Search Results (556)

Livestock represent a key asset in the livelihood of smallholder farmers and play a critical role in the social dynamics and nutritional security of resource-poor communities. However, within these resource-poor communities, livestock productivity remains low. This is often due to seasonal changes in the quantity and quality of available feed from the natural veld, which in turn also contributes to methane production. This study aimed to evaluate the effects of supplementing Eragrostis curvula hay with Searsia lancea leaf or silage meal on in vitro fermentation efficiency and gas and methane production. Therefore, an in vitro study using a semi-automated pressure transducer technique was conducted on grass hay alone (control) and grass hay supplemented with 15% or 30% of either S. lancea leaf or silage meal. The dietary treatments were arranged in a complete randomized design, with each treatment replicated four times. Total gas and methane production was recorded at 3, 6, 12, 24 and 48 h using a pressure transducer attached to a data logger. After incubation, samples were collected to determine volatile fatty acids. Supplementing grass hay with 15% S. lancea leaf meal increased gas production by 76%, 52%, 32% and 12% in the first 24 h of incubation. Similarly, increasing the supplementation level to 30% increased gas production by 75%, 63%, 45% and 14%. However, supplementing grass hay with silage meal at 15% significantly reduced gas production by 37% during the first 3 h of incubation, whereas supplementation at 30% had no effect. Supplementing grass hay with S. lancea meals effectively reduced methane production at 24 and 48 h. Grass hay supplemented with 15% or 30% silage meal reduced methane by 46% and 39% at 24 h, while at 48 h, methane was reduced by 39% and 49%, respectively. Supplementing grass hay with S. lancea meals, however, did not affect volatile fatty acids. In conclusion, S. lancea can be strategically used as a supplementary feed source to modulate the rumen ecosystem by attenuating enteric methane production. Further studies are required to determine the effect of S. lancea on rumen microbial composition and its metabolic function. Full article

This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation ${\mathrm{R}}^2$ of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation ${\mathrm{R}}^2$ was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s ${\mathrm{k}\mathrm{m}}^2$) using freely available satellite imagery and open-source machine learning frameworks. Full article

Canine atopic dermatitis (CAD) is a chronic, pruritic inflammatory disease that affects up to 15% of the global canine population. Its etiopathogenesis is multifactorial, involving genetic, immunological, environmental, and dietary factors. It is characterized by pruritus, erythema, alopecia, and secondary lesions, predominantly affecting the abdomen, extremities, and ears. This retrospective cross-sectional descriptive study analyzed 735 medical records of dogs diagnosed with CAD treated at the Veterinary Specialty Center (CEVET) in Quito, Ecuador, between January 2018 and July 2025. Demographic, clinical, housing, diet, and cohabitation data were collected and statistically analyzed using χ² for qualitative variables and the Kruskal–Wallis test for quantitative variables, with post hoc analysis as appropriate. Additionally, pruritus severity was assessed using the Pruritus Visual Analog Scale (pVAS). A composite Clinical Severity and Distribution Score (CSDS) was also developed to classify disease severity. A multivariate logistic regression model was performed to identify factors associated with severe CAD. The results showed a predominance of CAD in adult dogs (84.2%) and purebred dogs (74.97%), with a slight majority being males (52.38%). Pruritus was the most frequent initial symptom (80.27%), with most cases presenting moderate-to-severe pruritus (pVAS 7–10). The most affected areas were the abdomen (24.49%) and forelimbs (17.68%). The geographical distribution showed a predominance of urban areas (88.84%) and cold climates (86.39%). Based on the CSDS, 53.2% of cases were classified as severe, 44.4% as moderate, and 2.4% as mild. Multivariate analysis revealed that grass exposure was significantly associated with severe CAD (OR = 1.78; 95% CI: 1.22–2.60; p = 0.003), while urban environment showed a non-significant trend toward increased severity (OR = 1.41; p = 0.071). Significant associations were identified involving sex and body weight, age and affected area, and temporal variations in the severity of pruritus, age group, and distribution of lesions. Among breeds, French Bulldogs, Standard Schnauzers, and Shih Tzus had the highest prevalence of CAD. These findings provide the first systematic epidemiological and clinical characterization of CAD in Ecuador, highlighting the role of environmental factors in disease severity and supporting the use of composite clinical scoring approaches in retrospective studies, thereby contributing to understanding of the disease and serving as a reference for early diagnosis, clinical management, and the development of preventive strategies. Full article

