Search Results (367)

Several solutions are being explored to reduce methane intensity in dairy farms, but there is no consensus for commercial pastoral dairy systems in temperate zones. We evaluated the effects of essential oils (EO) supplementation on CH₄ intensity and performance in dairy cows within a commercial pasture-based system in southern Chile. Thirty multiparous cows were randomly assigned to a control group and a treated group, with a general average yield of 22.3 ± 5.37 kg/d and an average parity of 3.42 ± 1.13. The treated group received concentrate supplemented with a mixture of EOs. Enteric CH₄ emissions were measured using GreenFeed^®. Milk yield (kg/d), composition (% fat, % protein, urea, somatic cells), plasma biochemistry, and grassland proximal analysis (NIRs) were also evaluated. Results showed a significant increase in fat-corrected milk production (4.6 kg) in the treated group during the first trial period where the grassland was highly nutritious, offering 19.8% crude protein as well as a pool of long-chain fatty acids. Additionally, CH₄ intensity was significantly lower in the treated group (1.3 gCH₄/ECM) during the first phase. EO supplementation strategies represent a suitable non-invasive intervention suitable for commercial grassland-based systems in southern Chile that is strongly influenced by pasture quality. Full article

Affordable optical data from Unmanned Aerial Vehicles (UAVs) coupled with process-based models could constitute an integrative platform to map complex spatio-temporal patterns of nitrate leaching and reduce uncertainties in tightening the nitrogen (N) cycle of silvopastoral systems. This study uses field data from a commercial farm in Denmark with lactating sows housed in paddocks with pastures flanking a central zone of poplars, either pruned (P) or unpruned (tall, T), each with resources (feed and hut) on the same (S) or opposite side (O) of the tree zone. The poplar leaf area index derived from canopy cover using a computer vision approach on true-colour UAV imagery was fed to a process-based model alongside soil data and geostatistical analyses to derive the soil water balance across the paddocks and explicitly map the variation in soil nitrate leaching. The results showed clear patterns not seen before of nitrate leaching hotspots shifting from high values in the pre-study year without animals to diluted lower values in the main study year involving the pigs. The results also showed a seasonal and spatial variation of 7 to 860 kg N ha⁻¹ year⁻¹, a wide leaching range otherwise difficult to capture, by employing only a process-based model using mean effective parameters. Nitrate leaching was in the order PO > PS > TO > TS. The N cycle was tightened with T regardless of S/O. The approach could be improved with more machine learning-aided process-based modelling to operationally monitor complex silvopastoral systems to alleviate nitrate leaching in outdoor pig systems. Full article

Background/Objectives: Land management practices strongly influence soil biochemical processes, yet conventional soil measurements often overlook dynamic small-molecule variation underlying nutrient cycling and microbial activity. This study aimed to evaluate whether MS¹-based untargeted metabolomics can resolve meaningful biochemical differences among soil systems under distinct land management practices. Methods: Soils from six land-use types—conventional cultivation, organic cultivation, pasture, white pine, tulip poplar, and hardwood forest—were analyzed using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). Multivariate analyses, including PLS-DA, were performed to evaluate metabolic variation across systems. Both identified metabolites and unknown spectral features (MSI Level 4) were assessed, and biosynthetic class assignment of unknown features was performed using NPClassifier. Results: Metabolic features revealed clear separation between land management systems, demonstrating distinct chemical fingerprints across ecosystems. While conventional elemental ratios (e.g., C/N) showed minimal differentiation, phosphorus-related stoichiometric ratios (C/P and N/P) displayed strong land-use-dependent differences. NPClassifier superclasses highlighted unique chemical patterns, with forest soils enriched in diverse secondary metabolites, cultivated soils characterized by simplified profiles, and pasture soils dominated by microbial membrane lipids and alkaloids. Conclusions: Untargeted MS¹-based metabolomics effectively distinguished soil systems under different land-use practices and revealed ecologically meaningful variation even without complete structural identification. This study demonstrates that an MS¹-only workflow leveraging unknown spectral features can robustly distinguish soil systems, underscoring their value in untargeted metabolomics analyses. Full article

The use of plant growth simulation models, such as the Agricultural Crop Simulator (AgS), can support planning and management decisions in pasture-based animal production systems. AgS is a biophysical model that is being developed to focus on crops relevant to the Brazilian economy. Originally, the model was parameterized for Marandu palisadegrass (Urochloa brizantha cv. Marandu) under continuous stocking method and cutting regimes. The objective of this study was to parametrize and evaluate the performance of AgS in simulating Marandu palisadegrass biomass production under rotational stocking methods. Field data from an experiment assessing pre-grazing heights of Marandu palisadegrass grazed by beef cattle was used to evaluate the model. The simulations initially underestimated leaf and total biomass production, regardless of pre-grazing height. These results suggested that differences between cutting and grazing methods make additional model calibration necessary. Differences related to regrowth of leaves were addressed and the new calibration resulted in higher biomass allocation to leaves and stems, reducing the mean error in the 25 cm treatment from −1.001 to −253 kg ha⁻¹ and the rRMSE from 41% to 34%. AgS showed potential for simulating rotational stocking after adjustments were made, and future calibrations should consider different management and environmental conditions. Full article

