Search Results (5,000)

Rumen methane emissions contribute to global warming and energy loss in cattle. The use of plants rich in secondary metabolites to mitigate methane emissions has been extensively studied, with varying outcomes. The primary aim of this meta-analysis was to assess the impact of secondary metabolites on methane production, productive performance, and ruminal fermentation in beef cattle supplemented with plant-based additives. In accordance with the PRISMA guidelines, 43 of the 578 studies were selected for analysis, employing random-effects models to estimate the raw and standardized mean differences (RStudio: metafor and ggplot, with significance thresholds of p ≤ 0.05 and p ≤ 0.1). Heterogeneity was evaluated using the I² statistic, alongside a subgroup meta-regression examining variables such as dose, additive type, additive form, and forage-to-concentrate ratio. After performing Egger’s test and trim and fill analysis, it was determined that tannins reduced methane yield (g/kg DM, adjusted SDM = −0.463), increased propionate production (%, RMD = 0.066, p = 0.001), rumen pH (adjusted SMD = 0.307), and organic matter intake (RMD = 0.066, p = 0.01). Despite phenolic compounds, flavonoids, terpenes, and terpenoids successfully modulating methane production, fermentative parameters, and nutrient intake, the limited number of comparisons and publication bias suggest caution in generalizing these results. In conclusion, supplementation with plant-based additives containing tannins offers a promising nutritional strategy for reducing enteric methane emissions while maintaining beef cattle performance. Full article

In this study, we present a novel adaptive algorithm, social learning-enhanced deep reinforcement learning (SLDRL), which integrates social learning mechanisms into deep reinforcement learning (DRL) to improve agent performance in both discrete and continuous state-space environments. The proposed hybrid control architecture enables agents to autonomously decide when and how to exploit socially acquired behaviors, balancing social learning with individual exploration through an entropy-based intrinsic motivation mechanism. The framework incorporates online imitation and enactment mechanisms that allow agents to observe and selectively reuse behavioral sequences acquired from other agents during training. We evaluate SLDRL in a sparse-reward discrete grid-based foraging task and in the dense-reward continuous-state/discrete-action CartPole problem. In both domains, SLDRL agents outperform baseline DRL agents, achieving faster learning and higher cumulative rewards. The results show that socially acquired behaviors are utilized adaptively throughout training, with the balance between imitation and individual learning emerging dynamically according to the structure of the environment and the agent’s experience. Comparisons with a behavioral cloning baseline further indicate that selectively integrating observed behaviors can yield more robust long-term learning than direct imitation of demonstration trajectories. Overall, the results demonstrate that SLDRL can effectively leverage online social learning in diverse environments. Full article

Traditional grazing maintains essential ecosystem services, yet this activity is rapidly disappearing across Europe. Understanding the geobotanical features of traditionally grazed areas is critical for predicting biodiversity shifts driven by pastoral decline. This study provides a geobotanical characterisation of traditional sheep farms in north-western Spain. We integrated bioclimatic, phytosociological, and biogeographical approaches with spatial autocorrelation analyses, including global Moran’s I, Local Indicators of Spatial Association (LISA), and join-count tests, to assess spatial patterns in vegetation richness and plant community organisation. The results indicate that 28.22% of the studied farms were located in the Castilian Duero sector, 93.45% within the supramediterranean thermotype, and 75.46% within the subhumid ombrotype. A high diversity of vegetation was recorded, with 111 plant communities identified. These include several priority habitats of community interest within the European Union, notably belonging to the phytosociological classes Molinio-Arrhenatheretea, Festuco-Brometea, and Poetea bulbosae. This spatial approach characterises the vegetation mosaics within a fixed buffer around the holdings, although it does not directly measure actual forage use. As a key scientific novelty, this work provides, for the first time, a macro-regional and quantitatively validated integration that explicitly links broad environmental filters with localized pastoral vegetation mosaics. By providing a statistically robust diagnosis of landscape aggregation and segregation, this geobotanical characterisation serves as a fundamental tool for land managers and shepherds, contributing directly to the conservation and sustainable management of endangered traditional pastoral landscapes under changing environmental conditions. Full article

