Frey Lea Antonia, Baumann Philipp, Aasen Helge, Studer Bruno, Kölliker Roland
Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland.
Sustainable Agroecosystems, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland.
Front Plant Sci. 2020 Oct 15;11:569948. doi: 10.3389/fpls.2020.569948. eCollection 2020.
Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes. We assessed prediction performance of partial least square regression models (PLSR) using cross-validation, and validated model performance with an independent test set under controlled conditions. Starch content of the training set ranged from 0.1 to 120.3 mg g DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g DW. Model performance decreased when applying the trained model on the independent test set (RMSE = 29 mg g DW, = 0.36). Different variable selection methods did not increase model performance. Once validated in the field, the non-destructive spectral method presented here has the potential to detect large differences in leaf starch content of red clover genotypes. Breeding material could be sampled and selected according to their starch content without destroying the plant.
基于草地的反刍家畜生产为依赖浓缩饲料的集约化生产系统提供了一种可持续的替代方案。然而，基于草地的粗饲料往往缺乏满足现代家畜品种生产潜力所需的能量含量。诸如红三叶草等淀粉含量增加的饲用豆科植物可以部分替代玉米和谷物补充料。然而，培育高淀粉含量品种需要高效的表型分析方法。本研究的独特之处在于评估一种非破坏性的高光谱成像方法，以估算红三叶草叶片淀粉含量，从而实现高淀粉红三叶草基因型的高效培育。我们使用交叉验证评估了偏最小二乘回归模型（PLSR）的预测性能，并在可控条件下用独立测试集验证了模型性能。训练集的淀粉含量范围为0.1至120.3毫克/克干重。最佳交叉验证的PLSR模型解释了56%的实测变异，均方根误差（RMSE）为17毫克/克干重。将训练好的模型应用于独立测试集时，模型性能下降（RMSE = 29毫克/克干重，R² = 0.36）。不同的变量选择方法并未提高模型性能。一旦在田间得到验证，本文介绍的非破坏性光谱方法有潜力检测红三叶草基因型叶片淀粉含量的巨大差异。可以根据淀粉含量对育种材料进行采样和选择，而不破坏植株。
