Blanc Emmanuelle, Enjalbert Jérôme, Flutre Timothée, Barbillon Pierre
Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190, Gif-sur-Yvette, France.
Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris, 75005, Paris, France.
J Exp Bot. 2023 Nov 21;74(21):6722-6734. doi: 10.1093/jxb/erad339.
Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost, and, as a result, error propagation is usually ignored. Here we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. We used a Bayesian calibration method to jointly estimate the values of five parameters and quantify their uncertainty by fitting the model outputs to tillering dynamics data. We made recourse to Gaussian process metamodels in order to alleviate the computational cost of WALTer. These metamodels are built from an adaptive design that consists of successive runs of WALTer chosen by an efficient global optimization algorithm specifically adapted to this particular calibration task. The method presented here performed well on both synthetic and experimental data. It is an efficient approach for the calibration of WALTer and should be of interest for the calibration of other functional-structural plant models.
植物科学家越来越多地使用功能-结构植物模型来解决各种各样的问题。然而，这些复杂模型的校准通常具有挑战性，主要是因为其计算成本高，结果，误差传播通常被忽略。在这里，我们将一种自动方法应用于WALTer的校准：一个功能-结构小麦模型，该模型模拟了分蘖对光照竞争的可塑性。我们使用贝叶斯校准方法联合估计五个参数的值，并通过将模型输出拟合到分蘖动态数据来量化其不确定性。我们借助高斯过程元模型来减轻WALTer的计算成本。这些元模型是基于一种自适应设计构建的，该设计由WALTer的连续运行组成，这些运行由专门适用于此特定校准任务的高效全局优化算法选择。这里提出的方法在合成数据和实验数据上都表现良好。它是一种校准WALTer的有效方法，应该会引起其他功能-结构植物模型校准的兴趣。
