Sustainable Agricultural Practices Research Articles

Leveraging Cloud and Distributed Computing for Enhanced Precision Agriculture: A Yield Mapping Perspective

Abstract: This article explores the transformative impact of cloud and distributed computing technologies on precision agriculture, with a specific focus on enhancing yield mapping capabilities. Yield mapping, a critical component of modern farming, faces challenges such as data accuracy issues, data loss, edge effects, and temporal variability. The integration of cloud-based solutions and distributed computing addresses these challenges by facilitating the storage, processing, and analysis of large-scale agricultural datasets. Real-time processing and advanced analytics, powered by machine learning algorithms, enable farmers to gain deeper insights into spatial and temporal variations in crop performance. The article discusses the development of user-friendly interfaces and reporting tools that aid in yield map interpretation, as well as the integration of yield data with farm management systems for optimized decision-making. Furthermore, it examines the impact of these technologies on promoting sustainable farming practices, improving crop profitability, and reducing environmental footprints. The article also delves into future directions, including emerging technologies and potential obstacles to widespread adoption. By leveraging cloud and distributed computing, yield mapping evolves from a mere data collection tool to a sophisticated decision support system, paving the way for more efficient, profitable, and sustainable agricultural practices in the face of growing global food demand and environmental challenges.

Narrowband IoT in Livestock Farming: A Technological Innovation for Productivity and Sustainability

The integration of technology in livestock farming is crucial for enhancing production efficiency and animal welfare. This study aimed to develop and evaluate the implementation of a Narrowband IoT (NB-IoT)-based automated monitoring system in poultry farming. Using an experimental design, the research involved 30,000 day-old chicks at PT. Anugerah Teknologi Ternak in Central Java, Indonesia. The NB-IoT system collected real-time data on environmental parameters and poultry activity. Time-series analysis revealed non-stationary data, while correlation analysis showed a strong negative relationship between temperature and humidity (r = -0.8521). Anomaly detection identified 13.33% of observations as anomalous, demonstrating the system's capability for early issue detection. Regression modeling (R-squared = 0.7261) indicated that temperature and humidity significantly influence poultry productivity. The study concludes that NB-IoT implementation in poultry farming has significant potential for enhancing productivity through real-time monitoring and early anomaly detection, supporting more efficient and sustainable precision farming practices. However, limitations in data stationarity and sample generalizability suggest the need for further research to improve long-term predictions and broaden applicability across diverse farming contexts.

Foliar application of biostimulants improves nutritional and bioactive quality of walnuts.

Owing to their health benefits, walnuts are attracting interest as a good option for nutritious meals, thereby promoting their production. Furthermore, the adoption of ecologically and environmentally friendly agriculture strengthens biostimulant use as a sustainable complement to traditional fertilizers. This study evaluated the effects of different foliar-applied biostimulants in walnut tree orchards, in northeastern Portugal, on walnuts' chemical composition and bioactivity. Walnut samples were rich in fat (particularly the polyunsaturated linoleic acid), dietary fiber and protein. Sucrose was the most prevalent soluble sugar, followed by glucose and fructose. Studied samples also showed an antioxidant activity comparable (or superior) to that of Trolox. Some plant biostimulants (e.g. Sprint Plus®) had a positive impact on the nutritional composition of walnuts, more specifically by boosting tocopherol levels, besides improving the bioactivity of walnut extracts against specific bacteria. Overall, this research demonstrated that important quality traits of walnuts can be improved using sustainable agricultural bioproducts and practices. © 2024 Society of Chemical Industry.

An Expert Model Using Deep Learning for Image-based Pest Identification with the TSLM Approach for Enhancing Precision Farming

