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Background and aims: Deep roots are needed to allow uptake of nitrogen (N) and water available in the deeper soil layers, to help tolerate increasingly extreme climates. Yet few studies in the field have been able to identify genetic differences in deep roots and how this relates to N and water uptake. This study aimed to identify the relationship between deep roots and tolerance to drought, and how this varies by genotype and with differing N fertilization. Methods: We grew 14 diverse genotypes of winter wheat in a semi-field facility in Denmark, in 2019 and 2020, with a soil depth gradient and a rain-out shelter to create a water stress. We used minirhizotron tubes reaching to 2.5 m depth to quantify differences in deep roots. We applied isotope tracers (15N and 2H labelled water) at 1.6-1.8 m at anthesis to assess differences in root function. Grain and straw 13C were used to assess drought stress. Results: We found differences in deep roots between genotypes, and slightly less deep root growth when more N was applied. Deep roots were correlated with grain yield, uptake of deep-placed tracers of water and N, and tolerance to drought. Genotypes with deeper roots had the biggest decrease in water stress and increase in grain yield, when their roots had access to deeper soil. Conclusion: Deeper roots were related to drought tolerance and increased yields. This suggests that deep rooting should be considered in future breeding efforts for more climate resilient crops. [ABSTRACT FROM AUTHOR]

The possible influence of global climate changes on agricultural production is becoming increasingly significant, necessitating greater attention to improving agricultural production in response to temperature rises and precipitation variability. As one of the main winter wheat-producing areas in China, the temporal and spatial distribution characteristics of precipitation, accumulated temperature, and actual yield and climatic yield of winter wheat during the growing period in Shanxi Province were analysed in detail. With the utilisation of daily meteorological data collected from 12 meteorological stations in Shanxi Province in 1964–2018, our study analysed the change in winter wheat yield with climate change using GIS combined with wavelet analysis. The results show the following: (1) Accumulated temperature and precipitation are the two most important limiting factors among the main physical factors that impact yield. Based on the analysis of the ArcGIS geographical detector, the correlation between the actual yield of winter wheat and the precipitation during the growth period was the highest, reaching 0.469, and the meteorological yield and accumulated temperature during this period also reached its peak value of 0.376. (2) The regions with more suitable precipitation and accumulated temperature during the growth period of winter wheat in the study area had relatively high actual winter wheat yields. Overall, the average actual yield of the entire region showed a significant increasing trend over time, with an upward trend of 47.827 kg ha−1 yr−1. (3) The variation coefficient of winter wheat climatic yield was relatively stable in 2008–2018. In particular, there were many years of continuous reduction in winter wheat yields prior to 2006. Thereafter, the impact of climate change on winter wheat yields became smaller. This study expands our understanding of the complex interactions between climate variables and crop yield but also provides practical recommendations for enhancing agricultural practices in this region [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]

Background: The severity and frequency of drought are expected to increase substantially in the coming century and dramatically reduce crop yields. Manipulation of rhizosphere microbiomes is an emerging strategy for mitigating drought stress in agroecosystems. However, little is known about the mechanisms underlying how drought-resistant plant recruitment of specific rhizosphere fungi enhances drought adaptation of drought-sensitive wheats. Here, we investigated microbial community assembly features and functional profiles of rhizosphere microbiomes related to drought-resistant and drought-sensitive wheats by amplicon and shotgun metagenome sequencing techniques. We then established evident linkages between root morphology traits and putative keystone taxa based on microbial inoculation experiments. Furthermore, root RNA sequencing and RT-qPCR were employed to explore the mechanisms how rhizosphere microbes modify plant response traits to drought stresses. Results: Our results indicated that host plant signature, plant niche compartment, and planting site jointly contribute to the variation of soil microbiome assembly and functional adaptation, with a relatively greater effect of host plant signature observed for the rhizosphere fungi community. Importantly, drought-resistant wheat (Yunhan 618) possessed more diverse bacterial and fungal taxa than that of the drought-sensitive wheat (Chinese Spring), particularly for specific fungal species. In terms of microbial interkingdom association networks, the drought-resistant variety possessed more complex microbial networks. Metagenomics analyses further suggested that the enriched rhizosphere microbiomes belonging to the drought-resistant cultivar had a higher investment in energy metabolism, particularly in carbon cycling, that shaped their distinctive drought tolerance via the mediation of drought-induced feedback functional pathways. Furthermore, we observed that host plant signature drives the differentiation in the ecological role of the cultivable fungal species Mortierella alpine (M. alpina) and Epicoccum nigrum (E. nigrum). The successful colonization of M. alpina on the root surface enhanced the resistance of wheats in response to drought stresses via activation of drought-responsive genes (e.g., CIPK9 and PP2C30). Notably, we found that lateral roots and root hairs were significantly suppressed by co-colonization of a drought-enriched fungus (M. alpina) and a drought-depleted fungus (E. nigrum). Conclusions: Collectively, our findings revealed host genotypes profoundly influence rhizosphere microbiome assembly and functional adaptation, as well as it provides evidence that drought-resistant plant recruitment of specific rhizosphere fungi enhances drought tolerance of drought-sensitive wheats. These findings significantly underpin our understanding of the complex feedbacks between plants and microbes during drought, and lay a foundation for steering "beneficial keystone biome" to develop more resilient and productive crops under climate change. FGv_ocsEwwAXXw4e-N4Vhr Video Abstract [ABSTRACT FROM AUTHOR]