Monarch butterflies have declined in both eastern and western populations. Conservation initiatives that support this imperiled species are being implemented in lands managed by the energy and transportation sectors. Vegetation management strategies that encourage the presence of milkweed (Asclepias spp.), the larval host of monarch butterflies (Danaus plexippus), or floral resources to support pollinators are being practiced across North America; however, survey methods to evaluate the success of these strategies vary in accuracy and scalability. In this study, we compared five methods to quantify milkweed stem density and land cover estimates: (1) Site al, (2) Transect plot, (3) Square plot, (4) Large transect (informed by the Monarch CCAA methodology), and (5) Machine learning of images collected by UAVs. These methods encompass full coverage ground counts, partial ground counts, and aerial imagery using object-based image analysis. Sites included distribution, transmission, and gas line ROWs, solar arrays, and transportation easements. We found that Site al and Machine learning most consistently quantified milkweed stem density across sites. Partial ground count methods were likely to over or underestimate milkweed populations. Habitat characteristics (woody, broadleaf, grass, and bare ground) estimates were inconsistent across method and site. The intent of this study was to provide land managers with insight as to the most accurate, efficient, and economical approach to quantify milkweed populations and habitat characteristics. Full article

Identifying the plant species comprising a pasture, among other aspects, is crucial for assessing its nutritional value for grazing animals and facilitating its effective management. Traditionally, it requires labor-intensive visual inspection. Artificial Intelligence (AI) offers a solution for automatic classification, yet robust datasets for training such models in natural, uncontrolled environments are scarce. This data descriptor presents a dataset of 741 images collected in pasture lands in the Centre of Portugal using standard cameras at a height of 50 cm. A semi-automated annotation pipeline was employed, utilizing a Faster R-CNN model followed by manual verification and refinement. The dataset contains 1744 annotations across four categories: ‘Shrubs’, ‘Grasses’, ‘Legumes’, and ‘Others’. It includes diverse morphological variations and captures real-world challenges such as occlusion and lighting variability. This dataset serves as a benchmark for training object detection models in agricultural settings, facilitating the development of automated monitoring systems for precision agriculture. Such a mechanism could be incorporated into a mobile application, mounted on a drone, or embedded in an animal-worn device, enabling automated sampling and identification of the plant composition within a pasture. Full article

Accurate mapping of grass species in biodiverse ecosystems, such as wetlands, is critical for ecological protection. Rapid advancements in remote sensing have established satellite data as a critical tool for wetland grass species mapping; however, its relatively coarse spatial resolution and susceptibility to cloud contamination limit the distinction of co-occurring species at fine scales. While Unmanned Aerial Vehicle (UAV) remote sensing offers high resolution and operational flexibility, relying on single-source features is often insufficient for fine-scale wetland species mapping due to the spectral similarity of co-occurring species. On the other hand, the fusion of multi-source remote sensing features (i.e., spectral, textural, and geometric features) likely provides a promising solution for achieving accurate, fine-scale grass species mapping in biodiverse ecosystems. In this study, we developed a wetland grass species mapping framework integrating spectral, textural, and geometric features derived from UAV RGB and multispectral imagery. Using a dataset of 95,880 image objects representing 24 wetland grass species classes collected in two years in Dajiu Lake National Wetland Park of China, we evaluated three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—across various feature combinations. We found that while spectral features (i.e., red edge, normalized green–red difference index [NGRDI], and normalized difference vegetation index [NDVI]) (related to leaf pigment concentrations and cellular structures) exhibited the highest importance in wetland grass species mapping, textural (i.e., contrast) and geometric features (i.e., aspect ratio) significantly enhanced classification performance as complementary information, yielding improvements of up to 10.5% in overall accuracy (OA) and 0.103 in Macro-F1 scores. Specifically, the fusion of spectral, textural, and geometric features achieved optimal performance with an OA of 81.9% and a Macro-F1 of 0.807. Furthermore, the XGBoost model outperformed SVM and RF, improving OA by 9.4% and 2.8%, and Macro-F1 by 0.08 and 0.035, respectively. By identifying the optimal feature combination and machine learning algorithm, this study establishes an accurate method for wetland grass species mapping, offering new opportunities for ecological assessment and precision conservation in biodiverse landscapes. Full article

To address re-siltation from the secondary deposition of grass residues and the challenge of collecting residues after the slope mowing of farmland irrigation canals, as well as the precise maintenance requirements of “controlling grass without destroying it” for ecological canal slopes, an innovative cutting and conveying device for slope-protection grass was designed. The device includes a shaft equipped with Y-type flail knives and straight knives to achieve efficient cutting and initial throwing. The internal airflow field of the cutting device was simulated using FLUENT software, verifying the synergistic effect between the airflow field and mechanical throwing. The practicality of the lateral conveying device was verified through co-simulation using RecurDyn and EDEM. We determined the apparatus’ optimal operating parameters through field experiments and using a quadratic regression model. The results indicate that when the forward speed is 3.6 km·h⁻¹, the cutting blade shaft speed is 2050 rpm, and the conveyor belt linear speed is 1.2 m·s⁻¹, with the off-canal conveyance rate of the device reaching 85.44%. In this study, we provide a theoretical basis and support for the optimal design of cutting and conveying devices for farmland irrigation canals. Full article