The rise of digital agriculture has sparked considerable interest in its potential to revolutionize farming practices by integrating of advanced technologies. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a crucial tool in precision agriculture, offering diverse applications for real-world scenarios. This research focuses on harnessing the power of drones in the context of digital agriculture support, particularly in addressing the challenges of crop protection and pest management. The objective is to develop an innovative approach for crop-disease diagnosis using an upgraded Transfer-Driven Self-Adaptive Learning Model (TSLM) that leverages multispectral remote sensing data of drone-captured images to improve pest classification and streamline pesticide application. The study explores nine potent deep neural network models' capabilities for identifying plant diseases using various approaches within the digital agriculture framework. Transfer learning and advanced feature extraction techniques are employed to tailor these deep neural networks to the specific crop protection context. Using of pre-trained deep learning models for feature extraction and fine-tuning enhances the effectiveness of the proposed model. Evaluating the model's performance, precision, sensitivity, specificity, and F1-score metrics are assessed, leading to the discovery that deep feature extraction and Self-Adaptive Learning Model (SLM) classification outperform traditional transfer learning methods. The novelty of this work lies in its application of communication protocols to coordinate a fleet of drones for crop protection within the digital agriculture framework. By combining deep learning techniques with transfer-driven self-adaptive learning, the proposed approach significantly enhances the accuracy and efficiency of pest classification in precision agriculture. The study's findings offer valuable insights into optimizing drone-based technologies to combat plant disease epidemics, thereby contributing to the advancement of digital agriculture and its role in supporting sustainable farming practices.

Mitigating water deficit stress in lemon balm (Melissa officinalis L.) through integrated soil amendments: A pathway to sustainable agriculture.

Lemon balm (Melissa officinalis L.) is a valuable medicinal plant, but its growth can be significantly impacted by drought stress. This study aimed to mitigate the adverse effects of water deficit stress on lemon balm biomass by integrating poultry manure compost, poultry manure biochar, NPK fertilizer, Trichoderma harzianum, Thiobacillus thioparus, and elemental sulfur as soil amendments. The experiment was conducted in a greenhouse using a completely randomized design with a factorial arrangement, consisting of three replicates. It included a water deficit stress factor at three levels (95-100%, 75-80%, and 55-60% of field capacity) and a soil amendment treatment factor with eleven different fertilizer levels. Treatments included control (no amendment), NPK fertilizer, poultry manure compost, poultry manure biochar, and combinations of these with T. harzianum, T. thioparus, and elemental sulfur under various water deficit levels. Water deficit stress significantly reduced photosynthetic pigments, gas exchange parameters, chlorophyll fluorescence, relative water content, and antioxidant enzyme activity, while increasing membrane permeability and lipid peroxidation in lemon balm plants. However, the integrated application of organic, biological, and chemical amendments mitigated these negative impacts. The combined treatment of poultry manure compost, poultry manure biochar, NPK fertilizer, T. harzianum, T. thioparus, and elemental sulfur was the most effective in improving the morpho-physiological properties (1.97-60%) and biomass (2.31-2.76 times) of lemon balm under water deficit stress. The results demonstrate the potential of this holistic approach to enhance the resilience of lemon balm cultivation in water-scarce environments. The integration of organic, biological, and chemical amendments can contribute to sustainable agricultural practices by improving plant morphological and physiological properties and plant performance under drought conditions.

Potato disease detection and control measure recommendation using CNN

Potato cultivation will continue to be a crucial agricultural endeavor, significantly enrich to global food supply. and economic prosperity. However, the ongoing vulnerability of potatoes to diseases, particularly named early Blight and late Blight, will persist as a substantial threat to crop yield and quality. Traditional disease identification methods, reliant on expert visual inspection, are expected to remain time-consuming, error-prone, and inadequate for timely intervention. This paper will outline to find Potato diseases and recommend effective control measures using convolutional Neural-Networks (CNN). The project will adopt an object-oriented methodology to ensure modularity and maintainability, leveraging TensorFlow for comprehensive dataset collection, data cleaning, and model training. TensorFlow Lite will be applied to optimize and quantify the developed model, enhancing the efficiency of the deployment process. The frontend will be crafted with React Native to facilitate user accessibility and seamless interaction. The resulting deep learning technique is anticipated to exhibit high accuracy in identifying early-blight and late-blight in potato plants, enabling users to receive prompt and reliable disease predictions and actionable control suggestions. This innovative approach aims to downgrade crop losses, improve productivity, and foster sustainable agricultural practices, thereby bolstering global food security in the future.