The frequency of heat events is likely to increase due to global climate change, posing an increasing risk to wheat production. To optimize crop management strategies for coping with future climates, it is crucial to quantify the high-temperature occurrence during cropping seasons. Here, sixty-six years (1955~2020) of meteorological data during wheat reproductive growth were collected from six meteorological stations in the Huaibei Plain of Anhui Province. These data were analyzed to quantify the pattern and characteristics of post-anthesis heat stress for wheat crops. Five levels of annual mean daily maximum temperature (Tmax) were defined, from normal to extreme temperatures. Six crop developmental phases of winter wheat, i.e., phase i to phase vi, were divided from flowering to maturity. The data suggest an annual mean temperature of 17~24 °C from flowering to maturity, with an annual effective cumulative temperature ranging from 725 °C d to 956 °C d. The mean temperature and effective cumulative temperature increased as crop growth progressed, along with more frequent heat events during phase ii (8~14 days after anthesis) and phase iii (15~21 days after anthesis). We also found that the frequency of extremely high temperatures (≥33 °C) from 1990 to 2020 was significantly greater than that from 1957 to 1990. Interestingly, it was found that the intensity of post-anthesis night temperatures also increased with crop growth, i.e., from phase i to phase vi. Wheat grain yield increased with increasing effective accumulative temperature and Tmax, but it started to decline when thresholds of effective accumulative temperature and Tmax were reached. Overall, these findings could provide guidelines for winter wheat cropping in the Huaibei Plain, China, or similar climate and cropping regions. [ABSTRACT FROM AUTHOR]

Timely sowing and harvesting play important roles in agricultural production. The appropriate management decisions are necessary to cope with climate change and ensure high and stable crop yields. This study analyzed the effects of sowing date on the growth process of winter wheat and quantified the effects of climate resources and photothermal potential yield on theoretical yield at different stages of winter wheat. The analysis was based on the data from winter wheat interval sowing experiments conducted at the Hebei Gucheng Agricultural Meteorology National Observation and Research Station (Gucheng station) in north China (115°40′ E, 39°08′ N) during 2017–2019. The results showed that: (1) with the delay in sowing date, the growth process of winter wheat significantly advanced, the proportion of vegetative growth period significantly reduced (0.19% for per day delay), the proportion of reproductive growth period (RGP) significantly increased (0.12% for per day delay), and the prewintering light and temperature resources significantly reduced (12.2 °C·d accumulated temperature and 19.0 MJ·m−2 solar radiation for per day delay); (2) the theoretical yield of winter wheat showed a significant exponential relationship with the photothermal potential yield of the whole growth period: the minimum photothermal potential for yield formation was 26.6 t·ha−1, and the maximum theoretical yield was 12.6 t·ha−1; and (3) the wheat yield and yield stability were highest when the RGP photothermal potential yield was 16.0 t·ha−1 and the prewintering active accumulated temperature was 400 °C·d. This study also proposed a method to estimate the suitable sowing and harvesting dates to achieve high and stable yield of winter wheat, showing that the suitable sowing dates of winter wheat at Gucheng station from 1997 to 2021 ranged from 1 to 15 October, with no significant interannual variation; the suitable harvesting period ranged from 5 June to 10 July and showed a trend of gradual advance with the delay of the year. The results of the study provide a reference for sowing date adjustment of crops to adapt to climate change. [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]

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]

Crop phenology is considered to be an important indicator reflecting the biophysical and physiological processes of crops facing climate change. Therefore, quantifying crop phenology change and its relationship with climate variables is of great significance for developing agricultural management and adaptation strategies to cope with global warming. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) product, winter wheat green-up date, heading date, jointing date, and maturity date were first retrieved by Savitzky–Golay (S-G) filtering and threshold methods and then the variation of winter wheat phenology and its correlation with mean (Tmean), minimum (Tmin), and maximum (Tmax) temperature and precipitation (Pre) during 2003–2019 were comprehensively analyzed in Shandong Province, China. Results showed that green-up date, jointing date, heading date, and maturity date generally ranged from 50–70 DOY, 75–95 DOY, 100–120 DOY, and 130–150 DOY. Winter wheat phenology presented a spatial pattern of the South earlier than the North and the inland earlier than the coastal regions. For every 1° increase in latitude/longitude, green-up date, jointing date, heading date, and maturity date were respectively delayed by 3.93 days/0.43 days, 2.31 days/1.19 days, 2.80 days/1.14 days, and 2.12 days/1.09 days. Green-up date and jointing date were both advanced in the West and delayed in the Eastern coastal areas and the South, and heading date and maturity date respectively showed a widespread advance and a delayed tendency in Shandong Province, however, the trend of winter wheat phenological changes was generally insignificant. In addition, green-up date, jointing date, and heading date generally presented a significant negative correlation with mean/minimum temperature, while maturity date was positively associated with the current month maximum temperature, notably in the West of Shandong Province. Regarding precipitation, a generally insignificant relationship with winter wheat phenology was detected. Results in this study are anticipated to provide insight into the impact of climate change on winter wheat phenology and to supply reference for the agricultural production and field management of winter wheat in Shandong Province, China. [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]

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]

• 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]

