Ma Fumin, Shen Yinjuan, Su De, Adnan Muhammad, Wang Maoyao, Jiang Fuhong, Hu Qian, Chen Xiaoru, He Guanyong, Yao Wei, Zhang Muqing, Huang Jiangfeng
State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi Key Laboratory of Sugarcane Biology, Ministry Co-sponsored Collaborative Innovation Center of Canesugar Industry, Academy of Sugarcane and Sugar Industry, College of Agriculture, Guangxi University, Nanning, Guangxi, China.
Guangxi China-ASEAN Youth Industrial Park, Chongzuo Agricultural Hi-tech Industry Demo Zone, Chongzuo, Guangxi, China.
Front Plant Sci. 2023 Jul 20;14:1224268. doi: 10.3389/fpls.2023.1224268. eCollection 2023.
Sugarcane is a major industrial crop around the world. Lodging due to weak mechanical strength is one of the main problems leading to huge yield losses in sugarcane. However, due to the lack of high efficiency phenotyping methods for stalk mechanical strength characterization, genetic approaches for lodging-resistant improvement are severely restricted. This study attempted to apply near-infrared spectroscopy high-throughput assays for the first time to estimate the crushing strength of sugarcane stalks. A total of 335 sugarcane samples with huge variation in stalk crushing strength were collected for online NIRS modeling. A comprehensive analysis demonstrated that the calibration and validation sets were comparable. By applying a modified partial least squares method, we obtained high-performance equations that had large coefficients of determination ( > 0.80) and high ratio performance deviations (RPD > 2.4). Particularly, when the calibration and external validation sets combined for an integrative modeling, we obtained the final equation with a coefficient of determination () and ratio performance deviation (RPD) above 0.9 and 3.0, respectively, demonstrating excellent prediction capacity. Additionally, the obtained model was applied for characterization of stalk crushing strength in large-scale sugarcane germplasm. In a three-year study, the genetic characteristics of stalk crushing strength were found to remain stable, and the optimal sugarcane genotypes were screened out consistently. In conclusion, this study offers a feasible option for a high-throughput analysis of sugarcane mechanical strength, which can be used for the breeding of lodging resistant sugarcane and beyond.
甘蔗是全球主要的经济作物。由于机械强度较弱导致的倒伏是造成甘蔗产量大幅损失的主要问题之一。然而，由于缺乏用于茎秆机械强度表征的高效表型分析方法，抗倒伏改良的遗传方法受到严重限制。本研究首次尝试应用近红外光谱高通量分析来估算甘蔗茎秆的抗压强度。共收集了335个茎秆抗压强度差异巨大的甘蔗样本用于在线近红外光谱建模。综合分析表明，校准集和验证集具有可比性。通过应用改进的偏最小二乘法，我们获得了高性能方程，其决定系数较大（>0.80）且性能比偏差较高（RPD>2.4）。特别是，当校准集和外部验证集合并进行综合建模时，我们获得了最终方程，其决定系数（）和性能比偏差（RPD）分别高于0.9和3.0，显示出优异的预测能力。此外，所获得的模型被应用于大规模甘蔗种质的茎秆抗压强度表征。在一项为期三年的研究中，发现茎秆抗压强度的遗传特征保持稳定，并一致筛选出了最优的甘蔗基因型。总之，本研究为甘蔗机械强度的高通量分析提供了一种可行的选择，可用于抗倒伏甘蔗及其他相关品种的育种。
