Banca de DEFESA: ADRIANA DE AVILA E SILVA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : ADRIANA DE AVILA E SILVA
DATA : 27/01/2022
HORA: 08:00
LOCAL: Google Meet (videoconferência) - Causa: Pandemia
TÍTULO:

Characteristics and chemical attributes of the Amazonian soil under different managements

PALAVRAS-CHAVES:

Sustainability, fertility, agricultural soils, environmental quality, ecosystems.

PÁGINAS: 65
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Geociências
RESUMO:

The growth of world population has led to the expansion of agricultural areas to produce food that meet world demand, making it necessary to increase productivity and maintain sustainability in these areas. In this work was evaluated the effect of different Land Use and Land Cover (LULC) types, namely native forest, pasture, rice and soybean crops on the spatial variability of soil fertility and texture. Descriptive statistical analysis take base on pH, H+Al, Al, Ca, Mg, P, K, Cu, Fe, Mn, Zn, V, m, organic matter, clay, silt and sand values from soil samples about the different LULC. To verify the normality of the data, the Shapiro-Wilk test at 5% significance was performed, outlier analysis using boxplot graphics, principal component analysis and cluster analysis, using R software. In addition, the data were submitted to geostatistical analysis to verify the degree of spatial dependence of the variables through semivariograms, for interpolated kriging maps generated in the GS+ software. It was verified that the forest area has less fertility and greater acidity, whereas crop areas presented the opposite result. Except for silt, all variables were well represented in the factor map, in relation to PCA values the variability can be explained mainly by pH, V, Ca, K and Zn values 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. Through geostatistical analysis, spatial dependence ranging from moderate to strong was found, generating reliability in the prediction of most attributes in pasture, rice and soybean areas.

MEMBROS DA BANCA:
Presidente - 265126001 - CARLOS ANTONIO DA SILVA JUNIOR
Interno - 131948001 - MENDELSON GUERREIRO DE LIMA
Externo à Instituição - GUILHERME FERNANDO CAPRISTO SILVA - UFMT