Future Crop Research Articles

Azoles activate type I and type II programmed cell death pathways in crop pathogenic fungi

Triazoles are widely used to control pathogenic fungi. They inhibit the ergosterol biosynthetic pathway, but the precise mechanisms leading to fungicidal activities in many fungal pathogens are poorly understood. Here, we elucidate the mode of action of epoxiconazole and metconazole in the wheat pathogen Zymoseptoria tritici and the rice blast fungus Magnaporthe oryzae. We show that both azoles have fungicidal activity and reduce fluidity, but not integrity, of the plasma membrane. This impairs localisation of Cdc15-like F-BAR proteins, resulting in defective actin ring assembly and incomplete septation. However, mutant studies and pharmacological experiments in vitro and in planta show that azole lethality is due to a combination of reactive oxygen species-induced apoptosis and macroautophagy. Simultaneous inhibition of both programmed cell death pathways abolishes azole-induced cell death. Other classes of ergosterol biosynthesis inhibitors also induce apoptosis and macroautophagy, suggesting that activation of these two cell death pathways is a hallmark of ergosterol synthesis-targeting fungicides. This knowledge will inform future crop protection strategies.

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively.

Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms

The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate the advantages and challenges of UAV-based phenotyping techniques. This is a comprehensive overview summarizing the UAV platforms, sensors, and data processing while also introducing recent technological developments. Recently developed software and sensors greatly enhance the accessibility of UAV-based phenotyping, and a summary of recent research (publications 2019–2024) provides implications for future research. Researchers have focused on integrating multiple sensing data or utilizing machine learning algorithms, such as ensemble learning and deep learning, to enhance the prediction accuracies of crop physiological traits. However, this approach will require big data alongside laborious destructive measurements in the fields. Future research directions will involve standardizing the process of merging data from multiple field experiments and data repositories. Previous studies have focused mainly on UAV technology in major crops, but there is a high potential in minor crops or cropping systems for future sustainable crop production. This review can guide new practitioners who aim to implement and utilize UAV-based phenotyping.

CHALLENGES AND OPPORTUNITIES TO DESIGN FUTURE CROPS: GATEWAY TO SUSTAINABLE AGRICULTURE IN 21ST CENTURY

The global food production system is facing numerous challenges due to factors such as population growth, climate change, limited resources, and environmental preservation. To address these challenges, various strategies can be employed to develop future crops that are more productive, nutritious, and resilient. One strategy is to improve the yield potential of existing crops by developing new high-yielding cultivars. This can be achieved through the development of new varieties that can produce more grain under optimal conditions. Additionally, improving the nutritional quality of crops is important to address nutrient deficiencies. Synthetic biology and metabolic engineering methods can be used to develop crops with enhanced nutritional value. Efficient utilization of agricultural resources is another important aspect of crop improvement. This includes developing crops that use water and nutrients more efficiently, reducing the need for irrigation and fertilizers, and minimizing environmental impacts. Increasing the resistance of crops to pests, diseases, and extreme weather events can also help reduce the use of pesticides and minimize crop losses. The domestication of wild or semi-wild plants through genetic manipulation offers new opportunities for crop design. These plants may have high nutritional value, stress tolerance, and specialized metabolites that can be incorporated into cultivated crops. Similarly, the domestication of orphan or neglected plants can contribute to crop improvement by incorporating unique traits. Genetic improvement through the transfer of genes from wild relatives or other species can also enhance crop productivity. Advancements in genomics and genetic technologies can aid in the identification and transfer of beneficial alleles. Agronomic improvements, such as maximizing the effectiveness of crop protection agents and fertilizers while minimizing their environmental impact, can also contribute to crop performance. The emerging field of synthetic biology offers opportunities for developing novel biological devices and systems that can further enhance crop productivity and resilience. Overall, these strategies can help address the challenges faced by the global food production system.

