Paddy rice methane emissions, controlling factors, and mitigation potentials across Monsoon Asia.

Rice is a staple food for more than half of humanity, and 90 % of rice is grown and consumed in Asia. However, paddy rice cultivation creates an ideal environment for the production and release of methane (CH4). How to estimate regional CH4 emissions accurately and how to mitigate them efficiently have been of key concern. Here, with a machine learning method, we investigate the spatiotemporal changes, the major controlling factors and mitigation potentials of paddy rice CH4 emissions across Monsoon Asia at a resolution of 0.1° (~10 km). Spatially CH4 emissions are highly heterogeneous, with the Indo-Gangetic Plain, Deltas of the Mekong, and Yangtze River Basin as the hotspots. Nationwide, China, India, Bangladesh and Vietnam are the major emitters. Straw applied on season is a critical controlling factor for CH4 emission in rice fields. The single-season rice contributes to over 80 % of the total emissions. CH4 emissions from Monsoon Asia have notably declined since 2007. Three mitigation strategies, including water management techniques, off-season straw return, and straw to biochar, may reduce CH4 emissions by 27.66 %, 23.78 %, and 21.79 %, respectively, with the most effective strategy being rice cultivation type-specific and environment-specific. Our findings gain new insights into CH4 emissions and mitigations across Monsoon Asia, providing evidence to adopt precise mitigation strategies based on rice cultivation types and local environment.

Characterizing the 2020 summer floods in South China and effects on croplands.

Floods occur more frequently in the context of climate change; however, flood monitoring capacity has not been well established. Here, we used a synergic mapping framework to characterize summer floods in the middle and lower reaches of the Yangtze River Plain and the effects on croplands in 2020, from both flood extent and intensity perspectives. We found that the total flood extent was 4936 km2 from July to August, and for flood intensity, 1658, 1382, and 1896 km2 of areas experienced triple, double, and single floods. A total of 2282 km2 croplands (46% of the flooded area) were inundated mainly from Poyang and Dongting Lake Basins, containing a high ratio of moderate damage croplands (47%). The newly increased flooding extent in 2020 was 29% larger than the maximum ever-flooded extent in 2015-2019. This study is expected to provide a reference for rapid regional flood disaster assessment and serving mitigation.

Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates.

Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3-11% historically to 10-20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Variations in phenological, physiological, plant architectural and yield-related traits, their associations with grain yield and genetic basis.

BACKGROUND AND AIMS: Physiological and morphological traits play essential roles in wheat (Triticum aestivum) growth and development. In particular, photosynthesis is a limitation to yield. Increasing photosynthesis in wheat has been identified as an important strategy to increase yield. However, the genotypic variations and the genomic regions governing morphological, architectural and photosynthesis traits remain unexplored. METHODS: Here, we conducted a large-scale investigation of the phenological, physiological, plant architectural and yield-related traits, involving 32 traits for 166 wheat lines during 2018-2020 in four environments, and performed a genome-wide association study with wheat 90K and 660K single nucleotide polymorphism (SNP) arrays. KEY RESULTS: These traits exhibited considerable genotypic variations in the wheat diversity panel. Higher yield was associated with higher net photosynthetic rate (r = 0.41, P < 0.01), thousand-grain weight (r = 0.36, P < 0.01) and truncated and lanceolate shape, but shorter plant height (r = -0.63, P < 0.01), flag leaf angle (r = -0.49, P < 0.01) and spike number per square metre (r = -0.22, P < 0.01). Genome-wide association mapping discovered 1236 significant stable loci detected in the four environments among the 32 traits using SNP markers. Trait values have a cumulative effect as the number of the favourable alleles increases, and significant progress has been made in determining phenotypic values and favourable alleles over the years. Eleven elite cultivars and 14 traits associated with grain yield per plot (GY) were identified as potential parental lines and as target traits to develop high-yielding cultivars. CONCLUSIONS: This study provides new insights into the phenotypic and genetic elucidation of physiological and morphological traits in wheat and their associations with GY, paving the way for discovering their underlying gene control and for developing enhanced ideotypes in wheat breeding.

