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The use of predictive yield maps is an important tool for the delineation of within-field management zones. In particular, appropriate placement in the field of organically grown produce, as is the case for the processing tomato crop in this study, will ensure higher crop yield. Accurate estimation of yield can be used to plan the best time for harvesting and transport for industrial processing. Apparent electrical conductivity (ECa) and vegetation indices based on crop reflectance are two tools that can be used to help attain these objectives. Developments in the use of sensors have enabled massive georeferenced data sampling of these parameters. The aim of this article is to assess the ECa and normalized difference vegetation index (NDVI) using geostatistical techniques to optimize their use. Principal component analysis was used to evaluate the predictive yield maps developed. ECa was a reasonably good indicator of crop production potential throughout the plot as a whole, but NDVI was the best indicator, offering a better resolution than ECa and a reasonable estimation of yield distribution over the extensive tested crop surface area. [ABSTRACT FROM AUTHOR]

Identifying spatial patterns of soil and plant properties can be an efficient method for site-specific management in areas with homogeneous characteristics (i.e., management zones, MZs). In this study, the use of soil apparent electrical conductivity (ECa) is proposed as the main information source for evaluating the spatial variability of soil and plant properties when using this variability to determine potential MZs. This study was conducted in a commercial hedgerow olive grove. Spatial distribution maps of the main soil properties and normalized difference vegetation index (NDVI) were generated by regression-kriging in which ECa was used as a secondary variable. According to the results obtained by the validation process, all maps were accurate. Soil and plant properties and ECa were subjected to principal component analysis (PCA). Two MZs were determined using a fuzzy cluster classification. The MZ map was validated using data related to soil samples, yield, and NDVI. Establishing different MZs was useful for adapting the irrigation strategies to the soil conditions of the plot, which resulted in increased productivity of the hedgerow olive grove. [ABSTRACT FROM AUTHOR]