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The paper purpose was to assess the impact of climate change on wheat, maize and sunflower yield in the period 2017-2021, using statistical data from National Institute of Statistics (NIS) and National Administration of Meteorology (NAM) and other sources. Comparison method was used to evaluate the deviations between the registered air temperatures and precipitations and the 1981-2010 climatological norm. Graphic method reflected the dynamics of monthly air temperatures and rainfalls in each year. Descriptive statistics for mean, standard deviation and coefficient of variation reflected an more comprehensive image upon air temperature, precipitations and yield. Correlations and regression equations were used to establish the intensity of the kinks between climate factors and yield. The highest average air temperature In Romania was 12.130C in 2019. In the period 2017-2020, the average annual temperatures exceeded the climatological norm 1981-2010 accounting for 9.10C. The lowest rainfalls, 614.2 mm, were recorded in 2019, being below of 633.1 mm the norm for the period 1981-2010. The high temperatures associated with drought and low precipitations have deeply affected yield of many agricultural crops, including maize, wheat and sunflower. The highest wheat yield 4,888 kg/ha was achieved in 2017, and the lowest one, 2,966 kg/ha in 2020. Maize registered the highest performance of 7,644 kg/ha in 2018, and the lowest one, 3,977 kg/ha in 2020. Sunflower carried out the high performance 3,041 kg/ha in 2018, and the lowest one, 1,858 kg/ha in 2020. The worst agricultural years for these three crops was 2020, but 2017 favored wheat, while 2018 favored maize and sunflower. The correlation coefficient had in general small values between average temperature and yield, but a higher link with precipitations level. The multiple correlation between yield, air temperatures and precipitations was: r= 0.817 for wheat, r = 0.116 for maize and r = 0.504 for sunflower. In the South Eastern Dobrogea, the driest area in Romania, the rainfalls declined by 65% in the period 2018-2020 having a deep negative impact on sunflower seeds yield, which decreased from 4,282 kg/ha in 2018, to 1,503 kg/ha in 2020. The correlation between precipitations and sunflower yield positive and very strong (r = 0.737). As a conclusion, the climate conditions should be analyzed in each farm and farmers have to take measure to adapt the technologies for sustaining production. [ABSTRACT FROM AUTHOR]

The article examines the prospects of Ukraine on the world grain market and ways to increase the efficiency of maize cultivation using environmentally safe technologies in the conditions of the negative impact of climate change and other stress factors. The methodological approach takes into account the peculiarities of agrarian production based on the use of data of the Food and Agriculture Organization of the United Nations, United States Department of Agriculture, United Nations, Ministry of Agrarian Policy and Food of Ukraine and State Statistics Service of Ukraine. The dynamics of world production and export of maize grain, sown areas, production and yield of maize in Ukraine are demonstrated, which directly focuses attention on the competitiveness of Ukrainian maize on the world grain market. These arguments are confirmed by analytical results, which show that in 2022, among the world leaders of maize grain producers, Ukraine took fifth place, and among exporters - fourth. In order to mitigate the impact of climate change and other stress factors on the effective production and export of Ukrainian maize, adaptation measures are proposed, which consist in the application of modern technologies, which include: optimization of the structure of sown areas and scientifically based crop rotations with the effective use of competitive varieties and hybrids of agricultural crops, the introduction of organic and mineral fertilizers and plant protection products, the use of soil protective tillage, sideration and mulching, irrigation systems, and also the productive use of plant residues with the application of modern biodestructors. Their comprehensive implementation will contribute to the development of maize yield, grain quality, preservation of biodiversity and the solution of the grain-food problem in the world. [ABSTRACT FROM AUTHOR]

Agricultural practices of smallholder farming systems of Latin America can play an important role in reducing their exposure to the risks associated with climate extremes. To date, however, there is no systematic analysis of scientific evidence for the extent to which these practices can provide the multiple benefits needed for smallholders to adapt to climate extremes. In this paper, we searched scientific databases to review scientific evidence of the benefit provided by twenty-six practices in crops commonly farmed by smallholders in the region and highly relevant for their food and nutrition security; namely, coffee, maize and beans. We reviewed scientific documents (n = 304) published in the period 1953–2021 to register evidence of the practices' effects on fifty-five benefits. Our analysis of these documents found measurement records (n = 924) largely based on field experiments (85%). Our results show strong evidence of the multiple benefits that some ecosystem-based practices (e.g., tree-based practices for coffee and no tillage for maize) can provide to support the adaptation to climate extremes of smallholder farming systems and enhance a farm's natural assets (e.g., biodiversity, water, soil). We also found that the majority of research on practices in the region focused more on the socioeconomic dimension (54%) rather than on the capacity of practices to improve the natural assets of a smallholder farmers or reduce the impact of climate extremes. Given these knowledge gaps, we discuss the importance of a renovated investment in research to address existing knowledge gaps. Our concluding suggestions for future research include the need for systematizing existing knowledge from different sources (e.g., peer-reviewed, gray literature, farmers, extension agencies, etc.), and to assess the extent to which these practices can provide multiple benefits for smallholder farming systems by improving their wellbeing, reducing their vulnerability to different hydroclimatic extremes while also contributing to ecosystem services provision at the landscape level. [ABSTRACT FROM AUTHOR]

