Search Results (1,532)

The assessment of microplastics (MPs) in terrestrial ecosystems has garnered increasing global attention due to their accumulation and migration in soils, which may have potential impacts on soil health, biodiversity, and agricultural productivity. However, research on their distribution and interactions in soil remains limited, especially in tropical regions. This study aimed to characterize MPs extracted from tropical soil samples and relate their abundance to soil and terrain attributes under different land uses (forest, grassland, and agriculture). Soil samples were collected from an experimental farm in Lavras, Minas Gerais, Southeastern Brazil, to determine soil physical and chemical attributes and MP abundance in a micro-watershed. These locations were also used to obtain terrain attributes from a digital elevation model and the normalized difference vegetation index (NDVI). The majority of microplastics found in all samples were identified as polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and vinyl polychloride (PVC). The spatial distribution of MP was rather heterogeneous, with average abundances of 3826, 2553, and 3406 pieces kg⁻¹ under forest, grassland, and agriculture, respectively. MP abundance was positively related to macroporosity and sand content and negatively related to clay content and most chemical attributes. Regarding terrain attributes, MP abundance was negatively correlated with plan curvature, convergence index, and vertical distance to channel network, and positively related to topographic wetness index. These findings indicate that continuous water fluxes at both the landscape and soil surface scales play a key role, suggesting a tendency for higher MP accumulation in lower-lying areas and soils with greater porosity. These conditions promote MP transport and accumulation through surface runoff and facilitate their entry into the soil. Full article

Inside the transmission group of an agricultural tractor, the efficiency of power transfer to moving parts, their lubrication, and protection from wear are guaranteed by UTTO (Universal Tractor Transmission Oil) fluids, which are also used to operate the hydraulic system. These fluids, with mineral or synthetic origin, are characterized by excellent lubricating properties, high toxicity, and low biodegradability, which makes it important to replace them with more eco-sustainable fluids, such as those based on vegetable oils that are highly biodegradable and have low toxicity. It is also important to consider EU policies on the use of such fluids in sensitive environmental applications. To this end, several experimental bio-UTTO formulations were tested at CREA to evaluate—compared to conventional fluids—their suitability for use as lubricants for transmissions and hydraulic systems through endurance tests carried out in a Fluid Test Rig (FTR) specifically developed by CREA to apply controlled and repeatable work cycles to small volumes of oil, which are characterized by high thermal and mechanical stresses. The technical performance and the main physical–chemical parameters of the fluids were continuously monitored during the work cycles. Based on these experiences, this study describes the first application of a methodological approach aimed at testing an experimental biobased UTTO on a tractor used in normal farm activity. The method was based on a former test at the FTR in which the performance of the bio-UTTO was compared to that of the conventional UTTO recommended by the tractor manufacturer. Given the good results of the FTR test, bio-UTTO was introduced in a 20-year-old medium-power tractor, replacing the mineral fluid originally supplied, for the first reliability tests during its normal use on the CREA farm. After almost 600 h of work, the technical performance and the trend of chemical–physical parameters of bio-UTTO did not undergo significant changes. No damage to the tractor materials or oil leaks was observed. The test is still ongoing, but according to the results, in line with the indications provided by the FTR test, the experimental bio-UTTO seems suitable for replacing the conventional fluid in the tractor used in this study. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

This research proposes a novel location allocation framework that utilizes agent-based simulation for the efficient production of corn stover-based bioethanol, which requires dedicated pretreatment facilities for the feedstock. The framework comprises two main modules: (1) a Pretreatment Facility Module that assesses the performance of the corn stover-based bioethanol supply chain based on the interactions among three types of agents, namely order agent, pretreatment agent, and transport agent, and (2) an Optimization Module designed to determine the optimal supply chain configuration by selecting the most suitable number and locations for pretreatment facilities to achieve the lowest total operational cost. The framework is implemented in a case study for South Korea, which aims to raise the bioethanol blending ratio from 4% in 2025 to 8% by 2030. Experimental results reveal that, within the bioethanol supply chain comprising eight farms and four refineries, a 1% increase in bioethanol blending ratio leads to an increase in the demand for approximately 2229 kL of ethanol (10,225 tons of corn stover), and the proposed framework enables to identify the optimal location of pretreatment facilities in the subject supply chain according to the change in ethanol demand. Full article

