TY  - JOUR
A1  - Frieler, Katja
A1  - Schauberger, Bernhard
A1  - Arneth, Almut
A1  - Balkovic, Juraj
A1  - Chryssanthacopoulos, James
A1  - Deryng, Delphine
A1  - Elliott, Joshua
A1  - Folberth, Christian
A1  - Khabarov, Nikolay
A1  - Müller, Christoph
A1  - Olin, Stefan
A1  - Pugh, Thomas A. M.
A1  - Schaphoff, Sibyll
A1  - Schewe, Jacob
A1  - Schmid, Erwin
A1  - Warszawski, Lila
A1  - Levermann, Anders
T1  - Understanding the weather signal in national crop-yield variability
JF  - Earths future
N2  - 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 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.
Y1  - 2017
U6  - https://doi.org/10.1002/2016EF000525
SN  - 2328-4277
VL  - 5
SP  - 605
EP  - 616
PB  - Wiley
CY  - Hoboken
ER  -