Neural network and canonical interrelationships for the physiological aspects of soybean seedlings: effects of seed treatment
Abstract
The objective of this work was to analyze the performance of soybean seedlings in different seed treatments, with multivariate profiles and canonical interrelationships. The experiment was conducted in the county of Mineiros-GO. The soil was classified as a Quartzarenic Neosol. The experimental design used was a randomized block in a 5x4 factorial, corresponding to the seed treatments (WAT, CRU, FIP, FOR and STA) in 4 soybean cultivars (Bônus, Ultra, Extra and BKS7830), in 4 repetitions. Before sowing, pre-plant burndown was performed. The fertilizer used was 450 kg ha -1 of fertilizer 05-25-15 applied in the furrow and in a single dose next to the seeding. During the conduct of the experiment, the control of pests, diseases and weeds were carried out as they became necessary, respecting good practices and integrated management. The data obtained were submitted to the assumptions of the statistical model, verifying the normality and homogeneity of the residual variances, as well as the additivity of the model. Uni and multivariate tools were applied. The analyzes were performed on Rbio from R and Genes interfaces. The interaction of soybean cultivars and types of seed treatment led to variations in all analyzes evaluated in soybean seedlings. The best performances were found among the BRS 7380RR cultivars that expressed the highest shoot fresh mass when subjected to seed treatment with Cruiser, whereas the greatest root length was expressed in cultivar Ultra in the Fortenza seed treatment.
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