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Climate change and variability are projected to alter the geographic suitability of lands for crops cultivation. Accurately predicting changes in the potential current and future land suitability distribution dynamics of wheat (Triticum aestivum), soybean (Glycine max) and rice (Oryza sativa) crops due to climate change scenarios is critical to adapting and mitigating the impacts of bioclimatic changes, and plays a significant role in securing food security in East Asia region. This study compiled large datasets of wheat, soybean and rice occurrence locations from GBIF and 19 bioclimatic variables obtained from the WorldClim database that affect crops growth. We recognized potential future suitable distribution regions for crops under the one socioeconomic pathway, (SSP585) for 2021–2040 and 2041–2060, using the MaxEnt model. The accuracy of the MaxEnt was highly significant with mean AUC values ranging from 0.833 to 0.882 for all models evaluated. The jackknife test revealed that for wheat, Bio4 and Bio12 contributed 17.6% and 12.6%, for soybean Bio10 and Bio12 contributed 15.6% and 49.5%, while for rice Bio12 and Bio14 contributed 12.9% and 36.0% to the MaxEnt model. In addition, cultivation aptitude for wheat, soybean, and rice increased in southeast China, North Korea, South Korea, and Japan, while decreasing in Mongolia and northwest China. Climate change is expected to increase the high land suitability for wheat, soybean, and rice in East Asia. Simulation results indicate an average decrease of unsuitable areas of -98.5%, -41.2% and -36.3% for wheat, soybean and rice from 2060 than that of current land suitability. In contrast, the high land suitable for wheat, soybean and rice cultivation is projected to increase by 75.1%, 68.5% and 81.9% from 2060 as compared with current. The findings of this study are of utmost importance in the East Asia region as they present an opportunity for policy makers to develop appropriate adaptation and mitigation strategies required to sustain crops distribution under future climates. Although the risks of wheat, soybean and rice cultivation may be significantly higher in the future because of high temperatures, heat waves, and droughts caused by climate change. [ABSTRACT FROM AUTHOR]

Increasing phosphorus (P) fertiliser use efficiency (PUE) is essential for addressing global nutrition security, food production costs, and environmental pollution. However, a myriad of soil × climate × crop × management interactions control PUE and adaptive tools are needed to put this complexity into context at the field scale. We conducted a Morris global sensitivity analysis on an evaluated agricultural system model (APSIM) across 132 years of climate data to identify important drivers of PUE in a subtropical dryland cropping system. We selected soil, crop, and management parameters to understand their influence on yield, biomass, and grain P export as measures of PUE in a subtropical wheat crop. We then examined three P fertility scenarios ranging from low to high (10, 40, 70 mg available P kg−1 at 0–10 cm, with subsoils to 180 cm at 9–10 mg available P kg−1) resulting in 1,782,000 simulations. In scenarios with medium or high P fertility and higher in-crop rainfall (> 200 mm), nitrogen (N) fertiliser was the most important driver for increasing yield and associated PUE. At low P fertility and lower in-crop rainfall (< 200 mm), P fertiliser and P sorption isotherm parameters were the most important drivers. This highlighted potential constraints to PUE analysis in the region if rainfall data is not available, and to modelling activities if P isotherm data is not available. Our study is the first to use a Morris global sensitivity analysis method for defining the influence of N and P fertiliser, and in-crop rainfall, as short-term (year–year) drivers of PUE. This method may be adapted to investigate emerging challenges in increasing PUE in other agricultural systems, but future research should include interactions with deep P banding, the effects of climate change, and demonstrate drivers over the longer-term (5–10 years). [ABSTRACT FROM AUTHOR]

The paper considers a problem of modeling the impact of the future climate on the yield of crops (in the example of spring wheat). The authors have analyzed the sources of climatic data and values of agrometeorological indicators for past and future periods. Using agroclimatic parameters, they have developed a mathematical model of the dependence of the median of the distribution function of spring wheat yield on climatic changes. The authors performed the calculations using this model and analyzed the obtained results. The analysis indicates a significant increase in the yield level of spring wheat in 2030–2040 and an increase in the risk of losses of its yield compared to historical data. [ABSTRACT FROM AUTHOR]

