@article{silva souza_uneda-trevisoli_lana_fritsche-neto_2026, title={A Low-Cost RGB-Based Image Processing Method for High-Throughput Assessment of Rice Grain Chalkiness}, DOI={10.1016/j.rsci.2025.12.003}, abstractNote={Although numerous rice genotypes have been developed worldwide, post-harvest evaluation of chalkiness, a key grain trait, remains a significant challenge in breeding programs. Conventional phenotyping methods rely on manual grain separation and analysis, which limits the speed and performance of decision-making. This study aimed to assess the efficiency of a low-cost, image-based phenotyping method for characterizing rice grain chalkiness and morphology traits (grain length and width) in comparison with traditional evaluation methods. Grains from 270 rice samples were imaged using a hyperspectral camera (VNIR, 400–1000 nm) and a Nikon digital single-lens reflex (DSLR) camera. Only RGB information was used for analysis, including RGB channels extracted from hyperspectral imagery to simulate low-cost setups. Python scripts were used to segment grains, estimate morphological parameters, and calculate chalkiness degree. Results from both imaging systems were compared with reference data obtained from the SeedCount platform. Strong correlations were observed with SeedCount data, reaching 93% for hyperspectral-RGB extraction and 76% for the RGB system. Binary classification metrics showed high discriminative performance, with area under the curve (AUC) values above 0.90 for most traits. The proposed method enabled image acquisition and processing in approximately 21 s per sample, compared to 1.5 min required by the conventional platform. The findings demonstrate the feasibility of a rapid and low-cost image-based phenotyping strategy to support rice breeding programs, particularly for chalkiness quantification and grain morphology assessment. The complete image-processing pipeline is provided as supplementary material, reinforcing the transparency and reproducibility of the method.}, journal={Rice Science}, author={Silva Souza, Jardel and Uneda-Trevisoli, Sandra Helena and Lana, Felipe Dalla and Fritsche-Neto, Roberto}, year={2026}, month={Jan} }
 @article{silva viana_dovale_fritsche-neto_2026, title={Optimizing progeny size and number of crosses under genomic selection: insights into additive and epistatic contributions to long-term genetic gain}, DOI={10.1007/s00122-026-05164-2}, abstractNote={Designing genomic selection to capture the additive and epistatic effects. Genomic selection (GS) offers great potential to accelerate long-term genetic gain, but strategic decisions such as progeny size and number of crosses remain poorly established, particularly under contrasting resource scenarios. We conducted stochastic simulations of rice breeding programs over 50 years (10 cycles) using progeny sizes of 25, 50, 100, and 200 individuals, under both theoretical (unlimited resources) and practical (budget-constrained to 4000 F 2  individuals) contexts, and considering three levels of epistasis (absent, moderate, high). In theoretical scenarios, larger progenies consistently achieved higher gains. After 50 years, progenies of 200 individuals reached cumulative responses to selection of 2.39 (1.96% yr -1 ) with no epistasis, 3.20 (2.60% yr -1 ) under moderate epistasis, and 3.48 (3.34% yr -1 ) under high epistasis. These schemes also maximized prediction accuracy and efficiently converted additive and epistatic variance into genetic gain. Conversely, under budget constraints, smaller progenies combined with more crosses outperformed larger ones. Progenies of 25 and 50 individuals achieved the greatest responses-up to 2.58 (2.07% yr -1 ) without epistasis, 3.36 (2.76% yr -1 ) under moderate epistasis, and 2.72 (2.45% yr -1 ) under high epistasis-while maintaining higher genetic diversity across cycles. Our results demonstrate that in resource-unlimited conditions, larger progenies (200 individuals) maximize the capture of additive and epistatic effects, whereas in budget-constrained programs, smaller progenies (25-50 individuals) coupled with more crosses provide the most efficient strategy. These findings provide practical guidelines for breeders to design GS schemes that reconcile high long-term genetic gain with operational feasibility, highlighting the decisive role of epistasis in shaping gain trajectories.}, journal={Theoretical and Applied Genetics}, author={Silva Viana, Jesimiel and DoVale, Júlio César and Fritsche-Neto, Roberto}, year={2026}, month={Feb} }
 @article{viana_dovale_fritsche‐neto_2026, title={Using Stochastic Simulations to Shed Light on How to Deploy Speed Breeding and Genomic Selection in Self‐Pollinated Recurrent Breeding Programs}, DOI={10.1111/pbr.70068}, abstractNote={ABSTRACT Speed breeding can shorten breeding cycles and, when combined with genomic selection, can accelerate genetic gain. Yet it remains unclear how different integration strategies affect long‐term response, genetic variance and cost‐efficiency in small public programs. Here, we use simulations of a 20‐year breeding pipeline to compare speed breeding strategies with traditional pedigree selection and to identify designs that balance genetic gain, sustainability and cost. We simulated a breeding program in AlphaSimR , and all schemes used single‐seed descent. Three speed breeding scenarios, differing in the generation of genomic selection and in whether population size was recovered after selection, were contrasted with two traditional pedigree schemes that advanced fewer lines per cycle. For each design, we monitored population mean, additive genetic variance and prediction accuracy over 20 years. Speed breeding schemes delivered faster short‐term gains than traditional schemes. The design with genomic selection in F 2 and recovery of population size produced the largest cumulative response (3.55 genetic standard deviations) than the other speed scenarios. However, this design required genotyping and phenotyping 16,000 individuals per cycle, compared with 1200 in the best traditional scheme, resulting in lower efficiency per unit cost under current prices. Sensitivity analyses further showed that reducing the F 2 recovery proportion substantially improves the cost–gain trade‐off while retaining much of the long‐term advantage of the recovered‐population design. Designs without population‐size recovery reached lower plateaus of response (~2.4) but demanded far fewer resources and showed a higher return on investment. Here, we demonstrate that coupling speed breeding and genomic selection with recovery of population size is the most powerful strategy for long‐term gain, but its immediate adoption may be constrained in resource‐limited programs. In the short term, simpler speed breeding designs with smaller populations may be more realistic, whereas falling genotyping costs will favour high‐population designs. These results provide guidance for redesigning breeding pipelines that accelerate cultivar development while preserving sustainable genetic gains.}, journal={Plant Breeding}, author={Viana, Jesimiel da Silva and DoVale, Júlio César and Fritsche‐Neto, Roberto}, year={2026}, month={Feb} }
 @article{silva_famoso_linscombe_fritsche-neto_2025, title={110 Years of Rice Breeding at LSU: Realized Genetic Gains and Future Optimization}, url={https://doi.org/10.21203/rs.3.rs-4945684/v1}, DOI={10.21203/rs.3.rs-4945684/v1}, abstractNote={<title>Abstract</title> This research aimed to understand the critical role of adopting advanced breeding tools and optimizing breeding strategies to ensure the sustainability and success of public breeding programs in meeting future food security challenges. In this context, there are two main objectives: estimate the genetic gains achieved over 110 years in the rice breeding program of Louisiana State University (LSU); evaluate through stochastic simulations the impacts of modern selection tools such as genomic selection (GS) and high-throughput phenotyping (HTP) on future genetic gains. Considering the 110 years, the average increase was 4.55 kg/ha per generation (23 breeding cycles). However, from 1994 to 2018, we observed more substantial trends in genetic gains, particularly for grain yield, which increased by approximately 56.54 kg/ha per year. Based on simulations, integrating GS and HTP demonstrated significant advantages, including shorter breeding cycles, enhanced selection accuracy, and reduced costs. Also, simulation results showed that this approach yielded the highest response to selection (4.68% per year) due to the synergistic effects of combining advanced phenotyping techniques with GS. Finally, we assessed the effects of balancing the number of parents, crosses, and progeny sizes to maximize genetic gains and maintain genetic variability. Variance component analysis indicated that progeny size had the greatest impact on total variance (36%), followed by the number of crosses (23%) and the number of parents (3.4%). The findings highlight the need for strategic resource allocation in breeding programs to balance cost-effectiveness and genetic improvement.}, author={Silva, Allison and Famoso, Adam and Linscombe, Steve and Fritsche-Neto, Roberto}, year={2025}, month={Apr} }
 @article{gupta_angira_famoso_fritsche‐neto_2025, title={Assessing the stability and plasticity of rice quality traits through reaction norms on environmental covariates}, DOI={10.1002/agj2.70227}, abstractNote={Abstract We applied the reaction norm concept to assess the stability and plasticity of rice ( Oryza sativa L.) grain quality traits, specifically for whole milling, chalk, and length. For that, we used 15 days of planting trials from 2021 and 2022, which included 19 commercial varieties evaluated in randomized complete block design with three replications. The analysis was conducted in two phases: first, we obtained adjusted means for each line in each trial, followed by a joint analysis to calculate broad‐sense heritability and genotype‐by‐environment (G × E) interaction. Next, we used Finlay–Wilkinson's regression, genotype‐genotype × enviroment (GGE) biplot, and environmental covariates to dissect the G × E. All analyses were performed in R using SpATS, sommer, statgenGxE, metan, EnvRtpe, caret, and snpReady packages. Furthermore, to better understand the G × E effect, we employed linear and exponential regression models. Our results revealed a higher G × E interaction for whole milling and chalk, while grain length showed a lower interaction. Notably, the specific planting days were more critical for quality traits than planting windows. We identified key environmental covariates: potential evapotranspiration and relative humidity from pre‐flowering to flowering for whole milling; vapor pressure deficit and relative humidity from flowering to post‐flowering for chalk; and wind speed, potential evapotranspiration, and relative temperature anomaly during various growth stages for grain length. These covariates explained ∼76% of the total variation in these traits. Reaction norm curves provided insights into genotype‐specific responses to environmental factors, and the narrow‐sense heritability of reaction norm components (intercept and slope) revealed “new” heritable traits to be used for stability and adaptability selection.}, journal={Agronomy Journal}, author={Gupta, Kajal and Angira, Brijesh and Famoso, Adam and Fritsche‐Neto, Roberto}, year={2025}, month={Nov} }
 @article{lobo_silveira_pontes_machado_fritsche‐neto_dovale_2025, title={Association Mapping for Components of Reaction Norms to Environmental Covariates in Public Tropical Maize (<scp><i>Zea mays</i></scp>) Panel Under Water Stress}, url={https://doi.org/10.1111/pbr.13262}, DOI={10.1111/pbr.13262}, abstractNote={ABSTRACT Using reaction norm components instead of traditional phenotypic data in genetic association studies (GWAS) may allow the identification of genomic regions that are more influenced by environmental variables in terms of tolerance and responsiveness to water stress. To test this hypothesis, we used a public genetic diversity panel of tropical maize inbred lines, evaluated in eight environments, four in well‐watered (WW) and four in water stress (WS) conditions. Most SNPs explained at least 40% of the genetic variability, and some reached 67%. The identified genes and genomic regions revealed physiological responses and direct or indirect molecular mechanisms related to water deficit tolerance and responsiveness. This information will enable more assertive selections and subsidize breeding programs aimed at obtaining cultivars for water deficit conditions while reducing the costs of the evaluation processes of reaction standards.}, journal={Plant Breeding}, author={Lobo, Antonio Lucas Aguiar and Silveira, Maria Valnice de Souza and Pontes, Fernanda Carla Ferreira de and Machado, Ingrid Pinheiro and Fritsche‐Neto, Roberto and DoVale, Júlio César}, year={2025}, month={Feb} }
 @article{fritsche-neto_queiroz_viana_gupta_grover_dovale_2025, title={Combining genomic prediction and multi-trait indices through stochastic simulations: do index type and deployment order affect genetic gain?}, DOI={10.21203/rs.3.rs-8032657/v1}, abstractNote={<title>Abstract</title> Genomic selection (GS) has transformed plant breeding by increasing prediction accuracy and reducing cycle length, but its integration with classical multi-trait selection indices (SI) remains underexplored. In this study, we used stochastic simulations to evaluate seven alternative strategies combining GS with Smith–Hazel (SH), Pesek–Baker (PB), and empirical (EMP) indices in a rice breeding program targeting grain yield (GY), chalkiness rate (CR), and plant height (PH). For index construction, the relative importance assigned to the three target traits was: 70% for increasing grain yield (GY), 15% for decreasing chalky rice (CR), and 15% for maintaining plant height (PH) at a stable level. After a burn-in phase with phenotypic selection, ten recurrent cycles were simulated to compare strategies based on population mean, prediction accuracy, and additive variance. Results showed that the performance of genomic selection (GS) relative to traditional phenotypic indices (TRAD) depends strongly on the target trait and the type of selection index used. Overall, the order, GS or SI first, does not have a significant impact on GS-based and Traditional methods. Also, both the GS and traditional selection methods performed similarly, mainly because the framework and length were the same, even though, in practice, we expect many advantages of the GS-based methods over the traditional ones. Finally, Pesek-Baker provided the more balanced genetic gains among the selection indices, closest to the expectation.}, author={Fritsche-Neto, Roberto and Queiroz, Lorena Gabriela Coelho and Viana, Jesimiel and Gupta, Kajal and Grover, Kashish and DoVale, Júlio César}, year={2025}, month={Dec} }
 @article{de pontes_machado_silveira_lobo_sabadin_fritsche-neto_dovale_2025, title={Combining genotyping approaches improves resolution for association mapping: a case study in tropical maize under water stress conditions}, volume={15}, ISSN={1664-462X}, url={http://dx.doi.org/10.3389/fpls.2024.1442008}, DOI={10.3389/fpls.2024.1442008}, abstractNote={Genome-wide Association Studies (GWAS) identify genome variations related to specific phenotypes using Single Nucleotide Polymorphism (SNP) markers. Genotyping platforms like SNP-Array or sequencing-based techniques (GBS) can genotype samples with many SNPs. These approaches may bias tropical maize analyses due to reliance on the temperate line B73 as the reference genome. An alternative is a simulated genome called “Mock,” adapted to the population using bioinformatics. Recent studies show SNP-Array, GBS, and Mock yield similar results for population structure, heterotic groups definition, tester selection, and genomic hybrid prediction. However, no studies have examined the results generated by these different genotyping approaches for GWAS. This study aims to test the equivalence among the three genotyping scenarios in identifying significant effect genes in GWAS. To achieve this, maize was used as the model species, where SNP-Array genotyped 360 inbred lines from a public panel via the Affymetrix platform and GBS. The GBS data were used to perform SNP calling using the temperate inbred line B73 as the reference genome (GBS-B73) and a simulated genome “Mock” obtained in-silico (GBS-Mock). The study encompassed four above-ground traits with plants grown under two levels of water supply: well-watered (WW) and water-stressed (WS). In total, 46, 34, and 31 SNP were identified in the SNP-Array, GBS-B73, and GBS-Mock scenarios, respectively, across the two water levels, associated with the evaluated traits following the comparative analysis of each genotyping method individually. Overall, the identified candidate genes varied along the various scenarios but had the same functionality. Regarding SNP-Array and GBS-B73, genes with functional similarity were identified even without coincidence in the physical position of the SNPs. These genes and regions are involved in various processes and responses with applications in plant breeding. In terms of accuracy, the combination of genotyping scenarios compared to those isolated is feasible and recommended, as it increased all traits under both water conditions. In this sense, it is worth highlighting the combination of GBS-B73 and GBS-Mock scenarios, not only due to the increase in the resolution of GWAS results but also the reduction of costs associated with genotyping and the possibility of conducting genomic breeding methods.}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={de Pontes, Fernanda Carla Ferreira and Machado, Ingrid Pinheiro and Silveira, Maria Valnice de Souza and Lobo, Antônio Lucas Aguiar and Sabadin, Felipe and Fritsche-Neto, Roberto and DoVale, Júlio César}, year={2025}, month={Jan} }
 @article{crossa_martini_vitale_pérez-rodríguez_costa-neto_fritsche-neto_runcie_cuevas_toledo_li_et al._2025, title={Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software}, volume={1}, ISSN={1360-1385}, url={http://dx.doi.org/10.1016/j.tplants.2024.12.009}, DOI={10.1016/j.tplants.2024.12.009}, abstractNote={With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology.}, journal={Trends in Plant Science}, publisher={Elsevier BV}, author={Crossa, José and Martini, Johannes W.R. and Vitale, Paolo and Pérez-Rodríguez, Paulino and Costa-Neto, Germano and Fritsche-Neto, Roberto and Runcie, Daniel and Cuevas, Jaime and Toledo, Fernando and Li, H. and et al.}, year={2025}, month={Jan} }
 @article{montiel_moreno‐amores_punzalan_angira_cerioli_robbins_mccouch_fritsche‐neto_famoso_2025, title={The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice}, DOI={10.1002/tpg2.70161}, abstractNote={Genomic selection (GS) has revolutionized breeding practices by integrating genotype and phenotype data to predict genomic estimated breeding values, offering the potential to accelerate breeding cycles and intensify and enhance early-stage selections. This approach utilizes the concept of linkage disequilibrium (LD) between genetic markers and quantitative trait loci within populations. LD, the nonrandom association between alleles at different loci, provides valuable insights into historical recombination patterns, although it can change over time under strong selection or genetic drift. This study aimed to investigate the influence of recombination on haplotype sizes and LD, assess the impact of additive (A) versus additive + epistasis (A+I) genetic models on GS predictive ability (PA), and demonstrate how haplotype resolution in the training set (TS) impacts the PA of GS. For this, we used biparental (MP2) and multiparent (MP6-8) populations, where the main difference between them was the recombination rate. As expected, a strong correlation between LD decay and the number of recombination opportunities within populations was observed, with smaller haplotype blocks in populations experiencing more recombination. The use of A+I models increased heritability but did not improve PA. Finally, populations with smaller haplotype sizes in the TS exhibited enhanced PA. This study demonstrates the effect of haplotype size on GS accuracy, and its uniqueness lies in its focus on populations where the primary differentiating factor is haplotype size. It offers an important tool for breeders in designing GS strategies, providing valuable guidance for future breeding efforts.}, journal={The Plant Genome}, author={Montiel, Maria and Moreno‐Amores, Jose and Punzalan, Jomar and Angira, Brijesh and Cerioli, Tommaso and Robbins, Kelly and McCouch, Susan and Fritsche‐Neto, Roberto and Famoso, Adam}, year={2025}, month={Nov} }
 @article{montesinos-lópez_chavira-flores_kiasmiantini_crespo-herrera_saint piere_li_fritsche-neto_al-nowibet_montesinos-lópez_crossa_2024, title={A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding}, volume={11}, ISSN={1943-2631}, url={http://dx.doi.org/10.1093/genetics/iyae161}, DOI={10.1093/GENETICS/IYAE161}, abstractNote={Abstract Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population.}, journal={GENETICS}, publisher={Oxford University Press (OUP)}, author={Montesinos-López, Osval A and Chavira-Flores, Moises and Kiasmiantini and Crespo-Herrera, Leo and Saint Piere, Carolina and Li, HuiHui and Fritsche-Neto, Roberto and Al-Nowibet, Khalid and Montesinos-López, Abelardo and Crossa, José}, editor={Calus, MEditor}, year={2024}, month={Nov} }
 @article{silva_prado_campos_borges_yassue_husein_sposito_amorim_crossa_fritsche-neto_2024, title={Comparing strategies for genomic predictions in interspecific biparental populations: a case study with the Rubus genus}, url={https://doi.org/10.1007/s10681-024-03406-2}, DOI={10.1007/s10681-024-03406-2}, journal={Euphytica}, author={Silva, Allison Vieira and Prado, Melina and Campos, Gabriela Romêro and Borges, Karina Lima Reis and Yassue, Rafael Massahiro and Husein, Gustavo and Sposito, Marcel Bellato and Amorim, Lilian and Crossa, José and Fritsche-Neto, Roberto}, year={2024}, month={Sep} }
 @article{prado_da silva_campos_borges_yassue_husein_akens_sposito_amorim_behrouzi_et al._2024, title={Complementary approaches to dissect late leaf rust resistance in an interspecific raspberry population}, volume={14}, ISSN={2160-1836}, url={http://dx.doi.org/10.1093/g3journal/jkae202}, DOI={10.1093/G3JOURNAL/JKAE202}, abstractNote={Abstract Over the last 10 years, global raspberry production has increased by 47.89%, based mainly on the red raspberry species (Rubus idaeus). However, the black raspberry (Rubus occidentalis), although less consumed, is resistant to one of the most important diseases for the crop, the late leaf rust caused by Acculeastrum americanum fungus. In this context, genetic resistance is the most sustainable way to control the disease, mainly because there are no registered fungicides for late leaf rust in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late leaf rust in raspberries. For that, we used an interspecific multiparental population using the species mentioned above as parents, 2 different statistical approaches to associate the phenotypes with markers [GWAS (genome-wide association studies) and copula graphical models], and 2 phenotyping methodologies from the first to the 17th day after inoculation (high-throughput phenotyping with a multispectral camera and traditional phenotyping by disease severity scores). Our findings indicate that a locus of higher effect, at position 13.3 Mb on chromosome 5, possibly controls late leaf rust resistance, as both GWAS and the network suggested the same marker. Of the 12 genes flanking its region, 4 were possible receptors, 3 were likely defense executors, 1 gene was likely part of signaling cascades, and 4 were classified as nondefense related. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different candidate regions, potentially complementing the genetic control comprehension.}, number={10}, journal={G3: Genes, Genomes, Genetics}, publisher={Oxford University Press (OUP)}, author={Prado, Melina and da Silva, Allison Vieira and Campos, Gabriela Romêro and Borges, Karina Lima Reis and Yassue, Rafael Massahiro and Husein, Gustavo and Akens, Felix Frederik and Sposito, Marcel Bellato and Amorim, Lilian and Behrouzi, Pariya and et al.}, editor={Lipka, AEditor}, year={2024}, month={Aug} }
 @article{montesinos-lópez_solis-camacho_crespo-herrera_saint pierre_huerta prado_ramos-pulido_al-nowibet_fritsche-neto_gerard_montesinos-lópez_et al._2024, title={Data Augmentation Enhances Plant-Genomic-Enabled Predictions}, volume={15}, ISSN={2073-4425}, url={http://dx.doi.org/10.3390/genes15030286}, DOI={10.3390/GENES15030286}, abstractNote={Genomic selection (GS) is revolutionizing plant breeding. However, its practical implementation is still challenging, since there are many factors that affect its accuracy. For this reason, this research explores data augmentation with the goal of improving its accuracy. Deep neural networks with data augmentation (DA) generate synthetic data from the original training set to increase the training set and to improve the prediction performance of any statistical or machine learning algorithm. There is much empirical evidence of their success in many computer vision applications. Due to this, DA was explored in the context of GS using 14 real datasets. We found empirical evidence that DA is a powerful tool to improve the prediction accuracy, since we improved the prediction accuracy of the top lines in the 14 datasets under study. On average, across datasets and traits, the gain in prediction performance of the DA approach regarding the Conventional method in the top 20% of lines in the testing set was 108.4% in terms of the NRMSE and 107.4% in terms of the MAAPE, but a worse performance was observed on the whole testing set. We encourage more empirical evaluations to support our findings.}, number={3}, journal={Genes}, publisher={MDPI AG}, author={Montesinos-López, Osval A. and Solis-Camacho, Mario Alberto and Crespo-Herrera, Leonardo and Saint Pierre, Carolina and Huerta Prado, Gloria Isabel and Ramos-Pulido, Sofia and Al-Nowibet, Khalid and Fritsche-Neto, Roberto and Gerard, Guillermo and Montesinos-López, Abelardo and et al.}, year={2024}, month={Feb}, pages={286} }
 @article{pruthi_chaudhary_chapagain_abozaid_rana_kondi_fritsche-neto_subudhi_2024, title={Deciphering the genetic basis of salinity tolerance in a diverse panel of cultivated and wild soybean accessions by genome-wide association mapping}, volume={137}, ISSN={0040-5752 1432-2242}, url={http://dx.doi.org/10.1007/s00122-024-04752-4}, DOI={10.1007/S00122-024-04752-4}, abstractNote={Abstract Key message In a genome-wide association study involving 269 cultivated and wild soybean accessions, potential salt tolerance donors were identified along with significant markers and candidate genes, such as GmKUP6 and GmWRKY33 . Abstract Salt stress remains a significant challenge in agricultural systems, notably impacting soybean productivity worldwide. A comprehensive genome-wide association study (GWAS) was conducted to elucidate the genetic underpinnings of salt tolerance and identify novel source of salt tolerance among soybean genotypes. A diverse panel comprising 269 wild and cultivated soybean accessions was subjected to saline stress under controlled greenhouse conditions. Phenotypic data revealed that salt tolerance of soybean germplasm accessions was heavily compromised by the accumulation of sodium and chloride, as indicated by highly significant positive correlations of leaf scorching score with leaf sodium/chloride content. The GWAS analysis, leveraging a dataset of 32,832 SNPs, unveiled 32 significant marker-trait associations (MTAs) across seven traits associated with salt tolerance. These markers explained a substantial portion of the phenotypic variation, ranging from 14 to 52%. Notably, 11 markers surpassed Bonferroni’s correction threshold, exhibiting highly significant associations with the respective traits. Gene Ontology enrichment analysis conducted within a 100 Kb range of the identified MTAs highlighted candidate genes such as potassium transporter 6 ( GmKUP6 ), cation hydrogen exchanger ( GmCHX15 ), and GmWRKY33 . Expression levels of GmKUP6 and GmWRKY33 significantly varied between salt-tolerant and salt-susceptible soybean accessions under salt stress. The genetic markers and candidate genes identified in this study hold promise for developing soybean varieties resilient to salinity stress, thereby mitigating its adverse effects.}, number={10}, journal={Theoretical and Applied Genetics}, publisher={Springer Science and Business Media LLC}, author={Pruthi, Rajat and Chaudhary, Chanderkant and Chapagain, Sandeep and Abozaid, Mostafa Mohamed Elbasuoni and Rana, Prabhat and Kondi, Ravi Kiran Reddy and Fritsche-Neto, Roberto and Subudhi, Prasanta K.}, year={2024}, month={Sep} }
 @article{fritsche-neto_sabadin_oliveira couto_souza_alves_galli_junior_dovale_borges_garbuglio_2024, title={Deep purple - an open-pollinated variety to induce haploids in tropical maize}, url={https://doi.org/10.1590/1984-70332024v24n1a08}, DOI={10.1590/1984-70332024v24n1a08}, abstractNote={Deep purple is an open-pollinated variety, well adapted to tropical conditions, developed to induce maternal haploids in maize. The founders were W23, Stock6, and an experimental tropical maize population. The inducer population inherits the dominant the R1-nj marker, and the expected real haploid induction rate is 3%.}, journal={Crop Breeding and Applied Biotechnology}, author={Fritsche-Neto, Roberto and Sabadin, Felipe and Oliveira Couto, Evellyn Giselly and Souza, Pedro Henrique and Alves, Filipe Couto and Galli, Giovanni and Junior, Ronaldo Borsato and Dovale, Julio Cesar and Borges, Karina Lima Reis and Garbuglio, Deoclécio Domingos}, year={2024}, month={Jan} }
 @article{fritsche-neto_yassue_silva_prado_dovale_2024, title={Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops}, url={https://doi.org/10.21203/rs.3.rs-4355565/v1}, DOI={10.21203/rs.3.rs-4355565/v1}, abstractNote={<title>Abstract</title> In genomic selection, the prediction accuracy is heavily influenced by the training set (TS) composition. Currently, two primary strategies for building TS are in use: one involves accumulating historical phenotypic records from multiple years, while the other is the “test-and-shelf” approach. Additionally, studies have suggested that optimizing TS composition using genetic algorithms can improve the accuracy of prediction models. Most breeders operate in open systems, introducing new genetic variability into their populations as needed. However, the impact of elite germplasm introduction in GS models remains unclear. Therefore, we conducted a case study in self-pollinated crops using stochastic simulations to understand the effects of elite germplasm introduction, TS composition, and its optimization in long-term breeding programs. Overall, introducing external elite germplasm reduces the prediction accuracy. In this context, Test and Shelf seem more stable regarding accuracy in dealing with introductions despite the origin and rate, being useful in programs where the introductions come from different sources over the years. Conversely, using historical data, if the introductions come from the same source over the cycles, this negative effect is reduced as long as the cycles and this approach become the best. Thus, it may support public breeding programs in establishing networks of collaborations, where the exchange of germplasm will occur at a pre-defined rate and flow. In either case, the use of algorithms of optimization to trim the genetic variability does not bring a substantial advantage in the medium to long term.}, journal={Research Square (Research Square)}, author={Fritsche-Neto, Roberto and Yassue, Rafael Massahiro and Silva, Allison Vieira and Prado, Melina and DoVale, Júlio César}, year={2024}, month={May} }
 @article{fritsche‐neto_yassue_silva_prado_dovale_2024, title={Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs}, url={https://doi.org/10.1002/csc2.21384}, DOI={10.1002/csc2.21384}, abstractNote={Abstract In genomic selection (GS), the prediction accuracy is heavily influenced by the composition of the training set (TS). Currently, two primary strategies for building TS are used: one involves accumulating historical phenotypic records from multiple years, while the other is the “test‐and‐shelf” approach. Additionally, studies have suggested that optimizing TS composition using genetic algorithms can improve the accuracy of prediction models. Most breeders operate in open systems, introducing new genetic variability into their populations as needed. However, the impact of elite germplasm introduction in GS models remains unclear. Therefore, we conducted a case study in self‐pollinated crops using stochastic simulations to understand the effects of elite germplasm introduction, TS composition, and its optimization in long‐term breeding programs. Overall, introducing external elite germplasm reduces the prediction accuracy. In this context, test and shelf seem more stable regarding accuracy in dealing with introductions despite the origin and rate, being useful in programs where the introductions come from different sources over the years. Conversely, using historical data, if the introductions come from the same source over the cycles, this negative effect is reduced as long as the cycles and this approach become the best. Thus, it may support public breeding programs in establishing networks of collaborations where the exchange of germplasm will occur at a predefined rate and flow. In either case, the use of algorithms of optimization to trim the genetic variability does not bring a substantial advantage in the medium to long term.}, journal={Crop Science}, author={Fritsche‐Neto, Roberto and Yassue, Rafael Massahiro and Silva, Allison Vieira and Prado, Melina and DoVale, Júlio César}, year={2024}, month={Oct} }
 @article{montesinos-lópez_crespo-herrera_pierre_cano-paez_huerta-prado_mosqueda-gonzález_ramos-pulido_gerard_alnowibet_fritsche-neto_et al._2024, title={Feature engineering of environmental covariates improves plant genomic-enabled prediction}, volume={15}, ISSN={1664-462X}, url={http://dx.doi.org/10.3389/fpls.2024.1349569}, DOI={10.3389/FPLS.2024.1349569}, abstractNote={Introduction Because Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its practical implementation still needs to be improved in many breeding programs. For this reason, many strategies have been explored to improve the prediction performance of this methodology. Methods When environmental covariates are incorporated as inputs in the genomic prediction models, this information only sometimes helps increase prediction performance. For this reason, this investigation explores the use of feature engineering on the environmental covariates to enhance the prediction performance of genomic prediction models. Results and discussion We found that across data sets, feature engineering helps reduce prediction error regarding only the inclusion of the environmental covariates without feature engineering by 761.625% across predictors. These results are very promising regarding the potential of feature engineering to enhance prediction accuracy. However, since a significant gain in prediction accuracy was observed in only some data sets, further research is required to guarantee a robust feature engineering strategy to incorporate the environmental covariates.}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={Montesinos-López, Osval A. and Crespo-Herrera, Leonardo and Pierre, Carolina Saint and Cano-Paez, Bernabe and Huerta-Prado, Gloria Isabel and Mosqueda-González, Brandon Alejandro and Ramos-Pulido, Sofia and Gerard, Guillermo and Alnowibet, Khalid and Fritsche-Neto, Roberto and et al.}, year={2024}, month={May} }
