Spatial scales of climate information for simulating wheat and maize productivity: the case of the US Great Plains

Abstract

The spatial aggregation of climate and soils data for use in site-specific crop models to estimate regional yields is examined. The purpose of this exercise is to determine the optimum spatial resolution of observed climate and soils data for simulating major crops grown in the central Great Plains (maize, wheat), beginning at a scale of 2.8°×2.8° (T42), which is close to that of the European Centre for Medium-Range Forecasting (ECMWF) general circulation model (GCM) grid cell and progressively disaggregating climate and soils data to finer spatial scales. Using the Erosion Productivity Impact Calculator (EPIC) crop model, observed crop yields for the period 1984–1992 are compared with yields simulated with observed 1984–1992 climate. The goal is to identify the spatial resolution of climate and soils data which minimizes statistical error between observed and modeled yields. Agreement between simulated and observed maize and wheat was greatly improved when climate data was disaggregated to approximately 1°×1° resolution. No disaggregation results for hay were statistically significant. Disaggregation of climate data finer than the 1°×1° resolution gave no further improvement in agreement. Disaggregation of soils data gave no additional improvement beyond that of the disaggregation of climate data.