Biofabricated nanomaterials in sustainable agriculture: insights, challenges and prospects.

One ever-evolving and ever-demanding critical human endeavour is the provision of food security for the growing world population. It could be done by adopting sustainable agriculture through horizontal (expanding the aerable land area) and vertical (intensifying agriculture through sound technological approaches) interventions. Customised formulated nanomaterials have numerous advantages. With their specialised physicochemical properties, some nanoparticulised materials improve plant's natural development and stress tolerance and some other are good nanocarriers. Nanocarriers in agriculture often coat chemicals to form composites having utilities with crop productivity enhancement abilities, environmental management (like ecotoxicity reduction ability), and biomedicines (like the ability of controlled and targeted release of useful nanoscale drugs). The Ag, Fe, Zn, TiO2, ZnO, SiO2 and MgO nanoparticles often employed in advanced agriculture are covered here. Some nanoparticles used for various extended purposes in modern farming practices, including disease diagnostics and seed treatment are covered too. Thus, nanotechnology has revolutionised agrotechnology, which holds promises to transform agricultural (eco)system as a whole to ensure food security in future. Considering the available literature, the article further probes the emergent regulatory issues governing the synthesis and use of nanomaterials in the agriculture sector. If applied responsibly, nanomaterials could help improve soil health. The article provides an overview of the used nanomaterials in distribution of biomolecules, to aid in devising a safer and eco-friendly sustainable agriculture strategy. Through this, agri-systems depending on advanced farming practices might function more effectively and enhance agri-productivity to meet the food demand of the rising world population.

Melatonin differentially refines the metabolome to improve seed formation during grain developmental stages and enhances yield in two contrasting rice cultivars, grown in arsenic-contaminated soil.

The manuscript revealed the ameliorative effects of exogenous melatonin in two distinct reproductive stages, i.e., developing grains (20 days after pollination) and matured grains (40 days after pollination) in two contrasting indica rice genotypes, viz., Khitish (arsenic-susceptible) and Muktashri (arsenic-tolerant), irrigated with arsenic-contaminated water throughout their life-cycle. Melatonin administration improved yield-related parameters like rachis length, primary and secondary branch length, number of grains per panicle, number of filled and empty grains per panicle, grain length and breadth and 1000-grain per weight. Expression of GW2, which negatively regulates grain development, was suppressed, along with concomitant induction of positive regulators like GIF1, DEP1 and SPL14 in both Khitish and Muktashri. Melatonin lowered arsenic bioaccumulation in grains and tissue biomass, more effectively in Khitish. Unregulated production of reactive oxygen species, leading to cellular necrosis caused by arsenic, was reversed in presence of melatonin. Endogenous melatonin level was stimulated due to up-regulation of the key biosynthetic genes, SNAT and ASMT. Melatonin enhanced the production of diverse antioxidants like anthocyanins, flavonoids, total phenolics and ascorbic acid and also heightened the production of thiol-metabolites (cysteine, reduced glutathione, non-protein thiols and phytochelatin), ensuring effective chelation and arsenic detoxification. Altogether, our observation, supported by principal component analysis, proved that melatonin re-programs the antioxidative metabolome to enhance plant resilience against arsenic stress to mitigate oxidative damages and reduce arsenic translocation from the soil to tissue biomass and edible grains.

Anthropogenic fertilization influences a shift in barley rhizosphere microbial communities.

