• Energy Research

Abstract Potatoes are a mainstay of human diets and 4 million metric tons are produced annually in the United States. Simulations of future crop production show that climate change is likely to reduce the yields of the major grain crops around the world, but the impacts on potato production have yet to be determined. A model ensemble consisting of five process-based and one statistical model was used to estimate the impact of climate change on fully irrigated, well-fertilized potato crop across the USA under the RCP 8.5 scenario of high emissions. Results indicate that increasing temperature will reduce potato yields, but this will be mostly compensated by elevated atmospheric CO2. Yields are predicted to decline with climate change in the current highest-yielding areas, which might experience the highest rises in growing season temperature during short hot summers. Simulated yields increase slightly elsewhere in the southern regions of the USA. Planting potatoes earlier as adaptation to avoid hot summers might improve yields in most regions. Water use by the potato crop is predicted to decline despite higher temperatures, due to a shorter growing season and increased water use efficiency under elevated atmospheric CO2. With higher yields in many regions, crop uptake for (nitrogen + phosphorus + potassium) NPK fertilizer will increase, despite the reduced concentration of nutrients in potatoes due to a growth stimulus from elevated atmospheric CO2. With earlier planting, by 2050 water use will decline by 11.7%, NPK fertilizer uptake will increase by 10.4%, and yields of slightly less nutritious potatoes will increase by 14.9% nationally.

Solar radiation is the energy for all biological, physical, and chemical processes of the earth's surface system, and affects the growth and development of crops at all stages. But the diverse data sources and fusion algorithms lead to large differences in the radiation values in various climate datasets. Accurate estimates of the radiation data is not an easy task, the uncertainty of which and the impact on crop yield simulation remains unknown. In this study, the total solar radiation amounts from four independent global radiation datasets were shown considerable heterogeneity across regions and cropping seasons. Forcing the dynamic crop models with the four radiation inputs produced similarly great uncertainties of simulated yield in most regions, with the greatest uncertainty up to 30% of average yield for wheat in Europe. The global-scale uncertainty of simulated yield is increasing during the past three decades and would reach up to 20% of its averages in the future, equivalent to 300 million tons when converting to the global crop production. The results of this study suggest that the previously projected crop yield changes with climate change have large uncertainties propagated from solar radiation data sources used for projections. These uncertainties may mislead the assessment of future food security.

Soybean is an important oil crop in China, and the national focus of soybean production is in Northeast China. In order to achieve high-stable yield, it is crucial to acknowledge the impacts of mean climate and extreme climate indices on soybean yield and yield components. In this study, based on the weather data from 61 counties from 1981 to 2017 in Northeast China, we assessed the impacts of mean climate and extreme climate indices on soybean observed yield and simulated yield. Mean climate include effective growing degree days (GDD10), precipitation (Pre), and solar radiation (SR); extreme climate indices include the number of cool days during seed-filling period (C15), the number of cool days during 15 days before anthesis (C17), the number of hot days (H30), maximum amount of 5 Day accumulated precipitation (P5), and consecutive dry days (CDD)). We used the DSSAT-CROPGRO-Soybean model to identify the main yield components for soybean. The results showed that observed soybean yield reduced by 3.57% due to the collective changes in the eight study climate indices. Increases in GDD10, decreases in Pre, and decreases in SR caused a 3.96%, -3.89%, and - 0.48% change in soybean yield, respectively. Decreases in C15 and C17 led to a 5.36% increase in soybean yield; increases in H30, P5, and CDD caused a 5.75%, 0.30%, and 1.14% reduction in soybean yield, respectively. By comparing the response of observed and simulated soybean yield to climate indices (excluding P5) in the DSSAT-CROPGRO-Soybean model, we identified the key yield components for soybean as the number of pods and seed weight. The negative impacts on the number of pods and seed weight were mainly attributed to changes in Pre and H30 from anthesis to podding and during seed-filling period. Our results could be used to assist the local soybean community adapt to climate change.

Abstract Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with sink-source improved traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The sink-source traits emerged as climate neutral with 16% yield increase with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century RCP8.5 climate scenario, fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.

AbstractWheat growth is sensitive to temperature, but the effect of future warming on yield is uncertain. Here, focusing on China, we compiled 46 observations of the sensitivity of wheat yield to temperature change (SY,T, yield change per °C) from field warming experiments and 102 SY,T estimates from local process-based and statistical models. The average SY,T from field warming experiments, local process-based models and statistical models is −0.7±7.8(±s.d.)% per °C, −5.7±6.5% per °C and 0.4±4.4% per °C, respectively. Moreover, SY,T is different across regions and warming experiments indicate positive SY,T values in regions where growing-season mean temperature is low, and water supply is not limiting, and negative values elsewhere. Gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project appear to capture the spatial pattern of SY,T deduced from warming observations. These results from local manipulative experiments could be used to improve crop models in the future.

Significance Agricultural production is vulnerable to climate change. Understanding climate change, especially the temperature impacts, is critical if policymakers, agriculturalists, and crop breeders are to ensure global food security. Our study, by compiling extensive published results from four analytical methods, shows that independent methods consistently estimated negative temperature impacts on yields of four major crops at the global scale, generally underpinned by similar impacts at country and site scales. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops, with important implications for developing crop- and region-specific adaptation strategies to ensure future food supply of an increasing world population.