Improve Crop Yield Research Articles

Optimal Soil, Climate, and Management Factors for Maximizing Crop Yield and Soil Nutrients in a Rice–Oilseed Rotation System with Straw Return

Straw return (SR) has been widely recommended as a conservation agricultural practice in China. However, the effects of SR on crop yield and soil properties are inconsistent across studies of rice–oilseed rape cropping systems in China. This study aimed to investigate the effects of SR on crop yield and soil nutrient content in a rice–oilseed rotation system, and to understand the mechanism of straw return on the difference in yield increases between rice and oilseed rape. Additionally, suitable climate factors, soil properties, and agricultural practices were identified to achieve maximum increases in yield and soil nutrients in a rice–oilseed rotation under SR. This paper is based on a meta-analysis of 1322 observations from 83 peer-reviewed studies to evaluate the effects of climate, initial soil conditions, and agricultural management practices on rice and oilseed rape yields and soil nutrients under SR. The results showed that the responses of oilseed rape and rice yield remained positive, with 12.37% and 6.54% increases, and were significantly higher under SR than the control (no SR). Moreover, SR significantly increased the contents of several soil nutrients (soil organic carbon (SOC), total nutrients, available nutrients) and microbial biomass carbon (MBC) and nitrogen (MBN). Interestingly, the increase in crop yields was attributed to the increase in SOC, total nitrogen, and available potassium. Additionally, the increase in yields was mainly affected by climate factors, initial soil properties, and agronomic practices. For example, both mean annual temperature (MAT) and mean annual precipitation (MAP) had a positive correlation with crop yield increases under SR (p < 0.01). Initial soil conditions such as low SOC and total nitrogen content were more suitable for increased rice yield under SR, while the opposite was true for increased oilseed rape yield. Without fertilization, the SR did not significantly improve crop yield and soil nutrients, while it was more pronounced with N fertilization at 150–180 kg hm−2. The positive effect of SR on crop yields is more evident with plowing tillage, whereas the SR caused the highest increase in soil nutrients with the no-tillage condition. These findings have important implications for further improving crop yield, SOC, and soil nutrients in the Chinese rice–oilseed cropping system through straw return.

Deep Learning for Accurate Papaya Disease Identification Using Vision Transformers

This study investigates the application of Vision Transformers (ViTs) in deep learning for the accurate identification of papaya diseases. ViTs, known for their effectiveness in image classification tasks, are utilized to develop a robust model capable of precisely diagnosing various diseases that affect papaya plants. Through rigorous experimentation and validation, the study showcases the superior performance of ViTs compared to traditional convolutional neural networks (CNNs) in terms of classification accuracy and computational efficiency. The results highlight the potential of ViTs in real-world agricultural systems, enabling early and accurate disease detection to improve crop yield and ensure food security. This research contributes to the advancement of computer vision techniques in agriculture, emphasizing the importance of leveraging cutting-edge deep learning models like ViTs for enhanced disease management and sustainable agricultural practices.

Advanced Molecular Approaches for Improving Crop Yield and Quality: A Review

Advanced Molecular Approaches for Improving Crop Yield and Quality: A Review

Bioprospects of pink pigmented facultative methylotrophs (PPFMs)

PurposeThe purpose of this article is to provide information about interactions between pink-pigmented facultative methylotroph (PPFM) organisms and plants, their molecular mechanisms of methylotrophic metabolism, application of PPFMs in agriculture, biotechnology and bioremediation and also to explore lacuna in PPFMs research and direction for future research.Design/methodology/approachResearch findings on PPFM organisms as potent plant growth promoting organisms are discussed in the light of reports published by various workers. Unexplored field of PPFM research are detected and their application as a new group of biofertilizer that also help host plants to overcome draught stress in poorly irrigated crop field is suggested.FindingsPPFMs are used as plant growth promoters for improved crop yield, seed germination capacity, resistance against pathogens and tolerance against drought stress. Anti-oxidant and UV resistant properties of PPFM pigments protect the host plants from strong sunshine. PPFMs have excellent draught ameliorating capacity.Originality/valueTo meet the ever increasing world population, more and more barren, less irrigated land has to be utilized for agriculture and horticulture purpose and use of PPFM group of organisms due to their draught ameliorating properties in addition to their plant growth promoting characters will be extremely useful. PPFMs are also promising candidates for the production of various industrially and medicinally important enzymes and other value-added products. Wider application of this ecofriendly group of bacteria will reduce crop production cost thus improving economy of the farmers and will be a greener alternative of hazardous chemical fertilizers and fungicides.Graphicalabstract:

Virtual analysis of machine learning models for diseases prediction in muskmelon

Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real-time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability.

