Crop Quality Research Articles

Overexpression of NB-LRR Gene AtRPM1(D505V) Improved Drought and Salt Resistance and Decreased Cold Tolerance in Transgenic Rice

Abiotic stimuli severely restrict the growth and development of plants, resulting in massive losses in the quality and yield of crops. Exploring genes that can improve crop tolerance to abiotic stress is important. In a previous study, we found that overexpression of the Arabidopsis nucleotide-binding domain leucine-rich repeat (NB-LRR) gene AtRPM1(D505V) increased disease resistance in rice. In this research, we found that AtRPM1(D505V) transgenic plants were more sensitive to abscisic acid (ABA) than wild type (WT) plants. Abiotic-stress resistance in AtRPM1(D505V) transgenic plants was investigated. We found that AtRPM1(D505V) transgenic plants exhibited improved resistance to drought and salt stress; the phonotype and survival rates of transgenic rice were better than WT plants. The expression of stress responsive genes including OsDREB2A, OsDREB2B, OsRD22, and OsRD29A were significantly upregulated in AtRPM1(D505V) overexpressed plants than in WT plants. Moreover, the activities of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD) were significantly increased in AtRPM1(D505V) overexpressed plants than in WT plants under drought and salt stress. Under cold stress, the expression of stress responsive genes and the activities of antioxidant enzymes in AtRPM1(D505V) transgenic plants were significantly lower than in WT plants. Our research demonstrated that AtRPM1(D505V) confers drought and salt resistance to transgenic rice. Therefore, AtRPM1(D505V) could act as a potential candidate gene to cultivate drought- and salt-tolerant plants.

AI Driven IoT(AIIoT) for Smart Agriculture

The agricultural industry is witnessing a significant shift due to the emergence of Artificial Intelligence driven Internet of Things (AIIoT), which is providing farmers with unparalleled automation capabilities and insights. The goal of this paper is to present a thorough review of AIIoT in smart agriculture, emphasising its uses, advantages, and consequences for decision-making. Smart agriculture decision-making is a game-changing technology that gives farmers the ability to maximise their operations and make well-informed judgements. Through the utilisation of advanced analytics, real-time data, and decision support systems, farmers may optimise crop yields, minimise expenses, and minimise risks. The agricultural industry will become more sustainable and productive as a result of farmers' increased ability to make decisions as the field of smart agriculture develops. The application of AI and IoT in smart agriculture is a game-changing technology that might completely alter how we grow and prepare food. Farmers can increase crop yields and quality, streamline operations, and contribute to a more effective and sustainable food supply chain by utilising AI and IoT. The advantages of AIIoT in smart agriculture are evident, despite the fact that there are still certain obstacles to be addressed, and its use is anticipated to increase in the years to come.

Physiological responses and transcriptome analysis of Hemerocallis citrina Baroni exposed to Thrips palmi feeding stress

Physiological responses and transcriptome analysis of Hemerocallis citrina Baroni exposed to Thrips palmi feeding stress

Breeding for Resistance against Pest and Diseases in Tomatoes: A Review

Diseases and pests have a substantial effect on tomato production, greatly affecting both the quantity and quality of this crucial vegetable crop. Although fungicides and insecticides have been important in controlling plant diseases and pests, their excessive usage raises significant environmental issues. Vegetable breeders are increasingly concentrating on developing cultivars with natural tolerance to biotic stresses to promote sustainability and environmental friendliness. The change in focus is intended to cultivate tomato cultivars with inherent resistance to diseases and pests, hence decreasing the need for chemical treatments. Advancements in creating high-yielding genetically resistant tomato cultivars are a result of detailed study on the genetic basis of pest and disease resistance in tomato crops, as well as the complex interactions between the host plant and pathogens. For effective breeding programs and pre-breeding activities, scientists and breeders must have access to sources of resistance and a thorough grasp of the genetic complexities involved. This requires examining the genetic composition of both the tomato plants and the different infections that are impacting them. Breeders may generate tomato cultivars with strong resistance to common diseases and pests by using the inherent defensive mechanisms found in certain tomato types via selective crossing. Continuing to study how hosts and pathogens interact and the molecular processes involved in resistance is crucial. This information offers vital insights on how to improve and expand resistance, leading to the creation of cultivars with long-lasting and wide-ranging resistance. Currently, the emphasis on breeding is a proactive and sustainable strategy for transfer of resistances in high yielding tomato cultivars. Researchers aim to develop tomato cultivars that provide high yield and demonstrate tolerance to changing disease and pest stresses by integrating genetic knowledge with sophisticated breeding methods. This comprehensive method protects tomato crops and encourages environmental sustainability by decreasing the need on chemical inputs in agriculture.

