@article{rockstad_austin_gouveia_carbajal_milla-lewis_2023, title={Assessing unmanned aerial vehicle-based imagery for breeding applications in St. Augustinegrass under drought and non-drought conditions}, volume={12}, ISSN={["1435-0653"]}, url={https://doi.org/10.1002/csc2.21128}, DOI={10.1002/csc2.21128}, abstractNote={The use of imagery collected from small unmanned aerial vehicles (UAVs) in turfgrass breeding has rapidly increased, as has the demand to develop drought‐resistant cultivars. However, prior to adopting UAVs to help guide turfgrass selection under drought stress conditions, a clear understanding of the value and predictive ability of imagery‐based turfgrass characterization is required. In St. Augustinegrass, a major warm‐season turfgrass species grown in the southeastern United States, limited research has been published about characterizing drought stress using aerial imagery. Specifically, no efforts have compared the various vegetation indices (VIs) commonly used to evaluate vegetative health in other species and sought to identify the most useful index for phenotyping drought stress traits in St. Augustinegrass. In this study, traditional ground‐based approaches for measuring percent green cover (PGC) and normalized difference vegetation index (NDVI) were compared against their UAV‐derived counterparts as well as thirteen VIs under drought and non‐drought conditions, and broad‐sense heritability (H2) was calculated. A population of 115 genotypes from a ‘Raleigh’ × ‘Seville’ cross were analyzed at two environmentally distinct field sites in North Carolina. At both sites, a significant relationship between ground‐based and UAV‐derived measurements for PGC and NDVI was observed before and during drought (r = 0.82 to 0.95) and suggests a clear advantage to using UAVs for phenotyping drought traits given the reduced time and labor costs compared to on‐ground efforts. Among all VIs compared, UAV‐derived NDVI showed strong correlation with the PGC taken on the ground (r > 0.85), a similar trend over time, and a higher H2 estimate under drought conditions, suggesting that UAV‐derived NDVI has the potential to assist in the selection of St. Augustinegrass genotypes with the best phenotypic response to drought. Implementing UAV imagery‐based high‐throughput methods will allow breeders to evaluate germplasm with unbiased quantitative consistency, quickly and thoroughly, and with increased frequency—all without sacrificing the response to selection potential.This article is protected by copyright. All rights reserved}, journal={CROP SCIENCE}, author={Rockstad, Greta B. G. and Austin, Robert E. and Gouveia, Beatriz T. and Carbajal, Esdras M. and Milla-Lewis, Susana R.}, year={2023}, month={Dec} }
 @article{jones_austin_dunne_leon_everman_2023, title={Discrimination between protoporphyrinogen oxidase-inhibiting herbicide-resistant and herbicide-susceptible redroot pigweed (Amaranthus retroflexus) with spectral reflectance}, volume={5}, ISSN={["1550-2759"]}, url={https://doi.org/10.1017/wsc.2023.25}, DOI={10.1017/wsc.2023.25}, abstractNote={Abstract The current assays to confirm herbicide resistance can be time- and labor-intensive (dose–response) or require a skill set/technical equipment (genetic sequencing). Stakeholders could benefit from a rapid assay to confirm herbicide-resistant weeds to ensure sustainable crop production. Because protoporphyrinogen oxidase (PPO)-inhibiting herbicides rapidly interfere with chlorophyll production/integrity; we propose a new, rapid assay utilizing spectral reflectance to confirm resistance. Leaf disks were excised from two PPO-inhibiting herbicide-resistant (target-site [TSR] and non–target site [NTSR]) and herbicide-susceptible redroot pigweed (Amaranthus retroflexus L.) populations and placed into a 24-well plate containing different concentrations (0 to 10 mM) of fomesafen for 48 h. A multispectral sensor captured images from the red (668 nm), green (560 nm), blue (475 nm), and red edge (717 nm) wavebands after a 48-h incubation period. The green leaf index (GLI) was utilized to determine spectral reflectance ratios of the treated leaf disks. Clear differences of spectral reflectance were observed in the red edge waveband for all populations treated with the 10 mM concentration in the dose–response assays. Differences of spectral reflectance were observed for the NTSR population compared with the TSR and susceptible populations treated with the 10 mM concentration in the green waveband and the GLI in the dose–response assay. Leaf disks from the aforementioned A. retroflexus populations and two additional susceptible populations were subjected to a similar assay with the discriminating concentration (10 mM). Spectral reflectance was different between the PPO-inhibiting herbicide-resistant and herbicide-susceptible populations in the red, blue, and green wavebands. Spectral reflectance was not distinctive between the populations in the red edge waveband and the GLI. The results provide a basis for rapidly (∼48 h) detecting PPO-inhibiting herbicide-resistant A. retroflexus via spectral reflectance. Discrimination between TSR and NTSR populations was possible only in the dose–response assay, but the assay still has utility in distinguishing herbicide-resistant plants from herbicide-susceptible plants.}, journal={WEED SCIENCE}, author={Jones, Eric A. L. and Austin, Robert and Dunne, Jeffrey C. and Leon, Ramon G. and Everman, Wesley J.}, year={2023}, month={May} }
 @article{respess_austin_gatiboni_osmond_2022, title={Assessing the Agricultural Conservation Planning Framework toolbox in a Southern Piedmont landscape of the United States}, volume={77}, ISSN={["1941-3300"]}, DOI={10.2489/jswc.2022.00138}, abstractNote={The Agricultural Conservation Planning Framework (ACPF) is a geospatial decision support tool that was developed and is used in many areas of the Midwest of the United States to help with the prioritization and placement of conservation practices within agricultural watersheds. We evaluated the utility and extensibility of ACPF in two US Geological Survey 12-digit scale hydrologic units in the Southern Piedmont of North Carolina. The Southern Piedmont consists of less row crop agriculture and more pasture systems than the Midwest and has generally lower pollutant loads. Also, agricultural fields are comparatively smaller, irregularly shaped, and more sparsely distributed. For this study, local conservation experts were