Within-Field Temporal and Spatial Variability in Crop Productivity for Diverse Crops—A 30-Year Model-Based Assessment

Abstract

Within-field soil physical and chemical heterogeneity may affect spatio-temporal crop performance. Managing this heterogeneity can contribute to improving resource use and crop productivity. A simulation experiment based on comprehensive soil and crop data collected at the patchCROP landscape laboratory in Tempelberg, Brandenburg, Germany, an area characterized by heterogeneous soil conditions, was carried out to quantify the impact of within-field soil heterogeneities and their interactions with interannual weather variability on crop yield variability in summer and winter crops. Our hypothesis was that crop–soil water holding capacity interactions vary depending on the crop, with some crops being more sensitive to water stress conditions. Daily climate data from 1990 to 2019 were collected from a nearby station, and crop management model inputs were based on the patchCROP management data. A previously validated agroecosystem model was used to simulate crop growth and yield for each soil auger profile over the 30-year period. A total of 49 soil auger profiles were classified based on their plant available soil water capacity (PAWC), and the seasonal rainfall by crop was also classified from lowest to highest. The results revealed that the spatial variability in crop yield was higher than the temporal variability for most crops, except for sunflower. Spatial variability ranged from 17.3% for rapeseed to 45.8% for lupine, while temporal variability ranged from 10.4% for soybean to 36.8% for sunflower. Maize and sunflower showed a significant interaction between soil PAWC and seasonal rainfall, unlike legume crops lupine and soybean. As for winter crops, the interaction was also significant, except for wheat. Grain yield variations tended to be higher in years with low seasonal rainfall, and crop responses under high seasonal rainfall were more consistent across soil water categories. The simulated results can contribute to cropping system design for allocating crops and resources according to soil conditions and predicted seasonal weather conditions

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