@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{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{cammarano_stefanova_ortiz_ramirez-rodrigues_asseng_misra_wilkerson_basso_jones_boote_et al._2013, title={Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US}, volume={13}, ISSN={["1436-378X"]}, DOI={10.1007/s10113-013-0410-1}, journal={REGIONAL ENVIRONMENTAL CHANGE}, author={Cammarano, Davide and Stefanova, Lydia and Ortiz, Brenda V. and Ramirez-Rodrigues, Melissa and Asseng, Senthold and Misra, Vasubandhu and Wilkerson, Gail and Basso, Bruno and Jones, James W. and Boote, Kenneth J. and et al.}, year={2013}, month={Aug}, pages={S101–S110} }
 @article{lassiter_jordan_wilkerson_shew_brandenburg_2011, title={Influence of Cover Crops on Weed Management in Strip Tillage Peanut}, volume={25}, ISSN={["0890-037X"]}, DOI={10.1614/wt-d-11-00064.1}, abstractNote={Experiments were conducted in North Carolina during 2005, 2006, and 2007 to determine peanut and weed response when peanut was planted in strip tillage after desiccation of cereal rye, Italian ryegrass, oats, triticale, wheat, and native vegetation by glyphosate and paraquat before planting with three in-season herbicide programs. Control of common ragweed and yellow nutsedge did not differ among cover crop treatments when compared within a specific herbicide program. Applying dimethenamid orS-metolachlor plus diclosulam PRE followed by imazapic POST was more effective than a chloroacetamide herbicide PRE followed by acifluorfen, bentazon, and paraquat POST. Incidence of spotted wilt in peanut (caused by aTospovirus) did not differ when comparing cover crop treatments, regardless of herbicide program. Peanut yield increased in all 3 yr when herbicides were applied POST, compared with clethodim only. Peanut yield was not affected by cover crop treatment. Response to cover crop treatments was comparable, suggesting that growers can select cereal rye, Italian ryegrass, oats, or triticale as an alternative to wheat as a cover crop in peanut systems without experiencing differences associated with in-season weed management.}, number={4}, journal={WEED TECHNOLOGY}, author={Lassiter, Bridget R. and Jordan, David L. and Wilkerson, Gail G. and Shew, Barbara B. and Brandenburg, Rick L.}, year={2011}, pages={568–573} }
 @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{robinson_moffitt_wilkerson_jordan_2007, title={Economics and effectiveness of alternative weed scouting methods in peanut}, volume={21}, ISSN={["1550-2740"]}, DOI={10.1614/WT-05-190.1}, abstractNote={On-farm trials were conducted in 16 North Carolina peanut fields to obtain estimates of scouting times and quality of herbicide recommendations for different weed scouting methods. The fields were monitored for weed species and population density using four scouting methods: windshield (estimate made from the edge of the field), whole-field (estimate based on walk through the field), range (weed densities rated on 1–5 scale at six locations in the field), and counts (weeds estimated by counting at six locations in the field). The herbicide application decision support system (HADSS) was used to determine theoretical net return over herbicide investment and yield loss ($ and %) for each treatment in each field. Three scouts estimated average weed population densities using each scouting method. These values were entered into HADSS to obtain treatment recommendations. Independently collected count data from all three scouts were combined to determine the optimal treatment in each field and the relative ranking of each available treatment. When using the whole-field method, scouts observed a greater number of weed species than when using the other methods. The windshield, whole-field, and range scouting methods tended to overestimate density slightly at low densities and underestimate density substantially at high densities, compared to the count method. The windshield method required the least amount of time to complete (6 min per field), but also resulted in the greatest average loss. Even for this method, recommendations had theoretical net returns within 10% of the return for the optimal treatment 80% of the time. The count method appears to have less economic risk than the windshield, whole-field, and range scouting methods.}, number={1}, journal={WEED TECHNOLOGY}, author={Robinson, Bridget L. and Moffitt, Jodie M. and Wilkerson, Gail G. and Jordan, David L.}, year={2007}, pages={88–96} }
