Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize ( Zea mays L.)

José A. Martínez-Casasnovas, Alexandre Escolà and Jaume Arnó
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José A. Martínez-Casasnovas: Research Group in AgroICT and Precision Agriculture, Agrotecnio Center, University of Lleida, Av. Rovira Roure 191, 25198 Lleida, Catalonia, Spain
Alexandre Escolà: Research Group in AgroICT and Precision Agriculture, Agrotecnio Center, University of Lleida, Av. Rovira Roure 191, 25198 Lleida, Catalonia, Spain
Jaume Arnó: Research Group in AgroICT and Precision Agriculture, Agrotecnio Center, University of Lleida, Av. Rovira Roure 191, 25198 Lleida, Catalonia, Spain

Agriculture, 2018, vol. 8, issue 6, 1-18

Abstract: One of the fields of research in precision agriculture (PA) is the delineation of potential management zones (PMZs, also known as site-specific management zones, or simply management zones). To delineate PMZs, cluster analysis is the main used and recommended methodology. For cluster analysis, mainly yield maps, remote sensing multispectral indices, apparent soil electrical conductivity (ECa), and topography data are used. Nevertheless, there is still no accepted protocol or guidelines for establishing PMZs, and different solutions exist. In addition, the farmer’s expert knowledge is not usually taken into account in the delineation process. The objective of the present work was to propose a methodology to delineate potential management zones for differential crop management that expresses the productive potential of the soil within a field. The Management Zone Analyst (MZA) software, which implements a fuzzy c-means algorithm, was used to create different alternatives of PMZ that were validated with yield data in a maize ( Zea mays L.) field. The farmers’ expert knowledge was then taken into account to improve the resulting PMZs that best fitted to the yield spatial variability pattern. This knowledge was considered highly valuable information that could be also very useful for deciding management actions to be taken to reduce within-field variability.

Keywords: Sentinel-2; accumulated NDVI; apparent electrical conductivity; topography; cluster analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2018
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