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首页> 外文期刊>Journal of Experimental Botany >Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality
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Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality

机译：使用可识别性分析在设计饲料生产和质量造型中的表型实验中的使用

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Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phe-notyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.
机译：农业系统模型很复杂，并且往往相对于观察数据集被过度参数化。基于局部敏感性分析的实用可识别性分析证明有效地调查环境模型中可识别的参数集，但尚未应用于农业系统模型。在这里，我们证明可识别性分析改善了实验设计，以确保复杂草地模型的产量和质量输出的独立参数估计。牧场仿真模型（PASIM）用于证明实际可识别性分析在用多年生黑麦草在PHE展开实验中设计实验和测量方案的有效性。设计了三种因素的虚拟实验：测量频率，实验持续时间。和试验的位置。我们的结果表明，（i）PASIM在模拟生物质产量和多年生黑麦草的质量方面提供了足够的细节，用于繁殖，（ii）典型的育种试验不足以参数化所有有影响的参数，（iii）测量频率更多重要的是增长季节的数量，以提高PASIM参数的可识别性，（IV）可识别性分析提供了一种优化多位置试验设计的声音方法。实际可识别性分析可以在确保对表型数据和成本效益的多位置实验设计的适当开发方面发挥重要作用。考虑到仿真模型的越来越重要，本研究支持最近组织的表型网络中的实验和测量协议的设计。

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