2026-04-20T02:05:58Zhttps://recercat.cat/oai/request

oai:recercat.cat:10459.1/645192024-12-05T21:59:42Zcom_2072_3622col_2072_479130

00925njm 22002777a 4500 dc Martínez Casasnovas, José Antonio author Escolà i Agustí, Alexandre author Arnó Satorra, Jaume author 2018-06-15T11:13:22Z 2018-06-15T11:13:22Z 2018-06-13 2018-06-15T11:13:28Z 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. This research was funded by the contract C16022 between the University of Lleida and Ventafarinas, S.L. (Lleida, Spain). http://hdl.handle.net/10459.1/64519 Sentinel-2 accumulated NDVI Apparent electrical conductivity Topography cluster analysis Use of farmer knowledge in the delineation of potential management zones in precision agriculture: a case study in maize (Zea mays L.)