 @article{hore_balachiranjeevi_inabangan-asilo_deepak_palanog_hernandez_gregorio_dalisay_diaz_fritsche neto_et al._2024, title={Genomic prediction and QTL analysis for grain Zn content and yield in Aus-derived rice populations}, volume={33}, ISSN={0971-7811 0974-1275}, url={http://dx.doi.org/10.1007/s13562-024-00886-0}, DOI={10.1007/S13562-024-00886-0}, abstractNote={{"Label"=>"UNLABELLED"} Zinc (Zn) biofortification of rice can address Zn malnutrition in Asia. Identification and introgression of QTLs for grain Zn content and yield (YLD) can improve the efficiency of rice Zn biofortification. In four rice populations we detected 56 QTLs for seven traits by inclusive composite interval mapping (ICIM), and 16 QTLs for two traits (YLD and Zn) by association mapping. The phenotypic variance (PV) varied from 4.5% ( {"i"=>"qPN"} {"sub"=>{"i"=>"4.1"}}  ) to 31.7% ( {"i"=>"qPH"} {"sub"=>{"i"=>"1.1"}}  ).  {"i"=>"qDF"} {"sub"=>{"i"=>"1.1"}}  ,  {"i"=>"qDF"} {"sub"=>{"i"=>"7.2"}}  ,  {"i"=>"qDF"} {"sub"=>{"i"=>"8.1"}}  ,  {"i"=>"qPH"} {"sub"=>{"i"=>"1.1"}}  ,  {"i"=>"qPH"} {"sub"=>{"i"=>"7.1"}}  ,  {"i"=>"qPL"} {"sub"=>{"i"=>"1.2"}}  ,  {"i"=>"qPL"} {"sub"=>{"i"=>"9.1,"}} {"i"=>"qZn"} {"sub"=>{"i"=>"5.1"}}  ,  {"i"=>"qZn"} {"sub"=>{"i"=>"5.2"}}  ,  {"i"=>"qZn"} {"sub"=>{"i"=>"6.1"}}  and  {"i"=>"qZn"} {"sub"=>{"i"=>"7.1"}}  were identified in both dry and wet seasons;  {"i"=>"qZn"} {"sub"=>{"i"=>"5.1"}} {"i"=>", qZn"} {"sub"=>{"i"=>"5.2"}}  ,  {"i"=>"qZn"} {"sub"=>{"i"=>"5.3,"}} {"i"=>"qZn"} {"sub"=>{"i"=>"6.2,"}} {"i"=>"qZn"} {"sub"=>{"i"=>"7.1"}}  and  {"i"=>"qYLD"} {"sub"=>{"i"=>"1.2"}}  were detected by both ICIM and association mapping.  {"i"=>"qZn"} {"sub"=>{"i"=>"7.1"}}  had the highest PV (17.8%) and additive effect (2.5 ppm). Epistasis and QTL co-locations were also observed for different traits. The multi-trait genomic prediction values were 0.24 and 0.16 for YLD and Zn respectively.  {"i"=>"qZn"} {"sub"=>{"i"=>"6.2"}}  was co-located with a gene ( {"i"=>"OsHMA2"} ) involved in Zn transport. These results are useful for Zn biofortificatiton of rice. {"Label"=>"SUPPLEMENTARY INFORMATION", "NlmCategory"=>"UNASSIGNED"} The online version contains supplementary material available at 10.1007/s13562-024-00886-0.}, number={2}, journal={Journal of Plant Biochemistry and Biotechnology}, publisher={Springer Science and Business Media LLC}, author={Hore, Tapas Kumer and Balachiranjeevi, C. H. and Inabangan-Asilo, Mary Ann and Deepak, C. A. and Palanog, Alvin D. and Hernandez, Jose E. and Gregorio, Glenn B. and Dalisay, Teresita U. and Diaz, Maria Genaleen Q. and Fritsche Neto, Roberto Fritsche and et al.}, year={2024}, month={May}, pages={216–236} }
 @article{crossa_montesinos-lopez_costa-neto_vitale_martini_runcie_fritsche-neto_montesinos-lopez_pérez-rodríguez_gerard_et al._2025, title={Machine learning algorithms translate big data into predictive breeding accuracy}, volume={30}, ISSN={1360-1385}, url={http://dx.doi.org/10.1016/j.tplants.2024.09.011}, DOI={10.1016/j.tplants.2024.09.011}, abstractNote={Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multi-trait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets.}, number={2}, journal={Trends in Plant Science}, publisher={Elsevier BV}, author={Crossa, José and Montesinos-Lopez, Osval A. and Costa-Neto, Germano and Vitale, Paolo and Martini, Johannes W.R. and Runcie, Daniel and Fritsche-Neto, Roberto and Montesinos-Lopez, Abelardo and Pérez-Rodríguez, Paulino and Gerard, Guillermo and et al.}, year={2025}, month={Feb}, pages={167–184} }
 @article{borges_montiel_cerioli_angira_famoso_fritsche‐neto_2024, title={On the usefulness of genomic selection for rice ratoon performance in early breeding stages}, url={https://doi.org/10.1002/csc2.21420}, DOI={10.1002/csc2.21420}, abstractNote={Abstract Rice ( Oryza sativa L.) ratooning, a sustainable production system involves regrowing a second rice crop and it is a very common practice in southwest United States. Employing modern tools such as genomic selection (GS) can enhance breeding efficiency by enabling early selection. The Louisiana State University Rice Breeding Program has traditionally focused on developing superior varieties for the Louisiana's rice industry, however ratoon (RT) performance has typically been considered only in the late breeding stages, when there is little genetic variability available, and all the previous selections were made based on other qualitative and quantitative traits. Therefore, we aimed to verify if our pipeline for variety development is efficient in simultaneously selecting top grain yield performance lines for both harvest seasons: the main crop (H1) and the RT. In this context, we tested the following approaches: 1) Selection index, 2) Indirect selection, and 3) GS. Grain yield data evaluated over three years and three locations from the MP6‐8 population was used in this study. The results highlighted the genetic potential to be explored and the reliability of the data quality. Despite the low phenotypic and genotypic correlations between the first and second harvests (0.11 and 0.12, respectively), the plant response indices proved inefficient for dual‐season selection. Consequently, genotype ranking changed between harvest seasons, suggesting their relative independence. In simpler terms, the genotype that yields the highest productivity for H1 may not necessarily be the same for RT. Our study highlights the feasibility of using GS tools to perform early selections for RT and underscores it as a target trait in the breeding decision‐making process.}, journal={Crop Science}, author={Borges, Karina Lima Reis and Montiel, Maria Guadalupe and Cerioli, Tommaso and Angira, Brijesh and Famoso, Adam and Fritsche‐Neto, Roberto}, year={2024}, month={Dec} }
 @article{prado_famoso_guidry_fritsche-neto_2024, title={Optimizing Multi-Environment Trials in The US Rice Belt via Smart-Climate-Soil Prediction Based-Models and Economic Importance}, url={https://doi.org/10.1101/2024.07.02.601777}, DOI={10.1101/2024.07.02.601777}, abstractNote={Abstract Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: I) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trial’s allocation considering the counties’ economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed GxE. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, author={Prado, Melina and Famoso, Adam and Guidry, Kurt and Fritsche-Neto, Roberto}, year={2024}, month={Jul} }
 @article{prado_famoso_guidry_fritsche-neto_2024, title={Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance}, volume={15}, ISSN={1664-462X}, url={http://dx.doi.org/10.3389/fpls.2024.1458701}, DOI={10.3389/FPLS.2024.1458701}, abstractNote={Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trial’s allocation considering the counties’ economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed genotype-by-environment interaction. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy.}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={Prado, Melina and Famoso, Adam and Guidry, Kurt and Fritsche-Neto, Roberto}, year={2024}, month={Oct} }
 @article{fritsche-neto_montiel_moreno-amores_cerioli_angira_famoso_robbins_mccouch_2024, title={The Effect of Haplotype Size on Genomic Selection Accuracy and Epistasis: An Empirical Study in Rice}, url={https://doi.org/10.21203/rs.3.rs-3895233/v1}, DOI={10.21203/rs.3.rs-3895233/v1}, abstractNote={<title>Abstract</title> Genomic selection (GS) has revolutionized breeding practices by integrating genotype and phenotype data to predict genomic estimated breeding values (GEBVs), offering the potential to accelerate breeding cycles and intensify and enhance early-stage selections. This approach relies on the concept of linkage disequilibrium (LD) between genetic markers and quantitative trait loci (QTL) within populations. LD is the non-random association between alleles at different loci and provides valuable insights into recombination rates. This study aimed to investigate the influence of recombination on haplotype sizes and LD, assess the impact of additive (A) versus additive + epistasis (A+I) genetic models on GS prediction accuracy, and demonstrate how haplotype resolution in the training set (TS) impacts the prediction accuracy of GS. For this, we used biparental (MP2) and multiparent (MP6-8) populations, where the only difference between them was the recombination rate. Results revealed a direct relationship between LD decay and the number of recombination opportunities within populations, with smaller haplotype blocks observed in populations experiencing more recombination. The use of A+I models increased heritability but did not improve prediction accuracy. Finally, populations with smaller haplotype sizes in the TS exhibited enhanced prediction accuracy. This study demonstrates the effect of haplotype size on GS accuracy, and its uniqueness lies in its focus on populations where the sole differentiating factor is haplotype size. It offers an important tool for breeders in designing GS strategies, providing valuable guidance for future breeding efforts.}, author={Fritsche-Neto, Roberto and Montiel, Maria and Moreno-Amores, Jose and Cerioli, Tomasso and Angira, Brijesh and Famoso, Adam and Robbins, Kelly and McCouch, Susan}, year={2024}, month={Feb} }
 @article{machado_pontes_souza silveira_lobo_siqueira_fritsche-neto_dovale_2024, title={Unveiling early-stage responses of sensitive traits to water stress in tropical maize: a characterization study of a public panel}, url={https://doi.org/10.1007/s10681-024-03448-6}, DOI={10.1007/s10681-024-03448-6}, journal={Euphytica}, author={Machado, Ingrid Pinheiro and Pontes, Fernanda Carla Ferreira and Souza Silveira, Maria Valnice and Lobo, Antônio Lucas Aguiar and Siqueira, Michele Jorge Silva and Fritsche-Neto, Roberto and DoVale, Júlio César}, year={2024}, month={Dec} }
 @inbook{costa neto_resende_fritsche neto_heinemann_2023, place={Brasilia, DF}, edition={1}, title={Ambientômica}, booktitle={Melhoramento de Precisão: Aplicações e perspectivas na genética de plantas}, publisher={Embrapa}, author={Costa Neto, G.M.F. and Resende, R. and Fritsche Neto, R. and Heinemann, A.B.}, editor={Resende, Rafael Tassinari and Brondani, ClaudioEditors}, year={2023}, pages={195–226} }
 @article{de souza silveira_de pontes_machado_lobo_fritsche‐neto_dovale_2024, title={Association mapping for image‐based root traits in tropical maize under water stress in semi‐arid regions}, volume={116}, ISSN={0002-1962 1435-0645}, url={http://dx.doi.org/10.1002/agj2.21528}, DOI={10.1002/AGJ2.21528}, abstractNote={Abstract The root system is an organ that indicates signs of stress when a plant is subjected to water‐deficit conditions. However, its assessment is challenging. An alternative has been to obtain variables through image processing. In this way, it allows the rapid evaluation of genetic diversity panels. It contributes to identifying genomic regions or genes associated with the expression of the root system under water‐deficit conditions. Hence, a public diversity panel of 360 inbred lines of maize was evaluated under well‐watered (WW) and water stress (WS) conditions. Roots were phenotyped through image‐based processing. Then, genome‐wide association studies were conducted in WW and WS for each trait, using the Fixed and random model Circulating Probability Unification method. We found 23 genes or genomic regions with significant associations, of which eleven are exclusive to the WW condition, seven to the WS condition, and four are simultaneously associated with both WW and WS. All genomic regions related to the root system in the WS condition are associated with physiological mechanisms and molecular responses related to tolerance to water‐deficit conditions that can be explored in subsequent studies and by breeding programs to obtain varieties that are more tolerant and water efficient to this condition.}, number={3}, journal={Agronomy Journal}, publisher={Wiley}, author={de Souza Silveira, Maria Valnice and de Pontes, Fernanda Carla Ferreira and Machado, Ingrid Pinheiro and Lobo, Antônio Lucas Aguiar and Fritsche‐Neto, Roberto and DoVale, Júlio César}, year={2024}, month={May}, pages={1250–1264} }
 @book{gonçalves_fristche neto_2023, title={Biometria no Melhoramento de Plantas}, author={Gonçalves, Manoel Carlos and Fristche Neto, Roberto}, year={2023}, month={Nov} }
 @inproceedings{montiel_amores_angira_cerioli_hernandez_robbins_mccouch_famoso_fritsche-neto_sha_2023, title={Comparison Backward and Forward Genomic Selection Accuracy in Multiparent Populations in Rice (Oryza sativa L.)}, booktitle={Rice Technical Working Group Conference.}, author={Montiel, M. and Amores, J. and Angira, B. and Cerioli, T. and Hernandez, C. and Robbins, K. and McCouch, S. and Famoso, A. and Fritsche-Neto, R. and Sha, X.}, year={2023} }
 @article{campos_prado_reis borges_yassue_sabadin_da silva_morais de alcântara barbosa_bellato sposito_amorim_fritsche-neto_2023, title={Construction and genetic characterization of an interspecific raspberry hybrids panel aiming resistance to late leaf rust and adaptation to tropical regions}, volume={13}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/s41598-023-41728-8}, DOI={10.1038/S41598-023-41728-8}, abstractNote={Raspberries (Rubus spp) are temperate climate fruits with profitable high returns and have the potential for diversification of fruit growing in mid to low-latitude regions. However, there are still no cultivars adapted to climatic conditions and high pressure of diseases that occurs in tropical areas. In this context, our objective was to evaluate the genetic diversity from a 116 raspberry genotypes panel obtained from interspecific crosses in a testcross scheme with four cultivars already introduced in Brazil. The panel was genotyped via genotyping-by-sequencing. 28,373 and 27,281 SNPs were obtained, using the species R. occidentalis and R. idaeus genomes as references, respectively. A third marker dataset was constructed consisting of 41,292 non-coincident markers. Overall, there were no differences in the results when using the different marker sets for the subsequent analyses. The mean heterozygosity was 0.54. The average effective population size was 174, indicating great genetic variability. The other analyses revealed that the half-sibling families were structured in three groups. It is concluded that the studied panel has great potential for breeding and further genetic studies. Moreover, only one of the three marker matrices is sufficient for diversity studies.}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Campos, Gabriela Romêro and Prado, Melina and Reis Borges, Karina Lima and Yassue, Rafael Massahiro and Sabadin, Felipe and da Silva, Allison Vieira and Morais de Alcântara Barbosa, Caio and Bellato Sposito, Marcel and Amorim, Lilian and Fritsche-Neto, Roberto}, year={2023}, month={Sep} }
 @article{gevartosky_carvalho_costa-neto_montesinos-lópez_crossa_fritsche-neto_2023, title={Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize}, url={https://doi.org/10.1186/s12870-022-03975-1}, DOI={10.1186/s12870-022-03975-1}, abstractNote={{"Label"=>"BACKGROUND", "NlmCategory"=>"BACKGROUND"} Success in any genomic prediction platform is directly dependent on establishing a representative training set. This is a complex task, even in single-trait single-environment conditions and tends to be even more intricated wherein additional information from envirotyping and correlated traits are considered. Here, we aimed to design optimized training sets focused on genomic prediction, considering multi-trait multi-environment trials, and how those methods may increase accuracy reducing phenotyping costs. For that, we considered single-trait multi-environment trials and multi-trait multi-environment trials for three traits: grain yield, plant height, and ear height, two datasets, and two cross-validation schemes. Next, two strategies for designing optimized training sets were conceived, first considering only the genomic by environment by trait interaction (GET), while a second including large-scale environmental data (W, enviromics) as genomic by enviromic by trait interaction (GWT). The effective number of individuals (genotypes × environments × traits) was assumed as those that represent at least 98% of each kernel (GET or GWT) variation, in which those individuals were then selected by a genetic algorithm based on prediction error variance criteria to compose an optimized training set for genomic prediction purposes. {"Label"=>"RESULTS", "NlmCategory"=>"RESULTS"} The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested by up to 145% when compared to the model without enviromic data, and even more when compared to cross validation scheme with 70% of training set or pure phenotypic selection. Prediction models that include G × E or enviromic data + G × E yielded better prediction ability. {"Label"=>"CONCLUSIONS", "NlmCategory"=>"CONCLUSIONS"} Our findings indicate that a genomic by enviromic by trait interaction kernel associated with genetic algorithms is efficient and can be proposed as a promising approach to designing optimized training sets for genomic prediction when the variance-covariance matrix of traits is available. Additionally, great improvements in the genetic gains per dollar invested were observed, suggesting that a good allocation of resources can be deployed by using the proposed approach.}, journal={BMC Plant Biology}, author={Gevartosky, Raysa and Carvalho, Humberto Fanelli and Costa-Neto, Germano and Montesinos-López, Osval A. and Crossa, José and Fritsche-Neto, Roberto}, year={2023}, month={Jan} }
 @article{massahiro yassue_galli_james chen_fritsche‐neto_morota_2023, title={Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation}, volume={7}, ISSN={2475-4455 2475-4455}, url={http://dx.doi.org/10.1002/pld3.492}, DOI={10.1002/pld3.492}, abstractNote={Plant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386-1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growth-related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.}, number={4}, journal={Plant Direct}, publisher={Wiley}, author={Massahiro Yassue, Rafael and Galli, Giovanni and James Chen, Chun‐Peng and Fritsche‐Neto, Roberto and Morota, Gota}, year={2023}, month={Apr} }
 @article{da silva_costa_diniz_ramos_fritsche-neto_2023, title={Genomic and population characterization of a diversity panel of dwarf and tall coconut accessions from the International Coconut Genebank for Latin America and Caribbean}, volume={71}, ISSN={0925-9864 1573-5109}, url={http://dx.doi.org/10.1007/s10722-023-01652-2}, DOI={10.1007/S10722-023-01652-2}, number={2}, journal={Genetic Resources and Crop Evolution}, publisher={Springer Science and Business Media LLC}, author={da Silva, Allison Vieira and Costa, Emiliano Fernandes Nassau and Diniz, Leandro Eugenio Cardamone and Ramos, Semíramis Rabelo Ramalho and Fritsche-Neto, Roberto}, year={2023}, month={Jul}, pages={721–733} }
 @inproceedings{felix_gupta_volpato_mello filho_borém_fritsche-neto_2023, title={High-throughput Phenotyping of maturity, grain yield, and flowering date using UAV Multispectral and RGB Sensors: a case of study in soybean}, booktitle={Rice Field Day}, author={Felix, M.R. and Gupta, K. and Volpato, L. and Mello Filho, O.L. and Borém, A. and Fritsche-Neto, R.}, year={2023} }
 @article{fritsche-neto_ali_asis_allahgholipour_labroo_2023, title={Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance}, url={https://doi.org/10.21203/rs.3.rs-2860585/v1}, DOI={10.21203/rs.3.rs-2860585/v1}, abstractNote={Abstract One of the most common methods to improve hybrid performance is reciprocal recurrent selection (RRS). Genomic prediction (GP) can be used to increase genetic gain in RRS by reducing cycle length, but it is also possible to use GP to predict single-cross hybrid performance and recover higher-performing hybrids. The impact of the latter method on genetic gain has not been previously reported. Therefore, our study compared various phenotypic and genomics-assisted RRS breeding schemes which used GP to predict hybrid performance rather than reducing cycle length, which allows minimal changes to phenotypic schemes. We used stochastic simulation to compare compared five RRS breeding schemes in terms of genetic gain and best hybrid performance: Traditional (TRAD_RRS), drift (DRIFT_RRS), Traditional but updating testers every cycle (TRAD_RRS_ UP), Genomic Additive (GS_A_RRS), and Genomic Additive+Dominace (GS_AD_RRS). We also compared three breeding sizes which varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of the number of phenotyped hybrids, and the number of genomic predicted hybrids. Schemes which used genomic prediction of hybrid performance outperformed the others for both the average interpopulation hybrid population performance and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. Overall, the largest breeding size tested had the highest rates of genetic gain and in the lowest decrease in additive genetic variance due to drift, although cost was not considered. This study demonstrates the usefulness of single-cross prediction, which initially may be easier to implement than rapid-cycling RRS, and cyclical updating of testers. We also demonstrate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance, disregarding cost.}, journal={Research Square (Research Square)}, author={Fritsche-Neto, Roberto and Ali, Jauhar and Asis, Erik Jon De and Allahgholipour, Mehrzad and Labroo, Marlee Rose}, year={2023}, month={May} }
 @article{fritsche-neto_ali_de asis_allahgholipour_labroo_2024, title={Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance}, volume={137}, ISSN={0040-5752 1432-2242}, url={http://dx.doi.org/10.1007/s00122-023-04508-6}, DOI={10.1007/s00122-023-04508-6}, abstractNote={Schemes that use genomic prediction outperform others, updating testers increases hybrid genetic gain, and larger population sizes tend to have higher genetic gain and less depletion of genetic variance One of the most common methods to improve hybrid performance is reciprocal recurrent selection (RRS). Genomic prediction (GP) can be used to increase genetic gain in RRS by reducing cycle length, but it is also possible to use GP to predict single-cross hybrid performance. The impact of the latter method on genetic gain has yet to be previously reported. Therefore, we compared via stochastic simulations various phenotypic and genomics-assisted RRS breeding schemes which used GP to predict hybrid performance rather than reducing cycle length, which allows minimal changes to traditional breeding schemes. We also compared three breeding sizes scenarios that varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of hybrids evaluated, and the number of genomic predicted hybrids. Our results demonstrated that schemes that used genomic prediction of hybrid performance outperformed the others for the average interpopulation hybrid population and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. As expected, the largest breeding size tested had the highest rates of genetic improvement and the lowest decrease in additive genetic variance due to the drift. Therefore, this study demonstrates the usefulness of single-cross prediction, which may be easier to implement than rapid-cycling RRS and cyclical updating of testers. We also reiterate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance.}, number={1}, journal={Theoretical and Applied Genetics}, publisher={Springer Science and Business Media LLC}, author={Fritsche-Neto, Roberto and Ali, Jauhar and De Asis, Erik Jon and Allahgholipour, Mehrzad and Labroo, Marlee Rose}, year={2024} }
 @inproceedings{fritsche neto_ali_asis_allahgholipour_labroo_2023, title={Improving hybrid rice breeding programs via stochastic simulations: number of parents, single-crosses, tester update, and genomic recurrent selection}, booktitle={International Plant & Animal Genome Conference}, author={Fritsche Neto, R. and Ali, J. and Asis, E.J. and Allahgholipour, M. and Labroo, M.R.}, year={2023} }
 @article{carvalho_ferrão_galli_nonato_padilha_maluf_resende_fritsche-neto_guerreiro-filho_2023, title={On the accuracy of threshold genomic prediction models for leaf miner and leaf rust resistance in arabica coffee}, url={https://doi.org/10.1007/s11295-022-01581-8}, DOI={10.1007/s11295-022-01581-8}, journal={Tree Genetics & Genomes}, author={Carvalho, Humberto Fanelli and Ferrão, Luís Felipe Ventorim and Galli, Giovanni and Nonato, Juliana Vieira Almeida and Padilha, Lilian and Maluf, Mirian Perez and Resende, Márcio Fernando Ribeiro and Fritsche-Neto, Roberto and Guerreiro-Filho, Oliveiro}, year={2023}, month={Jan} }
 @article{machado_dovale_sabadin_fritsche-neto_2023, title={On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops}, volume={14}, ISSN={1664-462X}, url={http://dx.doi.org/10.3389/fpls.2023.1164555}, DOI={10.3389/FPLS.2023.1164555}, abstractNote={The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population's intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome.}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={Machado, Ingrid Pinheiro and DoVale, Júlio César and Sabadin, Felipe and Fritsche-Neto, Roberto}, year={2023}, month={Jun} }
 @inproceedings{fristche neto_famoso_2023, title={Optimizing Multi-Environment Trials in The US Rice Belt Via Smart-Climate-Soil Prediction Based-Models}, booktitle={Rice Technical Working Group Conference}, author={Fristche Neto, E. and Famoso, A.}, year={2023} }
 @article{platten_fritsche‐neto_2023, title={Optimizing quantitative trait loci introgression in elite rice germplasms: Comparing methods and population sizes to develop new recipients via stochastic simulations}, volume={142}, ISSN={0179-9541 1439-0523}, url={http://dx.doi.org/10.1111/pbr.13118}, DOI={10.1111/pbr.13118}, abstractNote={Abstract This study compared three strategies to develop new recipients for quantitative trait loci (QTL) introgression (background recovery [BG], selective sweep [SS] and breeding value [BV]) in a short‐term rice breeding programme (over five breeding cycles). Furthermore, we evaluated two different numbers of recipients (10 and 20) in the introgression process and how they influence the population performance and the QTL fixation over cycles. Finally, we used the International Rice Research Institute (IRRI) rice breeding framework as the model to perform the stochastic simulations. Each strategy was simulated and replicated 100 times. Regardless of the selection strategy used, the QTL introgression resulted in substantial penalties in yield performance. However, introducing fewer new parents to the augmentation process minimized this effect. Conversely, the time required to achieve fixation of target QTLs showed substantial differences, with selection for BV during augmentation outperforming other methods. Overall, the BV_10 strategy (10 parents selected based on genomic estimated BV) displayed the best trade‐off between reduced penalty from introducing new QTLs with a reasonable speed at which those QTLs can achieve fixation over subsequent breeding cycles.}, number={4}, journal={Plant Breeding}, publisher={Wiley}, author={Platten, John Damien and Fritsche‐Neto, Roberto}, year={2023}, month={May}, pages={439–448} }
 @inproceedings{famoso_angira_fritsche-neto_2023, title={Overview of the LSU AgCenter Rice Breeding Program and Future Directions.}, booktitle={Rice Technical Working Group Conference}, author={Famoso, A. and Angira, B. and Fritsche-Neto, R.}, year={2023} }
 @article{baptistella_blanchard_taylor_kimbeng_fritsche‐neto_gravois_reis_2023, title={Phenotypic plasticity and genetic trends in the past 30 years of sugarcane genetic improvement in Louisiana}, volume={64}, ISSN={0011-183X 1435-0653}, url={http://dx.doi.org/10.1002/csc2.21137}, DOI={10.1002/CSC2.21137}, abstractNote={Abstract Sugar yield, which results from the combination of stalk biomass (SB) and sugar content (SC), stands as the critical trait for sugarcane breeding programs in Louisiana. Nevertheless, it remains uncertain how SB and SC have individually contributed to the recent increase in sugar yield and how the environment governs their relationship. We applied linear mixed models and the Finlay–Wilkinson model to analyze historical data from the Louisiana outfield variety trials, from plant cane to the third ratoon in multiple environments. The primary objectives were to determine (i) SC and SB genetic gain, (ii) the nature of the relationship between SB and SC, and (iii) whether genotypes are widely or specifically adapted to the environment. Our results showed that the breeding increased sugar yield by relying mostly on SB (0.600–0.652 Mg ha −1 year −1 and 0.893%–0.950% per year) and less on SC (0.371–0.384 kg year −1 and 0.282%–0.292% per year). This was achieved by increasing genotypes SB sensitivity to environmental conditions (0.310% per year) on the plant cane rather than ratoon (nonsignificant). Additionally, the environment strongly controlled the relationship between SB and SC without a significant positive or negative trend on the population mean. From an environmental perspective, high‐yielding environments also provided conditions for high SC on plant cane. Our study highlights that environment characterization is fundamental to sugarcane breeding and emphasizes the opportunity to direct efforts on selecting genotypes that are responsive to the environmental quality by producing superior SB in the ratoon crop cycles.}, number={1}, journal={Crop Science}, publisher={Wiley}, author={Baptistella, João L. Corte and Blanchard, Brayden A. and Taylor, Zachary and Kimbeng, Collins A. and Fritsche‐Neto, Roberto and Gravois, Kenneth A. and Reis, André F. B.}, year={2023}, month={Nov}, pages={44–54} }
 @inproceedings{gupta_felix_angira_famoso_fritsche neto_2023, title={Reaction Norms on Environmental Covariates for Grain Quality in Rice,}, booktitle={Rice Field Day}, author={Gupta, K. and Felix, M.R. and Angira, B. and Famoso, A. and Fritsche Neto, Roberto}, year={2023} }
 @inproceedings{fritsche neto_angira_famoso_2023, title={Realized genetic gains in the LSU AgCenter Rice Breeding Program between 1994 and 2018}, booktitle={Rice Field Day}, author={Fritsche Neto, Roberto and Angira, B. and Famoso, A.}, year={2023} }
 @article{fritsche-neto_sabadin_dovale_souza_borges_crossa_garbuglio_2023, title={Realized genetic gains via recurrent selection in a tropical maize haploid inducer population and optimizing simultaneous selection for the next breeding cycles}, url={https://doi.org/10.21203/rs.3.rs-1952851/v2}, DOI={10.21203/rs.3.rs-1952851/v2}, abstractNote={<title>Abstract</title> Plant breeders widely use recurrent selection schemes to increase the frequency of favorable alleles for quantitative traits in a population. Although simultaneous selection is complex because it involves several traits combined with selection cycles, the use of selection indexes (SI) is applied to increase the chance of success of the breeding program. Despite many indices are available in the literature, therefore, simulations can help breeders to determine which selection index can be better adjusted considering the selection goals, the intensity, and the genetic correlation among traits over breeding cycles. In this context, we estimated the realized genetic gains in a tropical maize haploid inducer population after two cycles of recurrent selection, using external testers and optimizing the simultaneous selection for this breeding population in the long-term via stochastic simulations. Furthermore, we proposed a new approach to optimize the initial weights by applying Smith-Hazel method to maximize the genetic gains for all traits in a balanced way. Overall, the estimated gains in real induction rate were about 63% per cycle, improving the population performance from 0.8 to 2.8%. Moreover, our results confirm that the traditional Smith-Hazel approach outperformed other methods for long-term response to selection. Finally, recurrent selection with external testers is a suitable method to improve the haploid induction rate in tropical maize populations.}, author={Fritsche-Neto, Roberto and Sabadin, Felipe and doVale, Julio César and Souza, Pedro Henrique and Borges, Karina Lima Reis and Crossa, Jose and Garbuglio, Deoclécio Domingos}, year={2023}, month={Mar} }
 @article{fritsche‐neto_sabadin_dovale_borges_souza_crossa_garbuglio_2023, title={Realized genetic gains via recurrent selection in a tropical maize haploid inducer population and optimizing simultaneous selection for the next cycles}, url={https://doi.org/10.1002/csc2.21081}, DOI={10.1002/csc2.21081}, abstractNote={Abstract Plant breeders widely use recurrent selection schemes to increase the frequency of favorable alleles for quantitative traits in a population. Although simultaneous selection is complex because it involves several traits combined with selection cycles, the use of selection indexes (SI) is applied to increase the chance of success of the breeding program. Despite many indices are available in the literature, simulations can help breeders to determine which selection index can be better adjusted considering the selection goals, the intensity, and the genetic correlation among traits over breeding cycles. In this context, we estimated the realized genetic gains in a tropical maize haploid inducer population after two cycles of recurrent selection, using external testers and optimizing the simultaneous selection for this breeding population in the long term via stochastic simulations. Furthermore, we proposed a new approach to optimize the initial weights by applying Smith‐Hazel method to maximize the genetic gains for all traits in a balanced way. Overall, the estimated gains in real induction rate were about 63% per cycle, improving the population performance from 0.8% to 2.8%. Moreover, our results confirm that the traditional Smith‐Hazel approach outperformed other methods for long‐term response to selection. Finally, recurrent selection with external testers may be a suitable method to improve the haploid induction rate in tropical maize populations.}, journal={Crop Science}, author={Fritsche‐Neto, Roberto and Sabadin, Felipe and DoVale, Júlio César and Borges, Karina Lima Reis and Souza, Pedro Henrique and Crossa, Jose and Garbuglio, Deoclécio Domingos}, year={2023}, month={Aug} }
 @article{fritsche‐neto_2023, title={SoilType: An R package to interplay soil characterization in plant science}, volume={116}, ISSN={0002-1962 1435-0645}, url={http://dx.doi.org/10.1002/agj2.21383}, DOI={10.1002/agj2.21383}, abstractNote={Abstract Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. Therefore, soil‐related information can increase the predictive ability of genotype's performances across different locations. However, soil information is not always readily available worldwide or before the site or plot level growing season. Thus, in the current version, this tool has two functions. The first function retrieves soil samples and soil (from WoSIS Soil Profile Database) near your target location. Then it predicts 13 soil characteristics (physical and chemical). If the number of samples per location is greater than five, the function uses random forest to predict soil characteristics otherwise it averages the information. The output is a table with the target location and its latitude and longitude coordinates—the number of samples used, the root mean square error (RMSE), and the R ‐square for each prediction. From a couple of instances in a trial (location), the second function predicts soil characteristics at the plot level via random forest. The output is a table with the plot IDs, coordinates, number of samples used to make predictions, the RMSE and R ‐square for each prediction, the trait predicted, and the predicted value. As a proof‐of‐concept, we used the first function in the LSU Rice Breeding multi‐environmental trials (24 locations), identified the most important soil covariates to determine rice yield, and then clustered the locations. This tool can support breeders in better allocating trials in advance, borrow information from other regions, identify the best variety for each location, reduce costs, and increase accuracy.}, number={3}, journal={Agronomy Journal}, publisher={Wiley}, author={Fritsche‐Neto, Roberto}, year={2023}, month={Jun}, pages={848–854} }
