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

Abstract

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 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.

Comparison of temporal trends from multiple soil moisture data sets and precipitation: The implication of irrigation on regional soil moisture trend

“…Information on soil moisture is valuable in many disciplines includ-ing agronomy and environmental sciences, e.g., in application of vegetation growth/restoration in semiarid environments (Ahmad et al, 2010), soil erosion rate prediction (Wei et al, 2007) and crop yield estimation (Green and Erskine, 2004;Jaynes et al, 2003). Considering its importance in earth science, soil moisture is recognized as an Essential Climate Variable (GCOS, 2010), and the knowledge of its temporal trend and variability is of essential importance for understanding the effect of climate change on hydrological processes.…”

Soil moisture dynamics of typical ecosystems in response to precipitation: A monitoring-based analysis of hydrological service in the Qilian Mountains

“…Wang et al, 2013); yet continuous observations of soil moisture dynamics of different ecosystems are still need to be strengthened . Knowledge of soil moisture is critical for developing an understanding of numerous hydrological processes in soil hydrology, meteorology, and ecology research (Brocca et al, 2010;Green and Erskine, 2004;He et al, 2012). Soil moisture is regarded as the basis of quantitative research on hydrological dynamics and ecological patterns/processes (Rodriguez-Iturbel, 2000), participating as crony in the terrestrial hydrological cycle across the "lithosphere-biosphere-atmosphere-hydrosphere" and affecting the growth and succession of vegetation.…”

Spatial Analyses of Soil Chemical Properties from a Remodeled Paddy Field as Affected by Wet Land Leveling

“…This method can apply to quantify the interpolation of spatial patterns from point data (semivariogram) and to estimate distribution of properties at the interest scale (kriging) (Western et al, 1998). Semivariogram analysis has been applied to quantify variability of soil physical property distribution, such as porosity or pore structure (Cislerova and Votrabova, 2002;Posadas et al, 2003), soil moisture distribution (Western et al, 1998;Western et al, 2004;Brocca et al, 2007), microbiological property distribution (Goovaaerts, 1998;Matumbu et al, 2014), crop yield distribution (Miller et al, 1987;Green and Erskine, 2004) and soil chemical property distribution (Markus and McBratney, 2001;Yasrebi et al, 2009;Li et al, 2010).…”