Spatial dimensions of precision agriculture: a spatial econometric analysis of millet yield on Sahelian coversands
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. © 2002 Elsevier Science B.V. All rights reserved.
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- Florax, Raymond J. G. M. & Folmer, Hendrik & Rey, Sergio J., 2003.
"Specification searches in spatial econometrics: the relevance of Hendry's methodology,"
Regional Science and Urban Economics,
Elsevier, vol. 33(5), pages 557-579, September.
- Raymond J.G.M. Florax & Hendrik Folmer & Sergio J. Rey, 2002. "Specification Searches in Spatial Econometrics: The Relevance of Hendry's Methodology," Urban/Regional 0202001, EconWPA.
- Bongiovanni, Rodolfo & Lowenberg-DeBoer, James, 2001. "Precision Agriculture: Economics Of Nitrogen Management In Corn Using Site-Specific Crop Response Estimates From A Spatial Regression Model," 2001 Annual meeting, August 5-8, Chicago, IL 20650, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
- Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
- Thomas de Graaff & Raymond J.C.M. Florax & Peter Nijkamp & Aura Reggiani, 2001. "A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity," Journal of Regional Science, Wiley Blackwell, vol. 41(2), pages 255-276.
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