Acquiring spatiotemporal patterns of phenological information and its drivers is essential for understanding the response of crops to climate change and implementing adaptation measures. However, current approaches to obtain phenology and analyse its drivers have deficiencies such as sparse observations, excessive dependence of remote sensing inversion on sensors, and inevitable difficulties in upscaling site-based crop models into larger regions. Based on the Wang-Engel temperature response function, we improved the Crop Estimation through Resource and Environment Synthesis-Wheat (CERES-Wheat) model. First, we calibrated the model at the regional scale and evaluated its performance. Furthermore, the spatiotemporal changes in winter wheat phenology in China from 2000 to 2015 were analysed. The results showed that the improved model significantly enhanced the simulation accuracy of the anthesis and maturity dates by averages of 13% and 12% in most planting areas, especially in the Yunnan-Guizhou Plateau (YG) with improvements of 26% and 28%. The simulated phenology of winter wheat grown in a colder environment (e.g., the average temperatures during the vegetative growth period range from 0 to 5°C and from 15 to 20°C, and the reproductive growth period ranges from 10 to 15°C) also notably improved. These results confirmed that the original temperature response function indeed had limitations. Further analyses revealed that the key phenological dates and growth periods over the past 16 years were dominantly advanced and shortened. Specifically, the anthesis date, vegetative growth period (VGP), and reproductive growth period (RGP) indicated obviously spatial characteristics. For example, the anthesis date and VGP in the North China Plain (NCP) and the Middle-Lower Yangtze Plain (YZ) and the RGP in northwestern China (NW) showed opposite trends of delay and prolongation as comparing with the dominant patterns. Sensitivity analysis indicated that the key phenological dates and growth periods were advanced and shortened as the minimum (Tmin) and maximum temperatures (Tmax) rose, while they were postponed and prolonged with the increased precipitation. However, their responses to solar radiation did not show spatial consistency. Additionally, we found that the sensitivity of phenology to climatic factors differed across subregions. In particular, phenology in southwestern China and YG was more sensitive to Tmin, Tmax, and solar radiation than in the NCP and NW. Moreover, the sensitivity to precipitation in NW was higher than that in YZ. Totally, the improved crop model could provide more refined spatial characteristics of phenology at a large scale and benefit to explore its drivers more objectively. Furthermore, our results highlight that different planting areas should adopt suitable adaptation measures to cope with climate change impacts. Ultimately, the improved model is promising to enhance the accuracy of yield prediction and provide powerful tools for assessing regional climate change impact and adaptability. [ABSTRACT FROM AUTHOR]

The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeliness and robustness of the final yield prediction. In this study, the accumulated biomass (AB) simulated by the Agricultural Production Systems sIMulator (APSIM) model and multiple climate indices (e.g., climate suitability indices and extreme climate indices) were incorporated into SRM to predict the wheat yield in the North China Plain (NCP). The results showed that the prediction model based on the random forest (RF) algorithm outperformed the prediction models using other regression algorithms. The prediction for the wheat yield at SM (the period from the start of grain filling to the milky stage) based on RF can obtain a higher accuracy (r = 0.86, RMSE = 683 kg ha−1 and MAE = 498 kg ha−1). With the progression of wheat growth, the performances of yield prediction models improved gradually. The prediction of yield at FS (the period from flowering to the start of grain filling) can achieve higher precision and a longer lead time, which can be viewed as the optimum period providing the decent performance of the yield prediction and about one month's lead time. In addition, the precision of the predicted yield for the irrigated sites was higher than that for the rainfed sites. The APSIM-simulated AB had an importance of above 30% for the last three prediction events, including FIF event (the period from floral initiation to flowering), FS event (the period from flowering to the start of grain filling) and SM event (the period from the start of grain filling to the milky stage), which ranked first in the prediction model. The climate suitability indices, with a higher rank for every prediction event, played an important role in the prediction model. The winter wheat yield in the NCP was seriously affected by the low temperature events before flowering, the high temperature events after flowering and water stress. We hope that the prediction model can be used to develop adaptation strategies to mitigate the negative effects of climate change on crop productivity and provide the data support for food security. [ABSTRACT FROM AUTHOR]

Drought is a serious threat to agriculture particularly of rainfed regions like Potohar region of Pakistan. Wheat, staple food crop of this region, is mostly affected by drought, thereby impacting food security of the region. Effective drought monitoring and its impacts on wheat production have therefore been the key concerns of the farmers and policy makers in order to plan for any upcoming food crises in the region. A proactive drought management approach is needed in this regard. This study aims to assist the decision-making process for drought monitoring and yield predictions, as it informs drought assessment and its impacts on crop yield using drought and vegetation indices along with climate and crop yield data. This research quantifies recurrent drought events for Rubi (wheat crop) season (November–April) from 2000 to 2018 in the Potohar region using indices such as standardized precipitation index (SPI), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI) along with climatic parameters, i.e., mean temperature, rainfall and soil moisture. Results show a strong positive correlation of wheat yield with rainfall (r = 0.97) and soil moisture (r = 0.88), while a strong negative correlation with temperature (r = − 0.98). Three moderate (2000–01, 2001–02, 2009–10) and two weak (2011–12, 2017–18) drought events are identified using SPI, whereas two more drought events (2007–08 and 2016–17) are noticed when vegetation indices are used. Looking at Oceanic Niño Index (ONI), no definite pattern relating to ongoing La-Nina or El-Nino conditions during the specific drought years is observed for the Potohar region. Artificial neural network (ANN), a multilayer perception (MLP) model, is applied afterward to see the individual impact of each study parameter on wheat yield. Soil moisture is found to impact yield by 100%, temperature as 74%, rainfall as 61% and then rest of the indices. The findings of this study not only provide a scientific base for future studies on drought indicators, but assert that a cumulative approach is needed for effective drought management at national level. [ABSTRACT FROM AUTHOR]

