Search Results (779)

Livestock plays a crucial role in the global food system, not only as an important source of nutrients but also as a means of economic and social well-being. It constitutes a critical parameter of agricultural production in Mediterranean countries, with the majority of farms still having a relatively small herd size and depending largely on family labor. The purpose of this study is to record and evaluate the perceptions of livestock farmers in the Region of Western Macedonia, Greece (which represents a typical paradigm of an agricultural region), regarding the future prospects and the actions taken to ensure the sustainability of their farms. The research is based on a survey carried out from May to October, 2024, on ruminant farmers. Selective breeding and crossbreeding with higher-productivity breeds are some of the genetic improvements that are generally applied to increase productivity and were, therefore, investigated in this study. Through gradual crossbreeding, farmers attempt to improve the composition of their initial herds by incorporating high-productivity traits—although without officially participating in any recognized improvement program. This increases the risk of extinction for indigenous breeds, which are abandoned for use by the farmers. Our results also showed that most livestock farms derive from inheritances, with many livestock farmers practicing grazing mainly in mountainous areas and still rearing indigenous breeds. From the farmers’ point of view, more information and education regarding market conditions are needed. Furthermore, the sustainability of farms largely depends on subsidies, which are crucial due to difficulties in economic viability, particularly in mountainous areas. Encouraging the support of market differentiation and public awareness for the nutritional value of products derived from local breeds may serve as a promising agrobiodiversity conservation strategy. Full article

With the accelerating global energy transition, wind power has become a core pillar of renewable energy systems. However, its inherent intermittency and volatility pose significant challenges to the safe, stable, and economical operation of power grids—making ultra-short-term wind power prediction a critical technical link in optimizing grid scheduling and promoting large-scale wind power integration. Current forecasting techniques are plagued by problems like the inadequate representation of features, the poor separation of features, and the challenging clarity of deep learning models. This study introduces a method for the prediction of wind energy using spatiotemporal contrastive learning, employing seasonal trend decomposition to encapsulate the diverse characteristics of time series. A contrastive learning framework and a feature disentanglement loss function are employed to effectively decouple spatiotemporal features. Data on geographical positions are integrated to simulate spatial correlations, and a convolutional network of spatiotemporal graphs, integrated with a multi-head attention system, is crafted to improve the clarity. The proposed method is validated using operational data from two actual wind farms in Northwestern China. The research indicates that, compared with typical baselines (e.g., STGCN), this method reduces the RMSE by up to 38.47% and the MAE by up to 44.71% for ultra-short-term wind power prediction, markedly enhancing the prediction precision and offering a more efficient way to forecast wind power. Full article

Microminerals such as cobalt (Co), copper (Cu), iodine (I), iron (Fe), manganese (Mn), molybdenum (Mo), selenium (Se), and zinc (Zn) play key roles in cattle health. However, trace element imbalances are often underdiagnosed. This study retrospectively analyzed serum samples from 1273 cows across 117 herds in Spain, encompassing conventional dairy (n = 46), pasture-based dairy (n = 11), organic dairy (n = 25), and semi-extensive beef (n = 35) systems. Trace elements were determined by inductively coupled plasma mass spectrometry (ICP-MS). All herds were investigated for clinical or productive issues where mineral deficiencies were suspected. Significant differences were found in serum trace mineral concentrations between production systems. Adequacy rates were highest in conventional dairy herds receiving routine mineral supplementation, while deficiencies in Se, I, and Cu were frequently detected in pasture-based, organic, and beef herds. Zinc deficiencies were rare and typically involved complex, combined deficiencies. At the farm level, multielement deficiencies (≥3 elements) were detected in 39–45% of organic, pasture-based, and beef herds, but in only 5% of conventional dairy herds (p < 0.001). Principal component and cluster analyses produced consistent groupings of minerals according to dietary supplementation and soil-driven exposure. These findings highlight the increased vulnerability of low-input systems to complex micromineral imbalances and underline the importance of system-adapted mineral-monitoring and supplementation strategies in herd health management. However, as the study is based on diagnostic submissions rather than a randomized herd survey, the findings should be interpreted with caution due to potential selection bias. Full article

