Spatial variability in orchards after land transformation: Consequences for precision agriculture practices

Abstract

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.

Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities

“…As for yield mapping, production maps provide useful information for fruit growers. Fruit orchards usually show spatial variability due to soil variations, fertility, water irrigation, among others (Uribeetxebarria et al, 2018). An analysis of yield maps helps farmers to find the reasons for such variability and to determine which areas of lower productivity require special attention.…”

Sampling Stratification Using Aerial Imagery to Estimate Fruit Load in Peach Tree Orchards

“…Tree fruit differs from most annual crops in that plants are clonal, suggesting that there should be minimal variability between them compared with annual crops. However, orchard management and site-specific soil and climate effects accumulated over the years lead to significant within-orchard spatial variability that influences yield [11,12]. In this context, remote sensing could improve the efficiency of ground sampling protocols for yield assessments by (i) identifying the exact number of bearing trees in the orchard and (ii) detecting the spatial variability using auxiliary variables such as the Normalized Difference Vegetation Index (NDVI) or crown size [3] so that precision agriculture (PA) techniques would aid in classifying trees into homogeneous zones, which constitute sampling strata, lowering the MSS required.…”

“…In agriculture, many authors have shown that remote sensing and vegetation indexes such as Red Vegetation Index (RVI) or NDVI were correlated with the spatial variation of tree characteristics and yield [11][12][13][14][15][16]. However, contributions attempting to apply remote sensing in sampling designs are recent and mostly focused mostly in viticulture.…”

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

“…This is an important task in PA because it supposes the identification of areas within a field with different productive potential, allowing the opportunity for differential management to be assessed [7][8][9].…”