The availability of coherent time series of crop statistics is essential for better analyzing the past, understanding the present, and predicting future trends in yield, area, and production. Importantly, such data also underpin assessments and subsequent policy actions that can shape future food systems that are more resilient in the face of climate change and that are sustainable in terms of resource use efficiency. In the European Union (EU), there currently is no legal obligation for EU countries to provide subnational crop statistics. However, such data could improve in-season crop forecasts, climate change impacts and adaption need assessments, and evaluation of agri-environmental schemes. The dataset described in this paper includes a harmonized collection of subnational crop statistics on area, production, and yield, collected for the EU from National Statistical Institutes (NSIs) and the EUROSTAT REGIO database – subnational crop statistics voluntarily contributed by EU countries. The crops considered are wheat (including soft and durum wheat), barley (including winter and spring barley), grain maize, sunflower, and sugar beet. All data are harmonized towards the hierarchical structure of the EUROSTAT legend and the regional classification of NUTS (Nomenclature of Territorial Units for Statistics) version 2016, to provide coherent time series. A total of 344 282 records is presented here (115 974 for wheat, 122 705 for barley, 35 274 for grain maize, 34 916 for sugar beet, and 35 413 for sunflower) for a total of 961 regions in 27 EU countries. Statistics are reported from 1975 to 2020, with a median time-span range of 21 years. A flagging system details, for each data record, information on data sources, processing steps, and quality-checking results. This includes consistency checks between reported values for area, yield and production, identification of null values, missing and calculated data, information on crop legend matching, and NUTS versioning. We illustrate the value of this dataset by analyzing impacts on crops and production zone shifts in Europe due to climatic and economic factors. Recommendations and future developments of collecting subnational statistics at the EU level are briefly discussed. The dataset is accessible with European Commission Authentication Service (ECAS) login at 10.2905/685949ff-56de-4646-a8df-844b5bb5f835. [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]

In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha−1 and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability. [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]

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]

Highlights • A novel mathematical approach for crop yield forecast is proposed. • RBF interpolation models are based only on agrometeorological variables. • RBF models' predictions made four months prior to harvest have good quality. • The proposed mathematical approach can be embedded in mechanistic crop models. Abstract Durum wheat is an important crop in semi-arid Mediterranean regions as Andalusia, an autonomous community in the southern part of Spain. Accurate early predictions of durum wheat yield can provide precious information for within-season adjustment of crop managing as well as for economical and political stakeholders. In this study, an alternative methodology to mechanistic crop models is proposed for within-season early prediction of durum wheat yield in Spain based on estimates for its larger producer community, Andalusia. The proposed Radial Basis Functions (RBF) interpolation models are based on the sown area and a large number of climatic variables. Global warming and increasing occurrence of extreme weather events are only two of the factors that make crop yield forecast extremely difficult as they can lead to an increased interannual yield variability. Nevertheless, the RBF models proposed presented good quality yield predictions clearly outperforming multivariate linear models used as benchmark. Moreover, RBF models' predictions made four months prior to harvest are able to capture the trend of the yield series as well as near-harvest predictions. [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]

Multi-ovary wheat (three pistil) is a unique germplasm for the seed production of hybrid wheat. The purpose of the present study was to transfer the multi-ovary trait to semi-dwarf plants to increase the production of grains in wheat crops. Therefore, tall, semi-dwarf, and dwarf plants were crossed with plants with the three-pistil trait. A three-pistil tall plant was used as the female parent, while tall (Synthetic hexaploid), semi-dwarf, and dwarf plants were used as male parents. F1 and F2 progenies with parents were planted in 2015-16 using RCBD. The outcome of the crosses showed that multi-ovary tall plants gave significant difference for all five traits (days to maturity, plant height, number of seeds per spike, grain weight per spike, and grain yield per unit area) in both generations. The greatest number of grains per spike and grain yield per unit area were obtained from the cross of three-pistil tall and dwarf parent (P1/P6) in the F1 and F2 generations. The cross also resulted in a significant reduction in height (96 cm). Further heterosis studies conducted with crosses between three-pistil tall and dwarf parent (P1/P6) showed the greatest heterosis and heterobeltiosis for the number of grains per spike (60.0 and 26.19%, respectively) and grain yield per m2 (27.68 and 2.85%, respectively). In the case of grain weight per spike, the heterosis value was also positive and significant (17.7). Meanwhile, for other traits, their values were negative for heterosis and heterobeltiosis. High numbers of grains and grain weight were found to be associated with positive heterobeltiosis and in turn the grain yield per m2, but plant height and maturity had negative affirmation with heterobeltiosis. Heterosis studies also indicated the dominant gene action for the three-pistil trait. Thus, the study clearly signified that grain yield can be increased by using the multi-ovary genotype with the semi-dwarf height. This new germplasm will be helpful for breeders to increase the production of wheat crops in the southern climate of Pakistan. [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]

Clarifying the spatiotemporal variation of crop irrigation water requirement (IWR) under the background of climate change is an essential basis for water resource management, determining the irrigation quota and adjusting the planting structure. Using 61 years of climate data from 205 stations in Northwest China, this study investigated the spatiotemporal variations of climatic factors and IWR during the growth period of five main grain crops (spring wheat, winter wheat, spring maize, summer maize, and rice) and explored the dominant climatic driving factors of IWR variation. Results showed that (1) the IWR of grain crops showed distinct differences. Rice was the highest water consumption crop (mean of 753.78 mm), and summer maize was the lowest (mean of 452.90 mm). (2) The variation trends and average values of IWR of different grain crops have spatial heterogeneity across Northwest China. For most crops, high values and increasing trends of IWR were mainly located in eastern Xinjiang, western Gansu, and western Inner Mongolia. (3) Tmax (maximum temperature), Tmin (minimum temperature), and Peff (effective precipitation) showed an increasing trend during the growth period of each grain crop, while U10 (wind speed at 10 m height), SD (solar radiation), and RH (relative humidity) presented decreasing trends. (4) SD, Tmax, and U10 promoted the increase of grain crops' IWR, while Peff and RH inhibited it. The impacts of climatic factors on the grain crop IWR differed among different regions. Peff was the most influential factor to the IWR of all grain crops in most areas. Therefore, under the premise of a significant increase in T and uncertain precipitation mode in the future, it is urgent to take effective water-saving measures according to the irrigation needs of the region. To cope with the adverse impact of climate change on the sustainable development of agriculture in the northwest dry area, to ensure regional and national food security. [ABSTRACT FROM AUTHOR]

