FACCE-JPI Knowledge Hub: MACSUR-Partner 25

农业土地继续受到压力、粮食不安全和必须适应气候变化，因此，对未来农业生态系统发展进行综合评估和建立模型变得越来越重要。各种建模工具用于支持农业决策和规划（货车Ittersum等人，2008，Brouwer &货车Ittersum，2010; Ewert等人，2011年）。作物生长模拟模型的应用越来越多，特别是在与气候变化相关的农业影响评估中（Rosenzweig & Wilbanks，2010年;白色等人，2011年）。例如，对作物生产力未来变化的模型预测是在理解物理和生物过程的基础上做出的，例如特定作物如何应对供水减少、生长季节变暖或作物和土壤管理的变化（Chalcor等人，2009年; Chelseor，2011年; Rötter等人，2011年a）。尽管大多数作物生长模拟模型是在田间尺度上开发和评估的，因此不适用于大面积评估，但将其应用于评估农业影响和适应气候变异性以及从田间到（超）国家尺度的变化已成为常见做法（货车der Velde et al.，2009;货车Bussel，2011）。它已经被各种作者假设（例如Palosuo等人，2011; Rötter等人，提交; Asseng等人，在准备中），许多这些模型应用涉及巨大的不确定性。最近，在改进对与作物生长和产量预测相关的不确定性的理解和报告方面已经做出了新的努力（Rötter等人，2011 a; Ferrise等人，2011; Borgesen & Olesen，2011）。对不同的建模方法和模型进行比较可以揭示所涉及的不确定性。在模型相互比较中模型结果的变化还涉及与模型结构有关的不确定性，这可能是最重要的不确定性来源，也是最难以量化的。既需要量化作物模型产生的不确定性程度，也需要确定其不确定性在气候变化影响评估中的相对重要性（例如，Iizumi等人，2011年）。也就是说，有多少不确定性可归因于气候模型、作物模型和其他基本假设（如排放情景）。对不确定性的相对重要性以及如何减少不确定性的这种评估也是“农业模型相互比较和改进项目”（www.agmip.org）的核心。AgMIP确定了三个跨贸易、作物和气候建模的重要专题工作组：（一）有代表性的农业发展途径，（二）缩放方法和（三）不确定性分析。在这一结构中，全球农业生产计划举措与为农作物管理局确定的目标和任务以及与整个非洲农业生产和消费者信息交流机制-MACSUR重叠。然而，CropM和FACCE-MACSUR作为一个整体，在制定气候变化风险评估方法方面比AgMIP在地球仪其他地方走得更远。此外，欧洲作物和气候数据的高密度将允许分析缩放和模型连接方法，以及远远超出AgMIP在世界其他地区的能力的不确定性。模型的相互比较，当与被比较变量的实验数据相结合时，也可用于测试不同模型的性能。这种相互比较可以帮助确定模型中产生系统性误差和需要改进的部分。目前，欧洲各地有一些实验数据（小麦和大麦）可用于模型的相互比较。能够进行全面比较的综合数据集越来越少，需要共同努力为欧洲不同地点（农业气候条件）和作物开发这种高质量的数据集。

Continued pressure on agricultural land, food insecurity and required adaptation to climate change have made integrated assessment and modelling of future agro-ecosystems development increasingly important. Various modelling tools are used to support the decision making and planning in agriculture (van Ittersum et al., 2008, Brouwer & van Ittersum, 2010; Ewert et al., 2011). Crop growth simulation models are increasingly applied, particularly in climate change-related agricultural impact assessments (Rosenzweig & Wilbanks, 2010; White et al., 2011). Model-based projections of future changes in crop productivity, for instance, are made on the basis of understanding the physical and biological processes, such as how given crops respond to reduced water supply, warmer growing seasons or changed crop and soil management (Challinor et al., 2009; Challinor, 2011; Rötter et al., 2011a). Even though most of crop growth simulation models have been developed and evaluated at field scale, and were thus not meant for large area assessments, it has become common practice to apply them in assessing agricultural impacts and adaptation to climate variability and change from field to (supra-)national scale (van der Velde et al., 2009; van Bussel, 2011). It has been hypothesized by various authors (e.g. Palosuo et al., 2011; Rötter et al., submitted; Asseng et al., in preparation) that many of those model applications involve huge uncertainties. Recently, there have been renewed efforts in improving the understanding and reporting of the uncertainties related to crop growth and yield predictions (Rötter et al., 2011a; Ferrise et al., 2011; Borgesen & Olesen, 2011). Comparison of different modelling approaches and models can reveal the uncertainties involved. Variation of model results in model intercomparisons involves also the uncertainty related to model structure, which is probably the most important source of uncertainty and most difficult to quantify. There is both, a need for quantifying the degree of uncertainty resulting from crop models as well as to determine the relative importance of their uncertainties in climate change impact assessment (e.g. Iizumi et al., 2011). That is, how much of the uncertainty can be attributed to climate models, crop models and other basic assumptions (e.g. in emission scenarios). Such assessment of the relative importance of uncertainties and how to reduce them, is also at the core of "The Agricultural Model Intercomparison and Improvement project (AgMIP)" (www.agmip.org). AgMIP has identified three important thematic working groups cutting across trade, crop and climate modelling: they are (i) representative agricultural development pathways, (ii) scaling methods and (iii) uncertainty analysis. In that set-up, the global AgMIP initiative shows overlaps with the objectives and tasks defined for CropM, and with FACCE-MACSUR as a whole. However, CropM and FACCE-MACSUR as a whole have the ambition to go further in terms of developing climate change risk assessment methodology than AgMIP does in other parts of the globe. Also, the high density of crop and climate data in Europe will allow the analysis of scaling and model linking methods, and uncertainty which goes well beyond the capabilities of AgMIP in other world regions. Model intercomparisons, when combined with experimental data of the compared variables, may also be used to test the performance of different models. Such intercomparisons can help to identify those parts in models that produce systematic errors and require improvements. There is currently a number of experimental data (for wheat and barley) available across Europe which may be used for model intercomparisons. Comprehensive data sets that would allow thorough comparisons are getting increasingly scarce and call for concerted efforts to develop such high quality data sets for different locations (agro-climatic conditions) and crops in Europe.