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This study aims to assess the impact of climate change on productivity of maize in Khyber Pakhtunkhwa (KP) province of Pakistan over a period of thirty years from 1986 to 2015. According to the ecological landscape, KP is divided into three climatic regions; the northern climatic region, the central climatic region, and the southern climatic region. The study uses Autoregressive Distributed Lag (ARDL) model for maize crop in KP. This study reveals that the impacts of climatic variables are increasing from the southern climatic region to the northern climatic region. The study also reveals that any negative shock to the crops productivity can be overcome by increasing the area under maize cultivation. It is concluded that the variations in climatic variables have significant effects on maize productivity in the short-run as well as in the long-run in all the climatic regions of KP. [ABSTRACT FROM AUTHOR]

Various models and datasets related to aflatoxins in the maize and dairy production chain have been developed and used but they have not yet been linked with each other. This study aimed to investigate the impacts of climate change on aflatoxin B1 production in maize and its consequences on aflatoxin M1 contamination in dairy cow’s milk, using a full chain modelling approach. To this end, available models and input data were chained together in a modelling framework. As a case study, we focused on maize grown in Eastern Europe and imported to the Netherlands to be fed–as part of dairy cows’ compound feed–to dairy cows in the Netherlands. Three different climate models, one aflatoxin B1 prediction model and five different carryover models were used. For this particular case study of East European maize, most of the calculations suggest an increase (up to 50%) of maximum mean aflatoxin M1 in milk by 2030, except for one climate (DMI) model suggesting a decrease. Results from all combinations of carryover and climate models suggest a similar or slight increase (up to 0.6%) of the chance of finding aflatoxin M1 in milk above the EC limit of 0.05 μg/kg by 2030. Results varied mainly with the climate model data and carryover model considered. The model framework infrastructure is flexible so that forecasting models for other mycotoxins or other food safety hazards as well as other production chains, together with necessary input databases, can easily be included as well. This modelling framework for the first time links datasets and models related to aflatoxin B1 in maize and related aflatoxin M1 the dairy production chain to obtain a unique predictive methodology based on Monte Carlo simulation. Such an integrated approach with scenario analysis provides possibilities for policy makers and risk managers to study the effects of changes in the beginning of the chain on the end product. [ABSTRACT FROM AUTHOR]

As climate change continues to exert increasing pressure upon the livelihoods and agricultural sector of many developing and developed nations, a need exists to understand and prioritise at the sub national scale which areas and communities are most vulnerable. The purpose of this study is to develop a robust, rigorous and replicable methodology that is flexible to data limitations and spatially prioritizes the vulnerability of agriculture and rural livelihoods to climate change. We have applied the methodology in Vietnam, Uganda and Nicaragua, three contrasting developing countries that are particularly threatened by climate change. We conceptualize vulnerability to climate change following the widely adopted combination of sensitivity, exposure and adaptive capacity. We used Ecocrop and Maxent ecological models under a high emission climate scenario to assess the sensitivity of the main food security and cash crops to climate change. Using a participatory approach, we identified exposure to natural hazards and the main indicators of adaptive capacity, which were modelled and analysed using geographic information systems. We finally combined the components of vulnerability using equal-weighting to produce a crop specific vulnerability index and a final accumulative score. We have mapped the hotspots of climate change vulnerability and identified the underlying driving indicators. For example, in Vietnam we found the Mekong delta to be one of the vulnerable regions due to a decline in the climatic suitability of rice and maize, combined with high exposure to flooding, sea level rise and drought. However, the region is marked by a relatively high adaptive capacity due to developed infrastructure and comparatively high levels of education. The approach and information derived from the study informs public climate change policies and actions, as vulnerability assessments are the bases of any National Adaptation Plans (NAP), National Determined Contributions (NDC) and for accessing climate finance. [ABSTRACT FROM AUTHOR]