Most studies quantifying the impacts of climatic variability and warming on crop production have focused on yields and have overlooked potential areal and frequency responses, potentially biasing future projections of food security in a warming world. Here we analyze US winter wheat production from 1970 to 2017 and find that harvest area ratio (harvested area/planted area, HAR) has declined while yields have risen, standing in stark contrast to other US staple crops. Although lower profitability due to declining wheat prices appears to explain the HAR trend, fluctuating wheat yields—largely explained by temperature exposure—drive the interannual variation of HAR. Our analysis suggests that warming‐induced declines in HAR are comparable in magnitude to heat‐related yield losses, and lower wheat prices amplify the sensitivity of HAR to warming and yield variation. Although irrigation mitigates some temperature‐driven yield effects, it does little to change HAR, likely due to infrastructure cost and limited influence on relative profitability. Our results suggest that an accurate quantification of climate impacts on crop production must account for harvested area response, and that future adaptation strategies should not only target crop choice and management but also harvest incentives. [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]

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Projected warming and drying trends over the Mediterranean region represent a substantial threat for wheat production. The present study assesses winter wheat yield response to potential climate change and estimates the quantitative effectiveness of using early flowering cultivars and early sowing dates as adaptation options for the major wheat production region of Portugal. A crop model (STICS) is used for this purpose, which is calibrated for yield simulations before projecting future yields. Climate projections over 2021–2050 and 2051–2080 under two emission scenarios (RCP4.5 and RCP8.5) are retrieved from bias-adjusted datasets, generated by a ten-member climate model ensemble. Projected intensification of water deficits and more frequent high-temperature events during late spring (April–June), coinciding with the sensitive grain filling stage, primarily result in continuous mean yield losses (relative to 1981–2010) by − 14% (both scenarios) during 2021–2050 and by − 17% (RCP4.5) or − 27% (RCP8.5) during 2051–2080, also accompanied by increased yield variabilities. Of evaluated adaptation options at various levels, using earlier flowering cultivars reveals higher yield gains (26–38%) than that of early sowings (6–10%), which are able to reverse the yield reductions. The adopted early flowering cultivars successfully advance the anthesis onset and grain filling period, which reduces or avoids the risks of exposure to enhanced drought and heat stresses in late spring. In contrast, winter warming during early sowing window could affect vernalization fulfillment by slowing effective chilling accumulation, thus increasing the pre-anthesis growth length with limited effects on advancing reproductive stage. Crop yield projections and explored adaptation options are essential to assess food security prospects (availability and stability) of dry Mediterranean areas, providing crucial insights for appropriate policymaking. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) is a major staple food crop grown worldwide on >220 million ha. Climate change is regarded to have severe effect on wheat yields, and unpredictable drought stress is one of the most important factors. Breeding can significantly contribute to the mitigation of climate change effects on production by developing drought-tolerant wheat germplasm. The objective of our study was to determine the annual genetic gain for grain yield (GY) of the internationally distributed Semi-Arid Wheat Yield Trials, grown during 2002-2003 to 2013-2014 and developed by the Bread Wheat Breeding program at the CIMMYT. We analyzed data from 740 locations across 66 countries, which were classified in low-yielding (LYE) and medium-yielding (MYE) environments according to a cluster analysis. The rate of GY increase (GYC) was estimated relative to four drought-tolerant wheat lines used as constant checks. Our results estimate that the rate of GYC in LYE was 1.8% (38.13 kg ha-1 yr-1), whereas in MYE, it was 1.41% (57.71 kg ha-1 yr-1). The increase in GYC across environments was 1.6% (48.06 kg ha-1 yr-1). The pedigrees of the highest yielding lines through the coefficient of parentage analysis indicated the utilization of three primary sources--'Pastor', 'Baviacora 92', and synthetic hexaploid derivatives--to develop drought-tolerant, high and stably performing wheat lines. We conclude that CIMMYT's wheat breeding program continues to deliver adapted germplasm for suboptimal conditions of diverse wheat growing regions worldwide. [ABSTRACT FROM AUTHOR]

