A hybrid reanalysis-forecast meteorological forcing data for advancing climate adaptation in agriculture.

Climate variability in the growing season is well suited for testing adaptation measures. Adaptation to adverse events, such as heatwaves and droughts, increases the capacity of players in agri-food systems, not only producers but also transporters and food manufacturers, to prepare for production disruptions due to seasonal extremes and climate change. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the daily 0.5° global hybrid reanalysis-forecast meteorological forcing dataset from 2010 to 2021. The dataset consists of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast. Both are bias-corrected using the Delta method and integrated with a baseline climatology derived from the Environmental Research and Technology Development Fund's Strategic Research 14 Meteorological Forcing Dataset (S14FD). The dataset is called JCDS (JRA55-CPS2-Delta-S14FD) and offers a framework for monitoring and forecasting applications towards adaptation.

Contrasting area and yield responses to extreme climate contributes to climate-resilient rice production in Asia.

Climate impacts on crop production components other than yield, i.e., area and cropping intensity, remain under-studied. Here, we clarify climate-crop area relationships by analyzing subnational census area and yield data for six multi-rice cropping countries in South and Southeast Asia. Extreme climate has a greater influence on the departure of area and yield from long-term trends than the average seasonal climate; precipitation and temperature in the sowing period of the wet/rainfed season have a greater influence on variability of the total annual area than in the growing period. In 57% of the country-scenario cases showing significant changes in area and/or yield, the directions of the area and yield responses to climate are not synchronized, deriving non-significant production changes under projected climates. Climate-area relationships not only limit production shocks, but also clarify uncertainties associated with climate mitigation of agricultural land, where area markedly affects the scale of mitigation.

Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change.

The rapid expansion of soybean-growing areas across Europe raises questions about the suitability of agroclimatic conditions for soybean production. Here, using data-driven relationships between climate and soybean yield derived from machine-learning, we made yield projections under current and future climate with moderate (Representative Concentration Pathway (RCP) 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The selected model showed high R2 (>0.9) and low root-mean-squared error (0.35 t ha-1) between observed and predicted yields based on cross-validation. Our results suggest that a self-sufficiency level of 50% (100%) would be achievable in Europe under historical and future climate if 4-5% (9-11%) of the current European cropland were dedicated to soybean production. The findings could help farmers, extension services, policymakers and agribusiness to reorganize the production area distribution. The environmental benefits and side effects, and the impacts of soybean expansion on land-use change, would need further research.

Climate impacts on global agriculture emerge earlier in new generation of climate and crop models.

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project's Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to -6% (SSP126) and from +1% to -24% (SSP585)-explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The 'emergence' of climate impacts consistently occurs earlier in the new projections-before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Rising temperatures and increasing demand challenge wheat supply in Sudan.

Climate warming poses challenges for food production at low latitudes, particularly in arid regions. Sudan, where wheat demand could triple by 2050, has the world's hottest wheat-growing environments, and observed yield declines in hot seasons are prompting the national government to prepare for a warming of 1.5-4.2 °C. Using advanced crop modelling under different climate and socioeconomic scenarios, we show that despite the use of adjusted sowing dates and existing heat-tolerant varieties, by 2050, Sudan's domestic production share may decrease from 16.0% to 4.5-12.2%. In the relatively cool northern region, yields will need to increase by 3.1-4.7% per year, at non-compounding rates, to meet demand. In the hot central and eastern regions, improvements in heat tolerance are essential, and yields must increase by 0.2-2.7% per year to keep pace with climate warming. These results indicate the potential contribution of climate change adaptation measures and provide targets for addressing the wheat supply challenge.

The global dataset of historical yields for major crops 1981-2016.

Knowing the historical yield patterns of major commodity crops, including the trends and interannual variability, is crucial for understanding the current status, potential and risks in food production in the face of the growing demand for food and climate change. We updated the global dataset of historical yields for major crops (GDHY), which is a hybrid of agricultural census statistics and satellite remote sensing, to cover the 36-year period from 1981 to 2016, with a spatial resolution of 0.5°. Four major crops were considered: maize, rice, wheat and soybean. The updated version 1.3 was developed and then aligned with the earlier version 1.2 to ensure the continuity of the yield time series. Comparisons with different global yield datasets and published results demonstrate that the GDHY-aligned version v1.2 + v1.3 dataset is a valuable source of information on global yields. The aligned version dataset enables users to employ an increased number of yield samples for their analyses, which ultimately increases the confidence in their findings.

Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration.

Drought is a major risk in global agriculture. Building-up soil organic carbon (SOC) enhances soil fertility and efficient use of rainwater, which can increase drought tolerance in food production. SOC management demonstrates its benefit at various locations and is a promising means to achieve food security and climate mitigation at once. However, no global assessment of its potential and co-benefits gained from SOC enhancement has been presented. Here we evaluated the extent to which SOC build-up could reduce agricultural drought risk. Using statistical analysis of spatially-explicit global crop and soil datasets, we find that relatively small enhancement in topsoil (0-30 cm) organic carbon content (OCtop) could increase drought tolerance of the food production systems operating over 70% of the global harvested area (particularly drylands). By closing the gap between current and upper limit of tolerance levels through SOC addition of 4.87 GtC at the global scale, farmer's economic output in drought years would increase by ~16%. This level of SOC increase has co-benefit of reducing global decadal mean temperature warming by 0.011 °C. Our findings highlight that progress towards multiple development goals can be leveraged by SOC enhancement in carbon (C)-poor soils in drier regions around the world.

Evidence of crop production losses in West Africa due to historical global warming in two crop models.

Achieving food security goals in West Africa will depend on the capacity of the agricultural sector to feed the rapidly growing population and to moderate the adverse impacts of climate change. Indeed, a number of studies anticipate a reduction of the crop yield of the main staple food crops in the region in the coming decades due to global warming. Here, we found that crop production might have already been affected by climate change, with significant yield losses estimated in the historical past. We used a large ensemble of historical climate simulations derived from an atmospheric general circulation model and two process-based crop models, SARRA-H and CYGMA, to evaluate the effects of historical climate change on crop production in West Africa. We generated two ensembles of 100 historical simulations of yields of sorghum and millet corresponding to two climate conditions for each crop model. One ensemble is based on a realistic simulation of the actual climate, while the other is based on a climate simulation that does not account for human influences on climate systems (that is, the non-warming counterfactual climate condition). We found that the last simulated decade, 2000-2009, is approximately 1 °C warmer in West Africa in the ensemble accounting for human influences on climate, with more frequent heat and rainfall extremes. These altered climate conditions have led to regional average yield reductions of 10-20% for millet and 5-15% for sorghum in the two crop models. We found that the average annual production losses across West Africa in 2000-2009 associated with historical climate change, relative to a non-warming counterfactual condition (that is, pre-industrial climate), accounted for 2.33-4.02 billion USD for millet and 0.73-2.17 billion USD for sorghum. The estimates of production losses presented here can be a basis for the loss and damage associated with climate change to date and useful in estimating the costs of the adaptation of crop production systems in the region.

Uncertainties of potentials and recent changes in global yields of major crops resulting from census- and satellite-based yield datasets at multiple resolutions.

Global agriculture is under pressure to meet increasing demand for food and agricultural products. There are several global assessments of crop yields, but we know little about the uncertainties of their key findings, as the assessments are driven by the single best yield dataset available when each assessment was conducted. Recently, two different spatially explicit, global, historical yield datasets, one based on agricultural census and the other largely based on satellite remote sensing, became available. Using these datasets, we compare the similarities and differences in global yield gaps, trend patterns, growth rates and changes in year-to-year variability. We analyzed maize, rice, wheat and soybean for the period of 1981 to 2008 at four resolutions (0.083°, 0.5°, 1.0° and 2.0°). Although estimates varied by dataset and resolution, the global mean annual growth rates of 1.7-1.8%, 1.5-1.7%, 1.1-1.3% and 1.4-1.6% for maize, rice, wheat and soybean, respectively, are not on track to double crop production by 2050. Potential production increases that can be attributed to closing yield gaps estimated from the satellite-based dataset are almost twice those estimated from the census-based dataset. Detected yield variability changes in rice and wheat are sensitive to the choice of dataset and resolution, but they are relatively robust for maize and soybean. Estimates of yield gaps and variability changes are more uncertain than those of yield trend patterns and growth rates. These tendencies are consistent across crops. Efforts to reduce uncertainties are required to gain a better understanding of historical change and crop production potential to better inform agricultural policies and investments.

