Predicting spatial patterns of within-field crop yield variability

Over the last two decades, there has been significant advancements in the application of geospatial technologies in agriculture. Improved resolutions (spectral, spatial and temporal) of remotely sensed images, coupled with more precise on-the-ground systems (yield monitors, geophysical sensors) have allowed farmers to become more sensitive about the spatial and temporal variations of crop yields occurring in their fields. Previous research has extensively looked at spatial variability of crop yields at field scale, but studies designed to predict within-field spatial patterns of yield over a large number of fields as yet been reported. In this paper, we analyzed 571 fields with multiple years of yield maps at high spatial resolution to understand and predict within-field spatial patterns across eight states in the Midwest US and over corn, soybean, wheat and cotton fields. We examined the correlation between yield and 4 covariates, three derived from remote sensing imagery (red band spectral reflectance, NDVI and plant surface temperature) and the fourth from yield maps from previous years. The results showed that for spatial patterns that are stable over time the best predictor of the spatial variability is the historical yield map (previous years' yield maps), while for zones within the field that are more sensitive to weather and thus fluctuate from one year to the next the best predictor of the spatial patterns are the within-season images. The results of this research help quantify the role of historical yield maps and within-season remote sensing images to predict spatial patterns. The knowledge of spatial patterns within a field is critical not only to farmers for potential variable rate applications, but also to select homogenous zones within the field to run crop models with site-specific input to better understand and predict the impact of weather, soil and landscape characteristics on spatial and temporal patterns of crop yields to enhance resource use efficiency at field level.