The crop Water Footprint (WF) can provide a comprehensive knowledge of the use of water through the demarcation of the amount of the water consumed by different crops. The WF has three components: green (WFg), blue (WFb) and grey (WFgr) water footprints. The WFg refers to the rainwater stored in the root zone soil layer and is mainly utilized for agricultural, horticultural and forestry production. The WFb, however, is the consumptive use of water from surface or groundwater resources and mainly deals with irrigated agriculture, industry, domestic water use, etc. While the WFgr is the amount of fresh water required to assimilate pollutants resulting from the use of fertilizers/agrochemicals. This study was conducted on six agricultural fields in the Eastern region of Saudi Arabia, during the period from December 2015 to December 2016, to investigate the spatiotemporal variation of the WF of silage maize and carrot crops. The WF of each crop was estimated in two ways, namely agro-meteorological (WFAgro) and remote sensing (WFRS) methods. The blue, green and grey components of WFAgro were computed with the use of weather station/Eddy covariance measurements and field recorded crop yield datasets. The WFRS estimated by applying surface energy balance principles on Landsat-8 imageries. However, due to non-availability of Landsat-8 data on the event of rainy days, this study was limited to blue component (WFRS-b). The WFAgro of silage maize was found to range from 3545 m3 t-1 to 4960 m3 t-1; on an average, the WFAgro-g, WFAgro-b, and WFAgro-gr are composed of < 1%, 77%, and 22%, respectively. In the case of carrot, the WFAgro ranged between 297 m3 t-1 and 502 m3 t-1. The WFAgro-g of carrot crop was estimated at <1%, while WFAgro-b and WFAgro-gr was 67% and 32%, respectively. The WFAgro-b is occupied as a major portion in WF of silage maize (77%) and carrot (68%) crops. This is due to the high crop water demand combined with a very erratic rainfall, the irrigation is totally provided using groundwater delivered by center pivot irrigation systems. On the other hand, the WFRS-b estimated using Landsat-8 data was varied from 276 (±73) m3 t-1 (carrot) and 2885 (±441) m3 t-1 (silage maize). The variation (RMSE) between WFRS-b and WFAgro-b was about 17% and 14% for silage maize and carrot crops, respectively. [ABSTRACT FROM AUTHOR]

Sustainable crop production is the major challenge in the current global climate change scenario. Drought stress is one of the most critical abiotic factors which negatively impact crop productivity. In recent years, knowledge about molecular regulation has been generated to understand drought stress responses. For example, information obtained by transcriptome analysis has enhanced our knowledge and facilitated the identification of candidate genes which can be utilized for plant breeding. On the other hand, it becomes more and more evident that the translational and post-translational machinery plays a major role in stress adaptation, especially for immediate molecular processes during stress adaptation. Therefore, it is essential to measure protein levels and post-translational protein modifications to reveal information about stress inducible signal perception and transduction, translational activity and induced protein levels. This information cannot be revealed by genomic or transcriptomic analysis. Eventually, these processes will provide more direct insight into stress perception then genetic markers and might build a complementary basis for future marker-assisted selection of drought resistance. In this review, we survey the role of proteomic studies to illustrate their applications in crop stress adaptation analysis with respect to productivity. Cereal crops such as wheat, rice, maize, barley, sorghum and pearl millet are discussed in detail. We provide a comprehensive and comparative overview of all detected protein changes involved in drought stress in these crops and have summarized existing knowledge into a proposed scheme of drought response. Based on a recent proteome study of pearl millet under drought stress we compare our findings with wheat proteomes and another recent study which defined genetic marker in pearl millet. [ABSTRACT FROM AUTHOR]

