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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.

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.

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 Ton 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

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...

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.