• Cd is an exceptionally toxic and extremely mobile element in both soil and plant systems. • Zn foliar application effectually enhanced wheat quality and its tolerance against Cd. • Current study will give interesting outcomes to cope with Cd contaminated soils and improving plant performance. To address food production challenges and combat heavy metal toxicity, an economically viable and sustainable approach is essential. Cadmium (Cd) contamination poses risks to crops and human health. Leveraging the structural similarity between zinc (Zn) and cadmium, we explored foliar Zn spray's potential to alleviate Cd toxicity and enhance wheat yield. Our field study near Peshawar, Pakistan, during the 2021–2022 winter, employed a split-plot design to investigate Zn's impact on wheat biofortification. Across three wheat varieties (JANBAZ-10, Pirsabak (PS)-2013, PS-2015), four foliar Zn levels (0 %, 0.5 %, 1.0 %, 1.5 % solution) were tested at various growth stages. Pirsabak-2015 exhibited optimal results, with a 1 % foliar Zn treatment producing the highest grain yield (3409 kg ha−1), biological yield (9022 kg ha−1), grains per spike (56), and 1000-grain weight (40.6 g). Application of 1 % biochar also significantly increased grain yield. Zn concentration peaked in grains and leaves with 1.5 % Zn solution, followed by 1.0 % Zn in the PS-2015 cultivar. Moreover, 1 % Zn application enhanced photosynthetic parameters and significantly reduced cadmium concentrations in leaves and grains. Notably, 1.0 % Zn led to improved stress tolerance as indicated by decreased electrolyte leakage and increased superoxide dismutase and peroxidase activities. Applying 1.0 % Zn solution as a foliar spray during different growth stages demonstrates the potential to enhance wheat yield, increase grain Zn content, and mitigate Cd toxicity in Cd-contaminated soil. Further research across diverse cereal crops and climates will help establish 1.0 % Zn foliar spray as an effective Cd stress mitigation strategy. [ABSTRACT FROM AUTHOR]

The paper presents the behaviour of some wheat varieties under the years 2014, 2016, 2018, 2019, 2020, 2023 conditions in Dobrogea area in demonstrative plots carried out at S.C. SPORT AGRA S.R.L. Amzacea, Constanta County. The main aspects emphasized in this research area have been: climatic conditions, obtained yield, and economical efficiency. Looking to the pathogens, for wheat Septoria tritici showed an AR between 17-27% and Pyrenophora graminea showed a low atack (AR = 4.5-17.5%). The pathogen Puccinia striiformis was present in low precentage (AR = 0-13.5%) in the 2018 April observations. For crop protection against pathogens, 2 foliar tratments with fungicides were applied, Artea 330 EC(ciproconazol 80 g/l + propiconazol 250 g/l) 0,4 l/ha, in March and Priaxor EC (fluxapiroxad 150 g/l + piraclostrobin 75 g/l) 1l/ha and in April respectively. Fungal treatments have stopped the development of foliar and ear diseases. The beneficial effects were found in the good yields obtained in 2014, 2016, 2018, 2019, except the years 2020 and 2023. In 2020 Otilia reached 2,370 kg/ha, Miranda 2,260 kg/ha and Solindo variety 4,297 kg/ha and Ursita 4,165 kg/ha in 2023. All these special yields have been obtained under non-irrigated conditions. [ABSTRACT FROM AUTHOR]

