Artificial neural networks based on segmented model for adaptability and stability evaluation of soybean genotypes

Abstract

Unlike models based on simple linear regressions, segmented models can better assess the adaptability and stability of genotypes, demonstrating a nonlinear response pattern over environmental variation. However, these methods can be under statistical limitations, such as the Type Error II increase and biased estimates. Therefore, this work aimed to transpose the concepts of adaptability and stability from the statistical analysis of a segmented model to the discriminatory potential of an artificial neural network (ANN) and use it to classify soybean (Glycine max (L.) Merr.) genotypes. An ANN training was carried out with the grain yield of 7,200 soybean genotypes simulated in 15 different environments. The ANN topology chosen was the one that had less than 1% of error in the testing phase with 1,800 simulated genotypes. A total of 9,000 simulated soybean genotypes were previously arranged in 18 different classes, which represented the combination of nine classes of adaptability by the Verma and collaborators (VCM) method and two classes of stability (invariability concept) by the Finlay & Wilkinson (FW) method. Finally, the grain production of ten real soybean genotypes was inputted into the ANN-trained model, and the classification regarding adaptability and stability was obtained. There was 90% agreement between the ANN and VCM analyses regarding the adaptability classification and 20% regarding stability. With the methods presented in this work, it was demonstrated that the potential of using ANNs to assess the adaptability of genotypes is strong. In addition, since stability was introduced in the ANN as a different concept from that used to classify the genotypes by the statistical method, such classification needs to be reviewed and further improved