@article{jordan_buol_brandenburg_shew_wilkerson_lassiter_dunne_gorny_washburn_hoisington_et al._2022, title={A Risk Tool and Production Log Created using Microsoft Excel to Manage Pests in Peanut (Arachis hypogaea)}, volume={13}, ISSN={["2155-7470"]}, DOI={10.1093/jipm/pmac006}, abstractNote={Abstract}, number={1}, journal={JOURNAL OF INTEGRATED PEST MANAGEMENT}, author={Jordan, David L. and Buol, Greg S. and Brandenburg, Rick L. and Shew, Barbara B. and Wilkerson, Gail G. and Lassiter, Bridget R. and Dunne, Jeff and Gorny, Adrienne and Washburn, Derek and Hoisington, David and et al.}, year={2022}, month={Jan} }
 @article{jordan_buol_brandenburg_reisig_nboyine_abudulai_oteng-frimpong_mochiah_asibuo_arthur_et al._2022, title={Examples of Risk Tools for Pests in Peanut (Arachis hypogaea) Developed for Five Countries Using Microsoft Excel}, volume={13}, ISSN={["2155-7470"]}, url={https://doi.org/10.1093/jipm/pmac017}, DOI={10.1093/jipm/pmac017}, abstractNote={Abstract}, number={1}, journal={JOURNAL OF INTEGRATED PEST MANAGEMENT}, author={Jordan, David L. and Buol, Greg S. and Brandenburg, Rick L. and Reisig, Dominic and Nboyine, Jerry and Abudulai, Mumuni and Oteng-Frimpong, Richard and Mochiah, Moses Brandford and Asibuo, James Y. and Arthur, Stephen and et al.}, editor={Taylor, SallyEditor}, year={2022}, month={Jan} }
 @article{vann_drake-stowe_buol_dunphy_2021, title={Production practices that maximize soybean yield: What we have learned from the North Carolina soybean yield contest}, ISSN={["1435-0645"]}, DOI={10.1002/agj2.20728}, abstractNote={Abstract}, journal={AGRONOMY JOURNAL}, author={Vann, Rachel A. and Drake-Stowe, Katherine and Buol, Greg S. and Dunphy, E. Jim}, year={2021}, month={Jun} }
 @article{spivey_wilkerson_buol_edmisten_barnes_2018, title={USE OF CSM-CROPGRO-COTTON TO DETERMINE THE AGRONOMIC AND ECONOMIC VALUE OF IRRIGATION TO UPLAND COTTON PRODUCTION IN NORTH AND SOUTH CAROLINA}, volume={61}, ISSN={["2151-0040"]}, DOI={10.13031/trans.12801}, abstractNote={Abstract. Although prior research has shown that irrigation can increase cotton fiber yields in coastal plain soils of the Carolinas, only 2.7% of North Carolina’s and 7.8% of South Carolina’s planted hectares are irrigated, compared to 39% nationally. Little research has addressed the impact of compacted subsurface soil layers on the value of irrigation. Economic analysis of irrigation is also difficult due to the lack of long-term irrigation data for the region. The objectives of this study were to adapt the CSM-CROPGRO-Cotton simulation model to production conditions in the coastal plain of the Carolinas and use it to evaluate both the agronomic and economic value of irrigation to upland cotton production. Field data collected near Lewiston-Woodville, North Carolina, in 2015-2016 were used in model calibration and validation. Soil profiles were established using historical weather and cotton yield data for 16 cotton-producing counties in North and South Carolina from 1979 to 2015. Soil profiles were fit both with and without a root-restrictive (compacted) layer for each county. To evaluate the value of irrigation for these counties, simulations were conducted using ten irrigation levels, including non-irrigated, triggered when plant-available water (PAW) reached a maximum allowable depletion of 50%. The economic analysis made use of Cotton Incorporated’s Cotton Irrigation Decision Aid to determine the economic feasibility of irrigation using investment analysis tools such as cash flow, payback period, and net present value (NPV). Predicted agronomic and economic responses to irrigation were strongly dependent on seasonal rainfall. Fiber yield of non-irrigated cotton was reduced by more than 10% of fully irrigated cotton yield in more than 70% of the site-years simulated. This study suggests that irrigation is a feasible investment for cotton producers in North and South Carolina, as positive average cash flows and NPVs were observed over all counties and soils evaluated. Keywords: Cotton, CROPGRO, Crop simulation model, Economic analysis, Irrigation, Water use efficiency, Yield loss.}, number={5}, journal={TRANSACTIONS OF THE ASABE}, author={Spivey, T. A. and Wilkerson, G. G. and Buol, G. S. and Edmisten, K. L. and Barnes, E. M.}, year={2018}, pages={1627–1638} }
