A new framework for predicting and understanding flowering time for crop breeding
A new framework for predicting and understanding flowering time for crop breeding
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DOI：
10.1002/ppp3.10427
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发表时间：
2023-10-08
影响因子：
5.1
通讯作者：
Challinor，Andrew
中科院分区：
文献类型：
--
作者：
Deva，Chetan;Dixon，Laura;Challinor，Andrew
随着生长季节的变化，开发气候适应性强的作物品种已成为农业系统的一项重要适应措施。为气候变化培育新品种需要提高预测基因型和环境之间复杂相互作用的能力，这些相互作用决定了开花时间。通过对开花、环境和植物遗传学的观察，进行了数百次实验，建立了一个模型，可以预测各种普通豆类何时开花。该模型将有助于育种者探索其种质的物候特征，加快气候适应性的选择。随着生长季节的变化，作物改良计划必须确保他们开发的品种的物候特征仍然非常适合他们的目标群体的环境。迎接这一挑战将需要清楚地了解现有种质在基因型环境（G E）中的表现，以提高选择效率。最近的工作要求开发简单的模型，可以准确地模拟基因型变异的关键性状在整个目标人口的environments.However，我们开发了一个简单的机器学习框架，建模时间开花跨越G EEE和应用此常见的菜豆在赤道目标人口的环境。在这个框架内，我们测试了三个机器学习模型，发现表现最好的模型在整个GEE中显示出很高的准确性。我们通过证明热时间和累积蒸发量是所有三个模型中开花时间的强大预测因子，来推进对赤道条件下开花时间的环境驱动因素的理解。
Societal Impact StatementAs the growing season changes, the development of climate resilient crop varieties has emerged as a crucial adaptation in agricultural systems. Breeding new varieties for a changing climate requires enhanced capacity to predict the complex interactions between genotype and environment that determine flowering time. Hundreds of experiments with observations of flowering, the environment and plant genetics were used to build a model that can predict when a variety of common bean is going to flower. This model will help breeders to explore the phenological characteristics of their germplasm, speeding up selection for climate adaptation.SummaryThere is an urgent need to accelerate crop breeding for adaptation to a changing climate. As the growing season changes, crop improvement programmes must ensure that the phenological characteristics of the varieties they develop remain well suited to their target population of environments.Meeting this challenge will require a clear understanding of how existing germplasm behave across Genotype ∗ Environment (G ∗ E) to enhance the efficiency of selection. Recent work calls for the development of simple models that can accurately simulate genotypic variation in key traits across target population of environments.Accordingly, we develop a simple machine learning framework for modelling time to flowering across G ∗ E and apply this to common bean in an equatorial target population of environments. Within this framework, we test three machine learning models and find that the best performing models display high levels of accuracy across G ∗ E.We advance understanding of the environmental drivers of flowering time in equatorial conditions by showing that thermal time and accumulated evaporation are powerful predictors of flowering time across all three models.