Contribution of Millets in food and nutritional security to human being: Current status and future perspectives

Globally food and nutrition insecurity remain a serious challenge however the situation is more sever to the groups of people living under marginal and disadvantaged society. The causes of food and nutrition insecurity are multifaceted and complex, and influenced by a range of factors including high poverty, natural resource degradation, climate change, low level of market development, uncertain food support, and inadequate policy and institutional support. Considering the acute shortage of food and nutrition facing by global population, strengthening food and nutritional security is crucial in order to feed the ever-growing world population. One of the promising approaches of promoting millets, which requires low external inputs, a novel candidate for nutrients and adapted to thrive in harsh and dry environment. These crops play an important role in global food and nutrition security, and may have potential to contribute to sustainable food systems under changing climatic conditions. Keeping in view the importance of the millets in diversifying diet as well as a source of rich nutrition, we conducted an analysis by reviewing the research articles/reports/books as well as online databases to identify the prospects of millets crops crucial for continuous supply of food and nutrition, traditionally managed genetic resources for future crop improvement and making agricultural system resilient under changing climatic conditions. Evidences suggested from the meta-analysis that as a product of generations of agricultural landrace, there are range of millet crops are rich in nutrients, resilient, and adapted to location specific agricultural environments. Such millet crops in the existing cropping systems could support diverse food systems and nutrient suppliers and represent a broad gene pool to improving crops with suitable genetic interventions in the future. The study advocates for advancement in genomics coupled with molecular breeding for improving the genetic potential of millet crops and open avenues for developing sustainable food systems. The study also emphasis on developing strategies and roadmap for future research engagement and a policy interface to facilitate conservation and management of traditional landraces and associated indigenous knowledge of cultivation and consumption while adopting sustainable production of millet crops under marginal environmental conditions.

Genetic diversity and stability analysis of the improved cultivars of guava and their related species

Guava (Psidium guajava L.) is an important fruit crop in the tropical and sub-tropical world. The morphological, biochemical, and molecular (using 56 microsatellite markers) characterization was carried out on 18 guava genotypes and related species (P. guineense) during 2018–2022. The present study exhibited a wider range of variability: fruit weight (61.5–68.4 g); fruit diameter (4.20–7.91 cm); total soluble solids (8.50–13.40 °Brix); titrable acidity (0.20–0.64%); reducing sugar (2.55–7.00 mg glucose /g); ascorbic acid (76.4–196.3 mg/100 g); total phenol (125.9–305.4 mg GAE/100 g pulp); and total flavonoid content (172.30–948.5 mg QE/100 g pulp). The antioxidant activities (FRAP and DPPH) showed a favorable relationship with ascorbic acid, total phenol, total flavonoid, and total flavonol content. Red-fleshed genotypes were found superior for all the quality parameters over white-fleshed genotypes. The molecular analysis generated 213 alleles from 56 markers, with 2–9 alleles per locus (mean = 3.80). Apart from the genetic diversity, the improved cultivars were also distinguished by a set of markers: RCGH-4 (mPGCIR-184 and mPGCIR-194); RCGH-1 (mPGCIR-108 and mPGCIR-243); RCG-11 (mPGCIR-206 and mPGCIR-325); and RCGH-7 (mPGCIR-16 and mPGCIR-19). The cluster analysis indicated that P. guineense was the most diverse of the cultivated species, and all the red flesh genotypes were close to each other. Furthermore, RCG-11 had a lower seed content (58.89 per 100 g pulp), while RCGH-1 and RCGH-4 were stable for fruit weight and seed number, which can be promoted for commercial production and future crop improvement programs.

Crop Yield Prediction using Machine Learning

Weather profoundly impacts agricultural outcomes, making accurate crop prediction vital for farmers' decision-making. This abstract presents a comprehensive overview of weather-based crop prediction, emphasizing its significance, key components, and methodologies. The process begins with the collection and analysis of historical weatherdata encompassing variables such as temperature, precipitation, humidity, and sunlight. Utilizing Python programming and data visualization libraries like Pandas and Matplotlib facilitates the exploration and visualization of this data, revealing trends and patterns. Machine learning algorithms, including regression and ensemble methods, are employedto develop predictive models. These models leverage historical weather data to forecast future crop yields accurately. Python's extensive libraries, such as Scikit-learn and TensorFlow, offer robust tools for model development and evaluation. Incorporating advanced technologies like remote sensing and satellite imagery further refines the prediction process. These tools provide real-time insights into crop health and growth, enhancing the precision of forecasts. Ultimately, weather-based crop prediction serves as a valuable decision support tool for farmers, enabling informed choices regarding planting, irrigation, and harvesting practices. By harnessing historical weather data, machine learning algorithms, and innovative technologies, stakeholders can optimize agricultural productivity, mitigate risks, and contribute to global food security

