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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]

BACKGROUND Adaptive crop management is critical to food security in a changing climate, but the respective contributions of climate change and crop management to yields remain unclear. Thus, we distinguished and quantified the respective contribution of climate change and crop management on wheat yield between 1981 and 2018 in China, using first‐difference multivariate regression model. RESULTS: Wheat production in China has increased over the past four decades. Under the sole impact of climate change, wheat yield generally decreased (−5.45 to +1.09% decade−1). Crop management increased the wheat yield from 7.11 to 19.94% decade−1. Sensitivities of wheat yield to climatic variables (average temperature, accumulated sunshine hours, accumulated precipitation) were spatially heterogeneous; notably, in spring‐wheat planting areas, wheat yield was more susceptible to the negative impact of warming. In terms of relative contribution, the contribution of climate change to spring wheat yield was −24.08% to −5.41%, and the contribution to winter wheat was −4.98% to +34.69%. Crop management had a positive contribution to all wheat‐growing areas (65.31–96.84%). CONCLUSION: Crop management had a greater effect on wheat yield than climate change did. Among the three climatic variables investigated, average temperature had the dominant effect on wheat yield change; the impact of precipitation was minimal but mostly negative. The results provide insight regarding the contribution of climate change and crop management to wheat yield; adaptation measures may be more effective in planting areas where crop management contributes more, which will help stakeholders optimize crop management and adaptation strategies. © 2020 Society of Chemical Industry [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]

Phytohormones are important for the growth and development of plants. The objective of the experiment was to investigate the effect of foliar application of uniconazole (UCZ) at the four-leaf stage on hormone crosstalk and production of winter wheat. An experiment was carried out during 2015–2016 and 2016–2017 growth season in a semi-arid region, where UCZ at a concentration of 0 (CK, distilled water), 15 (FU15), 30 (FU30), and 45 (FU45) mg L−1 were sprayed on wheat crop at the four-leaf stage at a rate of 138.8 mL m−2. UCZ alters the endogenous hormone contents in flag leaves and in grains. UCZ inhibited gibberellic acid (GA) in flag leaves and in grains where the lower GA with UCZ improved the zeatin + zeatin riboside (Z + ZR) and abscisic acid (ABA) contents. The lower GA and higher Z + ZR and ABA contents with UCZ-treated plants improved the chlorophyll content and canopy apparent photosynthesis (CAP) as well as the grain-filling characteristics. The Z + ZR and ABA in flag leaves were positively correlated with chlorophyll content and CAP value while negatively with GA. Moreover, the Z + ZR and ABA were positively correlated with maximum grain weight, mean grain-filling rate, and maximum grain-filling rate, while negatively with GA level. Treatment FU30 significantly improved the chlorophyll content, CAP value, spike weight, grain-filling characteristics, and hormone contents of Z + ZR and ABA while it decreased the GA level. The hormone crosstalk with UCZ significantly increased the yield of wheat crop, where FU30 treatment performs better. [ABSTRACT FROM AUTHOR]

Comparing outputs of multiple climate and crop models is an option to assess the uncertainty in simulations in a changing climate. The use of multiple wheat models under five plausible future simulated climatic conditions is rarely found in literature. CERES-Wheat, DSSAT-Nwheat, CROPSIM-Wheat, and APSIM-Wheat models were calibrated with observed data form eleven sowing dates (15 October to 15 March) of irrigated wheat trails at Faisalabad, Pakistan, to explore close to real climate changing impacts and adaptations. Twenty-nine GCM of CMIP5 were used to generate future climate scenarios during 2040–2069 under RCP 8.5. These scenarios were categorized among five climatic conditions (Cool/Wet, Cool/Dry, Hot/Wet, Hot/Dry, Middle) on the basis of monthly changes in temperature and rainfall of wheat season using a stretched distribution approach (STA). The five GCM at Faisalabad and Layyah were selected and used in the wheat multimodels set to CO2 571 ppm. In the future, the temperature of both locations will elevate 2–3 °C under the five climatic conditions, although Faisalabad will be drier and Layyah will be wetter as compared with baseline conditions. Climate change impacts were quantified on wheat sown on different dates, including 1 November, 15 November, and 30 November which showed average reduction at semiarid and arid environment by 23.5%, 19.8%, and 31%, respectively. Agronomic and breeding options offset the climate change impacts and also increased simulated yield about 20% in all climatic conditions. The number of GCMs was considerably different in each quadrate of STA, showing the uncertainty in possible future climatic conditions of both locations. Uncertainty among wheat models was higher at Layyah as compared with Faisalabad. Under Hot/Dry and Hot/Wet climatic conditions, wheat models were the most uncertain to simulate impacts and adaptations. DSSAT-Nwheat and APSIM-Wheat were the most and least sensitive to changing temperature among years and climatic conditions, respectively. [ABSTRACT FROM AUTHOR]