 @article{wilkerson_buol_yang_peacock_mccready_steinke_chalmers_2015, title={Modeling Response of Warm-Season Turfgrass to Drought and Irrigation}, volume={107}, ISSN={["1435-0645"]}, DOI={10.2134/agronj14.0311}, abstractNote={When droughts occur, restrictions on outdoor water use are a frequently used tactic for reducing demand but are not always as effective as desired and can have negative impacts on homeowners and businesses. Our objective was to develop a simulation model for use in comparing irrigation strategies in terms of water usage and changes in turfgrass quality under varying levels of water restriction. Based on data from several experiments, we have developed a model for St. Augustinegrass [Stenotaphrum secundatum (Walter) Kuntze] and bermudagrass [Cynodon dactylon (L.) Pers.] that calculates a turfgrass drought index and a turfgrass quality index (TQI) on a daily basis. Turfgrass water demand is modeled as a function of TQI and reference evapotranspiration. Actual turf water uptake depends on plant‐available soil water as well as plant demand. Available soil water in the root zone is divided into two pools: an easily available pool and a less readily available pool. Turfgrass quality can increase when there is no drought stress and decline whenever drought stress exceeds a cultivar‐specific threshold. We used the generalized likelihood uncertainty estimation method to estimate five genetic coefficients for two cultivars of each species. The model was highly successful in predicting the observed values of TQI. Except for a few sample dates, simulated TQI was within the 95% confidence interval of the mean observed TQI. The model appears to respond accurately to both drought and irrigation and to capture species and cultivar differences in drought tolerance.}, number={2}, journal={AGRONOMY JOURNAL}, author={Wilkerson, Gail G. and Buol, Gregory S. and Yang, Zhengyu and Peacock, Charles and McCready, Mary S. and Steinke, Kurt and Chalmers, David}, year={2015}, pages={515–523} }
 @article{yang_wilkerson_buol_bowman_heiniger_2009, title={Estimating Genetic Coefficients for the CSM-CERES-Maize Model in North Carolina Environments}, volume={101}, ISSN={["1435-0645"]}, DOI={10.2134/agronj2008.0234x}, abstractNote={CSM‐CERES‐Maize has been extensively used worldwide to simulate corn growth and grain production, but has not been evaluated for use in North Carolina. The objectives of this study were to calibrate CSM‐CERES‐Maize soil parameters and genetic coefficients using official variety trial data, evaluate model performance in North Carolina, and determine the suitability of the fitting technique using variety trial data for model calibration. The study used yield data for 53 maize genotypes collected from multiple locations over a 10‐yr period. A stepwise calibration procedure was utilized: (i) two genetic coefficients which determine anthesis and physiological maturity dates were adjusted based on growing degree day requirements for each hybrid; and (ii) plant available soil water and rooting profile were adjusted iteratively with two other genetic coefficients affecting yield. Cross validation was used to evaluate the suitability of this approach for estimating soil and genetic coefficients. The root mean squared errors of prediction (RMSEPs) were similar to fitting errors. Results indicate that CSM‐CERES‐Maize can be used in North Carolina to simulate corn growth under nonlimiting N conditions and variety trial data can be used for estimating genetic coefficients. Hybrid average simulated yields matched measured yields well across a wide range of environments, and simulated hybrid yield rankings were in close agreement with rankings based on measured yields. Data from several site‐years could not be used in fitting genetic coefficients due to large root mean squared errors. In some cases, this could be attributed to a weather event, such as a late‐season hurricane.}, number={5}, journal={AGRONOMY JOURNAL}, author={Yang, Zhengyu and Wilkerson, Gail G. and Buol, Gregory S. and Bowman, Daryl T. and Heiniger, Ronnie W.}, year={2009}, pages={1276–1285} }
 @article{mera_niyogi_buol_wilkerson_semazzi_2006, title={Potential individual versus simultaneous climate change effects on soybean (C-3) and maize (C-4) crops: An agrotechnology model based study}, volume={54}, ISSN={["1872-6364"]}, DOI={10.1016/j.gloplacha.2005.11.003}, abstractNote={Landuse/landcover change induced effects on regional weather and climate patterns and the associated plant response or agricultural productivity are coupled processes. Some of the basic responses to climate change can be detected via changes in radiation (R), precipitation (P), and temperature (T). Past studies indicate that each of these three variables can affect LCLUC response and the agricultural productivity. This study seeks to address the following question: What is the effect of individual versus simultaneous changes in R, P, and T on plant response such as crop yields in a C3 and a C4 plant? This