Scaling-up crop models for climate variability applications

Although most dynamic crop models have been developed and tested for the scale of a homogeneous plot, applications related to climate variability are often at broader spatial scales that can incorporate considerable heterogeneity. This study reviews issues and approaches related to applying crop models at scales larger than the plot, Perfect aggregate prediction at larger scales requires perfect integration of a perfect model across the range of variability of perfect input data. Aggregation error results from imperfect integration of heterogeneous inputs, and includes distortions of either spatial mean values of predictions or year-to-year variability of the spatial means. Approaches for reducing aggregation error include sampling input variability in geographic or probability space, and calibration of model inputs or outputs. Implications of scale and spatial interactions for model structure and complexity are a matter of ongoing debate. Large-scale crop model applications must address limitations of soil, weather and management data. Distortion of weather sequences from spatial averaging is a particular danger. A case study of soybean in the state of Georgia, USA, illustrates several crop model scaling approaches, (C) 2000 Elsevier Science Ltd. All rights reserved.