UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon, France.
Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China.
Ann Bot. 2018 Aug 27;122(3):397-408. doi: 10.1093/aob/mcy080.
Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here.
First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the model's quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated.
Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method.
Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.
功能结构植物模型（FSPM）明确描述了器官到植物尺度上植物与其环境之间的相互作用。然而，结构或模型机制的高水平描述使得这种类型的模型非常复杂且难以校准。本文提出了一种两步法来简化校准过程。
首先，应用全局敏感性分析方法对校准损失函数进行分析。它提供了一阶和总阶灵敏度指标，可以根据重要性对参数进行排序，以选择最有影响的参数。其次，使用赤池信息量准则（AIC）来量化经过不同选择参数组合校准后的模型拟合质量。具有最低 AIC 的模型给出了最佳的参数选择组合。通过使用第二步中选择的参数在独立数据集（同一品种，另一年）上对模型进行校准，验证了该方法的有效性。所有参数均设置为其标称值；仅重新估计最有影响的参数。
应用于校准损失函数的敏感性分析是突出估计过程中最重要参数的相关方法。对于所研究的冬油菜模型，从 26 个估计参数中选择了 11 个。然后，通过仅重新估计用模型选择方法选择的三个参数，就可以对不同的数据集进行重新校准。
仅拟合少数几个参数可大大提高重新校准的效率，提高模型的稳健性，并有助于识别在不同环境条件下变化的主要来源。这种创新方法仍需要更广泛的验证，但已经为改进 FSPM 的校准提供了有趣的途径。