The economics of farming expansion in the Brazilian Cerrado under possible effects of climate change

Abstract This analysis assesses the financial viability of legally investing in native Cerrado vegetation deforestation for crop production, considering climate change. The study uses data from twelve different crop models based on three different climate models to predict potential future crop yields in cleared land for growing soy and maize. The outcomes show that in many micro-regions, investments in clearing land for crop production would destroy economic value, that is, generate a negative net present value because of low/negative and volatile cashflows driven primarily by future yields as affected by climate. Our analysis was carried out based on present agricultural practices and technology. As climate changes, farmers may adapt their practices, which can lead to more resilient and productive crops, or grow different crops, which could provide better returns on investment in clearing land than the ones resulting from our analysis. Despite various uncertainties, farmers, policy makers and financial institutions should be aware of the climatic and financial risks associated with land clearing in Brazil, mainly in micro-regions in which all scenarios resulted in negative outcomes in the investment analysis.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

Influence of plant growth regulators on qualitative attributes of acid lime Kuliana local in Odisha

Kuliana lime is an outstanding Odisha cultivar with significant potential to be included in future crop improvement operations due to its flavor and juice content. This research was conducted at the Horticultural Research Station, Department of Fruit Science and Horticultural Technology, Orissa University of Agriculture and Technology, Bhubaneswar during 2019–2020 and 2020–2021 to determine the optimal growth regulator in terms of quality metrics. certain growth regulators 2,4-D (2,4-D @ 10 ppm and 20 ppm) and GA3 (GA3 @ 50 ppm and 100 ppm) and NAA (NAA @ 100 ppm and 200 ppm) and SA (SA @ 100 ppm and 200 ppm) and SPMD (SPMD @ 0.01 ppm and 0.5 ppm) and PUT (PUT @ 0.01 ppm and 0.5 ppm) and BR (BR @ 0.1 ppm and 0.5 ppm) quality of Acid Lime kuliana local. The biometrical observations on various quality features were recorded in the same seasons over 2 years. The best was for fruit quality, which has been determined on the basis of growth regulators performances, as growth regulator variation has a significant effect on the quality of fruit. Brassinosteroids showed the best effects for TSS (7.17 oBrix), acidity (6.05 %), total sugar content (0.61 %), reducing sugar content (0.41 %), ascorbic acid (36.35 mg per 100 mL of juice), chlorophyll content (33.03 mg/100 g), flavonoid content (119.91 mg/100 g), protein (0.82 mg/100 g) and carbohydrate content (10.29 mg/100g) with the other treatments, including the untreated fruits in both years of the study. Our results revealed that the BR gave superior results in quality attributes for Kuliana lime in the East and Southeast Coastal Plain Zones of Odisha.

Unlocking the potential of Kodo millet: reviving an indigenous super grain for tomorrow's nutrition.

Kodo millet (Paspalum scrobiculatum L.) is an underutilized crop that encompasses nutritional benefits and climate resilience, making it a viable option for future crop development with nutraceutical properties. The cultivation of this crop has ancient roots, where it was revered for its ability to thrive in times of famine and was a vital companion crop to rice. Dishes made with Kodo millet are highly palatable and can be easily integrated into mainstream rice-based dishes. Among all cereals, Kodo millet is distinguished by its gluten-free composition, high phosphorus content, and significant antioxidant potential, which contributes to a diet that may reduce cardiovascular disease risk. Often grown in rainfed zones by marginal farmers, Kodo millet is valued for its grain and fodder. This less demanding crop can tolerate both biotic and abiotic stress, allowing it to thrive in soils with low organic matter and with minimal inputs, making it an ideal dual-purpose crop for rainfed areas. Despite its nutritional and agricultural benefits, Kodo millet's popularity is hindered by challenges such as low yield, market demand, lodging at harvest, and poor dehulling recovery, which necessitate the development of high-yielding varieties through the latest breeding advancements. Systematic investment and concerted breeding efforts are essential to harness the full potential of this nutrient-dense crop. The absence of whole genome sequence for Kodo millet poses a barrier to uncovering novel genetic traits. Consequently, there is an imperative to establish a millet-based value chain that elevates these underutilized crops, shaping smart cropping patterns and enhancing nutritional profiles for sustainable diets. Accordingly, this review highlights the significance of Kodo millet and the impact of breeding to establish it as a smart food choice for the future.