Global dimming, a decadal decrease in incident global radiation, is often accompanied with an increase in the diffuse radiation fraction, and, therefore, the impact of global dimming on crop production is hard to predict. A popular approach to quantify this impact is the statistical analysis of historical climate and crop data, or use of dynamic crop simulation modelling approach. Here, we show that statistical analysis of historical data did not provide plausible values for the effect of diffuse radiation versus direct radiation on rice or wheat yield. In contrast, our field experimental study of 3 years demonstrated a fertilization effect of increased diffuse radiation fraction, which partly offset yield losses caused by decreased global radiation, in both crops. The fertilization effect was not attributed to any improved canopy light interception but mainly to the increased radiation use efficiency (RUE). The increased RUE was explained not only by the saturating shape of photosynthetic light response curves but also by plant acclimation to dimming that gradually increased leaf nitrogen concentration. Crop harvest index slightly decreased under dimming, thereby discounting the fertilization effect on crop yields. These results challenge existing modelling paradigms, which assume that the fertilization effect on crop yields is mainly attributed to an improved light interception. Further studies on the physiological mechanism of plant acclimation are required to better quantify the global dimming impact on agroecosystem productivity under future climate change. [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]

Climate change also affects food production. However, what will be the response of agricultural crops to future climate conditions across Europe is one of the key issues for the development, implementation and proper implementation of the new European agricultural policy. This paper addresses the potential response of selected European agricultural crops (wheat [Triticum aestivum L.], maize [Zea Mays L.], potato [Solanum tuberosum L.] and olive [Olea europaea L.]) to climate scenarios (RCP2.6, RCP4.5 and RCP8.5) in the second half of the 21st century (2070) by considering different global climate models (CC, HE and MP). Bioclimatic data from the ECOCROP database (temperature and precipitation ranges) for each considered crop and the WorldClim climatic database for Europe were used to develop maps showing the future potential crop climate suitability. The results confirm existing assumptions that, in general, the optimal climatic conditions for the production of selected crops will shift towards higher latitudes or altitudes. Indeed, this potential spatial shift is, of course, species specific and depends on many other natural and socio-economic factors. [ABSTRACT FROM AUTHOR]

Understanding the impacts of climate change on crop yield is important for improving crop growth and yield formation in northwestern China. In this study, we evaluated the relationship between meteorological dryness/wetness conditions and spring wheat yield in the Ili river basin (IRB). The climate and yield data from 1961 to 2013 were collected to analyze characteristics and correlations between these two variables using the standardized precipitation evapotranspiration index (SPEI), yield detrending method, modified Mann-Kendall test and Spearman correlation analysis. Main results were as follows: (1) correlations between monthly SPEI values (MSV) and climatic yield of spring wheat indicated that the dryness/wetness condition in May was a key factor affecting yield in the whole region; (2) although the MSV in May and yield fluctuated from negative to positive values in time, the severely and extremely dryness events were in good agreement with the higher yield losses; (3) each increase of 0.5 MSV in May promoted over 3% increase of yield in most part of IRB; however, the larger variability of MSV in May resulted in larger yield fluctuations; and (4) the Tibetan Plateau index in April showed significant correlations with the MSV in May and yield, which provided a precursory signal for decision-makers to better understand potential yield fluctuations. [ABSTRACT FROM AUTHOR]