Understanding dynamics of soil organic carbon (SOC) stock in agroecosystems under climate change is imperative for maintaining soil productivity and offsetting greenhouse gas emissions. Simulations with the SPACSYS model were conducted to assess the effects of future climate scenarios (RCP2.6, RCP4.5 and RCP8.5) and fertilisation practices on crop yield and SOC stock by 2100 for a continuous winter wheat cropping system in southeast England. Weather data between 1921 and 2000 was considered as the baseline. SPACSYS was first calibrated and validated with the data of the Broadbalk continuous winter wheat experiment for over a century. Six treatments were used: no fertiliser, a combination of chemical nitrogen, phosphorus and potassium with three nitrogen application rates (N1PK, N3PK and N5PK), manure only (FYM, close N application rate to N5PK) and a combination of manure and chemical nitrogen application (FYMN, the same chemical N application rate as N3PK). Compared with the observations, SPACSYS was able to simulate grain yields and dynamics of SOC and TN stocks. Our predications showed that wheat yield would increase by 5.8–13.5% for all the fertiliser application treatments under future climate scenarios compared to that under the baseline because of a gradual increase in atmospheric CO 2 concentration. Meanwhile, the SOC stock can increase for the practices under the scenarios except the NPK fertiliser practices under RCP2.6. Increased C input through "CO 2 -fertilisation effects" can compensate C losses by soil respiration under the RCP scenarios. We concluded that manure application practices can be considered as a sustainable strategy for enhancing wheat yield and soil C sequestration under the future climate scenarios. [Display omitted] • SPACSYS was able to simulate wheat yield of different varieties, SOC and TN stocks. • Future climate change positively impacts wheat yield. • Climate change effects on SOC varied among RCP scenarios and fertiliser practices. • Manuring acts as a sustainable strategy for soil productivity and C sequestration. [ABSTRACT FROM AUTHOR]

In this study, an empirical assessment approach for the risk of crop loss due to water stress was developed and used to evaluate the risk of winter wheat loss in China, the United States, Germany, France and the United Kingdom. We combined statistical and remote sensing data on crop yields with climate data and cropland distribution to model the effect of water stress from 1982 to 2011. The average value of winter wheat loss due to water stress for the three European countries was about −931 kg/ha, which was higher than that in China (−570 kg/ha) and the United States (−367 kg/ha). Our study has important implications for the operational assessment of crop loss risk at a country or regional scale. Future studies should focus on using higher spatial resolution remote sensing data, combining actual evapotranspiration to estimate water stress, improving the method for downscaling of statistical crop yield data and establishing more sophisticated zoning methods. [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]

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]

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]

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]

Field experiments for six seasons (2008-09 to 2013-2014) and simulations for mid-century (2021-2050) were carried out to (i) understand impact of inter- and intra- seasonal temperature variability on wheat yield, and (ii) identify best management intervention in relation to temperature variability. In the field experiments, treatments were three dates of planting, two inbred varieties and two irrigation schedules replicated thrice. Simulation with CERES-Wheat model pointed that variability of 5.5 percent in Tmax and 3.8 percent in Tmin would cause 11.2 percent variability in yield. The variation in yield would also vary with date of planting. It was relatively less in Nov 05 sown wheat than other dates,showing that in mid-century yield can be sustained by planting wheat at this date. However, at present growing of longer duration varieties in last week of October with adequate irrigation, medium to longer duration in 1st week of November is the practical adaptive measure to minimize impact of temperature variability on wheat yield. [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]

Scab is one of the main diseases of winter wheat in Yangtze-Huaihe River region in China, whose monitoring and forecasting timely in large area will help to adjust the pesticide spraying measures reasonably and realize the purpose of reducing disaster and increasing yield. In this study, we carried out remote monitoring tests of winter wheat scab in 4 counties (Donghai, Lianshui, Taixing and Dafeng) of Jiangsu Province in Yangtze-Huaihe River region, analyzed the interaction and relationship between winter wheat scab characteristics, climatic factors, growth parameters and spectral information, selected major scab’s sensitive factors, and established the remote sensing estimation model of winter wheat scab disease index based on interactions between spectral information and climatic factors. The results showed that: 1) There is a good correlation between the daily mean temperatures in different time scales, of which the correlation between the daily mean temperature of 7 days and the daily mean temperature of 10 days is the highest, and the correlation coefficient is 0.966 5. The correlation coefficient between the daily mean temperature of 5 days and the winter wheat scab disease index is the largest, which is 0.772 6, indicating that the daily average temperature of 5 days in different time scales has the most obvious effect on the occurrence of scab in winter wheat. 2) Similar to the daily mean temperature, there are different degrees of correlation between the daily mean relative humidity at different time scales. The correlation coefficient is the largest between daily mean relative air humidity of 7 days and daily mean relative air humidity of 10 days, and its value is 0.933 7. The daily mean relative air humidity of 5 days has the highest correlation with winter wheat scab disease index, and its correlation coefficient is 0.784 2, higher than the daily mean temperature of 5 days, which shows that the daily mean relative air humidity of 5 days has a higher influence on winter wheat scab than the daily mean temperature of 5 days. 3) There is a positive linear correlation between winter wheat biomass, leaf area index (LAI) and leaf chlorophyll content and scab disease index, and the r values of the linear trend fitting are 0.608 4, 0.584 5 and 0.574 6, respectively, which reach the significant level, and indicate the large population density, high canopy density and over vigorous growth of winter wheat are the main incentive for scab. 4) Remote sensing vegetation index such as NDVI (normalized difference vegetation index), RVI (ratio vegetation index) and DVI (difference vegetation index) has a good correlation with winter wheat LAI, biomass and leaf chlorophyll content respectively, and their correlation coefficients are 0.851 6, 0.854 9 and 0.772 7 respectively. NDVI, RVI and DVI can be used to replace LAI, biomass and leaf chlorophyll content to participate in modeling. 5) Combining 5 sensitive factors i.e. NDVI, RVI, DVI, mean daily temperature of 5 days and average daily relative humidity of 5 days, we establish the remote sensing estimation model of winter wheat scab disease index based on interactions between spectral information and climatic factors. The estimated value of the model is consistent with the measured value, root mean square error (RMSE) is 5.3%, and the estimation accuracy is 90.46%. It shows that the estimation model in this study can effectively estimate winter wheat scab in Yangtze-Huaihe River region in China. [ABSTRACT FROM AUTHOR]