 @article{lassiter_burke_thomas_pline-srnic_jordan_wilcut_wilkerson_2007, title={Yield and physiological response of peanut to glyphosate drift}, volume={21}, ISSN={["0890-037X"]}, DOI={10.1614/WT-07-045.1}, abstractNote={Five experiments were conducted during 2001 and 2002 in North Carolina to evaluate peanut injury and pod yield when glyphosate was applied to 10 to 15 cm diameter peanut plants at rates ranging from 9 to 1,120 g ai/ha. Shikimic acid accumulation was determined in three of the five experiments. Visual foliar injury (necrosis and chlorosis) was noted 7 d after treatment (DAT) when glyphosate was applied at 18 g/ha or higher. Glyphosate at 280 g/ha or higher significantly injured the peanut plant and reduced pod yield. Shikimic acid accumulation was negatively correlated with visual injury and pod yield. The presence of shikimic acid can be detected using a leaf tissue assay, which is an effective diagnostic tool for determining exposure of peanut to glyphosate 7 DAT.}, number={4}, journal={WEED TECHNOLOGY}, author={Lassiter, Bridget R. and Burke, Ian C. and Thomas, Walter E. and Pline-Srnic, Wendy A. and Jordan, David L. and Wilcut, John W. and Wilkerson, Gall G.}, year={2007}, pages={954–960} }
 @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} }
 @article{weber_taylor_wilkerson_2006, title={Soil and herbicide properties influenced mobility of atrazine, metolachlor, and primisulfuron-methyl in field lysimeters}, volume={98}, DOI={10.2134/argonj2004.0221}, number={1}, journal={Agronomy Journal}, author={Weber, J. B. and Taylor, K. A. and Wilkerson, G. G.}, year={2006}, pages={8–18} }
 @article{weber_taylor_wilkerson_2006, title={Soil cover and tillage influenced metolachlor mobility and dissipation in field lysimeters}, volume={98}, DOI={10.2134/argonj2004.0222}, number={1}, journal={Agronomy Journal}, author={Weber, J. B. and Taylor, K. A. and Wilkerson, G. G.}, year={2006}, pages={19–25} }
 @article{weber_wilkerson_reinhardt_2004, title={Calculating pesticide sorption coefficients (K-d) using selected soil properties}, volume={55}, ISSN={["0045-6535"]}, DOI={10.1016/j.chemosphere.2003.10.049}, abstractNote={Pesticide soil/solution distribution coefficients ( Kd values), commonly referred to as pesticide soil sorption values, are utilized in computer and decision aid models to predict soil mobility of the compounds. The values are specific for a given chemical in a given soil sample, normally taken from surface soil, a selected soil horizon, or at a specific soil depth, and are normally related to selected soil properties. Pesticide databases provide Kd values for each chemical, but the values vary widely depending on the soil sample on which the chemicals were tested. We have correlated Kd values reported in the literature with the reported soil properties for an assortment of pesticides in an attempt to improve the accuracy of a Kd value for a specific chemical in a soil with known soil properties. Mathematical equations were developed from regression equations for the related properties. Soil properties that were correlated included organic matter content, clay mineral content, and/or soil pH, depending on the chemical properties of the pesticide. Pesticide families for which Kd equations were developed for 57 pesticides include the following: Carboxy acid, amino sulfonyl acid, hydroxy acid, weakly basic compounds and nonionizable amide/anilide, carbamate, dinitroaniline, organochlorine, organophosphate, and phenylurea compounds. Mean Kd values for 32 additional pesticides, many of which had Kd values that were correlated with specific soil properties but for which no significant Kd equations could be developed are also included.}, number={2}, journal={CHEMOSPHERE}, author={Weber, JB and Wilkerson, GG and Reinhardt, CF}, year={2004}, month={Apr}, pages={157–166} }
 @article{wilkerson_price_bennett_krueger_roberson_robinson_2004, title={Evaluating the potential for site-specific herbicide application in soybean}, volume={18}, ISSN={["0890-037X"]}, DOI={10.1614/WT-03-258R}, abstractNote={Field experiments were conducted on two North Carolina research stations in 1999, 2000, and 2001; on-farm in Lenoir, Wayne, and Wilson counties, NC, in 2002; and on-farm in Port Royal, VA, in 2000, 2001, and 2002 to evaluate possible gains from site-specific herbicide applications at these locations. Fields were scouted for weed populations using custom software on a handheld computer linked to a Global Positioning System. Scouts generated field-specific sampling grids and recorded weed density information for each grid cell. The decision aid HADSS™ (Herbicide Application Decision Support System) was used to estimate expected net return and yield loss remaining after treatment in each sample grid of every field under differing assumptions of weed size and soil moisture conditions, assuming the field was planted with either conventional or glyphosate-resistant (GR) soybean. The optimal whole-field treatment (that treatment with the highest expected net return summed across all grid cells within a field) resulted in average theoretical net returns of $79/ha (U.S. dollars) and $139/ha for conventional and GR soybean, respectively. When the most economical treatment for each grid cell was used in site-specific weed management, theoretical net returns increased by $13/ha (conventional) and $4.50/ha (GR), and expected yield loss after treatment was reduced by 10.5 and 4%, respectively, compared with the whole-field optimal treatment. When the most effective treatment for each grid cell was used in site-specific weed management, theoretical net returns decreased by $18/ha (conventional) and $4/ha (GR), and expected yield loss after treatment was reduced by 27 and 19%, respectively, compared with the whole-field optimal treatment. Site-specific herbicide applications could have reduced the volume of herbicides sprayed by as much as 70% in some situations but increased herbicide amounts in others. On average, the whole-field treatment was optimal in terms of net return for only 35% (conventional) and 57% (GR) of grid cells.}, number={4}, journal={WEED TECHNOLOGY}, author={Wilkerson, GG and Price, AJ and Bennett, AC and Krueger, DW and Roberson, GT and Robinson, BL}, year={2004}, pages={1101–1110} }
 @article{jordan_wilkerson_krueger_2003, title={Evaluation of scouting methods in peanut (Arachis hypogaea) using theoretical net returns from HADSS (TM)}, volume={17}, ISSN={["1550-2740"]}, DOI={10.1614/0890-037X(2003)017[0358:EOSMIP]2.0.CO;2}, abstractNote={A perceived limitation to incorporating herbicide application decision support system (HADSS™) into routine peanut weed management decisions is efficient scouting of fields. A total of 52 peanut fields were scouted from 1997 through 2001 in North Carolina to determine the weed density in a 9.3-m2 section for each 0.4-ha grid of the field. These weed populations and their spatial distributions were used to compare theoretical net return (TNR) over herbicide investment for various scouting methods and weed management approaches. HADSS was used to determine the expected net return for each treatment in each 0.4-ha section of every field under differing assumptions of weed size, soil moisture conditions, and pricing structures. The treatment with the highest net return averaged across all 0.4-ha grids was considered to be the optimal whole-field treatment. For all 52 fields, TNR for the best whole-field treatment and for site-specific weed management (applying the most economical recommendation on each 0.4-ha grid) averaged $414 and $435/ha, respectively. Estimated return from the commercial postemergence herbicide program of aciflurofen plus bentazon plus 2,4-DB followed by clethodim (where grass was present) averaged $316/ha across all 52 fields. For fields of 5 ha or more (17 fields) in which 12 or more samples were taken, TNR was $500, $510, and $516/ha for three-sample (one pass through the middle of the field with samples taken on both ends and the center of the field), six-sample (two passes through the field with three stops per pass), and full-sample (one stop for each 0.4 ha) approaches, respectively. Nomenclature: Peanut, Arachis hypogaea L. Additional index words: Economic thresholds, prescription weed management, weed interference, weed scouting, weed thresholds. Abbreviations: HADSS, herbicide application decision support system; TNR, theoretical net return over herbicide investment.}, number={2}, journal={WEED TECHNOLOGY}, author={Jordan, DL and Wilkerson, GG and Krueger, DW}, year={2003}, pages={358–365} }
 @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} }
 @article{welch_wilkerson_whiting_sun_vagts_buol_mavromatis_2002, title={Estimating soybean model genetic coefficients from private- sector variety performance trial data}, volume={45}, DOI={10.13031/2013.9925}, abstractNote={Genetic coefficients are constants that enable crop simulation models to mimic the phenological and physiological 
idiosyncrasies of individual varieties. Methods have been developed for estimating genetic coefficients from research data 
or from public variety trials. However, the degree of experimental design regularity present in public trials is largely absent 
from their private counterparts. The question therefore arises as to whether estimation techniques developed with public data 
will work in the private–sector environment. This was evaluated using a set of 1155 yield observations representing 
221 varieties grown in 1997, 1998, and 1999 at 105 site–year combinations as part of the on–going testing program of the 
Delta and Pine Land Company. 
