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Antônia Maria de Cássia Batista de Sousa, Verônica Brito da Silva, Ângela Célis de Almeida Lopes, Regina Lucia Ferreira Gomes and Leonardo Castelo Branco Carvalho

Abstract: 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.

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