Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data.

中文翻译：

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从户外高通量表型数据中自动离散化“蒸腾限制以增加 VPD”特征

在高蒸汽压亏缺（VPD）下限制蒸腾是一种有前途的干旱适应节水性状。然而，它通常是在受控条件和非常低的通量下测量的，不适合育种。存在一些高通量表型 (HTP) 研究，并且在分析该性状的基因型差异时仅考虑了最大蒸腾速率。此外，没有研究准确确定基因型在自然条件下限制蒸腾的 VPD 断点。因此，鹰嘴豆种群的户外 HTP 数据（15 分钟频率）用于自动生成平滑的蒸腾剖面，提取蒸腾对 VPD 响应的信息特征以实现最佳基因型离散化，识别 VPD 断点并比较基因型。从来自称重传感器数据的蒸腾速率曲线中提取了 15 个生物学相关特征。基因型被聚类（C1、C2、C3），并使用无监督的随机森林选择了 6 个最重要的特征（遗传力 > 0.5）。所有野生近缘种均在 C1 中发现，而 C2 和 C3 主要分别包含高 TE 和低 TE 系。对每个选定特征中不同 p 值组的评估揭示了代表对高 VPD 条件的蒸腾反应的特征的最高基因型变异。对多输出神经网络模型的敏感性分析（C1、C2、C3 的 R 分别为 0.931、0.944、0.953）发现 C1 具有最高的节水能力，在相对较低的 VPD 水平下限制蒸腾，56%（即3.52 kPa) 或 62% (即 3.90 kPa), 取决于其他环境变量的影响是最小还是最大。此外，VPD 似乎对蒸腾响应的影响最显着，与其他环境变量无关，而光、温度和相对湿度单独影响很小/没有影响。通过这项研究，我们提出了一种识别具有耐旱潜力的基因型的新方法，该方法克服了节水性状 HTP 中的挑战。六个选定的特征作为可靠的基因型离散化的代理表型。发现野生鹰嘴豆比浪费水的栽培鹰嘴豆更快地限制水分流失。这种分析方法可以直接用于规范育种应用，应用于其他性状，并有助于加快从 HTP 数据中最大限度地提取信息。