Publicação on-line; 26 ref.; 4 tables; 9 illus.; Summary (En)

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.

Keywords:
soil cover
Avena strigose
Chenopodium quinoa
Cichorium intybus
allelopathy
analysis discriminating