Evaluating GPFARM Crop Growth, Soil Water, and Soil Nitrogen Components for Colorado Dryland Locations

Abstract

Alternative agricultural management systems in the semi-arid Great Plains are receiving increasing attention. GPFARM is a farm/ranch decision support system (DSS) designed to assist in strategic management planning for land units from the field to the whole-farm level. This study evaluated the regional applicability and efficacy of GPFARM based on simulation model performance for dry mass grain yield, total soil profile water content, crop residue, and total soil profile residual NO3-N across a range of dryland no-till experimental sites in eastern Colorado. Field data were collected from 1987 through 1999 from an on-going, long-term experiment at three locations in eastern Colorado along a gradient of low (Sterling), medium (Stratton), and high (Walsh) potential evapotranspiration. Simulated crop alternatives were winter wheat (Triticum aestivum L.), corn (Zea mays L.), sorghum (Sorghum bicolor L.), proso millet (Panicum miliaceum L.), and fallow. Relative error (RE) of simulated mean, root mean square error (RMSE), and index of agreement (d) model evaluation statistics were calculated to compare modeled results to measured data. A one-way, fixed-effect ANOVA was also performed to determine differences among experimental locations. GPFARM simulated versus observed REs ranged from -3% to 35% for crop yield, 6% to 8% for total soil profile water content, -4% to 32% for crop residue, and -7% to -25% for total soil profile residual NO3-N. For trend analysis (magnitudes and location differences), GPFARM simulations generally agreed with observed trends and showed that the model was able to simulate location differences for the majority of model output responses. GPFARM appears to be adequate for use in strategic planning of alternative cropping systems across eastern Colorado dryland locations; however, further improvements in the crop growth and environmental components of the simulation model (including improved parameterization) would improve its applicability for short-term tactical planning scenarios.