After a remarkable 86% increase in cereal production from 1980 to 2005, recent crop yield growth in China has been slow. County level crop production data between 1980 and 2010 from eastern and middle China were used to analyze spatial and temporal patterns of rice, wheat and maize yield in five major farming systems that include around 90% of China's cereal production. Site-specific yield trends were assessed in areas where those crops have experienced increasing yield or where yields have stagnated or declined. We find that rice yields have continued to increase on over 12.3 million hectares (m. ha) or 41.8% of the rice area in China between 1980 and 2010. However, yields stagnated on 50% of the rice area (around 14.7 m. ha) over this time period. Wheat yields increased on 13.8 m. ha (58.2% of the total harvest area), but stagnated on around 3.8 m. ha (15.8% of the harvest area). Yields increased on a smaller proportion of the maize area (17.7% of harvest area, 5.3 m. ha), while yields have stagnated on over 54% (16.3 m. ha). Many parts of the lowland rice and upland intensive sub-tropical farming systems were more prone to stagnation with rice, the upland intensive sub-tropical system with wheat, and maize in the temperate mixed system. Large areas where wheat yield continues to rise were found in the lowland rice and temperate mixed systems. Land and water constraints, climate variability, and other environmental limitations undermine increased crop yield and agricultural productivity in these systems and threaten future food security. Technology and policy innovations must be implemented to promote crop yields and the sustainable use of agricultural resources to maintain food security in China. In many production regions it is possible to better match the crop with input resources to raise crop yields and benefits. Investments may be especially useful to intensify production in areas where yields continue to improve. For example, increased support to maize production in southern China, where yields are still rising, seems justified. [ABSTRACT FROM AUTHOR]

Climate is changing across the world, including the major maize-growing state of Iowa in the USA. To maintain crop yields, farmers will need a suite of adaptation strategies, and choice of strategy will depend on how the local to regional climate is expected to change. Here we predict how maize yield might change through the 21st century as compared with late 20th century yields across Iowa, USA, a region representing ideal climate and soils for maize production that contributes substantially to the global maize economy. To account for climate model uncertainty, we drive a dynamic ecosystem model with output from six climate models and two future climate forcing scenarios. Despite a wide range in the predicted amount of warming and change to summer precipitation, all simulations predict a decrease in maize yields from late 20th century to middle and late 21st century ranging from 15% to 50%. Linear regression of all models predicts a 6% state-averaged yield decrease for every 1°C increase in warm season average air temperature. When the influence of moisture stress on crop growth is removed from the model, yield decreases either remain the same or are reduced, depending on predicted changes in warm season precipitation. Our results suggest that even if maize were to receive all the water it needed, under the strongest climate forcing scenario yields will decline by 10–20% by the end of the 21st century. [ABSTRACT FROM AUTHOR]

The integration of local agricultural knowledge deepens the understanding of complex phenomena such as the association between climate variability, crop yields and undernutrition. Participatory Sensing (PS) is a concept which enables laymen to easily gather geodata with standard low-cost mobile devices, offering new and efficient opportunities for agricultural monitoring. This study presents a methodological approach for crop height assessment based on PS. In-field crop height variations of a maize field in Heidelberg, Germany, are gathered with smartphones and handheld GPS devices by 19 participants. The comparison of crop height values measured by the participants to reference data based on terrestrial laser scanning (TLS) results in R2 = 0.63 for the handheld GPS devices and R2 = 0.24 for the smartphone-based approach. RMSE for the comparison between crop height models (CHM) derived from PS and TLS data is 10.45 cm (GPS devices) and 14.69 cm (smartphones). Furthermore, the results indicate that incorporating participants’ cognitive abilities in the data collection process potentially improves the quality data captured with the PS approach. The proposed PS methods serve as a fundament to collect agricultural parameters on field-level by incorporating local people. Combined with other methods such as remote sensing, PS opens new perspectives to support agricultural development. [ABSTRACT FROM AUTHOR]

Competition or facilitation characterises intra- and interspecific interactions within communities of species that utilize the same resources. Temperature is an important factor influencing those interactions and eventual outcomes. The noctuid stemborers, Busseola fusca and Sesamia calamistis and the crambid Chilo partellus attack maize in sub-Saharan Africa. They often occur as a community of interacting species in the same field and plant at all elevations. The influence of temperature on the intra- and interspecific interactions among larvae of these species, was studied using potted maize plants exposed to varying temperatures in a greenhouse and artificial stems kept at different constant temperatures (15°C, 20°C, 25°C and 30°C) in an incubator. The experiments involved single- and multi-species infestation treatments. Survival and relative growth rates of each species were assessed. Both intra- and interspecific competitions were observed among all three species. Interspecific competition was stronger between the noctuids and the crambid than between the two noctuids. Temperature affected both survival and relative growth rates of the three species. Particularly at high temperatures, C. partellus was superior in interspecific interactions shown by higher larval survival and relative growth rates. In contrast, low temperatures favoured survival of B. fusca and S. calamistis but affected the relative growth rates of all three species. Survival and relative growth rates of B. fusca and S. calamistis in interspecific interactions did not differ significantly across temperatures. Temperature increase caused by future climate change is likely to confer an advantage on C. partellus over the noctuids in the utilization of resources (crops). [ABSTRACT FROM AUTHOR]

