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Irrigation Management Zones for Precision Viticulture According to Intra-Field Variability

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Irrigation Management Zones for Precision Viticulture According to Intra-Field Variability Irrigation management zones for precision viticulture according to intra-field variability J.A. Martínez-Casasnovas, D. Vallés Bigorda and M.C. Ramos University of Lleida, Department of Environment and Soil Science, Av. Rovira Roure 191, 25198 Lleida, Spain; [email protected] Abstract The present research shows a case study in precision viticulture to improve irrigation. The research was carried out in a vineyard field located in Raimat (NE Spain). This is a semi-arid area with continental Mediterranean climate and a total annual precipitation between 300-400 mm. The field (4.5 ha) is planted with Syrah vines in a 3×2 m pattern. The vines are irrigated by means of drips under a partial root drying schedule. The irrigation sectors have a quadrangular distribution, with a size of about 1 ha each. Yield presents a coefficient of variation of 32.2%, with an average yield of 6.9 t/ha. The re-design of the irrigation sectors was based on multi-variant statistics analysis of soil properties (pH, electric conductivity, organic matter content, calcium carbonate content, water retention availability for plants, texture and multi-temporal profile water volumetric content). The sampling density for these properties was of 8 samples/ha. Other data used in the analysis were the normalized difference vegetation index (NDVI) from Quickbird-2 satellite images of the years 2004 to 2007, and yield data acquired from a Canlink 3000 Farmscan monitor for the years 2004 to 2006. The results show that the best spatial prediction of yield is mainly explained by the NDVI of images acquired during veraison and the sand content. It explains 85.6% of variance. NDVI variability is mainly explained by the volumetric water content of the soil profile, explaining 72.1% of its variance. These properties: average yield, average NDVI, average volumetric water content and sand content, were used in a cluster analysis performed by means of the ISODATA algorithm implemented in Image Analyst for ArcGIS 9.1 to distinguish two management zones within the field. Those zones were finally used to re-design the irrigation sectors to adapt them to the variability expressed in the two zones. Keywords: vineyard, NDVI, yield, soil properties Introduction The application of Precision Agriculture (PA) techniques in viticulture is relatively recent. The first results began to be published from projects initiated in Australia in the wake of the appearance on the market of yield sensors and monitors (Bramley and Proffitt, 1999). Since then, a wide number of experiences and applications have been developed in this field, demonstrating that variable-rate application of inputs and selective harvesting at parcel level can be productive strategies which can provide significant benefits for winegrowers. Vineyard variability is a known phenomenon of which viticulturists are generally well aware, understanding that vine performance varies within their vineyards (Bramley and Hamilton, 2004). Recently, the development of the spatial information technologies tools in the last decades (geographical information systems, remote sensing, global position systems and electrical conductivity sensors, among other) and the advent of grape yield sensors and monitors has allowed obtaining information on vine performance as well as soil variability across the vineyard fields (Proffitt and Malcom, 2005; Proffitt et al., 2006). In this respect, variation in fruit and wine quality EFITA conference ’09 523 has been the focus of much of the work currently undertaken (Bramley and Lamb, 2003). The results of some of those research works have demonstrated that variation in yield as well as in fruit quality exhibit marked spatial structure, but that the patterns of variation are not necessarily the same. Nevertheless, due to the absence of an on-the-go sensor to monitor fruit quality parameters, in the same line as the existing yield monitors, it is suggested that zonal management to differentiate grape quality could proceed on the basis of zones of characteristic yield productivity (Bramley, 2005). This system of differential management has been referred to as zonal vineyard management (Bramley, 2005). Several examples of the commercial implementation of this approach to improve the uniformity of fruit parcels delivered to the winery have been already demonstrated (Proffitt and Pearse, 2004; Bramley et al., 2005). Mainly, those applications are addressed to selective harvesting, since the actuation in the fields to diminish crop variability are difficult because it is mainly related to soil property differences, which are difficult to change (Bramley, 2001). Other experiences to