Whole-plant soybean (WPS) is a high-protein forage resource, but its natural ensiling is often unsatisfactory due to low water-soluble carbohydrate content and high buffering capacity. This study investigated the effects of exogenous melatonin (ME) at 0 (CK), 5 (ME1), 10 (ME2), and 20 (ME3) mg/kg fresh matter on fermentation quality, chemical composition, in vitro rumen fermentation, and bacterial community structure of WPS silage. ME2 and ME3 had lower pH values and higher lactic acid contents than CK, with both treatments achieving pH values below 4.2. Crude protein concentration increased from 15.42% in CK to 19.96% in ME3, while neutral detergent fiber was lower in all ME treatments, and acid detergent fiber was lower in ME2 and ME3 than in CK. At 36 h, no overall treatment effect was detected for cumulative gas production, whereas in vitro dry matter digestibility differed only between ME2 and ME3. 16S rRNA gene sequencing revealed that ME altered the bacterial community, with community-weighted rrn copy number elevated in ME2 and ME3. Random forest analysis identified Enterococcus as the genus with the highest importance for treatment classification, and functional predictions indicated higher predicted abundances of amino acid biosynthesis pathways in ME treatment groups. These results indicate that ME has potential as an additive for improving WPS silage fermentation, but practical dosage recommendations require further validation through aerobic stability, animal performance, economic, and safety assessments. Full article

Gustatory perception in honeybees is a key determinant of foraging decisions and pollen source selection. However, the molecular mechanisms underlying this sensory discrimination remain poorly understood. To investigate these mechanisms during the collection of pollen from different floral sources, this study utilized antennae from worker bees foraging on pear and rapeseed pollen, and non-pollen-foraging workers as controls. Illumina high-throughput transcriptome sequencing was employed to identify differentially expressed genes (DEGs), perform functional annotation, and characterize gustatory receptor (GR) genes. Compared with the control group, 583 DEGs and 516 DEGs were identified in pear-pollen and rapeseed-pollen foragers, respectively, whereas only 73 DEGs were detected between the two pollen-foraging groups. Several DEGs were associated with chemosensory perception, signal transduction, energy metabolism, and immune responses. Notably, genes involved in membrane-associated signaling and stimulus response exhibited differential expression patterns among foraging groups, suggesting adaptive molecular responses to distinct floral resources. Gene Ontology (GO) analysis indicated that DEGs were primarily associated with cellular processes, membrane components, and binding functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment highlighted significant involvement in phagosome, phosphatidylinositol signaling system, oxidative phosphorylation, and extracellular matrix–receptor interaction. Notably, seven GR-related genes were identified in the antennal transcriptome, including five known GR genes and two novel candidates, all with complete open reading frames. Four of these genes featured the canonical seven-transmembrane domain structure of insect GRs. Phylogenetic analysis, in addition to the known sugar receptors AmelGR43a, AmelGR64f, and AmelGR64f-X1, based on GRs from Apis mellifera and Drosophila melanogaster suggested that AmelGR28b, AmelGR10, AmelGR12, and AmelGR13 may belong to the bitter taste receptor family. Quantitative real-time PCR (qRT-PCR) validation demonstrated that the expression patterns of the selected seven DEGs were consistent with the RNA-seq results. This study reveals differential expression patterns and potential functional divergence of gustatory receptor genes in Apis mellifera during pollen collection from different floral sources. It provides important molecular evidence for understanding how honeybees accurately recognize and preferentially forage specific pollen sources via gustatory perception, and offers valuable theoretical and practical insights for honeybee behavioral ecology and crop pollination management. Full article

