ANÁLISE MULTIVARIADA NA DETERMINAÇÃO DO RISCO DE EROSÃO EM SOLOS SOB IRRIGAÇÃO.
DOI:
https://doi.org/10.15809/irriga.2010v15n1p23Abstract
O objetivo deste trabalho foi abordar a utilização de técnicas de análise multivariada na discriminação do risco de erosão dos solos, sob pivô central, em diferentes classes de solos, relevo, uso e manejo. A área de estudo de 33 ha localizada na região de Carmo de Rio Claro, MG, sob pivô central, vem sendo cultivada com feijão, milho e café por um período de 7 anos. As amostragens foram feitas a intervalos regulares de 10 m na profundidade de 0,00-0,20m em uma transeção de 1050 m, perfazendo 59 amostras. Os parâmetros risco de erosão (A), potencial natural de erosão (PN) e expectativa de erosão (EE) foram avaliados por análise multivariada. A aplicação da análise multivariada mostrou uma boa associação entre os agrupamentos formados e os diferentes tipos de solos e, juntamente com os componentes principais, permitiram identificar dois grupos de maiores e menores perdas de solo, evidenciando que as áreas de maiores expectativas de perdas de solo estão correlacionadas com a classe de solo, o relevo e manejo do solo. O potencial natural da erosão do solo foi um fator importante para determinar os diferentes grupos. A análise multivariada mostrou que 98 % das variáveis foram classificadas dentro dos grupos e que estes pelo potencial erosivo requerem programas de manejo e conservação do solo.
UNITERMOS: Análise multivariada, componentes principais, solos, Equação Universal de Perdas de Solo.
BUENO, C. R. P.; ARRAES, C. L.; PEREIRA. G.T.; CORÁ. J.E.; CAMPOS, S. MULTIVARIANCE ANALYSIS ON EROSION RISK DETERMINATION IN SOIL UNDER IRRIGATION.
2 ABSTRACT
The objective of this work was to verify the application of cluster analysis to evaluate soil erosion risk for different soil classes, soil slopes and soil managements. The study was conducted in a 33 ha section of a large field located in Carmo do Rio Claro County, MG, Brazil. The field had been managed in a corn/bean rotation under conventional tillage and under coffee plantation for seven years, both under sprinkle irrigation. Soil samples were obtained at every 10 m at 0.20 m depth along a transect of 1050 m. Soil erosion risk (A), natural potential erosion (PN), and erosion expectation (EE) were determined and submitted to a cluster and principal component analysis. The application of clustering analysis showed high correlation between the clusters and soil types. With clustering analysis plus principal components analysis, it was possible to identify groups of high and low soil erosion expectation, showing that the areas with higher soil erosion expectation are correlated to the soil class, soil slope and soil management. Among the studied variables, the natural potential erosion (PN) showed to be the most important factor to identify different soil erosion groups. The cluster analysis showed that 98 % of the variables were classified within each group, and that they should be managed differently due to the soil erosive potential of each group,.
KEYWORDS: Cluster analysis, principal components analysis, soils, Universal Soil Loss Equation (USLE)
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