Data interpolation in the definition of management zones

摘要

Precision agriculture (PA) comprises the use of management zones (MZs). Sample data are usually interpolated to define MZs. Current research checks whether there is a need for data interpolation by evaluating the quality of MZs by five indices – variance reduction (VR), fuzzy performance index (FPI), modified partition entropy index (MPE), Kappa index and the cluster validation index (CVI), of which the latter has been focused in current assa y. Soil texture, soil resistance to penetration, elevation and slope in an experimental area of 15.5 ha were employed as attrib utes to the generation of MZ, correlating them with data of soybean yield from 2011-2012 and 2012-2013 harves ts. Data interpolation prior to MZs generation is important to achieve MZs as a smoother contour and for a greater reduction in data variance. The Kriging interpolator had the best performance. CVI in dex proved to be efficient in choosing MZs, with a less subjective decision on the best interpolator or number of MZs.