Since the late 1970s, brown spot of pear (BSP), a fungal disease caused by Stemphylium vesicarium (Wallr.) Simmons, has been one of the most important pear fungal diseases in Italy. To protect orchards from BSP, frequent fungicide application is essential throughout the period spanning petal fall to the onset of fruit maturation. In Italy, boscalid was the first succinate dehydrogenase inhibitor (SDHIs) fungicide authorised against BSP; subsequently, penthiopyrad and fluxapyroxad were authorised against the disease. In 2016 and 2017, SDHI compounds were applied against BSP as solo products at the University of Bologna’s experimental farm, showing a reduction in efficacy. Stemphylium vesicarium strains were isolated from leaves and fruit, and sensitivity assays and molecular analyses were performed. In vitro tests confirmed resistance to SDHIs, and two specific single-nucleotide polymorphisms were discovered, SDHB P230L and SDHC H134R, both leading to amino acid substitutions in succinate dehydrogenase subunits and confirming the resistant phenotype. Full article

Wheat is a crucial crop for human nutrition, and the demand for high-quality indicators within the “from farm to fork” concept is increasing. Based on this premise, this study examined how, at the farm level, the fertilization system can influence key quality indicators relevant to wheat production and final products. This research was conducted under specific conditions of the Western Plain of Romania at the Agricultural Research and Development Station (ARDS), Lovrin, during 2015–2017. Fertilization involved the autumn application of phosphorus (concentrated superphosphate; 0, 40, 80, 120, 160 kg ha⁻¹ active substance, a.s.) and potassium (potassium chloride; 0, 40, 80, 120 kg ha⁻¹ a.s.). Nitrogen (ammonium nitrate; 0, 30, 60, 90, 120 kg ha⁻¹ active substance) was applied in spring in two stages. The combination of these three fertilizers resulted in 18 fertilized variants (T2 to T19), tested alongside an unfertilized control (T1). The experimental variants were arranged in four randomized replications. Grain quality was assessed based on protein content (PRO, %), gluten (GLT, g 100 g⁻¹), gliadins (Gliad, %), glutenins (Glut, g 100 g⁻¹), high-molecular-weight glutenins (HMW, g 100 g⁻¹), low-molecular-weight glutenins (LMW, g 100 g⁻¹), and the gliadin/glutenin ratio (Gliad/Glut). Compared to the average values for each indicator across the experiment, certain variants produced values above the mean, with statistical significance. Variant T16 stood out by producing values above the mean for all indicators, with statistical confidence. Multivariate analysis showed that five indicators with very strong (PRO, GLT) and strong (HMW, Glut, LMW) influence grouped in PC1, while two indicators (Gliad, Gliad/Glut) with very strong and strong influence grouped in PC2. The analysis revealed varying levels of correlation between the applied fertilizers, with nitrogen (N) showing very strong and strong correlations with most indicators, while phosphorus and potassium showed moderate-to-weak correlations. Regression analysis generated mathematical models that statistically described how each indicator varied in relation to the fertilizers applied. Full article

Early and accurate detection of plant diseases is essential for ensuring food security and maintaining sustainable agricultural productivity. However, most deep learning models for plant disease classification rely heavily on large-scale annotated datasets, which are expensive, labor-intensive, and often impractical to obtain in real-world farming environments. To address this limitation, we propose a unified self-supervised learning (SSL) framework that leverages unlabeled plant imagery to learn meaningful and transferable visual representations. Our method integrates three complementary objectives—Bootstrap Your Own Latent (BYOL), Masked Image Modeling (MIM), and contrastive learning—within a ResNet101 backbone, optimized through a hybrid loss function that captures global alignment, local structure, and instance-level distinction. GPU-based data augmentations are used to introduce stochasticity and enhance generalization during pretraining. Experimental results on the challenging PlantDoc dataset demonstrate that our model achieves an accuracy of 77.82%, with macro-averaged precision, recall, and F1-score of 80.00%, 78.24%, and 77.48%, respectively—on par with or exceeding most state-of-the-art supervised and self-supervised approaches. Furthermore, when fine-tuned on the PlantVillage dataset, the pretrained model attains 99.85% accuracy, highlighting its strong cross-domain generalization and practical transferability. These findings underscore the potential of self-supervised learning as a scalable, annotation-efficient, and robust solution for plant disease detection in real-world agricultural settings, especially where labeled data is scarce or unavailable. Full article

