Effect of land uses and land cover on soil attributes in the southern Brazilian Amazon.
Autor: | de Avila E Silva A; State University of Mato Grosso (UNEMAT), Alta Floresta, Mato Grosso, Brazil., da Silva Junior CA; State University of Mato Grosso (UNEMAT), Sinop, Mato Grosso, Brazil. carlosjr@unemat.br., Boechat CL; Federal University of Piauí (UFPI), Bom Jesus, Piauí, Brazil., Della-Silva JL; State University of Mato Grosso (UNEMAT), PPG-Bionorte, Sinop, Mato Grosso, Brazil., Teodoro PE; Federal University of Mato Grosso Do Sul (UFMS), Chapadão Do Sul, Mato Grosso Do Sul, Brazil., Rossi FS; State University of São Paulo (UNESP), Jaboticabal, São Paulo, Brazil., Teodoro LPR; Federal University of Mato Grosso Do Sul (UFMS), Chapadão Do Sul, Mato Grosso Do Sul, Brazil., Pelissari TD; State University of São Paulo (UNESP), Ilha Solteira, São Paulo, Brazil., Baio FHR; Federal University of Mato Grosso Do Sul (UFMS), Chapadão Do Sul, Mato Grosso Do Sul, Brazil., Lima M; State University of Mato Grosso (UNEMAT), Alta Floresta, Mato Grosso, Brazil. |
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Jazyk: | angličtina |
Zdroj: | Environmental monitoring and assessment [Environ Monit Assess] 2022 Aug 25; Vol. 194 (10), pp. 709. Date of Electronic Publication: 2022 Aug 25. |
DOI: | 10.1007/s10661-022-10342-y |
Abstrakt: | 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. (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) |
Databáze: | MEDLINE |
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