Fine Mapping of Candidate Gene Controlling Anthocyanin Biosynthesis for Purple Peel in Solanum melongena L.

Fruit color is an intuitive quality of horticultural crops that can be used as an evaluation criterion for fruit ripening and is an important factor affecting consumers’ purchase choices. In this study, a genetic population from the cross of green peel ‘Qidong’ and purple peel ‘8 guo’ revealed that the purple to green color of eggplant peel is dominant and controlled by a pair of alleles. Bulked segregant analysis (BSA), SNP haplotyping, and fine genetic mapping delimited candidate genes to a 350 kb region of eggplant chromosome 10 flanked by markers KA2381 and CA8828. One ANS gene (EGP22363) was predicted to be a candidate gene based on gene annotation and sequence alignment of the 350-kb region. Sequence analysis revealed that a single base mutation of ‘T’ to ‘C’ on the exon green peel, which caused hydrophobicity to become hydrophilic serine, led to a change in the three-level spatial structure. Additionally, EGP22363 was more highly expressed in purple peels than in green peels. Collectively, EGP22363 is a strong candidate gene for anthocyanin biosynthesis in purple eggplant peels. These results provide important information for molecular marker-assisted selection in eggplants, and a basis for analyzing the regulatory pathways responsible for anthocyanin biosynthesis in eggplants.

Factors Influencing Seed Dormancy and Germination and Advances in Seed Priming Technology

Seed dormancy and germination play pivotal roles in the agronomic traits of plants, and the degree of dormancy intuitively affects the yield and quality of crops in agricultural production. Seed priming is a pre-sowing seed treatment that enhances and accelerates germination, leading to improved seedling establishment. Seed priming technologies, which are designed to partially activate germination, while preventing full seed germination, have exerted a profound impact on agricultural production. Conventional seed priming relies on external priming agents, which often yield unstable results. What works for one variety might not be effective for another. Therefore, it is necessary to explore the internal factors within the metabolic pathways that influence seed physiology and germination. This review unveils the underlying mechanisms of seed metabolism and germination, the factors affecting seed dormancy and germination, as well as the current seed priming technologies that can result in stable and better germination.

Yield Comparison of Sweet Potato Grown in Four Commercial Organic Soils

Aims: The experiment was conducted to observe the effects on comparative yields of sweet potato (Ipomea batatas ‘Beauregard’) grown in four different soilless media. Study Design: Mention the design of the study here. Place and Duration of Study: Pullen Farm at Murray State University, in Murray, Kentucky, USA., between May 2018 and August 2018. Methodology: 16 3.048m x .3046m troughs (four of each treatment) each containing five sweet potato plants were established. No treatments received supplemental fertilization. Descriptive statistics and quantitative analysis were performed on yield characteristics of the sweet potato crop (sweet-potato tuber and biomass yields). Results: While a significant difference in mass of above-ground biomass between the treatments could not be determined, growth parameters of the Organiloc treatments (A, B) were marginally greater than treatments C and D (Table 1). The fresh weight of treatments A (38.91 t ha-1) and B (42.75 t ha-1) were statistically greater than treatment C (21.23 t ha-1). Treatment B was also statistically greater that treatment D (23.92 t ha-1). The number of tubers in treatment A and B were not significantly different than treatment D, but were significantly greater than treatment C (Fig. 2). Conclusion: The results of this one cropping season study suggest that yields of sweet potatoes grown in these two biochar-based treatments (A + B), in which they were statistically greater than the Miracle Gro treatment (C), and Treatment B was statistically greater than the Magic Dirt treatment (D), may help to fulfill the increasing demand for locally-produced food and agricultural products while providing an efficient means of disposing of agricultural waste products and may promote sustainable crop production and food security.