High temperature typically decreases feed intake, milk production, and efficiency and increases metabolic disorders and health problems, greatly impacting farm economics. Supplements based on Saccharomyces cerevisiae have been suggested to benefit cows under heat stress, but effects on dairy cow performance are contradictory. This study aimed to evaluate the influence of heat stress on the effects of live yeast supplementation on the performance of dairy cows. Environmental temperature parameters were compared to two thermal humidity indices (THI1 and THI2) using wet bulb or dew point temperatures, as explanatory variables of dairy cow performance during the hot season. The experiment followed a randomized complete block design with 12 Holstein cows blocked by lactation number, days in milk, and milk production (two cows per block) and within each block, each cow was randomly assigned to a maize silage-based TMR with a concentrate mixture containing no yeast culture (Control) or 1 g/kg concentrate dry matter of a live yeast culture based on S. cerevisiae (Yeast) for 35 days. The experiment lasted for 35 d. Dry matter intake (DMI) was significantly higher for Yeast than it was for Control for all classes of temperature and THIs studied with an average increase of 2 kg DM per day, except for mean THI1 (from 54 to 60), for which the DMI was similar between treatments. Yeast promoted significantly higher milk yield than Control for all classes of daily maximum and mean temperature, averaging an increase of 4 kg of milk per day. Results suggest a more marked effect of temperature and indicate that yeast supplementation improved lactation performance of dairy cows exposed to hot weather. Full article

Farmers frequently face the decision to retain or replace dairy cows, with 20% to 40% of cows replaced annually. In Switzerland, this translates to over 100,000 cows replaced each year, representing a significant financial investment for farms and the dairy industry. The average productive lifespan of a dairy cow is currently three to four parities worldwide as in Switzerland, shorter than the optimal five to six parities, leading to financial losses from premature culling. Factors influencing suboptimal replacement decisions include inaccurate valuation of production parameters, replacement costs, and health issues. This study bridges the gap between theoretical models and real-world practices by analyzing replacement decisions from 29 Swiss dairy farmers over five years, comparing them to theoretical models and evaluating economic impacts. On average, suboptimal decisions resulted in an economic loss of 161 ± 164 CHF per farm per month (1.55 ± 1.58 CHF per cow per month), with losses from retaining unprofitable cows being approximately three times greater than those from premature culling. The results indicate that farmers typically make economically sound decisions regarding cow replacement; this contrasts with findings from previous studies on the topic. Nonetheless, replacing cows prematurely, particularly during their first parity, is not ideal from ecological, animal welfare, and sustainability standpoints. Consequently, enhancing animal health and fertility becomes essential for reducing culling rates and improving the longevity of dairy cows. Full article

The kinetics of milk release is influenced by several factors, including the milking facility, which affects the milk emission profile and quality. In dairy goats, the typical working vacuum level is 41–44 kPa; higher levels negatively impact health, quality, and milkability traits. This study, which was conducted on a commercial dairy farm located in the Latium region (central Italy), evaluates the impact of two vacuum levels (38 kPa and 42 kPa) on the milk emission profile and somatic cell content in Saanen goats. Statistical analysis (one-way ANOVA) of 400 milk flow curves recorded from 100 goats in four different afternoon milking sessions (200 at 42 kPa and 200 at 38 kPa) showed no significant differences in terms of milk yield, total milking time, or bimodal curve percentage when using two different operating vacuum levels. However, the milk emission time was longer at 38 kPa (1.86 vs. 1.71 min), while the peak flow rate (1.04 vs. 0.96 kg/min) and blind time (0.32 vs. 0.24 min) were higher at 42 kPa. Somatic cell content decreased significantly as the working vacuum level decreased (2470 vs. 2167 × 1000 cells/mL). This is in line with current studies which suggest that high vacuum levels increase the risk of udder injury and intramammary infection. In conclusion, adjusting the milking machine to a working vacuum level of 38 kPa, and performing proper maintenance and routine checks, significantly improves somatic cell content, and, consequently, milk quality, in goats. Full article