interviewed about conservation practices and their appropriate locations in the landscape. Interviewees demonstrated an extensive working knowledge of the land and producers on over 90% of the farmland. Many of the conservation practices identified by the local experts were “soil health” practices, such as cover crops or nutrient management, and are assumed in use before running ACPF. Results revealed that many of the conservation practices output by ACPF were not identified by the local experts in the Southern Piedmont watersheds due to their limited use in pasture conservation, conservation priorities, and landscape characteristics. Row crop agriculture was sparsely distributed in each study watershed and comprised less than 2% of the total catchment area. Contour buffer strips and grassed waterways were the conservation practices most identified by ACPF and were sited in 75% of cropped fields. A greater number of crop-related conservation practices (48 versus 15) were identified by ACPF than by local experts; however 80% of the conservation practices identified by the experts were outside the scope of ACPF and were mainly nutrient management or soil health practices. To evaluate ACPF for broader utility in the Southern Piedmont, alternative interpretations for existing outputs were considered: (1) ACPF “proxies” were identified to compare locally accepted practices with ACPF outputs that perform a similar function (e.g., strip cropping rather than contour buffer strips) and, (2) placing locally used conservation practices (e.g., exclusion fencing) based on existing ACPF data layers (hydrologically enforced flow paths). Alternative uses and interpretations surrounding ACPF outputs and data layers may provide opportunities for conservation planning outside the scope and intended use of ACPF in the Southern Piedmont.}, number={5}, journal={JOURNAL OF SOIL AND WATER CONSERVATION}, author={Respess, Z. M. and Austin, R. and Gatiboni, L. and Osmond, D.}, year={2022}, pages={441–449} }
 @article{marken_ricker_austin_2022, title={Combining Survey, Soil Coring, and GIS Methods to Improve Reservoir Capacity Estimates in the Maya Lowlands}, volume={10}, ISSN={["2326-3768"]}, url={https://doi.org/10.1017/aap.2022.6}, DOI={10.1017/aap.2022.6}, abstractNote={ABSTRACT This study reports water capacity estimates for four reservoirs within the Classic Maya city of El Perú-Waka’, Guatemala. Combining field survey, soil analysis, and a variety of GIS interpolation methods, it illustrates ways to more fully quantify a challenging resource—water—and its availability using an interdisciplinary approach. This is accomplished by comparing surface interpolation methods for estimating reservoir capacities to demonstrate that most provide reliable estimates. Reported estimates are further enhanced by analyzing internal reservoir soil morphology to better understand and quantify formation processes and refine estimates from field survey. These analyses document a multiscalar organization to water management within the Waka’ urban core that likely ran the gamut from individuals up to civic and state institutions. Although intricacies remain to be fully elucidated, this example offers an alternate path to theorizing about water management practices from traditional binary approaches.}, number={2}, journal={ADVANCES IN ARCHAEOLOGICAL PRACTICE}, publisher={Cambridge University Press (CUP)}, author={Marken, Damien B. and Ricker, Matthew C. and Austin, Robert}, year={2022}, month={May} }
 @article{larsen_austin_dunne_kudenov_2022, title={Drone-based polarization imaging for phenotyping peanut in response to leaf spot disease}, volume={12112}, ISBN={["978-1-5106-5100-5"]}, ISSN={["1996-756X"]}, DOI={10.1117/12.2623073}, abstractNote={Polarization imaging has been used extensively in applications related to atmospheric monitoring, remote sensing, and quality control. However, it has been used less extensively in agricultural applications, where color sensing - either red, green, and blue (RGB) imaging, multispectral, and/or hyperspectral cameras are more common. In this paper, we discuss our preliminary results related to the use of polarization imaging to quantify defoliation in peanut plants in response to leaf spot disease. A key metric for breeding resistant peanut varieties involves identifying the point at which defoliation occurs. Since defoliation is a geometrical property of the plant canopy, we investigated whether polarization imaging can provide a better-automated score when compared to conventional visual scoring. Initial results are presented, as well as a discussion of our drone-based platform and our experimental trials conducted during the 2021 North Carolina growing season.}, journal={POLARIZATION: MEASUREMENT, ANALYSIS, AND REMOTE SENSING XV}, author={Larsen, Joshua C. and Austin, Robert and Dunne, Jeffrey and Kudenov, Michael W.}, year={2022} }
 @article{havlin_austin_hardy_howard_heitman_2022, title={Nutrient Management Effects on Wine Grape Tissue Nutrient Content}, volume={11}, ISSN={["2223-7747"]}, url={https://doi.org/10.3390/plants11020158}, DOI={10.3390/plants11020158}, abstractNote={With limited research supporting local nutrient management decisions in North Carolina grape (Vitis vinifera) production, field studies (2015–17) were conducted to evaluate late season foliar nitrogen (N) application on leaf and petiole N concentration and yeast assimilable N (YAN) in the fruit. Foliar urea (1% v/v) was applied at different rates and application times beginning pre-and post-veraison. Compared to soil applied N, late season foliar N substantially enhanced petiole N and grape YAN. Smaller split N applications were generally more effective in increasing YAN than single larger N rates. These data demonstrate the value of assessing plant N content at full bloom with petiole N analysis or remote sensing to guide foliar N management decisions. Additional field studies (2008–11) were conducted to evaluate pre-bud soil applied phosphorus (P) and potassium (K) effects on petiole P and K nutrient status. Fertilizer P and K were initially broadcast applied (0–896 kg P2O5 ha−1; 0–672 kg K2O ha−1) prior to bud-break in 2008–09 and petiole P and K at full bloom soil test P and K were monitored for three to four years after application. Soil test and petiole P and K were significantly increased with increasing P and K rates, which subsequently declined to near unfertilized levels over the sampling time depending on site and P and K rate applied. These data demonstrate the value of annually monitoring petiole P and K levels to accurately assess plant P and K status to better inform nutrient management decisions.}, number={2}, journal={PLANTS-BASEL}, author={Havlin, John L. and Austin, Robert and Hardy, David and Howard, Adam and Heitman, Josh L.}, year={2022}, month={Jan} }