Crop Height Estimation Based on a Novel Semi-Empirical Model Considering Double-Bounce Scattering Using RADARSAT-2 PolSAR Data

Abstract. Obtaining precise and rapid crop height is essential to facilitate agricultural production services, field management, disaster monitoring, and yield assessment. With the capability to penetrate vegetation and record vertical structure information, Polarimetric Synthetic Aperture Radar (PolSAR) holds significant potential for application in vegetation height inversion. The Water Cloud Model (WCM) and its enhanced versions are extensively utilized for estimating crop heights from PolSAR data owing to their physical significance and simplicity. However, the method is not practical for stalk crops due to the neglect of double-bounce scattering considerations. Therefore, according to the growth characteristics of stalk crops, a three-component polarimetric coherent backscattering model considering crop target double-bounce scattering is established by simplifying the Random Volume over Ground (RVoG) coherent scattering model. The empirical coefficient is introduced to simplify the model into a semi-empirical for crop height inversion. The suitability of applying the RVoG-B three-component model for crop height inversion at the early stage in corn fields was assessed using Multi-temporal C-band PolSAR RADARSAT-2 data in three polarimetric channels. The results show that the HV channel exhibits superior potential in inverting the height of corn compared with the HH and the VV channels. The results of corn height inversion demonstrate that the RVoG-B three-component semi-empirical model performs effectively in estimating corn height, with its inversion accuracy having an RMSE ranging from 11.66cm to 24.51cm. This study demonstrates the potential application of the RVoG-B three-component semi-empirical model for inverting crop height at the early stage dominated by double-bounce scattering.

Investigating the Dynamics of Bayoud Disease in Date Palm Trees and Optimal Control Analysis

The fungus Fusarium oxysporum (f.sp. albedinis) causes Bayoud disease. It is one of the epiphytotic diseases that affects a wide range of palm species and has no known cure at present. However, preventive measures can be taken to reduce the effects of the disease. Bayoud disease has caused enormous economic losses due to decreased crop yield and quality. Therefore, it is essential to develop a mathematical model for the dynamics of the disease to propose some affordable methods for disease management. In this study, we propose a novel mathematical model that describes the transmission dynamics of the disease in date palm trees. The model incorporates various factors such as the contact rate of the fungi with date palm trees, the utilization of fungicides, and the introduction of a quarantine compartment to prevent disease dissemination. We first prove a few key properties of the proposed model to ensure that the model is well-posed and suitable for numerical investigations. We establish that the model has a unique positive solution that is bounded and stable over time. We use sensitivity analysis to identify the parameters that have the greatest effect on the reproduction number R0 and illustrate this effect graphically. We then formulate an optimal control problem to identify the most suitable and cost-effective disease control approaches. As a first approach, we solely focus on the application of fungicide to susceptible trees and determine the best spray rates for a greater decrease in exposed and infected trees. Secondly, we emphasize quarantining exposed and infected trees at optimal quarantine rates. Finally, we explore the combined effect of fungicide spraying and isolating infected trees on disease control. The findings of the last approach turn out to be the most rewarding and cost-effective for minimizing infections in date palm trees.

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

The segmentation of crops and weeds from camera-captured images is a demanding research area for advancing agricultural and smart farming systems. Previously, the segmentation of crops and weeds was conducted within a homogeneous data environment where training and testing data were from the same database. However, in the real-world application of advancing agricultural and smart farming systems, it is often the case of a heterogeneous data environment where a system trained with one database should be used for testing with a different database without additional training. This study pioneers the use of heterogeneous data for crop and weed segmentation, addressing the issue of degraded accuracy. Through adjusting the mean and standard deviation, we minimize the variability in pixel value and contrast, enhancing segmentation robustness. Unlike previous methods relying on extensive training data, our approach achieves real-world applicability with just one training sample for deep learning-based semantic segmentation. Moreover, we seamlessly integrated a method for estimating fractal dimensions into our system, incorporating it as an end-to-end task to provide important information on the distributional characteristics of crops and weeds. We evaluated our framework using the BoniRob dataset and the CWFID. When trained with the BoniRob dataset and tested with the CWFID, we obtained a mean intersection of union (mIoU) of 62% and an F1-score of 75.2%. Furthermore, when trained with the CWFID and tested with the BoniRob dataset, we obtained an mIoU of 63.7% and an F1-score of 74.3%. We confirmed that these values are higher than those obtained by state-of-the-art methods.

Identification and Characterization of Colletotrichum truncatum and C. destructivum Causing Stem Rot of White Clover in China.