Understanding the growth dynamics and ecological constraints of Porphyra spp. is essential for optimizing sustainable seaweed aquaculture. However, most existing models lack physiological detail and exhibit limited performance under variable environmental conditions. This study developed a mechanistic Dynamic Energy Budget (DEB) model to simulate structural biomass accumulation, carbon and nitrogen reserve dynamics, and blade area expansion of Porphyra under natural environmental conditions in Korean coastal waters. The model incorporates temperature, irradiance, and nutrient availability (NO₃⁻ and CO₂) as environmental drivers and was implemented using a forward difference numerical scheme. Field data from Beein Bay were used for model calibration and validation. Simulations showed good agreement with the observed biomass, reserve content, and blade area, with root-mean-square error (RMSE) typically within ±10%. Sensitivity analysis identified temperature-adjusted carbon assimilation and nitrogen uptake as the primary drivers of growth. The model was further used to estimate dynamic carrying capacity, revealing seasonal thresholds for sustainable biomass under current farming practices. Although limitations remain—such as the exclusion of reproductive allocation and tissue loss—the results demonstrate that DEB theory provides a robust framework for modeling Porphyra aquaculture. This approach supports scenario testing, spatial planning, and production forecasting, and it is adaptable for ecosystem-based management including integrated multi-trophic aquaculture (IMTA) and climate adaptation strategies. Full article

The vulnerability of regional agricultural systems continues to intensify under the influence of global climate change. Understanding the spatiotemporal variation in meteorological elements and their agricultural response mechanisms has become a critical scientific challenge for ensuring food security. This study focuses on the 852 Farm in the typical area of the albic soil region on the Sanjiang Plain in China. This research integrates multi-source meteorological observations and crop yield data from 2001 to 2024. Using methods such as wavelet analysis, grey relational analysis, and cross-wavelet analysis, this study systematically investigates the dynamic changes and cyclical evolution patterns of key meteorological factors and their impact on the yields of different staple crops. The results indicate that, in terms of trend evolution, air temperature, relative humidity, and surface temperature show no significant upward trend (Z > 0; p > 0.05), while precipitation significantly increases (Z > 0; p < 0.05). Evaporation and sunlight show a nonsignificant downward trend (Z < 0; p > 0.05). The yields of rice, soybean, and corn generally exhibit fluctuating upward trends (Z > 0; p > 0.05). In terms of periodic coupling characteristics, meteorological factors exhibit multi-time-scale oscillations at 22a, 12a, and 8a. The yields of the three staple crops form significant time–frequency couplings with meteorological factors in the 22a and 8a periods. Regarding the correlation, air temperature demonstrates the highest grey correlation degree (γ ≥ 0.8) and strong coherence with crop yields, followed by precipitation and sunlight. These findings provide a theoretical and quantitative basis for understanding the multi-scale interactive mechanisms of climate adaptation in agricultural systems of the albic soil region, as well as for managing and optimizing climate-resilient farming practices. Full article

In this study, the growth and welfare of the African catfish (Clarias gariepinus, Burchell 1822) were investigated under industrial farming conditions. For this purpose, the growing success (cm, g) and typical stress related parameters (glucose-, lactate-, cortisol-concentrations, growth hormone, HSI-liver index) were investigated on the African catfish (102–841 g) in relation to an external stressor (working light and noise) and different feeding regimes (day, night, and day and night feeding) over 83 days. As no significant effects were found among the experimental feeding treatments in relation to the growth performance and investigated stress parameters, the time of feeding seems to have less impact to the production success and stress reactions as suggested before. Regarding our results, the effect of feeding conditioning could have played a strong factor likewise the ageing process of the reared fish species which is known to be rather photophobic. Therefore, the factor of conditioning and its influence to the time shift in feeding regimes and the impact of noise and light stressors during feeding should be investigated separately in future experiments to obtain further results in this context and clarify the validity of the best feeding conditions for African catfish. Full article