 @article{carvalho_aguiar-perecin_clarindo_fristche-neto_mondin_2022, title={A Heterochromatic Knob Reducing the Flowering Time in Maize}, volume={12}, ISSN={1664-8021}, url={http://dx.doi.org/10.3389/fgene.2021.799681}, DOI={10.3389/fgene.2021.799681}, abstractNote={Maize flowering time is an important agronomic trait, which has been associated with variations in the genome size and heterochromatic knobs content. We integrated three steps to show this association. Firstly, we selected inbred lines varying for heterochromatic knob composition at specific sites in the homozygous state. Then, we produced homozygous and heterozygous hybrids for knobs. Second, we measured the genome size and flowering time for all materials. Knob composition did not affect the genome size and flowering time. Finally, we developed an association study and identified a knob marker on chromosome 9 showing the strongest association with flowering time. Indeed, modelling allele substitution and dominance effects could offer only one heterochromatic knob locus that could affect flowering time, making it earlier rather than the knob composition.}, journal={Frontiers in Genetics}, publisher={Frontiers Media SA}, author={Carvalho, Renata Flávia and Aguiar-Perecin, Margarida Lopes Rodrigues and Clarindo, Wellington Ronildo and Fristche-Neto, Roberto and Mondin, Mateus}, year={2022}, month={Feb} }
 @article{aono_francisco_souza_souza gonçalves_scaloppi_guen_fritsche-neto_gorjanc_quiles_souza_2022, title={A divide-and-conquer approach for genomic prediction in rubber tree using machine learning}, volume={3}, url={https://doi.org/10.1101/2022.03.30.486381}, DOI={10.1101/2022.03.30.486381}, abstractNote={Abstract Rubber tree ( Hevea brasiliensis ) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci (QTLs) have been constructed and suggested as a tool for marker-assisted selection (MAS). Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, publisher={Cold Spring Harbor Laboratory}, author={Aono, Alexandre Hild and Francisco, Felipe Roberto and Souza, Livia Moura and Souza Gonçalves, Paulo and Scaloppi, Erivaldo J. and Guen, Vincent Le and Fritsche-Neto, Roberto and Gorjanc, Gregor and Quiles, Marcos Gonçalves and Souza, Anete Pereira}, year={2022}, month={Mar} }
 @article{aono_francisco_souza_gonçalves_scaloppi junior_le guen_fritsche-neto_gorjanc_quiles_de souza_2022, title={A divide-and-conquer approach for genomic prediction in rubber tree using machine learning}, volume={12}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/s41598-022-20416-z}, DOI={10.1038/S41598-022-20416-Z}, abstractNote={Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Aono, Alexandre Hild and Francisco, Felipe Roberto and Souza, Livia Moura and Gonçalves, Paulo de Souza and Scaloppi Junior, Erivaldo J. and Le Guen, Vincent and Fritsche-Neto, Roberto and Gorjanc, Gregor and Quiles, Marcos Gonçalves and de Souza, Anete Pereira}, year={2022}, month={Oct} }
 @article{yassue_galli_borsato_cheng_morota_fritsche‐neto_2022, title={A low‐cost greenhouse‐based high‐throughput phenotyping platform for genetic studies: A case study in maize under inoculation with plant growth‐promoting bacteria}, url={https://doi.org/10.1002/ppj2.20043}, DOI={10.1002/ppj2.20043}, abstractNote={Abstract Greenhouse‐based high‐throughput phenotyping (HTP) presents a useful approach for studying novel plant growth‐promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize ( Zea Mays L.) cultivars exhibiting highly stable symbiosis with PGPB, greenhouse‐based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low‐cost greenhouse‐based HTP platform to collect growth‐related image‐derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen‐limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth in early developmental stages. Phenotypic correlations between the image‐derived phenotypes and manual measurements were at least 0.47 in the later stages of plant development. The genomic heritability estimates of the image‐derived phenotypes ranged from 0.23 to 0.54. Moderate‐to‐strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP‐based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low‐cost HTP platform for large‐scale genetic and management studies to capture plant growth.}, journal={The Plant Phenome Journal}, author={Yassue, Rafael Massahiro and Galli, Giovanni and Borsato, Ronaldo and Cheng, Hao and Morota, Gota and Fritsche‐Neto, Roberto}, year={2022}, month={Jan} }
 @inbook{fritsche neto_cavatte_dovale_2022, place={Viçosa}, edition={2nd}, title={A tolerância e a eficiência como respostas ao estresse}, booktitle={Melhoramento de plantas para estresses abióticos}, publisher={Editora UFV}, author={Fritsche Neto, Roberto and Cavatte, Paulo Cezar and Dovale, Julio Cesar}, editor={Fritsche-Neto, R. and Borém, A.Editors}, year={2022}, pages={31–40} }
 @article{nayak_habib_das_islam_hossain_karmakar_fritsche neto_bhosale_bhardwaj_singh_et al._2022, title={Adoption Trend of Climate-Resilient Rice Varieties in Bangladesh}, volume={14}, ISSN={2071-1050}, url={http://dx.doi.org/10.3390/su14095156}, DOI={10.3390/SU14095156}, abstractNote={Rice is a major crop in Bangladesh that supports both food security and livelihoods. However, a need remains for improved productivity and adaptation to the risks associated with climate change. To accomplish this, the increased adoption of climate-resilient and high-yielding rice varieties can be beneficial. Therefore, we conducted a study in Bangladesh over three consecutive years: 2016, 2017, and 2018. The scope of the study included the major cropping season (wet), Aman. The yield advantages of climate-resilient rice varieties were evaluated and compared with those of the varieties popular with farmers. We included new stress-tolerant varieties, such as submergence-tolerant rice (BRRI dhan51 and BRRI dhan52) and drought-tolerant rice (BRRI dhan56 and BRRI dhan71), along with farmer-chosen controls, in the study. We conducted the evaluation through on-farm trials to compare the varieties in both submergence- and drought-affected environments. The seasonal trials provided measured results of yield advantages. The participating farmers were also studied over the three-year-period to capture their varietal adoption rates. We calculated both the location estimated yield advantages (LEYA) and the location observed yield advantages (LOYA). The results revealed that, under non-stress conditions, the grain yields of climate-resilient varieties were either statistically similar to or higher than those of the farmer-chosen controls. Our study also revealed a year-to-year progressive adoption rate for the introduced varieties. The study suggests that the wide-scale introduction and popularization of climate-resilient varieties can ensure higher productivity and climate risk adaptation. The close similarity between LOYA and LEYA indicated that the observational and experiential conclusions of the host farmers were similar to the scientific performance of the varieties. We also found that comparison performed through on-farm trials was a critical method for enhancing experiential learning and obtaining an accurate estimation of yield advantages.}, number={9}, journal={Sustainability}, publisher={MDPI AG}, author={Nayak, Swati and Habib, Muhammad Ashraful and Das, Kuntal and Islam, Saidul and Hossain, Sk Mosharaf and Karmakar, Biswajit and Fritsche Neto, Roberto and Bhosale, Sankalp and Bhardwaj, Hans and Singh, Sudhanshu and et al.}, year={2022}, month={Apr}, pages={5156} }
 @article{galli_sabadin_yassue_galves_carvalho_crossa_montesinos-lópez_fritsche-neto_2022, title={Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids}, volume={13}, ISSN={1664-462X}, url={http://dx.doi.org/10.3389/fpls.2022.845524}, DOI={10.3389/FPLS.2022.845524}, abstractNote={Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images.” In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={Galli, Giovanni and Sabadin, Felipe and Yassue, Rafael Massahiro and Galves, Cassia and Carvalho, Humberto Fanelli and Crossa, Jose and Montesinos-López, Osval Antonio and Fritsche-Neto, Roberto}, year={2022}, month={Mar} }
 @article{yassue_galli_fritsche-neto_morota_2022, title={Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data}, volume={3}, url={https://doi.org/10.1101/2022.03.04.483003}, DOI={10.1101/2022.03.04.483003}, abstractNote={Abstract Recent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyper-spectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyper-spectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies. Core ideas Hyperspectral reflectance data can classify plant growth-promoting bacteria inoculation status Phenomic prediction performs better than genomic prediction depending on the target phenotype AutoML is a promising approach for automating hyperparameter tuning for classification and prediction}, journal={BioRxiv}, publisher={Cold Spring Harbor Laboratory}, author={Yassue, Rafael Massahiro and Galli, Giovanni and Fritsche-Neto, Roberto and Morota, Gota}, year={2022}, month={Mar} }
 @article{yassue_galli_fritsche‐neto_morota_2022, title={Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data}, volume={63}, ISSN={0011-183X 1435-0653}, url={http://dx.doi.org/10.1002/csc2.20836}, DOI={10.1002/CSC2.20836}, abstractNote={Abstract Recent technological advances in high‐throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyperspectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize ( Zea mays L.) lines with or without plant growth‐promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1,021 nm and 13,826 single‐nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth‐related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyperspectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral‐based phenotyping for management and phenomic prediction studies.}, number={1}, journal={Crop Science}, publisher={Wiley}, author={Yassue, Rafael Massahiro and Galli, Giovanni and Fritsche‐Neto, Roberto and Morota, Gota}, year={2022}, month={Nov}, pages={88–100} }
 @article{costa-neto_crespo-herrera_fradgley_gardner_bentley_dreisigacker_fritsche-neto_montesinos-lópez_crossa_2022, title={ENVIROME-WIDE ASSOCIATIONS ENHANCE MULTI-YEAR GENOME-BASED PREDICTION OF HISTORICAL WHEAT BREEDING DATA}, url={https://publons.com/wos-op/publon/59147392/}, DOI={10.1101/2022.08.14.503901}, abstractNote={ABSTRACT Linking high-throughput environmental data (enviromics) into genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G×E). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important G×E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes; (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. Then, the EPA outcomes were integrated into multi-environment GP models through a new single-step GBLUP. The wheat trial data used included 36 locations, 8 years and 3 target populations of environments (TPE) in India. Four prediction scenarios and 6 kernel-models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. However, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G×E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G×E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as “covariable selection” unfeasible because G×E is year-germplasm specific. We propose that the EPA effectively serves as a “reinforcement learner” algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, author={Costa-Neto, Germano and Crespo-Herrera, Leonardo and Fradgley, Nick and Gardner, Keith and Bentley, Alison R. and Dreisigacker, Susanne and Fritsche-Neto, Roberto and Montesinos-López, Osval A. and Crossa, Jose}, year={2022}, month={Aug} }
 @article{resende_chenu_rasmussen_heinemann_fritsche-neto_2022, title={Editorial: Enviromics in Plant Breeding}, volume={13}, ISSN={1664-462X}, url={http://dx.doi.org/10.3389/fpls.2022.935380}, DOI={10.3389/FPLS.2022.935380}, abstractNote={EDITORIAL article Front. Plant Sci., 30 June 2022Sec. Plant Breeding https://doi.org/10.3389/fpls.2022.935380}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={Resende, Rafael Tassinari and Chenu, Karine and Rasmussen, Soren K. and Heinemann, Alexandre Bryan and Fritsche-Neto, Roberto}, year={2022}, month={Jun} }
 @article{costa-neto_crespo-herrera_fradgley_gardner_bentley_dreisigacker_fritsche-neto_montesinos-lópez_crossa_2022, title={Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data}, url={https://doi.org/10.1093/g3journal/jkac313}, DOI={10.1093/g3journal/jkac313}, abstractNote={Abstract Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment–Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as “covariable selection” unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a “reinforcement learner” algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.}, journal={G3 Genes Genomes Genetics}, author={Costa-Neto, Germano and Crespo-Herrera, Leonardo and Fradgley, Nick and Gardner, Keith and Bentley, Alison R and Dreisigacker, Susanne and Fritsche-Neto, Roberto and Montesinos-López, Osval A and Crossa, Jose}, year={2022}, month={Dec} }
 @article{heinemann_costa-neto_fritsche-neto_da matta_fernandes_2022, title={Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil}, volume={286}, ISSN={0378-4290}, url={http://dx.doi.org/10.1016/j.fcr.2022.108628}, DOI={10.1016/J.FCR.2022.108628}, journal={Field Crops Research}, publisher={Elsevier BV}, author={Heinemann, Alexandre Bryan and Costa-Neto, Germano and Fritsche-Neto, Roberto and da Matta, David Henriques and Fernandes, Igor Kuivjogi}, year={2022}, month={Oct}, pages={108628} }
 @article{khanna_anumalla_catolos_bartholomé_fritsche-neto_platten_pisano_gulles_sta. cruz_ramos_et al._2022, title={Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines}, volume={15}, ISSN={1939-8425 1939-8433}, url={http://dx.doi.org/10.1186/s12284-022-00559-3}, DOI={10.1186/S12284-022-00559-3}, abstractNote={Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI's rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43-0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI's drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.}, number={1}, journal={Rice}, publisher={Springer Science and Business Media LLC}, author={Khanna, Apurva and Anumalla, Mahender and Catolos, Margaret and Bartholomé, Jérôme and Fritsche-Neto, Roberto and Platten, John Damien and Pisano, Daniel Joseph and Gulles, Alaine and Sta. Cruz, Ma Teresa and Ramos, Joie and et al.}, year={2022}, month={Mar} }
 @misc{crossa_montesinos-lópez_pérez-rodríguez_costa-neto_fritsche-neto_ortiz_martini_lillemo_montesinos-lópez_jarquin_et al._2022, title={Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction}, ISBN={9781071622049 9781071622056}, ISSN={1064-3745 1940-6029}, url={http://dx.doi.org/10.1007/978-1-0716-2205-6_9}, DOI={10.1007/978-1-0716-2205-6_9}, abstractNote={Abstract Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.}, journal={Methods in Molecular Biology}, publisher={Springer US}, author={Crossa, José and Montesinos-López, Osval Antonio and Pérez-Rodríguez, Paulino and Costa-Neto, Germano and Fritsche-Neto, Roberto and Ortiz, Rodomiro and Martini, Johannes W. R. and Lillemo, Morten and Montesinos-López, Abelardo and Jarquin, Diego and et al.}, year={2022}, pages={245–283} }
 @article{yassue_galli_chen_fritsche-neto_morota_2022, title={Genome-wide association analysis of hyperspectral reflectance data to dissect growth-related traits genetic architecture in maize under inoculation with plant growth-promoting bacteria}, url={https://doi.org/10.1101/2022.08.11.503682}, DOI={10.1101/2022.08.11.503682}, abstractNote={Abstract Plant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386–1,021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growthrelated traits under PGPB inoculation. Eight SNPs were detected, which were associated with manually measured and hyperspectral phenotypes. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multi-phenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.}, journal={BioRxiv}, author={Yassue, Rafael Massahiro and Galli, Giovanni and Chen, Chun-Peng James and Fritsche-Neto, Roberto and Morota, Gota}, year={2022}, month={Aug} }
 @article{dovale_carvalho_sabadin_fritsche-neto_2022, title={Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops}, url={https://doi.org/10.1007/s00122-022-04236-3}, DOI={10.1007/s00122-022-04236-3}, abstractNote={In genomic recurrent selection, the more markers, the better because they buffer the linkage disequilibrium losses caused by recombination over cycles, and consequently, provide higher responses to selection. Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, studies that focus on long selective breeding cycles generally do not consider GS models with the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models [additive, additive-dominant, general (GCA), and this plus specific combining ability (GCA + SCA)]. Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or GCA + SCA for reciprocal.}, journal={Theoretical and Applied Genetics}, author={DoVale, Júlio César and Carvalho, Humberto Fanelli and Sabadin, Felipe and Fritsche-Neto, Roberto}, year={2022}, month={Oct} }
 @book{fritsche-neto_borém_2022, place={Viçosa, MG}, title={Melhoramento de plantas para estresses abióticos}, publisher={UFV}, author={Fritsche-Neto, R. and Borém, A.}, year={2022} }
 @inbook{morosini_lyra_mendonca_vidotti_galli_santana_sabadin_fritsche neto_2022, place={Viçosa}, edition={2nd}, title={Melhoramento para eficiência no uso de nitrogênio}, booktitle={Melhoramento de plantas para estresses abiótico}, publisher={Editora UFV}, author={Morosini, Julia Sulva and Lyra, D.H. and Mendonca, L.F. and Vidotti, M.S. and Galli, G. and Santana, G.C. and Sabadin, Felipe and Fritsche Neto, R.}, editor={Fritsche-Neto, R. and Borém, A.Editors}, year={2022}, pages={93–114} }
 @article{platten_fritsche-neto_2022, title={Optimizing QTL introgression via stochastic simulations: an example of the IRRI rice breeding program}, url={https://doi.org/10.21203/rs.3.rs-1780978/v1}, DOI={10.21203/rs.3.rs-1780978/v1}, abstractNote={Abstract A key limitation in the ability of breeding programs to leverage benefits of major-gene marker-assisted selection is the availability of those genes in appropriate elite germplasm. In this context, our study compared three strategies to develop new recipients for QTL introgression (Background recovery (BG), Selective sweep (SS), and Breeding values (BV)) in a short-term breeding program (over five breeding cycles). Furthermore, we evaluated two different numbers of recipients (10 and 20) in the introgression process and how they influence the population performance and the QTL fixation over cycles. Finally, we used rice as a model of a self-pollinated crop and implemented stochastic simulations. Each strategy was simulated and replicated 40 times. Regardless of the selection strategy used, the QTL introgression resulted in substantial penalties in yield performance. However, introducing fewer new parents to the augmentation process minimized this effect. Conversely, the time required to achieve fixation of target QTLs showed substantial differences, with selection for BV during augmentation out-performing other methods. Overall, the BV_10 strategy (10 parents selected based on genomic estimated breeding values) displayed the best trade-off between reduced penalty from introducing new QTLs with a reasonable speed at which those QTLs can achieve fixation over subsequent breeding cycles.}, journal={Research Square (Research Square)}, author={Platten, John Damien and Fritsche-Neto, Roberto}, year={2022}, month={Jul} }
 @article{sabadin_dovale_platten_fritsche-neto_2022, title={Optimizing Self-Pollinated Crop Breeding Employing Genomic Selection: from Schemes to Updating Training Sets}, volume={3}, url={https://doi.org/10.21203/rs.3.rs-805463/v2}, DOI={10.21203/rs.3.rs-805463/v2}, abstractNote={Abstract Long-term breeding schemes employing genomic selection (GS) can boost the response to selection per year. Although several studies show that GS delivers a higher response to selection, only a few analyze the best strategy to employ it, specifically how often and in what manner the training set (TS) should be updated. Therefore, we used stochastic simulation to compare in a long-term breeding program of a hypothetical self-pollinated crop five different strategies to implement GS in the line fixation stage and four methods and sizes to update the TS. Moreover, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Finally, we compared these schemes to the traditional phenotypic selection and drift. Our results showed that the best scenario was using GS in F2 followed by the phenotypic selection of new parentals in F4. Furthermore, adding a new set of data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for many more generations. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that these results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.}, journal={Research Square (Research Square)}, publisher={Research Square Platform LLC}, author={Sabadin, Felipe and DoVale, Julio César and Platten, John and Fritsche-Neto, Roberto}, year={2022}, month={Mar} }
 @article{sabadin_dovale_platten_fritsche-neto_2022, title={Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets}, volume={13}, ISSN={1664-462X}, url={http://dx.doi.org/10.3389/fpls.2022.935885}, DOI={10.3389/FPLS.2022.935885}, abstractNote={Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.}, journal={Frontiers in Plant Science}, publisher={Frontiers Media SA}, author={Sabadin, Felipe and DoVale, Julio César and Platten, John Damien and Fritsche-Neto, Roberto}, year={2022}, month={Oct} }
 @article{fritsche-neto_sabadin_dovale_souza_borges_crossa_2022, title={Optimizing simultaneous selection in long-term breeding: a stochastic simulation study for a tropical corn haploid inducer population}, url={https://doi.org/10.21203/rs.3.rs-1952851/v1}, DOI={10.21203/rs.3.rs-1952851/v1}, abstractNote={<title>Abstract</title> Plant breeders widely use recurrent selection schemes to increase the frequency of favorable alleles for quantitative traits in a population. Although simultaneous selection is complex because it involves several traits combined with selection cycles, the use of selection indexes (SI) is applied to increase the chance of success of the breeding program. Moreover, many indices are available in the literature; therefore, simulations can help breeders determine which selection index can be adjusted better considering the selection goals, intensity, and genetic correlation among traits over breeding cycles. In this context, we aimed to optimize the simultaneous selection in long-term breeding programs via stochastic simulations using as an example a tropical maize inducer breeding. Furthermore, we proposed a new approach to optimize the initial weights for the Smith-Hazel method to maximize the genetic gains for all traits in a balanced way. Finally, our results confirm that the traditional Smith and Hazel approach outperformed other methods for the total and balanced response to selection for important traits in a tropical corn haploid inducer breeding population.}, author={Fritsche-Neto, Roberto and Sabadin, Felipe and doVale, Julio César and Souza, Pedro Henrique and Borges, Karina Lima Reis and Crossa, Jose}, year={2022}, month={Sep} }
 @inproceedings{fritsche neto_2022, title={Optimizing the Multi-Environment Trials in The US Rice Belt Via Smart-Climate-Soil Prediction Based-Models}, booktitle={LSU AgCenter Annual Meeting}, author={Fritsche Neto, Roberto}, year={2022} }
 @article{montesinos-lópez_montesinos-lópez_kismiantini_roman-gallardo_gardner_lillemo_fritsche-neto_crossa_2022, title={Partial Least Squares Enhances Genomic Prediction of New Environments}, volume={13}, ISSN={1664-8021}, url={http://dx.doi.org/10.3389/fgene.2022.920689}, DOI={10.3389/FGENE.2022.920689}, abstractNote={In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as “leave one environment out,” is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments.}, journal={Frontiers in Genetics}, publisher={Frontiers Media SA}, author={Montesinos-López, Osval A. and Montesinos-López, Abelardo and Kismiantini and Roman-Gallardo, Armando and Gardner, Keith and Lillemo, Morten and Fritsche-Neto, Roberto and Crossa, José}, year={2022}, month={Jul} }
 @article{rocha_benatti_de siqueira_de souza_bianchin_de souza_fernandes_oda_stape_yassue_et al._2022, title={Quantitative trait loci related to growth and wood quality traits in Eucalyptus grandis W. Hill identified through single- and multi-trait genome-wide association studies}, volume={18}, ISSN={1614-2942 1614-2950}, url={http://dx.doi.org/10.1007/s11295-022-01570-x}, DOI={10.1007/s11295-022-01570-x}, number={6}, journal={Tree Genetics & Genomes}, publisher={Springer Science and Business Media LLC}, author={Rocha, Lucas Fernandes and Benatti, Thiago Romanos and de Siqueira, Leandro and de Souza, Izabel Christina Gava and Bianchin, Isadora and de Souza, Aguinaldo José and Fernandes, Aline Cristina Miranda and Oda, Shinitiro and Stape, José Luiz and Yassue, Rafael Massahiro and et al.}, year={2022}, month={Oct} }
 @article{costa-neto_galli_carvalho_crossa_fritsche-neto_2021, title={<i>EnvRtype</i>: a software to interplay enviromics and quantitative genomics in agriculture}, volume={2}, url={https://doi.org/10.1093/g3journal/jkab040}, DOI={10.1093/g3journal/jkab040}, abstractNote={Abstract Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.}, journal={G3 Genes Genomes Genetics}, publisher={Oxford University Press (OUP)}, author={Costa-Neto, Germano and Galli, Giovanni and Carvalho, Humberto Fanelli and Crossa, José and Fritsche-Neto, Roberto}, editor={Koning, D-JEditor}, year={2021}, month={Feb} }
 @article{carvalho_aguiar-perecin_clarindo_fristche-neto_mondin_2021, title={A heterochromatic knob reducing the flowering time in maize}, url={https://doi.org/10.1101/2021.03.31.437909}, DOI={10.1101/2021.03.31.437909}, abstractNote={ABSTRACT Maize flowering time is an important agronomic trait, which has been associated with variations in the genome size and heterochromatic knobs content. We integrated three steps to show this association. Firstly, we selected inbred lines varying for heterochromatic knob composition at specific sites in the homozygous state. Then, we produced homozygous and heterozygous hybrids for knobs. Second, we measured the genome size and flowering time for all materials. Knob composition did not affect the genome size and flowering time. Finally, we developed an association study and identified a knob marker on chromosome 9 showing the strongest association with flowering time. Indeed, modelling allele substitution and dominance effects could offer only one heterochromatic knob locus that could affect flowering time, making it earlier rather than the knob composition.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, author={Carvalho, Renata Flávia and Aguiar-Perecin, Margarida Lopes Rodrigues and Clarindo, Wellington Ronildo and Fristche-Neto, Roberto and Mondin, Mateus}, year={2021}, month={Apr} }
 @article{yassue_galli_junior_cheng_morota_fritsche-neto_2021, title={A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: a case study in maize under inoculation with plant growth-promoting bacteria}, volume={8}, url={https://doi.org/10.1101/2021.08.12.456112}, DOI={10.1101/2021.08.12.456112}, abstractNote={Abstract Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP-based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth. Core ideas A low-cost greenhouse-based HTP platform was developed. Image-derived phenotypes presented moderate to high genomic heritabilities and correlations. Plant growth-promoting bacteria can improve plant resilience under nitrogen-limited conditions.}, journal={BioRxiv}, publisher={Cold Spring Harbor Laboratory}, author={Yassue, Rafael Massahiro and Galli, Giovanni and Junior, Ronaldo Borsato and Cheng, Hao and Morota, Gota and Fritsche-Neto, Roberto}, year={2021}, month={Aug} }
 @article{nonato_carvalho_borges_padilha_maluf_fritsche-neto_filho_2021, title={Association mapping reveals genomic regions associated with bienniality and resistance to biotic stresses in arabica coffee}, volume={217}, url={https://doi.org/10.1007/s10681-021-02922-9}, DOI={10.1007/s10681-021-02922-9}, abstractNote={The bienniality of production and the incidence of pests and diseases, such as coffee leaf miner and coffee leaf rust, stands out among the factors that limit coffee crop yield. Obtaining cultivars with greater stability in production and resistance to these biotic agents are among the main objectives of coffee breeding programs. In this way, biotechnological tools such as Genomic Wide Association Studies (GWAS) can increase these programs' efficacy since they allow the identification of molecular markers significantly associated with phenotypes of interest. In this context, the aim here is to identify genomic regions associated with yield, bienniality, and resistance to coffee leaf miner and coffee leaf rust in arabica coffee progenies. Thus, a population (n=597) was evaluated for resistance to biotic stresses and for the eight designed scenarios to study yield and bienniality. A matrix of 4,666 SNPs (Single Nucleotide Polymorphism) was built through Genotyping by Sequencing (GBS). After the genomic association analyses, we identified 12 potential SNPs markers associated with resistance to coffee leaf miner and coffee leaf rust, 32 associated with the eight designed scenarios to study yield and bienniality. Of the 44 SNPs significantly associated with this study's traits, 36 were noted in genomic regions responsible for biological processes related to plant response to biotic and abiotic stresses. In addition, four markers were coincident with yield and traits related to coffee leaf rust resistance. The genomic regions identified in this study can be incorporated into the coffee breeding program, through assisted selection, leading to more efficient breeding strategies in coffee.}, number={10}, journal={Euphytica}, publisher={Springer Science and Business Media LLC}, author={Nonato, Juliana Vieira Almeida and Carvalho, Humberto Fanelli and Borges, Karina Lima Reis and Padilha, Lilian and Maluf, Mirian Perez and Fritsche-Neto, Roberto and Filho, Oliveiro Guerreiro}, year={2021}, month={Sep} }
 @article{galli_sabadin_yassue_souza_carvalho_fritsche-neto_2021, title={Automated Machine Learning: a case study of genomic “image-based” prediction in maize hybrids}, url={https://doi.org/10.21203/rs.3.rs-840380/v2}, DOI={10.21203/rs.3.rs-840380/v2}, abstractNote={<title>Abstract</title> Machine learning methods such as Multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this sense, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images”. In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP but improved a little using only the additive genomic layer. It is expected that the average effect of allele substitution is mostly linear. Nevertheless, the methodology’s potential for GP is unprecedented because we can create “multispectral genome images,” including other effects and layers of data, such as dominance, epistasis, g × e, transcriptome, and so on, capturing linear and non-linear effects and boosting prediction accuracies. Hence, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.}, author={Galli, Giovanni and Sabadin, Felipe and Yassue, Rafael Massahiro and Souza, Cassia Galves and Carvalho, Humberto Fanelli and Fritsche-Neto, Roberto}, year={2021}, month={Aug} }
 @article{galli_sabadin_yassue_souza_carvalho_fritsche-neto_2021, title={Automated Machine Learning: a case study of genomic “image-based” prediction in maize hybrids}, url={https://doi.org/10.21203/rs.3.rs-840380/v1}, DOI={10.21203/rs.3.rs-840380/v1}, abstractNote={<title>Abstract</title> Machine learning methods such as Multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this sense, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images”. In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP but improved a little using only the additive genomic layer. It is expected that the average effect of allele substitution is mostly linear. Nevertheless, the methodology’s potential for GP is unprecedented because we can create “multispectral genome images,” including other effects and layers of data, such as dominance, epistasis, g × e, transcriptome, and so on, capturing linear and non-linear effects and boosting prediction accuracies. Hence, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.}, author={Galli, Giovanni and Sabadin, Felipe and Yassue, Rafael Massahiro and Souza, Cassia Galves and Carvalho, Humberto Fanelli and Fritsche-Neto, Roberto}, year={2021}, month={Aug} }
 @article{yassue_sabadin_galli_alves_fritsche-neto_2021, title={CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction}, volume={217}, url={https://doi.org/10.1007/s10681-021-02831-x}, DOI={10.1007/s10681-021-02831-x}, number={6}, journal={Euphytica}, publisher={Springer Science and Business Media LLC}, author={Yassue, Rafael Massahiro and Sabadin, Felipe and Galli, Giovanni and Alves, Filipe Couto and Fritsche-Neto, Roberto}, year={2021}, month={May} }
 @article{chen_saradadevi_vidotti_fritsche-neto_crossa_siddique_cowling_2021, title={Correction to: Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress}, volume={217}, url={https://doi.org/10.1007/s10681-021-02892-y}, DOI={10.1007/s10681-021-02892-y}, abstractNote={The article Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress, written by Sheng Chen, Renu Saradadevi, Miriam S. Vidotti, Roberto Fritsche-Neto, Jose Crossa, Kadambot H. M. Siddique, Wallace A. Cowling, was originally published Online First without Open Access. After publication in volume 217: 117 the author decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2021 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.}, number={8}, journal={Euphytica}, publisher={Springer Science and Business Media LLC}, author={Chen, Sheng and Saradadevi, Renu and Vidotti, Miriam S. and Fritsche-Neto, Roberto and Crossa, Jose and Siddique, Kadambot H. M. and Cowling, Wallace A.}, year={2021}, month={Jul} }
 @article{costa-neto_crossa_fritsche-neto_2021, title={Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize}, url={https://publons.com/wos-op/publon/52466942/}, DOI={10.3389/FPLS.2021.717552}, abstractNote={Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an “enviromic assembly approach,” which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.}, journal={Frontiers in Plant Science}, author={Costa-Neto, Germano and Crossa, Jose and Fritsche-Neto, Roberto}, year={2021}, month={Oct} }
 @article{costa-neto_crossa_fritsche-neto_2021, title={Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity}, volume={6}, url={https://doi.org/10.1101/2021.06.04.447091}, DOI={10.1101/2021.06.04.447091}, abstractNote={ABSTRACT Quantitative genetics states that phenotypic variation is a consequence of genetic and environmental factors and their subsequent interaction. Here, we present an enviromic assembly approach, which includes the use of ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as E-GP). We propose that the quality of an environment is defined by the core of environmental typologies (envirotype) and their frequencies, which describe different zones of plant adaptation. From that, we derive markers of environmental similarity cost-effectively. Combined with the traditional genomic sources (e.g., additive and dominance effects), this approach may better represent the putative phenotypic variation across diverse growing conditions (i.e., phenotypic plasticity). Additionally, we couple a genetic algorithm scheme to design optimized multi-environment field trials (MET), combining enviromic assembly and genomic kinships to provide in-silico realizations of the future genotype-environment combinations that must be phenotyped in the field. As a proof-of-concept, we highlight E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity using optimized phenotyping efforts. Our approach was tested using two non-conventional cross-validation schemes to better visualize the benefits of enviromic assembly in sparse experimental networks. Results on tropical maize show that E-GP outperforms benchmark GP in all scenarios and cases tested. We show that for training accurate GP models, the genotype-environment combinations’ representativeness is more critical than the MET size. Furthermore, we discuss theoretical backgrounds underlying how the intrinsic envirotype-phenotype covariances within the phenotypic records of (MET) can impact the accuracy of GP and limits the potentialities of predictive breeding approaches. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, publisher={Cold Spring Harbor Laboratory}, author={Costa-Neto, Germano and Crossa, Jose and Fritsche-Neto, Roberto}, year={2021}, month={Jun} }
