Search Results (228)

The use of Remotely Piloted Aircraft (RPA) for spraying coffee crops has expanded due to their practicality and cost reduction. This study aimed to evaluate spray rate effects on coffee crops using two RPA (T10 and T20). The study was conducted on a commercial farm with 10-year-old Coffea arabica Catucaí Amarelo. Two aircraft were used, T1 (hydraulic) and T2 (rotary nozzles). The application rates were established at 25 and 50 L ha⁻¹. The application quality was obtained by attaching Water-Sensitive Papers (WSPs) to the upper, middle, and lower parts of coffee trees, inside and outside the plants, in addition to the inter-row areas. The statistical Nested Crossed Design was applied to analyze the dataset for the experimental field with three distinct factors (RPA, application rate, and WSP position) and four replications. WSP position was the most determinant factor across all design effects, followed by RPA. The external layers of leaves received more droplets than internal parts of coffee trees. The WSP position information indicated that no droplets reached the middle interior parts of the plants or underneath them. The inter-row positions (soil) received significantly more drops than the coffee plants, regardless of application rate or RPA. The potential for drift to the soil was high in both applications. The Potential Drift Risks were more significant for RPA T2. Future studies may deepen understanding of the relationship between coverage and specific application models for coffee farming, as traditional application methods require improvements. Full article

Agri-food supply chains, particularly in the rice sector, face persistent challenges in transparency, quality control, and sustainability due to their complexity and fragmentation. Blockchain technology provides a promising solution by ensuring secure, immutable, and verifiable records of production and supply chain activities, supporting both consumer trust and compliance with the EU Common Agricultural Policy (CAP). This study reports on the TRACE-RICE Mediterranean pilot project, which developed a blockchain-enabled traceability system for rice production in Portugal. A Rice Field Data Recording App, built with ArcGIS Survey123, digitized agronomic and compliance records from Integrated Production systems and linked them to blockchain-verified QR codes on consumer packaging. The pilot conducted during the 2023 harvest demonstrated the potential to enhance data consistency and streamline field recording processes, thereby improving transparency in farming practices. A total of 174 QR code interactions, primarily from Lisbon, revealed consumer engagement patterns valuable for future business analysis. The scaling phase during the 2024 harvest confirmed the system’s adaptability to different varieties and production contexts, positioning blockchain as a replicable model for sustainable and competitive rice supply chains. Full article

The integration of machine learning (ML) techniques with unmanned aerial vehicle (UAV) imagery holds strong potential for improving yield prediction in agriculture. However, few studies have combined biophysical field variables with UAV-derived spectral data, particularly under conditions of limited sample size. This study evaluated the performance of different ML algorithms in predicting Arabica coffee (Coffea arabica) yield using field-based biophysical measurements and spectral variables extracted from multispectral UAV imagery. The research was conducted over two crop seasons (2020/2021 and 2021/2022) in a 1.2-hectare experimental plot in southeastern Brazil. Three modeling scenarios were tested with Random Forest, Gradient Boosting, K-Nearest Neighbors, Multilayer Perceptron, and Decision Tree algorithms, using Leave-One-Out cross-validation. Results varied considerably across seasons and scenarios. KNN performed best with raw data, while Gradient Boosting was more stable after variable selection and synthetic data augmentation with SMOTE. Nevertheless, limitations such as small sample size, seasonal variability, and overfitting, particularly with synthetic data, affected overall performance. Despite these challenges, this study demonstrates that integrating UAV-derived spectral data with ML can support yield estimation, especially when variable selection and phenological context are carefully addressed. Full article

The welfare of farmed hybrid striped bass remains largely unaddressed in U.S. aquaculture, despite the species’ economic significance and the scale of production. Physical handling during grading and inspection not only causes stress and increased incidence of injury, but also results in unmarketable fish and significant financial loss for producers. To address these issues, we present a prototype system that uses directed water currents to leverage the fish’s natural rheotactic behavior, enabling directed movement between tank regions without the need for direct physical contact. Our design allows for early identification of malformed individuals, who otherwise face prolonged suffering and starvation, so they can be humanely culled. In a small pilot study, we observed that fish moved into the destination region more frequently and with less behavioral variability when exposed to a directed current, suggesting this method as a viable alternative to traditional handling. While the system requires further refinement and testing at scale, these preliminary results offer a promising step toward ethical, commercially viable, and low-stress fish sorting systems in commercial aquaculture. Full article