With the rapid development of intensive aquaculture, unreasonable stocking density has become a major factor restricting the healthy growth of grass carp (Ctenopharyngodon idella). This study aimed to evaluate the effects of three stocking densities (0.57, 1.13, and 2.27 kg/m³) on the growth performance, stress response, antioxidant capacity, and immunity of grass carp. Grass carp with an initial body weight of 81.76 ± 17.69 g were randomly assigned to three groups with three replicates. After 75 days of cultivation, we randomly sampled and measured their growth performance. Reagent kits were used to detect serum biochemical indicators, kidney immune enzyme activity, and liver antioxidant indicators in each treatment group. The expression of spleen immune-related genes was detected using real-time quantitative PCR (RT-qPCR). Results showed that the final body weight, weight gain rate, specific growth rate, and condition factor were significantly higher in the medium-stocking-density group (p < 0.05). High stocking density significantly increased serum cortisol, glucose, transaminases, creatinine, and urea nitrogen, and decreased cholesterol and triglyceride levels (p < 0.05). For immune parameters, the activities of immunoglobulin M (IgM), lysozyme (LZM), antimicrobial peptide (AMP), alkaline phosphatase (AKP), and acid phosphatase (ACP) in the kidneys decreased with increasing density. The mRNA levels of IL-1β, IL-6, TNF-α, and IL-10 in the spleen were significantly upregulated, while IgM was downregulated in the high-density group (p < 0.05). Regarding antioxidant capacity, hepatic total antioxidant capacity (T-AOC), catalase (CAT), and glutathione (GSH) levels increased initially and then decreased with increasing density, while malondialdehyde (MDA) content increased continuously. Collectively, these findings suggest that high stocking density induces growth inhibition, oxidative stress, and immune dysfunction in grass carp. The medium density of 1.13 kg/m³ was found to be optimal for the growth and physiological health of grass carp in this study, providing a scientific basis for the optimization of intensive farming strategies. Full article

With the intensification of global climate change, the increasing frequency and severity of extreme weather events seriously affected agroecosystems and human health. Zoysia japonica Steud. (Z. japonica) is a warm season turfgrass with outstanding drought tolerance; therefore, gaining insight into the breeding and ecological restoration of drought-tolerant lawn grass species is of great significance. This study aimed to investigate the adaptive strategies of drought-resistant z047 and z388 by integrating transcriptome analysis and experimental physiological measurements in a drought field. Physiological experiments have demonstrated that z047 plants exhibited a stronger water retention capacity, lower cell membrane damage, and higher above-ground biomass. In addition, the relative water content and permanent wilting coefficient of z047 plants were superior to wild type plants. Our results verified that there were 108 and 208 significantly differentially expressed genes (DEGs) (fold change (FC) ≥ 4, p < 0.01) screened from z047 plants under drought stress for 7 and 14 days, respectively. Moreover, remarkable upregulation of MAPKKK17 and MAPKKK16 genes involved in the MAPK signalling pathway may be closely related to their drought tolerance. Collectively, this study reveals the molecular and physiological synergistic mechanism of drought tolerance in Z. japonica, thus providing a theoretical basis for molecular breeding of drought-tolerant plant cultivars and ecological restoration in arid areas. Full article

Sward height is a practical indicator for defining management targets that reflect pasture structure. The complexity of integrated systems, including the coexistence of trees, crops, and livestock, can modify animal grazing distribution and microhabitat conditions, leading to different degrees of sward heterogeneity and botanical composition. This study investigated sward-height distribution and species composition in four systems: livestock (L), livestock–forestry (LF), crop–livestock (CL), and crop–livestock–forestry (CLF). Data were collected over two years in pastures of black oat (Avena strigosa Schreb.), Aries grass (Megathyrsus maximus cv. Aries), Italian ryegrass (Lolium multiflorum Lam.), and other tropical grasses during summer, transition, and winter. Sward heights were classified into three categories (low, optimal, high) according to seasonal thresholds (winter: <18.0; 18–29.9; >30 cm; summer: <15.0; 15–24.9; >25 cm) and fitted to four probability distributions (normal, log-normal, Gamma, Weibull). Management based on target-height maintained 46% of observations within the optimal class, a satisfactory proportion for continuous stocking systems where structural heterogeneity is inherent. The CL system presented greater species diversity due to a higher frequency of Italian ryegrass and other grasses. Across systems and seasons, the Gamma distribution provided the best fit for sward-height frequencies. These findings offer a practical statistical tool for evaluating grazing management efficiency. Full article

