Table_2_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）在选择最优木薯基因型时构成了挑战，这往往源于对遗传估计的偏差。为了在多个性状的同时实现改进，并考虑到性能和产量的稳定性，已提出了各种策略。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），这两种方法均采用线性混合模型。本研究旨在评估遗传变异和GEI效应对鲜根产量（FRY）以及三个主要性状和三个次要性状的影响。通过在巴西47个不同的环境（年份×地点）中采用随机完全区组设计和三个重复，对22个基因型进行了全面评估。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，而基于地块的遗传力（hmɡ2）对于所有性状均持续超过0.90。GEI方差的高程度（σɡxe2）展示了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，共性（ɧ）对所有性状均超过87%。这些值共同表明，遗传变异的解释能力相当可观。在由表现出显著生产力和稳定性的主要性状基因型组成的Cluster 1中，基因型BRS Novo Horizonte和BR11-34-69成为提高FRY的首选候选者，而BRS Novo Horizonte和BR12-107-002则被指出可优化干物质含量。此外，使用30%的选择强度，MTMPS识别出七个具有高度稳定性的基因型。这种选择包括基于回归方差指数（Sdi2，R2和RMSE）考虑的多性状创新基因型。总的来说，将考虑稳定性和生产性能的方法融入其中，可以显著增强对新型木薯品种推荐的可信度。