This study aimed to assess the impact of climate change on spring wheat production and to provide more accurate projections by directly investigating the effect of climate change on crop development and yield. Based on ground observation data from 1981 (1986 Dingxi) to 2020 in four case areas in northwest China, and the relationships among spring wheat growth, yield, and climate factors were analyzed and determined. The results showed that the extreme arid (Dunhuang) and semiarid regions (Dingxi) tend to be warmer and wetter, whereas arid regions (Wuwei) and semihumid regions (Linxia) tend to be warmer and drier. Correlation analysis indicated that yield of spring wheat in Wuwei, Dingxi, and Linxia Stations increased significantly. The decrease in the number of days (≥30 °C) during the growth period at Wuwei resulted in a significant yield increase in the past 40 years. At Dingxi, yield increased over the last 35 years due to the significant increases in precipitation during growth, the number of grains per panicle, and thousand‐grain weight, and the decrease in infertile spikelets. Further increase in global temperature and changes in future precipitation patterns will likely affect spring wheat production in northwest China. Core Ideas: The relationship between climate factors and growth and yield of spring wheat are explored.Dominant climatic factors affecting the growth of spring wheat in different climatic regions of northwest China are studied.Regional differences in spring wheat growth and yield in response to climate change are assessed.Climate change trends in different climatic regions over time are examined.Impact of climate change on crop development and yield are assessed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding the precipitation–yield relationships is crucial for evaluating the impacts of climate variability on crop yield. As a north–south transitional zone and major wheat-producing province in China, the Anhui was chosen for this study. Data on county-level yield, daily precipitation, and monthly large-scale atmospheric circulation indices were collected, and then 13 precipitation indices were calculated during different wheat-growing stages. The K-means clustering algorithm, trend detection analysis, Pearson correlation and stepwise regression analysis were used to reflect the variation characteristics of wheat yield and to identify crucial precipitation indices influencing climate-driven wheat yield (CDWY). The main results were as follows: (1) the wheat yield from 74 counties in Anhui was divided into three sub-regions, i.e., southern, central, and northern Anhui. (2) While wheat yield had all increasing trends in three sub-regions, a notable inter-annual fluctuation was shown for CDWY in northern Anhui, which was dominated by the quasi 7-year oscillation. (3) Total precipitation of extreme rainy days in the filling-maturation period posed significant negative impacts on CDWY for southern and northern Anhui. In contrast, the number of light rain during the same period had a critical impact on the central region. Furthermore, these crucial precipitation indices were mainly influenced by different atmospheric circulations. (4) Optimal yield prediction models were constructed based on three predictors as year, crucial precipitation indices, and corresponding atmospheric circulations for each sub-region, which had an average relative error from 8.98% to 12.58%. These findings will provide a scientific basis for understanding yield fluctuations by combining crucial precipitation indices and atmospheric circulations in Anhui. [ABSTRACT FROM AUTHOR]

Climate change is expected to alter the growing conditions of agricultural crops. With increasing surface temperature, future suitable areas for crop production will see an altitude shift. Such shift is an adaptation response of crops to climate change. However, in the study area there are a limited number of studies that have dealt with geographical shifts of crops caused by climate change. This study was conducted with the aim of assessing impacts of climate change on altitudinal migration of crops and length of growing period (LGP). The climate and crop modeling study were carried out using ArcGIS, Diva GIS and MaxEnt using 30 years of climate data for the period 1980 to 2009. Results showed that wheat (Triticum aestivum) and barley (Hordeum vulgare L.) would migrate upward along the altitudinal gradients in the coming 80 years. However, areas under these crops are expected to drop by 16–100%. Highly impacted areas are expected to increase, whereas low impacted and new suitable areas are expected to decline significantly. Suitable areas for sorghum (Sorghum bicolor) and teff (Eragrostis tef Zucc.) production are expected to increase. While wheat and barley are projected to be highly affected by future climate change, sorghum and teff should be relatively stable. No significant difference was observed in LGP between the considered RCP 2.6 and RCP 8.5 climate scenarios. Therefore, this study concluded that upward movement of crops was one mechanism to adapt to climate change, and new varieties resilient to future climate change needs to be developed. [ABSTRACT FROM AUTHOR]

Durum wheat is used as raw material for many foods. Climate change might be responsible for larger or smaller changes in crop yields. For the combined assessment of climate and crop, growing degree days (GDDs) have a crucial role. Two experimental lines and twelve commercial wheat (Triticum durum) varieties from diverse backgrounds were cultivated to compare their crop properties, yield, and protein content in terms of GDDs. The experiment was established in typical Mediterranean environment, using a randomized complete block design with blocks of varieties and lines for two growing seasons. For all varieties, GDDs to head emergence was affected by factor year, whereas GDDs from head emergence to harvest were influenced by both varieties and year. Protein content (%) was not affected by genotypes. Factor of variety and interaction variety × year had an impact on vitreousness; it was ranged from 79.75 % ('Makaras' variety) to 44.00 % ('Levante' variety). Yield had no statistically significant difference among varieties/lines. In durum wheat cultivation, up to head emergence, when GDDs increased, yield would be declined in contrast to GDDs from emergence to harvest; with the increasing of GDDs to harvest, yield was climbed. Nowadays, the integrations of and interpretation of GDDs in the evaluation of crop performance seem vital. [ABSTRACT FROM AUTHOR]