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

Solar greenhouses provide a favorable climate environment for the production of counter-seasonal crops in northern China. The greenhouse environment is a key factor affecting crop growth, so accurate prediction of greenhouse environment changes helps to precisely regulate the crop growth environment and helps to promote the growth of fruits and vegetables. In this study, an environmental prediction model based on the combination of a gradient boosting tree and the Harris hawk optimization algorithm (IHHO-Catboost) is constructed, and in response to the problems of the HHO algorithm, such as the fact that the adjustment of the search process is not flexible enough, it cannot be targeted to carry out a stage search, and sometimes it will fall into the local optimum to make the algorithm’s search accuracy relatively poor, an algorithm based on the improved Harris hawk optimization (IHHO) algorithm-based parameter identification method is constructed. The model considers the internal and external environmental and regulatory factors affecting crop growth, which include indoor temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, outdoor temperature and humidity, light intensity, carbon dioxide concentration, wind direction, wind speed, and opening and closing of upper and lower air openings of the cotton quilt, and is input into a prediction model with a time series for training and testing. The experimental results show that the MAE (mean absolute error) values of temperature, relative humidity, carbon dioxide concentration, and light intensity of the model are reduced to 49.8%, 35.3%, 72.7%, and 32.1%, respectively, compared with LSTM (Long Short-Term Memory), which is a significant decrease in error. It shows that the proposed multi-parameter prediction model for solar greenhouse environments presents an effective method for accurate prediction of environmental data in solar greenhouses. The model not only improves prediction accuracy but also reduces dependence on large data volumes, reduces computational costs, and improves the transparency and interpretability of the model. Through this approach, an effective tool for greenhouse agriculture is provided to help farmers optimize the use of resources, reduce waste, and improve crop yield and quality, ultimately leading to a more efficient and environmentally friendly agricultural production system.

Weather and Crop Yield Prediction by Machine Learning Model and Spiking Neural Network

This paper, "Weather and Crop Yield Prediction by Machine Learning Model and Spiking Neural Network," is to improve crop yield prediction efficiency by utilizing cutting-edge technology and transform agricultural practices. The creation of two prediction models specifically for this purpose is the main goal of the study. The first model incorporates geographical information such as Normalized difference vegetation index (NDVI), Standard Precipitation Index (SPI), and Vector Choquet Integral (VCI) and uses a Genetic Algorithm to reduce the size of the input dataset. This reduction attempts to lessen learning process confusion brought on by comparable data with different values. The model improves its capacity to identify pertinent patterns for precise yield estimates by incorporating spatial data. The SoftMax function is employed by the second model, a Modified Convolutional Neural Network (MCNN), as an error correcting method. By reducing errors and raising the overall accuracy of the model, the SoftMax function improves the output and produces forecasts that are more accurate. The study emphasizes how crucial geographic data including satellite imagery is for supplying crucial insights for predicting crop yields. Farmers can make well-informed decisions by having a thorough understanding of agricultural dynamics, which is facilitated by spatial patterns and temporal shifts. Statistical and geographic data are used to completely study the effects of numerous variables, such as biological, economic, and environmental ones, on crop yield production. The goal of the project is to create precise and effective instruments that enable farmers to make knowledgeable decisions about their soil by combining machine learning with spiking neural networks.

