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.

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.

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.

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.

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.

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.

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.

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments.

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.

Multimodel ensembles improve predictions of crop-environment-management interactions.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Contributions of cultivar shift, management practice and climate change to maize yield in North China Plain in 1981-2009.

The impact of climate change on crop yield is compounded by cultivar shifts and agronomic management practices. To determine the relative contributions of climate change, cultivar shift, and management practice to changes in maize (Zea mays L.) yield in the past three decades, detailed field data for 1981-2009 from four representative experimental stations in North China Plain (NCP) were analyzed via model simulation. The four representative experimental stations are geographically and climatologically different, represent the typical cropping system in the study area, and have more complete weather/crop records for the period of 1981-2009. The results showed that while the shift from traditional to modern cultivar increased yield by 23.9-40.3 %, new fertilizer management increased yield by 3.3-8.6 %. However, the trends in climate variables for 1981-2009 reduced maize yield by 15-30 % in the study area. Among the main climate variables, solar radiation had the largest effect on maize yield, followed by temperature and then precipitation. While a significant decline in solar radiation in 1981-2009 (maybe due to air pollution) reduced yield by 12-24 %, a significant increase in temperature reduced yield by 3-9 %. In contrast, a non-significant increase in precipitation during the maize growth period increased yield by 0.9-3 % at three of the four investigated stations. However, a decline in precipitation reduced yield by 3 % in the remaining station. The study revealed that although the shift from traditional to modern cultivars and agronomic management practices contributed most to the increase in maize yield, the negative impact of climate change was large enough to offset 46-67 % of the trend in the observed yields in the past three decades in NCP. The reduction in solar radiation, especially in the most critical period of maize growth, limited the process of photosynthesis and thereby further reduced maize yield.

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Dynamic variability of the heading-flowering stages of single rice in China based on field observations and NDVI estimations.

Although many studies have indicated the consistent impact of warming on the natural ecosystem (e.g., an early flowering and prolonged growing period), our knowledge of the impacts on agricultural systems is still poorly understood. In this study, spatiotemporal variability of the heading-flowering stages of single rice was detected and compared at three different scales using field-based methods (FBMs) and satellite-based methods (SBMs). The heading-flowering stages from 2000 to 2009 with a spatial resolution of 1 km were extracted from the SPOT/VGT NDVI time series data using the Savizky-Golay filtering method in the areas in China dominated by single rice of Northeast China (NE), the middle-lower Yangtze River Valley (YZ), the Sichuan Basin (SC), and the Yunnan-Guizhou Plateau (YG). We found that approximately 52.6 and 76.3 % of the estimated heading-flowering stages by a SBM were within ±5 and ±10 days estimation error (a root mean square error (RMSE) of 8.76 days) when compared with those determined by a FBM. Both the FBM data and the SBM data had indicated a similar spatial pattern, with the earliest annual average heading-flowering stages in SC, followed by YG, NE, and YZ, which were inconsistent with the patterns reported in natural ecosystems. Moreover, diverse temporal trends were also detected in the four regions due to different climate conditions and agronomic factors such as cultivar shifts. Nevertheless, there were no significant differences (p > 0.05) between the FBM and the SBM in both the regional average value of the phenological stages and the trends, implying the consistency and rationality of the SBM at three scales.

Maize growing duration was prolonged across China in the past three decades under the combined effects of temperature, agronomic management, and cultivar shift.

