Assessing Spatial Variability in an Agricultural Experiment Station Field: Opportunities Arising from Spatial Dependence

Abstract

Spatio‐temporal field soil and crop processes are important for site‐specific farming. The objectives of this study were to spatially evaluate selected soil physical and chemical properties and their relationship to wheat (Triticum aestivum L.) yield, and to discuss stochastic approaches to help identify processes underlying yield variability in heterogeneous field sites. Modified grid sampling included 330 sites including a primary transect. Soil properties measured for the Ap, E if present, and upper B horizons at each site included pH, P, Zn, Cu, exchangeable cations, percentage base saturation, cation exchange capacity, bulk density, soil water contents at −10, −33, and −1500 kPa, texture, and humic matter content. Wheat grain and straw were hand‐harvested on 1‐ by 2‐m plots centered on each site. Soil water content on the primary transect was determined by neutron attenuation on nine dates. Field and primary transect means and semivariograms for a given soil or plant parameter were similar. The range of spatial dependence or autocorrelation of soil parameters ranged from 10 m for Ap horizon depth to 100 m for −1500 kPa water content of the Ap. Base saturation and available water storage capacity were cross‐correlated with grain yield to a distance of ±15 and 12.5 m, respectively. State‐space analysis was used to develop a grain yield model using these two variables. Spearman rank correlation of the soil water content data suggests that the temporal stability of soil water storage is less for shallow than for deeper soil layers.

Profile distribution of soil–water content and its temporal stability along a 1340-m long transect on the Loess Plateau, China

“…Below 180 cm, the mean r s gradually increased to 0.979 at 300 cm. The trend of r s along the profile was in disagreement with that of other studies (Cassel et al, 2000;Lin, 2006;Guber et al, 2008;Gao and Shao, 2012a;Penna et al, 2013), where the mean r s increased with depth. The different depths of sampling in these studies may not have been able to detect precise changes in mean r s along the soil profile.…”

Multifractal detrended fluctuation analysis in examining scaling properties of the spatial patterns of soil water storage

“…Therefore, the study of scaling properties in soil water content at the surface few centimeters (such as remote sensing measurement) is more complicated and is highly variable in nature (Biswas and Si, 2011a, b). On contrary, deep soil layers are less responsive to the changes in meteorological conditions (Hu et al, 2010), have less root activity (Cassel et al, 2000) and less disturbed soil structure (Guber et al, 2003;Pachepsky et al, 2005), which increase the buffering capacity of soil water changes in the deep layers and create an hydrological inertia (Martínez-Fernández and Ceballos, 2003) in soil water dynamics. Moreover, the rapid changes of soil water at the surface do not represent the actual changes in the vadosezone soil water storage.…”

A Review of Approaches for Measuring Soil Hydraulic Properties and Assessing the Impacts of Spatial Dependence on the Results

“…By using such techniques, the structure may be described in terms of autocorrelation functions and SARMA (Spatial Autoregressive-Moving Average) models with a view to estimating the stochastic properties of the data. Some of these applications in soil physics and hydrology include the studies by Morkoc et al, (1985); Anderson and Cassel, (1986); Wendroth et al, (1992); Cassel et al, (2000); Heuvelink and Webster (2001), Wendroth et all. (2006).…”

“…The state-space method (Kalman, 1960) is particularly interesting when the phenomenon in question satisfies certain systems of differential equations. The method has been used in economics (Shumway and Stoffer;2000) and has yielded good results in agronomic and soil science (Vieira et al, 1983;Morkoc et al, 1985;Wendroth et al, 1992;Comegna and Vitale, 1993;Wu et al, 1997;Cassel et al, 2000;Poulsen et al, 2003;Nielsen and Wendroth, 2003) Having set the initial values, we may obtain optimal forecasts and estimates of the nonobservable components by using the Kalman filter. At the same time, from many observations made of soil physical and hydraulic properties, the latter may plausibly have been generated by stationary isotropic processes with parameters independent of the individual measuring points:…”