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• The APEX model is used in a wide range of locations to evaluate conservation practices on farm land. • Of the several approximations to calculate Curve Number (CN) method for estimating runoff, the Variable Daily CN with Soil Moisture Index provided the best simulations of water content. • The diagnostic APEX performance in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site suggested reasonably accurate Evapotranspiration (ET), soil water calculations, and corn yields. • APEX performance in the OPE3 site varied by season (pre-plant, growing and post-plant). The Agricultural Policy/Environmental Extender (APEX) is a complex mechanistic simulation model designed to evaluate agricultural management strategies and their effects on yields, water and soil quality, and resource use. It is one of the main models used in the Conservation Effects Assessment Project (CEAP) assessment program and has been employed to evaluate the effects of conservation practices in the Chesapeake Bay Watershed (CBW). Calibration of model parameters is an important process in order to apply a simulation model to a particular area. The APEX model is normally parameterized for each application using selected datasets from the region. However, the APEX application area is usually very large and all areas may not be well represented by the calibration. Thus, additional tests of APEX without site-specific calibration for crop yield and soil water content can provide information on the stability of the parameters related to these variables in the CBW. The objective of this study is to evaluate the performance of APEX for corn yields and water balance in a new location using parameters previously developed for the CBW. This study used observations from the OPE3 site (Optimizing Production Inputs for Economic and Environmental Enhancement) located at the US Department of Agriculture, Henry A. Wallace Agricultural Research Center, Beltsville, Maryland, USA. Data from the OPE3 site consists of water content at various layers, corn yields, and ET covering the period from 1998 to 2013. The results for total water in the soil profile (up to 100 cm deep) showed that the simulated water amounts were about 4.4 cm higher than the observed values, but the trends were very similar to the observed trends. The statistical error analysis of R-square (RSQ), Nash-Sutcliffe (E NS) efficiency coefficient and Root Mean Square Error (RMSE) are 0.59, 0.51 and 0.21 cm•day−1, respectively. The average simulated yields were 0.3 t•ha−1 (4%) lower than observed yields with an average absolute deviation between APEX and OPE3 of 1.3 t•ha1 where the average observed yields over the period was 5.18 t•ha−1, The year-to-year trends were very similar to the measured trends. The monthly evapotranspiration data also showed reasonable trends between observed data and model results with the model RSQ, E NS and RMSE being 0.56, 0.41 and 4.49 mm•month−1, respectively. These results suggest that APEX parameters for yield and water use simulations calibrated for the CBW can be considered applicable to other areas of the CBW where calibration data are not available. [ABSTRACT FROM AUTHOR]

The Environmental Policy Integrated Climate (EPIC) model was used to assess the impacts of climate change and proposed adaptation measures on yields of corn ( Zea mays L.) and soybean ( Glycine max L.) as well as aggregated yields of C 3 [soybean, alfalfa ( Medicago sativa L.), winter wheat ( Triticum aestivum L.)] and C 4 [corn, sorghum ( Sorghum bicolor L.), pearl millet ( Pennisetum glaucum L.)] crop types from representative farms in ten Southeastern US states. Adaptations included annual biochar applications and irrigation. Historical baseline (1979–2009) and future (2041–2070) climate scenarios were used for simulations with baseline and future CO 2 concentrations of 360 ppmv and 500 ppmv, respectively. Four regional climate models (RCMs) nested within global climate models (GCMs) were used to run future simulations. The experiment was analyzed as randomized complete block design with split-plots in time for baseline vs. future comparisons, and as a randomized complete block design with repeated measures for comparisons between future periods within each RCM_GCM model. Compared to historical baseline scenario, increases in future corn yield ranged between 36–83%, but yields decreased by 5–13% towards 2066–2070 due to temperature stress. Future soybean yields decreased by 1–13% due to temperature and moisture stresses. Future aggregated C 4 crops produced higher yields compared to historical C 4 yields. There were no differences between future aggregated and historical C 3 crop yields. Both crop types were negatively affected by progressing climate change impacts towards the end of 2066–2070 simulation period. Reductions in future aggregated C 3 crop yields ranged between 10–22%, and between 6–10% for C 4 crops. We explained lower reductions in C 4 compared to C 3 crops due to a lesser degree of photorespiration, better water use efficiency, and better heat tolerance under conditions of high light intensities and increased temperatures in C 4 crops. Irrigation resulted in increased future corn yields between 29–33%, and 3–38% of aggregated C 4 crop yields, with no effect on soybean or aggregated C 3 crop yields. In some regions, biochar applications caused significant yield reductions of 9.5–20% for corn, 5–7% for aggregated C 3 , and 3–5% for aggregated C 4 crops, depending on the model. Yield reductions were ascribed to alterations in plant nutrient availability. It was concluded that under drier weather scenarios, irrigation may be a promising adaptation strategy for agriculture in the Southeastern US. [ABSTRACT FROM AUTHOR]