Maize phenology observations at 112 national agro-meteorological experiment stations across China spanning the years 1981-2009 were used to investigate the spatiotemporal changes of maize phenology, as well as the relations to temperature change and cultivar shift. The greater scope of the dataset allows us to estimate the effects of temperature change and cultivar shift on maize phenology more precisely. We found that maize sowing date advanced significantly at 26.0% of stations mainly for spring maize in northwestern, southwestern and northeastern China, although delayed significantly at 8.0% of stations mainly in northeastern China and the North China Plain (NCP). Maize maturity date delayed significantly at 36.6% of stations mainly in the northeastern China and the NCP. As a result, duration of maize whole growing period (GPw) was prolonged significantly at 41.1% of stations, although mean temperature (Tmean) during GPw increased at 72.3% of stations, significantly at 19.6% of stations, and Tmean was negatively correlated with the duration of GPw at 92.9% of stations and significantly at 42.9% of stations. Once disentangling the effects of temperature change and cultivar shift with an approach based on accumulated thermal development unit, we found that increase in temperature advanced heading date and maturity date and reduced the duration of GPw at 81.3%, 82.1% and 83.9% of stations on average by 3.2, 6.0 and 3.5 days/decade, respectively. By contrast, cultivar shift delayed heading date and maturity date and prolonged the duration of GPw at 75.0%, 94.6% and 92.9% of stations on average by 1.5, 6.5 and 6.5 days/decade, respectively. Our results suggest that maize production is adapting to ongoing climate change by shift of sowing date and adoption of cultivars with longer growing period. The spatiotemporal changes of maize phenology presented here can further guide the development of adaptation options for maize production in near future.

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 µmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

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.

Single rice growth period was prolonged by cultivars shifts, but yield was damaged by climate change during 1981-2009 in China, and late rice was just opposite.

Based on the crop trial data during 1981-2009 at 57 agricultural experimental stations across the North Eastern China Plain (NECP) and the middle and lower reaches of Yangtze River (MLRYR), we investigated how major climate variables had changed and how the climate change had affected crop growth and yield in a setting in which agronomic management practices were taken based on actual weather. We found a significant warming trend during rice growing season, and a general decreasing trend in solar radiation (SRD) in the MLRYR during 1981-2009. Rice transplanting, heading, and maturity dates were generally advanced, but the heading and maturity dates of single rice in the MLRYR (YZ_SR) and NECP (NE_SR) were delayed. Climate warming had a negative impact on growth period lengths at about 80% of the investigated stations. Nevertheless, the actual growth period lengths of YZ_SR and NE_SR, as well as the actual length of reproductive growth period (RGP) of early rice in the MLRYR (YZ_ER), were generally prolonged due to adoption of cultivars with longer growth period to obtain higher yield. In contrast, the actual growth period length of late rice in the MLRYR (YZ_LR) was shortened by both climate warming and adoption of early mature cultivars to prevent cold damage and obtain higher yield. During 1981-2009, climate warming and decrease in SRD changed the yield of YZ_ER by -0.59 to 2.4%; climate warming during RGP increased the yield of YZ_LR by 8.38-9.56%; climate warming and decrease in SRD jointly reduced yield of YZ_SR by 7.14-9.68%; climate warming and increase in SRD jointly increased the yield of NE_SR by 1.01-3.29%. Our study suggests that rice production in China has been affected by climate change, yet at the same time changes in varieties continue to be the major factor driving yield and growing period trends.

Observed changes in winter wheat phenology in the North China Plain for 1981-2009.

Climate change in the last three decades could have major impacts on crop phenological development and subsequently on crop productivity. In this study, trends in winter wheat phenology are investigated in 36 agro-meteorological stations in the North China Plain (NCP) for the period 1981-2009. The study shows that the dates of sowing (BBCH 00), emergence (BBCH 10) and dormancy (start of dormancy) are delayed on the average by 1.5, 1.7 and 1.5 days/decade, respectively. On the contrary, the dates of greenup (end of dormancy), anthesis (BBCH 61) and maturity (BBCH 89) occur early on the average by 1.1, 2.7 and 1.4 days/decade, respectively. In most of the investigated stations, GP2 (dormancy to greenup), GP3 (greenup to anthesis) and GP0 (entire period from emergence to maturity) of winter wheat shortened during the period 1981-2009. Due, however, to early anthesis, grain-filling stage occurs at lower temperatures than before. This, along with shifts in cultivars, slightly prolongs GP4 (anthesis to maturity). Comparison of field-observed CERES (Crop Environment Resource Synthesis)-wheat model-simulated dates of anthesis and maturity suggests that climate warming is the main driver of the changes in winter wheat phenology in the NCP. The findings of this study further suggest that climate change impact studies should be strengthened to adequately account for the complex responses and adaptations of field crops to this global phenomenon.

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.