Improving the operational speed of cabbage transplanters is essential for precision seed-ling placement and labor efficiency. In South Korea, manual cabbage transplanting can demand up to 184 person-hours per hectare, often leading to delays during peak periods due to labor shortages. Moreover, the environmental urgency to reduce plastic waste has accelerated the adoption of biodegradable pots in mechanized systems, supporting global sustainable development goals. This study aimed to determine optimal working conditions for a 1.54 kW semi-automatic single-row cabbage transplanter designed for biodegradable pots. The cam-follower-based planting mechanism was analyzed to identify ideal forward and rotational speeds, while evaluating power consumption and seedling placement quality. The mechanism includes a crank-driven four-bar linkage, with an added restoring spring for enhanced motion stability. A total of nine simulation trials were conducted across forward speeds of 250, 300, and 350 mm/s and planting unit speeds of 40, 50, and 60 rpm. Simulation and experimental results confirmed that a forward velocity of 300 mm/s and crank speed of 60 rpm produced optimal outcomes, achieving a vertical hopper displacement of 280 mm, minimal soil disturbance (2186.95 ± 2.27 mm²), upright seedling alignment, and the lowest power usage (17.42 ± 1.21 W). Comparative analysis showed that under the optimal condition, the characteristic coefficient λ = 1 minimized misalignment and power loss. These results support scalable and energy-efficient transplanting systems suitable for smallholder and mid-sized farms, offering an environmentally sustainable solution. Full article

The emergence and spread of pathogens pose significant challenges to the sustainability and productivity of aquaculture globally. For the Chilean salmon farming industry, salmonid rickettsial septicemia (SRS), caused by the facultative intracellular bacterium Piscirickettsia salmonis, constitutes one of the main disease challenges. In this study, the efficacy of various oral doses of florfenicol (FFC) (5, 7.5, and 10 mg/kg BW/day) against SRS was assessed in Atlantic salmon, when treatment was initiated at an early stage of infection. Since salmonids infected with P. salmonis typically lose appetite as the disease progresses, and the therapeutic FFC dose is dependent on a normal specific feeding rate (SFR), the treatments were administered 5 days post-challenge (DPC5). On the day of challenge, experimental fish were intraperitoneally (IP) injected with 0.2 mL of P. salmonis genogroup LF-89 inoculum (9.07 × 10⁷ CFU mL⁻¹). Fish mortality, behavior, clinical signs of disease, feed intake and SFR were monitored throughout the study. Conclusions: An important finding in this study was that all tested antibiotic doses halted disease progression and prevented mortality in fish challenged with P. salmonis when administered DPC5. In the control group, mortality reached 32.2% with fish displaying clinical signs of SRS. Full article

This study evaluates biogas production through the anaerobic digestion of food waste (FW), cow dung (CD), and green waste (GW), with the primary objective of determining the efficacy of co-digesting these organic wastes commonly generated by households and small farms in Central Queensland, Australia. The investigation focuses on both experimental and technoeconomic aspects to support the development of accessible and sustainable energy solutions. A batch anaerobic digestion process was employed using a 1 L jacketed glass digester, simulating small-scale conditions, while technoeconomic feasibility was projected onto a 500 L digester operated without temperature control, reflecting realistic constraints for decentralized rural or residential systems. Three feedstock mixtures (100% FW, 50:50 FW:CD, and 50:25:25 FW:CD:GW) were tested to determine their impact on biogas yield and methane concentration. Experiments were conducted over 14 days, during which biogas production and methane content were monitored. The results showed that FW alone produced the highest biogas volume, but with a low methane concentration of 25%. Co-digestion with CD and GW enhanced methane quality, achieving a methane yield of 48% while stabilizing the digestion process. A technoeconomic analysis was conducted based on the experimental results to estimate the viability of a 500 L biodigester for small-scale use. The evaluation considered costs, benefits, and financial metrics, including Net Present Value (NPV), Internal Rate of Return (IRR), and Dynamic Payback Period (DPP). The biodigester demonstrated strong economic potential, with an NPV of AUD 2834, an IRR of 13.5%, and a payback period of 3.2 years. This study highlights the significance of optimizing feedstock composition and integrating economic assessments with experimental findings to support the adoption of biogas systems as a sustainable energy solution for small-scale, off-grid, or rural applications. Full article