Social farming combines agricultural, social, and healthcare functions, and Animal-Assisted Interventions (AAIs) are increasingly being applied within this framework. Despite their potential, cattle are excluded from Italian guidelines and rarely studied. This pilot study explored the feasibility, effects, and economic sustainability of cow-assisted interventions within social farming in Umbria, Italy. It represents an original and innovative contribution, drawing attention to the therapeutic potential of the human–cow relationship. The study presents an experimental cow therapy protocol and proposes behavioral monitoring tools designed both for people with different disabilities and for the animals involved. Four Red Pied Valdostana cows were involved in structured sessions with three groups: adolescents removed from families, young adults with mental health disorders, and individuals with eating disorders. Activities included observation, feeding, grooming, problem solving, and leading. Human outcomes were assessed regarding emotional, relational, and behavioral dimensions, and animal welfare was continuously monitored. A cost analysis was also conducted for Animal-Assisted Activity (AAA), Animal-Assisted Education (AAE), and Animal-Assisted Therapy (AAT). Participants reported improved self-esteem, emotional expression, and social interaction; the eating disorder group showed greater openness toward dairy consumption. Animal welfare remained stable with high tolerance to handling. Costs were driven mainly by professional staff rather than animal care, with average hourly costs of €74.51 (AAA), €144.99 (AAE), and €172.41 (AAT). The comparative analysis demonstrates a clear trade-off: as the intervention shifts from recreational (AAA) to educational (AAE) and finally to therapeutic (AAT), the financial investment increases in parallel with the level of professionalization, personalization, and expected clinical outcomes. Cow-assisted interventions proved to be safe, feasible, and beneficial, supporting their potential inclusion in Italian guidelines on AAIs. Full article

Animal farming produces large volumes of underutilised by-products, such as poultry feathers (PF), often discarded in landfills or incinerated, causing environmental concerns. Transforming such residues into valuable energy carriers aligns with sustainable waste-to-energy (WtE) management. Pyrolysis represents a versatile thermochemical pathway for converting organic wastes into gaseous, liquid, and solid fuels. This study investigates slow pyrolysis of PF, lignite (LG), and their blends at pilot scale using a uniquely designed, patent-pending reactor bridging laboratory research with industrial practice. Experiments were conducted at 20 °C·min⁻¹, temperatures of 500–800 °C, and pressures from 0.1 to 1.0 MPa. PF pyrolysis produced mainly gas (70.1%), suitable for energy recovery, with smaller fractions of char (15.3%) and oil (14.6%). LG yielded predominantly char (59.9%), with lower gas (32.4%) and oil (7.7%) outputs. Co-pyrolysis revealed limited synergistic effects. Rising temperature promoted gas formation, reduced char, and improved its calorific value through carbon enrichment. Elevated pressure enhanced char yield and unexpectedly increased hydrogen content, suggesting complex thermochemical behaviour. The results confirm the scalability of laboratory findings and highlight pyrolysis as a practical WtE pathway for valorising protein-rich residues and low-rank coals, contributing to cleaner, more sustainable energy systems. Full article

Artificial Intelligence (AI) is widely recognized as a force that will fundamentally transform traditional chicken farming models. It can reduce labor costs while ensuring welfare and at the same time increase output and quality. However, the breadth of AI’s contribution to chicken farming has not been systematically quantified on a large scale; few people know how far current AI has actually progressed or how it will improve chicken farming to enhance the sector’s sustainability. Therefore, taking “AI + sustainable chicken farming” as the theme, this study retrieved 254 research papers for a comprehensive descriptive analysis from the Web of Science (May 2003 to March 2025) and analyzed AI’s contribution to the sustainable in recent years. Results show that: In the welfare dimension, AI primarily targets disease surveillance, behavior monitoring, stress detection, and health scoring, enabling earlier, less-invasive interventions and more stable, longer productive lifespans. In economic dimension, tools such as automated counting, vision-based weighing, and precision feeding improve labor productivity and feed use while enhancing product quality. In the environmental dimension, AI supports odor prediction, ventilation monitoring, and control strategies that lower emissions and energy use, reducing farms’ environmental footprint. However, large-scale adoption remains constrained by the lack of open and interoperable model and data standards, the compute and reliability burden of continuous multi-sensor monitoring, the gap between AI-based detection and fully automated control, and economic hurdles such as high upfront costs, unclear long-term returns, and limited farmer acceptance, particularly in resource-constrained settings. Environmental applications are also underrepresented because research has been overly vision-centric while audio and IoT sensing receive less attention. Looking ahead, AI development should prioritize solutions that are low cost, robust, animal friendly, and transparent in their benefits so that return on investment is visible in practice, supported by open benchmarks and standards, edge-first deployment, and staged cost–benefit pilots. Technically, integrating video, audio, and environmental sensors into a perception–cognition–action loop and updating policies through online learning can enable full-process adaptive management that improves welfare, enhances resource efficiency, reduces emissions, and increases adoption across diverse production contexts. Full article