Natural Recovery Dynamics of Alfalfa Field Soils under Different Degrees of Mechanical Compaction

Soil compaction in alfalfa fields has become increasingly severe due to the mechanization of animal husbandry and the increased use of heavy agricultural machinery. Perennial alfalfa land undergoes mechanical compaction several times during the planting period without mechanical tillage. The compacted soil structure may recover through moisture changes, freezing and thawing cycles, and plant growth, but the extent and rate of this recovery remain unknown. In this study, alfalfa plots with two different soil types (medium loam and sandy) in Gansu, China, were selected to address these issues. The areas of the plots were 120 m × 25 m and 80 m × 40 m, respectively. In the third year after sowing, three types of agricultural machinery with grounding pressures of 88 kPa, 69 kPa, and 48 kPa were used to compact the soil one, three, five, and seven times. The interval between replicates was 1 h. Each treatment had one plot of 10 m × 5 m, and the experiment was repeated 4 times, totaling 44 plots. Changes in soil bulk density, soil cone index, and saturated hydraulic conductivity were measured after 1, 4, 8, and 17 weeks, respectively. The results showed that the post-compaction soil bulk density and soil cone index largely influenced the recovery of the compacted soil. Recovery became problematic once the soil bulk density exceeded 1.5 g/cm3. The soil bulk density recovery rate varied across different soil layers, with the top layer recovering faster than more profound layers. The initial state could be restored when the change in post-compaction soil bulk density was minimal. Sandy soil recovered faster than medium-loam soil. The recovery of the soil cone index in each layer of medium-loam soil under lower compaction was more noticeable than that under severe compaction. However, with undergrounding pressures of 88 kPa and 69 kPa, the soil cone index could not fully recover after multiple compactions. The recovery of soil-saturated hydraulic conductivity in both soil types was slower and less pronounced. The recovery of soil-saturated hydraulic conductivity in medium-loam soil was slower than that in sandy loam. After 7 compactions and 17 weeks under a grounding pressure of 88 kPa, the saturated hydraulic conductivity remained below 20% of its initial value of 20 mm/h. In contrast, sandy soils recovered faster, reaching 60 mm/h within a week of each compaction event. This research is crucial for ensuring high and stable alfalfa yields and supporting sustainable agricultural practices.

Effect of Different Tillage and Organic Inputs on Soil Properties and Yield of Cotton on Vertisols

The present investigation was carried out at Research Farm, Department of Soil Science, Dr. PDKV, Akola during Kharif 2023-24 to study the effect of different tillage practices and organic inputs on soil properties and yield of cotton on Vertisols. Intensive tillage accelerates the loss of soil organic carbon (SOC) and reduces soil quality and yields, particularly in rainfed areas. This is further worsened by imbalanced fertilization and insufficient recycling of organic residues. Conservation tillage, when combined with organic inputs like FYM, vermicompost and phospho-compost, can help to restore soil structure, increase SOC and promote sustainable agricultural practices. The study aimed to assess the effect of different tillage practices and organic inputs on soil properties and on yield of cotton. The factorial randomized block design with two factors and four tillage treatments as factor A and four sources of manure as factor B were adopted. The treatments were composed of factor A consisting of four tillage operations [conventional tillage (T1), reduced tillage (T2), minimum tillage (T3) and zero tillage (T4)] and factor B consisting of organic manures such as farmyard manure (10 t ha-1) (M1), vermicompost (5 t ha-1) (M2), phospho-compost (5 t ha-1) (M3) and no manure (M4) replicated thrice. The results clearly indicated that conventional tillage resulted in the significantly highest seed cotton yield (14.10 ha-1), stalk yield (27.78 ha-1), total uptake of N (46.46 ha-1), P (7.86 ha-1) and K (33.05 ha-1) relative to other tillage practices examined. Among the organic manure treatments the highest seed cotton yield (11.21 ha-1), stalk yield (21.95 ha-1), total N (37.27 ha-1), P (6.57 ha-1) and K (31.25 ha-1) uptake by cotton was observed with phospho-compost application. Based on the observed results, it was notable that the available soil N, P and K were significantly influenced by the distinct tillage practices and organic manures. Among the tillage practices, the highest content of available soil N (182.14 kg ha-1), P (17.14 kg ha-1) and K (312.95 kg ha-1) were exhibited by the reduced tillage. The use of vermicompost resulted in the highest available soil N (185.93 kg ha-1) and K (316.68 kg ha-1), while the highest available soil P (18.22 kg ha-1) was observed with phospho-compost. The results revealed that conventional tillage combined with phospho-compost improves cotton yield and nutrient uptake, while reduced tillage along with the application of vermicompost and phospho-compost improves soil fertility. Therefore, combined use of tillage and organic inputs could be beneficial for enhancing soil properties and higher productivity of cotton in Vertisols as well as the whole of Maharashtra.

