Spatial variability of soil fertility under agroforestry system and native forest in eastern Amazonia, Brazil
DOI:
https://doi.org/10.14393/BJ-v39n0a2023-62830Palavras-chave:
Geostatistics, Income optimization, Kriging maps, Precision agriculture and silviculture, Soil chemical attributes.Resumo
The usage of spatial tools might be helpful in the optimization of decision-making regarding soil management, with technologies that assist in the interpretation of information related to soil fertility. Therefore, the present study evaluated the spatial variability of chemical attributes of the soil under an agroforestry system compared to a native forest in the municipality of Tomé-açu, Eastern Amazon, Brazil. Soil samples were performed at 36 points arranged in a 55 x 55 m grid. The soils were prepared and submitted to analysis in order to determine pH in H2O, exchangeable calcium, magnesium, potassium and aluminium, available phosphorus, potential acidity, organic matter, bases saturation and aluminium saturation. For each soil attribute, the spherical, gaussian and exponential models were adjusted. After the semivariograms fitting, data interpolation for assessment of spatial variability of the variables was performed through ordinary kriging. The spherical and gaussian models were the most efficient models in estimation of soil attributes spatial variability, in most cases. Most of variables presented a regular spatial variability in their respective kriging maps, with some exceptions. In general, the kriging maps can be used, and we can take them as logistical maps for management and intervention practices in order to improve the soil fertility in the study areas. The results principal components indicate the need for integrated management of soil chemical attributes, with localized application of acidity correctors, fertilizers and other types of incomes, using the spatial variability of these fertility variables.
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Copyright (c) 2023 Cassio Rafael Costa dos Santos, Augusto Takayuki Matsunaga, Luiz Rodolfo Reis Costa, Mario Lima dos Santos, Alberto Bentes Brasil Neto, Richard Pinheiro Rodrigues, Maria de Nazaré Martins Maciel, Vânia Silva de Melo
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