Background: Anthropogenic mediations contribute a significant role in stimulating positive reactions in soil-plant interactions; however, methodical reports on how anthropogenic activities impact soil microorganism-induced properties and soil health are still inadequate. In this study, we evaluated the influence of anthropogenic fertilization of farmland soil on barley rhizosphere microbial community structure and diversity, and the significant impacts on agro-ecosystem productivity. This will help validate the premise that soil amendment with prolonged synthetic fertilizers can lead to a significant reduction in bacterial abundance and diversity, while soils amended with organic fertilizers elicit the succession of the native soil microbial community and favor the growth of copiotrophic bacteria. Methods: The total metagenomic DNA was extracted from soils obtained from the barley rhizosphere under chemical fertilization (CB), organic fertilization (OB), and bulk soil (NB). Subsequently, these samples were sequenced using an amplicon-based sequencing approach, and the raw sequence dataset was examined using a metagenomic rast server (MG-RAST). Results: Our findings showed that all environments (CB, OB, and NB) shared numerous soil bacterial phyla but with different compositions. However, Bacteroidetes, Proteobacteria, and Actinobacteria predominated in the barley rhizosphere under chemical fertilization, organic fertilization, and bulk soils, respectively. Alpha and beta diversity analysis showed that the diversity of bacteria under organic barley rhizosphere was significantly higher and more evenly distributed than bacteria under chemical fertilization and bulk soil. Conclusion: Understanding the impact of conventional and organic fertilizers on the structure, composition, and diversity of the rhizosphere microbiome will assist in soil engineering to enhance microbial diversity in the agroecosystem.

Understanding plant-soil interactions underpins enhanced sustainability of crop production.

The Green Revolution transformed agriculture with high-yielding, stress-resistant varieties. However, the urgent need for more sustainable agricultural development presents new challenges: increasing crop yield, improving nutritional quality, and enhancing resource-use efficiency. Soil plays a vital role in crop-production systems and ecosystem services, providing water, nutrients, and physical anchorage for crop growth. Despite advancements in plant and soil sciences, our understanding of belowground plant-soil interactions, which impact both crop performance and soil health, remains limited. Here, we argue that a lack of understanding of these plant-soil interactions hinders sustainable crop production. We propose that targeted engineering of crops and soils can provide a fresh approach to achieve higher yields, more efficient sustainable crop production, and improved soil health.

The positive impact of honeybee activity on fennel crop production and sustainability.

This study investigates the ecological interaction between honeybees (Apis mellifera) and fennel (Foeniculum vulgare) plants, examining the mutual benefits of this relationship. Field experiments conducted in Egypt from December 2022 to May 2023 recorded diverse insect pollinators attracted to fennel flowers, especially honeybees. Assessing honeybee colonies near fennel fields showed improvements in sealed brood (357.5-772.5 cells), unsealed brood (176.3-343.8 cells), pollen collection (53.25-257.5 units), honey accumulation (257.5-877.5 units), and colony strength (7.75-10) over three weeks. Fennel exposure explained 88-99% of variability in foraging metrics. Comparing open versus self-pollinated fennel revealed enhanced attributes with bee pollination, including higher flower age (25.67 vs 19.67 days), more seeds per umbel (121.3 vs 95.33), bigger seeds (6.533 vs 4.400 mm), heavier seeds (0.510 vs 0.237 g/100 seeds), and increased fruit weight per umbel (0.619 vs 0.226 g). Natural variation in seed color and shape also occurred. The outcomes demonstrate the integral role of honeybees in fennel agroecosystems through efficient pollination services that improve crop productivity and quality. Fennel provides abundant nutritional resources that bolster honeybee colony health. This research elucidates the symbiotic bee-fennel relationship, underscoring mutualistic benefits and the importance of ecological conservation for sustainable agriculture.

Application of rhizobium inoculation in regulating heavy metals in legumes: A meta-analysis.

Rhizobium inoculation has been widely applied to alleviate heavy metal (HM) stress in legumes grown in contaminated soils, but it has generated inconsistent results with regard to HM accumulation in plant tissues. Here, we conducted a meta-analysis to assess the performance of Rhizobium inoculation for regulating HM in legumes and reveal the general influencing factors and processes. The meta-analysis showed that Rhizobium inoculation in legumes primarily increased the total HM uptake by stimulating plant biomass growth rather than HM phytoavailability. Inoculation had no significant effect on the average shoot HM concentration (p > 0.05); however, it significantly increased root HM uptake by 61 % and root HM concentration by 7 % (p < 0.05), indicating safe agricultural production while facilitating HM phytostabilisation. Inoculation decreased shoot HM concentrations and increased root HM uptake in Vicia, Medicago and Glycine, whereas it increased shoot HM concentrations in Sulla, Cicer and Vigna. The effects of inoculation on shoot biomass were suppressed by nitrogen fertiliser and native microorganisms, and the effect on shoot HM concentration was enhanced by high soil pH, organic matter content, and phosphorous content. Inoculation-boosted shoot nutrient concentration was positively correlated with increased shoot biomass, whereas the changes in pH and organic matter content were insufficient to significantly affect accumulation outcomes. Nitrogen content changes in the soil were positively correlated with changes in root HM concentration and uptake, whereas nitrogen translocation changes in the tissues were positively correlated with changes in HM translocation. Phosphorus solubilisation could improve HM phytoavailability at the expense of slight biomass promotion. These results suggest that the diverse growth-promoting characteristics of Rhizobia influence the trade-off between biomass-HM phytoavailability and HM translocation, impacting HM accumulation outcomes. Our findings can assist in optimising the utilisation of legume-Rhizobium systems in HM-contaminated soils.