Impact of Microplastics on the Carbon and Nitrogen of Vermicompost

ABSTRACTThe impact of microplastics (MPs) on carbon (C) and nitrogen (N) content in vermicompost is not yet fully understood. This study aims to evaluate the effect of MPs on the carbon and nitrogen ratio in vermicompost, which is important for soil quality and health. The study was carried out using a control treatment and six variations of MPs in the vermicomposting process. Observations were carried out at certain time intervals for 60 days with routine measurements of pH, electrical conductivity (EC), seed germination index (GI), survival rate (SR), and C/N ratio. The results showed that all types of MPs caused a decrease in the C/N ratio compared to the control, with the high‐density polyethylene (HDPE) MPs treatment showing the most significant decrease (from 21 to 9.42). Observations also showed that MPs had an impact on reducing the SR parameters (6.88%–20.41%), pH (6.8–6.1), and EC (525.3–398 µs/cm) in vermicompost, which had an impact on the final product quality thereby reducing the GI (10%–28%). MPs have a significant effect on the C/N ratio in vermicompost, reducing the quality of the compost. This study provides new insights into how MPs can affect the composting process and the quality of the final product, important for sustainable agricultural practices and waste management.

Impact of phosphorus fertilization on the yield and quality of various Alfalfa (Medicago sativa L.) varieties in light chestnut soils

This study investigates the impact of phosphorus fertilization on the yield and quality of various alfalfa (Medicago sativa L.) varieties grown in light chestnut soils. Conducted over a three-year period from 2013 to 2015, the research was carried out in the Karasay district of the Almaty region under irrigated conditions. The experiment included six alfalfa varieties: NS Alfa, VS Banat, Mediana, Nera, Niagara, and Kokoray. Four phosphorus treatments were applied: control (no phosphorus), 60 kg/ha (P60), 90 kg/ha (P90), and 120 kg/ha (P120), using double superphosphate as the phosphorus source. The results demonstrated that phosphorus fertilization significantly enhanced both the yield and quality of alfalfa. Across all varieties, the highest yield was observed with the application of 120 kg/ha phosphorus. For instance, NS Alfa's yield increased from 283.3 c/ha in the control to 349.7 c/ha with P120, reflecting a 23% increase. Similarly, VS Banat and Mediana exhibited yield increases of 23% and 25%, respectively, at the highest phosphorus level. The study also revealed improvements in the nutritional quality of alfalfa hay. Crude protein content increased from 20.3% to 22.0% in NS Alfa, while digestible protein content rose from 11.20% to 12.40%. Other quality parameters, including fat and carotene content, also improved significantly with higher phosphorus levels. Moreover, the availability of nitrate nitrogen and mobile phosphorus in the soil increased progressively with higher phosphorus application rates, contributing to better nutrient uptake and overall plant health. This research underscores the importance of phosphorus fertilization in maximizing alfalfa yield and quality. The findings suggest that the optimal phosphorus application rate for enhancing alfalfa production in light chestnut soils is 120 kg/ha, providing valuable insights for sustainable agricultural practices in similar agro-ecological zones.

The Impact of Technological Innovations on Agricultural Productivity and Environmental Sustainability in China

Agricultural productivity in China is a fundamental driver of food security and economic growth. Yet, the sector faces profound challenges due to environmental degradation and climate change, which threaten sustainable agricultural practices. This research examines the effects of technological innovations on agricultural Total Factor Productivity and environmental sustainability in China from 2012 to 2022. The study seeks to understand how technological advancements, when considered alongside socioeconomic variables, impact agricultural output while balancing ecological integrity. Employing a comprehensive methodological framework, this research integrates fixed-effects, random-effects, and multilevel mixed-effects models to analyze crucial factors including rural education, technological capability, and environmental conservation initiatives. The study further utilizes structural equation modeling to evaluate both the direct and indirect effects of these determinants on productivity. The results demonstrate that technological innovations substantially enhance agricultural productivity, particularly in provinces with higher socioeconomic development. Additionally, sustainable farming practices and tailored policy interventions are identified as vital in addressing regional productivity imbalances. The research concludes by underscoring the necessity for the continued integration of environmental considerations and emerging technologies to ensure the sustainability of agricultural growth in the long term.