For fruit harvesting robots, rapid and accurate detection of fruits and picking points is one of the main challenges for their practical deployment. Several fruits typically grow in clusters or bunches, such as grapes, cherry tomatoes, and blueberries. For such clustered fruits, it is desired for them to be picked by bunches instead of individually. This study proposes utilizing a low-cost off-the-shelf RGB-D sensor mounted on the end effector and a lightweight improved YOLOv8-Pose neural network to detect cherry tomato bunches and picking points for robotic harvesting. The problem of occlusion and overlap is alleviated by merging RGB and depth images from the RGB-D sensor. To enhance detection robustness in complex backgrounds and reduce the complexity of the model, the Starblock module from StarNet and the coordinate attention mechanism are incorporated into the YOLOv8-Pose network, termed StarBL-YOLO, to improve the efficiency of feature extraction and reinforce spatial information. Additionally, we replaced the original OKS loss function with the L1 loss function for keypoint loss calculation, which improves the accuracy in picking points localization. The proposed method has been evaluated on a dataset with 843 cherry tomato RGB-D image pairs acquired by a harvesting robot at a commercial greenhouse farm. Experimental results demonstrate that the proposed StarBL-YOLO model achieves a 12% reduction in model parameters compared to the original YOLOv8-Pose while improving detection accuracy for cherry tomato bunches and picking points. Specifically, the model shows significant improvements across all metrics: for computational efficiency, model size (−11.60%) and GFLOPs (−7.23%); for pickable bunch detection, mAP50 (+4.4%) and mAP50-95 (+4.7%); for non-pickable bunch detection, mAP50 (+8.0%) and mAP50-95 (+6.2%); and for picking point detection, mAP50 (+4.3%), mAP50-95 (+4.6%), and RMSE (−23.98%). These results validate that StarBL-YOLO substantially enhances detection accuracy for cherry tomato bunches and picking points while improving computational efficiency, which is valuable for resource-constrained edge-computing deployment for harvesting robots. Full article

Regenerative agriculture has emerged as a new organic farming movement, initially difficult to distinguish from similar approaches. Its core concerns, such as ecosystem degradation caused by intensive farming, align with those of many other organic systems. However, regenerative agriculture prioritizes soil health, biodiversity, and social equity, setting itself apart through its scalability and flexibility. Unlike other ecological farming methods, often limited to smaller scales, regenerative agriculture aims to be implemented on large farms, typically major contributors to pollution due to reliance on external inputs like fertilizers and pesticides. Notably, regenerative certification standards are more flexible, allowing the use of industrially synthesized inputs under specific conditions, provided that regenerative principles are upheld. This review systematically examines seven core regenerative practices: no-tillage farming, crop rotation, cover cropping, green manures, intercropping, perennial cover systems, and integrated crop-livestock systems. It outlines the practical advantages and ecological benefits of each, while identifying key adoption challenges, including costs, farm size, and institutional barriers. The paper argues that addressing these issues, particularly concerning scale and socio-economic constraints, is essential for broader adoption. By synthesizing recent evidence, this review clarifies the distinctiveness of regenerative agriculture and highlights pathways for its scalable implementation. Full article

The usage of land on small farms in subtropical regions varies with climatic conditions. Agricultural cultivation typically occurs during the spring and summer (of the southern hemisphere), with tobacco being the primary crop on most small farms. During these seasons, livestock graze in pastures and woodlots. After the tobacco harvest (March), farmers plant winter cover crops, and by May, livestock is moved from the pastures to the agricultural areas. This study aimed to examine how grazing influences soil density, water infiltration rates, and animal behavior across different land types (pasture, native forest, eucalyptus reforestation, and agriculture) during the tobacco-growing season, and the off-season when grazing occurs on agricultural lands. It was found that forage availability and climatic conditions determined grazing duration in pastures and forests, under Integrated Crop–Livestock (ICL) systems. Higher forage volume in the agriculture area reduced grazing time and increased resting periods. Eucalyptus reforestation areas had the best soil conditions due to minimal grazing occurring there. An increase in soil bulk density and a decrease in water infiltration rates were observed at the end of the grazing period in both pasture and woodland areas. Year-round ICL systems appear to enhance soil quality through fallow periods, improving forage availability, soil moisture retention, and water infiltration as well. Full article