The grassland ecosystem plays a crucial role in providing ample forage resources for grassland animal husbandry, ensuring its development. Identifying grassland forage is essential for understanding forage resources and cultivating high-quality forage. To address the low accuracy of forage image identification and the issue of some features being ignored during image preprocessing, we have proposed a novel forage identification model, Dual-attention Auto-Encoding Dense Convolution Network (DAEDN), which has not been applied to forage identification before. DAEDN simultaneously calculates the weighted features of both channels and spaces, and utilizes Auto-Encodings to better capture the data, thereby enhancing the feature extraction capability and identification classification performance for grassland forage data. Additionally, it enhances the analysis of edge, texture, and other detailed features by leveraging the feature reuse and direct connection of each layer in the dense convolution structure. We evaluated the model performance through six evaluation parameters including overall accuracy (OA) and average accuracy (AA) and verified the effectiveness of the model by comparing it with popular convolutional neural network models. Experimental results show that the identification accuracy of DAEDN is 98.31%. Experiments proved that DAEDN enhanced ability to extract forage features, improved identification accuracy, and offered a new approach for the identification research of forage hyperspectral images. Full article

Tef [Eragrostis tef (Zucc.) Trotter] is a multi-harvest annual cereal crop with outstanding fodder quality. Our overall goal was to evaluate tef as a multi-harvest summer fodder crop for Israeli dairy cows, and the objective of the study was to evaluate the effects of genotype earliness and harvest regimes on tef forage yield and quality. We report on the effects of genotype earliness and harvest timing on tef forage yield and quality for twelve genotypes (six early and six late) during two experimental years, each at different locations (hereafter environment 1-Revadim and 2-Shiller) in Central Israel. Under environment 1, forage yield was significantly greater at the grain-filling stage than at the heading stage (2100 vs. 1700 g DM/m², significant). However, cell wall carbohydrates and crude protein content were higher at heading than at the grain-filling stage (14% vs. 11%). Early-heading genotypes yielded more than late-heading at the grain-filling stage (2300 vs. 1800 g DM/m², significant). In contrast, under environment 2, late-heading genotypes yielded more than early-heading ones (2400 vs. 1400 g DM/m², significant). The in vitro dry matter digestibility (IVDMD) and in vitro neutral detergent fiber digestibility (IVNDFD) were higher at heading than at the grain-filling stage (75 vs. 53%, significant and 71 vs. 47%, significant, respectively). Late-heading genotypes had more IVDMD and IVNDFD than early-heading genotypes (54 vs. 52% and 48 vs. 46%, respectively). Under Mediterranean conditions, harvesting at heading maximizes crude protein, IVDMD, and IVNDFD, whereas harvesting at the grain-filling stage maximizes yield. All genotypes maintained high quality with low lignin, which is desirable for livestock forage. Our findings imply that tef is a high-quality multi-harvest summer crop, which could be a potential alternative fodder crop for Israeli dairy cows. Full article

This study investigates the ethnobiology of wild food plants in Arbëreshë (Albanian-speaking) and neighbouring Calabrian communities in north-eastern Calabria, inland southern Italy. It examines how traditional ecological knowledge, plant use patterns, and cultural perceptions are represented across two datasets, contributing to the understanding of biocultural dynamics in Mediterranean rural contexts. Fieldwork was conducted through forty-six semi-structured interviews in five villages in north-eastern Calabria, Southern Italy. Data were compared with an ethnobotanical dataset collected in the Vulture area (northern Lucania, southern Italy) during 2000–2001. The comparison is treated as cross-spatial and diachronic at the level of observed ethnobotanical records. Because the study areas differ in ecological and socio-economic conditions, comparisons are presented as descriptive contrasts rather than as direct temporal change. Taxa were classified by citation frequency, and comparisons were conducted at genus level to describe patterns of presence and variation in reported wild plant use. A total of 82 wild food taxa were documented. The dataset was dominated by vascular plants, with frequent representation of the families Asteraceae, Brassicaceae, Apiaceae, and Lamiaceae. Arbëreshë participants reported 60 genera, including seven genera not recorded in the comparative dataset (Asphodeline, Pimpinella, Hirschfeldia, Silene, Bellevalia, Leontodon, and Crocus). Calabrian participants reported 28 genera, including three not recorded among Arbëreshë participants (Clinopodium, Suillus, and Urospermum). Twenty-one genera were present in both datasets. Differences in citation frequency and genus composition are observed between datasets, with variation across groups and contexts. The results show a shared set of commonly reported wild food taxa across datasets, alongside variation in less frequently reported genera. The findings describe differences in ethnobotanical records across communities and time-separated datasets, reflecting combined influences of ecological context, sampling conditions, and local knowledge practices. Full article