The use of exogenous enzymes in the nutrition of dairy cows is an innovative and efficient strategy to maximize productivity and milk quality, with positive applications in the economic and environmental aspects of dairy farming. Therefore, the objective of this study was to evaluate whether the addition of a blend of exogenous enzymes to the diet of lactating Jersey cows has a positive effect on productive performance, milk quality, animal health, ruminal environment, and digestibility. Twenty-one primiparous Jersey cows, with 210 days in lactation (DL), were used. The exogenous enzymes used were blends containing mainly protease, in addition to cellulase, xylanase, and beta-glucanase. The animals were divided into three groups with seven replicates per group (each animal being the experimental unit), as follows: Control (T-0), basal diet without enzyme addition; Treatment (T-80), animals fed enzymes in the diet at a daily dose of 80 mg per kg of dry matter (DM); Treatment (T-160), animals fed enzymes in the diet at a daily dose of 160 mg per kg of DM. The study lasted 84 days, during which higher milk production was observed in the treated groups (T-80 and T-160) compared to the control group (p = 0.04). When calculating feed efficiency from days 1 to 84, greater efficiency was observed in both groups that received the blend compared to the control (p = 0.05). In the centesimal composition of the milk, it was observed that the percentage of protein in the milk of the T-160 group was higher compared to the control group (p = 0.03). The effect of the enzymes was verified for butyric (p = 0.05) and palmitic (p = 0.05) fatty acids. We also observed the effect of the enzyme blend on the amount of volatile fatty acids (VFAs), which were higher in the ruminal fluid of cows that received the enzymes (p = 0.01). Cows that consumed enzymes showed a higher apparent digestibility coefficient of crude protein (p = 0.01). In vitro, the main result is related to lower gas production in 24 and 48 h at T-160. We concluded that the use of a blend of exogenous enzymes in the diet of lactating Jersey cows was able to increase milk production in these animals, resulting in greater feed efficiency and also an increase in milk protein content, positively modulating the fatty acid profile in the rumen and improving the apparent digestibility of nutrients. Full article

The mechanisms through which different types of exogenous carbon enhance the soil organic carbon sequestration rate (C_seq), nitrogen use efficiency (NUE), and corn yield (CY) in rainfed farmland on the Loess Plateau remain inadequately elucidated. This study established a four-year fixed-site experiment in the context of organic materials to increase soil organic carbon storage and enhance corn yield in the dry-farmed areas of the mountainous southern Ningxia region. The research investigates the effects of adding different types of exogenous carbon materials on C_seq, NUE, and CY. The soil type at the experimental base is loessial soil (Huangmian soil), with a soil pH of 8.28 and a baseline organic carbon content of 8.20 g kg⁻¹. The main crop cultivated in this area is corn. The experimental treatments were as follows: (i) N, no fertilization; (ii) CK, 100% nitrogen, phosphorus, and potassium fertilizers; (iii) C, 50%CK + corn straw (pulverized); (iv) M, 50%CK + fermented cow manure; (v) C/M, 50%CK + fermented cow manure + corn straw (1:1). The results show that compared with the CK treatment, the C_seq of C, M, and C/M treatments increased by 488.89%, 355.56%, and 527.78%, respectively. Compared with the CK treatment, the NUE of C, M, and C/M treatments increased by 15.04%, 7.70%, and 12.20%, respectively. Compared with the CK treatment, the CY under the C, M, and C/M treatments were increased by 7.91%, 19.10%, and 11.59%, respectively. The linear regression results show that the C_seq had a significant positive effect on CY (R² = 0.37) and NUE, R² = 0.39) (p < 0.0001). The TOPSIS (technique for order preference by similarity to ideal solution) evaluation results indicate that the C/M treatment was the optimal measure for achieving increased corn yield while enhancing Cseq and NUE. Therefore, incorporating a 1:1 mixture of corn straw and cattle manure in rainfed farmland in the mountainous area of southern Ningxia may be the best strategy to improve C_seq and NUE. Full article

