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Development of a comprehensive evaluation system and models to determine soybean seed vigor
Industrial Crops and Products ( IF 5.6 ) Pub Date : 2024-12-19 , DOI: 10.1016/j.indcrop.2024.120329 Wentao Ding, Jianyu Lin, Chen Li, Zhen Zhu, Chao Wu, Jiqiu Cao, Dandan Liu, Yu Zhang, Qian Yang, Aishuang Xing, Shuqi Yao, Yanhui Sun, Na Guo, Han Xing, Jinming Zhao
Industrial Crops and Products ( IF 5.6 ) Pub Date : 2024-12-19 , DOI: 10.1016/j.indcrop.2024.120329 Wentao Ding, Jianyu Lin, Chen Li, Zhen Zhu, Chao Wu, Jiqiu Cao, Dandan Liu, Yu Zhang, Qian Yang, Aishuang Xing, Shuqi Yao, Yanhui Sun, Na Guo, Han Xing, Jinming Zhao
High-vigor soybean seeds are critical for efficient production owing to their favorable growth properties and high yield potential. The evaluation and identification of high-vigor germplasms are essential for increasing soybean production capacity. Currently, there is no universally accepted evaluation system to test for soybean seed vigor. In this study, 11 seed vigor-related traits were measured across 126 soybean landraces via an artificial accelerated aging technique. The ratios of these 11 traits, which were calculated before and after artificial accelerated aging, were used as vigor indicators in principal component analysis (PCA), ultimately yielding two principal component factors. These factors were then combined via membership function standardization to calculate a comprehensive seed vigor evaluation value (V value), thereby establishing an evaluation system. Cluster analysis based on the V value was used to classify seed vigor into five levels and identify seven high-vigor germplasms: ZDD12322, ZDD06438, ZDD11951, ZDD08251, ZDD12436, ZDD02315, and ZDD15624. Through stepwise regression analysis, the optimal seed vigor predictive model was defined as V = −0.026 + 0.625 × RSL + 0.485 × RGI. This model revealed that the relative seedling length (RSL) and relative germination index (RGI) had significant positive effects on seed vigor. This study provides a valuable framework for seed quality control and selection, facilitating presowing vigor assessments to increase soybean planting efficiency and yield.
中文翻译:
开发确定大豆种子活力的综合评估系统和模型
高活力大豆种子具有良好的生长特性和高产潜力,因此对于高效生产至关重要。高活力种质的评价和鉴定对于提高大豆生产能力至关重要。目前,没有普遍接受的评价系统来测试大豆种子的活力。在这项研究中,通过人工加速衰老技术在 11 个大豆地方品种中测量了 126 个与种子活力相关的性状。在人工加速衰老之前和之后计算的这 11 个性状的比率被用作主成分分析 (PCA) 的活力指标,最终产生两个主成分因子。然后,通过隶属函数标准化将这些因素进行组合,计算出综合种子活力评价值 (V 值),从而建立评价体系。采用基于 V 值的聚类分析将种子活力分为 5 个等级,鉴定出 7 种高活力种质:ZDD12322、ZDD06438、ZDD11951、ZDD08251、ZDD12436、ZDD02315 和 ZDD15624。通过逐步回归分析,最佳种子活力预测模型定义为 V = −0.026 + 0.625 × RSL + 0.485 × RGI。该模型揭示了相对苗长 (RSL) 和相对发芽指数 (RGI) 对种子活力有显著的正向影响。本研究为种子质量控制和选择提供了一个有价值的框架,有助于播种前活力评估,以提高大豆种植效率和产量。
更新日期:2024-12-19
中文翻译:
开发确定大豆种子活力的综合评估系统和模型
高活力大豆种子具有良好的生长特性和高产潜力,因此对于高效生产至关重要。高活力种质的评价和鉴定对于提高大豆生产能力至关重要。目前,没有普遍接受的评价系统来测试大豆种子的活力。在这项研究中,通过人工加速衰老技术在 11 个大豆地方品种中测量了 126 个与种子活力相关的性状。在人工加速衰老之前和之后计算的这 11 个性状的比率被用作主成分分析 (PCA) 的活力指标,最终产生两个主成分因子。然后,通过隶属函数标准化将这些因素进行组合,计算出综合种子活力评价值 (V 值),从而建立评价体系。采用基于 V 值的聚类分析将种子活力分为 5 个等级,鉴定出 7 种高活力种质:ZDD12322、ZDD06438、ZDD11951、ZDD08251、ZDD12436、ZDD02315 和 ZDD15624。通过逐步回归分析,最佳种子活力预测模型定义为 V = −0.026 + 0.625 × RSL + 0.485 × RGI。该模型揭示了相对苗长 (RSL) 和相对发芽指数 (RGI) 对种子活力有显著的正向影响。本研究为种子质量控制和选择提供了一个有价值的框架,有助于播种前活力评估,以提高大豆种植效率和产量。