Measurement, scaling, and topographic analyses of spatial crop yield and soil water content

The need to transfer information across a range of space-time scales (i.e. scaling) is coupled with the need to predict variables and processes of interest across landscapes (i.e. distributed simulation). Agricultural landscapes offer a unique set of problems and space-time data availability with the onset of satellite-based positioning and crop yield monitoring. The present study addresses quantification of the spatial variability of rainfed crop yield and near-surface soil water at farm field scales using two general methods: (1) geostatistical and fractal analyses; and (2) univariate linear regression using topographic attributes as explanatory variables. These methods are applied to 2 years of crop yield data from three fields in eastern Colorado, USA, and to soil-water content (depth-averaged over the top 30 cm) in one of these fields. Method 1 is useful for scaling each variable, and variogram shapes and their associated fractal dimensions of crop yield are related to those of topographic attributes. A new measure of fractal anisotropy is introduced and estimated from field data. Method 2 takes advantage of empirical and process knowledge of topographic controls on water movement and microenvironments. Topographic attributes, estimated from a digital elevation model at some scale (10 m by 10 m spacing here), help explain the spatial variability in crop yield. The topographic wetness index, for example, explained from 38 to 48% of the spatial variance in 1997 wheat yield. Soil water displays more random spatial variability, and its dynamic nature makes it difficult to predict in both space and time. Despite such variability, spatial structure is evident and can be approximated by simple fractals out to lag distances of about 450 m. In summary, these data and spatial analyses provide a basis and motivation for estimating the fractal behaviour, spatial statistics, and distributed patterns of crop yield from landscape topographic information.