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- ItemUnderstanding the weather signal in national crop‐yield variability(Hoboken, NJ : Wiley, 2017) Frieler, Katja; Schauberger, Bernhard; Arneth, Almut; Balkovič, Juraj; Chryssanthacopoulos, James; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Khabarov, Nikolay; Müller, Christoph; Olin, Stefan; Smith, Steven J.; Pugh, Thomas A.M.; Schaphoff, Sibyll; Schewe, Jacob; Schmid, Erwin; Warszawski, Lila; Levermann, AndersYear‐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 United States. 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 provide options to represent human‐management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.
- ItemShared Socio-economic Pathways for European agriculture and food systems: The Eur-Agri-SSPs(Amsterdam [u.a.] : Elsevier, 2020) Le Mouël, Chantal; Mathijs, Erik; Mehdi, Bano; Mittenzwei, Klaus; Mora, Olivier; Øistad, Knut; Øygarden, Lillian; Priess, Jörg A.; Reidsma, Pytrik; Schaldach, Rüdiger; Schönhart, Martin; Mitter, Hermine; Techen, Anja-K.; Sinabell, Franz; Helming, Katharina; Schmid, Erwin; Bodirsky, Benjamin L.; Holman, Ian; Kok, Kasper; Lehtonen, Heikki; Leip, AdrianScenarios describe plausible and internally consistent views of the future. They can be used by scientists, policymakers and entrepreneurs to explore the challenges of global environmental change given an appropriate level of spatial and sectoral detail and systematic development. We followed a nine-step protocol to extend and enrich a set of global scenarios – the Shared Socio-economic Pathways (SSPs) – providing regional and sectoral detail for European agriculture and food systems using a one-to-one nesting participatory approach. The resulting five Eur-Agri-SSPs are titled (1) Agriculture on sustainable paths, (2) Agriculture on established paths, (3) Agriculture on separated paths, (4) Agriculture on unequal paths, and (5) Agriculture on high-tech paths. They describe alternative plausible qualitative evolutions of multiple drivers of particular importance and high uncertainty for European agriculture and food systems. The added value of the protocol-based storyline development process lies in the conceptual and methodological transparency and rigor; the stakeholder driven selection of the storyline elements; and consistency checks within and between the storylines. Compared to the global SSPs, the five Eur-Agri-SSPs provide rich thematic and regional details and are thus a solid basis for integrated assessments of agriculture and food systems and their response to future socio-economic and environmental changes. © 2020 The Author(s)