Weed Management Methods for Herbaceous Field Crops: A Review

Weeds compete with crops for water and nutrients and can adversely affect crop growth and yield, so it is important to research effective weed control methods. This paper provides an overview of the impact of weeds on crop yield and describes the current state of research on weed management in field herbaceous crops. Physical weed control mainly refers to thermal technologies represented by flame weed control and laser weed control, which can efficiently and accurately remove weeds. Mechanical weed control requires a combination of sensor technologies, machine vision technology, and high-precision navigation to improve weed control accuracy. Biological weed control relies heavily on plant extracts and pathogens to create herbicides, but it is costly, and some can be toxic to mammals. Chemical weed control is a common method, resulting in environmental pollution and weed resistance. To reduce the use of chemical herbicides, scholars have proposed integrated weed management strategies, which combine biological control, control of the seed bank, and improve crop competitiveness. Integrated weed management strategies are considered to be the future direction of weed management. In conclusion, physical, mechanical, biological, and chemical weed control methods are commonly used in weed management. Each method has its applicable scenarios, and the implementation of integrated weed management strategies can lead to better weed control, improving crop yield and quality. The main objective of this review is to organize the research progress on weed management methods for herbaceous crops in the field and to provide a reference for the agricultural sector to develop weed control strategies. Specifically, this paper categorizes weed management methods into four groups, discusses and presents the advantages and disadvantages of the aforementioned weed control methods, and discusses future research directions.

A comparative analysis of the performance of common cropping systems used in the production of maize and selected grain legumes in Cameroon

Cropping systems and crop combinations are among vital practices to improve land utilisation with the limited arable land resource. Combinations of maize with legumes are common in Cameroon, but there is a knowledge gap on locality specificity of the best cropping system and maize/legume combination for sustained production. The experiment was conducted at the Faculty of Agriculture and Veterinary Medicine Teaching and Research farm, University of Buea. Different cropping systems and crop combinations were planted in a Randomized Complete Block Design with 13 treatments replicated three times. Data collected for vigour and yield parameters, were analyzed for significance using a two-way ANOVA and treatment means compared using Tukey HSD at 5 % probability, while additional data, were analysed for land equivalent and benefit-cost ratio. The findings reveal no significant differences in the yield of maize under different cropping systems, except for maize-dwarf bean mix cropping system (MDBM), which recorded the lowest yield (3.9 t/ha), with maize soybean intercropping (MSBI), recording the highest yield (5.9 t/ha) for maize. Indeterminate legumes (cowpea and climbing beans) recorded significant results for yield (MCPI = 1.7 t/ha; MCBI = 1.8 t/ha) under intercropping system. Likewise, determinate legumes (dwarf beans and soybeans) also recorded significant results for yield (DB = 2.2 t/ha; SB = 3.5 t/ha) under mono cropping system. Land equivalent ratios for intercropping and mixed cropping systems were highest with MCBI = 1.93 and MCBM = 1.76 and the benefit-cost ratios for intercropping and mixed cropping were >1, with MSBI recording the highest ratio of 3.5, followed by MSBM = 3.3. Intercropping and mixed cropping systems and crop combinations were advantageous over monocropping as they gave better land equivalent and benefit-cost ratio. This study provides meaningful findings in bridging the knowledge gap on locality specificity of the best cropping systems and maize/legume combination, for sustained production, locally and nationally. The contribution to the sustainable development goals (SDG), are multidimensional: The enhancement of food security through improved crop yields (SDG2); the promotion of agricultural practices that mitigate environmental degradation (SDG13) and lastly, advocating for biodiversity conservation through the adoption of sustainable cropping systems (SDG15).

Buckwheat (Fagopyrum esculentum) floral strips support natural enemies and maintain yields in organic green bean (Phaseolus vulgaris) crops

Arthropod natural enemies can contribute to pest management by consuming crop pests, but populations of these natural enemies may be low within crops due to a lack of resources. Floral strips can provide these resources and support natural enemy communities, which in turn can suppress pest populations and thereby improve crop yields. Buckwheat (Fagopyrum esculentum Moench) floral strips were added to experimental plots of organic green beans (Phaseolus vulgaris L.) and paired with control plots, without floral strips, to determine their impact on arthropod communities, crop yields, and pest control costs. Crop yields were maintained, but costs of pest control significantly lower (16%) in plots with floral strips. This was not explained by an increased abundance or diversity of natural enemies or a decrease of pests (i.e. all herbivores). Instead, the composition of arthropod natural enemy communities were similar between buckwheat floral strips and the crop, suggesting that the floral strips support natural enemies already present within crops. Semi-natural edge habitat in control plots, had a unique community of natural enemies, which had little spillover into the crop and therefore may have little impact on biological control. These results suggest that higher crop yields can be achieved with reduced inputs, such as insecticides, by adding floral strips to crops as they may support the natural enemies present within crops. Future studies should consider the impacts of specific natural enemies, rather than diversity, and direct measurements of biological control to connect the impact of floral strips to crop yields.