Cropping intensification is a solution to ensure global food security amid the threats of climate change and variability. Crop models are used extensively to evaluate the performance of single crop for a particular year under future climate. However, very few literatures are available on the application of crop models to evaluate intensive cropping systems. The study attempts to address this issue by evaluating the impact of projected climate derived from 29 GCMs during four future scenarios (mid and end-century under RCP4.5 and RCP8.5) on the performance of rice–wheat-groundnut cropping sequence in Mohanpur, India, using the sequence analysis module of DSSAT. A marked reduction in crop duration was simulated for rice (4 to 17 days) and wheat (1 to 16 days) during all four future scenarios. On the contrary, the duration of groundnut increased by 1–4 days. Under elevated CO2 concentration, an increase in biomass was simulated for all three crops. During future periods under elevated CO2, the yield of rice tends to increase under all the future scenarios whereas wheat and groundnut could decrease the magnitude of yield reduction due to an increase in temperature. Rice was benefited due to the carry-over effect of residue from preceding groundnut and, hence, could sustain the yield on the long-term. The uncertainty in simulation due to the selection of GCMs was also quantified. The study provided a better understanding on how a cropping sequence responds to projected climate rather than a single crop in a sequence, which can be used to develop better adaptation strategies. [ABSTRACT FROM AUTHOR]

• Growth stage-specific ECIs are important in estimating wheat yield. • Coupling crop model with machine learning can improve yield estimation accuracy. • Feature selection can reduce computation cost without affecting accuracy. • APSIM with RF and GA was the optimal model in estimating wheat yield. Assessing the impact of climate extremes on crop production is an important prerequisite for exploring agronomic practices to deal with changing climate. Process-based crop models are effective tools to assess the effect of climate change on crop yield, but cannot accurately express the impact of extreme climate events on crop yield. In this study, we developed a series of hybrid models by incorporating the APSIM model outputs and the most informative growth stage-specific extreme climate indices (ECIs) selected by two feature selection techniques (stepwise regression, SR and genetic algorithm, GA) into two machine learning algorithms (random forest, RF and light gradient boosting machine, LGBM) to evaluate impacts of climate extremes on wheat yields in the North China Plain (NCP). The results showed that the RF model outperformed the LGBM model in estimating wheat yield regardless of input variables. Applying feature selection to two machine learning algorithms can greatly reduce computational cost without significantly affecting model accuracy. The APSIM+RF-GA hybrid model was the optimal model for estimating wheat yield with explained 93 % of the observed yield variation and the accuracy of the model is improved by 33 % compared with the APSIM model alone. Extreme low temperature events before flowering and extreme high temperature events after flowering are the main extreme climate events causing the loss of wheat yield. In addition, we evaluated the impact of future climate change on wheat yield using the APSIM+RF-GA hybrid model and the APSIM model, respectively. Yields projected using a single APSIM model increased at all stations but yields projected using APSIM+RF-GA model decreased at 12.5–28.1 % of stations in the NCP under future climate scenarios. Compared to the APSIM+RF hybrid model, the future yield projected using single APSIM model might be overestimated by 12.7–19.2 % because of underestimating the yield loss caused by climate extremes. The increase of heat stress after flowering and frost stress during floral initiation to flowering were the main factors for future yield loss. Using the machine learning algorithm to make an external modification to the outputs of the APSIM model could improve the accuracy of yield estimation under extreme climate conditions and the method is more suitable for projecting future crop yield. This study is conducive to developing adaptation strategies to alleviate the negative impacts of future climate extremes on crop production. [ABSTRACT FROM AUTHOR]