The data were divided into three groups: varieties with 15 or more observations, those with 5 to 14 observations, and those 
with four observations or less. The first group (24 varieties) had sufficient data to support genetic coefficient estimation. Four 
of the 24 had 10 or more observations in each of the three years. These four were quarantined to ensure their independence 
and formed an Evaluation Set. The remaining variety groups were used to characterize locations. Corresponding weather data 
were obtained from the National Climatic Data Center. Soil features were first estimated by an automated procedure based 
on reported texture and then adjusted manually within published limits to reproduce maturity group average yields at each 
site. The Evaluation Set was not used in this latter process. Optimal variety and site parameters were then estimated by 
searching a prediction database pre–calculated by parallel processing. The variety parameters were critical short day length 
and an index interrelating a set of phenological attributes. The site parameters were rooting profiles that fine–tuned water 
availability. Cross–validation was used to estimate root mean square errors of yield prediction. 
Results indicate that it is possible to extract coefficient estimates from commercial data, and that these estimates can be 
used to predict outcomes in independent situations. However, those situations must be from the same statistical population 
as the original calibration data. The stability of the estimates obtained was strongly dependent on the manner in which 
varieties with 5 to 14 observations were utilized. Although more than one mechanism seemed to be at work, decreasing 
importance of rooting profile estimation and easing restrictions on free genetic coefficients were associated with improved 
parameter stability as new data were added. It was also apparent that actual management use of the resulting estimates would 
require better characterization of soils than is currently present in performance trial data.}, number={4}, journal={Transactions of the ASAE}, author={Welch, S. M. and Wilkerson, G. and Whiting, K. and Sun, N. and Vagts, T. and Buol, G. and Mavromatis, T.}, year={2002}, pages={1163–1175} }
 @article{mavromatis_boote_jones_wilkerson_hoogenboom_2002, title={Repeatability of model genetic coefficients derived from soybean performance trials across different states}, volume={42}, ISSN={["0011-183X"]}, DOI={10.2135/cropsci2002.0076}, abstractNote={Crop model testing in diverse environments is essential if modelers wish to make applications or extrapolations to those environments. A recent study demonstrated the effectiveness of optimization techniques for deriving cultivar coefficients for the CROPGRO-Soybean model from typical information provided by soybean performance tests. The objectives of this study were (i) to explore the extent to which cultivar coefficients developed by these approaches from crop performance tests are stable across different regions, (ii) to test the CROPGRO-Soybean model's ability to predict phenology and seed yield using cultivar coefficients that were developed in different regions, and (iii) to investigate whether 3 yr of crop performance data are adequate for developing stable genetic coefficients. A stepwise procedure was applied to derive cultivar coefficients for 10 common cultivars grown in different environments in Georgia and North Carolina. Regarding the transportability of cultivar coefficients across states, we found that the critical daylength coefficients were the most reliable cultivar traits. We found less stability of the cultivar traits that control genetic differences in seed yield potential. The estimated cultivar coefficients developed in Georgia enabled CROPGRO to predict yield and harvest maturity in North Carolina within 3.8% and 3.5 d, respectively, from the observed averages. Using the cultivar coefficients developed from North Carolina environments allowed us to simulate the actual mean yield and harvest maturity in Georgia to within 2.5% and 2.0 d. Furthermore, the model's ability to predict seed yield and maturity with cultivar coefficients developed from 3 yr of data was nearly as good as that derived from much larger data sets.}, number={1}, journal={CROP SCIENCE}, author={Mavromatis, T and Boote, KJ and Jones, JW and Wilkerson, GG and Hoogenboom, G}, year={2002}, pages={76–89} }
