Spatial variability of soil fertility under agroforestry system and native forest in eastern Amazonia, Brazil

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

doi:10.14393/BJ-v39n0a2023-62830

Authors

Cassio Rafael Costa dos Santos Universidade Federal Rural da Amazônia https://orcid.org/0000-0001-9356-0186
Augusto Takayuki Matsunaga Universidade Federal Rural da Amazônia
Luiz Rodolfo Reis Costa Universidade Federal Rural da Amazônia
Mario Lima dos Santos Universidade de Brasília https://orcid.org/0000-0003-1679-9796
Alberto Bentes Brasil Neto Instituto Federal de Educação, Ciência e Tecnologia do Pará https://orcid.org/0000-0003-2815-6085
Richard Pinheiro Rodrigues Universidade Federal Rural da Amazônia
Maria de Nazaré Martins Maciel Universidade Federal Rural da Amazônia
Vânia Silva de Melo Universidade Federal Rural da Amazônia

DOI:

https://doi.org/10.14393/BJ-v39n0a2023-62830

Keywords:

Geostatistics, Income optimization, Kriging maps, Precision agriculture and silviculture, Soil chemical attributes.

Abstract

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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