Uso de covariáveis ambientais para predição da interação de clones de cana-de-açúcar com ambientes
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Universidade Federal de Goiás
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The evaluation of genotypes in multi-environment trials that represent the
diversity of cultivation conditions is one of the main challenges of plant breeding
programs. In perennial or semi-perennial species, such as sugarcane, this challenge is
amplified by prolonged experimentation over several harvest seasons. The use of
environmental covariables has proven to be a promising approach to increase the
efficiency of genotype selection. This study aimed to evaluate different models for
predicting the performance of sugarcane clones in distinct environments using
environmental covariables. Phenotypic data for yield (tons of stalks per hectare) were
obtained from 53 trials conducted by the Sugarcane Breeding Program
(PMGCA/UFG/Ridesa) between 2012 and 2022. Envirotyping data for 32 environmental
covariates were collected from NASA's POWER platform. Six Bayesian ridge regression
models were developed, integrating different combinations of genotypic effects (G), trial
effects (E), environmental covariates (V), and their interactions. To evaluate the models,
two cross-validation schemes were used, 5-fold and leave-one-out, representing the
following scenarios, respectively: (1) the ability of the models to predict genotype
performance in trials where they were not evaluated; (2) the ability of models with
covariates to predict genotype performance in untested environments. Predictive ability
was assessed globally and by individual trial. In scenario 1, global predictive abilities
ranged from 0.93 to 0.13, while predictive ability by trial ranged from 0.50 to 0.16. In
scenario 2, global predictive abilities ranged from 0.47 to 0.01, and by trial from 0.58 to
0.21. Models without environmental covariates, G + E and G + E + G × E, proved suitable
for filling information gaps in breeding programs. The G + V model, although it did not
consider genotype × environmental covariables interactions, stood out as the most
promising option for cultivar recommendations in locations without prior data. The
results highlight differences in the performance of the tested models and the importance
of considering environmental variability in the interpretation of predictions. Identifying
trials with low predictive ability is essential to guide data collection strategies and
improve models capable of effectively handling environmental complexity.
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REIS, L. B. S. Uso de covariáveis ambientais para predição da interação de clones de cana-de-açúcar com ambientes. 2025. 63 f. Dissertação (Mestrado em Genética e Melhoramento de Plantas) – Escola de Agronomia, Universidade Federal de Goiás, Goiânia, 2025.