Breeding for resilience to climate change is a daunting prospect. Crop and climate models tell us that global wheat yields are likely to decline as the climate warms, causing a significant risk to global food security. High temperatures are known to affect crop development yet breeding for tolerance to heat stress is difficult to achieve in field environments. We conducted an active warming study over two years to quantify the effects of heat stress on genetic variation of soft red winter (SRW) wheat (Triticum aestivum L.). Forty SRW cultivars and breeding lines were chosen based on marker genotypes at photoperiod sensitivity and reduced height loci. These genotypes were planted in a randomized complete block design replicated twice across two environments, ambient and artificially warmed. Average heading date occurred 5 days earlier in the warmed environment than in the ambient environment over both years (p ≤ 0.05). On average, grain yield was significantly reduced in the warmed environment by 211.41 kg/ha (p ≤ 0.05) or 4.84%, though we identified 13 genotypes with increased yield in response to warming in both years. Of these genotypes, eight had significantly increased N uptake while six showed significantly increased N utilization efficiency under warming. Under warming, genotypes with wild‐type alleles at the Rht‐D1 locus display significantly greater yields (p ≤ 0.01) and biomass (p ≤ 0.001) than genotypes with reduced height alleles. Of the 13 genotypes with higher (p ≤ 0.01) yields under warming, nine have the wild‐type allele at the Rht‐D1 locus in addition to being photoperiod insensitive. The next steps will be to validate these findings in other populations and to develop an efficient breeding/phenotyping scheme that will lead to more resilient cultivars. Increasing global temperatures will reduce wheat yield and productivity. To identify genetic variation for resilience to warming, we screened a set of diverse wheat genotypes in a warmed and a control environment. On average, grain yield was significantly (p < 0.05) reduced in the warmed environment though we identified genotypes which had higher yields in response to warming in both years. [ABSTRACT FROM AUTHOR]

The paper aims to explore the biasness in the RegCM climate model outputs for diverse agro-climatic zones of Uttar Pradesh, India, with emphasis on wheat (Rabi growing season) and rice (Kharif growing season) yields with and without bias correction using quantile mapping approach for the baseline period of 1971-2000. The result shows that RCM highly underestimated the maximum and minimum temperature. There exists a bias towards lower precipitation in annual and Kharif and higher in Rabi along with strikingly low intense warm (maximum temperature > 45 °C and 40 °C) and high cold events (maximum temperature < 20 °C and minimum temperature < 5 °C) in the RCM simulation and biased towards low extreme rainfall > 50 mm/day. Bias correction through quantile mapping approach, however, showed excellent agreement for annual and seasonal maximum and minimum temperature and satisfactory for extreme temperatures but drastically failed to correct rainfall. The study also quantified the biasness in the simulated potential, irrigated, and rainfed wheat and rice yield using DSSAT (Decision Support System for Agro-technology Transfer) crop model by employing observed, RCM baseline, and RCM baseline bias-corrected weather data. The grain yields of RCM-simulated wheat and rice were high while the bias-corrected yield has shown good agreement with corresponding observed yield. Future research must account for the development of more reliable RCM models and explicitly bias correction method in specific to complement future analysis. [ABSTRACT FROM AUTHOR]

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures. [ABSTRACT FROM AUTHOR]

Winter wheat ( Triticum aestivum L.)-fallow (WF) remains a prominent cropping system throughout the Central Great Plains despite documentation confirming the inefficiency of precipitation storage during the summer fallow period. Wheat yield is greatly influenced by available soil water at planting, and that quantity is influenced by cropping intensity, tillage intensity, crop grown prior to winter wheat planting, and length of the non-crop period prior to winter wheat planting. The objective of this study was to determine the effect of cropping system intensity, tillage, and crop prior to wheat planting on available soil water at planting and the probabilities of having certain minimum amounts of available soil water at wheat planting. Available soil water was measured at wheat planting for nine cropping systems varying in fallow frequency, tillage intensity, and crop prior to wheat over a 25-yr period at Akron, CO. Available soil water at wheat planting was found to be greatest (average 218 mm) for no-tillage (NT) and reduced-tillage (RT) systems in which a fallow period of 12–14 months preceded wheat planting. The lowest amounts of available soil water at wheat planting were found for the continuously cropped rotations (average 100 mm). The cumulative probability exceedance graphs of available soil water at planting fell into three groupings: 1) the five rotations that had a NT or RT fallow period prior to the next wheat crop; 2) the WF (conventional till) rotation; 3) the three rotations with either pea ( Pisum sativum L.) or proso millet ( Panicum miliaceum L.) prior to the next wheat crop. These graphs provide a valuable decision support tool for helping farmers assess risk in moving from a WF cropping system to a system of greater cropping intensity and diversity. [ABSTRACT FROM AUTHOR]

