Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/58877
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Campo DCValorIdioma
dc.creatorValente, Gislayne Farias-
dc.creatorFerraz, Gabriel Araújo e Silva-
dc.creatorSantana, Lucas Santos-
dc.creatorFerraz, Patrícia Ferreira Ponciano-
dc.creatorMariano, Daiane de Cinque-
dc.creatorSantos, Crissogno Mesquita dos-
dc.creatorOkumura, Ricardo Shigueru-
dc.creatorSimonini, Stefano-
dc.creatorBarbari, Matteo-
dc.creatorRossi, Giuseppe-
dc.date.accessioned2024-02-01T14:46:49Z-
dc.date.available2024-02-01T14:46:49Z-
dc.date.issued2022-09-12-
dc.identifier.citationVALENTE, G. F. et al. Mapping soil and pasture attributes for buffalo management through remote sensing and geostatistics in Amazon biome. Animals, [S.l.], v. 12, n. 18, p. 1-17, Sept. 2022. DOI: 10.3390/ani12182374.pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/58877-
dc.description.abstractThe mapping of pastures can serve to increase productivity and reduce deforestation, especially in Amazon Biome regions. Therefore, in this study, we aimed to explore precision agriculture technologies for assessing the spatial variations of soil pH and biomass indicators (i.e., Dry Matter, DM; and Green Matter, GM). An experiment was conducted in an area cultivated with Panicum maximum (Jacq.) cv. Mombaça in a rotational grazing system for dairy buffaloes in the eastern Amazon. Biomass and soil samples were collected in a 10 m × 10 m grid, with a total of 196 georeferenced points. The data were analyzed by semivariogram and then mapped by Kriging interpolation. In addition, a variability analysis was performed, applying both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite remote sensing data. The Kriging mapping between DM and pH at 0.30 m depth demonstrated the best correlation. The vegetative index mapping showed that the NDVI presented a better performance in pastures with DM production above 5.42 ton/ha−1. In contrast, DM and GM showed low correlations with the NDWI. The possibility of applying a variable rate within the paddocks was evidenced through geostatistical mapping of soil pH. With this study, we contribute to understanding the necessary premises for utilizing remote sensing data for pasture variable analysis.pt_BR
dc.languageen_USpt_BR
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)pt_BR
dc.rightsacesso abertopt_BR
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceAnimalspt_BR
dc.subjectSpatial variabilitypt_BR
dc.subjectBuffalo farmingpt_BR
dc.subjectPrecision livestockpt_BR
dc.titleMapping soil and pasture attributes for buffalo management through remote sensing and geostatistics in Amazon biomept_BR
dc.typeArtigopt_BR
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