School of Plant Biology , University of Western Australia , Crawley , WA 6009 , Australia.
AoB Plants. 2011;2011:plr006. doi: 10.1093/aobpla/plr006. Epub 2011 Feb 20.
Simulations that integrate sub-models of important biological processes can be used to ask questions about optimal management strategies in agricultural and ecological systems. Building sub-models with more detail and aiming for greater accuracy and realism may seem attractive, but is likely to be more expensive and time-consuming and result in more complicated models that lack transparency. This paper illustrates a general integrated approach for constructing models of agricultural and ecological systems that is based on the principle of starting simple and then directly testing for the need to add additional detail and complexity.
The approach is demonstrated using LUSO (Land Use Sequence Optimizer), an agricultural system analysis framework based on simulation and optimization. A simple sensitivity analysis and functional perturbation analysis is used to test to what extent LUSO's crop-weed competition sub-model affects the answers to a number of questions at the scale of the whole farming system regarding optimal land-use sequencing strategies and resulting profitability.
The need for accuracy in the crop-weed competition sub-model within LUSO depended to a small extent on the parameter being varied, but more importantly and interestingly on the type of question being addressed with the model. Only a small part of the crop-weed competition model actually affects the answers to these questions.
This study illustrates an example application of the proposed integrated approach for constructing models of agricultural and ecological systems based on testing whether complexity needs to be added to address particular questions of interest. We conclude that this example clearly demonstrates the potential value of the general approach. Advantages of this approach include minimizing costs and resources required for model construction, keeping models transparent and easy to analyse, and ensuring the model is well suited to address the question of interest.
整合重要生物过程子模型的模拟可以用于询问农业和生态系统中最佳管理策略的问题。构建具有更多细节的子模型，并追求更高的准确性和现实性，可能看起来很有吸引力，但很可能更昂贵、耗时，并导致缺乏透明度的更复杂的模型。本文介绍了一种基于从简单开始然后直接测试添加额外细节和复杂性的必要性的构建农业和生态系统模型的通用综合方法。
该方法使用 LUSO（土地利用序列优化器）进行演示，这是一种基于模拟和优化的农业系统分析框架。简单的敏感性分析和功能干扰分析用于测试 LUSO 的作物-杂草竞争子模型在多大程度上影响整个农业系统规模的最佳土地利用序列策略和由此产生的盈利能力的一系列问题的答案。
LUSO 中的作物-杂草竞争子模型的准确性需求在一定程度上取决于所变化的参数，但更重要和有趣的是取决于模型所解决的问题类型。只有作物-杂草竞争模型的一小部分实际上会影响这些问题的答案。
本研究说明了基于测试是否需要添加复杂性来解决特定感兴趣问题的构建农业和生态系统模型的综合方法的一个示例应用。我们的结论是，该示例清楚地展示了该通用方法的潜在价值。该方法的优点包括最小化模型构建所需的成本和资源，保持模型透明且易于分析，并确保模型非常适合解决感兴趣的问题。