Wheat is an important cereal food crop providing key nutrients to humankind. Rusts are the most destructive pathogens of cereal crops, with the exception of rice, across the world and resistant cultivars have been widely employed to reduce the yield losses caused by them. The modern intensive monoculture of cultivars and changing climatic conditions has created congenial conditions for the emergence of new virulent races such as Ug99, which is a great concern for world food security. Conventional breeding efforts have not been effective in quickly developing new varieties with durable and broad‐spectrum resistance against the rapidly evolving rust pathogen races. However, in the last two decades, biotechnological methods such as marker‐assisted selection (MAS) and transgenic technology have provided novel strategies for enhancing resistance levels and durability in crop plants in a short span of time. Nevertheless, broad application of transgenics in agriculture is hindered by biosafety apprehensions. In recent years, improved versions of biotechnological breeding methods such as genomic selection, genome editing technologies, cisgenesis and intragenesis, RNA‐dependent DNA methylation (RdDM), agroinfiltration and reverse breeding are gaining popularity. These technologies provide a tremendous capability to manipulate crop plants more precisely than before and accelerate crop improvement efforts for sustained food production as well as overcoming safety concerns associated with food crops. [ABSTRACT FROM AUTHOR]

Sowing date is a crucial factor for wheat ( Triticum estivum L.) production. From traditional field experiments, optimum sowing date for wheat cultivation could be found out based on existing weather and soil conditions but not possible for futuristic sowing window to address climate change impacts. Crop simulation model can play an important role in this regards. So, a study was conducted at Regional Wheat Research Centre (RWRC), Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh to evaluate the CERES-wheat crop model in simulating optimum sowing window for wheat. Thirteen sowing dates starting from 21 October to 20 December at five days interval were tested with wheat cultivar BARI Gom-26. The model was calibrated and validated with one field experimental data followed by 30 years seasonal runs. Optimum sowing window for wheat is 15 November–30 November in Bangladesh. On an average, grain yield of wheat was reduced by 30–40 kg day −1  ha −1 when sown from 1 December to 20 December. Similarly, grain yield reduction was about 148–102 kg day −1  ha −1 with early sown wheat (21 October–14 November). [ABSTRACT FROM AUTHOR]

Polyamine metabolism is in relation with several metabolic pathways and linked with plant hormones or signalling molecules; in addition polyamines may modulate the up- or down-regulation of gene expression. However the precise mechanism by which polyamines act at the transcription level is still unclear. In the present study the modifying effect of putrescine pre-treatment has been investigated using the microarray transcriptome profile analysis under the conditions where exogenous putrescine alleviated osmotic stress in wheat plants. Pre-treatment with putrescine induced the unique expression of various general stress-related genes. Although there were obvious differences between the effects of putrescine and polyethylene glycol treatments, there was also a remarkable overlap between the effects of putrescine and osmotic stress responses in wheat plants, suggesting that putrescine has already induced acclimation processes under control conditions. The fatty acid composition in certain lipid fractions and the antioxidant enzyme activities have also been specifically changed under osmotic stress conditions or after treatment with putrescine. [ABSTRACT FROM AUTHOR]

Recently, sugarcane largely introduced in "Dera Ismail Khan" (D.1.Khan) Khyber Pakhtunkhwa Province, Pakistan, especially during the summer season. The current study was conducted at Arid Zone Research Centre (AZRC), Dera Ismail Khan (D.1.Khan) to evaluate cost and benefit, as well as climatic profile suitability for wheat cultivation, during 2014/2015 and 2015/2016 seasons. A sample of 200 respondents from 10 major wheat growing villages of the respective areas of the district was interviewed through pre-tested questionnaire. The study revealed that the cost of wheat production was Rs. 35,680 per acres, whereas output comes 1680 kg per acre (42 mounds) amounting Rs. 63,600 per acres. Farmers' margin also rises by adding the value of family labor and owned land, which is sufficient to sustain a normal family. Moreover, positive influence between return price and output of wheat was concluded from the study, whereas negative effect of cost was also observed. The output elasticity of Land preparation (LP), Seed and Sowing (SS), Farm inputs (Fl), Irrigation (Irr), Pesticides / insecticides (Pest) and Harvester threshing (HT) are 0.124587, 0.31244, 0.5874, 0.55461, 0.08248 and 0.65743, respectively. From climatic point of view, calculated accumulated growing degree days all over the studied seasons at the studied district were about 2663.5 degree days two seasons average. This is sufficient for the used type of wheat at this area to push the different growing stages and present economic crop yield. [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]