Sugarcane bagasse biochar boosts maize growth and yield in salt-affected soil by improving soil enzymatic activities.

Salinization is a leading threat to soil degradation and sustainable crop production. The application of organic amendments could improve crop growth in saline soil. Thus, we assessed the impact of sugarcane bagasse (SB) and its biochar (SBB) on soil enzymatic activity and growth response of maize crop at three various percentages (0.5%, 1%, and 2% of soil) under three salinity levels (1.66, 4, and 8 dS m-1). Each treatment was replicated three times in a completely randomized block design with factorial settings. The results showed that SB and SBB can restore the impact of salinization, but the SBB at the 2% addition rate revealed promising results compared to SB. The 2% SBB significantly enhanced shoot length (23.4%, 26.1%, and 41.8%), root length (16.8%, 20.8%, and 39.0%), grain yield (17.6%, 25.1%, and 392.2%), relative water contents (11.2%, 13.1%, and 19.2%), protein (17.2%, 19.6%, and 34.9%), and carotenoid (16.3, 30.3, and 49.9%) under different salinity levels (1.66, 4, and 8 dS m-1, respectively). The 2% SBB substantially drop the Na+ in maize root (28.3%, 29.9%, and 22.4%) and shoot (36.1%, 37.2%, and 38.5%) at 1.66, 4, and 8 dS m-1. Moreover, 2% SBB is the best treatment to boost the urease by 110.1%, 71.7%, and 91.2%, alkaline phosphatase by 28.8%, 38.8%, and 57.6%, and acid phosphatase by 48.4%, 80.1%, and 68.2% than control treatment under 1.66, 4 and 8 dS m-1, respectively. Pearson analysis showed that all the growth and yield parameters were positively associated with the soil enzymatic activities and negatively correlated with electrolyte leakage and sodium. The structural equational model (SEM) showed that the different application percentage of amendments significantly influences the growth and physiological parameters at all salinity levels. SEM explained the 81%, 92%, and 95% changes in maize yield under 1.66, 4, and 8 dS m-1, respectively. So, it is concluded that the 2% SBB could be an efficient approach to enhance the maize yield by ameliorating the noxious effect of degraded saline soil.

Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea.

