Evaluation of a Process-Based Agro-Ecosystem Model (Agro-IBIS) across the U.S. Corn Belt: Simulations of the Interannual Variability in Maize Yield

Abstract

A process-based terrestrial ecosystem model, Agro-IBIS, was used to simulate maize yield in a 13-state region of the U.S. Corn Belt from 1958 to 1994 across a 0.5° terrestrial grid. For validation, county-level census [U.S. Department of Agriculture (USDA)] data of yield were detrended to calculate annual yield residuals. Daily atmospheric inputs from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis were used in conjunction with the Climate Research Unit's (CRU's) monthly climate anomaly dataset at 0.5° resolution and a weather generator to drive the model at a 60-min time step. Multiple simulations were used to quantify model sensitivity to hybrid selection (defined by growing degree-day requirements), planting date, and soil type. The calibration of raw yields and model capability to replicate interannual variability were tested. The spatial patterns of simulated mean bias error (mbe) of raw yields were largely unresponsive to variations in soil texture, optimum hybrid choices, and planting dates typical for the region. Simulated 15-yr mean yields had a significant bias that was higher than observations, with an mbe of 0.97 Mg ha−1 and a root-mean-squared error (rmse) of 1.75 Mg ha−1. Simulations of interannual maize yield variability appeared to have a relatively weak response to changes in soil type and were more responsive to a planting date than a hybrid selection. The correlation (r2) between observed and simulated yield residuals within individual 0.5° grid cells ranged from 0.0 to 0.6, and increased as additional scenarios of land management decisions within 0.5° grid cells were taken into account. The correlations appeared to have a weak but significant relationship to a reported harvested area (r2 = 0.36). Model simulations produced a larger absolute magnitude of interannual variability than observations (positive and negative simulated yield residuals over the region averaged 18% and −21%, compared with 13% and −17% for observations), but spatial patterns were consistent. It was determined that the impact of irrigation on maize yield in the western Great Plains must be properly accounted for in future modeling scenarios to capture the increase (36%) in mean yields and significantly decreased interannual variability compared to rain-fed maize for future sensitivity studies.