Honey bees (Apis mellifera) are key pollinators in agricultural ecosystems that face increasing pressure from pathogens and environmental change. However, how these environmental factors interact remains incompletely understood. To assess associations between climate, landscape composition, and pathogen occurrence in real agroecosystems, we monitored honey bee colonies across 30 apiaries in southern Italy over two years, in summer and autumn. Molecular screening revealed widespread multi-pathogen exposure, with two viruses, Black Queen Cell Virus (BQCV) and Deformed Wing Virus (DWV), and gut trypanosomatid parasite (Lotmaria passim) being the most frequently detected. In contrast, Nosema ceranae, along with Bee Macula-like Virus (BeeMLV) and Acute Bee Paralysis Virus (ABPV), occurred at lower but still notable frequencies. Infections were generally more frequent in adult foragers than in in-hive bees and larvae, and overall pathogen occurrence tended to be higher in summer than in autumn. Higher humidity was associated with higher overall pathogen occurrence and coinfection levels, whereas higher temperature showed a weaker association with these outcomes. Associations between landscape composition and pathogen occurrence differed across pathogens: a higher proportion of semi-natural habitats was associated with lower viral occurrence, particularly BQCV and DWV; however, N. ceranae was more frequently detected under the same landscape conditions. In contrast, L. passim showed context-dependent responses, with landscape effects emerging only through interactions with humidity and temperature. Pathogen coinfections were more occurrent under warm, humid conditions, although this pattern was partially buffered in landscapes richer in semi-natural habitats. Together, these results indicate that, within the studied apiaries, honey bee pathogen occurrence was associated with climate, season, and land use. These findings suggest that environmental context should be considered when interpreting honey bee health monitoring data in heterogeneous agricultural landscapes, with potential implications for apiary management. Full article

Silage additives enhance forage preservation and resource efficiency by reducing dry matter (DM) losses and limiting aerobic spoilage. This study evaluated crop-specific lactic acid bacteria (LAB) inoculants by comparing treated (T) silage with untreated (U) controls for maize and grass. Silage quality was assessed using nutritional, fermentation, and microbial indicators, alongside aerobic stability (ASTA) and biogas yield. Additionally, a carbon footprint (CF) assessment, based on primary data from a farm in northern Germany with background datasets, quantified the implications for the biogas-to-electricity pathway. Two scenarios were modelled, with Scenario II accounting for changes in soil organic carbon (SOC). LAB additives improved preservation, with DM losses decreased from 18.48% in untreated grass (UG) to 13.17% in treated grass (TG) and from 21.53% in untreated maize (UM) to 5.21% in treated maize (TM). ASTA increased to 223 h for TM and 218 h for TG, alongside a lower presence of yeasts and moulds. In Scenario I, CF decreased by 5.00% for TG (293.53 g CO₂-eq kWh_el⁻¹) and 6.89% for TM (229.05 g CO₂-eq kWh_el⁻¹). Under Scenario II (including SOC), TG showed a value of 135.43 g CO₂-eq kWh_el⁻¹, and TM 321.48 g CO₂-eq kWh_el⁻¹. Overall, the additive improved silage stability and reduced climate impacts. Full article

