Complex biological models such as mechanistic research models often need to extend their current use to a broader audience. Simplification and faster simulations would increase their use. Here, a step-by-step methodology was developed and applied to partially metamodel, hence accelerate, the mechanistic model FlorSys. This is a process-based, multiannual and multispecies model ("virtual field") which simulates crop growth and weed dynamics and allows users to assess cropping systems for crop production and biodiversity. The model is relatively slow, which makes it difficult to test numerous and diverse cropping systems needed to identify those reconciling crop production and biodiversity. Here, we (1) identified the slowest submodel of FlorSys, i.e. the 3D voxelized light interception submodel, (2) identified and applied a relevant methodology to metamodel this submodel in the simplest situation, i.e. we predicted light interception and absorption directly at the scale of the plant instead of the voxel for a single plant in a field, and (3) extrapolated the method to more complex situations, i.e. a plant in diverse and heterogeneous crop:weed canopies, (4) replaced the original process-based FlorSys submodel by the metamodels, which required additional equations and decision rules, (5) evaluated the metamodelled FlorSys with independent field observations, showing an adequate prediction quality combined with an increased speed at fine-grained scale since the metamodelled version was 28 times faster than the process-based version. For steps 2 and 3, we used the global sensitivity method based on a truncated Legendre polynomial chaos expansion (PCE) whose coefficients were estimated by Partial Least Squares (PLS) regression to simultaneously (i) rank inputs with respect to their polynomial and total effects on outputs via the so-called PCE-PLS sensitivity indices, and (ii) provide metamodels predicting light interception and absorption at the plant level. These metamodels were then shortened into parsimonious metamodels via a LASSO-PLS method. The study showed that there was a trade-off between speed gain due to the metamodelled 3D light submodel and the speed loss due to the additional functions for neighbourhood effects. The metamodelled version is best used for testing complex systems where plant location must be modelled precisley (e.g., precision agriculture, intercropping with precision sowing) whereas the voxelized version with a large voxel size is better for simpler cropping systems. The present step-by-step process may be helpful for investigating and speeding up other complex simulation models with interacting objects/agents. It notably uses a hybrid approach, using a process-based (albeit simplified) approach for the most sensitive plant stage (newly emerged tiny plants) and separate sampling plans and metamodels to ensure that the more sensitive stages/components are adequately covered (small plants).

诸如机制研究模型之类的复杂生物学模型通常需要将其当前的使用范围扩大到更广泛的受众。简化和更快的仿真将增加它们的使用。在这里，逐步方法被开发出来并应用于部分元模型，从而加速了机械模型FlorSys。这是一个基于过程的，多年生和多物种模型（“虚拟田地”），可模拟作物生长和杂草动态，并允许用户评估作物生产和生物多样性的种植系统。该模型相对较慢，这使得难以测试确定与作物生产和生物多样性相协调的多种多样的种植系统。在这里，我们（1）确定了最慢的FlorSys子模型，即3D体素化的光拦截子模型，（2）在最简单的情况下确定并应用了相关的方法对该子模型进行元模型化，即我们直接在植物范围内预测了光的拦截和吸收，而不是在田野中单个植物的体素，（3）将该方法外推到更复杂的情况，即多种异质作物中的植物：杂草冠层，（4）用元模型替换了原始的基于过程的FlorSys子模型，这需要附加的方程式和决策规则，（5 ）评估了元模型FlorSys通过独立的现场观察，显示了足够的预测质量，并且细粒度的速度有所提高，因为元模型版本比基于过程的版本快28倍。对于第2步和第3步，我们使用了基于截断的Legendre多项式混沌展开（PCE）的全局灵敏度方法，其系数由偏最小二乘（PLS）回归估计得出，可以同时（i）根据多项式和总效应对输入进行排名通过所谓的PCE-PLS灵敏度指标在输出上进行分析；（ii）提供预测植物水平上的光拦截和吸收的元模型。然后通过LASSO-PLS方法将这些元模型简化为简约的元模型。研究表明，由于元模型化的3D光子模型而导致的速度增益与因邻域效应的附加功能而导致的速度损失之间存在一个权衡。最适合用于测试必须对工厂位置进行建模的复杂系统的元建模版本（例如，精准农业，间作和精密播种），而体素尺寸较大的体素化版本更适合于更简单的种植系统。当前的分步过程可能有助于调查和加速具有交互对象/代理的其他复杂仿真模型。它特别使用混合方法，对最敏感的植物阶段（新出现的小型植物）使用基于过程（尽管简化）的方法，并使用单独的采样计划和元模型来确保足够敏感的阶段/组件被覆盖（小型植物） ）。