Crop water requirement and irrigation scheduling under climate change scenario, and optimal cropland allocation in lower kulfo catchment

Crop water requirement and irrigation scheduling in Lower Kulfo Catchment of southern Ethiopia have not assessed under climate change scenarios, and the allocation of crop land also not optimal that signifcantly challenges to crop productivity.Therefore, this study was conducted to evaluate the effects of climate change on future crop water requirements, and irrigation scheduling, and to allocate cropland optimally. Bias of projected precipitation and temperature were corrected by utilizing Climate Model data with the hydrologic modeling tool (CMhyd). Alongside, crop water requirements and irrigation scheduling were assessed using Crop Water Assessment Tool. After estimating crop water requirement, crop land were allocated optimally using General Algebraic Modeling System programming with non-negativity constraints (scenario 1), and non-negativity constraints based on farmers adaptation (scenario 2). Average reference evapotranspiration from 2030 to 2050 and 2060 to 2080 was increased by 11.9 %, and 16.2 %, respectively compared with the reference period (2010–2022). The total seasonal crop water requirements were 4,529 mm, 4866.7 mm, and 5272.2 mm under 2010 to 2022, 2030 to 2050, and 2060 to 2080 climate change scenarios, respectively. The meean irrigation interval in 2010–2022, 2030 to 2050, and 2060 to 2080 climate change scenarios were 8 days, 7 days, and 5 days, respectively. This irrigation interval was decreased by 14 % (2030–2050), and 34 % (2060–2080) compared with the reference period. In 2030 to 2050 and 2026 to 2080 climate change scenarios, the required irrigation water at the inlet of main canal increased by 6.8 %, and 18 %, respectively. The optimal allocated area for tomato (60.4 %), maize (20.8 %), and watermelon (18.8 %) in scenario 1 with net benefit of 1.47*108 Ethiopian Birr. The allocated areas in scenario 2 were (48 %) for maize, (31.6 %) for tomato, and (20.4 %) for watermelon with 1.34*108 Ethiopian Birr net benefit it was reduced by 19.1 % compared with the net benefit in scenario 1. Fruit crops alone may not suffice for local food needs and to address this, small farmers should grow maize, tomato, and watermelon. This research aids policymakers in encouraging climate-resilient agriculture and improving small-scale farmers' awareness through conducting workshops and training.

Physiological and proteomic characterization revealed the response mechanisms underlying aluminium tolerance in lentil (Lens culinaris Medikus).

Aluminium (Al) toxicity causes major plant distress, affecting root growth, nutrient uptake and, ultimately, agricultural productivity. Lentil, which is a cheap source of vegetarian protein, is recognized to be sensitive to Al toxicity. Therefore, it is important to dissect the physiological and molecular mechanisms of Al tolerance in lentil. To understand the physiological system and proteome composition underlying Al tolerance, two genotypes [L-4602 (Al-tolerant) and BM-4 (Al-sensitive)] were studied at the seedling stage. L-4602 maintained a significantly higher root tolerance index and malate secretion with reduced Al accumulation than BM-4. Also, label-free proteomic analysis using ultra-performance liquid chromatography-tandem mass spectrometer exhibited significant regulation of Al-responsive proteins associated with antioxidants, signal transduction, calcium homeostasis, and regulation of glycolysis in L-4602 as compared to BM-4. Functional annotation suggested that transporter proteins (transmembrane protein, adenosine triphosphate-binding cassette transport-related protein and multi drug resistance protein), antioxidants associated proteins (nicotinamide adenine dinucleotide dependent oxidoreductase, oxidoreductase molybdopterin binding protein & peroxidases), kinases (calmodulin-domain kinase & protein kinase), and carbohydrate metabolism associated proteins (dihydrolipoamide acetyltransferase) were found to be abundant in tolerant genotype providing protection against Al toxicity. Overall, the root proteome uncovered in this study at seedling stage, along with the physiological parameters measured, allow a greater understanding of Al tolerance mechanism in lentil, thereby assisting in future crop improvement programmes.