 @article{gevartosky_carvalho_costa-neto_montesinos-lópez_crossa_fritsche-neto_2021, title={Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize}, volume={6}, url={https://doi.org/10.1101/2021.06.11.448049}, DOI={10.1101/2021.06.11.448049}, abstractNote={Abstract Genomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G × E) (M2), enviromic data (W) (M3), W+G × E (M4), and finally W+G × W (M5), where G × W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, publisher={Cold Spring Harbor Laboratory}, author={Gevartosky, Raysa and Carvalho, Humberto Fanelli and Costa-Neto, Germano and Montesinos-López, Osval A. and Crossa, José and Fritsche-Neto, Roberto}, year={2021}, month={Jun} }
 @article{costa-neto_fritsche-neto_2021, title={Enviromics: bridging different sources of data, building one framework}, url={https://doi.org/10.1590/1984-70332021v21sa25}, DOI={10.1590/1984-70332021v21sa25}, abstractNote={Enviromics is the field of applied data science that integrates databases of environmental factors into biostatistics and quantitative genetics. It can leverage plant ecophysiology knowledge to bridge the gaps about environment interactions with systems biology (genes, transcripts, proteins, and metabolites), which also boosts the ability to understand and model the phenotypic plasticity of the main agronomic traits. Recently, the plant breeding community has experienced reduced costs for acquiring environmental sensors to be installed in the field trials while increasing the reliability and resolution of the remote sensing techniques. The combination of those two factors has started the spring of enviromics-aided breeding in recent years. However, the use of environmental information in plant breeding is not a novelty approach developed a few years ago, but a core of efforts originated in the last 60 years, yet some basic ideas traced back to early 20th century attempts to establish a relationship between phenotypic and environmental variation. This review highlights the main concepts surrounding the construction of the “modern enviromics science”, tracing back to its origins in the last decades. Finally, we present how this field has helped integrate different data sources in prediction-based models or build one framework.}, journal={Crop Breeding and Applied Biotechnology}, author={Costa-Neto, Germano and Fritsche-Neto, Roberto}, year={2021}, month={Jan} }
 @article{chen_saradadevi_vidotti_fritsche-neto_crossa_siddique_cowling_2021, title={Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress}, volume={217}, url={https://doi.org/10.1007/s10681-021-02859-z}, DOI={10.1007/s10681-021-02859-z}, abstractNote={Abstract Oilseed rape ( Brassica napus L.) is sensitive to heat stress during the reproductive stage, but it is not clear whether the male and female reproductive organs differ in their sensitivity to heat stress. In this study, full diallel crossing experiments were conducted among four genotypes of B. napus under control, moderate and high heat stress conditions for five days immediately before and two days after crossing. General combining ability (GCA), specific combining ability (SCA) and reciprocal effects were analyzed to evaluate the genetic basis of heat stress tolerance in male and female reproductive organs. High female temperature (Tf) and high male temperature (Tm) reduced the number of fertile pods and seeds set per floret, and the significant Tf × Tm interaction indicated that female reproductive organs were more sensitive to heat stress than male reproductive organs. There were no overall GCA, SCA or reciprocal effects across all combinations of Tf and Tm. However, a significant reciprocal × Tf effect was found, suggesting that genotypes differed in their ability to set fertile pods and seeds as Tf increased. The relative heat tolerance of G1 as a female increased as Tf increased, and the relative heat tolerance of G2 as a male decreased as Tf increased. In summary, reciprocal diallel crossing has demonstrated that female reproductive organs of B. napus are more sensitive than male to transient heat stress at the early flowering stage, and genotypes differ in relative heat tolerance in the male and female reproductive organs as Tf increases.}, number={6}, journal={Euphytica}, publisher={Springer Science and Business Media LLC}, author={Chen, Sheng and Saradadevi, Renu and Vidotti, Miriam S. and Fritsche-Neto, Roberto and Crossa, Jose and Siddique, Kadambot H. M. and Cowling, Wallace A.}, year={2021}, month={May} }
 @article{paulino_almeida_bueno_song_fritsche-neto_carbonell_chiorato_benchimol-reis_2021, title={Genome-Wide Association Study Reveals Genomic Regions Associated with Fusarium Wilt Resistance in Common Bean}, url={https://publons.com/wos-op/publon/46412284/}, DOI={10.3390/GENES12050765}, abstractNote={Fusarium wilt (Fusarium oxysporum f. sp. phaseoli, Fop) is one of the main fungal soil diseases in common bean. The aim of the present study was to identify genomic regions associated with Fop resistance through genome-wide association studies (GWAS) in a Mesoamerican Diversity Panel (MDP) and to identify potential common bean sources of Fop’s resistance. The MDP was genotyped with BARCBean6K_3BeadChip and evaluated for Fop resistance with two different monosporic strains using the root-dip method. Disease severity rating (DSR) and the area under the disease progress curve (AUDPC), at 21 days after inoculation (DAI), were used for GWAS performed with FarmCPU model. The p-value of each SNP was determined by resampling method and Bonferroni test. For UFV01 strain, two significant single nucleotide polymorphisms (SNPs) were mapped on the Pv05 and Pv11 for AUDPC, and the same SNP (ss715648096) on Pv11 was associated with AUDPC and DSR. Another SNP, mapped on Pv03, showed significance for DSR. Regarding IAC18001 strain, significant SNPs on Pv03, Pv04, Pv05, Pv07 and on Pv01, Pv05, and Pv10 were observed. Putative candidate genes related to nucleotide-binding sites and carboxy-terminal leucine-rich repeats were identified. The markers may be important future tools for genomic selection to Fop disease resistance in beans.}, journal={Genes}, author={Paulino, Jean and Almeida, Caléo and Bueno, César and Song, Qijian and Fritsche-Neto, Roberto and Carbonell, Sérgio and Chiorato, Alisson and Benchimol-Reis, Luciana}, year={2021}, month={May} }
 @article{almeida_carvalho paulino_barbosa_moraes cunha gonçalves_fritsche-neto_carbonell_chiorato_benchimol-reis_2021, title={Genome-wide association mapping reveals race-specific SNP markers associated with anthracnose resistance in carioca common beans}, url={https://publons.com/wos-op/publon/46412283/}, DOI={10.1371/JOURNAL.PONE.0251745}, abstractNote={Brazil is the largest consumer of dry edible beans ( Phaseolus vulgaris L.) in the world, 70% of consumption is of the carioca variety. Although the variety has high yield, it is susceptible to several diseases, among them, anthracnose (ANT) can lead to losses of up to 100% of production. The most effective strategy to overcome ANT, a disease caused by the fungus Colletotrichum lindemuthianum , is the development of resistant cultivars. For that reason, the selection of carioca genotypes resistant to multiple ANT races and the identification of loci /markers associated with genetic resistance are extremely important for the genetic breeding process. Using a carioca diversity panel (CDP) with 125 genotypes and genotyped by BeadChip BARCBean6K_3 and a carioca segregating population AM (AND-277 × IAC-Milênio) genotyped by sequencing (GBS). Multiple interval mapping (MIM) and genome-wide association studies (GWAS) were used as mapping tools for the resistance genes to the major ANT physiological races present in the country. In general, 14 single nucleotide polymorphisms (SNPs) showed high significance for resistance by GWAS, and loci associated with multiple races were also identified, as the Co-3 locus . The SNPs ss715642306 and ss715649427 in linkage disequilibrium (LD) at the beginning of chromosome Pv04 were associated with all the races used, and 16 genes known to be related to plant immunity were identified in this region. Using the resistant cultivars and the markers associated with significant quantitative resistance loci (QRL), discriminant analysis of principal components (DAPC) was performed considering the allelic contribution to resistance. Through the DAPC clustering, cultivar sources with high potential for durable anthracnose resistance were recommended. The MIM confirmed the presence of the Co-1 4 locus in the AND-277 cultivar which revealed that it was the only one associated with resistance to ANT race 81. Three other loci were associated with race 81 on chromosomes Pv03, Pv10, and Pv11. This is the first study to identify new resistance loci in the AND-277 cultivar. Finally, the same Co-1 4 locus was also significant for the CDP at the end of Pv01. The new SNPs identified, especially those associated with more than one race, present great potential for use in marker-assisted and early selection of inbred lines.}, journal={PLoS ONE}, author={Almeida, Caléo Panhoca and Carvalho Paulino, Jean Fausto and Barbosa, Caio Cesar Ferrari and Moraes Cunha Gonçalves, Gabriel and Fritsche-Neto, Roberto and Carbonell, Sérgio Augusto Morais and Chiorato, Alisson Fernando and Benchimol-Reis, Luciana Lasry}, year={2021}, month={May} }
 @article{alves_galli_matias_vidotti_morosini_fritsche-neto_2021, title={Impact of the complexity of genotype by environment and dominance modeling on the predictive accuracy of maize hybrids in multi-environment prediction models}, url={https://publons.com/wos-op/publon/39689046/}, DOI={10.1007/S10681-021-02779-Y}, journal={Euphytica}, author={Alves, Filipe Couto and Galli, Giovanni and Matias, Filipe Inácio and Vidotti, Miriam Suzane and Morosini, Júlia Silva and Fritsche-Neto, Roberto}, year={2021}, month={Feb} }
 @article{sabadin_galli_borsato_gevartosky_campos_fritsche‐neto_2021, title={Improving the identification of haploid maize seeds using convolutional neural networks}, url={https://doi.org/10.1002/csc2.20487}, DOI={10.1002/csc2.20487}, abstractNote={Abstract A critical step toward the success of the doubled haploid (DH) technique is the haploid identification within induction crosses. The R1‐nj marker is the principal mechanism employed in this task enabling the selection of haploids at the seed stage. Although it seems easy to identify haploid seeds, this task is performed manually by visual classification, which becomes an inefficient process in terms of time and labor. Also, differential phenotypic expression of the R1‐nj marker results in high rates of false positives among haploid seeds. For the first time, an image‐based convolutional neural network (CNN) was trained to identify true positives among putative haploid seeds. The experiment was conducted using 3,000 maize ( Zea mays L.) seeds from induction crosses classified as haploid (1,000), diploid (1,000), and inhibited (1,000) class. Images were taken from each seed, and then seeds were planted in the field to confirm their ploidy. For putative haploids ( R1‐nj phenotype), the classification accuracy on average was 94.39%, 97.07% for the haploid class, and 91.71% for the diploid class. However, the CNN model was unable to distinguish true haploid seeds among the putative haploid class, which indicates that CNN did not recognize different patterns between them. Finally, we provided a highly accurate and trained CNN model to the scientific community to classify haploid maize seeds via R1‐nj , which can support maize breeders to optimize DH pipelines, mainly for small breeding programs with limited resources.}, journal={Crop Science}, author={Sabadin, Felipe and Galli, Giovanni and Borsato, Ronaldo and Gevartosky, Raysa and Campos, Gabriela Romêro and Fritsche‐Neto, Roberto}, year={2021}, month={Feb} }
 @article{barbosa_fritsche-neto_andrade_petroli_burgueño_galli_willcox_sonder_vidal-martínez_sifuentes-ibarra_et al._2021, title={Introgression of Maize Diversity for Drought Tolerance: Subtropical Maize Landraces as Source of New Positive Variants}, url={https://publons.com/wos-op/publon/52466943/}, DOI={10.3389/FPLS.2021.691211}, abstractNote={Current climate change models predict an increased frequency and intensity of drought for much of the developing world within the next 30 years. These events will negatively affect maize yields, potentially leading to economic and social instability in many smallholder farming communities. Knowledge about the genetic resources available for traits related to drought tolerance has great importance in developing breeding program strategies. The aim of this research was to study a maize landrace introgression panel to identify chromosomal regions associated with a drought tolerance index. For that, we performed Genome-Wide Association Study (GWAS) on 1326 landrace progenies developed by the CIMMYT Genetic Resources Program, originating from 20 landraces populations collected in arid regions. Phenotypic data were obtained from early testcross trials conducted in three sites and two contrasting irrigation environments, full irrigation (well-watered) and reduced irrigation (drought). The populations were genotyped using the DArTSeq ® platform, and a final set of 5,695 SNPs markers was used. The genotypic values were estimated using spatial adjustment in a two-stage analysis. First, we performed the individual analysis for each site/irrigation treatment combination. The best linear unbiased estimates (BLUEs) were used to calculate the Harmonic Mean of Relative Performance (HMRP) as a drought tolerance index for each testcross. The second stage was a joint analysis, which was performed using the HMRP to obtain the best linear unbiased predictions (BLUPs) of the index for each genotype. Then, GWAS was performed to determine the marker-index associations and the marker-Grain Yield (GY) associations for the two irrigation treatments. We detected two significant markers associated with the drought-tolerance index, four associated with GY in drought condition, and other four associated with GY in irrigated conditions each. Although each of these markers explained less than 0.1% of the phenotypic variation for the index and GY, we found two genes likely related to the plant response to drought stress. For these markers, alleles from landraces provide a slightly higher yield under drought conditions. Our results indicate that the positive diversity delivered by landraces are still present on the backcrosses and this is a potential breeding strategy for improving maize for drought tolerance and for trait introgression bringing new superior allelic diversity from landraces to breeding populations.}, journal={Frontiers in Plant Science}, author={Barbosa, Pedro Augusto Medeiros and Fritsche-Neto, Roberto and Andrade, Marcela Carvalho and Petroli, César Daniel and Burgueño, Juan and Galli, Giovanni and Willcox, Martha C. and Sonder, Kai and Vidal-Martínez, Víctor A. and Sifuentes-Ibarra, Ernesto and et al.}, year={2021}, month={Sep} }
 @article{yassue_carvalho_gevartosky_sabadin_souza_bonatelli_azevedo_quecine_fritsche-neto_2021, title={On the genetic architecture in a public tropical maize panel of the symbiosis between corn and plant growth-promoting bacteria aiming to improve plant resilience}, url={https://publons.com/wos-op/publon/49874990/}, DOI={10.1007/S11032-021-01257-6}, abstractNote={{"Label"=>"UNLABELLED"} Exploring the symbiosis between plants and plant growth-promoting bacteria (PGPB) is a new challenge for sustainable agriculture. Even though many works have reported the beneficial effects of PGPB in increasing plant resilience for several stresses, its potential is not yet widely explored. One of the many reasons is the differential symbiosis performance depending on the host genotype. This opens doors to plant breeding programs to explore the genetic variability and develop new cultivars with higher responses to PGPB interaction and, therefore, have higher resilience to stress. Hence, we aimed to study the genetic architecture of the symbiosis between PGPB and tropical maize germplasm, using a public association panel and its impact on plant resilience. Our findings reveal that the synthetic PGPB population can modulate and impact root architecture traits and improve resilience to nitrogen stress, and 37 regions were significant for controlling the symbiosis between PGPB and tropical maize. In addition, we found two overlapping SNPs in the GWAS analysis indicating strong candidates for further investigations. Furthermore, genomic prediction analysis with genomic relationship matrix computed using only significant SNPs obtained from GWAS analysis substantially increased the predictive ability for several traits endorsing the importance of these genomic regions for the response of PGPB. Finally, the public tropical panel reveals a significant genetic variability to the symbiosis with the PGPB and can be a source of alleles to improve plant resilience. {"Label"=>"SUPPLEMENTARY INFORMATION", "NlmCategory"=>"UNASSIGNED"} The online version contains supplementary material available at 10.1007/s11032-021-01257-6.}, journal={Molecular Breeding}, author={Yassue, Rafael Massahiro and Carvalho, Humberto Fanelli and Gevartosky, Raysa and Sabadin, Felipe and Souza, Pedro Henrique and Bonatelli, Maria Leticia and Azevedo, João Lúcio and Quecine, Maria Carolina and Fritsche-Neto, Roberto}, year={2021}, month={Oct} }
 @article{fritsche-neto_galli_borges_costa-neto_alves_sabadin_lyra_morais_andrade_granato_et al._2021, title={Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review}, url={https://publons.com/wos-op/publon/52466944/}, DOI={10.3389/FPLS.2021.658267}, abstractNote={The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype–environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.}, journal={Frontiers in Plant Science}, author={Fritsche-Neto, Roberto and Galli, Giovanni and Borges, Karina Lima Reis and Costa-Neto, Germano and Alves, Filipe Couto and Sabadin, Felipe and Lyra, Danilo Hottis and Morais, Pedro Patric Pinho and Andrade, Luciano Rogério Braatz and Granato, Italo and et al.}, year={2021}, month={Jul} }
 @article{sabadin_dovale_platten_fritsche-neto_2021, title={Optimizing self-pollinated crop breeding employing genomic selection: from schemes to updating training sets}, volume={8}, url={https://doi.org/10.21203/rs.3.rs-805463/v1}, DOI={10.21203/rs.3.rs-805463/v1}, abstractNote={<title>Abstract</title> Long-term breeding schemes employing genomic selection (GS) can boost the response to selection per year. Although several studies show that GS delivers a higher response to selection, only a few analyze the best strategy to employ it, specifically how often and in what manner the training set (TS) should be updated. Therefore, we used stochastic simulation to compare in a long-term breeding program of a hypothetical self-pollinated crop five different strategies to implement GS in the line fixation stage and four methods and sizes to update the TS. Moreover, among breeding schemes, we proposed a new approach for using GS to select the best individuals in each F2 progeny based on genomic estimated breeding and divergence and crossed them to generate a new recombination event. Finally, we compared these schemes to the traditional phenotypic selection and drift. Our results showed that using GS in F2 followed by the phenotypic selection of new parentals in F4 was the best scenario. Furthermore, adding a new set of training data every cycle (over 800) to update the TS maintains the accuracy at satisfactory levels for many more generations, showing that more data is better than optimizing the genetic relationship between TS and the targeted population in a closed system. Hence, we believe that these results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.}, publisher={Research Square Platform LLC}, author={Sabadin, Felipe and DoVale, Julio César and Platten, John and Fritsche-Neto, Roberto}, year={2021}, month={Aug} }
 @article{sabadin_carvalho_galli_fritsche-neto_2021, title={Population-tailored mock genome enables genomic studies in species without a reference genome}, volume={297}, url={https://doi.org/10.1007/s00438-021-01831-9}, DOI={10.1007/s00438-021-01831-9}, abstractNote={Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.}, number={1}, journal={Molecular Genetics and Genomics}, publisher={Springer Science and Business Media LLC}, author={Sabadin, Felipe and Carvalho, Humberto Fanelli and Galli, Giovanni and Fritsche-Neto, Roberto}, year={2021}, month={Nov}, pages={33–46} }
 @article{silva_dovale_fritsche-neto_marques_2021, title={Projeção GGE biplot na inferência de adaptabilidade e estabilidade da soja em um centro agrícola do Paraná, Brasil}, url={https://publons.com/wos-op/publon/48119343/}, DOI={10.5935/1806-6690.20210009}, abstractNote={The state of Paraná is among the main producers of soybeans not only in Brazil, but in the World. However, it presents considerable edaphoclimatic variation throughout its area. This is one of the main causes genotype-by-environment interactions, hindering the selective process as well as the recommendation of cultivars in the state. Therefore, the objective of this study was: (i) to identify the environment that make it possible to represent the conditions of the state of Paraná, in order to facilitate the selection and recommendation of cultivars in future breeding programs; (2) to identify soybean inbres lines stable and adapted to state of Paraná. For this, data from trials in the agricultural year of 2013/2014 conducted with 24 soybeansinbred lines in 18 locations. Genotypic differences were observed with a level of accuracy of 0.93. Despite the environmental differences, it was possible to explain approximately 70% of the global variation of the data with the first three main components. Based on the biplots, it was verified that the locality of Record-PR was the most discriminant and representative, whereas Iporã-PR provided lower. In general, inbred lines 5, 9, 23 and 24 showed good adaptability and stability as well as high grain yield.}, journal={Ciência Agronômica/Revista ciência agronômica}, author={Silva, Kadson Emmanuel Frutuoso and DoVale, Júlio César and Fritsche-Neto, Roberto and Marques, Jean Newton}, year={2021}, month={Jan} }
 @article{dovale_carvalho_sabadin_fritsche-neto_2021, title={Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops}, url={https://doi.org/10.1101/2021.03.05.434084}, DOI={10.1101/2021.03.05.434084}, abstractNote={ABSTRACT The selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using “static” training sets and populations, which are adequate only to predict a breeding program’s initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequili brium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, author={DoVale, Júlio César and Carvalho, Humberto Fanelli and Sabadin, Felipe and Fritsche-Neto, Roberto}, year={2021}, month={Mar} }
 @article{ladha_radanielson_rutkoski_buresh_dobermann_angeles_pabuayon_santos-medellín_fritsche-neto_chivenge_et al._2021, title={Steady agronomic and genetic interventions are essential for sustaining productivity in intensive rice cropping}, url={https://publons.com/wos-op/publon/50100430/}, DOI={10.1073/PNAS.2110807118}, abstractNote={Significance Steady agronomic and genetic interventions helped sustain high annual rice production in an intensive irrigated monoculture system under a changing climate. However, the system did not achieve the increases in yield required to keep pace with the growing global demand for rice because annual yield potential was stagnant, and apparent biotic constraints limited yield in the wet season.}, journal={Proceedings of the National Academy of Sciences}, author={Ladha, Jagdish K. and Radanielson, Ando M. and Rutkoski, Jessica Elaine and Buresh, Roland J. and Dobermann, Achim and Angeles, Olivyn and Pabuayon, Irish Lorraine B. and Santos-Medellín, Christian and Fritsche-Neto, Roberto and Chivenge, Pauline and et al.}, year={2021}, month={Nov} }
 @article{crossa_fritsche-neto_montesinos-lopez_costa-neto_dreisigacker_montesinos-lopez_bentley_2021, title={The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data}, url={https://publons.com/wos-op/publon/46362020/}, DOI={10.3389/FPLS.2021.651480}, abstractNote={OPINION article Front. Plant Sci., 16 April 2021Sec. Plant Breeding https://doi.org/10.3389/fpls.2021.651480}, journal={Frontiers in Plant Science}, author={Crossa, Jose and Fritsche-Neto, Roberto and Montesinos-Lopez, Osval A. and Costa-Neto, Germano and Dreisigacker, Susanne and Montesinos-Lopez, Abelardo and Bentley, Alison R.}, year={2021}, month={Apr} }
 @article{francisco_aono_silva_souza gonçalves_scaloppi_guen_neto_souza_souza_2021, title={Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches}, volume={8}, url={https://doi.org/10.1101/2021.08.16.456528}, DOI={10.1101/2021.08.16.456528}, abstractNote={Abstract Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, publisher={Cold Spring Harbor Laboratory}, author={Francisco, Felipe Roberto and Aono, Alexandre Hild and Silva, Carla Cristina and Souza Gonçalves, Paulo and Scaloppi, Erivaldo José and Guen, Vincent Le and Neto, Roberto Fritsche and Souza, Livia Moura and Souza, Anete Pereira}, year={2021}, month={Aug} }
 @article{francisco_aono_silva_gonçalves_junior_guen_fritsche-neto_souza_souza_2021, title={Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches}, url={https://publons.com/wos-op/publon/50746261/}, DOI={10.3389/FPLS.2021.768589}, abstractNote={Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.}, journal={Frontiers in Plant Science}, author={Francisco, Felipe Roberto and Aono, Alexandre Hild and Silva, Carla Cristina and Gonçalves, Paulo S. and Junior, Erivaldo J. Scaloppi and Guen, Vincent Le and Fritsche-Neto, Roberto and Souza, Livia Moura and Souza, Anete Pereira}, year={2021}, month={Dec} }
 @article{costa-neto_galli_carvalho_crossa_fritsche-neto_2020, title={<i>EnvRtype</i>: a software to interplay enviromics and quantitative genomics in agriculture}, url={https://doi.org/10.1101/2020.10.14.339705}, DOI={10.1101/2020.10.14.339705}, abstractNote={ABSTRACT Envirotyping is an essential technique used to unfold the non-genetic drivers associated with the phenotypic adaptation of living organisms. Here we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically-smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.}, journal={bioRxiv (Cold Spring Harbor Laboratory)}, author={Costa-Neto, Germano and Galli, Giovanni and Carvalho, Humberto Fanelli and Crossa, José and Fritsche-Neto, Roberto}, year={2020}, month={Oct} }
 @article{matos_neto_dovale_magalhães bertini_fritsche-neto_2020, title={A new proposal for the m + a methodology in segregating populations of cowpea}, volume={79}, url={https://doi.org/10.1590/1678-4499.20190271}, DOI={10.1590/1678-4499.20190271}, abstractNote={ABSTRACT The evaluation of segregating populations in plant breeding programs is an onerous and time-consuming process. Early identification of populations with genetic potential can be done by m + a methodology. However, the possibility of a modification in the traditional methodology in order to make it more efficient, that is, faster and cheaper, was envisaged. Thus, the objective of this study was to compare the genetic gains obtained by both methodologies, the traditional one and the proposed modification. For this, ten segregating bean-cowpea populations were evaluated at two distinct levels of homozygous F3:4 and F3:5. Genetic values were predicted by two different statistical genetic models. This was possible due to the methodology proposed here to present a much shorter execution time than the traditional methodology. Thus, with a shorter evaluation time, the breeding program manager can plan the evaluation of a larger number of populations in a short time.}, number={2}, journal={Bragantia}, publisher={FapUNIFESP (SciELO)}, author={Matos, Renata Fernandes and Neto, Antônio Moreira Barroso and DoVale, Júlio César and Magalhães Bertini, Cândida Hermínia Campos and Fritsche-Neto, Roberto}, year={2020}, month={May}, pages={242–249} }
 @article{galli_sabadin_costa-neto_fritsche-neto_2020, title={A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes}, url={https://publons.com/wos-op/publon/39359930/}, DOI={10.1007/S00122-020-03726-6}, abstractNote={It is possible to make inferences regarding the feasibility and applicability of plant high-throughput phenotyping via computer simulations. Protocol validation has been a key challenge to the establishment of high-throughput phenotyping (HTP) in breeding programs. We add to this matter by proposing an innovative way for designing and validating aerial imagery-based HTP approaches with in silico 3D experiments for plant breeding purposes. The algorithm is constructed following a pipeline composed of the simulation of phenotypic values, three-dimensional modeling of trials, and image rendering. Our tool is exemplified by testing a set of experimental setups that are of interest in the context of maize breeding using a comprehensive case study. We report on how the choice of (percentile of) points in dense clouds, the experimental repeatability (heritability), the treatment variance (genetic variability), and the flight altitude affect the accuracy of high-throughput plant height estimation based on conventional structure-from-motion (SfM) and multi-view stereo (MVS) pipelines. The evaluation of both the algorithm and the case study was driven by comparisons of the computer-simulated (ground truth) and the HTP-estimated values using correlations, regressions, and similarity indices. Our results showed that the 3D experiments can be adequately reconstructed, enabling inference-making. Moreover, it suggests that treatment variance, repeatability, and the choice of the percentile of points are highly influential over the accuracy of HTP. Conversely, flight altitude influenced the quality of reconstruction but not the accuracy of plant height estimation. Therefore, we believe that our tool can be of high value, enabling the promotion of new insights and further understanding of the events underlying the practice of high-throughput phenotyping.}, journal={Theoretical and Applied Genetics}, author={Galli, Giovanni and Sabadin, Felipe and Costa-Neto, Germano Martins Ferreira and Fritsche-Neto, Roberto}, year={2020}, month={Nov} }
 @article{yassue_sabadin_galli_alves_fritsche-neto_2020, title={CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction}, url={https://doi.org/10.1101/2020.11.11.376343}, DOI={10.1101/2020.11.11.376343}, abstractNote={Abstract Usually, the comparison among genomic prediction models is based on validation schemes as Repeated Random Subsampling (RRS) or K-fold cross-validation. Nevertheless, the design of training and validation sets has a high effect on the way and subjectiveness that we compare models. Those procedures cited above have an overlap across replicates that might cause an overestimated estimate and lack of residuals independence due to resampling issues and might cause less accurate results. Furthermore, posthoc tests, such as ANOVA, are not recommended due to assumption unfulfilled regarding residuals independence. Thus, we propose a new way to sample observations to build training and validation sets based on cross-validation alpha-based design (CV-α). The CV-α was meant to create several scenarios of validation (replicates x folds), regardless of the number of treatments. Using CV-α, the number of genotypes in the same fold across replicates was much lower than K-fold, indicating higher residual independence. Therefore, based on the CV-α results, as proof of concept, via ANOVA, we could compare the proposed methodology to RRS and K-fold, applying four genomic prediction models with a simulated and real dataset. Concerning the predictive ability and bias, all validation methods showed similar performance. However, regarding the mean squared error and coefficient of variation, the CV-α method presented the best performance under the evaluated scenarios. Moreover, as it has no additional cost nor complexity, it is more reliable and allows the use of non-subjective methods to compare models and factors. Therefore, CV-α can be considered a more precise validation methodology for model selection.}, journal={BioRxiv}, author={Yassue, Rafael Massahiro and Sabadin, José Felipe Gonzaga and Galli, Giovanni and Alves, Filipe Couto and Fritsche-Neto, Roberto}, year={2020}, month={Nov} }
 @article{freitas mendonça_galli_malone_fritsche‐neto_2020, title={Genomic prediction enables early but low‐intensity selection in soybean segregating progenies}, url={https://doi.org/10.1002/csc2.20072}, DOI={10.1002/csc2.20072}, abstractNote={Abstract In soybean [ Glycine max (L.) Merr.], new commercial lines are commonly obtained from biparental crosses, and the selection is performed as homozygosity increases. However, it is difficult to select for quantitative traits in the early steps of breeding, due to the high heterozygosity level and a vast number of new progenies, which sometimes lead breeders to randomly select for these traits in this phase. Therefore, we aimed to assess the impact of genomic selection in early generations of a soybean breeding program. Working on germplasm derived from two different maturity regions in Brazil, genotyped in F 2 and phenotyped in F 2:4 for grain yield, plant height, maturity rating, and days to maturity, we compared the composition of different training populations, models with and without the genotype × environment (G × E) interaction effect, and two types of relationship measurements (genetic similarity and Euclidian distance). Results showed superior performance of the Euclidian distance kernel over the standard VanRaden kernel in major scenarios tested. In general, G × E models did not obtain superior performance compared with mean principal models, and the training population composed only of the nearest progenies had the highest prediction ability. The best models achieved prediction abilities between 0.40 and 0.56, thereby enabling application of a low‐intensity selection in F 2 . As a result, half of the progenies could be discarded without missing a great part of the good ones. Our results show that through genomic prediction, it is possible to select for quantitative traits in the early steps of breeding, which might increase the efficiency of the program in the advanced phases.}, journal={Crop Science}, author={Freitas Mendonça, Leandro and Galli, Giovanni and Malone, Gaspar and Fritsche‐Neto, Roberto}, year={2020}, month={Mar} }
 @article{costa-neto_fritsche-neto_crossa_2020, title={Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials}, volume={126}, url={https://doi.org/10.1038/s41437-020-00353-1}, DOI={10.1038/s41437-020-00353-1}, abstractNote={Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype × environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (~up to 20%) under all model-kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (~up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way.}, number={1}, journal={Heredity}, publisher={Springer Science and Business Media LLC}, author={Costa-Neto, Germano and Fritsche-Neto, Roberto and Crossa, José}, year={2020}, month={Aug}, pages={92–106} }