Crop water productivity is an important indicator of agricultural water use. Understanding the impacts of climatic and agricultural factors variation on crop water productivity will provide a theoretical basis for regional crop productivity improvement. In this study, crop productivities at two scales were estimated based on the analysis of the agricultural water use in Hetao irrigation district, China. The sensitivity analysis was used to explore the sensitivity of crop water productivity to the variation of climatic and agricultural factors. Contribution rate analysis method was adopted to quantitatively evaluate the driving force of water productivity changes due to the variation of climatic and agricultural factors during the study period. Results show that crop water productivity of wheat had an average of 0.85 kg/m 3 over the study period at the field scale, and it had an average of 0.46 kg/m 3 at the regional scale. Both displayed an obvious upward during the study period. Among the climatic factors, water productivity of wheat at both the field and regional scales had higher sensitivity to relative humidity and wind speed, and had a lower sensitivity to sunshine hours. Among the agricultural variables, the crop water productivity had a higher sensitivity to irrigation efficiency, and is less sensitive to the amount of fertilizer used. The increase of agricultural inputs and irrigation efficiency were the main factors contributing to water productivity increases in Hetao Irrigation district. Climatic variations within the study period had less impact on the water productivity of wheat. [ABSTRACT FROM AUTHOR]

Wheat crop is influenced by different microclimatic parameters like solar radiation, canopy temperature etc. Agronomic manipulation like change in row spacing and row direction can be used as a strategy to modify the microclimate of crop. Keeping these facts in view, field trials were conducted during rabi 2012-13 and 2013-14 under two experiments in first experiment wheat varieties HD 2967, PBW 550 and PBW 343 were sown under three row spacing viz. 15 cm, 22.5 cm and 30 cm. In second experiment, wheat varieties HD 2967, PBW 550 and PBW 343 were sown under two row direction viz. North-South (N-S) and East-West (E-W). Short wave radiation interception and canopy temperature was recorded under different treatments at 15 days interval. Among different row spacing, short wave radiation interception and canopy temperature was maximum at 30 cm row spacing (77.7% and 25.1°C) followed by 22.5 cm (75.7% and 24.2°C) and 15 cm row spacing (73.9% and 23.2°C), whereas under row directions short wave radiation interception and canopy temperature was more (76.5% and 23.9°C) in E-W row direction as compared to N-S row direction (75% and 23.2°C). Relationships were developed between dry matter accumulation and canopy temperature. Polynomial relationships gave significant R² value (0.66 & 0.69) under different treatments. This two year study indicated that agronomic manipulations play an important role in microclimate modification and canopy temperature significantly influence dry matter accumulation under different crop geometry. [ABSTRACT FROM AUTHOR]

We build on the price transmission framework to identify domestic wheat price effects of wheat export controls. We explicitly take into account that a harvest failure causes domestic price effects. Moreover, the analysis at the regional level provides further evidence of the functioning of export controls in a large country. Results suggest a pronounced regional heterogeneity in the strength of domestic price effects of the 2010/11 export ban in Russia. The wheat price dampening effects amount to up to 67% and are strongest in the major wheat exporting region with direct access to the world market. This effect is transmitted to other regions by increased and reversed interregional trade flows. In contrast, we find that regional variation of export controls' domestic price effects in Ukraine is rather small. [ABSTRACT FROM AUTHOR]

The field experiments were carried out during rabi seasons of 2013-14 and 2014-15 at Research Farm, School of Climate Change and Agricultural Meteorology, PAU, Ludhiana to study the effect of different temperature regimes, nitrogen levels and post anthesis strategies on phenology growth, development and grain yield of wheat. The experiment was laid out in Split- Split plot design having three temperature regimes (D1=October 30, D2=November 15 and D3= November 30) in main plot, three nitrogen levels (N1=RDF (Recommended dose of N), N2=125% RDF (25% more than recommended N), N3=150% RDF (50% more than recommended N) in sub plot and four heat stress management post-anthesis strategies (P0=Control, P1=Water sprayed, P2=Foliar spray of ZnSO4.7H20 (0.5%), P3=Thiourea (10 mM) at anthesis and 20 days after anthesis in sub-sub plot during both years. The agrometeorological indices such as accumulated growing Degree Days (AGDD), heliothermal units (HTU), photo thermal units (PTU) and heat use efficiency (HUE) were calculated and their relation with grain yield was also observed. The results showed that the crop sown on October 30 with 150% RDF when spray at or after anthesis at regular intervals with any of stress alleviating chemicals such as ZnSO4.7H2O (0.5%), Thiourea (10 mM) or water accumulated more number of growing degree days, photo thermal units and had higher heat use efficiency than other treatments because crop exhibited best growth and development as the favorable environmental conditions concided with higher heat unit requirement of different phenophases and gave higher grain yield [ABSTRACT FROM AUTHOR]