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term. [ABSTRACT FROM AUTHOR]

The production of ethanol from pretreated plant biomass during fermentation is a strategy to mitigate climate change by substituting fossil fuels. However, biomass conversion is mainly limited by the recalcitrant nature of the plant cell wall. To overcome recalcitrance, the optimization of the plant cell wall for subsequent processing is a promising approach. Based on their phylogenetic proximity to existing and emerging energy crops, model plants have been proposed to study bioenergy-related cell wall biochemistry. One example is Brachypodium distachyon, which has been considered as a general model plant for cell wall analysis in grasses. To test whether relative phylogenetic proximity would be sufficient to qualify as a model plant not only for cell wall composition but also for the complete process leading to bioethanol production, we compared the processing of leaf and stem biomass from the C3 grasses B. distachyon and Triticum aestivum (wheat) with the C4 grasses Zea mays (maize) and Miscanthus x giganteus, a perennial energy crop. Lambda scanning with a confocal laser-scanning microscope allowed a rapid qualitative analysis of biomass saccharification. A maximum of 108–117 mg ethanol·g−1 dry biomass was yielded from thermo-chemically and enzymatically pretreated stem biomass of the tested plant species. Principal component analysis revealed that a relatively strong correlation between similarities in lignocellulosic ethanol production and phylogenetic relation was only given for stem and leaf biomass of the two tested C4 grasses. Our results suggest that suitability of B. distachyon as a model plant for biomass conversion of energy crops has to be specifically tested based on applied processing parameters and biomass tissue type. [ABSTRACT FROM AUTHOR]

Many studies have analyzed the impact of climate change on crop productivity, but comparing the performance of water management systems has rarely been explored. Because water supply and crop demand in agro-systems may be affected by global climate change in shaping the spatial patterns of agricultural production, we should evaluate how and where irrigation practices are effective in mitigating climate change effects. Here we have constructed simple, general models, based on biological mechanisms and a theoretical framework, which could be useful in explaining and predicting crop productivity dynamics. We have studied maize in irrigated and rain-fed systems at a provincial scale, from 1996 to 2009 in Spain, one of the most prominent “hot-spots” in future climate change projections. Our new approach allowed us to: (1) evaluate new structural properties such as the stability of crop yield dynamics, (2) detect nonlinear responses to climate change (thresholds and discontinuities), challenging the usual linear way of thinking, and (3) examine spatial patterns of yield losses due to water constraints and identify clusters of provinces that have been negatively affected by warming. We have reduced the uncertainty associated with climate change impacts on maize productivity by improving the understanding of the relative contributions of individual factors and providing a better spatial comprehension of the key processes. We have identified water stress and water management systems as being key causes of the yield gap, and detected vulnerable regions where efforts in research and policy should be prioritized in order to increase maize productivity. [ABSTRACT FROM AUTHOR]

Climate change implications are a severe risk to food security and the economy. Global warming could disturb the production of both rainfed and irrigated agriculture thru the amplify of yield water requests in many areas. In this study, the fast-track projections available through the Inter-Sectors Impact Model Intercomparison Project (ISI-MIP) were presented and analyzed to assess the effects of two global warming (GW) levels (1.5 and 2.0 °C) on the maize and wheat yields in Egypt. Outcomes proposed spatial variations in the effects of temperature change on crop yield. Compared with the referenced situation, an observed national average change in wheat yield about 5.0% (0.0% to 9.0%) and 5.0% (−3.0% to 14.0%) under GW1.5 and GW2.0 respectively. While for maize yield, the change in national average about −1.0% (−5.0% to 3.0%) and −4.0% (−8.0% to 2.0%) under GW1.5 and GW2.0 respectively. GW1.5 could be helpful for wheat yield, but the positive effect decayed when the warming level reached 2.0 °C overhead the pre-industrial level. Nevertheless, the possible deviations to Egypt's maize production under the GW1.5 and GW2.0 scenarios are unclear where the models do not agree with the sign of change. Adjusting the temperature rise within 1.5 °C would diminish the yield reduction, as it is an extraordinary priority to safeguard crop production. To achieve Progress of innovative agronomic managing plans and swapping to additional drought-resistant crops may be valuable for coping with climate change in regions vulnerable to yield decline. [ABSTRACT FROM AUTHOR]