Unpredicted variability in climate change on the planet is associated with frequent extreme high-temperature events impacting crop yield globally. Wheat is an economically and nutritionally important crop that fulfils global food requirements and each degree rise in temperature results in ∼6% of its yield reduction. Thus, understanding the impact of climate change, especially the terminal heat stress on global wheat production, becomes critically important for policymakers, crop breeders, researchers and scientists to ensure global food security. This review describes how wheat perceives heat stress and induces stress adaptation events by its morpho-physiological, phenological, molecular, and biochemical makeup. Temperature above a threshold level in crop vicinity leads to irreversible injuries, viz. destruction of cellular membranes and enzymes, generation of active oxygen species, redox imbalance, etc. To cope with these changes, wheat activates its heat tolerance mechanisms characterized by hoarding up soluble carbohydrates, signalling molecules, and heat tolerance gene expressions. Being vulnerable to heat stress, increasing wheat production without delay seeks strategies to mitigate the detrimental effects and provoke the methods for its sustainable development. Thus, to ensure the crop's resilience to stress and increasing food demand, this article circumscribes the integrated management approaches to enhance wheat's performance and adaptive capacity besides its alleviating risks of increasing temperature anticipated with climate change. Implementing these integrated strategies in the face of risks from rising temperatures will assist us in producing sustainable wheat with improved yield. • The rising temperature caused by climate change leads to abiotic stress harms crop growth and productivity. • Heat stress (HS) is limiting factor that declines and affects wheat production globally. • Perception and adaptation mechanism induced in response to stress explains the impact of climate change on crop "wheat". • Need of crop resilience to sustain the productivity matching the rising food demand. • Recently and successfully used management strategies improve stress tolerance and yield to cope with the existing/upcoming environmental changes. [ABSTRACT FROM AUTHOR]

Heading time is a critical feature determining wheat performance. Over the years, average global temperatures have increased, leading to adjustments in agronomic practices such as sowing dates and cultivar election. Bread wheat development is regulated by the effect of vernalization (Vrn), photoperiod sensitivity (Ps), and earliness per-se (Eps). The aim of this study was (i) to quantify the physiology behind wheat development for a wide range of contrasting commercial genotypes, and (ii) to design a simple wheat phenology prediction model that considers vernalization and photoperiod effects. Seventeen wheat genotypes that vary in VERNALIZATION-1 (Vrn1) , PHOTOPERIOD-1 (Ppd1) , and EARLINESS PER-SE D1 (Eps-D1) alleles were classified into four groups: Winter Sensitive (WS), Winter Insensitive (WI), Spring Sensitive (SS), and Spring Insensitive (SI). Field trials with vernalization treatments were carried out in different sites, years, and sowing dates. Genotypes with Vrn1 triple winter alleles exhibited the highest vernalization requirements, while cycle duration in spring genotypes remained constant. Photoperiod sensitivity was higher for those genotypes molecularly classified as Ppd1 sensitive but a differential response was evident when control and vernalization treatments were compared: after 6-week vernalization, Ps in WI decreased significantly without significant differences for WS, SS, and SI. For winter genotypes, the optimum photoperiod (Po) was the longest for the 6-week vernalized groups, and within that level of vernalization WI showed the longest Po values. The variability in Ps and Po values could be explained by the interaction between photoperiod and vernalization over Vrn1 upregulation and their consequent effect on wheat cycle duration. A wheat phenological model using photoperiod and vernalization correction factors was calibrated to predict heading dates in the Argentine wheat region. The model accurately predicted heading date with an error of 8.1, 6.5, 6.2, and 7.0 days for WI, WS, SI, and SS, respectively. The development of a model that uses simple universal process-based parameters is crucial for farmers, advisors, and researchers as it increases the precision in wheat cycle arrangement, but in a relatively parsimonious fashion. • Predicting phenology is essential for crop adaptation in a climate change scenario. • Photoperiod sensitivity had a differential response to increasing levels of pre-vernalization depending on Vrn1 and Ppd1. • Eps-D1 allele partially separated between precocity groups for average phase duration. • A heading date model using simple photoperiod and vernalization coefficients improves traditional wheat phenology prediction. [ABSTRACT FROM AUTHOR]