The aim of this study is to assess the factors determining the achievement of the Sustainable Development Goals (SDGs) in the context of improving the productivity of agricultural inputs—land, labor, and capital—and increasing farm income, using logistic regression. The analysis is based on primary data collected in 2023 during a pilot survey of 150 farms in the Central Pomerania region of Poland, conducted using the CATI (Computer-Assisted Telephone Interview) technique and a standardized questionnaire. The study examined factors related to farm income growth and to the productivity of land, labor, and capital. Logistic regression was applied to test 28 independent variables grouped into four categories: farm characteristics, production-related characteristics, farm asset-related characteristics, and household characteristics. Income growth was found to be associated with production-related and household characteristics. Land productivity showed associations with variables from all four categories, reflecting the complexity of this outcome. In the case of labor productivity, only household characteristics were significant, underscoring the strong connections between agricultural holdings and farming households. Capital productivity was associated with household characteristics, production-related characteristics, and farm asset-related characteristics. These findings highlight the need for an interdisciplinary approach to the sustainable development of agricultural holdings, integrating economic, production, and social dimensions. Full article

Ammonia (NH₃) emissions from swine manure pits contribute significantly to odor nuisance, health risks, and secondary PM_2.5 formation. This study assessed the pilot-scale performance of three source-control technologies: surface sealing with surfactant-based foam system (FOAM SYSTEM), swine manure wiping and removing system (WIPING SYSTEM), and belt-conveyor-based solid–liquid separator system (BELT SYSTEM). Each technology targets a different pathway in the ammonia generation process. The FOAM SYSTEM suppresses volatilization by forming a foam barrier at the air–liquid interface. The WIPING SYSTEM reduces precursor contact time by periodically removing feces. The BELT SYSTEM separates feces and urine upon excretion, inhibiting enzymatic ammonia formation. Among the individual systems, the BELT SYSTEM achieved the highest ammonia reduction efficiency of 91.7%, followed by the FOAM SYSTEM (73.6%) and WIPING SYSTEM (64.4%). The combined FOAM SYSTEM + BELT SYSTEM yielded the best performance with an ammonia reduction efficiency of 94.4%, showing modest synergy without operational interference. In contrast, the FOAM SYSTEM + WIPING SYSTEM configuration achieved 71.1%, slightly lower than the FOAM SYSTEM alone, likely due to foam disruption. Environmental sensitivity tests revealed that higher temperatures and alkaline pH elevated NH₃ emissions, whereas systems that maintained near-neutral pH, like the FOAM SYSTEM, demonstrated greater stability. These findings highlight the importance of integrating physical and source-control mechanisms while considering environmental variability for effective on-farm ammonia mitigation. Full article

Lack of food security is a major threat at the local and global levels. This research focused on the design and implementation of a school garden at Carlos Albán Holguín school as a strategy to ensure food for vulnerable communities and promote environmental education. This project was structured in six stages including diagnosis, characterization, formulation, and pilot validation. Data were collected through surveys and checklists, which evaluated the garden’s conditions and students’ understanding of environmental practices. Key findings revealed significant improvements in students’ knowledge of sustainable agriculture upon implementing the garden, with 75% demonstrating outstanding comprehension and 80% effectively applying organic farming principles. Promoting and implementing urban agriculture benefits surrounding communities, reduces environmental impact, promotes social awareness of current food security challenges, and promotes economic growth in cities. The main conclusion of this research is that integrating environmental education within the school curriculum can significantly enhance food security and foster environmental stewardship. This research underscores the importance of sustainable urban gardens in addressing nutritional deficiencies and promoting sustainable agriculture practices in urban settings. Full article

