Search Results (23,415)

This research aims to develop a model for cultivating talents in sustainable food literacy education in Taiwan. The project adopts the professional and theoretical axes of the food industry, sustainable development, and food literacy. The research employs a mixed-method approach, combining qualitative and quantitative techniques, to construct sustainable food literacy assessment indicators for Taiwan. In the first year, through literature analysis and qualitative research, the core content of “sustainable food literacy” in Taiwan was extracted, resulting in four major dimensions with 24 indicator items. Then, using the Fuzzy Delphi method, the indicators were constructed, defining the core content and dimension indicators of sustainable food literacy, which include “sustainable agriculture and production”, “healthy diet and culture”, “green environmental protection and consumption”, and “food social responsibility and ethics”, encompassing a total of 20 indicators. In the second year, based on the dimensions identified in the first year, a sustainable food literacy curriculum was developed. A 10-week quasi-experimental teaching curriculum was conducted for students enrolled in the “Vegetable and Fruit Carving” elective course in two classes of the Department of Food and Beverage Management at Jingwen University of Science and Technology. By comparing the pre-test and post-test scores of students’ sustainable food literacy and their sustainable food works, as well as analyzing student learning portfolios and teacher reflections, it was shown that the curriculum developed in this research significantly enhanced students’ sustainable food literacy and their performance. The results of this two-year study can be used for the assessment of sustainable food literacy talents in Taiwan, contributing both academically and practically. Full article

Microorganisms, as abundant biological resources, offer significant potential in the development of selenium-enrichment technologies. Selenium-enriched microorganisms not only absorb, reduce, and accumulate selenium efficiently but also produce various selenium compounds without relying on synthetic chemical processes. In particular, nano-selenium produced by these microorganisms during cultivation has garnered attention due to its unique physicochemical properties and biological activity, making it a promising raw material for functional foods and pharmaceutical products. This paper reviews selenium-enriched microorganisms, focusing on their classification, selenium metabolism, and transformation mechanisms. It explores how selenium is absorbed, reduced, and transformed within microbial cells, analyzing the biochemical processes by which inorganic selenium is converted into organic and nano-selenium forms. Finally, the broad applications of selenium-enriched microbial products in food, medicine, and agriculture are explored, including their roles in selenium-rich foods, nano-selenium materials, and disease prevention and treatment. Full article

Planting patterns and irrigation amounts are key factors affecting maize yield. This study adopted a two-factor experimental design, with planting pattern as the main plot and irrigation amount as the subplot, to investigate the effects of irrigation levels under different planting patterns (including uniform row spacing and alternating wide-narrow row spacing) on spring maize yield and water use efficiency in Xinjiang. Through this approach, the study examined the mechanisms by which planting pattern and irrigation amount influence maize growth, yield formation, and water use efficiency. Experiments conducted at the Agricultural Science Research Institute of the Ninth Division of Xinjiang Production and Construction Corps demonstrated that alternating wide-narrow row spacing combined with moderate irrigation (5400 m³/hm²) significantly optimized maize root distribution, improved water use efficiency, and increased leaf area index and net photosynthetic rate, thereby promoting dry matter accumulation and yield enhancement. In contrast, uniform row spacing under high irrigation levels increased yield but resulted in lower water use efficiency. The study also found that alternating wide-narrow row spacing enhanced maize nutrient absorption from the soil, particularly phosphorus utilization efficiency, by improving canopy structure and root expansion. This pattern exhibited comprehensive advantages in resource utilization, providing a theoretical basis and technical pathway for achieving water-saving and high-yield maize production in arid regions, which holds significant importance for promoting sustainable agricultural development. Full article