 @article{galli_alves_morosini_fritsche-neto_2020, title={On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids}, url={https://publons.com/wos-op/publon/31054879/}, DOI={10.1371/JOURNAL.PONE.0228724}, abstractNote={<div><p>Genome-wide association studies (GWAS) is one of the most popular methods of studying the genetic control of traits. This methodology has been intensely performed on inbred genotypes to identify causal variants. Nonetheless, the lack of covariance between the phenotype of inbred lines and their offspring in cross-pollinated species (such as maize) raises questions on the applicability of these findings in a hybrid breeding context. To address this topic, we incorporated previously reported parental lines GWAS information into the prediction of a low heritability trait in hybrids. This was done by marker-assisted selection based on significant markers identified in the lines and by genomic prediction having these markers as fixed effects. Additive-dominance GWAS of hybrids, a non-conventional procedure, was also performed for comparison purposes. Our results suggest that incorporating information from parental inbred lines GWAS led to decreases in the predictive ability of hybrids. Correspondingly, inbred lines and hybrids-based GWAS yielded different results. These findings do not invalidate GWAS on inbred lines for selection purposes, but mean that it may not be directly useful for hybrid breeding.</p></div>}, journal={PLoS ONE}, author={Galli, Giovanni and Alves, Filipe Couto and Morosini, Júlia Silva and Fritsche-Neto, Roberto}, year={2020}, month={Feb} }
 @article{galli_horne_collins_jung_chang_fritsche‐neto_rooney_2020, title={Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum}, volume={3}, url={https://doi.org/10.1002/ppj2.20010}, DOI={10.1002/ppj2.20010}, abstractNote={Abstract High‐throughput phenotyping (HTP) has enabled the acquisition of vast amounts of data. Therefore, finding the most informative phenological stage(s) and high‐throughput traits could lead to significant optimization of HTP‐assisted selection. An investigation as to when phenotypic data should be collected and how it should be processed from unmanned aerial system (UAS) imagery for the optimization and assessment of two primary traits in grain sorghum [ Sorghum bicolor (L). Moench], namely, grain yield and plant health (based on anthracnose scores) was conducted. By evaluating multiple flight dates across the growing season via multispectral UAS‐based imagery, a set of scenarios composed of combinations of flight dates and vegetation indices were constructed for analysis. In this sense, results showed no increase in predictive ability when combining multiple vegetation indices. Hence, using only an index with a higher predictive ability (e.g., normalized difference vegetation index (NDVI) or modified simple ratio (MSR) for plant health with 0.75; and any tested index but chlorophyll index (CIg) for grain yield with ∼0.55) is recommended. Likewise, the combining of multiple flights did not result in a significant increase in predictive ability for either primary trait. Thus, we observed that a single flight for each trait (e.g., 121 d after sowing with 0.81 for plant health; 104 d after sowing with 0.59 for grain yield) was optimal. Concerning, the predictive algorithms examined, partial least squares regression (PLSR) and neural network, results were similar, with PLSR generally outperforming. In addition, we discuss our findings from an application standpoint of a field‐based breeding program and suggest additional optimization options.}, number={1}, journal={The Plant Phenome Journal}, publisher={Wiley}, author={Galli, Giovanni and Horne, David W. and Collins, S. Delroy and Jung, Jinha and Chang, Anjin and Fritsche‐Neto, Roberto and Rooney, William L.}, year={2020}, month={Jan} }
 @article{sant’ana_espolador_granato_mendonça_fritsche-neto_borém_2020, title={Population structure analysis and identification of genomic regions under selection associated with low-nitrogen tolerance in tropical maize lines}, url={https://publons.com/wos-op/publon/44288296/}, DOI={10.1371/JOURNAL.PONE.0239900}, abstractNote={Increasing low nitrogen (N) tolerance in maize is an important goal for food security and agricultural sustainability. In order to analyze the population structure of tropical maize lines and identify genomic regions associated with low-N tolerance, a set of 64 inbred lines were evaluated under low-N and optimal-N conditions. The low-N Agronomic Efficiency index (LNAE) of each line was calculated. The maize lines were genotyped using 417,112 SNPs markers. The grouping based on the LNAE values classified the lines into two phenotypic groups, the first comprised by genotypes with high LNAE (named H_LNAE group), while the second one comprised genotypes with low LNAE (named L_LNAE group). The H_LNAE and L_LNAE groups had LNAE mean values of 3,304 and 1,644, respectively. The population structure analysis revealed a weak relationship between genetic and phenotypic diversity. Pairs of lines were identified, having at the same time high LNAE and high genetic distance from each other. A set of 29 SNPs markers exhibited a significant difference in allelic frequencies (Fst > 0.2) between H_LNAE and L_LNAE groups. The Pearson's correlation between LNAE and the favorable alleles in this set of SNPs was 0.69. These SNPs could be useful for marker-assisted selection for low-N tolerance in maize breeding programs. The results of this study could help maize breeders identify accessions to be used in the development of low-N tolerant cultivars.}, journal={PLoS ONE}, author={Sant’Ana, Gustavo César and Espolador, Fernando Garcia and Granato, Ítalo Stefanine Correia and Mendonça, Leandro Freitas and Fritsche-Neto, Roberto and Borém, Aluízio}, year={2020}, month={Sep} }
 @article{matias_sabadin_moreira_gomes_mira_fritsche-neto_otto_2020, title={Soil-app: a tool for soil analysis interpretation}, volume={78}, url={https://doi.org/10.1590/1678-992x-2019-0113}, DOI={10.1590/1678-992x-2019-0113}, abstractNote={New apps have changed the traditional way of learning and teaching; they are also applied as a quickly executed and effective method in agriculture. Soil-app is a web application with a friendly click-point interface built through packages lodged in R software. The app is an advanced model of an open-source platform to support teaching and learning activities in soil analyses and fertilizer recommendations. Soil-app includes soil test interpretation, soil amendment calculations (lime and gypsum), the fertilizer rate for the most important crops in Brazil, an NPK blend calculator, and NPK blend evaluation. It also includes experimental statistical analysis as applied to soil science. Soil-app is a user-friendly and high-performance tool, garnering fast adoption by both students and professionals. It is available for network use through the following link: http://www.genetica.esalq.usp.br/alogamas/R.html.}, number={1}, journal={Scientia Agricola}, publisher={FapUNIFESP (SciELO)}, author={Matias, Filipe Inácio and Sabadin, José Felipe Gonzaga and Moreira, Lílian Angélica and Gomes, Marcos Henrique Feresin and Mira, Acácio Bezerra and Fritsche-Neto, Roberto and Otto, Rafael}, year={2020}, month={Mar} }
 @article{freitas mendonça_fritsche‐neto_2020, title={The accuracy of different strategies for building training sets for genomic predictions in segregating soybean populations}, url={https://doi.org/10.1002/csc2.20267}, DOI={10.1002/csc2.20267}, abstractNote={Abstract The design of the training set is a key factor in the success of the genomic selection approach. The nature of line inclusion in soybean [ Sorghum bicolor (L.) Moench.] breeding programs is highly dynamic, so generating a training set that endures across the years and regions is challenging. Therefore, we aimed to define the best strategies for building training sets to apply genomic selection in segregating soybean populations for traits with different genetic architectures. We used two datasets for grain yield (GY) and maturity group (MG) from two different soybean breeding regions in Brazil. Five training set schemes were tested. In addition, we included a training set formed by an optimization algorithm based on the predicted error variance. The predictions achieved good values for both traits, reaching 0.5 in some scenarios. The best scenario changed according to the trait. Although the best performance was achieved with the use of full‐sibs in the MG dataset, for GY, full‐sibs and a set of advanced lines were equivalent. For both traits, no improvement in predictive ability resulted from training set optimization. Furthermore, the use of advanced lines from the same breeding program is recommended as a training set for GY, so the training set is continually renewed and closely related to the breeding populations, and no additional phenotyping is needed. On the other hand, to improve prediction accuracies for MG, it is necessary to use training sets with less genetic variability but with more segregation resolution.}, journal={Crop Science}, author={Freitas Mendonça, Leandro and Fritsche‐Neto, Roberto}, year={2020}, month={Jul} }
 @article{carvalho_galli_ferrão_nonato_padilha_maluf_filho_fritsche-neto_2020, title={The effect of bienniality on genomic prediction of yield in arabica coffee}, volume={216}, url={https://doi.org/10.1007/s10681-020-02641-7}, DOI={10.1007/s10681-020-02641-7}, number={6}, journal={Euphytica}, publisher={Springer Science and Business Media LLC}, author={Carvalho, Humberto Fanelli and Galli, Giovanni and Ferrão, Luís Felipe Ventorim and Nonato, Juliana Vieira Almeida and Padilha, Lilian and Maluf, Mirian Perez and Filho, Oliveiro Guerreiro and Fritsche-Neto, Roberto}, year={2020}, month={Jun} }
 @article{morais_akdemir_andrade_jannink_fritsche‐neto_borém_alves_lyra_granato_2020, title={Using public databases for genomic prediction of tropical maize lines}, url={https://doi.org/10.1111/pbr.12827}, DOI={10.1111/pbr.12827}, abstractNote={Abstract In this paper, the aims were (a) to test the usefulness of using genomic and phenotypic information from public databases (open access) to predict genetic values for tropical maize inbred lines regarding plant and ear height; (b) to identify how the population structure, the use of optimized training sets (OTSs) and the amount of information originating from public databases affect the predictive ability. Thus, 29 training sets (TSs) were defined considering three diversity panels: the University of São Paulo (USP—validation set (VS)) and the ASSO and USDA North Central Regional Plant Introduction Station (NCRPIS) (external public panels—predictors), which were divided into four scenarios with different TS configurations. We showed that it is possible to use public datasets as a primary TS and that population structure can modify the predictive abilities of GS. In the four scenarios proposed, very large or very small sets did not provide predictive abilities over 0.53 for GS. However, OTSs composed of 250 individuals were sufficient to achieve predictive abilities over this limit.}, journal={Plant Breeding}, author={Morais, Pedro Patric Pinho and Akdemir, Deniz and Andrade, Luciano Rogério Braatz and Jannink, Jean‐Luc and Fritsche‐Neto, Roberto and Borém, Aluízio and Alves, Filipe Couto and Lyra, Danilo Hottis and Granato, Ítalo Stefanine Correia}, year={2020}, month={May} }
 @article{vidotti_lyra_morosini_granato_quecine_azevedo_fritsche-neto_2019, title={Additive and heterozygous (dis)advantage GWAS models reveal candidate genes involved in the genotypic variation of maize hybrids to Azospirillum brasilense}, url={https://publons.com/wos-op/publon/20784578/}, DOI={10.1371/JOURNAL.PONE.0222788}, abstractNote={Maize genotypes can show different responsiveness to inoculation with Azospirillum brasilense and an intriguing issue is which genes of the plant are involved in the recognition and growth promotion by these Plant Growth-Promoting Bacteria (PGPB). We conducted Genome-Wide Association Studies (GWAS) using additive and heterozygous (dis)advantage models to find candidate genes for root and shoot traits under nitrogen (N) stress and N stress plus A. brasilense. A total of 52,215 Single Nucleotide Polymorphism (SNP) markers were used for GWAS analyses. For the six root traits with significant inoculation effect, the GWAS analyses revealed 25 significant SNPs for the N stress plus A. brasilense treatment, in which only two were overlapped with the 22 found for N stress only. Most were found by the heterozygous (dis)advantage model and were more related to exclusive gene ontology terms. Interestingly, the candidate genes around the significant SNPs found for the maize-A. brasilense association were involved in different functions previously described for PGPB in plants (e.g. signaling pathways of the plant's defense system and phytohormone biosynthesis). Our findings are a benchmark in the understanding of the genetic variation among maize hybrids for the association with A. brasilense and reveal the potential for further enhancement of maize through this association.}, journal={PLoS ONE}, author={Vidotti, Miriam Suzane and Lyra, Danilo Hottis and Morosini, Júlia Silva and Granato, Ítalo Stefanine Correia and Quecine, Maria Carolina and Azevedo, João Lúcio and Fritsche-Neto, Roberto}, year={2019}, month={Sep} }
 @article{matias_vidotti_meireles_barrios_valle_carley_fritsche‐neto_2019, title={Association Mapping Considering Allele Dosage: An Example of Forage Traits in an Interspecific Segmental Allotetraploid <i>Urochloa</i> spp. Panel}, url={https://publons.com/wos-op/publon/27374480/}, DOI={10.2135/CROPSCI2019.03.0185}, abstractNote={The breeding process in tropical segmental allopolyploid forage Urochloa is challenging due to the complex genetic control of the traits. Knowledge about genes associated with forage traits, expressed in the different cutting seasons, are extremely useful to support breeding programs and development of new cultivars. Thus, the aims of our study were (i) to identify genomic regions related to forage traits through genome‐wide association studies (GWAS), and (ii) to verify the influence of allele dosage on these results. A panel of 272 genotypes of Urochloa spp. [ U. brizantha (Hoscht. ex A. Rich.) R. Webster × U. ruziziensis (Hoscht. ex A. Rich.) R. Webster] was evaluated in both the wet and dry seasons. The GWAS analyses were performed with 26,535 single nucleotide polymorphisms (SNPs) obtained by genotyping‐by‐sequencing (GBS) using diploid and tetraploid allele dosage configurations. Furthermore, we evaluated scenarios including additive, dominance, and epistatic effects. Seven candidate genomic regions associated with the main forage traits of Urochloa spp. were identified. The importance of the diploid and tetraploid molecular configuration in GWAS analyses for segmental allopolyploid species was demonstrated to identify the genomic behavior of important regions. Results demonstrated that it is possible to identify the same regions using both ploidy configurations; however, in some cases, the allele substitution effect can be biased mainly for regions with dominance and epistatic effects. Finally, this study contributes to the understanding of genetic control of tropical forage traits and genomics to accelerate the selection and reduce the cost to release new cultivars.}, journal={Crop Science}, author={Matias, Filipe Inácio and Vidotti, Miriam Suzane and Meireles, Karem Guimarães Xavier and Barrios, Sanzio Carvalho Lima and Valle, Cacilda Borges and Carley, Cari A. Schmitz and Fritsche‐Neto, Roberto}, year={2019}, month={Aug} }
 @article{fritsche-neto_souza_pereira_faria_melo_novaes_brum_jannink_2019, title={Association mapping in common bean revealed regions associated with Anthracnose and Angular Leaf Spot resistance}, url={https://publons.com/wos-op/publon/19061746/}, DOI={10.1590/1678-992X-2017-0306}, abstractNote={Despite important biotic stresses to common bean, Anthracnose (ANT) and Angular Leaf Spot (ALS) can cause losses of up to 80 % and occur in more than 60 countries around the world. Genetic resistance is the most sustainable strategy to manage these diseases. Thus, we aimed to (1) identify new SNP markers associated with ALS and ANT resistance loci in elite common bean lines, and (2) provide a functional characterization of the DNA sequences containing the identified SNP markers. We evaluated 60 inbred lines, under field conditions, which represent the elite germplasm developed by the Embrapa common bean breeding program across 22 years, in terms of severity of the ALS and ANT. The lines were genotyped with 5,398 SNPs. Then, a Mixed Linear Model was run to determine the SNP-trait associations. We observed two-significant marker-trait associations reacting to ANT, both located on chromosome Pv-02. These markers explained 25 % of the phenotypic variation. For ALS, only one significant marker-trait association was observed, which is located in chromosome Pv-10 and explained 19 % of the phenotypic variation. These markers, along with others already used, will be useful to add or keep ANT and ALS resistance loci identified in this work in the new carioca and black seeded cultivars.}, journal={Scientia Agricola}, author={Fritsche-Neto, Roberto and Souza, Thiago Lívio Pessoa Oliveira and Pereira, Helton Santos and Faria, Luís Cláudio and Melo, Leonardo Cunha and Novaes, Evandro and Brum, Itaraju Junior Baracuhy and Jannink, Jean-Luc}, year={2019}, month={Mar} }
 @article{alves_granato_galli_lyra_fritsche-neto_campos_2019, title={Bayesian analysis and prediction of hybrid performance}, url={https://publons.com/wos-op/publon/14100985/}, DOI={10.1186/S13007-019-0388-X}, abstractNote={Genomic prediction can be a useful tool in pre-screening of hybrids and could contribute to the improvement of the efficiency and efficacy of maize hybrids breeding programs. The Bayesian framework offers a great deal of flexibility in modeling hybrid performance. The methodology can be used to estimate important genetic parameters and render predictions of the expected hybrid performance as well measures of uncertainty about such predictions.}, journal={Plant Methods}, author={Alves, Filipe Couto and Granato, Ítalo Stefanine Correa and Galli, Giovanni and Lyra, Danilo Hottis and Fritsche-Neto, Roberto and Campos, Gustavo}, year={2019}, month={Feb} }
 @article{matias_morosini_espolador_fritsche‐neto_2019, title={Be‐Breeder 2.0: A Web Application for Genetic Analyses in a Plant Breeding Context}, url={https://publons.com/wos-op/publon/21210882/}, DOI={10.2135/CROPSCI2018.10.0621LE}, journal={Crop Science}, author={Matias, Filipe Inácio and Morosini, Júlia Silva and Espolador, Fernando Garcia and Fritsche‐Neto, Roberto}, year={2019}, month={Jun} }
 @article{oliveira couto_cury_souza_granato_vidotti_garbuglio_crossa_burgueño_fritsche-neto_2019, title={Effect of F1 and F2 generations on genetic variability and working steps of doubled haploid production in maize}, url={https://publons.com/wos-op/publon/52466947/}, DOI={10.1371/JOURNAL.PONE.0224631}, abstractNote={For doubled haploid (DH) production in maize, F1 generation has been the most frequently used for haploid induction due to facility in the process. However, using F2 generation would be a good alternative to increase genetic variability owing to the additional recombination in meiosis. Our goals were to compare the effect of F1 and F2 generations on DH production in tropical germplasm, evaluating the R1-navajo expression in seeds, the working steps of the methodology, and the genetic variability of the DH lines obtained. Sources germplasm in F1 and F2 generations were crossed with the tropicalized haploid inducer LI-ESALQ. After harvest, for both induction crosses were calculated the haploid induction rate (HIR), diploid seed rate (DSR), and inhibition seed rate (ISR) using the total number of seeds obtained. In order to study the effectiveness of the DH working steps in each generation, the percentage per se and the relative percentage were verified. In addition, SNP markers were obtained for genetic variability studies. Results showed that the values for HIR, ISR, and DSR were 1.23%, 23.48%, and 75.21% for F1 and 1.78%, 15.82%, and 82.38% for F2, respectively. The effectiveness of the DH working step showed the same percentage per se value (0.4%) for F1 and F2, while the relative percentage was 27.2% for F1 and 22.4% for F2. Estimates of population parameters in DH lines from F1 were higher than F2. Furthermore, population structure and kinship analyses showed that one additional generation was not sufficient to create new genotype subgroups. Additionally, the relative efficiency of the response to selection in the F1 was 31.88% higher than F2 due to the number of cycles that are used to obtain the DH. Our results showed that in tropical maize, the use of F1 generation is recommended due to a superior balance between time and genetic variability.}, journal={PLoS ONE}, author={Oliveira Couto, Evellyn Giselly and Cury, Mayara Neves and Souza, Massaine Bandeira and Granato, Ítalo Stefanine Correia and Vidotti, Miriam Suzane and Garbuglio, Deoclécio Domingos and Crossa, José and Burgueño, Juan and Fritsche-Neto, Roberto}, year={2019}, month={Nov} }
 @article{matias_meireles_nagamatsu_barrios_valle_carazzolle_fritsche‐neto_endelman_2019, title={Expected Genotype Quality and Diploidized Marker Data from Genotyping‐by‐Sequencing of <i>Urochloa</i> spp. Tetraploids}, url={https://publons.com/wos-op/publon/31203968/}, DOI={10.3835/PLANTGENOME2019.01.0002}, abstractNote={Core Ideas Introduced concept of expected genotype quality (EGQ) and software to calculate it Provided read depth guidelines for GBS in tetraploids Developed software to generate diploidized genotype calls from VCF files Demonstrated value of aligning GBS reads to a mock reference genome for SNP discovery Recommend filtering based on GQ and read depth to prevent genotype bias Although genotyping‐by‐sequencing (GBS) is a well‐established marker technology in diploids, the development of best practices for tetraploid species is a topic of current research. We determined the theoretical relationship between read depth and the phred‐scaled probability of genotype misclassification conditioned on the true genotype, which we call expected genotype quality (EGQ). If the GBS method has 0.5% allelic error, then 17 reads are needed to classify simplex tetraploids as heterozygous with 95% accuracy (EGQ = 13) vs. 61 reads to determine allele dosage. We developed an R script to convert tetraploid GBS data in variant call format (VCF) into diploidized genotype calls and applied it to 267 interspecific hybrids of the tetraploid forage grass Urochloa . When reads were aligned to a mock reference genome created from GBS data of the Urochloa brizantha (Hochst. ex A. Rich.) R. D. Webster cultivar Marandu, 25,678 biallelic single nucleotide polymorphism (SNPs) were discovered, compared with ∼3000 SNPs when aligning to the closest true reference genomes, Setaria viridis (L.) P. Beauv. and S. italica (L.) P. Beauv. Cross‐validation revealed that missing genotypes were imputed by the random forest method with a median accuracy of 0.85 regardless of heterozygote frequency. Using the Urochloa spp. hybrids, we illustrated how filtering samples based only on genotype quality (GQ) creates genotype bias; a depth threshold based on EGQ is also needed regardless of whether genotypes are called using a diploidized or allele dosage model.}, journal={The Plant Genome}, author={Matias, Filipe Inácio and Meireles, Karem Guimarães Xavier and Nagamatsu, Sheila Tiemi and Barrios, Sanzio Carvalho Lima and Valle, Cacilda Borges and Carazzolle, Marcelo Falsarella and Fritsche‐Neto, Roberto and Endelman, Jeffrey B.}, year={2019}, month={Aug} }
 @article{souza_francisco_gonçalves_junior_guen_fritsche-neto_souza_2019, title={Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions}, url={https://publons.com/wos-op/publon/28934296/}, DOI={10.3389/FPLS.2019.01353}, abstractNote={Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals <i>via</i> genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (<i>H</i> <sup>2</sup>) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs.}, journal={Frontiers in Plant Science}, author={Souza, Livia M. and Francisco, Felipe R. and Gonçalves, Paulo S. and Junior, Erivaldo J. Scaloppi and Guen, Vincent Le and Fritsche-Neto, Roberto and Souza, Anete P.}, year={2019}, month={Oct} }
 @article{sousa_galli_lyra_granato_matias_alves_fritsche-neto_2019, title={Increasing accuracy and reducing costs of genomic prediction by marker selection}, url={https://publons.com/wos-op/publon/19061745/}, DOI={10.1007/S10681-019-2339-Z}, journal={Euphytica}, author={Sousa, Massaine Bandeira and Galli, Giovanni and Lyra, Danilo Hottis and Granato, Ítalo Stefanini Correia and Matias, Filipe Inácio and Alves, Filipe Couto and Fritsche-Neto, Roberto}, year={2019}, month={Jan} }
 @article{vidotti_matias_alves_pérez-rodríguez_beltran_burgueño_crossa_fritsche-neto_2019, title={Maize responsiveness to Azospirillum brasilense: Insights into genetic control, heterosis and genomic prediction}, url={https://publons.com/wos-op/publon/14585334/}, DOI={10.1371/JOURNAL.PONE.0217571}, abstractNote={Several studies have shown differences in the abilities of maize genotypes to facilitate or impede Azospirillum brasilense colonization and to receive benefits from this association. Hence, our aim was to study the genetic control, heterosis effect and the prediction accuracy of the shoot and root traits of maize in response to A. brasilense. For that, we evaluated 118 hybrids under two contrasting scenarios: i) N stress (control) and ii) N stress plus A. brasilense inoculation. The diallel analyses were performed using mixed model equations, and the genomic prediction models accounted for the general and specific combining ability (GCA and SCA, respectively) and the presence or not of G×E effects. In addition, the genomic models were fitted considering parametric (G-BLUP) and semi-parametric (RKHS) kernels. The genotypes showed significant inoculation effect for five root traits, and the GCA and SCA were significant for both. The GCA in the inoculated treatment presented a greater magnitude than the control, whereas the opposite was observed for SCA. Heterosis was weakly influenced by the inoculation, and the heterozygosity and N status in the plant can have a role in the benefits that can be obtained from this Plant Growth-Promoting Bacteria (PGPB). Prediction accuracies for N stress plus A. brasilense ranged from 0.42 to 0.78, depending on the scenario and trait, and were higher, in most cases, than the non-inoculated treatment. Finally, our findings provide an understanding of the quantitative variation of maize responsiveness to A. brasilense and important insights to be applied in maize breeding aiming the development of superior hybrids for this association.}, journal={PLoS ONE}, author={Vidotti, Miriam Suzane and Matias, Filipe Inácio and Alves, Filipe Couto and Pérez-Rodríguez, Paulino and Beltran, Gregório Alvarado and Burgueño, Juan and Crossa, José and Fritsche-Neto, Roberto}, year={2019}, month={Jun} }
 @article{matias_alves_meireles_barrios_valle_endelman_fritsche-neto_2019, title={On the accuracy of genomic prediction models considering multi-trait and allele dosage in Urochloa spp. interspecific tetraploid hybrids}, url={https://publons.com/wos-op/publon/27374481/}, DOI={10.1007/S11032-019-1002-7}, journal={Molecular Breeding}, author={Matias, Filipe Inácio and Alves, Filipe Couto and Meireles, Karem Guimarães Xavier and Barrios, Sanzio Carvalho Lima and Valle, Cacilda Borges and Endelman, Jeffrey B. and Fritsche-Neto, Roberto}, year={2019}, month={Jun} }
 @article{santos_amaral júnior_fritsche-neto_kamphorst_ferreira_amaral_vivas_santos_lima_khan_et al._2019, title={Relative importance of gene effects for nitrogen-use efficiency in popcorn}, url={https://publons.com/wos-op/publon/21270221/}, DOI={10.1371/JOURNAL.PONE.0222726}, abstractNote={The objective of this study was to evaluate the effects of additive and non-additive genes on the efficiency of nitrogen (N) use and N responsiveness in inbred popcorn lines. The parents, hybrids and reciprocal crosses were evaluated in a 10x10 triple lattice design at two sites and two levels of N availability. To establish different N levels in the two experiments, fertilization was carried out at sowing, according to soil analysis reports. However, for the experiments with ideal nitrogen availability, N was sidedressed according to the crop requirement, whereas for the N-poor experiments sidedressing consisted of 30% of that applied in the N-rich environment. Two indices were evaluated, the Harmonic Mean of the Relative Performance (HMRP) and Agronomic Efficiency under Low Nitrogen Availability (AELN), both based on grain yield at both N levels. Both additive and non-additive gene effects were important for selection for N-use efficiency. Moreover, there was allelic complementarity between the lines and a reciprocal effect for N-use efficiency, indicating the importance of the choice of the parents used as male or female. The best hybrids were obtained from inbred popcorn lines with contrasting N-use efficiency and N responsiveness.}, journal={PLoS ONE}, author={Santos, Adriano and Amaral Júnior, Antônio Teixeira and Fritsche-Neto, Roberto and Kamphorst, Samuel Henrique and Ferreira, Fernando Rafael Alves and Amaral, José Francisco Teixeira and Vivas, Janieli Maganha Silva and Santos, Pedro Henrique Araújo Diniz and Lima, Valter Jário and Khan, Shahid and et al.}, year={2019}, month={Sep} }
 @article{fristche-neto_akdemir_jannink_2018, title={Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs}, volume={131}, url={https://doi.org/10.1007/s00122-018-3068-8}, DOI={10.1007/s00122-018-3068-8}, abstractNote={Testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Even though many papers have been published about genomic prediction (GP) in maize, the best mating design to build the training population has not been defined yet. Such design must maximize the accuracy given constraints on costs and on the logistics of the crosses to be made. Hence, the aims of this work were: (1) empirically evaluate the effect of the mating designs, used as training set, on genomic selection to predict maize single-crosses obtained through full diallel and North Carolina design II, (2) and identify the possibility of reducing the number of crosses and parents to compose these training sets. Our results suggest that testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Moreover, North Carolina design II is the best training set to predict hybrids taken from full diallel. However, hybrids from full diallel and North Carolina design II can be well predicted using optimized training sets, which also allow reducing the total number of crosses to be made. Nevertheless, the number of parents and the crosses per parent in the training sets should be maximized.}, number={5}, journal={Theoretical and Applied Genetics}, publisher={Springer Nature}, author={Fristche-Neto, Roberto and Akdemir, Deniz and Jannink, Jean-Luc}, year={2018}, month={Feb}, pages={1153–1162} }
 @article{granato_cuevas_luna-vázquez_crossa_montesinos-lópez_burgueño_fritsche-neto_2018, title={BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models}, volume={7}, url={https://doi.org/10.1534/g3.118.200435}, DOI={10.1534/g3.118.200435}, abstractNote={Abstract One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option for creating genomic GE kernels and making genomic predictions.}, journal={G3 Genes Genomes Genetics}, publisher={Genetics Society of America}, author={Granato, Italo and Cuevas, Jaime and Luna-Vázquez, Francisco and Crossa, Jose and Montesinos-López, Osval and Burgueño, Juan and Fritsche-Neto, Roberto}, year={2018}, month={Jul}, pages={g3.200435.2018} }
 @article{matias_granato_fritsche-neto_2018, title={Be-Breeder: an R/Shiny application for phenotypic data analyses in plant breeding}, url={https://publons.com/wos-op/publon/19061741/}, DOI={10.1590/1984-70332018V18N2S36}, abstractNote={In order to successfully achieve the final goal of genotype selection in plant breeding programs, many aspects must be considered and carefully thought regarding cost, time, and efficiency. Thus, we have developed the Be-Breeder application to perform main biometric and statistical analyses using mixed and multivariate models. Implemented using the Shiny R package, this is one the first online platforms proposed in this context. Be-Breeder is available at http://www.genetica.esalq.usp.br/alogamas/R.html.}, journal={Crop Breeding and Applied Biotechnology}, author={Matias, Filipe Inácio and Granato, Italo and Fritsche-Neto, Roberto}, year={2018}, month={Apr} }
 @article{matias_granato_fritsche-neto_2018, title={Be-breeder: An R/Shiny application for phenotypic data analyses in plant breeding}, volume={18}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044132421&partnerID=MN8TOARS}, DOI={10.1590/1984-70332018v18n2a36}, number={2}, journal={Crop Breeding and Applied Biotechnology}, author={Matias, F.I. and Granato, I. and Fritsche-Neto, R.}, year={2018}, pages={241–243} }
 @article{matias_barrios_bearari_meireles_mateus_amaral_alves_valle_fritsche-neto_2018, title={Contribution of Additive and Dominance Effects on Agronomical and Nutritional Traits, and Multivariate Selection on <i>Urochloa</i> spp. Hybrids}, url={https://publons.com/wos-op/publon/18538944/}, DOI={10.2135/CROPSCI2018.04.0261}, abstractNote={A tropical forage breeding program contains several peculiarities, especially when it involves polyploid species and facultative apomixis. Urochloa spp. are excellent perennial forages, and the identification of superior genotypes depends on the selection of many characteristics under complex genetic control, with high cost and time‐consuming evaluation. Therefore, the use of tools such as multivariate analysis and diallel analyses could contribute to improving the efficiency of breeding programs. Thus, the objectives were to estimate (i) the contribution of additive and nonadditive effects on agronomical and nutritional traits in a population of interspecific hybrids of Urochloa spp., originated from a partial diallel between five apomictic and four sexual parents, and (ii) the accuracy of multivariate index selection efficiency. Genetic variability was detected between the parents, crosses, and hybrids for all the traits. There was no clear trend of the importance of the additive and nonadditive genetic effects on agronomical and nutritional traits. Furthermore, the predominant component of genetic variance changed depending on the characteristic. Moreover, there was no parent or cross that was outstanding for all traits simultaneously, showing the high variability generated from these crosses. The Mulamba and Mock index associated with principal components analysis allowed a more significant gain only for agronomic characteristics. However, the per se index, at the univariate level, promoted a more balanced response to selection for all traits.}, journal={Crop Science}, author={Matias, Filipe Inácio and Barrios, Sanzio Carvalho Lima and Bearari, Lucas Martins and Meireles, Karem Guimarães Xavier and Mateus, Rogério Gonçalves and Amaral, Pedro Nelson Cesar and Alves, Geovani Ferreira and Valle, Cacilda Borges and Fritsche-Neto, Roberto}, year={2018}, month={Sep} }
 @article{lyra_granato_morais_alves_santos_yu_guo_yu_fritsche-neto_2018, title={Controlling population structure in the genomic prediction of tropical maize hybrids}, url={https://publons.com/wos-op/publon/877710/}, DOI={10.1007/S11032-018-0882-2}, journal={Molecular Breeding}, author={Lyra, Danilo Hottis and Granato, Ítalo Stefanine Correia and Morais, Pedro Patric Pinho and Alves, Filipe Couto and Santos, Anna Rita Marcondes and Yu, Xiaoqing and Guo, Tingting and Yu, Jianming and Fritsche-Neto, Roberto}, year={2018}, month={Sep} }
