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A generic model to simulate the growth of forage legumes with contrasting architectures

机译:通用模型,采用对比体系结构模拟饲草豆类的生长

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Predicting the yield and botanical composition of multi-species grasslands remains a challenge, mainly because of the complex feedback loops of competition for resources taking place between plants above- and belowground. Individual-based models of plant communities are promising to help disentangle these interactions. In the present study, we present and evaluate a plant model dealing with the 3D morphogenesis, growth and N acquisition of forage legumes. The model was developed to account for herbaceous legume species regardless of their growth habits above- and belowground. The model was calibrated for six contrasting species on the basis of a series of greenhouse experiments. An independent outdoor experiment was carried out to assess model outputs under non-limiting water conditions. We confirmed that the generic set of production rules used in the model was able to capture the diversity of morphogenetic strategies encountered among forage legumes. Model predictions were accurate for shoot organogenesis, leaf area expansion, plant height, rooting depth and root density distribution for isolated plants of the different species. The contrasting responses of plant species to a defoliation event were correctly anticipated. However discrepancies were observed with respect to dry matter accumulation in one of the species, due to inaccurate representations of leaf area distribution and intercepted radiation. Further model evaluations are required concerning the responses induced by competition for resources in dense stands.
机译:预测多种草原的产量和植物组成仍然是一个挑战,主要是因为地上和地下植物之间发生的资源竞争的复杂反馈回路。基于个体的植物群落模型有望帮助消除这些相互作用。在当前的研究中,我们提出并评估了一种植物模型,其处理豆科植物的3D形态发生,生长和氮素吸收。该模型的开发考虑了草本豆类物种的生长,无论其地下和地下的生长习性如何。在一系列温室实验的基础上,针对六个对比物种对模型进行了校准。进行了独立的户外实验,以评估非限制性水条件下的模型输出。我们确认,模型中使用的通用生产规则集能够捕获饲草豆类中遇到的形态发生策略的多样性。模型预测对于不同物种的离体植物的枝条器官发生,叶面积扩展,植物高度,生根深度和根系密度分布是准确的。正确预期了植物物种对脱叶事件的不同反应。然而,由于叶面积分布和辐射截获的不准确表示,其中一种物种的干物质积累存在差异。需要有关密集林分中资源竞争所引起的响应的进一步模型评估。

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