 @article{jones_austin_dunne_cahoon_jennings_leon_everman_2022, title={Utilization of image-based spectral reflectance to detect herbicide resistance in glufosinate-resistant and glufosinate-susceptible plants: a proof of concept}, volume={12}, ISSN={["1550-2759"]}, url={https://doi.org/10.1017/wsc.2022.68}, DOI={10.1017/wsc.2022.68}, abstractNote={Abstract Glufosinate is an effective postemergence herbicide, and overreliance on this herbicide for weed control is likely to increase and select for glufosinate-resistant weeds. Common assays to confirm herbicide resistance are dose–response and molecular sequencing techniques; both can require significant time, labor, unique technical equipment, and a specialized skillset to perform. As an alternative, we propose an image-based approach that uses a relatively inexpensive multispectral sensor designed for unmanned aerial vehicles to measure and quantify surface reflectance from glufosinate-treated leaf disks. Leaf disks were excised from a glufosinate-resistant and glufosinate-susceptible corn (Zea mays L.), cotton (Gossypium hirsutum L.), and soybean [Glycine max (L.) Merr.] varieties and placed into a 24-well plate containing eight different concentrations (0 to 10 mM) of glufosinate for 48 h. Multispectral images were collected after the 48-h incubation period across five discrete wave bands: blue (475 to 507 nm), green (560 to 587 nm), red (668to 682 nm), red edge (717 to 729 nm), and near infrared (842 to 899 nm). The green leaf index (GLI; a metric to measure chlorophyll content) was utilized to determine relationships between measured reflectance from the tested wave bands from the treated leaf disks and the glufosinate concentration. Clear differences of spectral reflectance were observed between the corn, cotton, and soybean leaf disks of the glufosinate-resistant and glufosinate-susceptible varieties at the 10 mM concentration for select wave bands and GLI. Leaf disks from two additional glufosinate-resistant and glufosinate-susceptible varieties of each crop were subjected to a similar assay with two concentrations: 0 and 10 mM. No differences of spectral reflectance were observed from the corn and soybean varieties in all wave bands and the GLI. The leaf disks of the glufosinate-resistant and glufosinate-susceptible cotton varieties were spectrally distinct in the green, blue, and red-edge wave bands. The results provide a basis for rapidly detecting glufosinate-resistant plants via spectral reflectance. Future research will need to determine the glufosinate concentrations, useful wave bands, and susceptible/resistant thresholds for weeds that evolve resistance.}, journal={WEED SCIENCE}, author={Jones, Eric A. L. and Austin, Robert and Dunne, Jeffrey C. and Cahoon, Charles W. and Jennings, Katherine M. and Leon, Ramon G. and Everman, Wesley J.}, year={2022}, month={Dec} }
 @article{sanders_jones_austin_roberson_richardson_everman_2021, title={Remote Sensing for Palmer Amaranth (Amaranthus palmeri S. Wats.) Detection in Soybean (Glycine max (L.) Merr.)}, volume={11}, ISSN={["2073-4395"]}, DOI={10.3390/agronomy11101909}, abstractNote={Field studies were conducted in 2016 and 2017 to determine if multispectral imagery collected from an unmanned aerial vehicle (UAV) equipped with a five-band sensor could successfully identify Palmer amaranth (Amaranthus palmeri) infestations of various densities growing among soybeans (Glycine max [L.] Merr.). The multispectral sensor captures imagery from five wavebands: 475 (blue), 560 (green), 668 (red), 840 (near infrared [NIR]), and 717 nm (red-edge). Image analysis was performed to examine the spectral properties of discrete Palmer amaranth and soybean plants at various weed densities using these wavebands. Additionally, imagery was subjected to supervised classification to evaluate the usefulness of classification as a tool to differentiate the two species in a field setting. Date was a significant factor influencing the spectral reflectance values of the Palmer amaranth densities. The effects of altitude on reflectance were less clear and were dependent on band and density being evaluated. The near infrared (NIR) waveband offered the best resolution in separating Palmer amaranth densities. Spectral separability in the other wavebands was less defined, although low weed densities were consistently able to be discriminated from high densities. Palmer amaranth and soybean were found to be spectrally distinct regardless of imaging date, weed density, or waveband. Soybean exhibited overall lower reflectance intensity than Palmer amaranth across all wavebands. The reflectance of both species within blue, green, red, and red-edge wavebands declined as the season progressed, while reflectance in NIR increased. Near infrared and red-edge wavebands were shown to be the most useful for species discrimination and maintained their utility at most weed densities. Palmer amaranth weed densities were found to be spectrally distinct from one another in all wavebands, with greatest distinction when using the red, NIR and red-edge wavebands. Supervised classification in a two-class system was consistently able to discriminate between Palmer amaranth and soybean with at least 80% overall accuracy. The incorporation of a weed density component into these classifications introduced an error of 65% or greater into these classifications. Reducing the number of classes in a supervised classification system could improve the accuracy of discriminating between Palmer amaranth and soybean.}, number={10}, journal={AGRONOMY-BASEL}, author={Sanders, John T. and Jones, Eric A. L. and Austin, Robert and Roberson, Gary T. and Richardson, Robert J. and Everman, Wesley J.}, year={2021}, month={Oct} }