Effects of arbuscular mycorrhizal fungi on carbon assimilation and ecological stoichiometry of maize under combined abiotic stresses

Abstract Arbuscular mycorrhizal fungi (AMF) enhance plant tolerance to abiotic stresses like salinity and improve crop yield. However, their effects are variable, and the underlying cause of such variation remains largely unknown. This study aimed to assess how drought modified the effect of AMF on plant resistance to high calcium-saline stress. A pot experiment was performed to examine how AMF inoculation affects the growth, photosynthetic activity, nutrient uptake and carbon (C), nitrogen (N) and phosphorus (P) stoichiometric ratio (C:N:P) of maize under high calcium stress and contrasting water conditions. The results showed that high calcium stress significantly reduced mycorrhizal colonization, biomass accumulation, C assimilation rate and C:N stoichiometric ratio in plant tissues. Besides, the adverse effects of calcium stress on photosynthesis were exacerbated under drought. AMF inoculation profoundly alleviated such reductions under drought and saline stress. However, it barely affected maize performance when subjected to calcium stress under well-watered conditions. Moreover, watering changed AMF impact on nutrient allocation in plant tissues. Under well-watered conditions, AMF stimulated P accumulation in roots and plant growth, but did not induce leaf P accumulation proportional to C and N, resulting in increased leaf C:P and N:P ratios under high calcium stress. In contrast, AMF decreased N content and the N:P ratio in leaves under drought. Overall, AMF inoculation improved maize resistance to calcium-salt stress through enhanced photosynthesis and modulation of nutrient stoichiometry, particularly under water deficit conditions. These results highlighted the regulatory role of AMF in carbon assimilation and nutrient homeostasis under compound stresses, and provide significant guidance on the improvement of crop yield in saline and arid regions.

Analysis of Effects of Spatial Distributed Soil Properties and Soil Moisture Behavior on Hourly Streamflow Estimate through the Integration of SWAT and LSM

This study addresses the challenge of accurately estimating hourly flow and soil moisture by integrating the Soil and Water Assessment Tool (SWAT) with a Land Surface Model (LSM). Our approach enhances SWAT by incorporating spatially distributed soil properties and a physically-based soil moisture process, using the Noah LSM for hourly soil moisture estimation. This integration captures spatial variations in soil moisture and hydraulic properties from remote sensing across the watershed. The parameter sensitivity analysis and the calibration of hourly flow were significantly impacted by the physically-based hourly soil moisture routing and the incorporation of spatially distributed soil properties. Consequently, the modified SWAT model showed improved accuracy in hourly flow simulations for long-term and multiple rainfall events. Validation results showed significant improvements, with Coefficient of Determination (R2) and Nash and Sutcliffe Efficiency (NSE) increasing by 25.95% and 33.3%, respectively, and Percent Bias (PBIAS) decreasing by 85.8%. Notably, the average error for peak flows across eight events decreased by 49%. These findings highlight the importance of initializing soil parameters based on spatial soil moisture distribution and incorporating physical process-based moisture routing to enhance hourly flow simulation accuracy. Future research should focus on validating the physical feasibility of the soil parameter set in the study area with detailed hourly flow and soil moisture data and exploring its applicability in various regions. This study provides valuable insights for the scientific community, water resources, and agricultural decision-makers regarding integrated modeling of soil moisture and hourly flow, which can inform dam operation management, disaster planning, and crop yield improvement.

