Table_3_Multi-trait selection in multi-environments for performance and stability in cassava genotypes.docx

Genotype-environment interaction (GEI) presents challenges when aiming to select optimal cassava genotypes, often due to biased genetic estimates. Various strategies have been proposed to address the need for simultaneous improvements in multiple traits, while accounting for performance and yield stability. Among these methods are mean performance and stability (MPS) and the multi-trait mean performance and stability index (MTMPS), both utilizing linear mixed models. This study’s objective was to assess genetic variation and GEI effects on fresh root yield (FRY), along with three primary and three secondary traits. A comprehensive evaluation of 22 genotypes was conducted using a randomized complete block design with three replicates across 47 distinct environments (year x location) in Brazil. The broad-sense heritability (H2) averaged 0.37 for primary traits and 0.44 for secondary traits, with plot-based heritability (hmɡ2) consistently exceeding 0.90 for all traits. The high extent of GEI variance (σɡxe2) demonstrates the GEI effect on the expression of these traits. The dominant analytic factor (FA3) accounted for over 85% of the total variance, and the communality (ɧ) surpassed 87% for all traits. These values collectively suggest a substantial capacity for genetic variance explanation. In Cluster 1, composed of remarkably productive and stable genotypes for primary traits, genotypes BRS Novo Horizonte and BR11-34-69 emerged as prime candidates for FRY enhancement, while BRS Novo Horizonte and BR12-107-002 were indicated for optimizing dry matter content. Moreover, MTMPS, employing a selection intensity of 30%, identified seven genotypes distinguished by heightened stability. This selection encompassed innovative genotypes chosen based on regression variance index (Sdi2, R2, and RMSE) considerations for multiple traits. In essence, incorporating methodologies that account for stability and productive performance can significantly bolster the credibility of recommendations for novel cassava cultivars.

基因型-环境交互作用（GEI）在选择最佳木薯基因型时构成了挑战，这通常是由于遗传估计存在偏差所致。为解决同时改善多个性状的需求，同时考虑到性能和产量的稳定性，已经提出了各种策略。其中，包括平均性能和稳定性（MPS）以及多性状平均性能和稳定性指数（MTMPS），两者均采用线性混合模型。本研究旨在评估遗传变异和GEI效应对新鲜根产量（FRY）及三个主要性状和三个次要性状的影响。通过对22个基因型进行了全面评估，采用随机完全区组设计，在巴西47个不同的环境（年份×地点）中进行了三次重复。广义遗传力（H2）在主要性状中平均为0.37，在次要性状中为0.44，基于地块的遗传力（hmɡ2）对于所有性状均持续超过0.90。GEI方差（σɡxe2）的高程度显示了GEI对这些性状表达的影响。主导分析因子（FA3）解释了超过85%的总方差，所有性状的共变性（ɧ）均超过87%。这些值共同表明了遗传变异解释的巨大潜力。在第一集群中，由对主要性状表现出卓越的生产力和稳定性的基因型组成，基因型BRS Novo Horizonte和BR11-34-69被确定为提高FRY的首选候选者，而BRS Novo Horizonte和BR12-107-002则被指出可以优化干物质含量。此外，采用30%选择强度的MTMPS识别了七个稳定性增强的基因型。此选择包括基于回归方差指数（Sdi2、R2和RMSE）考虑的多个性状的创新基因型。本质上，纳入考虑稳定性和生产性能的方法可以显著增强对新型木薯品种推荐的可信度。