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• W4295548236 endingPage "2374" @default.
• W4295548236 startingPage "2374" @default.
• W4295548236 abstract "The 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." @default.
• W4295548236 created "2022-09-14" @default.
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• W4295548236 date "2022-09-12" @default.
• W4295548236 modified "2023-09-30" @default.
• W4295548236 title "Mapping Soil and Pasture Attributes for Buffalo Management through Remote Sensing and Geostatistics in Amazon Biome" @default.
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• W4295548236 doi "https://doi.org/10.3390/ani12182374" @default.
• W4295548236 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36139234" @default.
• W4295548236 hasPublicationYear "2022" @default.
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