• Energy Research

Abstract Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability.

Many studies have reported an increased mortality risk from heat waves comparing with non-heat wave days. However, how much the mortality rate change with the heat intensity-vulnerability curve-is still unknown. Such unknown information makes the related managers impossible to assess scientifically life losses from heat waves, consequently fail in conducting suitable integrated risk management measures.We used the heat wave intensity index (HWII) to characterize quantitatively the heat waves, then applied a distributed lag non-linear model to explore the area-specific definition of heat wave, and developed the vulnerability models on the relationships between HWII and mortality by age and by area. Finally, Monte Carlo method was run to assess and compare the event-based probabilistic heat wave risk during the periods of 1971-2015 and 2051-2095.We found a localized definition of heat wave for each corresponding area based on the minimum AIC (Akaike information criterion). Under the local heat wave events, the expected life loss during 1971-2015 does distinguish across areas, and decreases consistently in the order of WZ Chongqing, PK Nanjing and YX Guangzhou for each age group. More specifically, for the elders (≥65), the average annual loss (AAL) (and 95% confidence interval) would be 61.3 (30.6-91.9), 38 (3.8-72.2) and 18.7 (7.3-30) deaths per million people. With two stresses from warming and aging in future China, the predicted average AAL of the elders under four Representative Carbon Pathways (2.6, 4.5, 6.0, and 8.5) during 2051-2095 would be 2460, 1675, 465 deaths per million for PK Nanjing, YX Guangzhou and WZ Chongqing, respectively, approximately becoming 8~ 90 times of the AAL during 1971-2015.This study found that the non-linear HWII-mortality relationships vary by age and area. The heat wave mortality losses are closely associated with the social-economic level. With the increasing extreme climatic events and a rapid aging trend in China, our findings can provide guidance for policy-makers to take appropriate regional adaptive measures to reduce health risks in China.

ABSTRACTRice in China is increasingly suffered from extreme temperature stress (ETS) with ongoing climate change. It is projected that ETS would increase notably across the world in the future. However, the spatio‐temporal change of ETS in main rice planting areas in China is still unclear; and the future yield loss caused by ETS (YLETS) has seldom been investigated quantitatively. In this study, we first investigated the spatio‐temporal change of ETS across China under 20 climate change scenarios consisting of five global climate models and four Representative Carbon Pathways (2.6, 4.5, 6.0, and 8.5). Then, using a process‐based crop model (MCWLA‐Rice), its 30 sets of model parameters and the 20 climate change scenarios, we conducted a super‐ensemble assessment to investigate the YLETS over 2020–2049, relative to the baseline period (1980–2009), across China. The results showed that, an increased heat ETS and a decreased cold ETS would be expected for most areas. As a result, a large spatial variability of yield loss would be expected in the future, including severely cold stress for region I (northeastern China, single rice) and region IV (southern China, early rice), but severely heat stress for region III (the middle and lower reaches of Yangtze River, single rice) and region IV (southern China, late rice). Comparing yield loss from both ETS, a decreased change in yield loss would be mainly expected in region I (northeastern China, single rice), while an increased change for region III (the middle and lower reaches of Yangtze River, single rice) and IV (southern China, late rice), with less change for region II (southwestern China, single rice) and IV (southern China, early rice). Finally, some adaptation measures were proposed for the ETS‐sensitive areas. Our findings are useful to develop effective policies to cope with climate risk and relieve ETS disasters.

Responses of global crop yields to warmer temperatures are fundamental to sustainable development under climate change but remain uncertain. Here, we combined a global dataset of field warming experiments (48 sites) for wheat, maize, rice and soybean with gridded global crop models to produce field-data-constrained estimates on responses of crop yield to changes in temperature (ST) with the emergent-constraint approach. Our constrained estimates show with >95% probability that warmer temperatures would reduce yields for maize (−7.1 ± 2.8% K−1), rice (−5.6 ± 2.0% K−1) and soybean (−10.6 ± 5.8% K−1). For wheat, ST was 89% likely to be negative (−2.9 ± 2.3% K−1). Uncertainties associated with modelled ST were reduced by 12–54% for the four crops but data constraints do not allow for further disentangling ST of different crop types. A key implication for impact assessments after the Paris Agreement is that direct warming impacts alone will reduce major crop yields by 3–13% under 2 K global warming without considering CO2 fertilization effects and adaptations. Even if warming was limited to 1.5 K, all major producing countries would still face notable warming-induced yield reduction. This yield loss could be partially offset by projected benefits from elevated CO2, whose magnitude remains uncertain, and highlights the challenge to compensate it by autonomous adaptation. Global responses of crops to warmer temperatures will affect agricultural sustainability. This study of maize, rice, soybean and wheat projects yield reductions of 3–13% under 2 °C warming.