White clover (Trifolium repens L.) is an excellent quality forage legume species with superior planting efficiency, which reduces the cost of artificial weeding and nitrogen fertilizer inputs, and has feeding and economic value. However, from June to September 2022, severe stem rot affected the yield and quality of white clover crops in Harbin, Heilongjiang Province, China. The aim of this study was to identify the causative agents of the disease. Overall, Colletotrichum truncatum (6 isolates) and C. destructivum (10 isolates) were obtained from rotten white clover stems and identified based on morpho-molecular characteristics and phylogenetic analyses. Pathogenicity tests of the isolates revealed that C. destructivum had a higher pathogenicity to white clover than C. truncatum. In addition, all isolates were highly pathogenic to broad bean, fodder soybean, soybean, pak choi, and chickpea, were pathogenic to mint, and did not infect corn, wheat, or cilantro. C. destructivum and C. truncatum isolates were very sensitive to tebuconazole and pyraclostrobin, with EC50 values of 0.54 to 0.70 μg/ml and 0.42 to 0.62 μg/ml, respectively, efficacies ranging between 93.2 to 94.9% and 90.2 to 95.2% at 600 μg/ml and 450 μg/ml, respectively, and EC90 values of 1.88 to 13.36 μg/ml and 1.32 to 23.39 μg/ml, respectively. Therefore, intercropping of host and nonhost plants and chemicals can be considered to control stem rot in white clover. These results provide a basis for controlling C. destructivum and C. truncatum in white clover in China.

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Comprehensive physiology and proteomics analysis revealed the resistance mechanism of rice (Oryza sativa L) to cadmium stress

Cadmium contamination can lead to a decrease in crop yield and quality. However, Cd-tolerant rice can improve rice resistance genes, improve crop tolerance to heavy metals, and protect plants from oxidative damage. In this study, Japonica rice: Chunyou 987 and Indica rice: Chuanzhong you 3607 were used to reveal the molecular response mechanism of Cd-tolerant rice under cadmium concentration of 3 mg/kg through comparative experiments combined with physiology and proteomics. The results showed that compared with indica rice, japonica rice showed more robust resistance to Cd stress and effectively retained many Cd ions in roots. Moreover, it enhanced its enzymatic and non-enzymatic anti-oxidative stress mechanism, which increased the activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) by 47.37%, 21.75%, and 55.42%, respectively. The contents of non-enzymatic antioxidant substances ascorbic acid (AsA), glutathione (GSH), cysteine (Cys), proline (PRO), anthocyanins (OPC), and flavonoids were increased by 25.32%, 42.67%, 21.43%, 50.81%, 33.23%, and 72.16%, respectively. Through proteomics analysis, it was found that in response to the damage caused by cadmium stress, Japonica rice makes Photosynthesis functional proteins (psbO and PetH), Photosynthesis antenna proteins (LHCA and ASCAB9), Carbon fixation functional proteins (PEPC and OsAld), Porphyrin metabolism functional proteins (OsRCCR1 and SE5), Glyoxylate and dicarboxylate The expression of metabolism functional proteins (CATC and GLO4.) and Glutathione metabolism functional proteins (APX8 and OsGSTU13) were significantly up-regulated, which stimulated the antioxidant stress mechanism and photosynthetic system, and constructed a robust energy supply system to ensure the normal metabolic activities of life. Strengthening the mechanisms of plant homeostasis. In summary, this study revealed the molecular mechanism of tolerance to Cd stress in japonica rice, and the results of this study will provide a possible way to improve Cd-resistant rice seedlings.

Effects of Organic Fertilizer on Photosynthesis, Yield, and Quality of Pakchoi under Different Irrigation Conditions

Water scarcity and the overuse of chemical fertilizers present significant challenges to modern agriculture, critically affecting crop photosynthesis, yield, quality, and productivity sustainability. This research assesses the impact of organic fertilizer on the photosynthetic attributes, yield, and quality of pakchoi under varying irrigation water conditions, including fresh water and brackish water. Findings reveal that the modified rectangular hyperbolic model most accurately captures the photosynthetic reaction to organic fertilization, outperforming other evaluated models. The maximum net photosynthesis rate (Pnmax), yield, soluble sugar (SS), and soluble protein content (SP) all exhibited a downward-opening quadratic parabolic trend with increasing amounts of organic fertilizer application. Specifically, under fresh-water irrigation, the optimal Pnmax, yield, SS, and SP were obtained at organic fertilizer rates of 65.77, 74.63, 45.33, and 40.79 kg/ha, respectively, achieving peak values of 20.71 µmol/(m2·s), 50,832 kg/ha, 35.63 g/kg, and 6.25 g/kg. This investigation provides a foundational basis for further research into the intricate relationship between water salinity stress and nutrient management, with the goal of crafting more sophisticated and sustainable farming methodologies. The insights gained could significantly influence organic fertilizer practices, promoting not only higher yields but also superior quality in agricultural outputs.