The use of observation-dependent methods for crop productivity and food security assessment is challenging in data-sparse regions. This study presents a transferable framework and applies it to North Korea (NK) to assess rice productivity based on climate similarity, transferable machine-learning techniques, and extendable multi-source data. We initially divided the primary phenological stages of rice in the study region and extracted dynamic rice distributions based on Moderate Resolution Imaging Spectroradiometer products and phenological observations. We compared the performances of four representative environmentally driven models (Linear Regression, back-propagation Neural Network, Support Vector Machine, and Random Forest) in simulating rice productivity using an extensive dataset that included multi-angle vegetation monitoring, climate variables, and planting distribution information. The framework integrated an optimal environmentally driven model with agricultural management practices for transferability to predict rice productivity in NK over multiple years. Additionally, two crop growth scenarios (whole growth period (WGP) and seeding-heading period (SHP)) were compared to assess pre-harvest forecasting capabilities and identify dominant factors. Finally, independent datasets from the Food and Agriculture Organization, World Food Program, and Global Gridded Crop Models were used to validate the magnitude and spatial distribution of the predicted results. The results showed that phenological identification based on remote sensing can accurately capture rice growth characteristics and map rice distribution. Random Forest outperformed other models in simulating rice productivity variation, with r-squares of 0.87 and 0.83 in the WGP and SHP, respectively. The solar-induced chlorophyll fluorescence, maximum temperature, and evapotranspiration collectively determined approximately 40 % of the variation in yield simulated using Random Forest. Conversely, planting areas contributed over 42 % of the variation in rice production. Compared to Food and Agriculture Organization statistics, the environmentally driven framework explained 78.72 % and 76.89 % of the production variation and 69.42 % and 71.15 % of the yield variation in NK under the WGP and SHP, respectively. Moreover, the environmental management-driven framework captured over 90 % of the yield variation. The predicted spatial pattern of rice productivity exhibited significant concordance with the World Food Program and Global Gridded Crop Model reports. In summary, the proposed transferable framework for crop productivity assessment contributes to early warnings of production reduction and has the potential for scalability across various crops and data-sparse regions.

Implications of bacteria‒bacteria interactions within the plant microbiota for plant health and productivity. / 植物微生物群落中细菌-细菌互作对植物健康和生产力的影响.

Crop production currently relies on the widespread use of agrochemicals to ensure food security. This practice is considered unsustainable, yet has no viable alternative at present. The plant microbiota can fulfil various functions for its host, some of which could be the basis for developing sustainable protection and fertilization strategies for plants without relying on chemicals. To harness such functions, a detailed understanding of plant‒microbe and microbe‒microbe interactions is necessary. Among interactions within the plant microbiota, those between bacteria are the most common ones; they are not only of ecological importance but also essential for maintaining the health and productivity of the host plants. This review focuses on recent literature in this field and highlights various consequences of bacteria‒bacteria interactions under different agricultural settings. In addition, the molecular and genetic backgrounds of bacteria that facilitate such interactions are emphasized. Representative examples of commonly found bacterial metabolites with bioactive properties, as well as their modes of action, are given. Integrating our understanding of various binary interactions into complex models that encompass the entire microbiota will benefit future developments in agriculture and beyond, which could be further facilitated by artificial intelligence-based technologies.

Is the boom in staple crop production attributed to expanded cropland or improved yield? A comparative analysis between China and India.

The characteristics of cropland development and the dynamics of food production in China and India, the world's largest agricultural and most populous countries, are of great importance to global food security. However, there is a notable lack of a thorough comparison between China and India in this regard. Here, we systematically compare the differences between China and India using cropping intensity and crop production data, including cropland area, harvested area, total staple crop (i.e., cereal crops, tuber crops and pulse crops) production and yield capacity. The results are mainly as follows: (1) Both China and India experienced an increasing trend in cropland area and harvested area from 2001 to 2021, especially notable in India. In China, the cropland area and harvested area increased by 11.76 % and 14.36 %, respectively, while in India, they witnessed a more substantial increase of 31.10 % and 49.32 %, respectively. (2) The cropping intensity underwent significant transformations, primarily shifting between non-cropland, single-cropping, and double-cropping. Northwestern China exhibited a clear trend of non-cropland converting to single-cropping, whereas northeastern China showed a distinct pattern of single-cropping changing to non-cropland. The interconversion between single-cropping and double-cropping was also frequently observed in the main food-producing regions. In India, the cropland expansion and the adoption of double-cropping are highly pronounced, extending widely across most of the country. (3) From 2001 to 2021, the total staple crop production in China and India increased by 34.12 % and 55.81 %, respectively. Despite the rapid growth in India's total staple crop production, it still amounts to only about half of China's. The major crops production also showed different trends, China's cereal crops production increased significantly, while tuber and pulse crops production declined, and India's production of cereal, tuber, and pulse crops has all increased (4) China's yield capacity has increased by 17.28 %, while India's has only grown by 4.35 %. Despite the rapid increase in India's total staple crop production, the yield gap with China has widened. The boost in China's total staple crop production mainly resulted from improved yield capacity, whereas India relied more on the cropland area expansion, especially the increase in harvested area. Our comprehensive comparison of China and India in cropland development and staple crop production contributes to a deep understanding of the differences in agricultural production between the two countries, and provides lessons for global food security and sustainable agricultural development.