question is addressed by conducting model experiments for soybean (C3) and maize (C4) crops using the DSSAT: Decision Support System for Agrotechnology Transfer, CROPGRO (soybean), and CERES-Maize (maize) models. These models were configured over an agricultural experiment station in Clayton, NC [35.65°N, 78.5°W]. Observed weather and field conditions corresponding to 1998 were used as the control. In the first set of experiments, the CROPGRO (soybean) and CERES-Maize (maize) responses to individual changes in R and P (25%, 50%, 75%, 150%) and T (± 1, ± 2 °C) with respect to control were studied. In the second set, R, P, and T were simultaneously changed by 50%, 150%, and ± 2 °C, and the interactions and direct effects of individual versus simultaneous variable changes were analyzed. For the model setting and the prescribed environmental changes, results from the first set of experiments indicate: (i) precipitation changes were most sensitive and directly affected yield and water loss due to evapotranspiration; (ii) radiation changes had a non-linear effect and were not as prominent as precipitation changes; (iii) temperature had a limited impact and the response was non-linear; (iv) soybeans and maize responded differently for R, P, and T, with maize being more sensitive. The results from the second set of experiments indicate that simultaneous change analyses do not necessarily agree with those from individual changes, particularly for temperature changes. Our analysis indicates that for the changing climate, precipitation (hydrological), temperature, and radiative feedbacks show a non-linear effect on yield. Study results also indicate that for studying the feedback between the land surface and the atmospheric changes, (i) there is a need for performing simultaneous parameter changes in the response assessment of cropping patterns and crop yield based on ensembles of projected climate change, and (ii) C3 crops are generally considered more sensitive than C4; however, the temperature–radiation related changes shown in this study also effected significant changes in C4 crops. Future studies assessing LCLUC impacts, including those from agricultural cropping patterns and other LCULC–climate couplings, should advance beyond the sensitivity mode and consider multivariable, ensemble approaches to identify the vulnerability and feedbacks in estimating climate-related impacts.}, number={1-2}, journal={GLOBAL AND PLANETARY CHANGE}, author={Mera, Roberto J. and Niyogi, Dev and Buol, Gregory S. and Wilkerson, Gail G. and Semazzi, Fredrick H. M.}, year={2006}, month={Nov}, pages={163–182} }
 @misc{bennett_price_sturgill_buol_wilkerson_2003, title={HADSS (TM), pocket HERB (TM), and WebHADSS (TM): Decision aids for field crops}, volume={17}, ISSN={["1550-2740"]}, DOI={10.1614/0890-037X(2003)017[0412:HPHAWD]2.0.CO;2}, abstractNote={Row crop weed management decisions can be complex due to the number of available herbicide treatment options, the multispecies nature of weed infestations within fields, and the effect of soil characteristics and soil-moisture conditions on herbicide efficacy. To assist weed managers in evaluating alternative strategies and tactics, three computer programs have been developed for corn, cotton, peanut, and soybean. The programs, called HADSS™ (Herbicide Application Decision Support System), Pocket HERB™, and WebHADSS™, utilize field-specific information to estimate yield loss that may occur if no control methods are used, to eliminate herbicide treatments that are inappropriate for the specified conditions, and to calculate expected yield loss after treatment and expected net return for each available herbicide treatment. Each program has a unique interactive interface that provides recommendations to three distinct kinds of usage: desktop usage (HADSS), internet usage (WebHADSS), and on-site usage (Pocket HERB). Using WeedEd™, an editing program, cooperators in several southern U.S. states have created different versions of HADSS, WebHADSS, and Pocket HERB that are tailored to conditions and weed management systems in their locations. Nomenclature: Corn, Zea mays L.; cotton, Gossypium hirsutum L.; peanut, Arachis hypogea L; soybean, Glycine max L. Additional index words: Bioeconomic models, computer decision aids, decision support systems, weed management. Abbreviations: HADSS, Herbicide Application Decision Support System; PDS, postemergence-directed; POST, postemergence; PPI, preplant-incorporated; PRE, preemergence.}, number={2}, journal={WEED TECHNOLOGY}, author={Bennett, AC and Price, AJ and Sturgill, MC and Buol, GS and Wilkerson, GG}, year={2003}, pages={412–420} }