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An AgMIP framework for improved agricultural representation in IAMs
Environmental Research Letters ( IF 6.7 ) Pub Date : 2017-11-24 , DOI: 10.1088/1748-9326/aa8da6
Alex C Ruane 1 , Cynthia Rosenzweig 1 , Senthold Asseng 2 , Kenneth J Boote 2 , Joshua Elliott 3 , Frank Ewert 4, 5 , James W Jones 2, 6 , Pierre Martre 7 , Sonali P McDermid 8 , Christoph Müller 9 , Abigail Snyder 10 , Peter J Thorburn 11
Affiliation  

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

中文翻译:

用于改善 IAM 中农业代表性的 AgMIP 框架

综合评估模型(IAM)在评估社会经济发展、技术创新和气候条件变化如何塑造未来农业系统方面具有巨大潜力。通过与气候和作物模型模拟器相结合,IAM 有可能解决重要的农业反馈循环,并确定社会经济发展对农业系统的意外后果。在这里,我们提出了一个框架,用于在 IAM 内开发农业系统响应的稳健表征,将下游应用与模型开发以及从地方到全球尺度的关键气候响应的协调评估联系起来。我们调查了与农业模型比对和改进项目 (AgMIP) 相关的基于协议的评估的优缺点,每个评估都利用多个站点和模型来评估作物对核心气候变化的反应,包括二氧化碳浓度、温度和可用水量的变化,一些研究进一步探讨了氮水平和农业系统适应如何影响气候响应。采用精心校准的模型进行的实地研究涵盖了最多的活动;然而,它们捕捉全球农业系统多样性的全部能力有限。代表性站点网络比广泛采样的网络提供更有针对性的响应信息,但由于难以覆盖农业系统的多样性而受到限制。全球网格作物模型提供了全面的覆盖范围,但输入的校准和质量控制面临着巨大的挑战。气候响应的多样性强调作物模型模拟器必须区分区域和农业系统,同时认识到模型的不确定性。最后,为了弥合自下而上和自上而下方法之间的差距,我们建议部署混合气候响应系统,采用具有代表性的站点网络来纠正偏差综合网格模拟,从而为加速发展和广泛的应用程序。
更新日期:2017-11-24
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