Understanding the weather signal in national crop‐yield variability

Abstract

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.

The effects of climate extremes on global agricultural yields

“…We show that variables describing climate extremes contribute more than half of the explained variance of yield anomalies of maize, rice and soybeans and nearly half of spring wheat at the global scale, and hence are fundamental for understanding crop yield variations. The explained variances (R 2 ) presented here are of similar magnitude as previously reported in other studies: Lobell and Field (2007) and Ray et al (2015) estimate that growing season mean temperature and precipitation explain about one third of crop yield variations globally; Frieler et al (2017) determined the influence of weather variations on crop yields of main producers to be in the range of ∼5% (rice in India) to ∼80% (wheat in Australia). The reported values and findings are not directly comparable due to very different study designs and methods of calculating the explained variance.…”

“…Related to this, the relative contributions of temperature and precipitation anomalies to drought are difficult to disentangle-low rainfall and high temperatures both increase drought severity and are often significantly correlated (Trenberth andShea 2005, Zscheischler andSeneviratne 2017). Previous research (Frieler et al 2017) as well as our results show that the negative yield effects of high temperatures are intertwined with water stress and can be mitigated by irrigation. We discuss the collinearity of predictors and our approach to address these in SI section 2.1.…”

When Will Current Climate Extremes Affecting Maize Production Become the Norm?

“…These concerns motivate our analysis, which adopts statistical methods able to explain a significant portion of the observed interannual production variability (Frieler et al, ; Ray et al, ; Zampieri, Ceglar, Dentener, & Toreti, ). As in similar studies (Lobell, Schlenker, & Costa‐Roberts, ; Tebaldi & Lobell, ), we adopt a statistical approach to estimate the impacts of climate anomalies (namely, heat stress and drought) on the observed crop production variability and trend.…”

Loss and damage: A review of the literature and directions for future research

“…Close to 20% of papers focused solely on economic L&D. These studies did not necessarily discount NEL but clearly signaled their intention to focus on economic/tangible dimensions. For example, Frieler et al () used a process‐based model, which accounts for more variables than has hitherto been modeled, to historically simulate the effect of weather variability on crop yields for major producers of wheat and rice. They argued that this type of study is “important in the context of the debate on the attribution of L&D to climate change” and found that up to 50% of crop yield variation can be attributed to weather related variability, with water stress being the “main driver of historical reported yield fluctuations” (Frieler et al, , pp.…”