In recent years, solar energy has emerged as one of the most advanced renewable energy sources, with its production capacity steadily growing. To maximize output and efficiency, choosing the right configuration for a specific location for these installations is crucial. This study uniquely integrates detailed multi-variant fixed-tilt PV system simulations with comprehensive economic evaluation under temperate climate conditions, addressing site-specific spatial constraints and grid integration considerations that have rarely been combined in previous works. In this paper, an energy and economic efficiency analysis for a photovoltaic power plant, located in central Poland, designed in eight variants (10°, 15°, 20°, 25°, 30° PV module inclination angle for a south orientation and 10°, 20°, 30° for an east–west orientation) for a limited building area of approximately 300,000 m² was conducted. In PVSyst computer simulations, PVGIS-SARAH2 solar radiation data were used together with the most common data for describing the Polish local solar climate, called Typical Meteorological Year data (TMY). The most energy-efficient variants were found to be 20° S and 30° S, configurations with the highest surface production coefficient (249.49 and 272.68 kWh/m²) and unit production efficiency values (1123 and 1132 kWh/kW, respectively). These findings highlight potential efficiency gains of up to approximately 9% in surface production coefficient and financial returns exceeding 450% ROI, demonstrating significant economic benefits. In economic terms, the 15° S variant achieved the highest values of financial parameters, such as the return on investment (ROI) (453.2%), the value of the average annual share of profits in total revenues (56.93%), the shortest expected payback period (8.7 years), the value of the levelized cost of energy production (LCOE) (0.1 EUR/kWh), and one of the lowest costs of building 1 MWp of a photovoltaic farm (664,272.7 EUR/MWp). Among the tested variants of photovoltaic farms with an east–west geographical orientation, the most advantageous choice is the 10° EW arrangement. The results provide valuable insights for policymakers and investors aiming to optimize photovoltaic deployment in temperate climates, supporting the broader transition to renewable energy and alignment with national energy policy goals. Full article

Obtaining the echo of individual fish is an important prerequisite for fisheries acoustic applications, such as in situ measurement of fish target strength and assessment of fish abundance using the counting method. It is also the foundation for evaluating the growth status of farmed fish and managing aquaculture risks. The density of farmed fish populations is typically higher, and such high-density aquaculture further increases the difficulty of obtaining individual fish echoes in practical applications. Building upon previous research and considering the behavioral characteristics of fish in aquaculture settings, this study conducted performance simulations, live fish experiments in simulated aquaculture cages, and comparative evaluations of three individual fish broadband echo detection methods based on a broadband signal system: the amplitude pulse width method (APM) based on echo envelopes, the peak detection and time delay estimation method (PDM), and the peak time delay combined with instantaneous frequency method (PDIM). This study assumed a dorsolateral fish orientation, which limits its research scope and applicability. The results showed that the PDIM achieved a detection accuracy of 78.34% and a false recognition rate of 1.32%. The APM based on echo envelopes was insensitive to individual fish echoes and had lower recognition accuracy. The PDM exhibited better individual fish echo capture capabilities, while the PDIM demonstrated superior overlapping echo rejection capabilities. Full article

Produce supply chains play a critical role in ensuring fruits and vegetables reach consumers efficiently, affordably, and at optimal freshness. In recent decades, hub-and-spoke network models have emerged as valuable tools for optimizing sustainable cold food supply chains. Traditional optimization efforts typically focus on removing inefficiencies, minimizing lead times, refining inventory management, strengthening supplier relationships, and leveraging technological advancements for better visibility and control. However, the majority of models rely on deterministic approaches that overlook the inherent uncertainties of crop yields, which are further intensified by climate variability. Rising atmospheric CO₂ concentrations, along with shifting temperature patterns and extreme weather events, have a substantial effect on crop productivity and availability. Such uncertainties can prompt distributors to seek alternative sources, increasing costs due to supply chain reconfiguration. This research introduces a stochastic hub-and-spoke network optimization model specifically designed to minimize transportation expenses by determining optimal distribution routes that explicitly account for climate variability effects on crop yields. A use case involving a cold food supply chain (CFSC) was carried out using several weather scenarios based on climate models and real soil data for California. Strawberries were selected as a representative crop, given California’s leading role in strawberry production. Simulation results show that scenarios characterized by increased rainfall during growing seasons result in increased yields, allowing distributors to reduce transportation costs by sourcing from nearby farms. Conversely, scenarios with reduced rainfall and lower yields require sourcing from more distant locations, thereby increasing transportation costs. Nonetheless, supply chain configurations may vary depending on the choice of climate models or weather prediction sources, highlighting the importance of regularly updating scenario inputs to ensure robust planning. This tool aids decision-making by planning climate-resilient supply chains, enhancing preparedness and responsiveness to future climate-related disruptions. Full article