improve labour at pruning or to yield forecasting have also been reported (Martínez-Casasnovas and Bordes, 2005; Proffit and Malcom, 2005). Nevertheless, some experiences have been specifically addressed to apply cultural practices differentially, as for example irrigation water, with distinct amounts in different management zones along the growing season (Proffitt and Pearse, 2004; Proffit and Malcom, 2005). In this reported case study in the Margaret river region (Proffit and Malcom, 2005), irrigation was managed differentially in high vigorous areas with respect to less vigorous areas, so that water was restricted in the vigorous areas in order to reduce vegetative growth. The application of less water during the season appeared to reduce vegetative growth, with the greatest decrease in canopy surface area being recorded in the most vigorous areas. In view of this background, and according to the interest of precision viticulture as a tool for improving the efficient use of inputs and for diminishing vigour crop variability across the vineyard fields to deliver a more uniform output to the winery (in terms of yield as well as of fruit quality), the objective of the present work is to present a case study in precision viticulture to re-define irrigation management zones according to intra-field variability in a commercial vineyard block located in Raimat (NE Spain). Material and methods Study area The research was carried out in a commercial vineyard field located in Raimat (Lleida, NE Spain) (X= 292420, Y= 4614740, UTM 31n). It is included in the Costers del Segre Designation of Origin. This is a semi-arid area with continental Mediterranean climate and a total annual precipitation between 300-400 mm. The field, with an extension of 4.5 ha, is planted with Syrah vines in a 3×2 m pattern. The vines are irrigated by means of drips under a partial root drying schedule. At present, the irrigation sectors are have a quadrangular distribution, irrigating a homogeneous area of about 1 ha each. The sectors were designed previous to the plantation in 2002, without having into account the possible spatial variability of soil characteristics. At present, yield in this field presents a coefficient of variation of 32.2%, with an average yield of 6.9 t/ha. Spatial variability of yield, vigour and soil properties The re-design of the irrigation sectors was based on multi-variant statistics analysis of yield, plant vigour, through the normalized difference vegetation index – NDVI (Rouse et al., 1973) and soil properties. Yield data was acquired from a Canlink 3000 Farmscan monitor for the years 2004, 2005 and 2006. For each year, yield maps were produced following the protocol of Bramley and Williams (2001). 524 EFITA conference ’09 The re-design of the irrigation sectors was based on multi-variant statistics analysis of yield, plant vigour, through the normalized difference vegetation index – NDVI (Rouse et al., 1973) and soil properties. Yield data was acquired from a Canlink 3000 Farmscan monitor for the years 2004, 2005 and 2006. For each year, yield maps were produced following the protocol of Bramley and Williams (2001). Data refinement involved normalising the data (μ = 0, s = 1) after removal of data recordsData with refinement zero yield involved or GPS erronormalisingrs, and thenthe dataremoving (μ = 0, recordss = 1) after for removalwhich the of data records with zero normalised yieldyield was or greater GPS errors,or less andthan then ±3 st removingandard deviations records fromfor which the mean. the normalised The resulting yield was greater or less yield data werethan used ±3 to standardinterpolate deviations 3 m grid byfrom local the block mean. kriging The resulting (10 m x10 yield m blocks) data were using used to interpolate 3 m VESPER (Minasnygrid byet al. local, 2005). block kriging (10×10 m blocks) using VESPER (Minasny et al., 2005). In addition, three Quickbird-2 satellite images where acquired and processed for plant vigour In addition, three Quickbird-2 satellite images where acquired and processed for plant vigour monitoring. The dates of images acquisition were: 29-07-2004, 13-07-2005 and 13-07-2006. They monitoring. The dates of images acquisition were: 29-07-2004, 13-07-2005 and 13-07-2006. They are withinare the within range theof ±2range weeks of ±2 the weeks mome thent of moment veraison, of whichveraison, has which been referred has been to referred be to be the optimal the optimal timetime for for image image ac acquisitionquisition in inprecisi precisionon viticulture viticulture applications applications (Lamb (Lamb et etal. al.,, 2004). The spatial 2004). The spatialresolution resolution of the of multi-spectral the multi-spectral images images was 2.8was m.2.8 The m. imagesThe images were werecorrected for atmospheric corrected for atmosphericscattering by scattering applying bythe aCOSTpplying model
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