A field experiment was conducted at Ranchi (Jharkhand) during rabi season of 2009-10 to 2011- 12 with wheat cultivar K 9107 sown on three dates (Nov 20,Dec 5 and Dec 20) to calibrate and validate the CERES-wheat model. There was a good agreement between simulated and observed wheat yields, days to anthesis and days to maturity. The validated model was used to study the impact of intra seasonal temperature variation on wheat. The sensitivity analysis of CERES wheat model, with respect to temperature variations and wheat yield, revealed that increase in temperature above the normal values to 3 °C, showed considerable yield reduction (12- 17 per cent) under different sowing dates. Similarly, with decrease in seasonal temperature up to 3°C, the yield enhancement by 12-16 per cent was observed. Sensitivity of CERES wheat model to incremental units of intra seasonal temperature showed a gradual decrease in yield as well as days to maturity under varied sowing dates. Increase in temperature ( 3 °C from normal) during 2nd fortnight of January (16-31st ) was observed to be the most detrimental for wheat crop with respect to its yield for timely sowing date (20thNov) whereas it would be maximum during 2nd fortnight of February (15-28th) for moderately normal sown (5th Nov) and late sown crops (20thDec). [ABSTRACT FROM AUTHOR]

The CERES-wheat crop growth simulation model has been calibrated and evaluated for two wheat cultivars (PBW 343 and PBW 542) for three sowing dates (30 October, 15 November and 30 November) during 2008-09 and 2009-10 to study partitioning of leaf, stem and grains at Ludhiana, Punjab, India. The experimental data and simulated model data were analysed on partitioning of leaf, stem and grains, and validated. It was found that the model closely simulated the field data from phenological events and biomass. Simulated biological and grain yield was in accordance with-field experiment crop yield within the acceptable range. The correlation coefficient between field-experiment and simulated yield data and biomass data varied significantly from 0.81 and 0.96. The model showed overestimation from field-experimental yield for both cultivars. The cultivar PBW 343 gave higher yield than cultivar PBW 542 on 15 November during both years. The model performance was evaluated and it was found that CERES-wheat model could predict growth parameters like days to anthesis and maturity, biomass and yield with reasonably good accuracy (error less than 8%) and also correlation coefficient between field-experimental and simulated yield data and biomass data varied from 0.94 and 0.95. The results showed that the correlation coefficient between simulated and districts yield varied from 0.41 to 0.78 and also significantly at all six selected districts. The results may be used to improve and evaluate the current practices of crop management at different growth stages of the crop to achieve better production potential. [ABSTRACT FROM AUTHOR]

Heat waves and drought periods are expected to become more frequent due to climate change. This may cause a critical decline in future crop yields. However, insufficient knowledge of the interactive effects of high temperature and drought stress at specific growth stages is the cause of numerous uncertainties in modeling impacts of climate change on field crop growth and yield. Hence, the aim of this study was to investigate the effects of interactions between the short-term exposure (3 and 7 days) of two winter wheat genotypes to elevated temperature and drought stress on yield formation and photosynthetic parameters. Winter wheat plants grown under ambient conditions were subjected to four temperature regimes (with maxima at 26, 32, 35 and 38 °C) and drought in growth chambers at three critical growth stages (beginning of stem elongation – DC 31, beginning of anthesis – DC 61, and medium milk ripe – DC 75). The response of yield formation parameters was obviously modulated by variety and growth stage. Grain number was more affected by drought at DC 31 and by the temperature at DC 61. Grain weight per spike was reduced by drought stress similarly at all growth stages, but the results indicated the increasing sensitivity of this parameter to a temperature at the later growth stage. Although yield parameters only changed slightly with the length of heat and drought stress, the photosynthetic parameters were strongly affected, particularly by longer drought and the interactive effect of high temperature and drought stress. Higher temperature significantly increased the negative impact of drought on CO 2 assimilation rate. Photosynthetic parameters were less affected by combined high temperatures and drought stress at DC 61 as compared to other growth stages investigated. The larger effect at the later growth stage (DC 75) can be attributed to induced senescence, among other factors, particularly in the Bohemia variety. The Tobak variety appears to be more tolerant to combined high temperatures and drought stress in terms of photosynthetic parameters. Based on the relationships between heat degree-days (HDD) and grain weight per spike we demonstrated the potential of HDD to integrate length and intensity of heat stress at different growth stages, particularly for estimation effects on yield parameters. It can be summarized that although the short-term combination of high temperatures and drought causes significant reductions in photosynthetic parameters, the impact on yield formation is much lower, indicating fast recovery of photosynthetic processes and compensation in yield formation parameters. [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]

A prerequisite for application of crop models is a careful parameterization based on observational data. However, there are limited studies investigating the link between quality and quantity of observed data and its suitability for model parameterization. Here, we explore the interactions between number of measurements, noise and model predictive skills to simulate the impact of 2050′s climate change (RCP8.5) on winter wheat flowering time. The learning curve of two winter wheat phenology models is analysed under different assumptions about the size of the calibration dataset, the measurement error and the accuracy of the model structure. Our assessment confirms that prediction skills improve asymptotically with the size of the calibration dataset, as with statistical models. Results suggest that less precise but larger training datasets can improve the predictive abilities of models. However, the non-linear relationship between number of measurements, measurement error, and prediction skills limit the compensation between data quality and quantity. We find that the model performance does not improve significantly with a theoretical minimum size of 7–9 observations when the model structure is approximate. While simulation of crop phenology is critical to crop model simulation, more studies are needed to explore data needs for assessing entire crop models. [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]