Linking genetic markers with an eco-physiological model to pyramid favourable alleles and design wheat ideotypes.

Genetic markers can be linked with eco-physiological crop models to accurately predict genotype performance and individual markers' contributions in target environments, exploring interactions between genotype and environment. Here, wheat (Triticum aestivum L.) yield was dissected into seven traits corresponding to cultivar genetic coefficients in an eco-physiological model. Loci for these traits were discovered through the genome-wide association studies (GWAS). The cultivar genetic coefficients were derived from the loci using multiple linear regression or random forest, building a marker-based eco-physiological model. It is then applied to simulate wheat yields and design virtual ideotypes. The results indicated that the loci identified through GWAS explained 46%-75% variations in cultivar genetic coefficients. Using the marker-based model, the normalized root mean square error (nRMSE) between the simulated yield and observed yield was 13.95% by multiple linear regression and 13.62% by random forest. The nRMSE between the simulated and observed maturity dates was 1.24% by multiple linear regression and 1.11% by random forest, respectively. Structural equation modelling indicated that variations in grain yield could be well explained by cultivar genetic coefficients and phenological data. In addition, 24 pleiotropic loci in this study were detected on 15 chromosomes. More significant loci were detected by the model-based dissection method than considering yield per se. Ideotypes were identified by higher yield and more favourable alleles of cultivar genetic traits. This study proposes a genotype-to-phenotype approach and demonstrates novel ideas and tools to support the effective breeding of new cultivars with high yield through pyramiding favourable alleles and designing crop ideotypes.

Future water security in the major basins of China under the 1.5 °C and 2.0 °C global warming scenarios.

Freshwater is an essential resource for human lives, agriculture, industry, and ecology. Future water supply, water withdrawal, and water security under the impacts of climate change and human interventions have been of key concern. Numerous studies have projected future changes in river runoff and surface water resources under climate change. However, the changes in the major water withdrawal components including agricultural irrigation water, industrial, domestic and ecological water withdrawal, as well as the balance between water supply and withdrawal, under the joint impacts of climate change and socio-economic development have been seldom investigated, especially at the basin and national scales. In this study, changes in surface water resources, agricultural irrigation water, industrial, domestic and ecological water withdrawal, as well as the balances between water supply and withdrawal, under the baseline climate (2006-2015), 1.5 °C and 2.0 °C warming scenarios (2106-2115) in the 10 major basins across China, were investigated by combining modelling and local census data. The results showed that water withdrawal exceeded water supply in the basins of Liao River, Northwest River, Hai River, Yellow River and Huai River in the baseline period. Under the 1.5 °C and 2.0 °C warming scenarios, the shortage of water resources would aggravate in the above-mentioned basins and the Songhua River basin. And the surplus of water resources would reduce substantially in the basins of Yangtze River, Southeast River and Pearl River. Overall, the difference between water supply and water withdrawal was 435.88 billion m3 during the baseline period, and would be 261.84 and 218.39 billion m3, respectively, under the 1.5 °C and 2.0 °C warming scenarios. This study provides a comprehensive perspective on future water security in the 10 major basins across China, has important implications for water resources management and climate change adaptation.

Wheat traits and the associated loci conferring radiation use efficiency.