Enabling Intelligence on the Edge: Leveraging Edge Impulse to Deploy Multiple Deep Learning Models on Edge Devices for Tomato Leaf Disease Detection

Tomato diseases, including Leaf blight, Leaf curl, Septoria leaf spot, and Verticillium wilt, are responsible for up to 50% of annual yield loss, significantly impacting global tomato production, valued at approximately USD 87 billion. In Ghana, there is a yield gap of about 50% in tomato production, which requires drastic measures to increase the yield of tomatoes. Conventional diagnostic methods are labor-intensive and impractical for real-time application, highlighting the need for innovative solutions. This study addresses these issues in Ghana by utilizing Edge Impulse to deploy multiple deep-learning models on a single mobile device, facilitating the rapid and precise detection of tomato leaf diseases in the field. This work compiled and rigorously prepared a comprehensive Ghanaian dataset of tomato leaf images, applying advanced preprocessing and augmentation techniques to enhance robustness. Using TensorFlow, we designed and optimized efficient convolutional neural network (CNN) architectures, including MobileNet, Inception, ShuffleNet, Squeezenet, EfficientNet, and a custom Deep Neural Network (DNN). The models were converted to TensorFlow Lite format and quantized to int8, substantially reducing the model size and improving inference speed. Deployment files were generated, and the Edge Impulse platform was configured to enable multiple model deployments on a mobile device. Performance evaluations across edge hardware provided metrics such as inference speed, accuracy, and resource utilization, demonstrating reliable real-time detection. EfficientNet achieved a high training accuracy of 97.12% with a compact 4.60 MB model size, proving its efficacy for mobile device deployment. In contrast, the custom DNN model is optimized for microcontroller unit (MCU) deployment. This edge artificial intelligence (AI) technology integration into agricultural practices offers scalable, cost-effective, and accessible solutions for disease classification, enhancing crop management, and supporting sustainable farming practices.

Enabling Precision Agriculture through a Web-Based Fertilization Management System for Nawungan Selopamioro Fruit Orchards

Precision Agriculture (PA) is an integrated farming system based on information and technology for managing agriculture to identify, analyze, and manage spatial and temporal diversity information in specific locations to obtain optimum and sustainable benefits while minimizing unwanted environmental impacts. Fertilization is one of the crucial phases in agricultural production process considering technical cultivation aspects, costs, and environmental impacts. The current fertilization process at Kebun Buah Nawungan Selopamioro (KBNS) is still conventional, so there is no standard rule in determining the fertilization dose. Therefore, a PA approach is needed to provide suitable fertilizer doses for agricultural production needs. This objective of this study was to develop of a web-based fertilizer management system, integrating with orchard management to enhance accessibility and decision-making. The system calculates fertilizer requirements by analyzing soil nutrient availability (N, P, K), cultivation area, crop type and age, and available fertilizer types. The development followed the waterfall methodology, encompassing stages from requirement analysis to system maintenance. The outcome is a web application that manages land assets, administrative activities, and fertilizer needs tailored to specific land blocks, crop characteristics, and nutrient inventories. Subsequent validation against field conditions ensures the accuracy of its recommendations. Although comprehensive testing confirmed a 100% success rate in functionality, the system currently operates within a limited scope of variables. Future enhancements are planned to incorporate broader agronomic factors, such as soil pH and texture, to augment the system's precision. Despite its limitations, this system represents a significant technological advance in precision agriculture, promising to improve fertilizer application efficiency and support sustainable farming practices.