In the current era, Indian agriculture faces a significant demand for increased food production, which has led to the integration of advanced technologies to enhance efficiency and productivity. Drones have emerged as transformative tools for enhancing precision agriculture, reducing costs, and improving sustainability. This study provides a comprehensive review of drone adoption in Indian agriculture by examining its effects on precision farming, crop monitoring, and pesticide application. This research evaluates technological advancements, regulatory frameworks, infrastructure, farmers’ perceptions, and the financial accessibility of drone technology in the Indian agricultural context. Key findings indicate that, while drone adoption enhances efficiency and sustainability, challenges such as high costs, lack of training, and regulatory barriers hinder widespread implementation. This paper also explores the growing market for agricultural drones in India, highlighting key industry players and projected market growth. Furthermore, it addresses regional differences in adoption rates and emphasizes the increasing social acceptance of drones among Indian farmers. To bridge the gap between potential and practice, the study proposes several policy and institutional recommendations, including government-led financial incentives, training programs, and public–private partnerships to facilitate drone integration. Moreover, this review article also highlights technological advancements, such as AI and IoT, in agriculture. Finally, open issues and future research directions for drones are discussed. Full article

Evaluation of the effectiveness of state policy instruments is a permanent element of economic science. This paper addresses the issue of investment support under the Common Agricultural Policy (CAP). Using data on Polish farms from 2015–2023, a Propensity Score Matching–Difference in Differences (PSM-DiD) analysis was conducted to assess changes in the economic results of agricultural producers that invest using this support. The comparison of the economic results achieved by supported investors with both non-investing agricultural producers and unsupported investors is a distinguishing element of this study. The relatively rarely used Competitivness Index (CI), which measures the ratio of earned income to the sum of the alternative use of the owned means of production, was used. The positive change in the CI during the analyzed period was 0.14 higher for supported investors than non-investors. No statistically significant change was found were compared to unsupported investors. A clear increase in income, total fixed assets, liabilities, and the level of production in the population of producers using support in relation to non-investors and investing without CAP support was also observed. However, in relationships with investors using their own funds, these differences were mainly due to the difference in the level of investments and were not statistically significant when introducing a correction regarding the scale of the investment. The obtained results remain in line with the results of research shown by a significant part of economists undertaking a similar issue. Full article

This study aimed to assess whether dietary supplementation with probiotics could alleviate intestinal injury in lipopolysaccharide (LPS)-challenged piglets. Healthy weaned piglets were randomly allocated to four individual groups (n = 6): (1) a control group; (2) an LPS group; (3) an LPS + Lactobacillus group; and (4) an LPS + Bacillus group. The control and LPS groups received a basal diet, while the probiotic groups were provided with the same basal diet supplemented with 6 × 10⁶ cfu/g of Lactobacillus casei (L. casei) or a combination of Bacillus subtilis (B. subtilis) and Bacillus licheniformis (B. licheniformis) at a dosage of 3 × 10⁶ cfu/g, respectively. On day 31 of the trial, overnight-fasted piglets were killed following the administration of either LPS or 0.9% NaCl solution. Blood samples and intestinal tissues were obtained for further analysis several hours later. The results indicate that dietary supplementation with probiotics significantly exhibited health-promoting effects compared with the control group and effectively reduced LPS-induced histomorphological damage to the small intestine, impairments in barrier function, and dysregulated immune responses via modulation of enzyme activity and the expression of relevant genes, such as nuclear factor-kappa B (NF-κB), interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 10 (IL-10), claudin-1, nuclear-associatedantigenki-67 (Ki-67), and β-defensins-1 (pBD-1). Collectively, these results suggest that dietary supplementation with probiotics could alleviate LPS-induced intestinal injury by enhancing the immunity and anti-inflammatory responses in piglets. Our research provides a theoretical basis for the rational application of probiotics in the future. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

