The monitoring and prediction of climate-induced variations in crop yields, production and export prices in major food-producing regions have become important to enable national governments in import-dependent countries to ensure supplies of affordable food for consumers. Although the El Niño/Southern Oscillation (ENSO) often affects seasonal temperature and precipitation, and thus crop yields in many regions, the overall impacts of ENSO on global yields are uncertain. Here we present a global map of the impacts of ENSO on the yields of major crops and quantify its impacts on their global-mean yield anomalies. Results show that El Niño likely improves the global-mean soybean yield by 2.1-5.4% but appears to change the yields of maize, rice and wheat by À 4.3 to þ 0.8%. The global-mean yields of all four crops during La Niña years tend to be below normal ( À 4.5 to 0.0%). Our findings highlight the importance of ENSO to global crop production.

International audienc

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.

Requirement of mineral elements in different plant tissues is not often consistent with their transpiration rate; therefore, plants have developed systems for preferential distribution of mineral elements to the developing tissues with low transpiration. Here we took silicon (Si) as an example and revealed an efficient system for preferential distribution of Si in the node of rice (Oryza sativa). Rice is able to accumulate more than 10% Si of the dry weight in the husk, which is required for protecting the grains from water loss and pathogen infection. However, it has been unknown for a long time how this hyperaccumulation is achieved. We found that three transporters (Lsi2, Lsi3, and Lsi6) located at the node are involved in the intervascular transfer, which is required for the preferential distribution of Si. Lsi2 was polarly localized to the bundle sheath cell layer around the enlarged vascular bundles, which is next to the xylem transfer cell layer where Lsi6 is localized. Lsi3 was located in the parenchyma tissues between enlarged vascular bundles and diffuse vascular bundles. Similar to Lsi6, knockout of Lsi2 and Lsi3 also resulted in decreased distribution of Si to the panicles but increased Si to the flag leaf. Furthermore, we constructed a mathematical model for Si distribution and revealed that in addition to cooperation of three transporters, an apoplastic barrier localized at the bundle sheath cells and development of the enlarged vascular bundles in node are also required for the hyperaccumulation of Si in rice husk.node | rice transporter | apoplastic barrier | silicon distribution | mathematical model

Understanding how climate warming has an impact on the life cycle schedule of terrestrial organisms is critical to evaluate ecosystem vulnerability to environmental change. Despite recent advances identifying the molecular basis of temperature responses, few studies have incorporated this knowledge into predictive models. Here we develop a method to forecast flowering phenology by modelling regulatory dynamics of key flowering-time genes in perennial life cycles. The model, parameterized by controlled laboratory experiments, accurately reproduces the seasonal changes in gene expression, the corresponding timing of floral initiation and return to vegetative growth after a period of flowering in complex natural environments. A striking scenario forecast by the model under climate warming is that the shift in the return time to vegetative growth is greater than that in floral initiation, which results in a significant reduction of the flowering period. Our study demonstrates the usefulness of gene expression assessment to predict unexplored risks of climate change.