Wheat (Triticum aestivum L.) radiation use efficiency (RUE) must be raised through crop breeding to further increase the yield potential, as the harvest index is now close to its theoretical limit. Field experiments including 209 wheat cultivars which have been widely cultivated in China since the 1940s were conducted in two growing seasons (2018-2019 and 2019-2020) to evaluate the variations of phenological, physiological, plant architectural, and yield-related traits and their contributions to RUE and to identify limiting factors for wheat yield potential. The average annual genetic gain in grain yield was 0.60% (or 45.32 kg ha-1 year-1 ; R2 = 0.44, P < 0.01), mainly attributed to the gain in RUE (r = 0.85, P < 0.01). The net photosynthetic rates were positively and closely correlated with grain RUE and grain yield, suggesting source as a limiting factor to future yield gains. Thirty-four cultivars were identified, exhibiting not only high RUE, but also traits contributing to high RUE and 11 other critical traits - of known genetic basis - as potential parents for breeding to improve yield and RUE. Our findings reveal wheat traits and the associated loci conferring RUE, which are valuable for facilitating marker-assisted breeding to improve wheat RUE and yield potential.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Spatiotemporal changes in greenhouse gas emissions and soil organic carbon sequestration for major cropping systems across China and their drivers over the past two decades.

Chinese agricultural systems have experienced dramatic changes in crop planting area, cropping system, irrigation and fertilization managements, and crop yields in recent decades. These changes can substantially affect greenhouse gases (GHG) emissions and soil organic carbon (SOC) sequestration in croplands. However, the spatiotemporal patterns, as well as their driving factors and mechanisms, have not been well understood. Here, the Denitrification-Decomposition model is calibrated and validated to estimate nitrous oxide (N2O) and methane (CH4) emissions and SOC sequestration for seven major cropping systems in China during 2001-2020. The Logarithmic Mean Divisia Index method is further applied to attribute the net GHG emissions (NGEs) trend to various drivers. The results show that the total N2O emissions, CH4 emissions, and SOC sequestration were approximately 23.7, 182.0, and 177.6 Tg CO2-eq/year in the croplands across China. The national average NGEs per unit area ranged from -8705 to 8431 kg CO2-eq ha-1 year-1 across the major cropping systems. During 2001-2020, the trend in national annual NGEs was 0.66 kg CO2-eq ha-1 year-2, ranging from -78.9 to 82.2 kg CO2-eq ha-1 year-2 across the major cropping systems. The paddy lands were mainly a carbon source due to the large amount of CH4 emissions while the uplands could be a carbon sink owing to SOC sequestration. As a whole, the cropland in China was a carbon source with the NGEs equal to 28.4 Tg CO2-eq/year, and the NGEs increased by 0.047 Tg CO2-eq/year2 in the past 20 years. Nationally, changes in crop planting area and yields reduced the NGEs whereas changes in nitrogen use efficiency and cropping systems increased them, although the major factors and their impacts varied greatly among regions. Optimizing cropping systems and nitrogen fertilization based on the local genotype, environment and management should be the most effective method to reduce the NGEs in croplands.

Interactive impacts of climate change and agricultural management on soil organic carbon sequestration potential of cropland in China over the coming decades.

Cropland plays an important role in Soil Organic Carbon (SOC) sequestration. Although the SOC stock and its dynamic in the past decades have been extensively investigated, the information as to where, how much, and how SOC could be potentially sequestered in the coming decades has rarely been available. Here, the Rothamsted Carbon model was applied to investigate the spatiotemporal pattern of SOC sequestration potential for China's cropland in 2021-2040 at 1 km resolution, as well as the interactive impacts of climate change and agricultural management on SOC sequestration. Under the combined impacts of climate change and C input, the SOC sequestration of China's cropland in 2021-2040 would be about 0.56 Mg C ha-1 (0.06% yr-1), 1.33 Mg C ha-1 (0.15% yr-1), 2.10 Mg C ha-1 (0.24% yr-1), and 3.65 Mg C ha-1 (0.41% yr-1), with no increase, 5%, 10%, and 20% increase of C input, respectively. Therefore, a >20% increase in C input would be necessary to realize the promise of the '4 per 1000' initiative. Climate change would decrease SOC sequestration by 26.6-27.6 Tg yr-1 (or 60.4-62.7%). An increase of C input by 0%, 5%, 10%, and 20% relative to business as usual (BAU) would increase SOC sequestration by 4.8 (or 10.8%), 6.6 (or 14.9%), 13.1 (or 29.8%), and 26.2 (or 59.6%) Tg yr-1, respectively. The contributions of temperature, precipitation, and C input to SOC sequestration will be averagely 18.6%, 22.4%, and 59.0%, respectively. Our findings quantify the SOC sequestration in 2021-2040 at a high spatial resolution under the interactive impacts of climate change and agricultural management, which help to identify potential foci and develop region-specific measures to increase SOC sequestration efficiently.