Crop production is inherently sensitive to variability in climate. Temperature is a major determinant of the rate of plant development and, under climate change, warmer temperatures that shorten development stages of determinate crops will most probably reduce the yield of a given variety. Earlier crop flowering and maturity have been observed and documented in recent decades, and these are often associated with warmer (spring) temperatures. However, farm management practices have also changed and the attribution of observed changes in phenology to climate change per se is difficult. Increases in atmospheric [CO2] often advance the time of flowering by a few days, but measurements in FACE (free air CO2 enrichment) field-based experiments suggest that elevated [CO2] has little or no effect on the rate of development other than small advances in development associated with a warmer canopy temperature. The rate of development (inverse of the duration from sowing to flowering) is largely determined by responses to temperature and photoperiod, and the effects of temperature and of photoperiod at optimum and suboptimum temperatures can be quantified and predicted. However, responses to temperature, and more particularly photoperiod, at supraoptimal temperature are not well understood. Analysis of a comprehensive data set of time to tassel initiation in maize (Zea mays) with a wide range of photoperiods above and below the optimum suggests that photoperiod modulates the negative effects of temperature above the optimum. A simulation analysis of the effects of prescribed increases in temperature (0–6 °C in +1 °C steps) and temperature variability (0% and +50%) on days to tassel initiation showed that tassel initiation occurs later, and variability was increased, as the temperature exceeds the optimum in models both with and without photoperiod sensitivity. However, the inclusion of photoperiod sensitivity above the optimum temperature resulted in a higher apparent optimum temperature and less variability in the time of tassel initiation. Given the importance of changes in plant development for crop yield under climate change, the effects of photoperiod and temperature on development rates above the optimum temperature clearly merit further research, and some of the knowledge gaps are identified herein. [ABSTRACT FROM PUBLISHER]

Drought stress causes heavy damages to crop growth and productivity under global climatic changes. Transcription factors have been extensively studied in many crops to play important roles in plant growth and defense. However, there is a scarcity of studies regarding WRKY transcription factors regulating drought responses in maize crops. Previously, ZmWRKY79 was identified as the regulator of maize phytoalexin biosynthesis with inducible expression under different elicitation. Here, we elucidated the function of ZmWRKY79 in drought stress through regulating ABA biosynthesis. The overexpression of ZmWRKY79 in Arabidopsis improved the survival rate under drought stress, which was accompanied by more lateral roots, lower stomatal aperture, and water loss. ROS scavenging was also boosted by ZmWRKY79 to result in less H2O2 and MDA accumulation and increased antioxidant enzyme activities. Further analysis detected more ABA production in ZmWRKY79 overexpression lines under drought stress, which was consistent with up-regulated ABA biosynthetic gene expression by RNA-seq analysis. ZmWRKY79 was observed to target ZmAAO3 genes in maize protoplast through acting on the specific W-boxes of the corresponding gene promoters. Virus-induced gene silencing of ZmWRKY79 in maize resulted in compromised drought tolerance with more H2O2 accumulation and weaker root system architecture. Together, this study substantiates the role of ZmWRKY79 in the drought-tolerance mechanism through regulating ABA biosynthesis, suggesting its broad functions not only as the regulator in phytoalexin biosynthesis against pathogen infection but also playing the positive role in abiotic stress response, which provides a WRKY candidate gene to improve drought tolerance for maize and other crop plants. [ABSTRACT FROM AUTHOR]

Maize is known to be susceptible to drought stress, which negatively affects vegetative growth and biomass production, as well as the formation of reproductive organs and yield parameters. In this study, 27 responsive traits of germination (G) and seedlings growth were evaluated for 40 accessions of the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) germplasm collection, under no stress and simulated drought stress treatments by 10%, 15%, and 20% of polyethylene glycol (PEG). The three treatments significantly reduced G% and retarded seedlings growth, particularly the 15% and 20% PEG treatments; these two treatments also resulted in a significant increase of abnormal seedlings (AS). The heritability (H2) and correlations of the traits were estimated, and drought tolerance indices (DTIs) were calculated for traits and accessions. The H2 of G% values were reduced, and H2 for AS% increased as the PEG stress increased. Positive correlations were found between most trait pairs, particularly shoot and root traits, with 48 highly significant correlations under no stress and 25 highly significant correlations under the 10% PEG treatments, particularly for shoot and root traits. The medium to high heritability of shoot and root seedling traits provides a sound basis for further genetic analyses. PCA analysis clearly grouped accessions with high DTIs together and the accessions with low DTIs together, indicating that the DTI indicates the stress tolerance level of maize germplasm. However, the resemblance in DTI values does not clearly reflect the origin or taxonomic assignments to subspecies and varieties of the examined accessions. [ABSTRACT FROM AUTHOR]