 @misc{wilkerson_wiles_bennett_2002, title={Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach}, volume={50}, ISSN={["1550-2759"]}, DOI={10.1614/0043-1745(2002)050[0411:WMDMPP]2.0.CO;2}, abstractNote={Abstract The use of scouting and economic thresholds has not been accepted as readily for managing weeds as it has been for insects, but the economic threshold concept is the basis of most weed management decision models available to growers. A World Wide Web survey was conducted to investigate perceptions of weed science professionals regarding the value of these models. Over half of the 56 respondents were involved in model development or support, and 82% thought that decision models could be beneficial for managing weeds, although more as educational rather than as decision-making tools. Some respondents indicated that models are too simple because they do not include all factors that influence weed competition or all issues a grower considers when deciding how to manage weeds. Others stated that models are too complex because many users do not have time to obtain and enter the required information or are not necessary because growers use a zero threshold or because skilled decision makers can make better and quicker recommendations. Our view is that economic threshold–based models are, and will continue to be, valuable as a means of providing growers with the knowledge and experience of many experts for field-specific decisions. Weed management decision models must be evaluated from three perspectives: biological accuracy, quality of recommendations, and ease of use. Scientists developing and supporting decision models may have hindered wide-scale acceptance by overemphasizing the capacity to determine economic thresholds, and they need to explain more clearly to potential users the tasks for which models are and are not suitable. Future use depends on finding cost-effective methods to assess weed populations, demonstrating that models use results in better decision making, and finding stable, long-term funding for maintenance and support. New technologies, including herbicide-resistant crops, will likely increase rather than decrease the need for decision support.}, number={4}, journal={WEED SCIENCE}, author={Wilkerson, GG and Wiles, LJ and Bennett, AC}, year={2002}, pages={411–424} }
 @article{weber_wilkerson_linker_wilcut_leidy_senseman_witt_barrett_vencill_shaw_et al._2000, title={A proposal to standardize soil/solution herbicide distribution coefficients}, volume={48}, ISSN={["0043-1745"]}, DOI={10.1614/0043-1745(2000)048[0075:APTSSS]2.0.CO;2}, abstractNote={Abstract Herbicide soil/solution distribution coefficients (Kd) are used in mathematical models to predict the movement of herbicides in soil and groundwater. Herbicides bind to various soil constituents to differing degrees. The universal soil colloid that binds most herbicides is organic matter (OM), however clay minerals (CM) and metallic hydrous oxides are more retentive for cationic, phosphoric, and arsenic acid compounds. Weakly basic herbicides bind to both organic and inorganic soil colloids. The soil organic carbon (OC) affinity coefficient (Koc) has become a common parameter for comparing herbicide binding in soil; however, because OM and OC determinations vary greatly between methods and laboratories, Koc values may vary greatly. This proposal discusses this issue and offers suggestions for obtaining the most accurate Kd, Freundlich constant (Kf), and Koc values for herbicides listed in the WSSA Herbicide Handbook and Supplement. Nomenclature: Readers are referred to the WSSA Herbicide Handbook and Supplement for the chemical names of the herbicides.}, number={1}, journal={WEED SCIENCE}, author={Weber, JB and Wilkerson, GG and Linker, HM and Wilcut, JW and Leidy, RB and Senseman, S and Witt, WW and Barrett, M and Vencill, WK and Shaw, DR and et al.}, year={2000}, pages={75–88} }