Multifractal analysis of maize and soybean DNA

This paper investigates the complexity of DNA sequences in maize and soybean using the multifractal detrended fluctuation analysis (MF-DFA) method, chaos game representation (CGR), and the complexity-entropy plane approach. The study aims to understand the patterns and structures of these DNA sequences, which can provide insights into their genetic makeup and improve crop yield and quality. The results show that maize and soybean DNA sequences exhibit fractal properties, indicating a complex and self-organizing structure. We observe the persistence trend between sequences of base pairs, which indicates long-range correlations between base pairs. We also identified the stochastic nature of the DNA sequences of both species.

The Role of Bio-fortification in Enhancing the Nutritional Quality of Vegetables: A Review

Bio-fortification is a process that enhances the nutritional quality of crops, including vegetables, by increasing their micronutrient content. It can be achieved through different approaches such as agronomic, conventional breeding, and transgenic/biotechnological methods. Vegetables are known to be rich in micronutrients, vitamins, antioxidants, and other health-benefiting compounds, making them essential for a balanced diet. However, malnutrition and hidden hunger continue to be global challenges, particularly in developing countries. Micronutrient deficiencies, such as iron, zinc, iodine, and vitamin A, are prevalent in these populations. Conventional breeding focuses on selecting genotypes with desirable nutritional traits without compromising agricultural productivity. Transgenic/biotechnological approaches involve the synthesis of transgenes to enhance the bioavailability of micronutrients in plants. Bio-fortification of vegetables is particularly important as they are rich sources of micronutrients, vitamins, and other health-benefiting compounds. However, improving the nutritional quality of vegetables through conventional breeding has had limited success, and modern molecular tools and techniques offer potential for handling complex traits and developing nutrient-dense varieties. Bio-fortification offers a sustainable solution to address these deficiencies by increasing the nutrient content of crops, particularly plant-based foods. These techniques have shown promising results in increasing the concentration of nutrients, such as iron, in vegetables, thereby improving their nutritional quality.

The Function of RNA Interference (RNAi) in Crop Enhancement- Mechanism and Applications: A Review

RNA interference (RNAi) is a conserved molecular mechanism that plays a critical role in post-transcriptional gene silencing across diverse organisms. This review delves into the role of RNAi in plant functional genomics and its applications in crop improvement, highlighting its mechanistic insights and practical implications. The overview starts with the fundamental finding of the mechanism of RNAi, following its journey from petunias to its ubiquitous presence in a wide range of species. The discovery of several kinds of regulatory non-coding small RNAs, including as phasiRNAs, miRNAs, and siRNAs, has broadened our knowledge of RNAi-mediated gene regulation beyond what is previously thought. These RNA types have an impact on gene expression through complex post-transcriptional and epigenetic mechanisms. RNA interference (RNAi) has become a potent tool for comprehending gene activities in the context of crop improvement. It has shown to be useful in determining the functions of genes for metabolic pathways, stress tolerance, and more. Furthermore, RNAi-based strategies have potential for sustainable agriculture and integrated pest management, supporting international efforts to ensure food security. The many uses of RNA interference (RNAi) are covered in this article, including improving crop nutrition, changing plant architecture, and increasing shelf life. The challenges and future prospects of RNAi technology are also explored. The worldwide geography of RNA interference research is emphasized, with notable contributions from North America, Europe, and China. In conclusion, RNA interference (RNAi) continues to be a flexible and essential tool in contemporary plant research, providing new pathways for understanding gene activities and enhancing crop characteristics. Its combination with other biotechnological techniques, such as gene editing, might influence how agriculture and sustainable food production develop in the future.

