Table_1_A Comparative Study for Assessing the Drought-Tolerance of Chickpea Under Varying Natural Growth Environments.pdf

This study was planned with the purpose of evaluating the drought tolerance of advanced breeding lines of chickpea in natural field conditions. Two methods were employed to impose field conditions; the first: simulating drought stress by growing chickpea genotypes at five rainfed areas, with Faisalabad as the non-stressed control environment; and the second: planting chickpea genotypes in spring to simulate a drought stress environment, with winter-sowing serving as the non-stressed environment. Additive main effects and multiplicative interaction (AMMI) and generalized linear models (GLM) models were both found to be equally effective in extracting main effects in the rainfed experiment. Results demonstrated that environment influenced seed yield, number of primary and secondary branches, number of pods, and number of seeds most predominantly; however, genotype was the main source of variation in 100 seed weight and plant height. The GGE biplot showed that Faisalabad, Kallur Kot, and Bhakkar were contributing the most in the GEI, respectively, while Bahawalpur, Bhawana, and Karor were relatively stable environments, respectively. Faisalabad was the most, and Bhakkar the least productive in terms of seed yield. The best genotypes to grow in non-stressed environments were CH39/08, CH40/09, and CH15/11, whereas CH28/07 and CH39/08 were found suitable for both conditions. CH55/09 displayed the best performance in stress conditions only. The AMMI stability and drought-tolerance indices enabled us to select genotypes with differential performance in both conditions. It is therefore concluded that the spring-sown experiment revealed a high-grade drought stress imposition on plants, and that the genotypes selected by both methods shared quite similar rankings, and also that manually computed drought-tolerance indices are also comparable for usage for better genotypic selections. This study could provide sufficient evidence for using the aforementioned as drought-tolerance evaluation methods, especially for countries and research organizations who have limited resources and funding for conducting multilocation trials, and performing sophisticated analyses on expensive software.

本研究旨在评估自然田间条件下鹰嘴豆先进育种品系的抗旱性。本研究采用两种方法设置田间试验环境：其一为在五个雨养区种植鹰嘴豆基因型，以费萨拉巴德作为非胁迫对照环境，以此模拟干旱胁迫；其二为在春季播种鹰嘴豆基因型以构建干旱胁迫环境，以冬播作为非胁迫环境。研究表明，加性主效应与乘积交互作用（Additive main effects and multiplicative interaction, AMMI）模型与广义线性模型（Generalized linear models, GLM）在雨养试验中提取主效应的效果相当。结果显示，环境对种子产量、一级与二级分枝数、荚果数及种子数的影响最为显著；而百粒重与株高的变异主要来源于基因型差异。GGE双标图分析结果表明，费萨拉巴德、卡鲁尔科特与巴卡尔分别在基因型-环境互作（Genotype-Environment Interaction, GEI）中贡献度最高，而巴哈瓦尔布尔、巴瓦纳与卡罗尔则为相对稳定的试验环境。就种子产量而言，费萨拉巴德为最高产环境，巴卡尔则为最低产环境。在非胁迫环境下表现最优的基因型为CH39/08、CH40/09与CH15/11；CH28/07与CH39/08则适用于两种试验环境。仅在胁迫环境下表现最佳的基因型为CH55/09。通过AMMI稳定性指数与抗旱性指数，可筛选出在两种环境下表现存在差异的基因型。本研究得出结论：春播试验可对植株施加高强度干旱胁迫，两种试验方法筛选出的基因型排名较为一致，且人工计算的抗旱性指数同样可用于精准的基因型筛选，具备良好的应用价值。本研究可为上述抗旱性评价方法的推广提供充分依据，尤其适用于资源与经费有限、难以开展多地点试验及使用昂贵软件进行复杂分析的国家与研究机构。