 @article{krueger_wilkerson_coble_gold_2000, title={An economic analysis of binomial sampling for weed scouting}, volume={48}, ISSN={["1550-2759"]}, DOI={10.1614/0043-1745(2000)048[0053:AEAOBS]2.0.CO;2}, abstractNote={Abstract Full-count random sampling has been the traditional method of obtaining weed densities. Currently it is the recommended scouting procedure when using HERB, a herbicide selection decision aid. However, alternative methods of scouting that are quicker and more economical need to be investigated. One possibility that has been considered is binomial sampling. Binomial sampling is the procedure by which density is estimated from the number of random quadrats in which the count of individuals is equal to or less than a specified cutoff value. This sampling method has been widely used for insect scouting. There has also been interest in using binomial sampling for weed scouting. However, an economic analysis of this sampling method for weeds has not been performed. In this paper, the results of an economic analysis using simulations with binomial sampling and the HERB model are presented. Full-count sampling was included in the simulations to provide a benchmark for comparison. The comparison was made in terms of economic losses incurred when the estimated weed density obtained from sampling was inaccurate and a herbicide treatment was selected that did not maximize profits. These types of losses are referred to as opportunity losses. The opportunity losses obtained from the simulations indicate that in some situations binomial sampling may be a viable economic alternative to full-count sampling for fields with weed populations that follow a negative binomial distribution, assuming no prior knowledge of weed densities or negative binomial k values. Nomenclature: Glycine max, soybeans.}, number={1}, journal={WEED SCIENCE}, author={Krueger, DW and Wilkerson, GG and Coble, HD and Gold, HJ}, year={2000}, pages={53–60} }
 @article{irmak_jones_mavromatis_welch_boote_wilkerson_2000, title={Evaluating methods for simulating soybean cultivar responses using cross validation}, volume={92}, ISSN={["0002-1962"]}, DOI={10.2134/agronj2000.9261140x}, abstractNote={Crop simulation models are used in research worldwide, and efforts are now being made to incorporate them into decision‐support systems for farmers and their advisors. However, their on‐farm acceptance will be limited unless methods can be found to determine model coefficients for new cultivars that are released by public and private breeders. The availability of data to determine coefficients is usually limited; however, soybean breeders routinely collect data for new cultivars from variety trials. Objectives of this research were to (i) estimate soybean crop‐model prediction errors for anthesis, maturity, and yield using variety trial data; (ii) determine the effectiveness of cross validation for estimating prediction errors of the soybean model; and (iii) compare these errors with those based on regression equations relating specific cultivar yields to simulated maturity group (MG) yields. Root mean squared errors of prediction (RMSEP) were used for comparisons. Georgia variety trial data from 1987 through 1996 for six MG VII cultivars were divided into sets for fitting model coefficients and independent validation. The RMSEP using cross validation were similar to fitting errors when all  or only half of the data were used to fit cultivar coefficients. These errors were similar to those computed using independent data. The RMSEP for yield using linear regression were better than using generic MG coefficients but not as good as that found by fitting model coefficients. We conclude that soybean yield can be simulated for specific cultivars using either crop model or regression approaches, but the latter was not adequate for predicting cultivar anthesis and maturity dates.}, number={6}, journal={AGRONOMY JOURNAL}, author={Irmak, A and Jones, JW and Mavromatis, T and Welch, SM and Boote, KJ and Wilkerson, GG}, year={2000}, pages={1140–1149} }
 @article{scott_wilcut_wilkerson_1999, title={Cotton herb: a new decision making tool for weed management in cotton}, volume={1}, number={1999}, journal={Beltwide Cotton Conferences. Proceedings}, author={Scott, G. H. and Wilcut, J. W. and Wilkerson, G. G.}, year={1999}, pages={752–753} }
 @article{krueger_coble_wilkerson_1998, title={Software for mapping and analyzing weed distributions: gWeedMap}, volume={90}, ISSN={["1435-0645"]}, DOI={10.2134/agronj1998.00021962009000040018x}, abstractNote={Abstract}, number={4}, journal={AGRONOMY JOURNAL}, author={Krueger, DW and Coble, HD and Wilkerson, GG}, year={1998}, pages={552–556} }
 @article{wilkerson_jones_coble_gunsolus_1990, title={SOYWEED - A SIMULATION-MODEL OF SOYBEAN AND COMMON COCKLEBUR GROWTH AND COMPETITION}, volume={82}, ISSN={["0002-1962"]}, DOI={10.2134/agronj1990.00021962008200050033x}, abstractNote={Abstract}, number={5}, journal={AGRONOMY JOURNAL}, author={WILKERSON, GG and JONES, JW and COBLE, HD and GUNSOLUS, JL}, year={1990}, pages={1003–1010} }