Drought limits sustainable forage production in arid and semi-arid regions, where poor crop establishment is common, especially under inadequate irrigation. To address this challenge, research has turned to simple, rapid solutions that improve crop performance under variable conditions. Among green technologies, seed priming has gained attention as an eco-friendly and cost-effective approach. This study evaluates the effectiveness of seed hydropriming in alleviating the effects of water-deficit stress (40% of field capacity) on tall fescue (Festuca arundinacea Schreb.) at the seedling stage. Without priming, drought stress reduced root and shoot growth, tissue hydration, and nutrient uptake. By contrast, hydropriming improved drought tolerance by improving relative water content and ion supply and sustaining photosynthetic activity. These effects were accompanied by greater accumulation of proline and soluble sugars. Hence, (i) a regulated supply with essential mineral nutrients, (ii) an osmotic adjustment using the absorbed ions (such as K⁺, Ca²⁺, and Mg²⁺) in the vacuole and the accumulated osmotica (such as proline and soluble sugars) in the cytoplasm, and (iii) a maintained photosynthetic machinery together with improved root development are associated with enhanced drought tolerance in F. arundinacea plants issued from hydroprimed seeds. It seems that seed priming emerges more and more as a practical, low-cost technique to enhance drought resilience and support sustainable forage production under limited water conditions. Full article

The aim of this study was to isolate and molecularly identify lactic acid bacteria (LAB) associated with the epiphytic microflora and silage of wheatgrass (Agropyron spp.), as well as to evaluate their biotechnological potential as starter cultures for the ensiling of difficult-to-ensile forage crops under the climatic conditions of northern Kazakhstan. A total of 63 bacterial isolates were obtained and grown on MRS medium under different temperature conditions. Based on growth characteristics, pH values, and titratable acidity, 15 highly active strains were selected, demonstrating stable acidification (pH 3.99–4.75) and high metabolic activity. All isolates were catalase negative and capable of fermenting a wide range of carbohydrates and polyols, although pronounced strain-specific differences were observed. The selected strains exhibited proteolytic and antagonistic activity against test microorganisms and showed high tolerance to osmotic stress, maintaining growth at NaCl concentrations of up to 8–10%. Molecular identification based on 16S rRNA gene sequencing revealed that nine technologically significant strains belonged to the species Lactococcus garvieae, Pediococcus acidilactici, Lactiplantibacillus plantarum, Enterococcus faecalis and Enterococcus faecium. The results obtained in this study demonstrate the high environmental adaptability of the isolated strains and confirm their potential for the development of effective microbial inoculants aimed at improving fermentation processes and enhancing the preservation of difficult-to-ensile forage crops under cold-climate conditions. Full article

Virtual fencing is increasingly used in grazing systems as a flexible alternative to physical fencing, yet detailed assessments of cattle behavior within such systems remain limited. This study investigates the use of collar-mounted tri-axial accelerometers combined with supervised machine learning to characterize cattle behavior in a virtual fencing system. Seven free-ranging Angus cattle were monitored using accelerometers mounted on a virtual fencing system, GNSS positioning, and virtual fence warning logs. A random forest classifier was developed and trained to identify key behaviors (grazing/feeding, ruminating, lying, standing and locomotion) using features derived from tri-axial accelerometer data. The model achieved high classification performance for grazing/feeding, ruminating, and lying (mean accuracy = 0.87, range = 0.83–0.90), enabling estimation of individual behavioral time budgets. Daily activity patterns were generally stable over time and across individuals. Spatial analyses revealed significant differences in behavior between areas near the virtual fence boundary and interior pasture locations, with increased grazing and reduced ruminating near the boundary, potentially reflecting spatial variation in habitat type or forage availability. In the virtual fencing system, cattle are equipped with collars that emit an auditory warning when they approach a virtual boundary, followed by a low-energy electrical impulse when the warning is ignored over a directional distance of 5–10 m. Event-based analyses showed no consistent short-term changes in either movement intensity and direction nor locomotion following auditory warning events, indicating that cattle habituated to the system did not exhibit uniform behavioral disturbance in response to warnings. These results suggest that accelerometer-based behavior classification can provide fine-scale, non-invasive insights into spatio-temporal cattle behavior in virtual fencing systems. The finding indicates that, in a habituated herd, virtual fencing was not associated with pronounced disruption to the measured behavioral patterns, while highlighting the potential of embedded sensor data for animal-based behavioral monitoring. Full article

