A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina

Abstract

The gap between data analysis and site-specific recommendations has been identified as one of the key constraints on widespread adoption of precision agriculture technology. This disparity is in part due to the fact that analytical techniques available to understand crop GIS layers have lagged behind development of data gathering and storage technologies. Yield monitor, sensor and other spatially dense agronomic data is often autocorrelated, and this dependence among neighboring observations violates the assumptions of classical statistical analysis. Consequently, reliability of estimates may be compromised. Spatial regression analysis is one way to more fully exploit the information contained in spatially dense data. Spatial regression techniques can also adjust for bias and inefficiency caused by spatial autocorrelation. The objective of this paper is to compare four spatial regression methods that explicitly incorporate spatial correlation in the economic analysis of variable rate technology: (1) a regression approach adopted from the spatial econometric literature; (2) a polynomial trend regression approach; (3) a classical nearest neighbor analysis; and (4) a geostatistical approach. The data used in the analysis is from a variable rate nitrogen trial in the Co´rdoba Province, Argentina, 1999. The spatial regression approaches offered stronger statistical evidence of spatial heterogeneity of corn yield response to nitrogen than ordinary least squares. The spatial econometric analysis can be implemented on relatively small data sets that do not have enough observations for estimation of the semivariogram required by geostatistics. The nearest neighbor and polynomial trend analyses can be implemented with ordinary least squares routines that are available in GIS software. The main result of this study is that conclusions drawn from marginal analyses of this variable rate nitrogen trial were similar for each of the spatial regression models, although the assumptions about spatial process in each model are quite different.

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

“…As demonstrated by Anselin [24], spatial correlation of regression residuals should be critically considered in the analysis of yield monitor data. Following this rationale, the application of spatial econometric methods incorporates a simultaneous autoregressive model of order one for the error term (SAR error model or SPL) and considers spatial neighborhood dependence structure [24][25][26].…”

Spatial analysis of yield monitor data: case studies of on-farm trials and farm management decision making

“…Spatial analysis is an inferential spatial statistical technique that explicitly accounts for spatial heterogeneity as well as the spatial interaction structure of neighboring observations. Spatial analysis techniques have been applied to site-specific yield monitor data before (Anselin et al 2004;Griffin et al 2005Griffin et al , 2006bLambert et al 2004;Hurley et al 2005). Three farmers conducted five field-scale on-farm trials and received spatial analysis reports.…”

Current status and future directions of precision aerial application for site-specific crop management in the USA

“…In a case study to model spatially varied yield monitor data for corn nitrogen response, Lowenberg-DeBoer (2000, 2002) determined that spatial regression analysis of yield monitor data could be used to estimate the site-specific crop nitrogen response needed to fine tune variable-rate fertilization strategies for maize and soybean. Lambert and Lowenberg-DeBoer (2003) demonstrated that the spatial econometric, geostatistical approach and spatial trend analysis offered stronger statistical evidence of spatial heterogeneity of nitrogen response than the ordinary least squares or nearest neighbor analysis. Yao et al (2003) investigated soil nutrient mapping by a co-located co-kriging estimator using soil sampling data and aerial hyperspectral image.…”