IJAZ-based approach to engineer lepidopteran pest resistance in multiple crop species.

The fall armyworm (FAW) poses a significant threat to global crop production. Here we showed that overexpression of jasmonate ZIM-domain (JAZ) protein GhJAZ24 confers resistance to cotton bollworm and FAW, while also causing sterility in transgenic cotton by recruiting TOPLESS and histone deacetylase 6. We identified the NGR motif of GhJAZ24 that recognizes and binds the aminopeptidase N receptor, enabling GhJAZ24 to enter cells and disrupt histone deacetylase 3, leading to cell death. To overcome plant sterility associated with GhJAZ24 overexpression, we developed iJAZ (i, induced), an approach involving damage-induced expression and a switch from intracellular to extracellular localization of GhJAZ24. iJAZ transgenic cotton maintained fertility and showed insecticidal activity against cotton bollworm and FAW. In addition, iJAZ transgenic rice, maize and tobacco plants showed insecticidal activity against their lepidopteran pests, resulting in an iJAZ-based approach for generating alternative insecticidal proteins with distinctive mechanisms of action, thus holding immense potential for future crop engineering.

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

Nutritional and bioactive properties and antioxidant potential of Amaranthus tricolor, A. lividus, A viridis, and A. spinosus leafy vegetables

Climate change results in continuous warming of the planet, threatening sustainable crop production around the world. Amaranth is an abiotic stress-tolerant, climate-resilient, C4 leafy orphan vegetable that has grown rapidly with great divergence and potential usage. The C4 photosynthesis allows amaranth to be grown as a sustainable future food crop across the world. Most amaranth species grow as weeds in many parts of the world, however, a few amaranth species can be also found in cultivated form. Weed species can be used as a folk medicine to relieve pain or reduce fever thanks to their antipyretic and analgesic properties. In this study, nutritional value, bioactive pigments, bioactive compounds content, and radical scavenging potential (RSP) of four weedy and cultivated (WC) amaranth species were evaluated. The highest dry matter, carbohydrate content, ash, content of iron, copper, sodium, boron, molybdenum, zinc, β-carotene and carotenoids, vitamin C, total polyphenols (TP), RSP (DPPH), and RSP (ABTS+) was determined in Amaranthus viridis (AV). On the other hand, A. spinosus (AS) was found to have the highest content of protein, fat, dietary fiber, manganese, molybdenum, and total flavonoids (TF). In A. tricolor (AT) species the highest total chlorophyll, chlorophyll a and b, betaxanthin, betacyanin, and betalain content was determined. A. lividus (AL) was evaluated as the highest source of energy. AV and AT accessions are underutilized but promising vegetables due to their bioactive phytochemicals and antioxidants.

Zonal Chemical Signal Pathways Mediating Floral Induction in Apple.

Phytohormones that trigger or repress flower meristem development in apple buds are thought to be locally emitted from adjacent plant tissues, including leaves and fruitlets. The presence of fruitlets is known to inhibit adjacent buds from forming flowers and thus fruits. The resulting absence of fruitlets the following season restores flower-promoting signalling to the new buds. The cycle can lead to a biennial bearing behaviour of alternating crop loads in a branch or tree. The hormonal stimuli that elicit flowering is typically referred to as the floral induction (FI) phase in bud meristem development. To determine the metabolic pathways activated in FI, young trees of the cultivar 'Ruby Matilda' were subjected to zonal crop load treatments imposed to two leaders of bi-axis trees in the 2020/2021 season. Buds were collected over the expected FI phase, which is within 60 DAFB. Metabolomics profiling was undertaken to determine the differentially expressed pathways and key signalling molecules associated with FI in the leader and at tree level. Pronounced metabolic differences were observed in trees and leaders with high return bloom with significant increases in compounds belonging to the cytokinin, abscisic acid (ABA), phenylpropanoid and flavanol chemical classes. The presence of cytokinins, namely adenosine, inosine and related derivatives, as well as ABA phytohormones, provides further insight into the chemical intervention opportunities for future crop load management strategies via plant growth regulators.