This paper presents a novel literature survey on leveraging electrolysis for grid frequency stabilization in power systems with high penetration of renewable energy sources (RESs), uniquely integrating global research findings with specific insights into the Polish energy context—a region facing acute grid challenges due to rapid RES growth and infrastructure limitations. The intermittent nature of wind and solar power exacerbates frequency fluctuations, necessitating dynamic demand-side management solutions like hydrogen production via electrolysis. By synthesizing over 30 studies, the survey reveals key results: electrolysis systems, particularly PEM and alkaline electrolyzers, can reduce frequency deviations by up to 50% through fast frequency response (FFR) and primary reserve provision, as demonstrated in simulations and real-world pilots (e.g., in France and the Netherlands); however, economic viability requires enhanced compensation schemes, with current models showing unprofitability without subsidies. Technological advancements, such as transistor-based rectifiers, improve efficiency under partial loads, while integration with RES farms mitigates overproduction issues, as evidenced by Polish cases where 44 GWh of solar energy was curtailed in March 2024. The survey contributes actionable insights for policymakers and engineers, including recommendations for deploying electrolyzers to enhance grid resilience, support hydrogen-based transportation, and facilitate Poland’s target of 50.1% RESs by 2030, thereby advancing the green energy transition amid rising instability risks like blackouts in RES-heavy systems. Full article

Mangoes are an important part of Hainan’s tropical characteristic agriculture. In response to the requirements of building an ecological civilization pilot demonstration zone in Hainan, China, green and sustainable development will be the future development trend of the mango planting system. However, the economic benefits and environmental impact during its planting and management process remain unclear. This paper combines emergy, life cycle assessment (LCA), and economic analysis to compare the system sustainability, environmental impact, and economic benefits of the traditional mango cultivation system (TM) in Dongfang City, Hainan Province, and the early-maturing mango cultivation system (EM) in Sanya City. The emergy evaluation results show that the total emergy input of EM (1.37 × 10¹⁶ sej ha⁻¹) was higher than that of TM (1.32 × 10¹⁶ sej ha⁻¹). From the perspective of the emergy index, compared with TM, EM exerted less pressure on the local environment and has better stability and sustainability. This was due to the higher input of renewable resources in EM. The LCA results showed that based on mass as the functional unit, the potential environmental impact of the EM is relatively high, and its total environmental impact index was 18.67–33.19% higher than that of the TM. Fertilizer input and On-Farm emissions were the main factors causing environmental consequences. Choosing alternative fertilizers that have a smaller impact on the environment may effectively reduce the environmental impact of the system. The economic analysis results showed that due to the higher selling price of early-maturing mango, the total profit and cost–benefit ratio of the EM have increased by 55.84% and 36.87%, respectively, compared with the TM. These results indicated that EM in Sanya City can enhance environmental sustainability and boost producers’ annual income, but attention should be paid to the negative environmental impact of excessive fertilizer input. These findings offer insights into optimizing agricultural inputs for Hainan mango production to mitigate multiple environmental impacts while enhancing economic benefits, aiming to provide theoretical support for promoting the sustainable development of the Hainan mango industry. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Agricultural education of future farmers includes a period of practice in Denmark. How can this be shaped to best support the education and future life as farmers? This question motivated a pilot study of how different factors influenced farmer apprentices’ learning in their on-farm apprentice period, based on interviews of farmer students and host farmers, and to explore this in the historical and social context of Danish agriculture. Three focus group interviews with 24 farmer apprentices were conducted, as well as 14 qualitative semi-structured phone interviews with host farmers. Four major themes emerged from the interviews with host farmers and apprentices: There is a potential conflict between the two perspectives in themes one and two: The host farm primarily as a learning site, versus the apprentice being primarily a farm laborer learning through working on the host farm as a workplace. Thirdly, the on-farm and social environment was suggested to have significant importance for developing into a future farmer, which resonates with the concept of Communities of Practice (CoP). This pilot study raised important issues regarding how future farmers are motivated and educated, highlighting that agricultural colleges play a pivotal role in articulating and guiding how the apprentice learning on-farm can be meaningfully integrated into the other parts of the farmer education. Full article

The cognition of large farm animals is important for understanding how cognitive abilities are shaped by evolution and domestication. Valid testing methods are needed with the development of cognitive studies in more species. Here, a step-by-step method for training four naïve domestic goats to use a touchscreen in cognitive tests is described. The goats made accurate touches smoothly after training. Follow-up tests were conducted to confirm that they could do cognitive tests on a touchscreen. In the pilot test of odd-item search, all the goats had above-chance level performances in some conditions. In the subsequent odd-item search tasks using multiple novel stimulus sets, one goat could achieve the criterion and complete several stages, and the results showed a learning effect. These suggest a potential ability to learn the rule of odd-item search. Not all goats could pass the criteria, and there were failures in the transfer, indicating a perceptual strategy rather than using the odd-item search rule. The experiment confirmed that goats could use the touchscreen testing system for cognitive tasks and demonstrated their approaches in tackling this problem. We also hope that these training methods will help future studies training and testing naïve animals. Full article