Optimizing Agricultural Yields with Artificial Intelligence-Based Climate Adaptation Strategies

In the face of climate change, agricultural productivity is severely threatened by unpredictable weather patterns and changing environmental conditions, underscoring the critical need for innovative solutions to bolster agricultural resilience and optimize yields. This study delves into the potential of artificial intelligence (AI), specifically through the use of machine learning and deep learning techniques, to develop climate adaptation strategies aimed at enhancing agricultural outcomes. By integrating AI with climatological data, the research predicts and mitigates the adverse impacts of climate on crop yields, utilizing a combination of machine learning and deep learning models to analyze historical climate data alongside crop performance. These models, trained on datasets including temperature, rainfall, soil moisture, and crop genetic information, are adept at forecasting future agricultural outcomes under varied climatic scenarios and suggest optimal adaptation strategies that significantly improve crop yields. Consequently, these AI-based models serve as robust tools for farmers and agricultural policymakers, enabling them to make informed decisions that are aligned with anticipated climatic conditions. The findings not only underscore the efficacy of AI in transforming data into actionable insights that enhance agricultural productivity but also contribute to the field of agricultural science by providing a technologically advanced approach to climate adaptation. Furthermore, this research paves the way for future studies on the integration of AI with real-time environmental sensing technologies, thereby offering a dynamic framework for agricultural management that supports sustainable farming practices and global food security amid climate challenges.

Influencia de microorganismos benéficos en el comportamiento agronómico del cultivo de la papa cv. “Bicentenaria”

The excessive use of chemical fertilizers causes alterations in soil microbial activity, environmental pollution and high production costs in potato cultivation (Solanum tuberosum L.). One way to avoid this effect is with the use of beneficial microorganisms, due to their ability to capture atmospheric nitrogen, produce growth-promoting substances such as indole acetic acid and solubilize inorganic phosphorus from insoluble compounds, which in turn time improves crop yields. The objective of this research was to evaluate the effect of the beneficial microorganisms as biofertilizers on growth and yield of potato crop. In a population of 1,600 potato plants cv. “Bicentenaria” four treatments were evaluated: Trichoderma harzianum (0.5 g.L-1), Glomus spp. (30 g.plant-1), efficient microorganisms (EM) (50 mL.L-1) and a control treatment (no application of microorganisms). A completely randomized experimental design with four repetitions per treatment was used. The effect of the treatments was evaluated using vegetative and reproductive variables. It was evident that the treatment with T. harzianum significantly favored the rest of the treatments in plant height at 90 days (43.60 cm), tuber weight (154 g) and yield (57.13 t.ha-1). The use of Glomus spp. and EM, had only a partial effect on the growth of the plants. The treatment with T. harzianum could represent an ecological agricultural alternative for potato production.

MAPK/NF-κB signaling mediates atrazine-induced cardiorenal syndrome and antagonism of lycopene

Atrazine (ATZ) is the most prevalent herbicide that has been widely used in agriculture to control broadleaf weeds and improve crop yield and quality. The heavy use of ATZ has caused serious environmental pollution and toxicity to human health. Lycopene (LYC), is a carotenoid that exhibits numerous health benefits, such as prevention of cardiovascular diseases and nephropathy. However, it remains unclear that whether ATZ causes cardiorenal injury or even cardiorenal syndrome (CRS) and the beneficial role of LYC on it. To test this hypothesis, mice were treated with LYC and/or ATZ for 21 days by oral gavage. This study demonstrated that ATZ exposure caused cardiorenal morphological alterations, and several inflammatory cell infiltrations mediated by activating NF-κB signaling pathways. Interestingly, dysregulation of MAPK signaling pathways and MAPK phosphorylation caused by ATZ have been implicated in cardiorenal diseases. ATZ exposure up-regulated cardiac and renal injury associated biomarkers levels that suggested the occurrence of CRS. However, these all changes were reverted, and the phenomenon of CAR was disappeared by LYC co-treatment. Based on our findings, we postulated a novel mechanism to elucidate pesticide-induced CRS and indicated that LYC can be a preventive and therapeutic agent for treating CRS by targeting MAPK/NF-κB signaling pathways.