How do water, food, and energy resources impact environmental conditions in Ghana? A novel dynamic ARDL simulation approach

Rising water, food, and energy demands pose significant resource risks and environmental threats. Despite the critical nature of this issue, there is a notable absence of empirical research linking the consumption of these resources to environmental degradation in Ghana. To fill this gap, this study investigates the short-term and long-term effects of water, food, and energy resource consumption on environmental degradation in Ghana, analyzing data from 1994 to 2020. Ghana's increasing environmental degradation accentuates the urgency of this research, its vulnerability to climate change, and the imperative to sustain human life and protect the environment. Utilizing the novel dynamic ARDL simulation method, this research offers new perspectives that diverge from previous findings. The study's findings demonstrate that water resource consumption positively impacts environmental degradation in the short and long term. However, food and energy resource consumption mitigates environmental degradation in the short and long term. Therefore, the study advocates adopting resource management strategies emphasizing efficiency and sustainability. It calls for policies that improve water efficiency, boost renewable energy uptake, and encourage sustainable farming practices. These measures are intended to lessen the negative impacts of resource exploitation and aid in environmental preservation efforts.

Effects of Plant Extracts on Growth Promotion, Antioxidant Enzymes, and Secondary Metabolites in Rice (Oryza sativa) Plants

Plant extracts are widely used in sustainable agriculture practices to enhance crop production and reduce chemical usage in agriculture. This study employed several extraction solutions of various plant extracts to synthesize planting and spraying strategies, assess the persistence efficacy of rice, and investigate the influence of selected water extracts on secondary chemicals at different rice planting stages. Among 17 water extracts that were evaluated on rice seeds, 7 were enhanced to align with the lengths of rice roots 50–70% and shoots 40–50%. The analysis of extraction, spraying, and planting experiments revealed that water extracts, soil application, and transplanting were the most efficient methods for stimulating rice growth, especially 0.1 and 0.5% concentrations. The efficacy of the extracts remained intact also after 14 days of treatment. This study showed that photosynthesis and antioxidant activities may play crucial roles in plant growth. Rice growth stimulation has been linked to photosynthesis, flavonoid contents, and antioxidant enzymes, providing a balanced supply of nutrients for plant growth. Among all tested water extracts, Psidium guajava, Aloe vera, Allium sativum, and Medicago sativa extracts can be used to promote plant growth in organic farming.

Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review.

This study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change; in particular, it evaluates the effectiveness of various non-destructive remote sensing platforms (RGB, thermal imaging, and hyperspectral imaging) and AI techniques (machine learning, deep learning, ensemble methods, GAN, and XAI) in monitoring and predicting crop water stress. The analysis focuses on variability in precipitation due to climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity. Furthermore, this study is expected to contribute to improving sustainable agricultural practices and mitigating the negative impacts of climate change on crop yield and quality.

Optimizing Sour Orange Growth and Chemical Properties through Foliar Application of Nano and Organic Fertilizers

This study, conducted at the Lath House of Agriculture College, Al-Qasim Green University during the 2021-2022 growth season, aimed to evaluate the effects of KHARZA nanoparticles (NPs) and organic fertilizer (Nutirgreen) on sour orange seedlings. Different concentrations of KHARZA NPs (0, 1, 2, and 3 g L-1) and organic fertilizer (0, 1.5, 3, and 4.5 ml L-1) were applied. The experimental design followed a randomized complete block design (RCBD) with two factors, each having four treatments, and each treatment replicated thrice. Our findings reveal that spraying KHARZA NPs at a concentration of 3 g.L-1 significantly enhanced various vegetative parameters, such as plant height, stem diameter, leaf number, and leaf area, alongside improvements in chemical properties such as mineral concentration, chlorophyll content, and carbohydrate levels in leaves. Similarly, applying organic fertilizer at a rate of 4.5 ml L-1 led to notable enhancements in both vegetative and chemical characteristics compared to the control group. Notably, combined application of KHARZA NPs at 3 g L-1 and organic fertilizer at 4.5 ml L-1 resulted in synergistic effects, yielding the most favorable outcomes in terms of vegetative growth and chemical properties. These results underscore the potential of utilizing KHARZA nanoparticles and organic fertilizers in enhancing the growth and physiological properties of sour orange seedlings, offering promising avenues for sustainable agricultural practices. Keywords: Optimizing, spray nutrients, KHARZA, organic fertilizer, growth, orange seedlings

Tailoring Core-Shell Metal Coordination for Smart Seed Coatings in Sustainable Agriculture.

