Daba Sintayehu D, Kiszonas Alecia M, McGee Rebecca J
USDA-ARS Western Wheat & Pulse Quality Laboratory, Pullman, WA 99164, USA.
USDA-ARS Grain Legume Genetics and Physiology Research Unit, Pullman, WA 99164, USA.
Plants (Basel). 2023 Jun 16;12(12):2343. doi: 10.3390/plants12122343.
A large amount of data on various traits is accumulated over the course of a breeding program and can be used to optimize various aspects of the crop improvement pipeline. We leveraged data from advanced yield trials (AYT) of three classes of peas (green, yellow, and winter peas) collected over ten years (2012-2021) to analyze and test key aspects fundamental to pea breeding. Six balanced datasets were used to test the predictive success of the BLUP and AMMI family models. Predictive assessment using cross-validation indicated that BLUP offered better predictive accuracy as compared to any AMMI family model. However, BLUP may not always identify the best genotype that performs well across environments. AMMI and GGE, two statistical tools used to exploit GE, could fill this gap and aid in understanding how genotypes perform across environments. AMMI's yield by environmental IPCA1, WAASB by yield plot, and GGE biplot were shown to be useful in identifying genotypes for specific or broad adaptability. When compared to the most favorable environment, we observed a yield reduction of 80-87% in the most unfavorable environment. The seed yield variability across environments was caused in part by weather variability. Hotter conditions in June and July as well as low precipitation in May and June affected seed yield negatively. In conclusion, the findings of this study are useful to breeders in the variety selection process and growers in pea production.
在育种计划过程中积累了大量关于各种性状的数据，这些数据可用于优化作物改良流程的各个方面。我们利用了在十年（2012 - 2021年）间收集的三类豌豆（绿豌豆、黄豌豆和冬豌豆）的高级产量试验（AYT）数据，来分析和测试豌豆育种的关键基础方面。使用六个平衡数据集来测试BLUP和AMMI家族模型的预测成功率。使用交叉验证的预测评估表明，与任何AMMI家族模型相比，BLUP具有更好的预测准确性。然而，BLUP可能并不总是能识别出在不同环境中表现良好的最佳基因型。AMMI和GGE这两种用于利用基因型与环境互作（GE）的统计工具，可以填补这一空白，并有助于理解基因型在不同环境中的表现。AMMI的产量与环境IPCA1、WAASB与产量小区以及GGE双标图被证明在识别具有特定或广泛适应性的基因型方面很有用。与最有利的环境相比，我们观察到在最不利的环境中产量降低了80 - 87%。环境间种子产量的变异性部分是由天气变异性引起的。6月和7月较热的条件以及5月和6月的低降水量对种子产量产生了负面影响。总之，本研究的结果对育种者在品种选择过程中以及豌豆种植者在生产中都很有用。