Study of timing of irrigation after foliar application of putrescine on phytochemical and physiological responses of sage

AbstractSalvia officinalis L. (sage) is a valuable medicinal plant with various secondary metabolites. These compounds are usually inducible by different stress conditions, for example, water‐deficit stress. Therefore, cost‐effective and eco‐friendly approaches for alleviating water‐deficit stress in medicinal plant crops are in high demand. Polyamines (PAs), such as putrescine, have a significant effect on improving crop yield under water‐deficit stress conditions. Therefore, a factorial experiment based on a completely randomized design using two factors and three replications was conducted. Treatments included irrigation regime (IR) and timing of IR after foliar application of putrescine (TIRFP) as follows: irrigation after depletion of 20%, 40%, 60%, and 80% available soil water (ASWD). TIRFP was performed at 0, 24, 48, and 72 h. The treatments affected morphological, physiological, molecular, and biochemical attributes. TIRFP of 72 h under IR of 80% ASWD improved the leaf area index, relative water content, antioxidant enzyme activities, and total radical scavenging capacity compared with the controlled treatment. The results of real‐time quantitative polymerase chain reaction (qRT‐PCR) and gas chromatography/mass spectrometry (GC/MS) analysis showed the highest level of sabinene synthase (SS) and bornyl diphosphate synthase (BPPS), the highest contents of 1,8‐cineole, β‐thujone, and camphor appeared in plants after 72 h of TIRFP under IR of 80% ASWD. The findings of this study confirm the protective role of putrescine against water stress. In conclusion, putrescine foliar application allows for balanced water retention. It preserves antioxidant capacity resulting in efficient photosynthesis and the restricted risk of oxidative damage under water‐deficit stress. It improves sage plants’ physiological attributes and valuable monoterpenes components of essential oil.

Irrigation Scheduling Based on Evapotranspiration of Tomato (Solanumm Lycopersicum) Using Atmometer in The Upland Rolling Production Area

Objective : The goal is to provide the right amount of water to the crop, at the right time, to meet the crop's needs. It was conducted to estimate evapotranspiration using atmometers for irrigation scheduling that may be highly beneficial in soil water balance, water allocation and budgeting, irrigation management, and improving crop yield and production for greater income. Methods: This study was conducted at the Organic Agricultural Research and Development Innovation Center at the Bataan Peninsula State University – Abucay Campus, Bangkal, Abucay, Bataan, Philippines (North 14°44'28” East 120°27'04”). The effects of using different evapotranspiration estimation methods on the timing and amounts of water application were then evaluated by using a computed irrigation scheduling model. Findings: The atmometer ( and Penman-Monteith ( values were statistically analyzed using linear regression (y=0.8573x + 1.586), coefficients of determination (R² = 0.7236), root mean – squared error (RMSD = 0.73), mean bias error (MBE = 0.03), and the t-statistics (significant at 5% level). The study revealed that the ETo-A data strongly correlated with the ETo-PM data. Using an atmometer for scheduling irrigation on tomatoes would have resulted in an equally similar distribution of irrigation events, more water would have been provided throughout the season compared to using ETo-PM data. Novelty : The calibrated evapotranspiration data from the atmometer may guide appropriate irrigation depth and schedule for tomatoes. By aligning irrigation intervals with other crops’ water requirements, farmers can optimize water usage, minimize water stress, and achieve higher crop yields. Keywords: Atmometer, Bataan Philippines, Evapotranspiration, Tomato, Penman-­Montheith Equation

Role of calcium signaling in cadmium stress mitigation by indol-3-acetic acid and gibberellin in chickpea seedlings.

Given the adverse impacts of heavy metals on plant development and physiological processes, the present research investigated the protective role of indole-3-acetic acid (IAA) and gibberellic acid (GA3) against cadmium (Cd)-induced injury in chickpea seedlings. Therefore, seeds germinated for 6 days in a medium containing 200 μM Cd alone or combined with 10 μM GA3 or 10 μM IAA. Both GA3 and IAA mitigated Cd-imposed growth delays in roots and shoots (80% and 50% increase in root and shoot length, respectively). This beneficial effect was accompanied by a significant reduction in Cd2+ accumulation in both roots (74% for IAA and 38% for GA3) and shoots (68% and 35%, respectively). Furthermore, these phytohormones restored the cellular redox state by reducing the activity of NADPH oxidase and downregulating the transcription level of RbohF and RbohD genes. Likewise, hydrogen peroxide contents were reduced by GA3 and IAA supply. Additionally, GA3 and IAA countered the Cd-induced reduction in total phenols, flavonoids, and reducing sugars in both roots and shoots. The exogenous effectors enhanced the activities of catalase, ascorbate peroxidase, and thioredoxin, as well as the corresponding gene expressions. Interestingly, adding GA3 and IAA to the Cd-contaminated germination media corrected the level of calcium (Ca2+) ion within seedling tissues. This effect coincided with the upregulation of key genes associated with stress sensing and signal transduction, including auxin-binding protein (ABP19a), mitogen-activated protein kinase (MAPK2), calcium-dependent protein kinase (CDPK1), and calmodulin (CaM). Overall, the current results suggest that GA3 and IAA sustain the Ca2+ signaling pathway, resulting in metal phytotoxicity relief. Amendment of agricultural soils contaminated with heavy metals with GA3 or IAA could represent an effective practice to improve crop yield.