Changes in wheat traits under future climate change and their contributions to yield changes in conventional vs. conservational tillage systems.

Exploring the changes in wheat traits under future climate change and their contributions to yield changes is essential to improve the understanding of climate impact mechanisms and develop climate-resilient cultivars, which however has been seldom conducted. In this study, using a process-based crop model (APSIM-Wheat), meta-regression analyses, and machine learning approaches, we assessed the impacts of different warming levels on soil environments and wheat traits; investigated the impacts of future climate change on wheat traits, growth and development; and identified the favorable wheat traits for breeding under future climate change conditions. Meta-analyses showed that climate warming could significantly advance anthesis date by 3.50% and shorten the entire growth duration by 1.18%, although the duration from anthesis to maturity could be elongated by 7.72%. It could also increase grain yield slightly by 2.72% in the North China Plain, mainly due to the increase in biomass by 6.66%, grain weight by 3.86% and the elongating grain-filling period. However, high temperatures could significantly reduce aboveground biomass. The APSIM-Wheat model was validated based on three years' high-quality environment-controlled experimental data in the long-term warming and conservation tillage fields at Yucheng comprehensive experiment station in the North China Plain. The results showed that the mean yield would decrease under RCP4.5 for both tillage managements (conservational tillage: 0.55%, no-tillage: 6.88%), but increase conservational tillage yield (7.7%) under RCP8.5, relative to 1980-2010, owing to the interactive impacts of climate, CO2 and tillage on wheat traits. Soil moisture would play a more important role in biomass, yield, height, LAI, and grain number for conventional tillage than for no-tillage system, and in the future than in the historical period. Our findings gained insights into the impacts of climate change on wheat traits and yield under different tillage managements, which are essential to understand climate change impact mechanisms and develop climate-resilient cultivars.

Crop loss identification at field parcel scale using satellite remote sensing and machine learning.

Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define crop loss. This study investigates the feasibility of using satellite images to train machine learning (ML) models to classify agricultural field parcels into those with and without crop loss. The reference data for this study was provided by Finnish Food Authority (FFA) containing crop loss information of approximately 1.4 million field parcels in Finland covering about 3.5 million ha from 2000 to 2015. This reference data was combined with Normalised Difference Vegetation Index (NDVI) derived from Landsat 7 images, in which more than 80% of the possible data are missing. Despite the hard problem with extremely noisy data, among the four ML models we tested, random forest (with mean imputation and missing value indicators) achieved the average AUC (area under the ROC curve) of 0.688±0.059 over all 16 years with the range [0.602, 0.795] in identifying new crop-loss fields based on reference fields of the same year. To our knowledge, this is one of the first large scale benchmark study of using machine learning for crop loss classification at field parcel scale. The classification setting and trained models have numerous potential applications, for example, allowing government agencies or insurance companies to verify crop-loss claims by farmers and realise efficient agricultural monitoring.

A coupled human-natural system analysis of water yield in the Yellow River basin, China.

In response to the potential water conflict caused by climate change and increased population, an integrated water yield analysis from the perspective of the coupled human-natural system is clearly required. This paper conducted an integrated water yield analysis in the Yellow River basin (YRB), China, with applications for irrigated cropland water modeling and many field, statistical and satellite images. We found the following during 2000-2017: (1) The irrigation water consumption, rain-fed water consumption of cropland and rain-fed water consumption of natural ecosystems all increased significantly. (2) Ecological restoration caused a consequence of the 81.7 108 m3 water consumption transfer from cropland to natural ecosystems. (3) Water consumption variability was strongly related to irrigation expansion and ecological restoration, and this variability dominated the high water yield variability in the midstream YRB (95.73% ± 0.5%). (4) The increased downstream human water use stress was mainly affected by increased downstream water use and upstream water yield change, with contribution ratios of 1.67 and -0.72, respectively. The study declares the intense relationship between ecological restoration, crop production and socioeconomic activities within the water-limited river basin. This research also highlights that synthetic river basin management is essential to balance the water demand between different sectors and between the upper stream and downstream sections of a basin.