Successful establishment of resource-efficient perennial crops that can thrive and produce economically viable yields under metal stress conditions requires a clear understanding of macronutrient uptake and metal detoxification regulation mechanisms particularly during crop establishment period. Therefore, this study aimed to evaluate the partitioning of macronutrients and metals in miscanthus and switchgrass grown on metal-contaminated soils, and to evaluate the effect of biostimulant treatments on early crop establishment and biomass productivity. Field trials were conducted with two perennial C₄ grasses, miscanthus (Miscanthus lutarioriparius) and switchgrass (Panicum virgatum L.), under three treatments: control (CK), humic acid (HA), and humic acid combined with microbial inoculants (HAM). At final growth stages, agronomic traits, biomass quality, and macronutrient (N, P, K) and metal (Cd, Cr, Pb, Cu, Zn) contents were analyzed. To investigate metal and macronutrient partitioning dynamics, samples were collected from October to December. The HAM treatment significantly enhanced biomass yield and morphological parameters in both species, particularly in miscanthus. Both HA and HAM improved cellulose and hemicellulose while reducing the lignin content, thereby improving biomass quality. For both crops, roots served as the primary organ for metal accumulation across growth stages. In miscanthus roots from October to December, the proportions of Cd, Cr, and Pb increased (10.5%, 10.8%, 13.6%), while Zn and Cu decreased (6.5%, 11.6%). Over the same period, Pb increased slightly (4.4%), but Cd, Cr, and Cu declined (26%, 1.9%, 12.9%) in switchgrass roots. Coupling and principal component analyses revealed weak macronutrient–metal synchronization in both miscanthus and switchgrass across growth stages. Full article

Direct seeded rice, being less water- and labor-intensive, can be an alternative approach to conventional rice planting methods. However, uneven and poor stand establishment caused by deep sowing in the field is one of the major hurdles in the adoption of direct seeding technology. Varieties with the potential to emerge from deeper layers of soil may have a positive impact on crop establishment. To evaluate the behavior of ten rice cultivars against their potential to emerge from different soil depths (0, 2.5, and 5.0 cm), a pot experiment was conducted under semi-controlled conditions at the PARC Rice Programme, Kala Shah Kaku, Lahore. Data on different seedling parameters were collected. The results showed that the highest mean seedling emergence percentage (95%) was achieved by the tested genotypes at a 2.5 cm seeding depth, while surface sowing and placement of seeds at a 5 cm depth demonstrated a similar mean emergence percentage (89%). Seeding depth, genotypes, and their interactions significantly affected mean emergence time, mesocotyl and coleoptile lengths, and root and shoot lengths. Sowing seeds at a 5 cm depth increased mean emergence time by 28%. However, increasing sowing depth increased the coleoptile length, mesocotyl length, first leaf sheath length, and shoot length of rice seedlings. Mesocotyls and coleoptile lengths showed a linear relationship with mean emergence time. Mesocotyl and coleoptile are key structures of the apical–basal axis in grasses that elongate to facilitate the emergence of germinating seeds under deep sowing. The longest coleoptiles (1.47 cm) and mesocotyls (3.27 cm) were measured from seedlings sown at a depth of 5 cm. Among genotypes, PK-1121 exhibited maximum coleoptile elongation (2.10 cm) under deep sowing (5 cm), while the longest mesocotyls were recorded from deep-sown (5 cm) seedlings of Chenab Basmati. Root length was found to be inversely proportional to sowing depth. PK-1121 aromatic, Kisan Basmati, Punjab Basmati, and Chenab Basmati produced longer shoots (22.61, 23.37, 23.32, and 21.05 cm, respectively) and took a relatively short time for emergence when sown deep. These varieties may have better potential to emerge from deeper soil layers, which may have a positive impact on even germination and better crop stand establishment. Full article

