Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation

Limitations on water resources for agriculture in places such as Colorado, USA, have caused farmers to consider limited irrigation as an alternative to full irrigation practices, where the crop is intentionally stressed during specific growth stages in an effort to maximize yield per unit water consumed, or evapotranspiration (ET). While crop growth models such as CERES-Maize provide the ability to evaluate numerous management scenarios without the costs associated with multiyear field experiments, recent studies have shown that CERES-Maize performs well under full irrigation but overestimates ET of corn under limited irrigation management. The primary objective of this study was to improve CERES-Maize ET simulation under limited irrigation management while maintaining accuracy of other important model output responses. Field experiments with corn were performed in northern Colorado, USA from 2006 to 2010, where four replicates each of full (ET requirement supplied by irrigation throughout the season) and limited (no irrigation before the V12 growth stage unless necessary for emergence, then full irrigation afterwards) irrigation treatments were analyzed. The local sensitivity of model input parameters affecting ET was evaluated, prompting changes to the model code with a new dynamic crop coefficient (KCD) as a function of the crop leaf area index. The modified CERES-Maize model more accurately represented ET under full and limited irrigation, for example reducing late-season ET potential from a plant with reduced canopy and more closely matched FAO-56 crop coefficient curves under full irrigation. Using the limited irrigation data for evaluation, the modified model showed significant decreases in model error for seasonal cumulative ET (root mean square deviation RMSD from 80.9 mm to 49.9 mm) and water productivity (RMSD from 5.97 kg ha−1mm−1 to 2.86 kg ha−1mm−1) as compared to the original model. The modified model was subsequently applied to several hypothetical irrigation management strategies, indicating that reducing weekly vegetative state water applications from 20 mm to 2.5 mm can increase simulated water productivity by over 15%. While these synthetic water production functions may not be feasible in a production field with natural climate variability, the modified ET model indicates promise for limited irrigation management increasing water productivity

Simulating landscape catena effects in no-till dryland agroecosystems using GPFARM

This study evaluated the site-specific applicability and efficacy of the GPFARM decision support system (DSS) based on underlying simulation model performance for dry mass grain yield, crop residue, total soil profile water content, and total soil profile residual NO3-N across a landscape catena for dryland no-till experimental locations in eastern Colorado. Relative error of simulated mean, normalized objective function (root mean square error divided by the observed mean), and index of agreement evaluation statistics were calculated to compare modeled results to observed data. A one-way, fixed-effect ANOVA was also performed to determine differences among experimental locations and summit, sideslope, and toeslope landscape positions. GPFARM simulations matched observed data trends, with the model correctly distinguishing variations between the summit and toeslope landscape positions. In addition, experimental observations and GPFARM simulations both indicated that the toeslope landscape position was the most productive for grain yield and also exhibited higher amounts of crop residue, total soil profile water content, and total soil residual NO3-N. The GPFARM crop model performed adequately but was inconsistent in simulating winter wheat, corn, and sorghum dry mass grain yield. GPFARM performance in simulating crop residue was poorer than for crop grain yield. GPFARM predicted mean total soil profile water content was generally within ±20% of the observed mean across locations and landscape positions, with the model somewhat biased towards overpredicting total soil profile water content at the summit and sideslope landscape positions. Total soil profile residual NO3-N was underpredicted by GPFARM across all locations and landscape positions by an average of 30%. Although GPFARM appears to have reasonably simulated long-term output responses across a landscape catena for the eastern Colorado experimental locations (especially given the simplifying assumptions in many of the GPFARM simulation components and the inherent variability present at the experimental plot level), different interpretations of GPFARM performance can be made depending on the evaluation statistic of interest. Furthermore, the model cannot fully account for water and chemical movement across the landscape catena; simulation results suggest that addition of a spatially-distributed routing component should offer improvements in GPFARM prediction accuracy across a catena where surface runoff or lateral subsurface flow is occurring.Landscape catena GPFARM Agroecosystem modeling Crop yield Soil water Soil nitrogen