Wheat morpho-physiological traits and radiation use efficiency under interactive effects of warming and tillage management.

Understanding the interactive effects of different warming levels and tillage managements on crop morphological and physiological traits and radiation use efficiency (RUE) is essential for breeding climate-resilient cultivars. Here, we conducted temperature free-air controlled enhancement (T-FACE) experiments on winter wheat during two growth seasons in the North China Plain. The experiments consisted of three warming treatments and two tillage treatments (CT: conventional tillage and NT: no-tillage). In the normal season, warming had significant positive effects on major morphological and physiological traits and increased significantly RUE of yield (RUEY ) and biomass (RUEDM ) by 13.3 and 11.3%, 19.3 and 12.4%, 42.3 and 43.7%, respectively, under the treatments of CTT1, CTT2 and NTT1 relative to the control (CTN, NTN). By contrast, in the warmer season, warming had negative effects on leaf width, light extinction coefficient, light-saturated net photosynthetic rate, aboveground, stems and spike biomass and RUE from anthesis to maturity, and consequently grain yield under conventional tillage, but positive effects under no-tillage. Our findings bring new insights into the mechanisms on the interactive effects of warming and tillage treatments on wheat growth and productivity; provide valuable information on crop ideotypic traits for breeding climate-resilient crop cultivars.

Impacts of climate and vegetation leaf area index changes on global terrestrial water storage from 2002 to 2016.

Terrestrial water storage (TWS) has a major impact on the water, energy, and biogeochemical fluxes of the land surface. The spatiotemporal features of TWS variations, as well as the roles of climate change and human activities in TWS variations, have been of key concerns and need to be further investigated. Here, using the data products from the Gravity Recovery and Climate Experiment and the Global Land Data Assimilation System, together with some auxiliary data on climate, evapotranspiration, and vegetation leaf area index (LAI), we investigated the spatiotemporal variations of global TWS and how TWS was partitioned into its different components. We further quantified the sensitivity of TWS to changes in climate and vegetation LAI, as well as the impacts of climate and vegetation LAI changes on TWS during 2002-2016. The results showed that global TWS declined at a rate of 0.04 mm month-1 during 2002-2016 with a spatially explicit pattern and a distinct seasonal pattern, although the trend was negligible before October 2009 and only became obvious after that. With the seasonal variations of precipitation, temperature, and LAI together, the seasonal variations of TWS were able to be explained by ≥50% in the tropical and subtropical regions. With the joint changes in LAI, temperature, and precipitation, TWS increased by ≤ ~10 mm month-1 in the subarctic and inland temperate regions, and some tropical and subtropical regions; by contrast declined by ≤ ~10 mm month-1 in some regions such as the Central Africa. Our study shed light on the spatiotemporal characteristic of the TWS variations and quantified the impacts of climate and vegetation LAI changes on TWS. The findings can provide not only important evidence for the effectiveness of past ecosystem management intervention programs in water retention service but also guidance for planning ecosystem management intervention programs in the near future.

Correction: Is Yield Increase Sufficient to Achieve Food Security in China?

[This corrects the article DOI: 10.1371/journal.pone.0116430.].

Economic losses due to ozone impacts on human health, forest productivity and crop yield across China.

