Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

The aim of this study was to compare the Multiple Linear Regression and Artificial Neural Network models in prediction of grain yield of ten landrace varieties of lima bean and evaluate adaptability and stability through the Lin and Binns method for identification of the best performing variety. Tri...

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Bibliographic Details
Published in:Crop breeding and applied biotechnology 2020, Vol.20 (1)
Main Authors: Sousa, Antônia Maria de Cássia Batista de, Silva, Verônica Brito da, Lopes, Ângela Célis de Almeida, Gomes, Regina Lucia Ferreira, Carvalho, Leonardo Castelo Branco
Format: Article
Language:English
Subjects:
Adaptability
AGRONOMY
Artificial neural networks
Beans
BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Crop yield
Genotypes
Municipalities
Neural networks
Phaseolus lunatus
Predictions
Regression analysis
Stability analysis
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Summary:The aim of this study was to compare the Multiple Linear Regression and Artificial Neural Network models in prediction of grain yield of ten landrace varieties of lima bean and evaluate adaptability and stability through the Lin and Binns method for identification of the best performing variety. Trials were conducted in the municipalities of Teresina, PI, and São Domingos do Maranhão, MA, through measurement of 12 traits, except for grain yield in São Domingos do Maranhão. The parameters of Pearson and Spearman correlation, root mean square error, mean absolute error, and coefficient of determination were used to compare the models. The Artificial Neural Network proved to be more adequate for prediction of grain yield. Adaptability and stability analyses indicated that the environments are discriminant for selection of promising genotypes, and that the landrace variety Mulatinha can be recommended for planting in the municipalities.
ISSN:1518-7853
1984-7033
1984-7033
DOI:10.1590/1984-70332020v20n1a15