• Assessing impacts of climate change on wheat production in climate-suitable area. • Quantifying projected uncertainty of wheat production under future climate change. • SDM dominated projected uncertainty of wheat production in climate-suitable area. • CM was more certain when projecting future wheat production in climate-suitable area. Climate change poses a great challenge to global food security. Recently the combination of crop models (CMs), global climate models (GCMs), and species distribution models (SDMs) has been applied to assess the impacts of climate change on crop production with consideration of changes of crop climate-suitable regions. However, little is known about the uncertainty sources in the wheat production projections with consideration of crop climatic suitability under future climate. In this study, an integration method based on multiple CMs, SDMs, and GCMs was adopted to assess the impacts of climate change on winter wheat production in the Loess Plateau of China. A comprehensive analysis of different uncertainty sources (i.e. CM, GCM, SDM, Emission Scenario or Scen , and their interactions) was conducted through the ANOVA (Analysis of variance) method. Based on the projections of CM ensemble and ensemble-SDMs driven by 27 GCMs, multi-model mean winter wheat production would increase by 14.6% and 19.7% in 2041–2060 and 4.9% and 3.5% in 2081–2100 under SSP245 and SSP585, respectively. We found that the changes in climate-suitable areas of winter wheat caused larger changes in winter wheat production than the changes of per unit yield. SDM was the largest uncertainty contributor among the four main factors of CM, GCM, SDM, and Scen in the projections of winter wheat production under future climate in the Loess Plateau, accounting for about 20.3% of total uncertainty. At the same time, CM was the lowest uncertainty contributor and accounted for only about 3.0% of total uncertainty. Thus, CM was proved more certain in future projections of winter wheat production when considering the changes of crop climate-suitable areas. The efforts in this study could help to rationally integrate the crop modeling, species distribution modeling, and climate models on the projections of global wheat production under future global climate change. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-source environmental data were used to predict wheat yield in China. • We developed a robust machine learning model to predict wheat yield. • The random forest model using NIRv had better performance than models using NDVI and EVI. • NIRv from jointing to heading was the most important predictor in determining yield. Crop yield is controlled by different environmental factors. Multi-source data for site-specific soils, climates, and remotely sensed vegetation indices are essential for yield prediction. Algorithms of data-model fusion for crop growth monitoring and yield prediction are complicated and need to be optimized to deal with model uncertainty. This study integrated multi-source environmental variables (e.g., satellite-based vegetation indices, climate data, and soil properties) into random forest (RF) and support vector machine (SVM) models for wheat yield prediction in China. The performance of both RF and SVM models was investigated using different types of vegetation indices associated with other predictors. Relative importance and partial dependence analyses were used to identify the main predictors and their relationships with wheat yield. We found that using remotely sensed vegetation indices improved our model precision, and that near-infrared reflectance of terrestrial vegetation (NIRv) was slightly better than normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in predicting yield. NIRv was better in detecting climate stress on crops, and could capture more information regarding crop growth and yield formation. Compared with the SVM model, the RF model with NIRv and other covariates had better performance in wheat yield prediction, with R2 and RMSE being 0.74 and 758 kg/ha respectively. We also found that NIRv from jointing to heading was the most important predictor in determining yield, followed by solar radiation (especially during tillering–heading), relative humidity (during planting–tillering), soil organic carbon, and wind speed (throughout the growing season). In addition, wheat yield exhibited threshold-like responses to most factors based on our RF model. These threshold values can help to better understand how different environmental factors limit wheat yield, which will provide useful information for climate-adaptive crop management. Our findings demonstrated the potential of using NIRv for yield prediction. This approach is broadly applicable to other regions globally using publicly available data. [ABSTRACT FROM AUTHOR]