 @article{fritsche-neto_akdemir_jannink_2018, title={Correction to: Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs}, volume={131}, url={https://doi.org/10.1007/s00122-018-3118-2}, DOI={10.1007/s00122-018-3118-2}, abstractNote={Unfortunately, the first author name of the above-mentioned article was incorrectly published in the original publication. The complete correct name should read as follows.}, number={7}, journal={Theoretical and Applied Genetics}, publisher={Springer Science and Business Media LLC}, author={Fritsche-Neto, Roberto and Akdemir, Deniz and Jannink, Jean-Luc}, year={2018}, month={May}, pages={1603–1603} }
 @article{cuevas_granato_fritsche-neto_montesinos-lopez_burgueño_sousa_crossa_2018, title={Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials}, volume={8}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044726281&partnerID=MN8TOARS}, DOI={10.1534/g3.117.300454}, abstractNote={Abstract In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy.}, number={4}, journal={G3 Genes Genomes Genetics}, publisher={Genetics Society of America}, author={Cuevas, Jaime and Granato, Italo and Fritsche-Neto, Roberto and Montesinos-Lopez, Osval A and Burgueño, Juan and Sousa, Massaine Bandeira and Crossa, José}, year={2018}, month={Feb}, pages={1347–1365} }
 @article{galli_lyra_alves_granato_sousa_fritsche-neto_2018, title={Impact of Phenotypic Correction Method and Missing Phenotypic Data on Genomic Prediction of Maize Hybrids}, volume={58}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85049031287&partnerID=MN8TOARS}, DOI={10.2135/cropsci2017.07.0459}, abstractNote={Phenotypic datasets in plant breeding are commonly incomplete due to missing phenotypic information. The best approach for correcting these datasets for a stage‐wise genomic prediction (GP) is not unanimous in the scientific community. Therefore, this study evaluates a two‐step GP based on different methods of phenotypic correction considering complete and incomplete datasets of maize ( Zea mays L.) single crosses. The dataset consists of 325 hybrids evaluated for grain yield and plant height in four sites. Sequential levels of data loss were simulated to the original dataset (from 0 to 30%) to assess the impact of missing information. The prediction was performed by an additive genomic best linear unbiased prediction model (GBLUP) using best linear unbiased estimations (BLUEs), best linear unbiased predictions (BLUPs), and deregressed BLUPs as the response variable. Mean reliability and predictive ability slightly decreased as missing phenotypic information increased, irrespective of the response variable. Regarding phenotypic correction, all methods yielded similar results for these parameters over most missing information percentages. The coincidence of selection between single‐ and two‐stage GP was not systematically affected by response variable across multiple selection intensities, and missing data only led to a minor decrease in coincidence. Therefore, from a breeding standpoint, regardless of phenotypic correction method and missing data level, a similar set of genotypes tend to be selected.}, number={4}, journal={Crop Science}, author={Galli, Giovanni and Lyra, Danilo Hottis and Alves, Filipe Couto and Granato, Ítalo Stefanine Correia and Sousa, Massaine Bandeira and Fritsche-Neto, Roberto}, year={2018}, month={Jun}, pages={1481–1491} }
 @article{lyra_galli_alves_granato_vidotti_sousa_morosini_crossa_fritsche-neto_2018, title={Modeling copy number variation in the genomic prediction of maize hybrids}, url={https://publons.com/wos-op/publon/19061744/}, DOI={10.1007/S00122-018-3215-2}, abstractNote={Our study indicates that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids. Moreover, predicting hybrid phenotypes by combining additive-dominance effects with copy variants has the potential to be a viable predictive model. Non-additive effects resulting from the actions of multiple loci may influence trait variation in single-cross hybrids. In addition, complementation of allelic variation could be a valuable contributor to hybrid genetic variation, especially when crossing inbred lines with higher contents of copy gains. With this in mind, we aimed (1) to study the association between copy number variation (CNV) and hybrid phenotype, and (2) to compare the predictive ability (PA) of additive and additive-dominance genomic best linear unbiased prediction model when combined with the effects of CNV in two datasets of maize hybrids (USP and HELIX). In the USP dataset, we observed a significant negative phenotypic correlation of low magnitude between copy number loss and plant height, revealing a tendency that more copy losses lead to lower plants. In the same set, when CNV was combined with the additive plus dominance effects, the PA significantly increased only for plant height under low nitrogen. In this case, CNV effects explicitly capture relatedness between individuals and add extra information to the model. In the HELIX dataset, we observed a pronounced difference in PA between additive (0.50) and additive-dominance (0.71) models for predicting grain yield, suggesting a significant contribution of dominance. We conclude that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids, although the inclusion of CNVs into datasets does not return significant gains concerning PA.}, journal={Theoretical and Applied Genetics}, author={Lyra, Danilo Hottis and Galli, Giovanni and Alves, Filipe Couto and Granato, Ítalo Stefanine Correia and Vidotti, Miriam Suzane and Sousa, Massaine Bandeira and Morosini, Júlia Silva and Crossa, José and Fritsche-Neto, Roberto}, year={2018}, month={Oct} }
 @article{akdemir_beavis_fritsche-neto_singh_isidro-sánchez_2018, title={Multi-objective optimized genomic breeding strategies for sustainable food improvement}, volume={122}, url={https://doi.org/10.1038/s41437-018-0147-1}, DOI={10.1038/s41437-018-0147-1}, abstractNote={The purpose of breeding programs is to obtain sustainable gains in multiple traits while controlling the loss of genetic variation. The decisions at each breeding cycle involve multiple, usually competing, objectives; these complex decisions can be supported by the insights that are gained by applying multi-objective optimization principles to breeding. The discussion in this manuscript includes the definition of several multi-objective optimized breeding approaches within the phenotypic or genomic breeding frameworks and the comparison of these approaches with the standard multi-trait breeding schemes such as tandem selection, independent culling and index selection. Proposed methods are demonstrated with two empirical data sets and simulations. In addition, we have described several graphical tools that can aid breeders in arriving at a compromise decision. The results show that the proposed methodology is a viable approach to answer several real breeding problems. In simulations, the newly proposed methods resulted in gains larger than the methods previously proposed including index selection: Compared to the best alternative breeding strategy, the gains from multi-objective optimized parental proportions approaches were about 20-30% higher at the end of long-term simulations of breeding cycles. In addition, the flexibility of the multi-objective optimized breeding strategies were displayed with methods and examples covering non-dominated selection, assignment of optimal parental proportions, using genomewide marker effects in producing optimal mating designs, and finally in selection of training populations for genomic prediction.}, number={5}, journal={Heredity}, publisher={Springer Nature}, author={Akdemir, Deniz and Beavis, William and Fritsche-Neto, Roberto and Singh, Asheesh K. and Isidro-Sánchez, Julio}, year={2018}, month={Sep}, pages={672–683} }
 @article{morais_sousa_galli_andrade_fritsche-neto_oliveira_2018, title={Yield components and reproductive, physiological, and root traits used in early selection for nitrogen use efficiency in corn}, url={https://publons.com/wos-op/publon/19061743/}, DOI={10.1590/S0100-204X2018000500011}, abstractNote={Abstract: The objective of this work was to examine the possibility of using yield components and reproductive, physiological, and root traits in early selection for nitrogen use efficiency (NUE) in corn. Sixty-four inbred lines were evaluated under two nitrogen fertilization levels: ideal and low. The evaluations were performed at three phenological stages: eight fully-expanded leaves, tasseling stage, and physiological maturity. It is possible to select superior lines for NUE, but the yield components did not show differential behavior under the different nitrogen levels evaluated. Root, reproductive, and physiological traits are not promising for early selection of corn lines with high NUE. Likewise, the eight-leaves and tasseling stages were not promising for this purpose, since NUE should be estimated taking grain yield into account. However, indirect selection for NUE can be performed via number of ears or using the selection index considering number and weight of ears.}, journal={Pesquisa Agropecuária Brasileira}, author={Morais, Pedro Patric Pinho and Sousa, Massaine Bandeira and Galli, Giovanni and Andrade, Luciano Rogério Braatz and Fritsche-Neto, Roberto and Oliveira, Aluízio Borém}, year={2018}, month={May} }
 @article{granato_galli_oliveira couto_souza_mendonça_fritsche-neto_2018, title={snpReady: a tool to assist breeders in genomic analysis}, url={https://publons.com/wos-op/publon/16264125/}, DOI={10.1007/S11032-018-0844-8}, journal={Molecular Breeding}, author={Granato, Italo S. C. and Galli, Giovanni and Oliveira Couto, Evellyn Giselly and Souza, Massaine Bandeira and Mendonça, Leandro Freitas and Fritsche-Neto, Roberto}, year={2018}, month={Jul} }
 @article{freitas mendonça_granato_alves_morais_vidotti_fritsche-neto_2017, title={Accuracy and simultaneous selection gains for N-stress tolerance and N-use efficiency in maize tropical lines}, volume={74}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85027246872&partnerID=MN8TOARS}, DOI={10.1590/1678-992x-2016-0313}, abstractNote={Maize plants can be N-use efficient or N-stress tolerant. The first have high yields in favorable environments but is drastically affected under stress conditions; whereas the second show satisfactory yields in stressful environments but only moderate ones under optimal conditions. In this context, our aim was to assess the possibility of selecting tropical maize lines that are simultaneously N-stress tolerant and N-use efficient and check for differences between simultaneous selection statistical methods. Sixty-four tropical maize lines were evaluated for Nitrogen Agronomic Efficiency (NAE) and Low Nitrogen Tolerance (LNTI) response indices and two per se selection indices, Low Nitrogen Agronomic Efficiency (LNAE) and Harmonic Mean of Relative Performance (HMRP). We performed eight selection scenarios: LNAE; HMRP; Additive index; Mulamba-Mock index; and Independent culling levels. The last three was predicted by REML/BLUP single-trait and multi-trait using genotypic values of NAE and LNTI. The REML/BLUP multi-trait analysis was superior to the single-trait analysis due to high unfavorable correlation between NAE and LNTI. However, the accuracy and genotypic determination coefficient of NAE and LNTI were too low. Thus, neither single- nor multi-trait analysis achieved a good result for simultaneous selection nor N-use efficiency nor N-stress tolerance. LNAE obtained satisfactorily accurate values and genotypic determination coefficient, but its performance in selection gain was worse than HMRP, particularly in terms of N-use efficiency. Therefore, because of the superior performance in accuracy, genotypic determination coefficient and selection, HMRP was considered the best simultaneous selection methodology of the scenarios tested for N-use efficiency and N-stress tolerance.}, number={6}, journal={Scientia Agricola}, author={Freitas Mendonça, Leandro and Granato, Ítalo Stefanine Correia and Alves, Filipe Couto and Morais, Pedro Patric Pinho and Vidotti, Miriam Suzane and Fritsche-Neto, Roberto}, year={2017}, month={Aug}, pages={481–488} }
 @article{morosini_freitas mendonça_lyra_galli_vidotti_fritsche-neto_2017, title={Association mapping for traits related to nitrogen use efficiency in tropical maize lines under field conditions}, volume={421}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85032953225&partnerID=MN8TOARS}, DOI={10.1007/s11104-017-3479-3}, number={1-2}, journal={Plant and Soil}, author={Morosini, Júlia Silva and Freitas Mendonça, Leandro and Lyra, Danilo Hottis and Galli, Giovanni and Vidotti, Miriam Suzane and Fritsche-Neto, Roberto}, year={2017}, month={Nov}, pages={453–463} }
 @article{matias_granato_dequigiovanni_fritsche-neto_2017, title={Be-Breeder - an application for analysis of genomic data in plant breeding}, volume={17}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000394514700008&KeyUID=WOS:000394514700008}, DOI={10.1590/1984-70332017v17n1n8}, abstractNote={Be-Breeder is an application directed toward genetic breeding of plants, developed through the Shiny package of the R software, which allows different phenotype and molecular (marker) analysis to be undertaken. The section for analysis of molecular data of the Be-Breeder application makes it possible to achieve quality control of genotyping data, to obtain genomic kinship matrices, and to analyze genomic selection, genome association, and genetic diversity in a simple manner on line. This application is available for use in a network through the site of the Allogamous Plant Breeding Laboratory of ESALQ-USP (http://www.genetica.esalq.usp.br/alogamas/R.html).}, number={1}, journal={Crop Breeding and Applied Biotechnology}, author={Matias, Filipe Inácio and Granato, Italo Stefanine Correa and Dequigiovanni, Gabriel and Fritsche-Neto, Roberto}, year={2017}, month={Feb}, pages={54–58} }
 @article{santos_amaral júnior_nascimento ferreira kurosawa_gerhardt_neto_2017, title={GGE Biplot projection in discriminating the efficiency of popcorn lines to use nitrogen}, volume={41}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85014536482&partnerID=MN8TOARS}, DOI={10.1590/1413-70542017411030816}, abstractNote={ABSTRACT Nitrogen is essential for sustaining life on the planet, and it is the most important nutrient for obtaining high agricultural production. However, their use leads to the release of nitrous oxide with a global warming potential 296 times higher than the CO2 molecule, making it a challenge to reduce their use in agriculture. The objective of this research was to identify efficient popcorn inbred lines and responsive nitrogen use and exhibit a good expansion volume. For this, 29 inbred lines from the Germplasm Collection of Darcy Ribeiro North Fluminense State University (UENF) were evaluated at two contrasting levels of nitrogen availability (low and ideal) at two representative locations in the north and northwest of the state of Rio de Janeiro, Brazil, arranged in a randomized block design with three replicates. These inbred lines were discriminated against efficient use of nitrogen by multivariate GGE Biplot. Selective accuracy was close to 1, showing that the genotypes were enough to provide contrasting success in selection procedures. The first two main components (PC) retained 93.82% of the total variation, and PC1 furnished an information ratio (IR) that was unaffected by noise. L77 was the most unstable line, while P7, P2, P6, P3, P5, P4, P9, P10, P8, P9, L70, L74, and L55 were efficient and responsive. The GGE biplot method is recommended for the reliable identification of popcorn lines that are efficient and responsive to the use of nitrogen.}, number={1}, journal={Ciência e Agrotecnologia}, author={Santos, Adriano and Amaral Júnior, Antônio Teixeira and Nascimento Ferreira Kurosawa, Railan and Gerhardt, Ismael Fernando Schegoscheski and Neto, Roberto Fritsche}, year={2017}, month={Feb}, pages={22–31} }
 @article{matias_galli_granato_fritsche‐neto_2017, title={Genomic Prediction of Autogamous and Allogamous Plants by SNPs and Haplotypes}, volume={57}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85032018858&partnerID=MN8TOARS}, DOI={10.2135/cropsci2017.01.0022}, abstractNote={The implementation of single‐nucleotide polymorphism (SNP)‐based genomic selection has demonstrated great predictive potential in plants. However, its application is sometimes limited to the biallelism of the marker. In this context, the use of haplotype blocks as multiallelic markers might improve genomic prediction. This study was performed to compare the predictive ability of Bayesian genomic prediction models using haplotypes (confidence interval and four‐gamete), individual SNPs, and sets of SNPs selected according to haplotype construction. The use of haplotype matrices increased the predictive ability and selection coincidence with the phenotypic selection for the maize ( Zea mays L.) breeding population. However, this was not observed for the rice ( Oryza sativa L.) population, in which the use of the nonreduced SNP matrix was more efficient. Overall, the use of reduced SNP matrices did not lead to better predictive abilities. No difference was observed between the genomic prediction methods used. We found that the use of haplotypes has potential to increase predictive ability of genomic prediction in breeding populations of allogamous plants or plants with high multiallelism.}, number={6}, journal={Crop Science}, author={Matias, Filipe Inacio and Galli, Giovanni and Granato, Italo Stefanine Correia and Fritsche‐Neto, Roberto}, year={2017}, month={Sep}, pages={2951–2958} }
 @article{sousa_cuevas_oliveira couto_pérez-rodríguez_jarquín_fritsche-neto_burgueño_crossa_2017, title={Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction}, volume={7}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020239577&partnerID=MN8TOARS}, DOI={10.1534/g3.117.042341}, abstractNote={Abstract Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.}, number={6}, journal={G3 Genes Genomes Genetics}, author={Sousa, Massaine Bandeira and Cuevas, Jaime and Oliveira Couto, Evellyn Giselly and Pérez-Rodríguez, Paulino and Jarquín, Diego and Fritsche-Neto, Roberto and Burgueño, Juan and Crossa, Jose}, year={2017}, month={Apr}, pages={1995–2014} }
 @article{lyra_freitas mendonça_galli_alves_granato_fritsche-neto_2017, title={Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids}, volume={37}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020627463&partnerID=MN8TOARS}, DOI={10.1007/s11032-017-0681-1}, number={6}, journal={Molecular Breeding}, author={Lyra, Danilo Hottis and Freitas Mendonça, Leandro and Galli, Giovanni and Alves, Filipe Couto and Granato, Ítalo Stefanine Correia and Fritsche-Neto, Roberto}, year={2017}, month={Jun} }
 @article{reis_pereira_granato_dovale_fritsche-neto_2017, title={Tropical maize selection indexes genotypes for efficiency in use of nutrients: phosphorus}, volume={64}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85023189688&partnerID=MN8TOARS}, DOI={10.1590/0034-737X201764030007}, abstractNote={ABSTRACT Brazil generates an annual demand for more than 2.83 million tons of phosphate fertilizers. Part of this is due to low P use efficiency (PUE) by plants, particularly in current maize cultivars. Thus, the aim of this study was to create indexes that allow accurate selection of maize genotypes with high PUE under conditions of either low or high P availability. The experiment was conducted in a greenhouse (20º45'14"S; 42º52'53"W) at the Universidade Federal de Viçosa in October 2010. We evaluated 39 experimental hybrid combinations and 14 maize inbred lines with divergent PUE under two conditions of P availability. The relative importance of the traits studied was analyzed and estimated by principal component analysis, factor analysis, and establishment of selection indexes. To obtain genotypes responsive to high P availability, the index SIHP (selection index for high phosphorus) = 0.3985 RDM + 0.3099 SDM + 0.5567 RLLAT + 0.2340 PUEb - 0.1139 SRS is recommended. To obtain genotypes tolerant to low P availability, the index SILP (selection index for low phosphorus) = 0.3548 RDM + 0.3996 RLLAT + 0.3344 SDM + 0.0041 SH/RS - 0.1019 SRS is suggested.}, number={3}, journal={Revista CERES}, author={Reis, Gabriel Gonçalves and Pereira, Felipe Bermudez and Granato, Italo Stefanine Correia and DoVale, Júlio César and Fritsche-Neto, Roberto}, year={2017}, month={Jun}, pages={266–273} }
 @article{coutinho_filho_neto_frizzo_2017, title={Viabilidade da seleção precoce de Pinus taeda L. em diâmetro a altura do peito em programa de melhoramento genético}, volume={45}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020278127&partnerID=MN8TOARS}, DOI={10.18671/scifor.v45n113.21}, abstractNote={The superior genotypes selection of Pinus taeda L. using genetic field trials require many years, due to the rotation age of commercial plantations, which consumes a lot of financial resources. Regarding this, many researchers have put their efforts in evaluate the selection in early ages prior to the final age of rotation. The goals of this study are to estimate, at the early age of eight years and final rotation age of fifteen years, the genetic parameters and the predicted genetic gains between and within the progenies of Pinus taeda L. half-sib family, with experiments established in three distinct locations in the state of Paraná, Brazil. There were significant correlations observed for the progenies ranking and for the linear correlation of the additive genetic gain between the age of eight and fifteen years. The obtained heritability in this study were greater in Campo do Tenente, were it was observed a greater growth, also concurring with the location of the evaluated half-sib family selection. For the Campo do Tenente experiment, the eight years old measures displayed the same predicted genetic gain of those from the fifteen years old measures, for either the parents or the best individual selection. For the Morungava experiment, located in the municipality of Senges, having similar soil and climate conditions of Campo do Tenente, however further away from the parents selection location, it was only possible to succeed in the best individual selection at eight years old age, whereas the parents selection at this age have not showed significant statistical difference apart the control. For the best individual selection at early age, it is recommended to utilize the maximum number of individuals per genitor restriction, due to showing the same statistical results of the selection at fifteen years old, but with greater genetic variability. For the Mocambo experiment, also in the municipality of Senges, but with soil and climate conditions very different, it was not possible to detect statistical differences for neither the parents nor the best individual selection.}, number={113}, journal={Scientia Forestalis}, author={Coutinho, Rodrigo Toledo and Filho, João Carlos Bespalhok and Neto, Roberto Fritsche and Frizzo, Caroline}, year={2017}, month={Mar}, pages={205–219} }
 @article{mendonca_fritsche-neto_granato_alves_2016, title={Accuracy and simultaneous selection gains for grain yield and earliness in tropical maize lines}, volume={61}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000393132900010&KeyUID=WOS:000393132900010}, number={3}, journal={Maydica}, author={Mendonca, L. F. and Fritsche-Neto, R. and Granato, I. S. C. and Alves, F. C.}, year={2016} }
 @article{accuracy and simultaneous selection gains for grain yield and earliness in tropical maize lines_2016, url={https://publons.com/wos-op/publon/9094979/}, journal={Maydica}, year={2016} }
 @article{fritsche-neto_matias_2016, title={Be-Breeder - Learning: a new tool for teaching and learning plant breeding principles}, volume={16}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000391702500011&KeyUID=WOS:000391702500011}, DOI={10.1590/1984-70332016v16n3n36}, abstractNote={The Be-Breeder application is an on-line tool constructed through the R software for the purpose of assisting in some of the main genetic and statistical analyses related to the area of plant breeding. In addition, Be-Breeder provides a section called “Learning”, which in a simple click-point manner allows explanation of theories related to the effect of inbreeding, population structure, qualitative and quantitative traits, heterosis, population size, effect of selection, and composition of hybrids. Be-Breeder is available for network use on the website of the Allogamous Plant Breeding Laboratory (Laboratório de Melhoramento de Plantas Alógamas) of ESALQ-USP through the link: http:// www.genetica.esalq.usp.br/alogamas/R.html.}, number={3}, journal={Crop Breeding and Applied Biotechnology}, author={Fritsche-Neto, Roberto and Matias, Filipe Inácio}, year={2016}, month={Sep}, pages={240–245} }
 @article{granato_fritsche-neto_resende_silva_2016, title={Effects of using phenotypic means and genotypic values in GGE biplot analyses on genotype by environment studies on tropical maize (Zea mays)}, volume={15}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84990966442&partnerID=MN8TOARS}, DOI={10.4238/gmr.15048747}, abstractNote={The objective of this study was to examine the effects of the type and intensity of nutritional stress, and of the statistical treatment of the data, on the genotype x environment (G x E) interaction for tropical maize (Zea mays). For this purpose, 39 hybrid combinations were evaluated under low- and high-nitrogen and -phosphorus availability. The plants were harvested at the V6 stage, and the shoot dry mass was estimated. The variance components and genetic values were assessed using the restricted maximum likelihood/best linear unbiased prediction method, and subsequently analyzed using the GGE biplot method. We observed differences in the performances of the hybrids depending on both the type and intensity of nutritional stress. The results of relationship between environments depended on whether genotypic values or phenotypic means were used. The selection of tropical maize genotypes against nutritional stress should be performed for each nutrient availability level within each type of nutritional stress. The use of phenotypic means for this purpose provides greater reliability than do genotypic values for the analysis of the G x E interaction using GGE biplot.}, number={4}, journal={Genetics and Molecular Research}, author={Granato, I.S.C. and Fritsche-Neto, R. and Resende, M.D.V. and Silva, F.F.}, year={2016}, month={Jan} }
 @article{andrade_neto_granato_sant’ana_morais_borém_2016, title={Genetic Vulnerability and the Relationship of Commercial Germplasms of Maize in Brazil with the Nested Association Mapping Parents}, volume={11}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000389046900006&KeyUID=WOS:000389046900006}, DOI={10.1371/journal.pone.0163739}, abstractNote={A few breeding companies dominate the maize (Zea mays L.) hybrid market in Brazil: Monsanto® (35%), DuPont Pioneer® (30%), Dow Agrosciences® (15%), Syngenta® (10%) and Helix Sementes (4%). Therefore, it is important to monitor the genetic diversity in commercial germplasms as breeding practices, registration and marketing of new cultivars can lead to a significant reduction of the genetic diversity. Reduced genetic variation may lead to crop vulnerabilities, food insecurity and limited genetic gains following selection. The aim of this study was to evaluate the genetic vulnerability risk by examining the relationship between the commercial Brazilian maize germplasms and the Nested Association Mapping (NAM) Parents. For this purpose, we used the commercial hybrids with the largest market share in Brazil and the NAM parents. The hybrids were genotyped for 768 single nucleotide polymorphisms (SNPs), using the Illumina Goldengate® platform. The NAM parent genomic data, comprising 1,536 SNPs for each line, were obtained from the Panzea data bank. The population structure, genetic diversity and the correlation between allele frequencies were analyzed. Based on the estimated effective population size and genetic variability, it was found that there is a low risk of genetic vulnerability in the commercial Brazilian maize germplasms. However, the genetic diversity is lower than those found in the NAM parents. Furthermore, the Brazilian germplasms presented no close relations with most NAM parents, except B73. This indicates that B73, or its heterotic group (Iowa Stiff Stalk Synthetic), contributed to the development of the commercial Brazilian germplasms.}, number={10}, journal={PLoS ONE}, author={Andrade, Luciano Rogério Braatz and Neto, Roberto Fritsche and Granato, Ítalo Stefanine Correia and Sant’Ana, Gustavo César and Morais, Pedro Patric Pinho and Borém, Aluízio}, year={2016}, month={Oct} }
 @article{caixeta_fritsche-neto_granato_oliveira_galvão_2015, title={Early indirect selection for nitrogen use efficiency in maize}, volume={46}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000352272800016&KeyUID=WOS:000352272800016}, DOI={10.5935/1806-6690.20150016}, abstractNote={"Several studies to evaluate nitrogen use efficiency (NUE) have been carried out using early growth stages. However, there are no scientific reports on the ideal stage for evaluation and on which characteristics have the highest correlation with the NUE at that stage. The aim therefore was to identify the phenological stages and secondary characteristics which maximize accuracy in early indirect selection for NUE in maize. To do this, three endogamic maize strains were evaluated in a completely randomised design with five replications, in a triple factorial scheme (strains x N levels x phenological stage), at two contrasting nitrogen levels: low and high nitrogen. The plants were evaluated at five growth stages: stage nine (V9), with 14 fully-developed leaves (V14), tasseling (VT), flowering (R1) and physiological maturity (R6). The following characteristics were evaluated: efficiency in the usage, absorption, use and translocation of nitrogen; activity of nitrate reductase and glutamine synthetase; length of the lateral and axial roots; specific root area; chlorophyll content; number of leaves; plant height; stem diameter; and the levels of phosphorus and potassium. Considering the estimated direct and indirect gains, it can be concluded that the activities of glutamine synthetase at the V9 and V14 stages permit early indirect selection for nitrogen use efficiency in maize under conditions of low and high N availability respectively."}, number={2}, journal={Ciência Agronômica/Revista ciência agronômica}, author={Caixeta, Débora Santos and Fritsche-Neto, Roberto and Granato, Ítalo Stefanine Correia and Oliveira, Lucimar Rodrigues and Galvão, João Carlos Cardoso}, year={2015}, month={Jan}, pages={369–378} }
 @article{fritsche-neto_borém_2015, title={Phenomics}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84944628510&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-13677-6}, journal={Phenomics: How Next-Generation Phenotyping is Revolutionizing Plant Breeding}, author={Fritsche-Neto, Roberto and Borém, Aluízio}, year={2015}, month={Jan}, pages={1–142} }
 @article{dovale_fritsche-neto_2015, title={Root Phenomics}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84944580921&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-13677-6_4}, journal={Phenomics}, author={DoVale, Júlio César and Fritsche-Neto, Roberto}, year={2015}, month={Jan}, pages={49–66} }
 @article{borém_fritsche-neto_2014, title={Biotechnology and Plant Breeding}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84904141126&partnerID=MN8TOARS}, DOI={10.1016/C2013-0-06808-9}, journal={Elsevier eBooks}, author={Borém, A. and Fritsche-Neto, R.}, year={2014}, month={Jan}, pages={1–257} }
 @article{biotechnology and plant breeding applications and approaches for developing improved cultivars preface_2014, url={https://publons.com/wos-op/publon/19793013/}, journal={Biotechnology and Plant Breeding: Applications and Approaches for Developing Improved Cultivars}, year={2014} }
 @article{biotechnology and plant breeding: applications and approaches for developing improved cultivars_2014, url={https://publons.com/wos-op/publon/19793017/}, journal={Biotechnology and Plant Breeding: Applications and Approaches for Developing Improved Cultivars}, year={2014} }
 @article{fritsche-neto_garbuglio_borém_2014, title={Double Haploids}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84904139460&partnerID=MN8TOARS}, DOI={10.1016/B978-0-12-418672-9.00009-X}, journal={Elsevier eBooks}, author={Fritsche-Neto, Roberto and Garbuglio, Deoclecio Domingos and Borém, Aluízio}, year={2014}, month={Jan}, pages={201–224} }
 @book{borém_fritsche-neto_2014, title={Forewod}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84930882435&partnerID=MN8TOARS}, journal={Omics in Plant Breeding}, author={Borém, A. and Fritsche-Neto, R.}, year={2014}, pages={xiii} }
 @article{diola_fritsche-neto_2014, title={Genes Prospection}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84904147644&partnerID=MN8TOARS}, DOI={10.1016/B978-0-12-418672-9.00006-4}, journal={Elsevier eBooks}, author={Diola, Valdir and Fritsche-Neto, Roberto}, year={2014}, month={Jan}, pages={135–156} }
 @article{granato_bermudez_reis_dovale_miranda_fritsche-neto_2014, title={Index selection of tropical maize genotypes for nitrogen use efficiency}, volume={73}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=SCIELO&KeyUT=SCIELO:S0006-87052014000200009&KeyUID=SCIELO:S0006-87052014000200009}, DOI={10.1590/brag.2014.021}, abstractNote={Nitrogen (N) limitation in maize crops is related to the fact that the efficiency of nitrogen fertilization in maize does not exceed 50%, primarily due to volatilization, denitrification and soil leaching. Therefore, the development of new nitrogen use efficient (NUE) cultivars is necessary. The aim of the present study was to develop indices for the accurate selection of NUE maize genotypes for use in conditions of both high and low N availability. The experiment was conducted in a greenhouse (20º45'14"S; 42º52'53"W) at the Federal University of Viçosa during October 2010. A total of 39 experimental hybrid combinations and 14 maize lines differing in NUE were evaluated under two N availability conditions. We determined the relative importance of the studied characters using principal component analysis, factor analysis and by developing efficient selection indices. We conclude that indirect and early selection of tropical maize genotypes can be performed using the indices I HN = 0.022 SDM + 0.35 RSDM + 0.35 RL A + 0.35 NUE for high N availability environments and I LN = -0.06 RSDM + 0.35 RSA A + 0.35 RL A + 0.39 SDM for low N availability environments.}, number={2}, journal={Bragantia}, author={Granato, Ítalo Stefanine Correia and Bermudez, Felipe Pereira and Reis, Gabriel Gonçalves and Dovale, Julio César and Miranda, Glauco Vieira and Fritsche-Neto, Roberto}, year={2014}, month={Jun}, pages={153–159} }
 @article{borém_fritsche-neto_2014, title={Omics in Plant Breeding}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84927572603&partnerID=MN8TOARS}, DOI={10.1002/9781118820971}, journal={Omics in Plant Breeding}, author={Borém, A. and Fritsche-Neto, R.}, year={2014}, month={Jun}, pages={1–230} }
 @article{omics in plant breeding foreword_2014, url={https://publons.com/wos-op/publon/19793018/}, journal={Omics in Plant Breeding}, year={2014} }
 @article{omics: opening up the "black box" of the phenotype_2014, url={https://publons.com/wos-op/publon/19793019/}, journal={Omics in Plant Breeding}, year={2014} }
