Abstract
ABSTRACT: Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.
Highlights
Precision Agriculture (PA) is a relatively new technology in grapevine, where it is called Precision Viticulture (PV), highlighting the pioneering research conducted in the United States (WAMPLE et al, 1998) and in Australia (BRAMLEY and PROFFITT, 1999; PROFFITT et al, 2006)
The vegetation indexes (VIs) that presented the highest number of correlations by Pearson in relation to the parameter of agronomic variables tested were Simple Ratio (SR)-SP, NPQI-SP and Pl1-SP, with 14 significant correlations each
Considering the magnitude of the results, the vegetation indexes with the highest number of moderate positive significant correlations were: Modified normalized Difference vegetation index (mNDVI)-CP, Photochemical Reflectance Index (PRI)-CP, mNDVI-SP, Pl1-SP and ARI-SP, presenting 4 correlations each, while SR- SP obtained the highest number of negative moderate correlations with 5 significant correlations
Summary
Precision Agriculture (PA) is a relatively new technology in grapevine, where it is called Precision Viticulture (PV), highlighting the pioneering research conducted in the United States (WAMPLE et al, 1998) and in Australia (BRAMLEY and PROFFITT, 1999; PROFFITT et al, 2006). It was later adopted by winemakers from Europe such as France and Spain and South America (MIELE et al, 2014). Application of proximal or distal hyperspectral remote sensing for studies related to evaluating agricultural vegetation will allow the monitoring of important crop variables, including stresses (as those caused by water, insects, pollution, etc.), agricultural production, productivity, carbon sequestration, phenology, crop maturation, among others
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