 @article{duncan_respess_ryan_austin_royer_osmond_kleinman_2021, title={The Agricultural Conservation Planning Framework: Opportunities and challenges in the eastern United States}, volume={6}, ISSN={["2471-9625"]}, DOI={10.1002/ael2.20054}, abstractNote={The Agriculture Conservation Planning Framework (ACPF) applies high‐spatial resolution soils and topographic data, now available for many areas of the United States, to precisely locate opportunities for the placement of conservation practices in agricultural watersheds. Application of the ACPF, developed in midwestern landscapes, to watersheds in the eastern United States represents both opportunity and challenge to conservation planning. Based on experience in applying ACPF to eight watersheds in the eastern United States, from Vermont to North Carolina, we assess the toolbox's application in the eastern United States through the lens of strengths, weaknesses, opportunities, and threats (SWOT) analysis framework. We see a great future for the ACPF, but its adoption and utility require interaction with scientists and conservation planners familiar with the region to avoid misapplication and ensure appropriate adaptation and interpretation.}, number={3}, journal={AGRICULTURAL & ENVIRONMENTAL LETTERS}, author={Duncan, Jonathan M. and Respess, Zachary and Ryan, William and Austin, Robert and Royer, Matthew and Osmond, Deanna and Kleinman, Peter}, year={2021} }
 @article{whitaker_austin_holden_jones_michel_peak_thompson_duckworth_2021, title={The structure of natural biogenic iron (oxyhydr)oxides formed in circumneutral pH environments}, volume={308}, ISSN={["1872-9533"]}, DOI={10.1016/j.gca.2021.05.059}, abstractNote={Biogenic iron (Fe) (oxyhydr)oxides (BIOS) partially control the cycling of organic matter, nutrients, and pollutants in soils and water via sorption and redox reactions. Although recent studies have shown that the structure of BIOS resembles that of two-line ferrihydrite (2LFh), we lack detailed knowledge of the BIOS local coordination environment and structure required to understand the drivers of BIOS reactivity in redox active environments. Therefore, we used a combination of microscopy, scattering, and spectroscopic methods to elucidate the structure of BIOS sampled from a groundwater seep in North Carolina and compare them to 2LFh. We also simulated the effects of wet-dry cycles by varying sample preparation (e.g., freezing, flash freezing with freeze drying, freezing with freeze drying and oven drying). In general, the results show that both the long- and short-range ordering in BIOS are structurally distinct and notably more disordered than 2LFh. Our structure analysis, which utilized Fe K-edge X-ray absorption spectroscopy, Mössbauer spectroscopy, X-ray diffraction, and pair distribution function analyses, showed that the BIOS samples were more poorly ordered than 2LFh and intimately mixed with organic matter. Furthermore, pair distribution function analyses resulted in coherent scattering domains for the BIOS samples ranging from 12-18 Å, smaller than those of 2LFh (21-27 Å), consistent with reduced ordering. Additionally, Fe L-edge XAS indicated that the local coordination environment of 2LFh samples consisted of minor amounts of tetrahedral Fe(III), whereas BIOS were dominated by octahedral Fe(III), consistent with depletion of the sites due to small domain size and incorporation of impurities (e.g., organic C, Al, Si, P). Within sample sets, the frozen freeze dried and oven dried sample preparation increased the crystallinity of the 2LFh samples when compared to the frozen treatment, whereas the BIOS samples remained more poorly crystalline under all sample preparations. This research shows that BIOS formed in circumneutral pH waters are poorly ordered and more environmentally stable than 2LFh.}, journal={GEOCHIMICA ET COSMOCHIMICA ACTA}, author={Whitaker, Andrew H. and Austin, Robert E. and Holden, Kathryn L. and Jones, Jacob L. and Michel, F. Marc and Peak, Derek and Thompson, Aaron and Duckworth, Owen W.}, year={2021}, month={Sep}, pages={237–255} }
 @article{piskackova_reberg-horton_richardson_austin_jennings_leon_2020, title={Creating Predictive Weed Emergence Models Using Repeat Photography and Image Analysis}, volume={9}, url={https://doi.org/10.3390/plants9050635}, DOI={10.3390/plants9050635}, abstractNote={Weed emergence models have the potential to be important tools for automating weed control actions; however, producing the necessary data (e.g., seedling counts) is time consuming and tedious. If similar weed emergence models could be created by deriving emergence data from images rather than physical counts, the amount of generated data could be increased to create more robust models. In this research, repeat RGB images taken throughout the emergence period of Raphanus raphanistrum L. and Senna obtusifolia (L.) Irwin and Barneby underwent pixel-based spectral classification. Relative cumulative pixels generated by the weed of interest over time were used to model emergence patterns. The models that were derived from cumulative pixel data were validated with the relative emergence of true seedling counts. The cumulative pixel model for R. raphanistrum and S. obtusifolia accounted for 92% of the variation in relative emergence of true counts. The results demonstrate that a simple image analysis approach based on time-dependent changes in weed cover can be used to generate weed emergence predictive models equivalent to those produced based on seedling counts. This process will help researchers working on weed emergence models, providing a new low-cost and technologically simple tool for data collection.}, number={5}, journal={Plants}, publisher={MDPI AG}, author={Piskackova, Theresa Reinhardt and Reberg-Horton, Chris and Richardson, Robert J and Austin, Robert and Jennings, Katie M and Leon, Ramon G}, year={2020}, month={May}, pages={635} }