During four growing seasons (2013–2017), 282 leaf rust progress curves were characterized across experimental plots according to a number of climate-crop-disease variables in Kermanshah province, Iran. Leaf rust severity differed significantly between levels of cultivar, planting date, sampling time and year factors. Area under disease progress curve (AUDPC) based on disease severity was ranked using Kruskal–Wallis one-way ANOVA. H-test comparison between two disease-onset levels indicated lower mean AUDPC value for late disease onset than that for early disease onset. Highest and lowest AUDPC values were obtained in spring 2016 and 2015, respectively. According to principal component analysis, three principal components accounting for 73% of data variance verified dependence of leaf rust intensity on air temperature and wetness, cultivar resistance, date of disease onset, wheat maturity, and planting date. Based on principal component regression model, disease-onset and maturity date, mean 6-monthly temperature, number of days with minimum temperatures within the range of 5–25 °C and maximum RH > 60%, resistance index, and planting date justified 74% of variability in AUDPC dataset. These findings recommend involvement of cultivar resistance, dates of wheat maturity and planting along with weather predictors in wheat-leaf-rust studies to increase prediction value of future models, durability of host resistance to disease, and sustainability of disease control methods. [ABSTRACT FROM AUTHOR]

The extreme temperatures and uneven distribution of rainfall associated with climate change are expected to affect agricultural productivity and food security. A study was conducted to evaluate the impact of climate change on wheat in southeastern regions of Turkey. The CERES-wheat crop simulation model was calibrated and evaluated with data from eight surveyed farms. The four farms were used for calibration and four for evaluation. Climate change scenarios were developed for the middle (2036–2065) and late 21st century (2066–2095) under representative concentration pathways (RCPs 4.5 and 8.5) for study sites in Islahiye and Nurdagi. Model calibration results showed a good agreement between observed and simulated yield with only a 1 to 11% range of error. The model evaluation results showed good fit between observed and simulated values of all parameters with % error ranged from 0.51 to 13.3%. Future climate change projections showed that maximum temperature (Tmax) will increase between 1.6 °C (RCP4.5) and 2.3 °C (RCP8.5), while minimum temperature (Tmin) will increase between 1.0 °C (RCP4.5) and 1.5 °C (RCP8.5) for mid-century. At the end of the century, Tmax is projected to increase from 2 °C (RCP4.5) to 4 °C (RCP8.5) and Tmin from 1.3 °C (RCP4.5) to 3.1 °C (RCP8.5). Climate change impacts results showed that future rise in temperature will reduce wheat yield by 16.3% in mid-century and 16.8% at the end of the century at Islahiye and for Nurdagi, while 13.0% in mid and 14.4% end of the century. The use of climate and crop modeling technique provides useful information in evaluating the climate change impacts and may assist stakeholders to make decisions to overcome the negative impacts in the near and long term. [ABSTRACT FROM AUTHOR]

Key message: Selecting contrasting environments allows decreasing phenotyping intensity but still maintaining high accuracy to assess yield stability. Abstract: Improving yield stability of wheat varieties is important to cope with enhanced abiotic stresses caused by climate change. The objective of our study was to (1) develop and implement an improved heritability estimate to examine the required scale of phenotyping for assessing yield stability in wheat, (2) compare yield performance and yield stability of wheat hybrids and inbred lines, (3) investigate the association of agronomic traits with yield stability, and (4) explore the possibility of selecting subsets of environments allowing to portray large proportion of the variation of yield stability. Our study is based on phenotypic data from five series of official winter wheat registration trials in Germany each including 119-132 genotypes evaluated in up to 50 environments. Our findings suggested that phenotyping in at least 40 environments is required to reliably estimate yield stability to guarantee heritability estimates above 0.7. Contrasting the yield stability of hybrids versus lines revealed no significant differences. Absence of stable associations between yield stability and further agronomic traits suggested low potential of indirect selection to improve yield stability. Selecting posteriori contrasting environments based on the genotype-by-environment interaction effects allowed decreasing phenotyping intensity, but still maintaining high accuracy to assess yield stability. The huge potential of the developed strategy to select contrasting and informative environments has to be validated as a next step in an a priori scenario based on genotype-by-location interaction effects. [ABSTRACT FROM AUTHOR]

Paddy-wheat crop rotation is dominant in the Punjab state covering about 28.40 lakh hectares of cultivable land. This paddy-wheat rotation also assesses the potential of new technology i.e. happy seeder technology to address the problem. The straw management system should be necessary for all the combine harvesters. The total 28 field demonstrations on happy seeder technology were conducted in village Killi Nihal Singh of district Bathinda of Punjab state during the years 2013-14 & 2014-15.The results showed that by using this technology, the nutrients i.e Urea, DAP and Potash fertilizer were saved worth Rs. 424.15, Rs.366.25 and Rs.1989 respectively totaling the amount to Rs.2779.40 per hectare besides improving the physical properties over longer period of time. The highest incremental B: C ratio of happy seeder plots were 4.36 during the year 2013-14. Apart from saving the burning of the paddy straw, the happy seeder technology was able to save Rs.2311.25 per hectare over the normal sown wheat during the field preparation, sowing and management operations of the crop. In the context of burning of paddy straw, this happy seeder technology is a new initiative under taken by Krishi Vigyan Kendra Bathinda for checking of environmental pollution which will be beneficial for the society as a whole because. Bathinda district lies in the south-western region of Punjab which is not much mechanized in agriculture as compared to other districts of the state. [ABSTRACT FROM AUTHOR]