Ectopic overexpression of Eleusine coracana CAX3 confers tolerance to metal and ion stress in yeast and Arabidopsis

Ionic and metal toxicity in plants is still a global problem for the environment, agricultural productivity and ultimately poses human health threats when these metal ions accumulate in edible organs of plants. Metal and ion transport from cytosol to the vacuole is considered an important component of metal and ion tolerance and a plant's potential utility in phytoremediation. Finger millet (Eleusine coracana) is an orphan crop but has prominent nutritional value in comparison to other cereals. Previous transcriptomic studies suggested that one of the calcium/proton exchanger (EcCAX3) is strongly upregulated during different developmental stages of spikes development in plant. This finding led us to speculate that high calcium accumulation in the grain might be because of CAX3 function. Moreover, phylogenetic analysis shows that EcCAX3 is more closely related to foxtail millet, sorghum and rice CAX3 protein. To decipher the functional role of EcCAX3, we have adopted complementation of yeast triple mutant K677 (Δpmc1Δvcx1Δcnb1), which has defective calcium transport machinery. Furthermore, metal tolerance assay shows that EcCAX3 expression conferred tolerance to different metal stresses in yeast. The gain-of-function study suggests that EcCAX3 overexpressing Arabidopsis plants shows better tolerance to higher concentration of different metal ions as compared to wild type Col-0 plants. EcCAX3-overexpression transgenic lines exhibits abundance of metal transporters and cation exchanger transporter transcripts under metal stress conditions. Furthermore, EcCAX3-overexpression lines have higher accumulation of macro- and micro-elements under different metal stress. Overall, this finding highlights the functional role of EcCAX3 in the regulation of metal and ion homeostasis and this could be potentially utilized to engineer metal fortification and generation of stress tolerant crops in near future.

Polystyrene nanoplastics in soil impair drought priming-induced low temperature tolerance in wheat

Drought priming is known to enhance plant low temperature tolerance, whereas polystyrene nanoplastic contamination exerts detrimental effects on plant growth. This study investigates the less-explored influence of nanoplastic contamination on cold stress tolerance in drought-primed plants. We compared the photosynthetic carbon assimilation, carbohydrate metabolism, reactive oxygen species metabolism, and grain yield between the non-primed and drought-primed wheat grown in both nanoplastic-contaminated and healthy soils. Our results reveal that the beneficial effects of drought priming on photosynthetic carbon assimilation and the efficiency of the “water-water” cycle were compromised in the presence of nanoplastics (nPS). Additionally, nPS exposure disturbed carbohydrate metabolism, which impeded source-to-sink transport of sugar and resulted in reduced grain yield in drought-primed plants under low temperature conditions. These findings unveil the suppression of nPS on drought-primed low-temperature tolerance (DPLT) in wheat plants, suggesting an intricate interplay between the induction of stress tolerance and responses to nPS contamination. The study raises awareness about a potential challenge for future crop production.

Guard cell and subsidiary cell sizes are key determinants for stomatal kinetics and drought adaptation in cereal crops.

Climate change-induced drought is a major threat to agriculture. C4 crops have a higher water use efficiency (WUE) and better adaptability to drought than C3 crops due to their smaller stomatal morphology and faster response. However, our understanding of stomatal behaviours in both C3 and C4 Poaceae crops is limited by knowledge gaps in physical traits of guard cell (GC) and subsidiary cell (SC). We employed infrared gas exchange analysis and a stomatal assay to explore the relationship between GC/SC sizes and stomatal kinetics across diverse drought conditions in two C3 (wheat and barley) and three C4 (maize, sorghum and foxtail millet) upland Poaceae crops. Through statistical analyses, we proposed a GCSC-τ model to demonstrate how morphological differences affect stomatal kinetics in C4 Poaceae crops. Our findings reveal that morphological variations specifically correlate with stomatal kinetics in C4 Poaceae crops, but not in C3 ones. Subsequent modelling and experimental validation provide further evidence that GC/SC sizes significantly impact stomatal kinetics, which affects stomatal responses to different drought conditions and thereby WUE in C4 Poaceae crops. These findings emphasize the crucial advantage of GC/SC morphological characteristics and stomatal kinetics for the drought adaptability of C4 Poaceae crops, highlighting their potential as future climate-resilient crops.