 @article{austin_osmond_shelton_2019, title={Optimum Nitrogen Rates for Maize and Wheat in North Carolina}, volume={111}, ISSN={["1435-0645"]}, DOI={10.2134/agronj2019.04.0286}, abstractNote={Nitrogen decision making and the selection of the “right” N rate in wheat ( Triticum aestivum L.) and maize ( Zea mays L.) are difficult due to complex interactions in the N cycle with weather, management, and genetics. An adaptive management approach utilizing farmer networks and participatory learning was established to refine N rate decisions. On‐farm trials were established to reflect grower N rate with additional treatments of ±25% N. In 79 site‐years of wheat, N −25% , N std , and N +25% rate treatments were best in 37, 35, and 28% of the trials, respectively. In 100 site‐years of maize, N −25% , N std , and N +25% rate treatments were best in 58, 30, and 12% of the trials, respectively. Grower’s selected N rates in wheat were similar to recommendations from the North Carolina Realistic Yield Expectation (RYE) database while maize N rates were an average 48 kg ha −1 higher; however, N −25% rates, which were best 58% of the time, were similar to RYE N rate. Doppler‐based estimates of total precipitation from the National Weather Center explained 90% of the average maize yield variability. However, site‐yield was independent of location, N rate, and total precipitation. Measures of performance (N factor productivity and N balance) varied with achieved yields but indicate most growers apply N adequate to maintain organic N lost through mineralization. Results suggest that improved approaches to N rate selection and N efficiency will likely require in‐season adjustments to yield‐based N rates that incorporate local management and environmental conditions throughout the growing season. Core Ideas Yield level and response is independent of location, N rate, and total precipitation. Doppler‐based rainfall estimates help explain seasonal trends in yield. Growers often select N rates greater than recommended for maize but not wheat.}, number={5}, journal={AGRONOMY JOURNAL}, author={Austin, Robert and Osmond, Deanna and Shelton, Shelby}, year={2019}, pages={2558–2568} }
 @article{sanders_everman_austin_roberson_richardson_2019, title={Weed species differentiation using spectral reflectance and image classification}, volume={11007}, ISSN={["1996-756X"]}, url={http://dx.doi.org/10.1117/12.2519306}, DOI={10.1117/12.2519306}, abstractNote={Advancements in efficient unmanned aerial platforms and affordable sensors has led to renewed interest in remote sensing by agricultural producers and land managers for use as an efficient and convenient method of evaluating crop status and pest issues in their fields. For remote sensing to be employed as a viable and widespread tool for weed management, the accurate detection of distinct weed species must be possible through the use of analytical procedures on the resultant imagery. Additionally, the remote sensing platform and subsequent analysis must be capable of identifying these species across a wide range of heights. In 2017, a field study was performed to identify any weed height thresholds on the accurate detection and subsequent classification of three common broadleaf weed species in the southeastern United States: Palmer amaranth (Amaranthus palmeri), common ragweed (Ambrosia artemisiifolia) and sicklepod Senna obtusifolia) as well as the classification accuracy of image classifications performed on the species scale. Pots of the three species at heights of 5, 10, 15, and 30 cm were randomly arranged in a grid and 5-band multispectral imagery was collected at 15 m. Image analysis was used to identify the spectral reflectance behavior of the weed species and height combinations and to evaluate the accuracy of species based supervised classifications involving the three species. Supervised classification was able to discriminate between the three weed species with between 24-100% accuracy depending on height and species. Palmer amaranth classification accuracy was consistently 100%. Increased height of sicklepod and common ragweed plants did not reliably confer improved accuracy but the species were correctly identified with at least 24% and 60% accuracy, respectively.}, journal={ADVANCED ENVIRONMENTAL, CHEMICAL, AND BIOLOGICAL SENSING TECHNOLOGIES XV}, author={Sanders, J. T. and Everman, W. J. and Austin, R. and Roberson, G. T. and Richardson, R. J.}, year={2019} }
 @article{osmond_austin_shelton_es_sela_2018, title={Evaluation of Adapt-N and Realistic Yield Expectation Approaches for Maize Nitrogen Management in North Carolina}, volume={82}, ISSN={["1435-0661"]}, DOI={10.2136/sssaj2018.03.0127}, abstractNote={Core Ideas The North Carolina nitrogen database made better recommendations than Adapt‐N for plot studies. Different labs produce distinctly different soil organic matter percentages from the same soil. Farmer strip trial results demonstrated approximately 60% of the time that Grower‐25% N yielded similarly to the other treatments. Farmer strip trial results showed overall similar performance for Adapt‐N and Grower‐Consultant rates. Nitrogen decision making for maize ( Zea mays L.) is difficult because of seasonal weather fluctuations. New tools have emerged based on dynamic simulation models that account for weather variability. We evaluated the performance of the Adapt‐N tool relative to North Carolina's Realistic Yield Expectation (RYE) framework through six multi‐N rate maize plot trials in three physiographic regions and 38 strip trials on commercial farms in the coastal plain. Yield response and profit were evaluated with quadratic plateau (QP) response curves. The RYE framework generally estimated the agronomic optimum N rate (AONR) well, as did Adapt‐N after modification to account for high soil organic matter (SOM) and C/N ratio mineral‐organic coastal plain soil; there was, however, much greater site variability with Adapt‐N. Adapt‐N was sensitive to SOM content inputs, which varied based on method and laboratory. The RYE provided overall higher dollar return than Adapt‐N; Adapt‐N returns varied based on SOM and yield goal inputs, since they strongly impact N rate recommendations. In the on‐farm strip‐trials, 58% of yields were not statistically different between lower N rates and other treatments including Adapt‐N. On average, Adapt‐N performed similar to grower‐consultant practice with modest tradeoffs between reduced N rates and yield. Adapt‐N recommendations and grower rates yielded higher than RYE and required more N. Overall, the RYE approach performed better than Adapt‐N in N research farm trials; in on‐farm strip trials Adapt‐N performed similarly to grower‐consultant practices and in various cases provided economical yield increases over RYE.}, number={6}, journal={SOIL SCIENCE SOCIETY OF AMERICA JOURNAL}, author={Osmond, Deanna and Austin, Robert and Shelton, Shelby and Es, Harold and Sela, Shai}, year={2018}, pages={1449–1458} }
