Department of Microbial, Cellular and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia.
Bio and Emerging Technology Institute, Addis Ababa, Ethiopia.
PLoS One. 2023 Feb 1;18(2):e0277499. doi: 10.1371/journal.pone.0277499. eCollection 2023.
Spatial variation and genotype by environment (GxE) interaction are common in varietal selection field trials and pose a significant challenge for plant breeders when comparing the genetic potential of different varieties. Efficient statistical methods must be employed for the evaluation of finger millet breeding trials to accurately select superior varieties that contribute to agricultural productivity. The objective of this study was to improve selection strategies in finger millet breeding in Ethiopia through modeling of spatial field trends and the GxE interaction. A dataset of seven multi-environment trials (MET) conducted in randomized complete block design (RCBD) with two replications laid out in rectangle (row x column) arrays of plots was used in this study. The results revealed that, under the linear mixed model, the spatial and factor analytic (FA) models were efficient methods of data analysis for this study, and this was demonstrated with evidence of heritability measure. We found two clusters of correlated environments that helped to select superior and stable varieties through ranking average Best Linear Unbiased Predictors (BLUPs) within clusters. The first cluster was chosen because it contained a greater number of environments with high heritability. Based on this cluster, Bako-09, 203439, 203325, and 203347 were the top four varieties with relatively high yield performance and stability across correlated environments. Hence, scaling up the use of this efficient analysis method will improve the selection of superior finger millet varieties.
空间变异和基因型与环境互作（GxE）在品种选择田间试验中很常见，当比较不同品种的遗传潜力时，这给植物育种者带来了重大挑战。为了准确选择对农业生产力有贡献的优良品种，必须采用有效的统计方法来评估珍珠粟育种试验。本研究的目的是通过模拟空间田间趋势和 GxE 互作，改进埃塞俄比亚珍珠粟的选育策略。本研究使用了在随机完全区组设计（RCBD）下进行的七个多环境试验（MET）的数据集，采用矩形（行 x 列）排列的小区进行了两次重复。结果表明，在线性混合模型下，空间和因子分析（FA）模型是本研究数据分析的有效方法，这一点从遗传力度量的证据中得到了证明。我们发现了两个相关环境聚类，通过在聚类内对最佳线性无偏预测值（BLUP）进行排名，可以帮助选择优良和稳定的品种。第一个聚类被选中，是因为它包含了更多具有高遗传力的环境。基于这个聚类，Bako-09、203439、203325 和 203347 是前四个产量表现和相关环境稳定性相对较高的品种。因此，扩大这种高效分析方法的使用将有助于选择优良的珍珠粟品种。