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

Heat Waves Coupled with Nanoparticles Induce Yield and Nutritional Losses in Rice by Regulating Stomatal Closure.

The frequency, duration, and intensity of heat waves (HWs) within terrestrial ecosystems are increasing, posing potential risks to agricultural production. Cerium dioxide nanoparticles (CeO2 NPs) are garnering increasing attention in the field of agriculture because of their potential to enhance photosynthesis and improve stress tolerance. In the present study, CeO2 NPs decreased the grain yield, grain protein content, and amino acid content by 16.2, 23.9, and 10.4%, respectively, under HW conditions. Individually, neither the CeO2 NPs nor HWs alone negatively affected rice production or triggered stomatal closure. However, under HW conditions, CeO2 NPs decreased the stomatal conductance and net photosynthetic rate by 67.6 and 33.5%, respectively. Moreover, stomatal closure in the presence of HWs and CeO2 NPs triggered reactive oxygen species (ROS) accumulation (increased by 32.3-57.1%), resulting in chloroplast distortion and reduced photosystem II activity (decreased by 9.4-36.4%). Metabolic, transcriptomic, and quantitative real-time polymerase chain reaction (qRT-PCR) analyses revealed that, under HW conditions, CeO2 NPs activated a stomatal closure pathway mediated by abscisic acid (ABA) and ROS by regulating gene expression (PP2C, NCED4, HPCA1, and RBOHD were upregulated, while CYP707A and ALMT9 were downregulated) and metabolite levels (the content of γ-aminobutyric acid (GABA) increased while that of gallic acid decreased). These findings elucidate the mechanism underlying the yield and nutritional losses induced by stomatal closure in the presence of CeO2 NPs and HWs and thus highlight the potential threat posed by CeO2 NPs to rice production during HWs.

Linear modeling of zonal level crop production in Ethiopia.

Accounting for zonal-level variations and identifying factors that have linear effects on crop production help to make better decisions and plan new policies for effective crop production and food security. The main objective of this study is to identify potential subsets of covariates and estimate their linear effects on crop production. A linear mixed effects model (random--intercept) is used on agricultural sample survey data for Meher seasons from 2012/13 to 2019/20 to explore and identify the best variance-covariance structure for the longitudinal data on 90 zones with eight repeated observations and different sampling weights. The minimum, mean, and maximum crop production by farmers across the country are 1.616, 8.693, and 147.843 quintals, respectively, and about 98 % of farmers produced less than 25 quintals. There is a small rate of increase in mean and median crop production by farmers across the years, and the variability between zones is highest in the year 2019/20 and in the Somali region. The histogram, kernel density, and P-P plots suggested a common logarithm transformation on the crop production variable. Results from the data exploration and variance-covariance structure selection methods suggested a heterogeneous compound symmetry (CSH) structure. Covariates region, year, proportion of farmers who practice pure-agriculture and other-agriculture types, proportion of farmers who use any type of fertilizer, farmer's age, area used, farmer association crop production, indigenous seed used, improved seed used, UREA fertilizer used, other fertilizers used, and percentage of crop damaged are significant in linearly explaining/affecting log crop production, and among these area used, farmers association crop production, UREA fertilizer used, and indigenous seed used have relatively highest effect on log crop production. Zones Wolayita, North-Shewa (Am), West-Arsi, West-Welega, Dawro, and Guji are top/good performers while zones Southwest-Shewa, Waghimra, Guraghe, South-Omo, Keffa, North-Wello, South-Wello, and Eastern Tigray are bottom/poor performers in crop production. Model assumptions and influence diagnostics results suggested the linearity of the model and normality of random effects and residuals are not violated, even though some zones have influences on either model parameters, precisions of estimates of these parameters, and predicted values.

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity.

In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp's permittivity-water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θm-εa0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εa-σb, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type.