In order to study the effective water-saving cultivation of wheat in Sichuan under drought conditions to reduce grain loss, the effects of different irrigation methods (natural rainfall, artificial irrigation, and infiltrating irrigation) on soil physical characteristics and main agronomic characters and yield of wheat were analyzed. The results showed that different irrigation treatments at different growth stages had different effects on soil physical characteristics and agronomic characters of wheat. At the seedling and jointing stage, there were small differences between the treatments in soil compactness, soil bulk density, and soil water content. At the flowering and maturation stage, different irrigation treatments had great impacts on soil compactness, soil bulk density, soil water content, and agronomic characters of wheat. In the hilly areas of Sichuan Basin, infiltrating irrigation at the jointing stage was the best, and wheat yield increased significantly, 2 113.46 kg/hm² higher than that in the control. [ABSTRACT FROM AUTHOR]

The Crop Environment Resource Synthesis (CERES) models have been developed and utilized for the last 30 years to simulate crop growth in response to climate, soil, genotypes and management across locations throughout the world. We reviewed 215 papers found in the literature that contained field observed data where the CERES models were tested. Over 30 simulated variables of the CERES models have been tested in 43 different countries under various experimental treatments. Across all testing conditions, the CERES models simulated grain yield with a root mean square error (RMSE) of less than 1400 kg/ha (~10% relative error, RE), 1200 kg/ha (~20% RE) and 800 kg/ha (~10% RE) for maize, wheat, and rice, respectively. Phenological development was simulated with less than 7 days difference from the observations in most studies. The CERES models simulated aboveground biomass, harvest index, evapotranspiration, and soil water reasonably well too. The simulations of grain number (up to 4340 root mean square error, RMSE), grain weight (up to 22% error), intercepted photosynthetically active radiation (IPAR, up to 0.41 MJ/plant), leaf area index (LAI, 31.9% error), soil temperature (over 10°C difference), and nitrogen (N) dynamics (up to 80% error) were less accurate. In fact the average error of CERES model simulations tends to be higher under marginal crop growing conditions such as extreme heat or cold, water and nutrient deficit conditions. [ABSTRACT FROM AUTHOR]

Heat stress affects a number of physiological and morphological traits in crops. The present study was undertaken to identify heat tolerant wheat genotypes based on their response for days to flowering and days to maturity. A set of 95 wheat genotypes was evaluated under normal and late sowing conditions. Data analysis revealed that location, sowing time and genotype has marked effect on days to flowering; and sowing time and genotype has significant effect on days to maturity. Based on the time taken by the genotypes to flower and mature under late sowing condition, six genotypes showing heat tolerance in at least two locations were found to be resistant. Heat susceptibility index value was used to identify a total of thirteen genotypes as tolerant to heat for both the traits. The genotypes identified as heat tolerant would form an important resource for the development of high-yielding varieties under heat stress. [ABSTRACT FROM AUTHOR]

Climate is changing world over and is expected to continue to do so in future also and has potential impact on crop productivity. Punjab has a distinct sub-tropical climate with most rainfall occurring during summer mansoon. The main factors limiting wheat production in this region are rainfall and temperature. To investigate the weather variability impacts on wheat productivity in Ludhiana and Bathinda of Punjab, the present investigation was undertaken at Ludhiana and Bathinda with three dates of sowing i.e. 30th Oct. (D1, 15th Nov. (D2) and 30th Nov.(D3) and the three irrigation levels i.e. I1(recommended schedule), I2 (one additional irrigation, after CRI stage) and I3 (delayed irrigation by one week after heading). Two wheat cultivars viz. PBW 343 (V1) and WH 542 (V2) were also taken for studies and experiments were conducted during 2008-09 and 2009-10. A drastic decline in yield was observed during 2009-10 as compared to 2008-09 at both the locations. The crop sown on 30th Oct. and 15th Nov. gave better yield than the crop sown on 30th Nov. The reduction in yield was more during 2009-10 at Bathinda as compared to Ludhiana. The analysis showed no significant effect in different irrigation schedules under three dates of sowing. Cultivar PBW 343 performed better than WH 542 during both the years under study and application of one additional irrigation after CRI stage increased grain yield over recommended irrigation schedule. [ABSTRACT FROM AUTHOR]