Lychee, an entomophilous fruit crop cultivated in tropical and subtropical regions, depends heavily on pollination services for optimal fruit yield and the economic sustainability of farmers. However, information on its pollinator interactions remains limited. This study was conducted over three flowering seasons to document the pollinator assemblage of lychee and examine variation in their activity under different physical conditions, including time of day, panicle height, and light–shade environments. Several insects (here, 47, including many butterflies, bees and flies) were recorded as flower visitors of lychee. The most effective pollinators were Apis cerana, Apis dorsata, Apis florea, Braunsapis mixta, and Tetragonula pagdeni. Pollinator abundance, species richness, diversity, and foraging traits (e.g., flower visitation rate and flower handling time) varied with daytime, inflorescence height, and light availability (light versus shade). Greater abundance, richness, and diversity were documented between 8:00 and 12:00 h, at mid-canopy height (2–6 m), and on well-lit inflorescences. Flower visitation rate was higher under these conditions, whereas flower handling time was lower. This study uncovered the key pollinators of lychee and demonstrated that plant–pollinator interactions vary across different physical conditions. These findings may help to improve pollinator management and enhance pollination services in lychee cultivation. Full article

Early senescence of alpine pasture grass, manifested as rapid yellowing at the onset of autumn on the Qinghai–Tibet Plateau, constrains the sustainable development of grassland animal husbandry. Breeding stay-green forage germplasm is key to mitigating this problem. To identify superior stay-green germplasm and preliminarily elucidate the main drivers of senescence, we evaluated six stay-green lines of Elymus sibiricus with non-stay-green materials as controls. Fixed-site field observations were conducted for three consecutive years in Haiyan County, Qinghai Province. We quantified dynamic changes in phenotypic, photosynthetic, and physiological traits during senescence, applied mixed-effects models to identify factors associated with stay-green, and used the TOPSIS model for comprehensive evaluation. The results showed that plant height, green leaf area, chlorophyll content, net photosynthetic rate, and root activity of stay-green E. sibiricus were significantly higher than those of non-stay-green materials at all planting years, and the senescence rate was significantly slower. All traits performed optimally at the third year. Relative to HB-2, HB-4, HB-8, HB-10, HB-11, and HB-15 (stay-green E. sibiricus), plant height, green leaf area, chlorophyll content, net photosynthetic rate, and root activity of CK (non-stay-green E. sibiricus) were 0.83, 0.95, 0.79, 0.82, 0.80, and 0.78; 0.37, 0.37, 0.34, 0.35, 0.31, and 0.35; 0.82, 0.84, 0.80, 0.86, 0.82, and 0.75; 0.86, 0.86, 0.74, 0.89, 0.77, and 0.70; and 0.72, 0.74, 0.66, 0.78, 0.70, and 0.61, respectively. Mixed-effects modeling identified chlorophyll, root vitality, soluble sugars, and photosynthesis as the primary determinants of stay-green in E. sibiricus. The TOPSIS model indicated that HB-15 maintained the highest fitting degree values in years 2–4. These values were 0.69, 0.62, and 0.71, respectively. Therefore, HB-15 was the most ideal stay-green germplasm. These findings provide a theoretical basis and elite parental materials for breeding new stay-green varieties of E. sibiricus. Full article