 @article{ryan_adamson_aktipis_andersen_austin_barnes_beasley_bedell_briggs_chapman_et al._2018, title={The role of citizen science in addressing grand challenges in food and agriculture research}, volume={285}, ISSN={0962-8452 1471-2954}, url={http://dx.doi.org/10.1098/rspb.2018.1977}, DOI={10.1098/rspb.2018.1977}, abstractNote={The power of citizen science to contribute to both science and society is gaining increased recognition, particularly in physics and biology. Although there is a long history of public engagement in agriculture and food science, the term ‘citizen science’ has rarely been applied to these efforts. Similarly, in the emerging field of citizen science, most new citizen science projects do not focus on food or agriculture. Here, we convened thought leaders from a broad range of fields related to citizen science, agriculture, and food science to highlight key opportunities for bridging these overlapping yet disconnected communities/fields and identify ways to leverage their respective strengths. Specifically, we show that (i) citizen science projects are addressing many grand challenges facing our food systems, as outlined by the United States National Institute of Food and Agriculture, as well as broader Sustainable Development Goals set by the United Nations Development Programme, (ii) there exist emerging opportunities and unique challenges for citizen science in agriculture/food research, and (iii) the greatest opportunities for the development of citizen science projects in agriculture and food science will be gained by using the existing infrastructure and tools of Extension programmes and through the engagement of urban communities. Further, we argue there is no better time to foster greater collaboration between these fields given the trend of shrinking Extension programmes, the increasing need to apply innovative solutions to address rising demands on agricultural systems, and the exponential growth of the field of citizen science.}, number={1891}, journal={Proceedings of the Royal Society B: Biological Sciences}, publisher={The Royal Society}, author={Ryan, S. F. and Adamson, N. L. and Aktipis, A. and Andersen, L. K. and Austin, R. and Barnes, L. and Beasley, M. R. and Bedell, K. D. and Briggs, S. and Chapman, B. and et al.}, year={2018}, month={Nov}, pages={20181977} }
 @article{rajkovich_osmond_weisz_crozier_israel_austin_2017, title={Evaluation of Nitrogen-Loss Prevention Amendments in Maize and Wheat in North Carolina}, volume={109}, ISSN={["1435-0645"]}, DOI={10.2134/agronj2016.03.0153}, abstractNote={1811 Variability in crop N requirements across states, regions, seasons, crops, and even fields complicates the selection of an appropriate N rate, and has led to concerns of the environmental impacts that may result from over-application. Nitrogen from agricultural sources can be lost through volatilization or denitrification, but nitrate (NO3) leaching constitutes the largest percent of loss and can consequently contaminate ground and surface water (David et al., 1997; Jaynes et al., 2001; Randall and Goss, 2001). Aside from the well documented negative health effects of NO3 in drinking water (Spalding and Exner, 1993; Sogbedji et al., 2001), excess NO3 can also cause eutrophication and hypoxia of lakes and coastal waters (Spalding and Exner, 1993; Randall and Goss, 2001), a consequence that can be devastating to ecosystem biodiversity (Kronvang et al., 2001). Minimizing N rates can lower leaching losses and protect water quality while maintaining crop yields, but identifying appropriate N rates can be a challenge. In North Carolina, the realistic yield expectations (RYE) database provides recommended N rates for 32 different crops by soil series (North Carolina Nutrient Management Workgroup, 2003). The database was developed and validated through N rate trials across the state. More recently maize N recommendations were updated to reflect changes in N use efficiency related to new hybrid releases and farmer management practices (Rajkovich et al., 2015). Nationally, the “4Rs” (right rate, right placement, right timing, and right source) have been promoted by private and public organizations (Natural Resources Conservation Service, 2005; The Fertilizer Institute, 2011; International Plant Nutrition Institute, 2012) as a platform for farmers to think about their N applications in a logical, interconnected way. Beyond typical soil-based N rate recommendations, farmers also have access to fertilizer N-loss prevention amendments intended to reduce the amount of N lost; essentially to bolster the “right source” aspect of an N management plan. These N-loss prevention amendments, which can be added to N fertilizers, have various modes of action and reported levels of effectiveness in the literature. The N-loss prevention amendment NBPT+DCD (AgrotainPlus; Koch Agronomic Services LLC, Wichita, KS), contains the urease inhibitor N-(n-butyl)thiophosphoric triamide (NBPT at 30–70 g kg–1 a.i.) and the Evaluation of Nitrogen-Loss Prevention Amendments in Maize and Wheat in North Carolina}, number={5}, journal={AGRONOMY JOURNAL}, author={Rajkovich, Shelby and Osmond, Deanna and Weisz, Randy and Crozier, Carl and Israel, Daniel and Austin, Robert}, year={2017}, pages={1811–1824} }
 @article{hesterberg_polizzotto_crozier_austin_2016, title={Assessment of trace element impacts on agricultural use of water from the Dan River following the Eden coal ash release}, volume={12}, ISSN={["1551-3793"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84941010545&partnerID=MN8TOARS}, DOI={10.1002/ieam.1669}, abstractNote={Catastrophic events require rapid, scientifically sound decision making to mitigate impacts on human welfare and the environment. The objective of this study was to analyze potential impacts of coal ash‐derived trace elements on agriculture following a 35 000‐tonne release of coal ash into the Dan River at the Duke Energy Steam Station in Eden, North Carolina. We performed scenario calculations to assess the potential for excessive trace element loading to soils via irrigation and flooding with Dan River water, uptake of trace elements by crops, and livestock consumption of trace elements via drinking water. Concentrations of 13 trace elements measured in Dan River water samples within 4 km of the release site declined sharply after the release and were equivalent within 5 d to measurements taken upriver. Mass–balance calculations based on estimates of soil trace‐element concentrations and the nominal river water concentrations indicated that irrigation or flooding with 25 cm of Dan River water would increase soil concentrations of all trace elements by less than 0.5%. Calculations of potential increases of trace elements in corn grain and silage, fescue, and tobacco leaves suggested that As, Cr, Se, Sr, and V were elements of most concern. Concentrations of trace elements measured in river water following the ash release never exceeded adopted standards for livestock drinking water. Based on our analyses, we present guidelines for safe usage of Dan River water to diminish negative impacts of trace elements on soils and crop production. In general, the approach we describe here may serve as a basis for rapid assessment of environmental and agricultural risks associated with any similar types of releases that arise in the future. Integr Environ Assess Manag 2016;12:353–363. © 2015 SETAC}, number={2}, journal={INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT}, author={Hesterberg, Dean and Polizzotto, Matthew L. and Crozier, Carl and Austin, Robert E.}, year={2016}, month={Apr}, pages={353–363} }
