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.

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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.

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.

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