The present-day issues related to the cotton-growing industry, namely yield estimation, pest effect, and growth phase diagnostics, call for integrated, scalable monitoring solutions. This write-up reveals Cotton Multitask Learning (CMTL), a transformer-driven multitask framework that launches three major agronomic tasks from UAV pictures at one go: boll detection, pest damage segmentation, and phenological stage classification. CMTL does not change separate pipelines, but rather merges these goals using a Cross-Level Multi-Granular Encoder (CLMGE) and a Multitask Self-Distilled Attention Fusion (MSDAF) module that both allow mutual learning across tasks and still keep their specific features. The biologically guided Stage Consistency Loss is the part of the architecture of the network that enables the system to carry out growth stage transitions that occur in reality. We executed CMTL on a tri-source UAV dataset that fused over 2100 labeled images from public and private collections, representing a variety of crop stages and conditions. The model showed its virtues state-of-the-art baselines in all the tasks: setting 0.913 mAP for boll detection, 0.832 IoU for pest segmentation, and 0.936 accuracy for growth stage classification. Additionally, it runs at the fastest speed of performance on edge devices such as NVIDIA Jetson Xavier NX (Manufactured in Shanghai, China), which makes it ideal for deployment. These outcomes evoke CMTL’s promise as a single and productive instrument of aerial crop intelligence in precision cotton agriculture. Full article

The increasing adoption of digital technologies in the agriculture sector has significantly contributed to optimizing on-farm routines, especially in data-driven decision-making. This study aimed to develop an application to determine the slaughter point of lambs by predicting subcutaneous fat thickness (SFT) using pre-slaughter parameters such as body weight (BW), body condition score (BCS), and skinfold measurements at the brisket (BST), lumbar (LST), and tail base (TST), obtained using an adipometer. A total of 45 Pantaneiros lambs were evaluated, finished in feedlot, and slaughtered at different body weights. Each pre-slaughter weight class showed a distinct carcass pattern when all parameters were included in the model. Exploratory analysis revealed statistical significance for all variables (p < 0.001). BW and LST were selected to construct the predictive equation (R² = 55.44%). The regression equations were integrated into the developed application, allowing for in-field estimation of SFT based on simple measurements. Compared to conventional techniques such as ultrasound or visual scoring, this tool offers advantages in portability, objectivity, and immediate decision-making support. In conclusion, combining accessible technologies (e.g., adipometer) with traditional variables (e.g., body weight), represents an effective alternative for production systems aimed at optimizing and enhancing the value of lamb carcasses. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

The rainy season characteristics are directly modulated by atmospheric circulation and moisture transport dynamics. Focusing on the characteristics of the rainy season onset date (RSOD), this study aims to advance the understanding and prediction of climate change impacts on agricultural production and disaster mitigation strategies. Based on rainfall data from 66 meteorological stations in northeast China (NEC) from 1961 to 2020, this study determined the patterns of the RSOD in the region and established its mechanistic linkages with atmospheric circulation and water vapor transport mechanisms. This study identifies a climatic regime shift around 2000, with the RSOD transitioning from low to high interannual variability in NEC. Further analysis reveals a strong correlation between the RSOD and atmospheric circulation characteristics: cyclonic vorticity amplifies before the RSOD and dissipates afterward. Innovatively, this study reveals a significant transition in the water vapor transport paths during the early rainy season in NEC around 2000, shifting from eastern Mongolia–Sea of Japan to the northwestern Pacific region. Moreover, the advance or delay of the RSOD directly influences the water vapor transport intensity—an early (delayed) RSOD is associated with enhanced (weakened) water vapor transport. These findings provide a new perspective for predicting the RSOD in the context of climate change while providing critical theoretical underpinnings for optimizing agricultural strategies and enhancing disaster prevention protocols. Full article

The increasing demand for food necessitates that agri-food systems adopt innovative techniques to enhance food production while optimizing the use of limited resources, such as water. In agriculture, hydrogels are being increasingly used to enhance water retention and reduce irrigation requirements. However, most of these materials are based on synthetic polymers that are not biodegradable. This raises serious environmental and health concerns, highlighting the urgent need for sustainable, biodegradable alternatives. Biomass-derived from agro-industrial waste presents a substantial potential for producing hydrogels, which can effectively function as water collectors and suppliers for crops. This review article provides a comprehensive overview of recent advancements in the application of agro-industrial waste for the formulation of hydrogels. Additionally, it offers a critical analysis of the development of hydrogels utilizing natural and compostable materials. Agro-industrial and food waste, which are rich in hemicellulose and cellulose, have been utilized to enhance the mechanical properties and water absorption capacity of hydrogels. These biomaterials hold significant potential for the development of effective hydrogels in agricultural applications; they can be either hybrid or natural materials that exhibit efficacy in enhancing seed germination, improving water retention capabilities, and facilitating the controlled release of fertilizers. Natural hydrogels derived from agro-industrial waste present a sustainable technological alternative that is environmentally benign. Full article

