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The change from traditional to a more mechanized and technical agriculture has involved, in many cases, land transformations. This has supposed alteration of landforms and soils, with significant consequences. The effects of induced soil variability and the subsequent implications in site-specific crop management have not been sufficiently studied. The present work investigated the application of a resistivity soil sensor (Veris 3100), to map the apparent electrical conductivity (ECa), and detailed multispectral airborne images to analyse soil and crop spatial variability to assist in site-specific orchard management. The study was carried out in a peach orchard (Prunus persica (L.) Stokes), in an area transformed in the 1980 decade to change from rainfed arable crops to irrigated orchards. A total of 40 soil samples at two depths (0-30cm and 30-60cm) were analysed and compared to ECa and the normalised difference vegetation index (NDVI). Two types of statistical analysis were performed between ECa or NDVI classes with soil properties: a linear correlation analysis and multivariate analysis of variance (MANOVA). The results showed that the land transformation altered the spatial distribution and continuity of soil properties. Although a relationship between ECa and peach tree vigour could be expected, it was not found, even in the case of trees planted in soils with salts content above the tolerance threshold. Two types of management zones were proposed: a) zones delineated according to ECa classes to leach salts in the high ECa zones, and b) zones delineated according to NDVI classes to regulate tree vigour and yield. These strategies respond to the alteration of the original soil functions due to the land transformation carried out in previous years.

Different sampling schemes were tested to estimate yield (kg/tree), fruit firmness (kg) and the refractometric index (°Baumé) in a peach orchard. In contrast to simple random sampling (SRS), the use of auxiliary information (NDVI and apparent electrical conductivity, ECa) allowed sampling points to be stratified according to two or three classes (strata) within the plot. Sampling schemes were compared in terms of accuracy and efficiency. Stratification of samples improved efficiency compared to SRS. However, yield and quality parameters may require different sampling strategies. While yield was better estimated using stratified samples based on the ECa, fruit quality (firmness and °Baumé) showed better results when stratifying by NDVI.

POSTPRINT of the article: Uribeetxebarria, A., Martínez-Casasnovas, J.A, Escolà, A., Rosell-Polo, J.R., Arnó, J.2018. Stratified sampling in fruit orchards using cluster-based ancillary information maps: a comparative analysis to improve yield and quality estimates. Precis.Agric.

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