Effect of land uses and land cover on soil attributes in the southern Brazilian Amazon

• Autor(a) principal: Silva, Adriana de Avila e
• Data de Publicação: 2022
• Outros Autores: Silva Junior, Carlos Antonio da, Boechat, Cácio Luiz, Della-Silva, João Lucas, Teodoro, Paulo Eduardo, Rossi, Fernando Saragosa [UNESP], Teodoro, Larissa Pereira Ribeiro, Pelissari, Tatiane Deoti [UNESP], Baio, Fábio Henrique Rojo, Lima, Mendelson
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/s10661-022-10342-y; http://hdl.handle.net/11449/240707
• topic: Agricultural soils; Environmental quality; Fertility; Sustainability

The growth of the world population has led to the expansion of agricultural areas to produce food that meets world demand, making it necessary to increase productivity and maintain environmental sustainability in these areas. Seeking sustainable food production, the agricultural use of soil must be assessed in view of optimal use or land as natural resource, as well as minimize the effects of global warming related to land use and land cover (LULC). We hypothesize that different LULC affects Amazonian soil attributes. In this study, the effect of different LULC in the southern Brazilian Amazon, namely, native forest, pasture, and rice and soybean crops, on the spatial variability of soil fertility and texture was assessed, seeking to obtain information that will guide farmers in the near future to better exploit their areas and contribute to a more sustainable agriculture. Descriptive statistical analysis was performed for the pH, H + Al, Al, Ca, Mg, P, K, Cu, Fe, Mn, Zn, V, m, organic matter, clay, silt, and sand values from soil samples under different LULC. To verify the data normality, the Shapiro–Wilk test at 5% significance was performed. Outlier analysis using boxplot graphics, principal component analysis (PCA), and cluster analysis was performed. Data were submitted to geostatistical analysis to verify the spatial dependence degree of the variables through semivariograms for interpolated kriging maps. Except for silt, all variables were well represented in the factor map. PCA revealed that the data variability can be explained mainly by pH, V, Ca, K, and Zn values, which are inversely proportional to m, P, and sand. Through geostatistical analysis, spatial dependence ranging from moderate to strong was observed, generating reliability in the prediction of most attributes in pasture, rice, and soybean areas. Yet, a spatial dependence ranging from moderate to strong was found, generating reliability in the prediction of most attributes in pasture, rice, and soybean areas. Our findings reveal a lower fertility and higher acidity in forest areas, whereas crop areas presented the opposite result.