Studies on the Effects of Liming Acidic Soil on Improving Soil Physicochemical Properties and Yield of Crops: A Review

Soil acidity is a major constraint to crop production globally by potentially limiting agricultural productivity and causing environmental challenges, especially in temperate and tropical regions of the world where there is high precipitation. The review article summarizes the works of literature and gathers information on the effects of liming on soil's physicochemical properties and the yield of crops. Soil acidity is caused by natural ways, such as the high amount of precipitation that exceeds evapotranspiration that leaches appreciable amounts of exchangeable bases from the soil surface, weathering, and decomposition of organic matter and by human interference (by the use of nitrogen fertilizer mainly ammonia and urea fertilizers). Application of lime improved soil pH and neutralize the effect of toxic elements. Liming directly improves some physicochemical properties of the soil, such as aggregates, density, and porosity as physical properties and reduction of exchangeable acidity, Al saturation, micronutrients (Cu, Fe, Mn, and Zn) in the soil solutions, from exchange complex to the levels required and increasing soil pH, exchangeable cations (Na+, K+, Ca+2, and Mg+2) as chemical properties. Soil aggregation, density, and porosity of soil undergo changes with the application of lime. The long-term lime application resulted in increased soil chemical properties. Lime application contributed to increased crop productivity and crop quality. The effects of liming can be explained by the flocculation and cementing action of Calcium ions in the short term. In the long term, increases in productivity induced by liming, result in increasing soil pH, available phosphorus, cation exchange capacity, basic cations, microbial activity, organic carbon, total nitrogen, and decreasing leaching of nutrients, exchangeable aluminum, and acidity, all favoring the physical and chemical properties of the soil.

Zinc Oxide Nanoparticles Alleviate Salt Stress in Cotton (Gossypium hirsutum L.) by Adjusting Na+/K+ Ratio and Antioxidative Ability

Soil salinization poses a threat to the sustainability of agricultural production and has become a global issue. Cotton is an important cash crop and plays an important role in economic development. Salt stress has been harming the yield and quality of many crops, including cotton, for many years. In recent years, soil salinization has been increasing. It is crucial to study the mechanism of cotton salt tolerance and explore diversified materials and methods to alleviate the salt stress of cotton for the development of the cotton industry. Nanoparticles (NPs) are an effective means to alleviate salt stress. In this study, zinc oxide NPs (ZnO NPs) were sprayed on cotton leaves with the aim of investigating the intrinsic mechanism of NPs to alleviate salt stress in cotton. The results show that the foliar spraying of ZnO NPs significantly alleviated the negative effects of salt stress on hydroponic cotton seedlings, including the improvement of above-ground and root dry and fresh weight, leaf area, seedling height, and stem diameter. In addition, ZnO NPs can significantly improve the salt-induced oxidative stress by reducing the levels of MDA, H2O2, and O2− and increasing the activities of major antioxidant enzymes, such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). Furthermore, RNA-seq showed that the foliar spraying of ZnO NPs could induce the expressions of CNGC, NHX2, AHA3, HAK17, and other genes, and reduce the expression of SKOR, combined with the CBL-CIPK pathway, which alleviated the toxic effect of excessive Na+ and reduced the loss of excessive K+ so that the Na+/K+ ratio was stabilized. In summary, our results indicate that the foliar application of ZnO NPs can alleviate high salt stress in cotton by adjusting the Na+/K+ ratio and regulating antioxidative ability. This provides a new strategy for alleviating the salt stress of cotton and other crops, which is conducive to the development of agriculture.

Agromet Advisories to Mitigate the Effects of Aberrant Weather in Himachal Pradesh

Weather is a pivotal factor in agricultural production, influencing crop growth, disease and pest occurrences, water and fertilizer needs, and the effectiveness of cultural and preventive measures. Extreme weather events can cause physiological stress, physical damage, soil erosion, and crop quality degradation. In India, agriculture's vulnerability to adverse weather is a significant concern, with natural disasters like floods, droughts, and cyclones leading to crop failure, food insecurity, and economic impacts. To mitigate these challenges, Agrometeorological Advisory Services (AAS) play a crucial role in providing farmers with weather-based advisories to manage agricultural risks. The Integrated Agro-Meteorological Advisory Service (IAAS), run by the Indian Ministry of Earth Sciences and the India Meteorological Department (IMD), offers district-level weather forecasts and agromet advisories to support farmers in adapting to weather fluctuations. These advisories cover various weather-related risks, including cold injury and frost, high temperatures, wind damage, hailstorms, and floods. Sample advisories suggest measures like irrigation for frost protection, mulching to reduce heat stress, and shelter belts to mitigate wind damage. Effective dissemination of advisories involves communication through electronic media, Krishi Vigyan Kendras (Agriculture Science Centres), and Kisan (Farmer) Call Centres. Thrust areas for enhancing AAS include improved scientific understanding, advanced monitoring and prediction of severe weather events, satellite-based communication technologies, and real-time forecasting. By integrating these strategies, farmers can better prepare for extreme weather and mitigate its impact on agriculture.