Spatial dimensions of precision agriculture: a spatial econometric analysis of millet yield on Sahelian coversands

Abstract

The identification of local soil variability caused by within-field differences of macronutrients and ecological features is of paramount importance for the effectiveness of precision agriculture. We present several spatial statistical and econometric techniques to capture local differences in soil variation, ecological characteristics, and yield more effectively than the analytical techniques traditionally used in agronomy. The application of these techniques is illustrated in a case study dealing with precision agriculture in the West African Sahel. The production of millet on acid sandy soils constitutes a typical example of low soil fertility areas exhibiting small absolute but large relative differences in crop production conditions over short distances. (R.J.G.M. Florax), r.l.voortman@sow.vu.nl (R.L. Voortman), brouwbar@bos.nl (J. Brouwer). URLs: http://www.feweb.vu.nl/re/master-point, http://www.sow.vu.nl/ Tel.: +31-20-4449321; fax: +31-20-4449325. 0169-5150/02/$see front matter 0 2002 Elsevier Science B.V. All rights reserved.PII: S 0 1 6 9 -5 1 5 0 (0 2 ) 0 0 0 6 8 -3

Spatial Econometric Analysis: Potential Contribution to the Economic Analysis of Smallholder Development

“…Florax [26] undertook a spatial econometric analysis of millet yield in the West African Sahel. The method enabled them to capture local differences in soil variation, ecological characteristics and yield.…”

A Comparison of Geostatistical and Spatial Autoregressive Approaches for Dealing With Spatially Correlated Residuals In Regression Analysis for Precision Agriculture Applications

“…For this paper we choose to use the spatial-error model specification and do not pursue further explanations on the spatial lag specification. We note in passing that the use of the spatial lag model in exploratory analyses of this sort, where the explanatory variables have a strong spatial structure themselves as is the case in Long (1998) and Florax et al (2002), is questionable and can obscure the true influence of the field properties under investigation on the dependent variable (see for example comments in Upton and Fingleton, p. 373 about a similar situation in a study by Cook and Pocock).…”

“…The effect of correlated residuals on parameter estimates has been recognized in agricultural research for a long time, but in practice this has been almost exclusively addressed in experimental studies involving variety trials, where the use of some variation of the nearest neighbors approach has become common, (Wilkinson et al, 1983, Bhatti et al, 1991 and other recently adopted methods based on geostatistics (Ball et al, 1993, Brownie et al, 1993, Stroup et al,1994. In a reduced number of regression analyses in the area of precision agriculture, spatial correlation was accounted for in the estimation of β by the use of basically three different approaches: nearest-neighbors analysis (Mamo et al, 2003, Bermudez andMallarino, 2003), direct covariance representation (Lark and Wheeler, 2003;Kaspar et al, 2004) and spatial autoregression (SAR, Long, 1998;Florax et al, 2002;Anselin et al, 2002). In this paper, for purposes of accounting for spatial correlation in regression residuals, we deal only with the direct covariance representation and SAR methods.…”

Spatial Econometric Issues for Bio‐Economic and Land‐Use Modelling

“…Ignoring spatial considerations, as was the practice in previous crop yield studies, can lead to incorrect inferences and poor model performance. Therefore, Florax et al (2002) utilise spatial econometric techniques in their study of millet production on Sahelian coversands in South-West Niger. Their results indicate that there is (1) positive spatial correlation among yield values (their dependent variable) and (2) the spatial lag model outperforms standard OLS estimates of millet yield.…”