Author
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Submitted to: Hydrological Processes
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 1/15/2003 Publication Date: 6/15/2004 Citation: Green, T.R., Erskine, R.H. 2004. Measurement, scaling, and topographic analyses of spatial crop yield and soil water content. Hydrological Processes. June 2004. Volume 18, Issue 8, pages 1447-1465. Interpretive Summary: : 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 methods: 1) geostatistical and fractal analyses; and 2) univariate linear regression using topographic attributes for explanatory variables. These methods are applied to two years of crop yield data from three fields in eastern Colorado, USA, and to soil-water content in the top 30 cm of one of these fields. Method 1 is useful for scaling the spatial moments of each variable, and it may be applied to determining appropriate scales of measurement and management. 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 here), help explain the spatial variability in crop yield. The topographic wetness index, for example, explained up to 48% of the spatial variance in 1997 wheat yield. Soil water (top 30 cm) 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. Future work will extend the present point-to-point univariate regression with topographic attributes to multiple regression and non-parametric pattern matching at the field-scales of interest for land unit delineation. Technical Abstract: : 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 methods: 1) geostatistical and fractal analyses; and 2) univariate linear regression using topographic attributes for explanatory variables. These methods are applied to two years of crop yield data from three fields in eastern Colorado, USA, and to soil-water content in the top 30 cm of one of these fields. Method 1 is useful for scaling the spatial moments of each variable, and it may be applied to determining appropriate scales of measurement and management. 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 here), help explain the spatial variability in crop yield. The topographic wetness index, for example, explained up to 48% of the spatial variance in 1997 wheat yield. Soil water (top 30 cm) 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. Future work will extend the present point-to-point univariate regression with topographic attributes to multiple regression and non-parametric pattern matching at the field-scales of interest for land unit delineation. |
