2021
DOI: 10.1590/1678-992x-2019-0243
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Abstract: Characterization of the spatial variability of vegetative vigor in vineyards can help improve the performance of site-specific management practices, or the management of vineyards with different rates. Characterization using canopy proximal sensing has been a widely disseminated technique; however, vineyards in southeastern Brazil, where the utilization of annual double pruning results in a winter harvest, knowledge of the role of variability in improving vineyard management has not yet been applied. This stud… Show more

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Cited by 4 publications
(4 citation statements)
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“…The spatial distribution of the normalized difference vegetation index (NDVI) in this vineyard was determined by Oldoni et al (2021) in 2017 and 2018 growing seasons (Figure 2 A, B), and those authors reported that it was positively correlated with vine's vegetative vigor. In that ocasion, a Crop Circle ACS-430 active canopy sensor was used to collect reflectance data on wavelengths of 670 (red) and 780 nm (near infrared) for the determination of NDVI.…”
Section: Homogeneous Zonesmentioning
confidence: 98%
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“…The spatial distribution of the normalized difference vegetation index (NDVI) in this vineyard was determined by Oldoni et al (2021) in 2017 and 2018 growing seasons (Figure 2 A, B), and those authors reported that it was positively correlated with vine's vegetative vigor. In that ocasion, a Crop Circle ACS-430 active canopy sensor was used to collect reflectance data on wavelengths of 670 (red) and 780 nm (near infrared) for the determination of NDVI.…”
Section: Homogeneous Zonesmentioning
confidence: 98%
“…Soil in area 1 was categorized according to the U.S. Soil Taxonomy (Soil Survey Staff, 2014) as a complex of Inceptisols and Entisols (Orthents) while soil in area 2 was classified as an association of the same soil types (Oldoni et al, 2021). The soil layers of 0-0.2 m and 0.2-0.4 m presented, respectively, 379 and 441g kg -1 of clay; 125 and 129 g kg -1 silt; 494 and 430 g kg -1 sand.…”
Section: Experimental Sitementioning
confidence: 99%
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“…Understanding spatial variability in perennial crops, such as winegrapes, is important for farmers and those downstream of the crop, but in the absence of appropriate sensors, the cost of sample collection necessary to create maps of productivity constrains most operations (Bramley 2021). Vegetation indices, derived from remote or proximal sensors, are tools that can alleviate some of this burden by allowing practitioners of precision agriculture to collect strategic samples based on the assignment of management zones to areas considered to contain similarly productive plants (Acevedo‐Opazo et al 2008, Tagarakis et al 2013, Bonilla et al 2015, Meyers et al 2020, Oldoni et al 2021, Sams et al 2022b). Nevertheless, direct measurements may be necessary to characterise variability in regions where image collection is difficult due to atmospheric conditions, or where the variable of interest is not closely related to the spectral response of canopies (Sams et al 2022a,b).…”
Section: Introductionmentioning
confidence: 99%