Regional rapid forecasting of vertical ocean temperature profiles is increasingly important for marine aquaculture, as these profiles directly affect habitat management and the physiological responses of farmed species. However, observational temperature profile data with sufficient temporal resolution are often unavailable, limiting their use in regional rapid forecasting. In addition, traditional numerical ocean models suffer from intensive computational demands and limited operational flexibility, making them unsuitable for regional rapid forecasting applications. To address this gap, we propose PICA-Net (Physics-Inspired CNN–Attention–BiLSTM Network), a hybrid deep learning model that coordinates large-scale satellite observations with local-scale, continuous in situ data to enhance predictive fidelity. The model also incorporates weak physical constraints during training that enforce temporal–spatial diffusion consistency, mixed-layer homogeneity, and surface heat flux consistency, enhancing physical consistency and interpretability. The model uses hourly historical inputs to predict temperature profiles at 6 h intervals over a period of 24 h, incorporating features such as sea surface temperature, sea surface height anomalies, wind fields, salinity, ocean currents, and net heat flux. Experimental results demonstrate that PICA-Net outperforms baseline models in terms of accuracy and generalization. Additionally, its lightweight design enables real-time deployment on edge devices, offering a viable solution for localized, on-site forecasting in smart aquaculture. Full article

In crab pond environments, obstacles such as long aerobic pipelines, aerators, and ground cages are usually sparsely distributed. Automatic feeding boats can navigate while avoiding obstacles and execute feeding tasks along planned paths, thus improving feeding quality and operational efficiency. In large-scale crab pond farming, a single feeding operation often fails to achieve the complete coverage of the bait casting task due to the limited boat load. Therefore, this study proposes a multi-voyage path planning scheme for feeding boats. Firstly, a complete coverage path planning algorithm is proposed based on an improved genetic algorithm to achieve the complete coverage of the bait casting task. Secondly, to address the issue of an insufficient bait loading capacity in complete coverage operations, which requires the feeding boat to return to the loading wharf several times to replenish bait, a multi-voyage path planning algorithm is proposed. The return point of the feeding operation is predicted by the algorithm. Subsequently, the improved Q-Learning algorithm (I-QLA) is proposed to plan the optimal multi-voyage return paths by increasing the exploration of the diagonal direction, refining the reward mechanism and dynamically adjusting the exploration rate. The simulation results show that compared with the traditional genetic algorithm, the repetition rate, path length, and the number of 90° turns of the complete coverage path planned by the improved genetic algorithm are reduced by 59.62%, 1.27%, and 28%, respectively. Compared with the traditional Q-Learning algorithm, average path length, average number of turns, average training time, and average number of iterations planned by the I-QLA are reduced by 20.84%, 74.19%, 48.27%, and 45.08%, respectively. The crab pond experimental results show that compared with the Q-Learning algorithm, the path length, turning times, and energy consumption of the I-QLA algorithm are reduced by 29.7%, 77.8%, and 39.6%, respectively. This multi-voyage method enables efficient, low-energy, and precise feeding for crab farming. Full article

Although 5–20% of global crop production is lost to arthropod damage, current biomonitoring programs are extremely limited. This study evaluates the feasibility of using metabarcoding to assess overall insect diversity and detect pest species in agricultural settings. It introduces a curated DNA barcode reference library for Canadian insects that are agricultural pests and applies it to metabarcoding data from the analysis of Malaise trap samples from two experimental farms in Southern Ontario. A total of 7707 arthropod species were collected across the two farms, and projections indicate that another 4000 await detection. These taxa included 231 registered pest species. The composition of the overall arthropod community composition was more heavily influenced by site location than crop type, but pest species composition was influenced by the crop. This study confirms that metabarcoding enables the evaluation of the species composition of arthropod communities in agroecosystems, allowing pest species to be tracked. Full article