Analysis of Emission Reduction Measures and Simulation of PM2.5 Concentrations in the Main Cotton Production Areas of Xinjiang in 2025

Cotton production in Xinjiang is increasing year by year, and the improved crop yields have had an impact on the environment. This study investigated the changes in six significant pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) from 2017 to 2022 in Xinjiang. We compiled a biomass burning emission inventory to make the MEIC emission inventory more complete. The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was employed to simulate air quality in different reduction scenarios in 2025, and it explored ways to alleviate air pollution in the main cotton areas of Xinjiang. The result shows that the main pollutant in Xinjiang is particulate matter (PM particles with aerodynamic diameters less than 2.5 µm and 10 µm), and the concentration of particulate matter decreased from the northern mountains toward the south. The concentrations of O3 (ozone) were highest in summer, while the concentrations of other pollutants were high in autumn and winter. If the pollution is not strictly controlled in terms of emission reduction, it is impossible to achieve the target of a 35 μg/m3 PM2.5 concentration in the planting area. In the scenario of enhanced emission reduction measures and the scenario of higher intensity emission reduction measures, there was a failure to reach the target, despite the reduction in the PM2.5 concentration. In the best emission reduction scenario, PM2.5 in Xinjiang is expected to drop to 22.5 μg/m3 in November and 34 μg/m3 in March, respectively. Therefore, in the optimal emission reduction scenario, the target of 35 μg/m3 will be reached. This study emphasized the importance of future air pollution mitigation and identified a feasible pathway to achieve the target of 35 μg/m3 PM2.5 concentration by 2025. The research findings provide useful insights for the local government which can be used to develop strategies aimed at mitigating substantial pollution emissions.

Severity of natural calamities and crop yield prediction using hybrid deep learning model in Uttar Pradesh

AbstractCrop yield prediction has gained major potential for global food production. Predicting crop yields based on specific parameters like soil, environment, crop, and water has been an interesting research topic in recent decades. To accurately predict crop yields, measuring the severities of natural calamities including water level is mainly required. However, the existing studies failed to predict crop yields accurately because of various issues like overfitting problems, difficulty in training, inability to handle large data, and reduced learning capability. Thus, the proposed study develops an efficient mechanism for accurately predicting crop yields by analyzing several natural calamities. Here, the input samples are initially pre‐processed to remove unwanted noises using data normalization and standardization. To enhance the performance of crop yield prediction, natural calamities are computed by using an Extreme Gradient Boosting (XGBoost) model based on parameters like the Palmer Drought Severity Index (PDSI), Severe Hail Index (SHI), and Storm Severity Index (SSI). Also, the hyperparameters of XGBoost model are tuned by utilizing Sheep Flock Optimization Algorithm (SFOA). Finally, the crop yield is predicted by proposing a new one‐dimensional convolutional gated recurrent unit neural network (1D‐CGRU). The proposed classifier predicts the crop yields with reduced error rates like mean square error (MSE) of 0.4363, root mean square error (RMSE) of 0.1904, normalized root mean squared error (NRMSE) of 0.00101, mean absolute error (MAE) of 0.2437, and R‐squared (R2) of .2756. Also, the significant findings of the proposed study positively indicate that this study can be applicable to real‐time agricultural practices and is highly suitable for water quality predictions. Also, it can assist farmers and farming businesses in predicting the yield of crops in a specific season when to harvest and crop a plant for attaining improved crop yields.