Stability of the hypocotyl length of soybean cultivars using neural networks and traditional methods

Abstract

ABSTRACT: The length of the hypocotyl has been highlighted as a potential descriptor of the soybean crop. However, there is no information available in the published literature about its behavior over several planting times. The present study aimed to identify soybean cultivars with stability and predictability of hypocotyl length behavior through neural networks and traditional adaptability and stability methodologies. We analyzed 16 soybean cultivars in 6 planting seasons under greenhouse conditions. In each season, a randomized block design with 4 replications was adopted. The experimental unit was composed of 3 plants. The plot mean was used in the analysis. Hypocotyl length data were analyzed by analysis of variance and Tukey’s test. Then analyses were carried out using the Traditional Method, Plaisted and Peterson, Wricke, Eberhart and Russell, and Artificial Neural Networks. A significant effect (p<0.01 by the F test) was identified for Cultivars versus Planting Season and Planting Seasons and Cultivars. Cultivars BRS810C, BRSMG760SRR, TMG1175RR, and BMX Tornado RR showed lower averages, high stability, and general adaptability regarding soybean hypocotyl length whereas the cultivar BG4272 presented higher mean, high stability, and general adaptability. Identification of soybean cultivars of predictable and stable behavior as to hypocotyl length contributes to Soybean Improvement as it further our knowledge on the potential descriptor and the possibility of increasing the number of descriptors.

Keywords

• Glycine max, interação genótipos x ambientes, Eberhart e Russell, inteligência artificial
• comprimento de hipocótilo