Understanding the weather signal in national crop-yield variability.,Earth's Future

当前位置： X-MOL 学术 › Earths Future › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Understanding the weather signal in national crop-yield variability.
Earth's Future Pub Date : 2017-05-17 , DOI: 10.1002/2016ef000525
Katja Frieler ₁ , Bernhard Schauberger ₁ , Almut Arneth ₂ , Juraj Balkovic _{3,

4} , James Chryssanthacopoulos ₅ , Delphine Deryng _{5,

6} , Joshua Elliott _{5,

7} , Christian Folberth ₃ , Nikolay Khabarov ₃ , Christoph Müller ₁ , Stefan Olin ₈ , Thomas A M Pugh _{2,

9} , Sibyll Schaphoff ₁ , Jacob Schewe ₁ , Erwin Schmid ₁₀ , Lila Warszawski ₁ , Anders Levermann _{1,

11,

12}

Affiliation

Potsdam Institute for Climate Impact Research, Potsdam, Germany.
Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany.
International Institute for Applied System Analysis, Laxenburg, Austria.
Department of Soil Science, Faculty of Natural Sciences, Comenius University, Bratislava, Slovak Republic.
Center for Climate Systems Research, Columbia University, New York, New York, USA.
Climate Analytics, Berlin, Germany.
ANL Computation Institute, University of Chicago, Chicago, Illinois.
Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.
School of Geography, Earth and Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK.
University of Natural Resources and Life Sciences, Vienna, Austria.
Institute of Physics, Potsdam University, Potsdam, Germany.
Lamont-Doherty Earth Observatory, Columbia University, New York, New York.

Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the US. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.

更新日期：2019-11-01

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文