Chlorophylls and carotenoids are key plant metabolites involved in photosynthesis, stress responses, and antioxidant activity. This study aimed to examine intrapopulation variability in Melica uniflora Retz. (wood melick), focusing on chlorophyll and carotenoid content in relation to the developmental stage and environmental conditions. Research was carried out over three consecutive years (2021–2023) in the Ślęża Massif near Sobótka, Lower Silesia, Poland. Leaf blades samples were collected annually from ten natural forest sites at two time points: summer (July) and autumn (October), and analyzed for chlorophyll a, chlorophyll b, and total carotenoids using spectrophotometry. Statistical analyses, including ANOVA, were used to assess the effects of year, harvest time, and site on pigment concentrations. The average (±SD) pigment content in M. uniflora was 1.44 ± 0.73 mg∙g⁻¹ DM for chlorophyll a, 0.67 ± 0.40 mg∙g⁻¹ DM for chlorophyll b, and 0.46 ± 0.28 mg∙g⁻¹ DM for total carotenoids. Among the factors studied, year and developmental stage had the strongest statistically significant influence on chlorophyll and carotenoid levels, while site-specific differences contributed to intrapopulation variability to a lesser extent (p < 0.001). Interestingly, the first year of the study showed higher average pigment levels across both harvest times. Summer-collected plants had higher concentrations of all pigments than those collected in autumn. Differences among sites further indicated intrapopulation variability within this species. These findings provide new insights into the natural variability of photosynthetic metabolites in forest grasses and may serve as a reference for studies on the adaptive and biochemical responses of woodland plant species to environmental factors. Full article

Estimating nitrogen (N) and the corresponding crude protein (CP) content in forage crops is essential for optimizing fertilization and livestock nutrition. However, standard methods such as the Dumas and Kjeldahl techniques are destructive, costly, and impractical for field use in certain regions of developing countries. This study evaluated four non-destructive approaches—morphometric measurements, Pantone^® color scales, smartphone-based RGB analysis (ColorDetector app), and SPAD chlorophyll readings—for predicting N and CP in Megathyrsus maximus (Mombasa grass). A total of 120 samples were collected under three nitrogen fertilization levels and assessed using linear mixed-effects models with cross-validation. Morphometric variables showed poor performance (R² < 0.01), indicating low correlation with nutrient content. Pantone-based RGB models provided slightly better predictions (R² ≈ 0.30) but were limited by subjectivity and discrete data. SPAD-based models demonstrated moderate predictive accuracy (R² ≈ 0.53; RMSE ≈ 0.46%). The highest accuracy was achieved with smartphone-derived RGB data, where full RGB models reached R² = 0.60 and RMSE = 0.45%. Based on these results, a practical green color scale was developed from RGB values to support real-time, in-field nitrogen and crude protein assessment. This study highlights smartphone imaging as a scalable, low-cost, and accurate tool for non-destructive estimation of nitrogen and crude protein in tropical forages, offering an accessible alternative to laboratory methods for producers and field technicians. Full article

The accumulation of municipal sewage sludge is a worldwide problem, although when properly treated, it can be utilized for various purposes in industry and agriculture. Due to its high nutrient content, one of its possible uses is the application as fertilizer on agricultural or degraded lands with the purpose of non-food plant production. In the present study, the sustainability of dehydrated sewage sludge application was tested in Szarvasi-1 energy grass (Thinopyrum obtusiflorum cv. Szarvasi-1) plantations, with special focus on the turnover of nutrients and trace elements in two experiments conducted outdoors between 2016 and 2019. Experiment 1 was conducted in 1 m³ containers, and the treatment was started on two-year old plants in 0, 15, 22.5, and 30 Mg ha⁻¹ doses per year applied in two or three portions to reveal the upper limit of sludge application. Experiment 2 was conducted in 100 m² field quadrates with 0, 7.5, 15, and 22.5 Mg ha⁻¹ doses per year applied once a year, which is in the range of the currently permitted application dose in Hungary. Soil, sludge, and plant samples, as well as physiological data, were collected. Aboveground biomass yield was measured 2–3 times per year. Increasing doses of sewage sludge significantly increased the yield compared to the controls, but the increment between the second and third doses was small. Chlorophyll content (SPAD values) increased tendentiously and partly significantly. The maximal quantum efficiency of PSII and the stomatal conductance was not improved compared to the control, whereas the relative water content of the plants was increased in Experiment 1 but not in Experiment 2 compared to the control. Malondialdehyde concentration was increased by the largest dose in Experiment 1. The concentration of macroelements, Ca, Mg, N, and S, increased in the aboveground biomass with increasing doses of sewage sludge, but even after three years, the cumulative amount removed with the harvested biomass was much smaller than the amount remaining in the soil. The total amount of K in the harvested biomass exceeded that introduced to the soil by the treatments. Micro- and trace-element concentrations did not show increasing tendency in the biomass, suggesting a slower uptake and removal rate than macroelements. The results point to the necessity to assess the real nutrient requirement and trace-element uptake by the plants as compared to the sewage sludge treatment to avoid their uncontrolled accumulation in the soil and ensure a sustainable fertilization of the plantations. Full article