Responses of crop yield growth to global temperature and socioeconomic changes.

Although biophysical yield responses to local warming have been studied, we know little about how crop yield growth-a function of climate and technology-responds to global temperature and socioeconomic changes. Here, we present the yield growth of major crops under warming conditions from preindustrial levels as simulated by a global gridded crop model. The results revealed that global mean yields of maize and soybean will stagnate with warming even when agronomic adjustments are considered. This trend is consistent across socioeconomic assumptions. Low-income countries located at low latitudes will benefit from intensive mitigation and from associated limited warming trends (1.8 °C), thus preventing maize, soybean and wheat yield stagnation. Rice yields in these countries can improve under more aggressive warming trends. The yield growth of maize and soybean crops in high-income countries located at mid and high latitudes will stagnate, whereas that of rice and wheat will not. Our findings underpin the importance of ambitious climate mitigation targets for sustaining yield growth worldwide.

Prediction of future methane emission from irrigated rice paddies in central Thailand under different water management practices.

There is concern about positive feedbacks between climate change and methane (CH4) emission from rice paddies. However, appropriate water management may mitigate the problem. We tested this hypothesis at six field sites in central Thailand, where the irrigated area is rapidly increasing. We used DNDC-Rice, a process-based biogeochemistry model adjusted based on rice growth data at each site to simulate CH4 emission from a rice-rice double cropping system from 2001 to 2060. Future climate change scenarios consisting of four representative concentration pathways (RCPs) and seven global climate models were generated by statistical downscaling. We then simulated CH4 emission in three water management practices: continuous flooding (CF), single aeration (SA), and multiple aeration (MA). The adjusted model reproduced the observed rice yield and CH4 emission well at each site. The simulated CH4 emissions in CF from 2051 to 2060 were 5.3 to 7.8%, 9.6 to 16.0%, 7.3 to 18.0%, and 13.6 to 19.0% higher than those from 2001 to 2010 in RCPs 2.6, 4.5, 6.0, and 8.5, respectively, at the six sites. Regionally, SA and MA mitigated CH4 emission by 21.9 to 22.9% and 53.5 to 55.2%, respectively, relative to CF among the four RCPs. These mitigation potentials by SA and MA were comparable to those from 2001 to 2010. Our results indicate that climate change in the next several decades will not attenuate the quantitative effect of water management practices on mitigating CH4 emission from irrigated rice paddies in central Thailand.

Impacts of El Niño Southern Oscillation on the global yields of major crops.

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 -4.3 to +0.8%. The global-mean yields of all four crops during La Niña years tend to be below normal (-4.5 to 0.0%). Our findings highlight the importance of ENSO to global crop production.

How much has the increase in atmospheric CO2 directly affected past soybean production?

Understanding the effects of climate change is vital for food security. Among the most important environmental impacts of climate change is the direct effect of increased atmospheric carbon dioxide concentration ([CO2]) on crop yields, known as the CO2 fertilization effect. Although several statistical studies have estimated past impacts of temperature and precipitation on crop yield at regional scales, the impact of past CO2 fertilization is not well known. We evaluated how soybean yields have been enhanced by historical atmospheric [CO2] increases in three major soybean-producing countries. The estimated average yields during 2002-2006 in the USA, Brazil, and China were 4.34%, 7.57%, and 5.10% larger, respectively, than the average yields estimated using the atmospheric [CO2] of 1980. Our results demonstrate the importance of considering atmospheric [CO2] increases in evaluations of the past effects of climate change on crop yields.

ELPIS-JP: a dataset of local-scale daily climate change scenarios for Japan.

We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs) LARS-WG and, in part, WXGEN. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum and minimum temperatures; precipitation; solar radiation; relative humidity; and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models (GCMs) used in the coupled model intercomparison project (CMIP3) and multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981-2000 is assessed using several statistical tests and quantile-quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (i) the transient scenarios throughout the twenty-first century using projections from 10 CMIP3 GCMs under three emission scenarios (A1B, A2 and B1) and (ii) the time-slice scenarios for the period 2081-2100 using projections from three S-5-3 regional climate models. The ELPIS-JP dataset is designed to be used in conjunction with process-based impact models (e.g. crop models) for assessment, not only the impacts of mean climate change but also the impacts of changes in climate variability, wet/dry spells and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. The ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.