Understanding the weather signal in national crop-yield variability

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 limi ... mehrtation 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.