Multivariate analysis using a discriminant method for evaluating the techniques of weed management in soybean crop

Abstract

Background: The analysis of information generated from experiments involving different treatments, can be done by multivariate statistical analysis techniques, such as discriminant analysis, to analyze data obtained from predefined groups. Objective: Verify, through discriminant analysis, the differences among cover crop (Avena strigosa, Chenopodium quinoa, Cichorium intybus, and fallow land) treatments with respect to main crop soybean yield. Methods: For weed control, these cover crops were subjected to different management techniques, namely mowing, the application of glyphosate or the application of paraquat. The experimental design consisted of completely randomized blocks in a 4 × 3 × 2 factorial scheme, with four replications, consisting of the following factors: Factor A: (treatment) cover crops of A. strigosa, C. quinoa, C. intybus, and fallow land; Factor B: (management) plots were subdivided and treated with the application of paraquat or glyphosate, or the mowing of cover plants; Factor C: the plots were sub-subdivided and managed by one or two applications of a post-emergence herbicide. In order to evaluate the percentage of correct classifications of the different management techniques and treatments, a data matrix was elaborated for evaluation of variables relating to the soybean crop and the data were standardized by log - log 10 - log (n; 10). Multivariate analysis was performed using Fisher’s linear discriminant method. Results: Discriminant analysis selected four variables with discriminatory power relating to the A. strigosa, C. quinoa, C. intybus and fallow, which contributed to 100% of the explained variance. Conclusions: Treatment with oats used as a cover crop provided higher soybean crop yield, whereas in terms of management, weed control using glyphosate provided the best results with all cover crops.