Remote sensing-derived wheat crop yield-climate models were developed to highlight the impact of temperature variation during thermo-sensitive periods (anthesis and grain-filling; TSP) of wheat crop development. Specific questions addressed are: can the impact of temperature variation occurring during the TSP on wheat crop yield be detected using remote sensing data and what is the impact? Do crop critical temperature thresholds during TSP exist in real world cropping landscapes? These questions are tested in one of the world's major wheat breadbaskets of Punjab and Haryana, north-west India. Warming average minimum temperatures during the TSP had a greater negative impact on wheat crop yield than warming maximum temperatures. Warming minimum and maximum temperatures during the TSP explain a greater amount of variation in wheat crop yield than average growing season temperature. In complex real world cereal croplands there was a variable yield response to critical temperature threshold exceedance, specifically a more pronounced negative impact on wheat yield with increased warming events above 35 °C. The negative impact of warming increases with a later start-of-season suggesting earlier sowing can reduce wheat crop exposure harmful temperatures. However, even earlier sown wheat experienced temperature-induced yield losses, which, when viewed in the context of projected warming up to 2100 indicates adaptive responses should focus on increasing wheat tolerance to heat. This study shows it is possible to capture the impacts of temperature variation during the TSP on wheat crop yield in real world cropping landscapes using remote sensing data; this has important implications for monitoring the impact of climate change, variation and heat extremes on wheat croplands. [ABSTRACT FROM AUTHOR]

Wheat is a foremost staple food crop of Pakistan and plays a vital role for stability of country's economy and people's food requirement. Shortage of water has remained a consistent problem for the farmers over past few years and different agronomic techniques have been introduced into the limelight. But there is an immense scope of making some genetic manipulations to improve/enhance the drought tolerance of wheat. It has been observed by many researchers that yield in drought stress conditions, is a fine fusion of the traits like days taken by crop to reach physical maturity, water use efficiency, crop water use and harvest index. Drought being one of the main limiting factors of wheat production should be highly preferred in the future wheat improvement programs. [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]

Traits such as plant height (PH), juvenile growth habit (GH), heading date (HD), and tiller number are important for both increasing yield potential and improving crop adaptation to climate change. In the present study, these traits were investigated by using the same bi-parental population at early (F2 and F2-derived F3 families) and late (F6 and F7, recombinant inbred lines, RILs) generations to detect quantitative trait loci (QTLs) and search for candidate genes. A total of 176 and 178 lines were genotyped by the wheat Illumina 25K Infinium SNP array. The two genetic maps spanned 2486.97 cM and 3732.84 cM in length, for the F2 and RILs, respectively. QTLs explaining the highest phenotypic variation were found on chromosomes 2B, 2D, 5A, and 7D for HD and GH, whereas those for PH were found on chromosomes 4B and 4D. Several QTL detected in the early generations (i.e., PH and tiller number) were not detected in the late generations as they were due to dominance effects. Some of the identified QTLs co-mapped to well-known adaptive genes (i.e., Ppd-1, Vrn-1, and Rht-1). Other putative candidate genes were identified for each trait, of which PINE1 and PIF4 may be considered new for GH and TTN in wheat. The use of a large F2 mapping population combined with NGS-based genotyping techniques could improve map resolution and allow closer QTL tagging. [ABSTRACT FROM AUTHOR]

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics. [ABSTRACT FROM AUTHOR]

China, as one of the largest food consumers in the world, is currently experiencing obvious impacts of climate change. Exploring the impact of climate change on the production potential of maize, wheat, and rice in China is of great significance in adapting to climate change and safeguarding global food security. In this study the Global Agro-Ecological Zone (GAEZ) model and the Extreme-Point Symmetric Mode Decomposition (ESMD) model were used to explore the trends in production potential of China's three major crops in the context of climate change from 1960 to 2010. There was a quasi-3-year and quasi-5-year cycle in changes in maize, wheat, and rice production potential due to climate change in China. Climate change increased maize and rice production potential and reduced wheat production potential. The increase in minimum temperature was the main reason for the increased maize and rice production potential. Reduced wheat production potential was mainly caused by increased maximum temperature. Decreases in precipitation had a substantial negative impact on the production potential of all three crops. These results suggest that priority should be given to adjusting the structure of agricultural cultivation and appropriately expanding the planting area of maize and rice to adapt to climate change. Accelerating the construction of agricultural infrastructure to reduce the negative impact of declining precipitation and increasing daytime temperature on agriculture is also a high priority in order to safeguard food security. • Climate change increased maize and rice production potential of China • Increased minimum temperature was the primary reason for increased yields • Wheat production potential decreased mainly because of maximum temperature stresses • There was a quasi-3&5-year cycle in changes in crop production potentials [ABSTRACT FROM AUTHOR]

