Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments

A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bang...

小麦（Triticum aestivum）改良的主要选择目标是籽粒产量。然而，籽粒产量的选择受限于田间试验的范围、环境波动以及获取多年评估所需的时间。光谱反射率（spectral reflectance）和冠层温度（CT）等次级性状可在整个生长季快速多次测量，且常与籽粒产量相关，因此可用于大群体的间接选择——尤其适用于育种周期中重复产量测试前的早期世代。尽管近端传感（proximal sensing）数据采集正越来越多地通过高通量平台实现，此类平台能提供强大且经济实惠的信息，但高效且有效地利用这些数据仍面临挑战。本研究的目标是在孟加拉国常见的极端终端热胁迫（extreme terminal heat stress）条件下，利用高密度近端传感测量监测小麦育种试验中的小麦生长状况，并预测其籽粒产量。