 @article{gillispie_austin_rivera_bolich_duckworth_bradley_amoozegar_hesterberg_polizzotto_2016, title={Soil Weathering as an Engine for Manganese Contamination of Well Water}, volume={50}, ISSN={["1520-5851"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84988464669&partnerID=MN8TOARS}, DOI={10.1021/acs.est.6b01686}, abstractNote={Manganese (Mn) contamination of well water is recognized as an environmental health concern. In the southeastern Piedmont region of the United States, well water Mn concentrations can be >2 orders of magnitude above health limits, but the specific sources and causes of elevated Mn in groundwater are generally unknown. Here, using field, laboratory, spectroscopic, and geospatial analyses, we propose that natural pedogenetic and hydrogeochemical processes couple to export Mn from the near-surface to fractured-bedrock aquifers within the Piedmont. Dissolved Mn concentrations are greatest just below the water table and decrease with depth. Solid-phase concentration, chemical extraction, and X-ray absorption spectroscopy data show that secondary Mn oxides accumulate near the water table within the chemically weathering saprolite, whereas less-reactive, primary Mn-bearing minerals dominate Mn speciation within the physically weathered transition zone and bedrock. Mass-balance calculations indicate soil weathering has depleted over 40% of the original solid-phase Mn from the near-surface, and hydrologic gradients provide a driving force for downward delivery of Mn. Overall, we estimate that >1 million people in the southeastern Piedmont consume well water containing Mn at concentrations exceeding recommended standards, and collectively, these results suggest that integrated soil-bedrock-system analyses are needed to predict and manage Mn in drinking-water wells.}, number={18}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Gillispie, Elizabeth C. and Austin, Robert E. and Rivera, Nelson A. and Bolich, Rick and Duckworth, Owen W. and Bradley, Phil and Amoozegar, Aziz and Hesterberg, Dean and Polizzotto, Matthew L.}, year={2016}, month={Sep}, pages={9963–9971} }
 @article{rivera_kaur_hesterberg_ward_austin_duckworth_2015, title={Chemical Composition, Speciation, and Elemental Associations in Coal Fly Ash Samples Related to the Kingston Ash Spill}, volume={29}, ISSN={["1520-5029"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84923296129&partnerID=MN8TOARS}, DOI={10.1021/ef501258m}, abstractNote={Environmental impacts of potentially toxic trace elements from coal fly ash are controlled in part by the mineralogy of the ash matrix and the chemical speciation of the trace elements. Our objective was to characterize the chemical and mineralogical composition of fly ash samples that are pertinent to the 2008 release of coal ash from a containment area at the Tennessee Valley Authority (TVA) Kingston fossil plant, which left 4 to 500 t of trace elements in adjoining river systems. Three fly ash samples were analyzed for elemental composition by digestion or neutron activation analysis, mineralogy and macroelement speciation by conventional and synchrotron-based X-ray diffraction (XRD and SXRD) and X-ray absorption spectroscopy (XAS), and for spatial associations of elements by electron probe microanalysis (EPMA). Ash samples were mainly composed of Si (20–27% w/w), Al (10–14% w/w), Fe (4–6% w/w), and Ca (4–6% w/w). Concentrations of selected trace elements ranged from 8 to 1480 mg kg–1, with the followi...}, number={2}, journal={ENERGY & FUELS}, author={Rivera, Nelson and Kaur, Navdeep and Hesterberg, Dean and Ward, Colin R. and Austin, Robert E. and Duckworth, Owen W.}, year={2015}, month={Feb}, pages={954–967} }
 @article{walker_robarge_austin_2014, title={Modeling of ammonia dry deposition to a pocosin landscape downwind of a large poultry facility}, volume={185}, ISSN={["1873-2305"]}, DOI={10.1016/j.agee.2013.10.029}, abstractNote={A semi-empirical bi-directional flux modeling approach is used to estimate NH3 air concentrations and dry deposition fluxes to a portion of the Pocosin Lakes National Wildlife Refuge (PLNWR) downwind of a large poultry facility. Meteorological patterns at PLNWR are such that some portion of the refuge is downwind of the poultry facility 52%, 66%, 57%, and 50% of time during winter, spring, summer, and fall, respectively. Air concentrations and dry deposition rates are highest in the northeasterly direction from the facility, consistent with prevailing wind patterns. Dry deposition rates along the axis of highest concentrations are 10.1 kg N ha−1 yr−1 at the refuge boundary closest to the facility, decreasing to 5.4 kg N ha−1 yr−1 1.5 km further downwind and continuing to decrease non-linearly to a deposition rate of 1.4 kg N ha−1 yr−1 8–10 km downwind. Approximately 10% of the refuge model domain receives ≥3.0 kg N ha−1 yr−1 as dry NH3 deposition. Depending on the definition of the background air concentration, annual nitrogen loading to the refuge from background NH3 dry deposition and NH3 dry deposition associated with elevated concentrations downwind of the facility is between 41% and 79% higher than background dry NH3 deposition alone. Relative to the total N deposition budget for the refuge, which includes all nitrogen compounds, total background N deposition plus NH3 dry deposition associated with elevated concentrations downwind of the facility is, correspondingly, 6–10% greater than background total N deposition alone. From a process standpoint, predicted fluxes are most sensitive to uncertainty in the parameterization of the cuticular resistance.}, journal={AGRICULTURE ECOSYSTEMS & ENVIRONMENT}, author={Walker, John T. and Robarge, Wayne P. and Austin, Robert}, year={2014}, month={Mar}, pages={161–175} }