 @article{fritsche‐neto_borém_2014, title={Omics: Opening up the “Black Box” of the Phenotype}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84927577689&partnerID=MN8TOARS}, DOI={10.1002/9781118820971.ch1}, abstractNote={With a huge range of sequences being deposited in databases, geneticists are faced with a challenge as great as that which propelled the "genomics era": correlating structure with function. This challenge has given rise to functional genomics, the science of the "era of omics." Omics is the neologism used to refer to the fields of biotechnology with the suffix omics: genomics, proteomics, transcriptomics, metabolomics, and physiognomics, among others. In recent years, genetics and omics tools have revolutionized plant breeding, greatly increasing the available knowledge of the genetic factors responsible for complex traits and developing a large amount of resources that can be used in the selection of superior genotypes. Post-transcriptional gene silencing (PTGS), or RNA interference (RNAi) has assisted the development of transgenic plants capable of suppressing the expression of endogenous genes and foreign nucleic acids.}, journal={Omics in Plant Breeding}, author={Fritsche‐Neto, Roberto and Borém, Aluízio}, year={2014}, month={Jun}, pages={1–11} }
 @article{fritsche‐neto_borém_cobb_2014, title={Phenomics}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84927580966&partnerID=MN8TOARS}, DOI={10.1002/9781118820971.ch7}, abstractNote={The development of phenotyping technology requires increased automation and collaborations between biologists, agronomists, engineers, and bioinformaticians. Phenomics involves a series of high-throughput techniques to increase and automate the capacity and accuracy of phenotypic evaluation, so as to empower the discovery of the genes, transcripts, proteins, and metabolites that interact with the environment to produce the biodiversity prevalent today and drive the generation of new diversity that will serve to diminish the impact of climate change and other global challenges moving forward. One of the earliest phenotyping tools widely used to increase automation and throughput was the infrared gas analyzer (IRGA). Next-generation phenotyping facilitates the discovery of genes as well as improve gain from selection in a breeding context. Its application ranges from germplasm characterization, to genomic selection (GS), to QTL identification.}, journal={Omics in Plant Breeding}, author={Fritsche‐Neto, Roberto and Borém, Aluízio and Cobb, Joshua N.}, year={2014}, month={Jun}, pages={127–146} }
 @article{phenomics_2014, url={https://publons.com/wos-op/publon/10966115/}, DOI={10.1201/B16437}, journal={Omics in Plant Breeding}, year={2014}, month={Jan} }
 @article{borém_diola_fritsche-neto_2014, title={Plant Breeding and Biotechnological Advances}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84904142582&partnerID=MN8TOARS}, DOI={10.1016/B978-0-12-418672-9.00001-5}, journal={Elsevier eBooks}, author={Borém, Aluízio and Diola, Valdir and Fritsche-Neto, Roberto}, year={2014}, month={Jan}, pages={1–17} }
 @book{borém_fritsche-neto_2014, title={Preface}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84904150461&partnerID=MN8TOARS}, journal={Biotechnology and Plant Breeding: Applications and Approaches for Developing Improved Cultivars}, author={Borém, A. and Fritsche-Neto, R.}, year={2014} }
 @article{coimbra_fritsche-neto_coimbra_naoe_cardoso_raoni_miranda_2014, title={RELATIONSHIP BETWEEN MAIZE TOLERANCE TO LOW PHOSPHORUS CONTENT IN THE SOIL AND THE PHOSPHORUS RESPONSIVENESS}, volume={30}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000331950300004&KeyUID=WOS:000331950300004}, number={2}, journal={Bioscience Journal}, author={Coimbra, Ronaldo Rodrigues and Fritsche-Neto, Roberto and Coimbra, Diego Barbosa and Naoe, Lucas Koshy and Cardoso, Expedito Alves and Raoni, Diego and Miranda, Glauco Vieira}, year={2014}, pages={332–339} }
 @article{relationship between maize tolerance to low phosphorus content in the soil and the phosphorus responsiveness_2014, url={https://publons.com/wos-op/publon/19061731/}, journal={Bioscience Journal}, year={2014} }
 @article{coimbra_fritsche-neto_coimbra_naoe_cardoso_raoni_miranda_2014, title={Relationship between maize tolerance to low phosphorus content in the soil and the phosphorus responsiveness,Relação entre tolerância do milho a baixo teor de fosforo no solo e responsividade a adubação fosfatada}, volume={30}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84894254368&partnerID=MN8TOARS}, number={2}, journal={Bioscience Journal}, author={Coimbra, R.R. and Fritsche-Neto, R. and Coimbra, D.B. and Naoe, L.K. and Cardoso, E.A. and Raoni, D. and Miranda, G.V.}, year={2014}, pages={332–339} }
 @article{galvão_miranda_trogello_fritsche-neto_2014, title={Sete décadas de evolução do sistema produtivo da cultura do milho}, volume={61}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84939211662&partnerID=MN8TOARS}, DOI={10.1590/0034-737X201461000007}, abstractNote={Objetivou-se com este trabalho comparar o sistema de produção de milho, recomendado nos anos 40, com o atualmente empregado. Para isso, utilizou-se como base o artigo publicado por Antônio Secundino de São José, na Revista Ceres, em 1944, comparando-se as práticas agrícolas recomendadas para a cultura do milho na época com as atualmente empregadas. Naquela época, não havia preocupação direta com os aspectos conservacionistas de solo e água. Todavia, iniciava-se o processo de elevação da produtividade de grãos, com base no uso de mais insumos, todos obtidos na propriedade, como o esterco bovino, e de obtenção das próprias sementes. A cultura do milho era tratada de maneira individualizada, sem os conceitos de integração de lavoura, pecuária e conservação de solo e água. Atualmente, muitos conceitos recomendados há 70 anos ainda são utilizados na agricultura orgânica e familiar. Por outro lado, no cultivo em grande escala da cultura do milho utilizam-se os mais variados insumos, como fertilizantes sintéticos, herbicidas, inseticidas, sementes de híbridos (com ou sem eventos transgênicos), aplicação de fungicidas, plantio e colheita mecanizados. Conclui-se que nos últimos 70 anos ocorreram muitas mudanças no sistema de produção de milho e que estas mudanças foram fundamentais para que a produtividade aumentasse 3,79 vezes no período analisado. Todo o sistema de produção foi modificado em relação aos fatores de construção e proteção da produtividade, que por sua vez, deram suporte para que o Brasil chegasse a posição de terceiro maior produtor e exportador de milho do mundo, saltando de 5,6 milhões de toneladas em 1944 para 81,5 milhões de toneladas em 2013.}, journal={Revista CERES}, author={Galvão, João Carlos Cardoso and Miranda, Glauco Vieira and Trogello, Emerson and Fritsche-Neto, Roberto}, year={2014}, month={Dec}, pages={819–828} }
 @article{oliveira_miranda_delima_fritsche-neto_galvão_2013, title={Eficiência na absorção e utilização de nitrogênio e atividade enzimática em genótipos de milho}, url={https://publons.com/wos-op/publon/19061730/}, DOI={10.1590/S1806-66902013000300025}, abstractNote={The aim of this study was to investigate the employment of components of the nitrogen efficiency use (NUE), and of the activities of the nitrate reductase and glutamine synthetase enzymes in the selection of nitrogen use efficient maize genotypes of maize. Ten maize genotypes at V4 stage were evaluated under high and low N. The experiment consisted of a 2 × 10 factorial (two levels of N and ten maize genotypes), in a randomized complete block design with three replicates. The following traits were evaluated: N uptake efficiency (NUpE), N utilizationefficiency (NUtE) and N use efficiency (NUE), and activities of the glutamine synthetase (GS) and nitrate reductase (NR) enzymes. For traits which the effect of genotype was significant (p≥0.05) in the variance analysis forlevel of N, the mean were compared using the t test (p≥0.05). The indirect gains in NUE and their components with selection over the activities of the enzymes NR and GS were estimated under low N. We conclude that: under high N, NupE is efficient to differentiate nitrogen use efficient maize genotypes; under low N, NupE and NUtE efficient to differentiate nitrogen use efficient maize genotypes, the activity of NR enzyme is not a good physiological parameter for differentiate nitrogen use efficient maize genotypes, and selection over activity of GS enzyme enables the indirect selection of nitrogen use efficient maize genotypes.}, journal={Ciência Agronômica/Revista ciência agronômica}, author={Oliveira, Lucimar Rodrigues and Miranda, Glauco Vieira and DeLima, Rodrigo Oliveira and Fritsche-Neto, Roberto and Galvão, João Carlos Cardoso}, year={2013}, month={May} }
 @article{dovale_fritsche-neto_2013, title={Genetic control of traits associated with phosphorus use efficiency in maize by REML/BLUP}, volume={44}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84880580673&partnerID=MN8TOARS}, DOI={10.1590/S1806-66902013000300018}, abstractNote={The improvement phosphorus use efficiency (PUE) allows to reach satisfactory yields with lower costs. A breeding strategy for PUE is to increase its components, the phosphorus acquisiton efficiency (PAE) and phosphorus utilization efficiency (PUtE). Thus, this study aimed to identify: i) the relative importance of the components of PUE, in high and low phosphorus; ii)the relationship of root system length and shoot dry mass (SDM) with PUE and its components and; iii) the genetic control of traits associated with the PUE. Forty-one hybrid combinations were evaluated in two experimental environments representing contrasting conditions of availability of phosphorus: low (LP) and high (HP). A randomized block design with two replications were used in simple factorial (hybrid combination x availability of phosphorus) arrangement. Independently of phosphorus availability, PAE was the mostimportant component of the PUE and non-additive genetic effects were moreimportant to the traits associated with the PUE. It was observed that the estimates of specific combining ability for SDM and PUE, in LP and HP, showed similar behavior and magnitude, indicating that the selection based on performance of hybrid combinations for SDM, allows to obtain genotypes with high PUE.}, number={3}, journal={Ciência Agronômica/Revista ciência agronômica}, author={DoVale, Júlio César and Fritsche-Neto, Roberto}, year={2013}, month={May}, pages={554–563} }
 @article{dovale_fritsche-neto_2013, title={Genetic control of traits associated with phosphorus use efficiency in maize by REML/BLUP}, volume={44}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000320007700018&KeyUID=WOS:000320007700018}, number={3}, journal={Revista Ciencia Agronomica}, author={DoVale, Julio Cesar and Fritsche-Neto, Roberto}, year={2013}, pages={554–563} }
 @article{oliveira_miranda_delima_fritsche-neto_cardoso galvao_2013, title={Nitrogen uptake and utilization efficiency and enzymatic activity in maize genotypes}, volume={44}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000320007700025&KeyUID=WOS:000320007700025}, number={3}, journal={Revista Ciencia Agronomica}, author={Oliveira, Lucimar Rodrigues and Miranda, Glauco Vieira and DeLima, Rodrigo Oliveira and Fritsche-Neto, Roberto and Cardoso Galvao, Joao Carlos}, year={2013}, pages={614–621} }
 @article{nitrogen uptake and utilization efficiency and enzymatic activity in maize genotypes,eficiência na absorčão e utilizačão de nitrogênio e atividade enzimática em genótipos de milho_2013, volume={44}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84880586970&partnerID=MN8TOARS}, number={3}, journal={Revista Ciencia Agronomica}, year={2013}, pages={614–621} }
 @article{caixeta_fritsche-neto_batista_carvalho_dovale_lanes_miranda_2013, title={Relationship between heterosis and genetic divergence for phosphorus use efficiency and its components in tropical maize}, volume={43}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000312670300011&KeyUID=WOS:000312670300011}, number={1}, journal={Ciencia Rural}, author={Caixeta, Debora Santos and Fritsche-Neto, Roberto and Batista, Lorena Guimaraes and Carvalho, Humberto Fanelli and DoVale, Julio Cesar and Lanes, Eder Cristian and Miranda, Glauco Vieira}, year={2013}, pages={60–65} }
 @article{pereira_dovale_carneiro_fritsche-neto_2013, title={Relação entre os caracteres determinantes das eficiências no uso de nitrogênio e fósforo em milho}, volume={60}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84891302085&partnerID=MN8TOARS}, DOI={10.1590/S0034-737X2013000500006}, abstractNote={O melhoramento genético das eficiências no uso de N (EUN) e P (EUP) é um dos meios para se obterem produtividades de grãos satisfatórias, com menores custos e de modo sustentável. Todavia, pouco se sabe a respeito da relação entre os caracteres determinantes dessas eficiências, o que tem dificultado o uso da seleção precoce e indireta. Portanto, objetivou-se, com este trabalho, identificar a relação entre os caracteres determinantes das eficiências no uso de nitrogênio e fósforo, em milho. Para isso, avaliaram-se 14 linhagens e 39 híbridos simples, em dois experimentos, em baixa e alta disponibilidade de N e P, em delineamento inteiramente ao acaso, com duas repetições, em esquema fatorial simples. Os experimentos foram conduzidos em telado. Foram utilizados tubos cilíndricos de PVC, com 4 dm³ de capacidade, preenchidos com dois tipos de substrato, de acordo com o experimento. As soluções nutritivas foram fornecidas a partir do sétimo dia após o transplantio, aplicando-se 250 ml tubo-1, a cada dois dias. As plantas foram colhidas em estádio de seis folhas completamente expandidas (V6) e os caracteres avaliados foram: massa da parte aérea seca (MPS), área de raiz específica (ARE), comprimento de raízes laterais (CRLat) e axiais (CRAxi) e os dois componentes da EUN e EUP, as eficiências de utilização (EUt) e a de absorção (EAb). Foram realizadas análises de variância e de trilha dos dados coletados. Os caracteres de raiz não apresentaram efeitos significativos sobre as EUN e EUP. A MPS é o principal determinante das EUN e EUP, independentemente da disponibilidade nutricional.}, number={5}, journal={Revista CERES}, author={Pereira, Felipe Bermudez and DoVale, Júlio César and Carneiro, Pedro Crescêncio Souza and Fritsche-Neto, Roberto}, year={2013}, month={Oct}, pages={636–645} }
 @article{borém_ramalho_fritsche-neto_2012, title={Abiotic Stresses: Challenges for Plant Breeding in the Coming Decades}, volume={9783642305535}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84929937059&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-30553-5_1}, journal={Plant Breeding for Abiotic Stress Tolerance}, author={Borém, Aluízio and Ramalho, Magno Antonio Patto and Fritsche-Neto, Roberto}, year={2012}, month={Jan}, pages={1–12} }
 @article{silva_carvalho_vieira_fritsche-neto_2012, title={Adaptabilidade e estabilidade de populações de cenoura}, url={https://publons.com/wos-op/publon/17960327/}, DOI={10.1590/S0102-05362012000100014}, abstractNote={No processo de desenvolvimento de novas cultivares de cenoura, é imprescindível o conhecimento do comportamento das populações em fase final de melhoramento, frente aos possíveis ambientes para os quais elas possam vir a ser indicadas. Isso pode ser verificado por meio de análises de adaptabilidade e estabilidade. O objetivo desse estudo foi quantificar a adaptabilidade e estabilidade de populações de cenoura do grupo Brasilia. O trabalho foi conduzido nos anos agrícolas de 2007/2008, 2008/2009 e 2009/2010 em cinco locais: três com cultivo convencional em São Gotardo (MG); Irecê (BA) e Gama (DF); e dois com cultivo orgânico no Programa de Assentamento Dirigido do Distrito Federal (PAD-DF) e em Gama (DF). O delineamento experimental utilizado foi de blocos ao acaso com três repetições e parcelas de 1 m². Foram avaliadas quatro populações de cenoura do grupo Brasília: 0912532, 0912520, BRS Planalto e Brasília. Aos 100 dias após a semeadura, foi determinada em cada parcela, em gramas, a massa fresca das raízes com padrão comercial. Foi utilizada a metodologia REML/BLUP para a avaliação da adaptabilidade e estabilidade. Pôde-se verificar que as populações com melhor adaptabilidade e estabilidade foram BRS Planalto e 0912520. BRS Planalto destacou-se principalmente na safra de 2009/2010; enquanto a população 0912520 destacou-se em 2007/2008 e 2008/2009; Brasília teve o pior desempenho na maioria dos locais, porém apresentou bom desempenho ou adaptabilidade específica para Irecê (BA).}, journal={Horticultura Brasileira}, author={Silva, Giovani Olegário and Carvalho, Agnaldo Donizete F and Vieira, Jairo V and Fritsche-Neto, Roberto}, year={2012}, month={Mar} }
 @article{silva_carvalho_vieira_fritsche-neto_2012, title={Adaptability and stability of carrot populations}, volume={30}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000303202800014&KeyUID=WOS:000303202800014}, number={1}, journal={Horticultura Brasileira}, author={Silva, Giovani Olegario and Carvalho, Agnaldo Donizete F. and Vieira, Jairo V. and Fritsche-Neto, Roberto}, year={2012}, pages={80–83} }
 @article{silva_carvalho_vieira_fritsche-neto_2012, title={Adaptability and stability of carrot populations,Adaptabilidade e estabilidade de populações de cenoura}, volume={30}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84860850650&partnerID=MN8TOARS}, number={1}, journal={Horticultura Brasileira}, author={Silva, G.O. and Carvalho, A.D.F. and Vieira, J.V. and Fritsche-Neto, R.}, year={2012}, pages={80–83} }
 @article{matsuo_ferreira_sediyama_ferraz_borém_fritsche-neto_2012, title={Breeding for Nematode Resistance}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84930244894&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-33087-2_5}, journal={Plant Breeding for Biotic Stress Resistance}, author={Matsuo, Éder and Ferreira, Paulo Afonso and Sediyama, Tuneo and Ferraz, Silamar and Borém, Aluízio and Fritsche-Neto, Roberto}, year={2012}, month={Jan}, pages={81–102} }
 @article{dovale_delima_fritsche-neto_2012, title={Breeding for Nitrogen Use Efficiency}, volume={9783642305535}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84929895914&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-30553-5_4}, journal={Plant Breeding for Abiotic Stress Tolerance}, author={DoVale, Júlio César and DeLima, Rodrigo Oliveira and Fritsche-Neto, Roberto}, year={2012}, month={Jan}, pages={53–65} }
 @article{fritsche-neto_dovale_2012, title={Breeding for Stress-Tolerance or Resource-Use Efficiency?}, volume={9783642305535}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84929874765&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-30553-5_2}, journal={Plant Breeding for Abiotic Stress Tolerance}, author={Fritsche-Neto, Roberto and DoVale, Júlio César}, year={2012}, month={Jan}, pages={13–19} }
 @article{almeida silva_santos_labate_guidetti-gonzalez_santana borges_ferreira_delima_fritsche-neto_2012, title={Breeding for Water Use Efficiency}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84893968463&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-30553-5_6}, journal={Plant Breeding for Abiotic Stress Tolerance}, author={Almeida Silva, Marcelo and Santos, Claudiana Moura and Labate, Carlos Alberto and Guidetti-Gonzalez, Simone and Santana Borges, Janaina and Ferreira, Leonardo Cesar and DeLima, Rodrigo Oliveira and Fritsche-Neto, Roberto}, year={2012}, month={Jan}, pages={87–102} }
 @article{fritsche-neto_dovale_ferreira_ferreira_silva_2012, title={Breeding for Weed Management}, volume={9783642330872}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84930247358&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-33087-2_8}, journal={Plant Breeding for Biotic Stress Resistance}, author={Fritsche-Neto, Roberto and DoVale, Júlio César and Ferreira, Lino Roberto and Ferreira, Francisco Affonso and Silva, Antônio Alberto}, year={2012}, month={Jan}, pages={137–164} }
 @article{borém_fritsche-neto_2012, title={Challenges for Plant Breeding to Develop Biotic-Resistant Cultivars}, volume={9783642330872}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84930246546&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-33087-2_1}, journal={Plant Breeding for Biotic Stress Resistance}, author={Borém, Aluízio and Fritsche-Neto, Roberto}, year={2012}, month={Jan}, pages={1–11} }
 @article{vale_soares_cornélio_reis_borges_bisi_soares_fritsche-neto_2012, title={Contribuição genética na produtividade do arroz irrigado em Minas Gerais no período de 1998 a 2010}, volume={71}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84873558740&partnerID=MN8TOARS}, DOI={10.1590/S0006-87052012000400002}, abstractNote={O objetivo desse estudo foi quantificar o ganho genético para produtividade de grãos do programa de melhoramento do arroz irrigado de Minas Gerais, no período de 1998 a 2010. Foram utilizados dados dos ensaios comparativos avançados realizados em quatro localidades diferentes de Minas Gerais. Nem todos os locais foram contemplados em todos os anos agrícolas e para estimativas com maior acurácia, utilizou-se o método de modelos mistos. O ganho genético para produtividade de grãos no período considerado foi de 107,42 kg ha-1, o que representou uma proporção de 17,88% da estimativa total do progresso. Esse ganho correspondeu ao aumento de 8,95 kg ha-1 ano-1, ou ainda, aumento de 1,99% em produtividade de grãos por ano. Apesar de o ganho genético ter sido satisfatório, novas alternativas de melhoramento podem ser empreendidas para que o incremento produtivo seja maior nos próximos anos.}, number={4}, journal={Bragantia}, author={Vale, Júlio César Do and Soares, Plínio César and Cornélio, Vanda Maria Oliveira and Reis, Moizés Souza and Borges, Vanderley and Bisi, Rayane Barcelos and Soares, Antônio Alves and Fritsche-Neto, Roberto}, year={2012}, month={Jan}, pages={460–466} }
 @article{sediyama_souza carneiro_fritsche-neto_sediyama_barbosa_galvão_souza_2012, title={Contribution of the universities to the development of field crop cultivars}, url={https://publons.com/wos-op/publon/19061729/}, DOI={10.1590/S1984-70332012000500013}, abstractNote={Public and private research institutions employ their best efforts to produce new cultivars, which are intended to ensure productivity, reduce ecological footprint and present characteristics that meet consumer expectations. Some Brazilian universities, which are usually originated from schools of higher education in agriculture, have contributed to the breeding of some crops. These universities also aimed to solve the problems of the Brazilian agricultural sector, and became essential tool to make Brazil an important player in the agribusiness world. In the last decades, regarding the five species presented here, the universities have developed 35 oat cultivars and made the country self-sufficient in this grain; they have also developed cultivars of common beans (27), sugarcane (59), soybean (62), and wheat (03), besides countless corn hybrids, since works in this species date before the establishment of the national cultivar registration system.}, journal={Crop Breeding and Applied Biotechnology}, author={Sediyama, Carlos Sigueyuki and Souza Carneiro, José Eustáquio and Fritsche-Neto, Roberto and Sediyama, Tuneo and Barbosa, Márcio Henrique Pereira and Galvão, João Carlos Cardoso and Souza, Moacil Alves}, year={2012}, month={Dec} }
 @article{sediyama_souza carneiro_fritsche-neto_sediyama_pereira barbosa_cardoso galvao_souza_2012, title={Contribution of the universities to the development of field crop cultivars}, volume={12}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000314538700013&KeyUID=WOS:000314538700013}, journal={Crop Breeding and Applied Biotechnology}, author={Sediyama, Carlos Sigueyuki and Souza Carneiro, Jose Eustaquio and Fritsche-Neto, Roberto and Sediyama, Tuneo and Pereira Barbosa, Marcio Henrique and Cardoso Galvao, Joao Carlos and Souza, Moacil Alves}, year={2012}, pages={121–130} }
 @article{dovale_fritsche-neto_bermudez_miranda_2012, title={Efeitos gênicos de caracteres associados à eficiência no uso de nitrogênio em milho}, volume={47}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84862329717&partnerID=MN8TOARS}, DOI={10.1590/S0100-204X2012000300010}, abstractNote={Os objetivos deste trabalho foram determinar o controle genético da eficiência no uso do nitrogênio (EUN), identificar a importância das eficiências na absorção (EAN) e na utilização (EUtN) na sua composição, e quantificar relação entre produção de matéria seca da parte aérea (MPS) e do sistema radicular com a EUN e com seus componentes. Foram avaliadas 41 combinações híbridas em duas disponibilidades de N: baixa (BN) e alta (AN). Utilizou-se o delineamento de blocos ao acaso com duas repetições, em arranjo fatorial simples (combinação híbrida x disponibilidade de N). As análises estatísticas foram realizadas por meio das equações de modelos mistos. Correlações de elevada magnitude foram detectadas entre EAN e EUN, bem como entre essas eficiências e a MPS, tanto em BN como em AN. Em ambas as disponibilidades de N, efeitos genéticos aditivos apresentaram maior importância para os caracteres associados à EUN. Dessa forma, a seleção baseada no desempenho individual de linhagens quanto à MPS pode possibilitar a obtenção de genótipos com alta EUN. Independentemente da disponibilidade de N, a EAN é o componente mais importante da EUN.}, number={3}, journal={Pesquisa Agropecuária Brasileira}, author={DoVale, Júlio César and Fritsche-Neto, Roberto and Bermudez, Felipe and Miranda, Glauco Vieira}, year={2012}, month={Mar}, pages={385–392} }
 @article{dovale_soares_oliveira cornelio_reis_borges_bisi_soares_fritsche-neto_2012, title={Genetic contribution in yield of irrigated rice in Minas Gerais State between 1998 and 2010}, volume={71}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000319618200002&KeyUID=WOS:000319618200002}, number={4}, journal={Bragantia}, author={DoVale, Julio Cesar and Soares, Plinio Cesar and Oliveira Cornelio, Vanda Maria and Reis, Moizes Souza and Borges, Vanderley and Bisi, Rayane Barcelos and Soares, Antonio Alves and Fritsche-Neto, Roberto}, year={2012}, pages={460–466} }
 @article{genetic contribution in yield of irrigated rice in minas gerais state between 1998 and 2010_2012, url={https://publons.com/wos-op/publon/12806050/}, journal={Bragantia}, year={2012} }
 @article{dovale_fritsche-neto_bermudez_miranda_2012, title={Genetic effects of traits associated to nitrogen use efficiency in maize}, volume={47}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000303939700010&KeyUID=WOS:000303939700010}, number={3}, journal={Pesquisa Agropecuaria Brasileira}, author={DoVale, Julio Cesar and Fritsche-Neto, Roberto and Bermudez, Felipe and Miranda, Glauco Vieira}, year={2012}, pages={385–392} }
 @article{dovale_maia_fritsche-neto_miranda_cavatte_2012, title={Genetic responses of traits relationship to components of nitrogen and phosphorus use efficiency in maize}, volume={35}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000316257800004&KeyUID=WOS:000316257800004}, DOI={10.4025/actasciagron.v35i1.15237}, abstractNote={This study had three objectives: i) to observe maize inbred lines response to nitrogen (N) and phosphorus (P) acquisition as well as their utilization efficiencies and traits of root morphology in contrasting levels of these nutrients, ii) to study the relationship between root morphology and nutrient use efficiencies for both nutrients, and iii) to identify contrasting parents with components N and P use efficiency for an inheritance study. We evaluated 15 inbred lines in two experiments conducted in contrasting conditions of N and P. We evaluated the shoot, root traits and nutritional efficiencies and observed the genetic variability for most traits. Selection can be practiced for most of these traits in specific environments. Under conditions of nutritional stress, average and heritability estimates were of lesser magnitude. In this study, the shoot and root morphology traits were shown to be associated with the acquisition efficiency of both N and P in all of the environments that were evaluated.}, number={1}, journal={Acta Scientiarum Agronomy}, author={DoVale, Júlio César and Maia, Ciro and Fritsche-Neto, Roberto and Miranda, Glauco Vieira and Cavatte, Paulo Cézar}, year={2012}, month={Dec}, pages={31–38} }
 @article{fritsche-neto_dovale_lanes_resende_miranda_2012, title={Genome-Wide Selection for tropical maize root traits under conditions of nitrogen and phosphorus stress}, volume={34}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000316257000005&KeyUID=WOS:000316257000005}, DOI={10.4025/actasciagron.v34i4.15884}, abstractNote={The objective of this study was to verify the accuracy of the Genome-Wide Selection (GWS) method in tropical maize breeding for root traits under conditions of nitrogen and phosphorus stress. Forty-one single-crosses were evaluated in two experiments. The first experiment considered low nitrogen availability, and the second experiment considered low phosphorus availability. A randomized block design with two replicates was used. The lateral and axial root lengths were measured using WinRhizo software. The analysis of deviance was calculated using the Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) method. Eighty microsatellite markers were used to genotype the estimation population. The Random Regression method was used to analyze the GWS (RR-BLUP/GWS) data. The gains per unit time of the GWS and the phenotypic selection method were compared, as the standard phenotypic selection methods were considered to be the Recurrent Selection. The GWS accuracy was higher than the phenotypic selection accuracy for all of the traits evaluated. Thus, the GWS method may significantly increase the genetic gains for root traits that are obtained in tropical maize breeding programs for nutritional stress conditions.}, number={4}, journal={Acta Scientiarum Agronomy}, author={Fritsche-Neto, Roberto and DoVale, Júlio César and Lanes, Éder Cristian Malta and Resende, Marcos Deon Vilela and Miranda, Glauco Vieira}, year={2012}, month={Sep}, pages={389–395} }
 @article{fritsche-neto_borém aluízio_2012, title={Plant Breeding for Abiotic Stress Tolerance}, volume={9783642305535}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84929860419&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-30553-5}, journal={Plant Breeding for Abiotic Stress Tolerance}, author={Fritsche-Neto, Roberto and Borém Aluízio}, year={2012}, month={Jan}, pages={1–175} }
 @article{fritsche-neto_borém_2012, title={Plant Breeding for Biotic Stress Resistance}, volume={9783642330872}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84949178944&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-33087-2}, journal={Plant Breeding for Biotic Stress Resistance}, author={Fritsche-Neto, Roberto and Borém, Aluízio}, year={2012}, month={Jan}, pages={v} }
 @article{caixeta_fritsche-neto_batista_carvalho_dovale_lanes_miranda_2012, title={Relationship between heterosis and genetic divergence for phosphorus use efficiency and its components in tropical maize}, volume={43}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84871689319&partnerID=MN8TOARS}, DOI={10.1590/S0103-84782012005000133}, abstractNote={The objective of this study was to determine the relationship between heterosis and genetic divergence for phosphorus use efficiency (PUE) in tropical maize. It was used two groups of genitors, each consisting of seven lines, contrasting with each other in the nitrogen and phosphorus use efficiency. It was obtained 41 hybrid combinations between these groups, which were evaluated in low phosphorus. Randomized complete block design with two replications was used. For obtaining the components of variance and the breeding values were used REML/BLUP method. In the genotyping of the parental lines were used 80 microsatellite markers. Through the correlation between genetic distance obtained by the markers and specific combining ability it was not possible to determine with accuracy by molecular markers, the crosses that produced hybrids with the highest heterosis for PUE. Thus, is possible to conclude that there is no relationship between genetic divergence and heterosis for phosphorus use efficiency and its components in tropical maize.}, number={1}, journal={Ciência Rural}, author={Caixeta, Débora Santos and Fritsche-Neto, Roberto and Batista, Lorena Guimarães and Carvalho, Humberto Fanelli and DoVale, Júlio César and Lanes, Éder Cristian Malta and Miranda, Glauco Vieira}, year={2012}, month={Nov}, pages={60–65} }
 @article{fritsche-neto_resende_miranda_dovale_2012, title={Seleção genômica ampla e novos métodos de melhoramento do milho}, volume={59}, url={https://publons.com/wos-op/publon/18728200/}, DOI={10.1590/S0034-737X2012000600009}, abstractNote={Os objetivos deste trabalho foram verificar a acurácia do método da Seleção Genômica Ampla (GWS) no melhoramento de milho nas condições de estresse nutricional e propor novos métodos de melhoramento baseados em GWS. Foram estimados os dois componentes da eficiência no uso de nitrogênio e de fósforo (eficiência de absorção e de utilização) em 41 combinações híbridas, em dois experimentos, sob baixa e alta disponibilidades de N e P. Para a genotipagem da população de estimação, foram utilizados 80 marcadores microssatélites. As estimativas dos parâmetros genéticos foram obtidas via REML/BLUP, e a predição dos valores genéticos genômicos, via regressão aleatória (Random Regression - RR) aplicada à seleção genômica ampla (RR-BLUP/GWS). Para os caracteres em que a GWS apresentou altos valores de acurácia, essa foi comparada com os métodos de Seleção Recorrente Intra e Interpopulacional. Com o uso da GWS houve aumento significativo na acurácia seletiva e nos ganhos genéticos por unidade de tempo.}, number={6}, journal={Revista CERES}, author={Fritsche-Neto, Roberto and Resende, Marcos Deon Vilela and Miranda, Glauco Vieira and DoVale, Júlio César}, year={2012}, month={Dec}, pages={794–802} }
 @article{dovale_silva_fialho_mariguele_fritsche-neto_2011, title={Repeatability and number of growing seasons for the selection of custard apple progenies}, url={https://publons.com/wos-op/publon/8140856/}, DOI={10.1590/S1984-70332011000100008}, abstractNote={This study aimed to estimate the repeatability coefficient and determine the minimum number of samples required for effective selection for yield of custard apple. Twenty progenies were evaluated in randomized blocks, five replications and four plants per plot. The fruits were collected, counted and weighed every two days of the year. Estimates of the repeatability coefficients were obtained by the methods of analysis of variance - ANOVA and principal components - PC. The estimates from the repeatability analysis of biennial data are higher than those based on individual years. The estimates of the PC method were accurate even in the first harvest, unlike ANOVA. Four biennia were sufficient to ensure effective progeny selection of custard apple.}, journal={Crop Breeding and Applied Biotechnology}, author={DoVale, Julio César and Silva, Paulo Sérgio Lima and Fialho, Gustavo Sessa and Mariguele, Keny Henrique and Fritsche-Neto, Roberto}, year={2011}, month={Mar} }
 @article{dovale_silva_fialho_mariguele_fritsche-neto_2011, title={Repeatability and number of growing seasons for the selection of custard apple progenies}, volume={11}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000291668400008&KeyUID=WOS:000291668400008}, number={1}, journal={Crop Breeding and Applied Biotechnology}, author={DoVale, Julio Cesar and Silva, Paulo Sergio and Fialho, Gustavo Sessa and Mariguele, Keny Henrique and Fritsche-Neto, Roberto}, year={2011}, pages={59–63} }
 @article{dovale_silva_fialho_mariguele_fritsche-neto_2011, title={Repeatability and number of growing seasons for the selection of custard apple progenies,Repetibilidade e número de colheitas para a seleção de progênies de pinheiras}, volume={11}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79955890611&partnerID=MN8TOARS}, number={1}, journal={Crop Breeding and Applied Biotechnology}, author={DoVale, J.C. and Silva, P.S. and Fialho, G.S. and Mariguele, K.H. and Fritsche-Neto, R.}, year={2011}, pages={59–63} }