Assessing the impacts of crop production on climate change: An in-depth analysis of long-term determinants and policy implications.

This research investigates the long-term determinants of carbon emissions in three diverse regions-Europe and Central Asia (ECA), Sub-Saharan Africa (SSA), and the Middle East and North Africa (MENA)-spanning 1990 to 2020. Utilizing advanced econometric models and analyses, including the Regularized Common Correlated Effects Estimator (rCCE), Common Correlated Effects Estimator (CCE), and Mean-Group (MG) approach, the study explores the intricate relationships between carbon emissions, crop production, emissions per agricultural production, energy consumption, renewable energy consumption, per capita GDP, and population. Region-specific nuances are uncovered, highlighting the varying dynamics: ECA exhibits intricate and non-significant relationships, SSA showcases significant effects of population dynamics and green technology adoption, and the MENA region reveals a nuanced interplay between emissions per agricultural production.The findings underscore the universal efficacy of green technology adoption for mitigation. Strategies for mitigating carbon emissions in the agricultural sector require diversified energy transition approaches, emphasizing efficiency enhancements, green technology adoption, and tailored population management strategies based on regional intricacies. Counterfactual simulations indicate the potential efficacy of strategic measures targeting crop production to reduce carbon emissions, while acknowledging the nuanced relationship between economic growth and emissions. Policymakers are urged to recognize the persistence in emission patterns, emphasizing the importance of targeted interventions to transition towards more sustainable trajectories. Overall, the research provides essential insights for crafting effective policies at both regional and global scales to address the complexities of climate change mitigation in the agricultural sector.

Decline in Honeybees and Its Consequences for Beekeepers and Crop Pollination in Western Nepal.

In understudied regions of the world, beekeeper records can provide valuable insights into changes in pollinator population trends. We conducted a questionnaire survey of 116 beekeepers in a mountainous area of Western Nepal, where the native honeybee Apis cerana cerana is kept as a managed bee. We complemented the survey with field data on insect-crop visitation, a household income survey, and an interview with a local lead beekeeper. In total, 76% of beekeepers reported declines in honeybees, while 86% and 78% reported declines in honey yield and number of beehives, respectively. Honey yield per hive fell by 50% between 2012 and 2022, whilst the number of occupied hives decreased by 44%. Beekeepers ranked climate change and declining flower abundance as the most important drivers of the decline. This raises concern for the future food and economic security of this region, where honey sales contribute to 16% of total household income, and where Apis cerana cerana plays a major role in crop pollination, contributing more than 50% of all flower visits to apple, cucumber, and pumpkin. To mitigate further declines, we promote native habitat and wildflower preservation, and using well-insulated log hives to buffer bees against the increasingly extreme temperature fluctuations.

Modulation of Drought-Induced Stress in Cowpea Genotypes Using Exogenous Salicylic Acid.

Plant endogenous mechanisms are not always sufficient enough to mitigate drought stress, therefore, the exogenous application of elicitors, such as salicylic acid, is necessary. In this study, we assessed the mitigating action of salicylic acid (SA) in cowpea genotypes under drought conditions. An experiment was conducted with two cowpea genotypes and six treatments of drought stress and salicylic acid (T1 = Control, T2 = drought stress (stress), T3 = stress + 0.1 mM of SA, T4 = stress + 0.5 mM of SA, T5 = stress + 1.0 mM of SA, and T6 = stress + 2.0 mM of SA). Plants were evaluated in areas of leaf area, stomatal conductance, photosynthesis, proline content, the activity of antioxidant enzymes, and dry grain production. Drought stress reduces the leaf area, stomatal conductance, photosynthesis, and, consequently, the production of both cowpea genotypes. The growth and production of the BRS Paraguaçu genotype outcompetes the Pingo de Ouro-1-2 genotype, regardless of the stress conditions. The exogenous application of 0.5 mM salicylic acid to cowpea leaves increases SOD activity, decreases CAT activity, and improves the production of both genotypes. The application of 0.5 mM of salicylic acid mitigates drought stress in the cowpea genotype, and the BRS Paraguaçu genotype is more tolerant to drought stress.

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations.

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.