 @article{vepraskas_heitman_austin_2009, title={Future directions for hydropedology: quantifying impacts of global change on land use}, volume={13}, ISSN={["1607-7938"]}, DOI={10.5194/hess-13-1427-2009}, abstractNote={Abstract. Hydropedology is well positioned to address contemporary issues resulting from climate change. We propose a six-step process by which digital, field-scale maps will be produced to show where climate change impacts will be greatest for two land uses: a) home sites using septic systems, and b) wetlands. State and federal laws have defined critical water table levels that can be used to determine where septic systems will function well or fail, and where wetlands are likely to occur. Hydrologic models along with historic rainfall and temperature data can be used to compute long records of water table data. However, it is difficult to extrapolate such data across land regions, because too little work has been done to test different ways for doing this reliably. The modeled water table data can be used to define soil drainage classes for individual mapping units, and the drainage classes used to extrapolate the data regionally using existing digital soil survey maps. Estimates of changes in precipitation and temperature can also be input into the models to compute changes to water table levels and drainage classes. To do this effectively, more work needs to be done on developing daily climate files from the monthly climate change predictions. Technology currently exists to use the NRCS Soil Survey Geographic (SSURGO) Database with hydrologic model predictions to develop maps within a GIS that show climate change impacts on septic system performance and wetland boundaries. By using these maps, planners will have the option to scale back development in sensitive areas, or simply monitor the water quality of these areas for pathogenic organisms. The calibrated models and prediction maps should be useful throughout the Coastal Plain region. Similar work for other climate-change and land-use issues can be a valuable contribution from hydropedologists.}, number={8}, journal={HYDROLOGY AND EARTH SYSTEM SCIENCES}, author={Vepraskas, M. J. and Heitman, J. L. and Austin, R. E.}, year={2009}, pages={1427–1438} }
 @article{anderson_thompson_crouse_austin_2006, title={Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM}, volume={132}, ISSN={["1872-6259"]}, DOI={10.1016/j.geoderma.2005.06.004}, abstractNote={Terrain analysis of digital elevation models (DEM) has become an important technique to assess landscape and watershed scale hydrologic and pedologic processes and the spatial variability of soil and ecologic properties. Light detecting and ranging (LIDAR) elevation data sets provide the flexibility needed to produce multiple horizontal resolutions of DEM from the same data source. A series of 61 LIDAR tiles (100 ha) were collected from the North Carolina Flood Mapping Program covering the spatial extent of the Hofmann Forest in the Lower Coastal Plain of Eastern North Carolina. The LIDAR data set was reduced to 50%, 25%, 10%, 5%, and 1% of the original density. We created 5-, 10-, and 30-m DEM with 0.1 m vertical precision for each density level and used paired t-test to determine if the true mean of their differences were equal to zero. Differences indicated that for the 30-m DEM, LIDAR data sets could be reduced to 10% of their original data density without statistically altering the produced DEM. However, the 10-m DEM could only be reduced to 25% of the original data set before statistically altering the DEM. Data reduction was more limited for the 5-m DEM with possible reduction only to 50% of their original density without producing statistically different DEM. Our evaluation provides some indication as to the minimum required LIDAR data density to produce a DEM of a given horizontal resolution. However, evaluation of additional horizontal resolutions and additional density reduction is required to provide a clearer understanding of the effect of LIDAR data density.}, number={3-4}, journal={GEODERMA}, author={Anderson, Eric S. and Thompson, James A. and Crouse, David A. and Austin, Rob E.}, year={2006}, month={Jun}, pages={406–415} }
 @article{anderson_thompson_austin_2005, title={LIDAR density and linear interpolator effects on elevation estimates}, volume={26}, ISSN={["1366-5901"]}, DOI={10.1080/01431160500181671}, abstractNote={Linear interpolation of irregularly spaced LIDAR elevation data sets is needed to develop realistic spatial models. We evaluated inverse distance weighting (IDW) and ordinary kriging (OK) interpolation techniques and the effects of LIDAR data density on the statistical validity of the linear interpolators. A series of 10 forested 1000‐ha LIDAR tiles on the Lower Coastal Plain of eastern North Carolina was used. An exploratory analysis of the spatial correlation structure of the LIDAR data set was performed. Weighted non‐linear least squares (WNLS) analysis was used to parameterize best‐fit theoretical semivariograms on the empirical data. Tile data were sequentially reduced through random selection of a predetermined percentage of the original LIDAR data set, resulting in data sets with 50%, 25%, 10%, 5% and 1% of their original densities. Cross‐validation and independent validation procedures were used to evaluate root mean square error (RMSE) and kriging standard error (SE) differences between interpolators and across density sequences. Review of errors indicated that LIDAR data sets could withstand substantial data reductions yet maintain adequate accuracy (30 cm RMSE; 50 cm SE) for elevation predictions. The results also indicated that simple interpolation approaches such as IDW could be sufficient for interpolating irregularly spaced LIDAR data sets.}, number={18}, journal={INTERNATIONAL JOURNAL OF REMOTE SENSING}, author={Anderson, ES and Thompson, JA and Austin, RE}, year={2005}, month={Sep}, pages={3889–3900} }
 @article{austin_crouse_havlin_hodges_2000, title={The Spatial Information Research Laboratory at North Carolina State University}, journal={Proceedings of the 5th International conference on precision agriculture, Bloomington, Minnesota, USA, 16-19 July, 2000}, publisher={Madison, WI : Precision Agriculture Center, University of Minnesota, ASA-CSSA-SSSA}, author={Austin, R. E. and Crouse, D. A. and Havlin, J. L. and Hodges, S. C.}, year={2000}, pages={1} }