The widespread use of pesticides in modern agriculture has significantly enhanced food production by managing pests and diseases; however, their degradation in soil can lead to unintended consequences for soil fertility and nutrient cycling. This review explores the mechanisms of pesticide degradation, both abiotic and biotic, and the soil factors influencing these processes. It critically examines how degradation products impact soil microbial communities, organic matter decomposition, and key nutrient cycles, including nitrogen, phosphorus, potassium, and micronutrients. This review highlights emerging evidence linking pesticide residues with altered enzymatic activity, disrupted microbial populations, and reduced nutrient bioavailability, potentially compromising soil structure, water retention, and long-term productivity. Additionally, it discusses the broader environmental and agricultural implications, including decreased crop yields, biodiversity loss, and groundwater contamination. Sustainable management strategies such as bioremediation, the use of biochar, eco-friendly pesticides, and integrated pest management (IPM) are evaluated for mitigating these adverse effects. Finally, this review outlines future research directions emphasizing long-term studies, biotechnology innovations, and predictive modeling to support resilient agroecosystems. Understanding the intricate relationship between pesticide degradation and soil health is crucial to ensuring sustainable agriculture and food security. Full article

Excessive nitrogen (N) fertilizer usage in agriculture has prompted the exploration of sustainable strategies to enhance nitrogen use efficiency (NUE) while maintaining crop yield and quality. Processed tomatoes (Solanum lycopersicum L.) were grown for two years (2023 and 2024) following a two-way factorial randomized complete block (RCBD) design, considering three biostimulants and three N regimes as two factors, to assess their morphophysiological, biochemical, anatomical and yield performances. Nitrogen application significantly influenced biomass accumulation, the leaf area index (LAI), nitrogen uptake and yield with notable comparable values between reduced and optimal nitrogen dose, indicating improved nitrogen use efficiency. Biostimulants showed limited effects alone but enhanced plant performance under reduced nitrogen conditions, particularly improving chlorophyll content, crop growth, N uptake, yield and anatomical adaptations. Moreover, compared to 2024, biostimulant application enhanced tomato growth more evidently in 2023 due to environmental variations, likely due to the occurrence of stress conditions. Importantly, biostimulants, together with N regimes, i.e., optimal and reduced doses, showed improved anatomical traits, especially regarding leaf thickness and thickness between the two epidermises, indicating adaptive responses that may support sustained productivity under N-limited conditions. Among the biostimulants used, the processed tomatoes responded better to protein hydrolysate and endophytic N-fixing bacteria than to seaweed extract. These findings suggest that although biostimulants alone were not affected, integrating them with reduced N fertilization provides a viable strategy for optimizing tomato production, conserving resources and minimizing the environmental impact without compromising yield or quality. Full article

The genus Trichoderma plays a pivotal role in sustainable agriculture through its multifaceted contributions to plant health and productivity. This review explores Trichoderma’s biological functions, including its roles as a biocontrol agent, plant growth promoter, and stress resilience enhancer. By producing various enzymes, secondary metabolites, and volatile organic compounds, Trichoderma effectively suppresses plant pathogens, promotes root development, and primes plant immune responses. This review details the evolutionary adaptations of Trichoderma, which has transitioned from saprotrophism to mycoparasitism and established beneficial symbiotic relationships with plants. It also highlights the ecological versatility of Trichoderma in colonizing plant roots and improving soil health, while emphasizing its role in mitigating both biotic and abiotic stressors. With increasing recognition as a biostimulant and biocontrol agent, Trichoderma has become a key player in reducing chemical inputs and advancing eco-friendly farming practices. This review addresses challenges such as strain selection, formulation stability, and regulatory hurdles and concludes by advocating for continued research to optimize Trichoderma’s applications in addressing climate change, enhancing food security, and promoting a sustainable agricultural future. Full article