Agricultural vulnerability assessment is a comprehensive and powerful analytical tool to locate hotspots with states of susceptibility to harm and powerlessness of agricultural system. It plays an important role in guiding policy makers to plan and implement adaptation practices to mitigate potential climate risks to crop. However, due to the diversity in the methodology of vulnerability assessment, there are still knowledge gaps in assessing and comparing crop vulnerability to climate in different regions of the world, including Australia. Our main objectives were to: (1) present a vulnerability analytical method for wheat yield, which can be applied to different areas where long-term crop yield and climate data are available. (2) quantify temporal changes of the vulnerability of wheat yield to historical climate. (3) identify the most vulnerable region in study area to provide guidance for climate mitigation. Our study developed an indicator-based method using exposure, sensitivity, and adaptive capacity to assess the vulnerability of crop yield. We used the long-term recorded wheat yield data, combining with comprehensive exposure index to assess climate vulnerability of historical yield with a case study area of south-eastern Australia's wheat belt. The results showed that from the 1930s to the 1990s, both climate exposure and sensitivity had large inter-annual variations with no significant trends detected. However, adaptive capacity increased by 34% from 1930s to 1950s, 54% from 1950s to 1970s, and 54% from 1970s to 1990s. By contrast, climate vulnerability across the wheat belt decreased by 13% from 1930s to 1950s, 15% from 1950s to 1970s, and 33% from 1970s to 1990s. This is mainly due to increased adaptive capacity with the improvement of agronomic management practices, technological and socio-economic progress. We identified the areas with the highest vulnerability were in the northwestern parts of wheat belt while the least vulnerable areas located in the southeast. We expect that these identified vulnerable hotspots can be used by different landholders to allocate natural resources and policymakers to plan the priority mitigation to adapt to climate change in the local scale. Moreover, the method of vulnerability assessment used in this study can be applied to other regions around the world where long-term crop yield and climate data are available. [Display omitted] • A modified indicator-based method was developed to assess wheat yield vulnerability. • Machine learning was used to obtain weights of climate indices when calculating exposure. • Historical vulnerability of wheat yield decreased as the improved adaptive capacity. • The most vulnerable areas were the north-western parts of the wheat belt. • This vulnerability assessment method can be applied to other regions with long-term data of crop yield and climate. [ABSTRACT FROM AUTHOR]

In the era of global climate change, extreme weather events frequently occur. Many kinds of agro-meteorological disasters that are closely related to environmental conditions (such as sunshine hours, temperature, precipitation, etc.) are witnessed all over the word. However, which factor dominates winter wheat production in the middle and lower reaches of the Yangtze River remains unresolved. Quantifying the key limiting meteorological factor could deepen our understanding of the impact of climate change on crops and then help us to formulate disaster prevention and mitigation measures. However, the relative role of precipitation, sunshine hours and maximum daily temperature in limiting winter wheat yield in the middle and lower reaches of the Yangtze River is not clear and difficult to decouple. In this study, we used statistical methods to quantify the effect of precipitation, maximum temperature and sunshine hours extremes on winter wheat (Triticum aestivum L.) yield based on long time-series, county-level yield data and a daily meteorological dataset. According to the winter wheat growing season period (October of the sowing year to May of the following year), anomaly values of cumulative precipitation, average sunshine hours and average daily maximum temperature are calculated. With the range of −3 σ to 3 σ of anomaly and an interval of 0.5 σ (σ is the corresponding standard deviation of cumulative precipitation, mean maximum temperature and mean sunshine hours, respectively), the corresponding weighted yield loss ratio (WYLR) represents the impact of this kind of climate condition on yield. The results show that excessive rainfall is the key limiting meteorological factor that can reduce winter wheat yield to −18.4% in the middle and lower reaches of the Yangtze River, while it is only −0.24% in extreme dry conditions. Moreover, yield loss under extreme temperature and sunshine hours are negligible (−0.66% for extremely long sunshine hours and −8.29% for extreme cold). More detailed analysis results show that the impact of excessive rainfall on winter wheat yield varies regionally, as it causes severe yield reductions in the Huai River basin and the middle to southern part with low elevation and rainy areas of the study area, while for drier areas in the Hubei province, there is even an increase in yield. Our results disclosed with observational evidence that excessive precipitation is the key meteorological limiting factor leading to the reduction in winter wheat yield in the middle and lower reaches of the Yangtze River. The knowledge of the possible impact of climate change on winter wheat yield in the study area allows policy-makers, agronomists and economists to better forecast a plan that differs from the past. In addition, our results emphasized the need for better understanding and further process-based model simulation of the excessive rainfall impact on crop yield. [ABSTRACT FROM AUTHOR]