Spatial and multivariate analysis of soybean productivity and soil physical-chemical attributes

• Autor(a) principal: Buss,Ricardo N.
• Data de Publicação: 2019
• Outros Autores: Silva,Raimunda A., Siqueira,Glécio M., Leiva,Jairo O. R., Oliveira,Osmann C. C., França,Victor L.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019000600446
• topic: management zones; geostatistics; principal components

ABSTRACT The objective of this study was to evaluate the spatial variability of soybean yield, carbon stock, and soil physical attributes using multivariate and geostatistical techniques. The attributes were determined in Oxisols samples with clayey and cohesive textures collected from the municipality of Mata Roma, Maranhão state, Brazil. In the study area, 70 sampling points were demarcated, and soybean yield and soil attributes were evaluated at soil depths of 0-0.20 and 0.20-0.40 m. Data were analysed using multivariate analyses (principal component analysis, PCA) and geostatistical tools. The mean soybean yield was 3,370 kg ha-1. The semivariogram of productivity, organic carbon (OC), and carbon stock (Cst) at the 0-0.20 m layer were adjusted to the spherical model. The PCA explained 73.21% of the variance and covariance structure between productivity and soil attributes at the 0-0.20 m layer [(PCA 1 (26.89%), PCA 2 (24.10%), and PCA 3 (22.22%)] and 68.64% at the 0.20-0.40 m layer [PCA 1 (31.95%), PCA 2 (22.83%), and PCA 3 (13.85%)]. The spatial variability maps of the PCA eigenvalue scores showed that it is possible to determine management zones using PCA 1 in the two studied depths; however, with different management strategies for each of the layers in this study.