China's economic growth has significantly increased emissions of tropospheric ozone (O3) precursors, resulting in increased regional O3 pollution. We analyzed data from >1400 monitoring stations and estimated the exposure of population and vegetation (crops and forests) to O3 pollution across China in 2015. Based on WHO metrics for human health protection, the current O3 level leads to +0.9% premature mortality (59,844 additional cases a year) with 96% of populated areas showing O3-induced premature death. For vegetation, O3 reduces annual forest tree biomass growth by 11-13% and yield of rice and wheat by 8% and 6%, respectively, relative to conditions below the respective AOT40 critical levels (CL). These CLs are exceeded over 98%, 75% and 83% of the areas of forests, rice and wheat, respectively. Using O3 exposure-response functions, we evaluated the costs of O3-induced losses in rice (7.5 billion US$), wheat (11.1 billion US$) and forest production (52.2 billion US$) and SOMO35-based morbidity for respiratory diseases (690.9 billion US$) and non-accidental mortality (7.5 billion US$), i.e. a total O3-related cost representing 7% of the China Gross Domestic Product in 2015.

Impact of warming climate, sowing date, and cultivar shift on rice phenology across China during 1981-2010.

Climate change would exert significantly impact on crop yield by altering crop growth and development processes. Therefore, to ensure food security, it is necessary to assess the response and adaptation of crop phenology to the natural (mainly climate change) and artificial (including sowing date (SD) change and cultivar shift) factors. In this study, using field data from 113 agro-meteorological experiment stations across China, along with the Agricultural Production System Simulator (APSIM) oryza model, we investigated the trends of rice phenology in relation to climate change and agronomic factors (i.e., SD change and cultivar shift) from 1981 to 2010. We found that flowering date (FD) and maturity date (MD) of single-rice were delayed by 0.3 and 1.4 days 10a-1, respectively, but FD and MD of double-rice were advanced by 0.7-0.8 and 0.2-1.1 days 10a-1, respectively. Climate change advanced FD and MD of rice at representative stations except FD of late-rice, and shortened length of rice growth period. SD change of rice mainly affected duration of vegetative growth phase (VGP, from SD to FD), but had no significant impact on duration of reproductive growth phase (RGP, from FD to MD). Cultivar shift delayed FD and MD of rice at all representative stations except late-rice at Lianhua. Moreover, cultivar shift prolonged the duration of rice RGP by 0.2-2.8 days 10a-1. Overall, the results suggested that rice phenology was adapting to ongoing climate change by SD change and adoption of cultivars with longer RGP. Therefore, crop phenological characteristics should be sufficiently taken into account to develop adaptation strategies in the future.

Optimizing genotype-environment-management interactions to enhance productivity and eco-efficiency for wheat-maize rotation in the North China Plain.

Agricultural production is facing unprecedented challenges to ensure food security by increasing productivity and in the meantime lowering environmental risk, especially in China. To enhance productivity and eco-efficiency of the typical winter wheat-summer maize rotation simultaneously in the North China Plain (NCP), we optimized the Genotype (G) × Environment (E) × Management (M) interactions to propose the optimal agronomic management practices and cultivars for four representative sites, with the Agricultural Production Systems sIMulator (APSIM) model and detailed field trial data. The results showed that an appropriate delay in sowing date could mitigate climatic negative effects and a proper increase in sowing density could increase yield. The optimal nitrogen application rate could be 180 kg N ha-1 year-1 for maize. For the cropping system, 240 mm of irrigation for wheat and 330-390 kg N ha-1 year-1 of nitrogen application rate (150-210 kg N ha-1 year-1 for wheat and 180 kg N ha-1 year-1 for maize) were suitable to sustain high yield, resource use efficiency, and lower N2O emissions. These recommended levels were, respectively, 40% less than the current irrigation and N application rate commonly used by local farmers. The recommended management practices could increase groundwater recharge while reducing nitrogen leaching and N2O emissions without reducing yield. The maize cultivars with a long growth duration, large grain number and grain-filling rate are desirable. The desirable wheat cultivars are characterized with a medium vernalization sensitivity and high grain filling rate. The present study demonstrated an effective approach to develop sustainable intensification options for producing more with less environmental costs through optimizing G × E × M interactions.

Global wheat production with 1.5 and 2.0°C above pre-industrial warming.

Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.