The international agriculture and food security sector is grappling with challenges like low crop yields, soil health deficiencies, and inefficient agrochemical use. The application of smart nanotechnology in agriculture, particularly surface functionalization, holds promise but has limited implementation. Engineered nanomaterials used as seed treatments, known as nanopriming, offer a simple technology to improve crop yield and stress tolerance. In this study, a multicomponent platform called Phelm (Phenolic network with a lipid core and metal coordinated shell) is proposed for encapsulating a commercial plant growth regulator, indole-3 acetic acid (IAA). Phelm comprises a hydrophobic solid lipid core, loaded with IAA, and an outer metal coordinated phenolic shell of tannic acid (TA) and Fe3+. The platform aims to treat seeds with encapsulated IAA, which can be controllably released, as well as protect the germination process at high salt concentrations. Phelm showed a remarkable increase in growth parameters of wheat seeds up to 58.6%, despite being irrigated with high concentrations of saltwater (100 mM). These findings suggest that nanopriming of seeds can effectively increase their efficacy even under abiotic stress conditions, which can drastically improve crop yields. Moreover, we envisage that the Phelm core/shell assembly can encapsulate a wide range of agrochemicals and biostimulants to promote sustainable and smart agricultural practices.

Predation Efficiency and Biological Control Potential of Micromus angulatus Against Aphis craccivora

Micromus angulatus (Neuroptera: Hemerobiidae) is a widely distributed and highly effective predator that shows promise as a biological control agent against agricultural pests, particularly Aphis craccivora, the cowpea aphid, which threatens leguminous crops globally. This study aimed to evaluate the predation behaviour, search efficiency, and intraspecific interference of M. angulatus at different developmental stages, including first- to third-instar larvae and adults, in controlling adult A. craccivora populations. The results demonstrated that all developmental stages of M. angulatus exhibited predatory behaviour towards adult aphids, with the functional response fitting the Holling Type II model. The instantaneous attack rates for first-, second-, and third-instar larvae and adults were 1.0017, 1.0448, 0.9581, and 0.9508, respectively; the handling times were 0.0158, 0.0051, 0.0016, and 0.0011 days, respectively; and the theoretical maximum daily predation rates were 63.2911, 196.0784, 625, and 909.0909 aphids, respectively. The pest control efficacies were 63.3989, 204.8672, 598.8311, and 864.3192, respectively. The search efficiency at each developmental stage was negatively correlated with aphid density, which decreased as the prey density increased, with second-instar larvae showing the greatest decrease and adults the least. When the aphid density was fixed, the daily predation rate of individual M. angulatus decreased with increasing conspecific density, indicating that predation was affected by its own density, with the interference effect equation being E = 0.6194P−0.87. These findings indicate that M. angulatus, especially in the third-instar larval and adult stages, has considerable potential as a biological control agent for managing A. craccivora populations in agricultural settings. This study contributes valuable insights for developing sustainable agricultural practices by decreasing reliance on chemical pesticides.

Unveiling Indian farmers’ adoption of climate information services for informed decision-making: a path to agricultural resilience

ABSTRACT Amidst the intensifying ramifications of climate change on economic conditions, there exists an imperative for farmers to adopt climate forecast information in agricultural decision-making proactively. The research employed an extended theory of planned behaviour to examine the factors influencing farmers’ intentions to incorporate climate information in their decision-making process. Data from 343 male farmers in India were collected using a multi-stage sampling technique and analysed through Partial Least Squares Structural Equation Modelling (PLS-SEM). The results confirm that attitude, subjective norms, perceived behavioural control, perceived benefits and epistemic values significantly and positively affect the farmers’ intention to use climate forecasts in making informed decisions in farming. The findings carry significant importance for advancing adaptation strategies within the agricultural sector, calling for essential measures like fostering the development of farmers’ associations and promoting fruitful collaboration in climate prediction initiatives. Such actions are instrumental in equipping farmers with the necessary tools and knowledge to tackle climate-related challenges and ensure sustainable agricultural practices effectively.