 @article{dovale_fritsche-neto_silva_2011, title={Selection index of maize cultivars with twice fitness: Baby corn and green corn,Índice de seleção para cultivares de milho com dupla aptidão: Minimilho e milho verde}, volume={70}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84856789482&partnerID=MN8TOARS}, number={4}, journal={Bragantia}, author={DoVale, J.C. and Fritsche-Neto, R. and Silva, P.S.L.}, year={2011}, pages={781–787} }
 @article{dovale_fritsche-neto_silva_2011, title={Selection index of maize cultivars with twice fitness: baby corn and green corn}, volume={70}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000301402900008&KeyUID=WOS:000301402900008}, number={4}, journal={Bragantia}, author={DoVale, Julio Cesar and Fritsche-Neto, Roberto and Silva, Paulo Sergio}, year={2011}, pages={781–787} }
 @article{maia_dovale_fritsche-neto_cavatte_miranda_2011, title={The difference between breeding for nutrient use efficiency and for nutrient stress tolerance}, volume={11}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-80054831221&partnerID=MN8TOARS}, DOI={10.1590/S1984-70332011000300010}, abstractNote={This study aimed to verify the relationship between breeding for tolerance to low levels of soil nutrients and for nutrient use efficiency in tropical maize. Fifteen inbred lines were evaluated in two greenhouse experiments under contrasting levels of N and P. The relationship between nutritional efficiency and tolerance to nutritional stress was estimated by the Spearman ranking correlation between the genotypes for the traits related to N and P use efficiency and phenotypic plasticity indices. The lack of relationship between the traits, in magnitude as well as significance, indicates that these characters are controlled by different gene groups. Consequently, simultaneous selection for both nutrient use efficiency and tolerance to nutritional stress is possible, if the mechanisms that confer efficiency and tolerance are not competitive.}, number={3}, journal={Crop Breeding and Applied Biotechnology}, author={Maia, Ciro and DoVale, Júlio César and Fritsche-Neto, Roberto and Cavatte, Paulo Cezar and Miranda, Glauco Vieira}, year={2011}, month={Sep}, pages={270–275} }
 @article{maia_dovale_fritsche-neto_cavatte_miranda_2011, title={The difference between breeding for nutrient use efficiency and for nutrient stress tolerance}, volume={11}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000296047800010&KeyUID=WOS:000296047800010}, number={3}, journal={Crop Breeding and Applied Biotechnology}, author={Maia, Ciro and DoVale, Julio Cesar and Fritsche-Neto, Roberto and Cavatte, Paulo Cezar and Miranda, Glauco Vieira}, year={2011}, pages={270–275} }
 @article{fritsche-neto_vieira_scapim_miranda_rezende_2011, title={Updating the ranking of the coefficients of variation from maize experiments}, volume={34}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000302050000014&KeyUID=WOS:000302050000014}, DOI={10.4025/actasciagron.v34i1.13115}, abstractNote={The objective of this study was to update the ranking of the coefficients of variation (CVs) from maize experiments and evaluate the accuracy of the data from the latest Brazilian publications. We rank-ordered the CVs for grain yield, plant and ear heights, number of ears per plant, and weight of commercial ears, except for the weight of 100 grains. The data were obtained from 143 scientific papers published from 2005 to 2010. The classification was based on the average (m) and standard deviation (SD) and the CVs were ranked as low, intermediate, high and very high. All of the random variables had the CVs normally distributed. For most of the traits, we observed a large difference between the ranks from Scapim and Pimentel Gomes. In summary, the coefficients of variation have to be classified for each variable, significant improvement occurred in the accuracy of the national maize experiments in the last 15 years, and they still require periodic updating.}, number={1}, journal={Acta Scientiarum Agronomy}, author={Fritsche-Neto, Roberto and Vieira, Rafael Augusto and Scapim, Carlos Alberto and Miranda, Glauco Vieira and Rezende, Luciano Moreira}, year={2011}, month={Dec}, pages={99–101} }
 @article{dovale_fritsche-neto_silva_2011, title={Índice de seleção para cultivares de milho com dupla aptidão: minimilho e milho verde}, url={https://publons.com/wos-op/publon/12806047/}, DOI={10.1590/S0006-87052011000400008}, abstractNote={Este trabalho teve por objetivo a elaboração de um índice que permita a seleção acurada de cultivares de milho com aptidão tanto para a produção de minimilho quanto para de milho verde. Os experimentos foram realizados no ano agrícola de 2002/2003 com dez cultivares comerciais de milho em dois experimentos. O primeiro quanto ao rendimento de minimilho e o segundo quanto ao de milho verde, delineados em blocos ao acaso com três repetições. A importância relativa dos caracteres estudados foi estimada por meio do método dos componentes principais e o agrupamento destes foi realizado pela análise de fatores. O seguinte índice foi obtido: I = 0,031 NEM + 0,013 MEM + 0,207 NEV + 0,243 MEV - 0,16 AP - 0,058 MFP, em que, NEM, MEM, NEV, MEV, AP e MFP são o número e a massa de espigas empalhadas de minimilho e de milho verde, altura de planta e massa de pendão fresca respectivamente. Esse índice indicou que híbridos triplos DKB 350, AG 8080 e AG 6690 e o duplo DKB 747 revelaram os melhores desempenhos para as produções de minimilho e de milho verde.}, journal={Bragantia}, author={DoVale, Júlio César and Fritsche-Neto, Roberto and Silva, Paulo Sérgio Lima}, year={2011}, month={Jan} }
 @article{fritsche-neto_miranda_delima_souza_2010, title={Factor analysis and SREG GGE biplot for the genotype × environment interaction stratification in maize}, volume={40}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000279803900007&KeyUID=WOS:000279803900007}, DOI={10.1590/S0103-84782010000500007}, abstractNote={The objective of this study was to evaluate the use of SREG GGE biplot methodology and factor analysis to stratify the genotype×environment interaction in maize. Forty-nine early maize hybrids were evaluated in nine environments. The experimental design used was a 7×7 square lattice with two replicates. Each plot consisted of two 5m long rows spaced 0.90m apart. Grain yield data were used to perform the analysis. The results indicated the existence of two mega-environments in the State of Minas Gerais, Brazil, for early maize hybrids. The stratification of the environment by factor analysis was more selective to join the similarity the according with cultivar performance. However, this approach did not identify specific genotype x environment interactions, which is possible through SREG GGE biplot analysis.}, number={5}, journal={Ciência Rural}, author={Fritsche-Neto, Roberto and Miranda, Glauco Vieira and DeLima, Rodrigo Oliveira and Souza, Heraldo Namorato}, year={2010}, month={May}, pages={1043–1048} }
 @article{fritsche-neto_miranda_delima_souza_silva_2010, title={Herança de caracteres associados à eficiência de utilização do fósforo em milho}, volume={45}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000280479200005&KeyUID=WOS:000280479200005}, DOI={10.1590/S0100-204X2010000500005}, abstractNote={O objetivo deste trabalho foi identificar os efeitos genéticos que controlam a herança de caracteres associados à eficiência de utilização do fósforo (EUP) em milho e determinar as relações entre esses caracteres. Foi realizado um dialelo entre seis cultivares de milho do qual foram obtidas 15 combinações híbridas. Estas cultivares foram avaliadas em alta e baixa disponibilidade de fósforo, em casa de vegetação e campo. Foram medidas a resposta das plantas à disponibilidade de fósforo (RU) e as características associadas às eficiências de utilização e translocação do nutriente. As combinações híbridas apresentaram interação significativa com a disponibilidade de P, para os caracteres massa de matéria seca da planta (MSP), teor de P na planta (PAP) e razão entre as massas de matéria seca da parte aérea e da raiz (RMS). A interação entre capacidade geral de combinação e RU foi significativa para PAP e RMS e, entre capacidade específica de combinação e RU, para MSP e PAP. As EUP em baixa e alta disponibilidade do nutriente estiveram correlacionadas entre si e com RU. A EUP em baixa disponibilidade de P não se correlacionou com a produtividade de grãos. Contudo, sob alta disponibilidade do nutriente, esses parâmetros se correlacionaram. Os efeitos não aditivos têm maior importância para caracteres relacionados à EUP, de modo que a seleção deve ser realizada nas combinações híbridas.}, number={5}, journal={Pesquisa Agropecuária Brasileira}, author={Fritsche-Neto, Roberto and Miranda, Glauco Vieira and DeLima, Rodrigo Oliveira and Souza, Leandro Vagno and Silva, Jaeveson}, year={2010}, month={May}, pages={465–471} }
 @article{fritsche-neto_gonçalves_vencovsky_junior_2010, title={Prediction of genotypic values of maize hybrids in unbalanced experiments}, url={https://publons.com/wos-op/publon/17960330/}, DOI={10.12702/1984-7033.V10N01A05}, abstractNote={The objective of this study was to evaluate whether the REML/BLUP can be useful for predicting the genotypic values of maize hybrids in a group of unbalanced experiments. A set of 256 single-crosses were evaluated in 13 environments for grain yield, plant height and plant lodging. Sets of hybrids within environments and sets of environments were withdrawn from the experiments to simulate unbalanced data, and the hybrid predictions of the unbalanced data were computed by the REML/BLUP, simulated using the bootstrap resampling procedure. The coefficients of determination and percentage of selection coincidence were computed for the predicted genotypic values of unbalanced data and their means from the balanced data. The REML/BLUP method accurately predicted the genotypic values of missing hybrids under losses of up to 20% of hybrids within environments or a reduction of 23% of the environments, even in the presence of significant and complex hybrid × environment interaction.}, journal={Crop Breeding and Applied Biotechnology}, author={Fritsche-Neto, R. and Gonçalves, M.C. and Vencovsky, R. and Junior, C.L. Souza}, year={2010}, month={Mar} }
 @article{fritsche-neto_goncalves_vencovsky_souza junior_2010, title={Prediction of genotypic values of maize hybrids in unbalanced experiments}, volume={10}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000279643400005&KeyUID=WOS:000279643400005}, number={1}, journal={Crop Breeding and Applied Biotechnology}, author={Fritsche-Neto, Roberto and Goncalves, Manoel Carlos and Vencovsky, Roland and Souza Junior, Claudio Lopes}, year={2010}, pages={32–39} }
 @article{fritsche-neto_gon?alves_vencovsky_souza junior_2010, title={Prediction of genotypic values of maize hybrids in unbalanced experiments,Predição de valores genotípicos de híbridos de milho em experimentos desbalanceados}, volume={10}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-77952714100&partnerID=MN8TOARS}, number={1}, journal={Crop Breeding and Applied Biotechnology}, author={Fritsche-Neto, R. and Gon?alves, M.C. and Vencovsky, R. and Souza Junior, C.L.}, year={2010}, pages={32–39} }
 @article{silva_pereira_souza_carvalho_fritsche neto_2009, title={Estimation of the combining ability in early generations of potato selection}, volume={27}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000276494200003&KeyUID=WOS:000276494200003}, number={3}, journal={Horticultura Brasileira}, author={Silva, Giovani O. and Pereira, Arione da S. and Souza, Velci Q. and Carvalho, Fernando Iraja F. and Fritsche Neto, Roberto}, year={2009}, pages={275–279} }
 @article{silva_pereira_souza_carvalho_neto_2009, title={Estimation of the combining ability in early generations of potato selection,Estimativa de capacidades de combinação em gerações iniciais de seleção de batata}, volume={27}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-77952607012&partnerID=MN8TOARS}, number={3}, journal={Horticultura Brasileira}, author={Silva, G.O. and Pereira, A.S. and Souza, V.Q. and Carvalho, F.I.F. and Neto, R.F.}, year={2009}, pages={275–279} }
 @article{silva_s pereira arione_souza_carvalho_neto_2009, title={Estimativa de capacidades de combinação em gerações iniciais de seleção de batata}, url={https://publons.com/wos-op/publon/17960331/}, DOI={10.1590/S0102-05362009000300002}, abstractNote={Verificou-se as estimativas das capacidades de combinação de genitores de batata em gerações iniciais de seleção. Os experimentos foram realizados na Embrapa Clima Temperado, Pelotas (RS). Foram avaliadas 20 famílias derivadas de nove genitores cruzados em esquema parcial 4 x 5 (C-1750-15-95; 2CRI-1149-1-78; C-1786-6-96 e 'Eliza'; 'White Lady'; 'Asterix'; 'BP-1'; 'Vivaldi' e 'Ágria'). As famílias foram avaliadas na geração de plântula e na primeira geração de campo, utilizando o delineamento experimental de blocos com tratamentos ao acaso, com três repetições. A parcela consistiu de uma amostra de 15 genótipos de uma família. Nas duas gerações foram avaliados os caracteres rendimento, número e massa média de tubérculo. Os dados foram submetidos às análises de variância conjunta e dialélica parcial. Para rendimento verificou-se efeito gênico predominantemente aditivo, enquanto para número e massa média de tubérculo constatou-se tanto efeito aditivo e não aditivo, igualmente importantes. 2CRI-1149-1-78 e 'White Lady' revelaram-se os genitores mais promissores para os três caracteres, enquanto 'Ágria' destacou-se para massa média. As gerações expressaram estimativas similares de capacidade geral de combinação.}, journal={Horticultura Brasileira}, author={Silva, Giovani O and S Pereira Arione and Souza, Velci Q and Carvalho, Fernando Irajá F and Neto, Roberto Fritsche}, year={2009}, month={Sep} }
 @article{silva_pereira_castro_souza_carvalho_fritsche neto_2009, title={Repeatability and the importance of characters in the evaluation of a potato active germplasm collection}, volume={27}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000276494200006&KeyUID=WOS:000276494200006}, number={3}, journal={Horticultura Brasileira}, author={Silva, Giovani O. and Pereira, Arione S. and Castro, Caroline M. and Souza, Velci Q. and Carvalho, Fernando I. F. and Fritsche Neto, Roberto}, year={2009}, pages={290–293} }
 @article{repeatability and the importance of characters in the evaluation of a potato active germplasm collection_2009, url={https://publons.com/wos-op/publon/17960332/}, journal={Horticultura Brasileira}, year={2009} }
 @article{silva_pereira_castro_souza_carvalho_neto_2009, title={Repeatability and the importance of characters in the evaluation of a potato active germplasm collection,Repetibilidade e importância de caracteres para avaliação de coleção ativa de germoplasma de batata}, volume={27}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-77952640689&partnerID=MN8TOARS}, number={3}, journal={Horticultura Brasileira}, author={Silva, G.O. and Pereira, A.S. and Castro, C.M. and Souza, V.Q. and Carvalho, F.I.F. and Neto, R.F.}, year={2009}, pages={290–293} }
 @article{silva_pereira_castro_souza_carvalho_neto_2009, title={Repetibilidade e importância de caracteres para avaliação de coleção ativa de germoplasma de batata}, volume={27}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=SCIELO&KeyUT=SCIELO:S0102-05362009000300005&KeyUID=SCIELO:S0102-05362009000300005}, DOI={10.1590/s0102-05362009000300005}, abstractNote={Como a base genética da batata cultivada Solanum tuberosum L. é estreita, torna-se importante a utilização do germoplasma existente nos programas de melhoramento, associada a um eficiente método de caracterização. O objetivo foi verificar a repetibilidade, o número de avaliações necessárias e a importância relativa de caracteres fenotípicos na caracterização de uma coleção ativa de germoplasma de batata. Um conjunto de 77 cultivares e clones elite de batata foi cultivado no campo experimental da Embrapa Clima Temperado, nas primaveras de 1999, 2000, 2001, 2002 e 2003, em parcelas de fileira simples de 15 plantas, espaçadas de 0,30 x 0,80 m, dentro e entre fileiras, respectivamente. Foram avaliados 31 caracteres nas plantas e nos tubérculos, os quais fazem parte dos descritores mínimos da batata. Os dados foram submetidos à análise de variância, de repetibilidade e de importância de caracteres. Concluiu-se que os caracteres com maior importância na caracterização do germoplasma avaliado são pigmentação da haste e intensidade de coloração da base do broto, tanto pela porcentagem de contribuição quanto pela repetibilidade da expressão nos diferentes anos de cultivo, refletindo no reduzido número de avaliações necessárias. Os caracteres coalescência da folha e pigmentação da nervura, presença de asas, tipo de folhagem, inserção da folha, largura dos folíolos, tamanho dos folíolos, cor da película, freqüência de flores, pubescência da base do broto, pigmentação do pedúnculo e pigmentação externa da corola, aspecto do ápice e aspereza da película, por apresentarem menores repetibilidades e/ou menores contribuições para a dissimilaridade, podem receber menos ênfase nas avaliações}, number={3}, journal={Horticultura Brasileira}, author={Silva, Giovani O and Pereira, Arione S and Castro, Caroline M and Souza, Velci Q and Carvalho, Fernando IF and Neto, Roberto Fritsche}, year={2009}, month={Sep}, pages={209–293} }
 @article{trevisan_franzon_neto_silva gonçalves_gonçalves_antunes_2008, title={Enraizamento de estacas herbáceas de mirtilo: influência da lesão na base e do ácido indolbutírico}, volume={32}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000257575300009&KeyUID=WOS:000257575300009}, DOI={10.1590/s1413-70542008000200009}, abstractNote={O mirtilo (Vaccinium sp.) é uma espécie de clima temperado que, por ocasião do período vegetativo, produz abundante quantidade de material vegetal que pode ser utilizado na propagação. Objetivou-se verificar o potencial de enraizamento de estacas herbáceas de diferentes cultivares de mirtilo, tratadas ou não com ácido indolbutírico e com e sem lesão na base. O trabalho foi realizado em dois experimentos, testando a capacidade de enraizamento das cultivares Florida, Woodard, Bluegem, Bluebele, Clímax e Briteblue. No primeiro experimento, as estacas das cultivares foram tratadas com AIB (0, 2500, 5000 e 7500mgL-1). No segundo experimento utilizou-se 2000mgL-1 de AIB, em estacas com e sem lesão na base. O delineamento utilizado foi completamente casualizado com repetições e unidades experimentais adequadas para cada experimento. O uso do ácido indolbutírico e a lesão nas estacas, não proporcionaram estímulo na emissão de raízes adventícias; as cultivares apresentam potencial genético de enraizamento diferenciado, sendo que, a Bluebelle apresentou maiores porcentuais de estacas enraizadas, e a Clímax, os menores porcentuais.}, number={2}, journal={Ciência e Agrotecnologia}, author={Trevisan, Renato and Franzon, Rodrigo Cezar and Neto, Roberto Fritsche and Silva Gonçalves, Rafael and Gonçalves, Emerson Dias and Antunes, Luis Eduardo Corrêa}, year={2008}, month={Apr}, pages={402–406} }
 @article{carvalho_neto_geraldi_2008, title={Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares}, url={https://publons.com/wos-op/publon/10595516/}, DOI={10.12702/1984-7033.V08N03A06}, abstractNote={The aim of this study was to compare REML/BLUP and Least Square procedures in the prediction and estimation of genetic parameters and breeding values in soybean progenies. F2:3 and F4:5 progenies were evaluated in the 2005/06 growing season and the F2:4 and F4:6 generations derived thereof were evaluated in 2006/07. These progenies were originated from two semi-early experimental lines that differ in grain yield. The experiments were conducted in a lattice design and plots consisted of a 2 m row, spaced 0.5 m apart. The trait grain yield per plot was evaluated. It was observed that early selection is more efficient for the discrimination of the best lines from the F4 generation onwards. No practical differences were observed between the least square and REML/BLUP procedures in the case of the models and simplifications for REML/BLUP used here.}, journal={Crop Breeding and Applied Biotechnology}, author={Carvalho, A.D.F. and Neto, R. Fritsche and Geraldi, I.O.}, year={2008}, month={Sep} }
 @article{de carvalho_neto_geraldi_2008, title={Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares}, volume={8}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-55249095085&partnerID=MN8TOARS}, number={3}, journal={Crop Breeding and Applied Biotechnology}, author={De Carvalho, A.D.F. and Neto, R.F. and Geraldi, I.O.}, year={2008}, pages={230–235} }
 @article{carvalho_fritsche neto_geraldi_2008, title={Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares}, volume={8}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000262009700006&KeyUID=WOS:000262009700006}, number={3}, journal={Crop Breeding and Applied Biotechnology}, author={Carvalho, Agnaldo Donizete and Fritsche Neto, Roberto and Geraldi, Isaias Olivio}, year={2008}, pages={219–224} }
 @article{da silva_da silva pereira_de souza_de carvalho_de oliveira_bertan_neto_2008, title={Importance of characters in the dissimilarity of potato progenies in early generations,Importância de caracteres na dissimilaridade de progénies de batata em gerações iniciais de seleção}, volume={67}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-43949086877&partnerID=MN8TOARS}, number={1}, journal={Bragantia}, author={Da Silva, G.O. and Da Silva Pereira, A. and De Souza, V.Q. and De Carvalho, F.I.F. and De Oliveira, A.C. and Bertan, I. and Neto, R.F.}, year={2008}, pages={141–144} }
 @article{silva_silva pereira_souza_carvalho_oliveira_bertan_neto_2008, title={Importância de caracteres na dissimilaridade de progênies de batata em gerações iniciais de seleção}, volume={67}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=SCIELO&KeyUT=SCIELO:S0006-87052008000100017&KeyUID=SCIELO:S0006-87052008000100017}, DOI={10.1590/s0006-87052008000100017}, abstractNote={O trabalho teve por objetivo verificar as implicações da utilização da análise de importância de caracteres na eliminação de caracteres avaliados para o cálculo da distância genética de progênies de batata (Solanum tuberosum L.) nas primeiras gerações de seleção. Os experimentos foram desenvolvidos em casa plástica (geração de seedlings) e em campo (primeira geração clonal), na Embrapa Clima Temperado, Pelotas, Estado do Rio Grande do Sul em outono de 2004 e outono de 2005. O estudo demonstrou que a análise de importância de caracteres foi eficiente para diminuir o número de caracteres necessários à classificação das progênies de batata em relação à dissimilaridade. O formato de tubérculo foi o caráter com maior eficiência para o estudo de dissimilaridade em progênies de batata.}, number={1}, journal={Bragantia}, author={Silva, Giovani Olegário and Silva Pereira, Arione and Souza, Velci Queiroz and Carvalho, Fernando Irajá Félix and Oliveira, Antônio Costa and Bertan, Ivandro and Neto, Roberto Fritsche}, year={2008}, month={Jan}, pages={141–144} }
 @article{neto_pereira_raseira_silva_souza_2008, title={Methods for pollen evaluation and influence of gibberellic acid on potato crossing}, volume={32}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000257575300019&KeyUID=WOS:000257575300019}, number={2}, journal={Ciencia E Agrotecnologia}, author={Neto, R. F. and Pereira, A. D. and Raseira, M. D. B. and Silva, G. O. and Souza, V. Q.}, year={2008}, pages={469–473} }
 @article{methods for pollen evaluation and influence of gibberellic acid on potato crossing_2008, url={https://publons.com/wos-op/publon/19061728/}, journal={Ciencia E Agrotecnologia}, year={2008} }
 @article{neto_pereira_raseira_silva_souza_2008, title={Methods for pollen evaluation and influence of gibberellic acid on potato crossing,Métodos de avaliação de pólen e influência do ácido giberélico em cruzamentos de batata}, volume={32}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-70350157051&partnerID=MN8TOARS}, number={2}, journal={Ciencia e Agrotecnologia}, author={Neto, R.F. and Pereira, A.S. and Raseira, M.C.B. and Silva, G.O. and Souza, V.Q.}, year={2008}, pages={469–473} }
 @article{neto_silva pereira_carmo bassols raseira_silva_souza_2008, title={Métodos de avaliação de pólen e influência do ácido giberélico em cruzamentos de batata}, volume={32}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=SCIELO&KeyUT=SCIELO:S1413-70542008000200019&KeyUID=SCIELO:S1413-70542008000200019}, DOI={10.1590/s1413-70542008000200019}, abstractNote={Objetivou-se neste trabalho comparar métodos de avaliação da qualidade de pólen e avaliar a influência do ácido giberélico (GA3), na eficiência de cruzamento de genótipos de batata (Solanum tuberosum L.). Ambos os estudos foram conduzidos na sede da Embrapa Clima Temperado, Pelotas-RS. O primeiro estudo foi conduzido no Laboratório de Melhoramento Genético, na primavera de 2003 e outono-inverno de 2004. Foram avaliados os métodos do carmin propiônico e da fertilidade do pólen, com dois meios de ágar, açúcar e boro (meio 1 e meio 2), utilizando cinco genótipos de batata. O segundo estudo foi realizado em casa-de-vegetação, no outono-inverno de 2004. Uma solução de 25mg.L-1 de GA3 foi pulverizada em 15 genótipos de batata, que são utilizados como genitores no programa de melhoramento. Conclui-se que ambos os meios de cultura podem ser utilizados na avaliação da qualidade do pólen de batata, e a influência do GA3 na eficiência de cruzamento é dependente do genótipo.}, number={2}, journal={Ciência e Agrotecnologia}, author={Neto, Roberto Fritsche and Silva Pereira, Arione and Carmo Bassols Raseira, Maria and Silva, Giovani Olegário and Souza, Velci Queiroz}, year={2008}, month={Apr}, pages={469–473} }
 @article{trevisan_franzon_neto_gon?alves_gon?alves_antunes_2008, title={Rooting of herbaceous blueberry cuttings: Influence of the base incision and indolbutiric acid,Enraizamento de estacas herbáceas de mirtilo: Influência da lesão na base e do ácido indolbutírico}, volume={32}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-68949198085&partnerID=MN8TOARS}, number={2}, journal={Ciencia e Agrotecnologia}, author={Trevisan, R. and Franzon, R.C. and Neto, R. Fritsche and Gon?alves, R.S. and Gon?alves, E.D. and Antunes, L.E.C.}, year={2008}, pages={402–406} }
 @article{silva_pereira_souza_carvalho_fritsche neto_2008, title={Selection for components of tuber appearance and yield in potato seedlings}, volume={26}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000261419900006&KeyUID=WOS:000261419900006}, number={3}, journal={Horticultura Brasileira}, author={Silva, Giovani Olegario and Pereira, Arione da S. and Souza, Velci Q. and Carvalho, Fernando Iraja and Fritsche Neto, Roberto}, year={2008}, pages={325–329} }
 @article{silva_s pereira arione_souza_carvalho_neto_2008, title={Seleção para caracteres componentes de aparência e rendimento de tubérculo em plântulas de batata}, volume={26}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-57649136967&partnerID=MN8TOARS}, DOI={10.1590/S0102-05362008000300007}, abstractNote={O objetivo do presente trabalho foi verificar os ganhos esperados com a seleção correlacionada na geração de plântula, para caracteres componentes do rendimento e aparência geral de tubérculos de batata. Foram avaliadas duas populações em casa de vegetação nos cultivos de outono e primavera de 2004. Estimaram-se os ganhos esperados com a seleção direta e ganhos esperados por meio da seleção de caracteres correlacionados geneticamente. Verificou-se que para alguns caracteres os ganhos correlacionados esperados podem ser obtidos na geração de plântula, sendo uma boa opção quando favoreceram caracteres com menores herdabilidades. A curvatura de tubérculo foi o caráter que proporcionou maiores ganhos correlacionados esperados para aparência geral de tubérculo.}, number={3}, journal={Horticultura Brasileira}, author={Silva, Giovani Olegario and S Pereira Arione and Souza, Velci Q and Carvalho, Fernando Irajá Félix and Neto, Roberto Fritsche}, year={2008}, month={Sep}, pages={325–329} }
 @article{souza_silva pereira_silva_neto_oliveira_2007, title={Consistency of two stability analysis methods in potatoes}, volume={37}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=SCIELO&KeyUT=SCIELO:S0103-84782007000300009&KeyUID=SCIELO:S0103-84782007000300009}, DOI={10.1590/s0103-84782007000300009}, abstractNote={The objective of this research was to compare the consistency of the bi-segmented and AMMI (additive main effects and multiplicative interaction analysis) methods for estimating yield stability in potatoes. Data of ten genotypes evaluated in 34 environments (local, growing season and year combinations) of the Rio Grande do Sul state, Brazil, in 1994 and 1995 were used. Three data sets were analyzed: 34-environment data set and two 17-environment data subsets, which were chosen by randomly dividing the total data set. For the 34-environment data set, the models gave similar results in relation to the stable genotypes, but they differed with regard to the unstable genotypes. For the 17-environment data sets, the bi-segmented model showed more consistent results, either between subsets or between these and the total data set. For the AMMI model, only the Santo Amor genotype showed consistency between one of the subsets and the total data set. In this work, the bi-segmented method was shown to be more consistent than the AMMI model.}, number={3}, journal={Ciência Rural}, author={Souza, Velci Queiroz and Silva Pereira, Arione and Silva, Giovani Olegário and Neto, Roberto Fritsche and Oliveira, Antônio Costa}, year={2007}, month={Jun}, pages={656–661} }
 @article{de souza_pereira_da silva_neto_de oliveira_2007, title={Consistency of two stability analysis methods in potatoes}, volume={37}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-34250010030&partnerID=MN8TOARS}, number={3}, journal={Ciencia Rural}, author={De Souza, V.Q. and Pereira, A.D.S. and Da Silva, G.O. and Neto, R.F. and De Oliveira, A.C.}, year={2007}, pages={656–661} }
 @article{da silva_da silva pereira_de souza_de carvalho_neto_2007, title={Correlations between appearance and yield characters, and path analysis for potato tuber appearance,Correlações entre caracteres de aparência e rendimento e análise de trilha para aparência de batata}, volume={66}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-34848820001&partnerID=MN8TOARS}, number={3}, journal={Bragantia}, author={Da Silva, G.O. and Da Silva Pereira, A. and De Souza, V.Q. and De Carvalho, F.I.F. and Neto, R.F.}, year={2007}, pages={381–388} }
 @article{silva_silva pereira_souza_carvalho_neto_2007, title={Correlações entre caracteres de aparência e rendimento e análise de trilha para aparência de batata}, volume={66}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=SCIELO&KeyUT=SCIELO:S0006-87052007000300003&KeyUID=SCIELO:S0006-87052007000300003}, DOI={10.1590/s0006-87052007000300003}, abstractNote={O objetivo deste trabalho foi verificar correlações entre caracteres da aparência e do rendimento de tubérculo, bem como a influência de caracteres componentes da aparência na expressão do caráter aparência de tubérculo e suas implicações na seleção. Utilizou-se uma população híbrida de batata com 15 famílias e 60 genótipos cada família. Os experimentos foram desenvolvidos no campo experimental da Embrapa Clima Temperado, Pelotas, Rio Grande do Sul, no outono de 2004 e 2005. Aparência de tubérculo correlacionou-se mais estreitamente com formato, curvatura, apontamento e sobrancelha de tubérculo, na geração de seedlings; e com uniformidades de formato e tamanho de tubérculo na primeira geração clonal. O caráter rendimento foi mais fortemente associado com tamanho, número e massa médio de tubérculo, na geração de seedlings, e com tamanho, número, massa média, achatamento de tubérculo e vigor de planta, na primeira geração clonal. Verificou-se que curvatura de tubérculo foi o caráter mais efetivo na seleção indireta para a melhoria da aparência em ambas as gerações. Os resultados sugerem que na geração de seedlings o apontamento de tubérculo seja considerado em conjunto com curvatura de tubérculo na seleção para aparência de tubérculo.}, number={3}, journal={Bragantia}, author={Silva, Giovani Oleário Da and Silva Pereira, Arione and Souza, Velcio Queiroz and Carvalho, Fernando Irajá Félix and Neto, Roberto Fritsche}, year={2007}, month={Jan}, pages={381–388} }
 @article{s pereira arione_neto_s silva roberta_bender_schünemann_ferri_vendruscolo_2007, title={Genótipos de batata com baixo teor de açúcares redutores}, url={https://publons.com/wos-op/publon/17960334/}, DOI={10.1590/S0102-05362007000200018}, abstractNote={Os objetivos deste trabalho foram avaliar genótipos de batata quanto ao teor de açúcares redutores e condições pós-colheita que favoreçam a identificação daqueles com baixo teor. Oito clones, previamente selecionados para cor clara de fritura, e a cultivar Atlantic foram avaliados nos períodos de outono de 2004 e 2005. Os experimentos foram conduzidos na Embrapa Clima Temperado, Pelotas-RS, Brasil (31°52'S, 52°21'W), em blocos ao acaso com quatro repetições. Em 2004, o teor de açúcares redutores foi quantificado após três condições: cura; quatro semanas refrigerados a 4°C; e refrigeração seguida por recondicionamento durante duas semanas. Em 2005, os açúcares redutores foram analisados somente após a refrigeração. O teste F revelou diferenças significativas entre genótipos para todas as variáveis, exceto para açúcares redutores após a cura dos tubérculos. Os clones 'C-1883-22-97', 'C-1881-16-97', 'C-1786-9-96', 'C-1786-7-96' e 'C-1787-14-96' contiveram os teores mais baixos de açúcares redutores. A refrigeração foi a condição que permitiu a identificação de genótipos com baixos teores de açúcares redutores.}, journal={Horticultura Brasileira}, author={S Pereira Arione and Neto, Roberto Fritsche and S Silva Roberta and Bender, Carolina I and Schünemann, Ana Paula and Ferri, Núbia Marilin L and Vendruscolo, João Luiz}, year={2007}, month={Jun} }
 @article{pereira_fritsche neto_silva_bender_schuenemann_ferri_vendruscolo_2007, title={Potato genotypes with low reducing sugar content}, volume={25}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000254829400017&KeyUID=WOS:000254829400017}, number={2}, journal={Horticultura Brasileira}, author={Pereira, Arione da S. and Fritsche Neto, Roberto and Silva, Roberta da S. and Bender, Carolina I. and Schuenemann, Ana Paula and Ferri, Nubia Marilin L. and Vendruscolo, Joiio Luiz}, year={2007}, pages={220–223} }
 @article{pereira_neto_silva_bender_schünemann_ferri_vendruscolo_2007, title={Potato genotypes with low reducing sugar content,Genótipos de batata com baixo teor de açúcares redutores}, volume={25}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-36049018090&partnerID=MN8TOARS}, number={2}, journal={Horticultura Brasileira}, author={Pereira, A.D.S. and Neto, R.F. and Silva, R.D.S. and Bender, C.I. and Schünemann, A.P. and Ferri, N.M.L. and Vendruscolo, J.L.}, year={2007}, pages={220–223} }
 @article{da silva_de souza_pereira_de carvalho_neto_2006, title={Early generation selection for tuber appearance affects potato yield components}, volume={6}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33748678907&partnerID=MN8TOARS}, number={1}, journal={Crop Breeding and Applied Biotechnology}, author={Da Silva, G.O. and De Souza, V.Q. and Pereira, A.D.S. and De Carvalho, F.I.F. and Neto, R.F.}, year={2006}, pages={73–78} }
 @article{neto_de souza_pereira_da silva_garcia_2006, title={Estimate of cross efficiency of potato parents}, volume={6}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-41149094674&partnerID=MN8TOARS}, number={3}, journal={Crop Breeding and Applied Biotechnology}, author={Neto, R.F. and De Souza, V.Q. and Pereira, A.D.S. and Da Silva, G.O. and Garcia, S.M.}, year={2006}, pages={242–249} }