• Adverse weather conditions were analysed using a range of adverse weather indices. • Future UK climate is expected to remain favourable for wheat production. • Wetter winter and spring could, however, increase the risk of waterlogging. • Use of global climate model ensembles to quantify prediction uncertainty is advised. Winter wheat is an important crop in the UK, suited to the typical weather conditions in the current climate. In a changing climate the increased frequency and severity of adverse weather events, which are often localised, are considered a major threat to wheat production. In the present study we assessed a range of adverse weather conditions, which can significantly affect yield, under current and future climates based on adverse weather indices. We analysed changes in the frequency, magnitude and spatial patterns of 10 adverse weather indices, at 25 sites across the UK, using climate scenarios from the CMIP5 ensemble of global climate models (GCMs) and two greenhouse gas emissions (RCP4.5 and RCP8.5). The future UK climate is expected to remain favourable for wheat production, with most adverse weather indicators reducing in magnitude by the mid-21st century. Hotter and drier summers would improve sowing and harvesting conditions and reduce the risk of lodging. The probability of late frosts and heat stress during reproductive and grain filling periods would likely remain small in 2050. Wetter winter and spring could cause issues with waterlogging. The severity of drought stress during reproduction would generally be lower in 2050, however localised differences suggest it is important to examine drought at a small spatial scale. Prolonged water stress does not increase considerably in the UK, as may be expected in other parts of Europe. Climate projections based on the CMIP5 ensemble reveal considerable uncertainty in the magnitude of adverse weather conditions including waterlogging, drought and water stress. The variation in adverse weather conditions due to GCMs was generally greater than between emissions scenarios. Accordingly, CMIP5 ensembles should be used in the assessment of adverse weather conditions for crop production to indicate the full range of possible impacts, which a limited number of GCMs may not provide. [ABSTRACT FROM AUTHOR]

• Multifactorial experiment with three spring wheat varieties in climate chambers. • Varieties' growth responses were affected differently by climate factor combinations. • Yield losses due to chronic ozone exposure were significant in all varieties. • The landrace variety was more sensitive to ozone than the two modern varieties were. • Response to temperature change overrode responses to changes in other factors. When assessing potentials for crop production under future climatic conditions, multiple environmental parameters need to be included. An increase in carbon dioxide [CO 2 ], higher temperatures, and regional changes in tropospheric ozone [O 3 ] will influence the growth responses of existing crop species and varieties. Ozone is phytotoxic and a plant stressor at current concentrations, reducing yields worldwide, but possible interactions with changes in other abiotic factors have been considered very little. In this study, we have used eight combinations: two levels of temperature, two levels of [CO 2 ], and three [O 3 ] exposure regimes to assess the impact of medium-to-high ozone concentrations (80–100 ppb) on wheat growth when other abiotic factors change. Two modern spring wheat varieties (KWS Bittern and Lennox) and a landrace variety (Lantvete) were grown to maturity in climate chambers. We examined plant performance during growth as development rate, rate of photosynthesis, stomatal conductance, water use, and at harvest as total aboveground dry matter and grain yield. All three varieties lost yield in all treatments compared to the ambient treatment that had the following settings: Lowest temperature, ambient [CO 2 ], and very low [O 3 ]. For episodic ozone exposure in the ambient or high [CO 2 ] and high-level temperature treatment, the yield losses were 18 and 25%, respectively, for KWS Bittern; 44 and 34% for Lennox; and 16 and 37% for Lantvete. The yields of the modern varieties are significantly higher than the landrace variety in two out of eight treatments, although they are higher by weight in seven of the eight treatments. The landrace variety's fraction loss from its highest grain yield in the ambient treatment to the high-[CO 2 ]-and high-level-temperature-treatments was smaller than the modern varieties', showing a comparably higher degree of plasticity of performance. Current crop varieties might be more sensitive to ozone than older varieties, emphasizing the need of future breeding programs to expand the gene pool to provide more climate robust crops. [ABSTRACT FROM AUTHOR]