Accurate pasture biomass (PB) estimation is critical for tactical grazing management, yet traditional satellite-derived vegetation indices such as Normalised Difference Vegetation Index (NDVI) saturate when canopy density exceeds about 3 t DM ha⁻¹. This limits predictive accuracy because the spectral signal plateaus under dense vegetation, masking further biomass increases. To address this limitation, this study integrated multiple data sources to improve PB estimation in dairy systems. The dataset combined Sentinel-2 spectral bands, rising plate-meter (RPM) PB measurements, daily weather data, and paddock management features. A total of 3161 paired RPM–satellite observations were collected from 80 paddocks across 16 New South Wales dairy farms between November 2021 and July 2024. Eight regression algorithms and four predictor configurations were evaluated using robust cross-validation, including an 80:20 farm/paddock-stratified train–test-set split. The XGBoost model using full-band reflectance and concurrent weather data achieved strong baseline performance (R² = 0.63; MAE = 243 kg DM ha⁻¹) on non-interpolated data, outperforming NDVI-based models. To address temporal gaps between field readings and satellite imagery, Multiquadric interpolation was applied to RPM data, adding roughly 30% new observations. This enhanced dataset improved test performance to R² = 0.70 and MAE = 216 kg DM ha⁻¹, with gains maintained on external validations (R² = 0.41/0.48; MAE = 267/235 kg DM ha⁻¹). A progressive training strategy, which refreshed model parameters with seasonally aligned data, further reduced errors by 30% compared to static models and sustained performance even when farms or seasons were excluded. This fortified Sentinel-2 modelling workflow, combining RPM interpolation and progressive calibration, achieved accuracy comparable to the commercial Pasture.io platform (R² = 0.66; MAE = 240 kg DM ha⁻¹) which uses satellite imagery with higher temporal and spatial resolution, demonstrating potential for automated recalibration and near real-time, paddock-level decision support in pasture-based dairy systems. Full article

Improving sustainability in agricultural systems depends on increasing the efficiency of nitrogen (N) use and recycling. This study evaluated whether animal supplementation and legume-based pastures can enhance N balance and residual N availability in an integrated crop-livestock system (ICLS). The experiment was conducted in two phases—livestock and cropping—using three treatments: a control pasture (oat + ryegrass), a legume mixture (oat + ryegrass + arrowleaf clover), and a supplementation treatment (oat + ryegrass with concentrate supplementation at 1% of live weight), each replicated three times. Soybeans were grown during the cropping phase. Supplementation increased the stocking rate by 21%, while both supplementation and legumes led to a 30% increase in residual N returned via feces and urine, without negatively affecting soybean yield (~4.1 Mg ha⁻¹). N off-take by soybean grain was approximately 9% higher in these treatments, while N exported via cattle carcasses remained unchanged across treatments, averaging 8.2 kg ha⁻¹. Overall, soybeans accounted for 96–97% of total N export, and animals for only 3–4%. These results demonstrate that animal supplementation and legume integration enhance N use efficiency and contribute to nutrient recycling in ICLS, offering a viable strategy to reduce dependence on synthetic fertilizers. The findings support the development of more sustainable livestock and crop systems by maximizing nutrient retention, maintaining yield, and improving soil fertility. Furthermore, the implications for soybean yield and the sustainability of livestock systems indicate a potential positive economic and environmental impact for producers and policymakers. Full article

Soil respiration, the exchange of gases between soil and the atmosphere (O₂ consumption and CO₂ production), plays a key role in ecosystem functioning and climate regulation. This study investigated the short-term temporal variability of soil CO₂ emissions and O₂ influx and their relationship with tropical climate conditions and soil attributes in the Cerrado region, Selvíria, MS, Brazil. Soil CO₂ emissions were measured using the LI-8100 portable system, while soil O₂ influx was estimated by linear interpolation of O₂ variation inside the chamber using a UV Flux 25% (ultraviolet light) sensor. Soil temperature and moisture were measured simultaneously in three land use types: pasture (~11 years) and reforested areas with native species and eucalyptus (~35 years). Soils were classified as Oxisoils according to Soil Taxonomy. Significant short-term temporal variability was observed in CO₂ emissions (mean 3.2 ± 0.5 µmol m⁻² s⁻¹), O₂ influx (mean 1.8 ± 0.3 mg O₂ m⁻² s⁻¹), soil temperature and moisture across the land use types. Pasture areas exhibited the lowest CO₂ emission rates, associated with improved soil attributes (soil organic matter, sum of bases and pH) due to management practices, while reforested areas showed overlapping soil respiration patterns and higher temporal variability. Principal component analysis revealed strong coupling between O₂ influx and CO₂ emission in reforested soils. These findings highlight the influence of land use on short-term soil respiration dynamics and underscore the importance of sustainable pasture management and reforestation in the Brazilian Cerrado. The results also support public policies aimed at restoring degraded pastures, reducing deforestation and burning, and enhancing soil carbon sequestration to mitigate climate change. Full article

This study aimed to assess the potential of machine learning models applied to high spatial resolution images from UAVs for estimating the aboveground biomass (AGB) of forage grass cultivated in the Brazilian semiarid region. The fresh and dry AGB were determined in Cenchrus ciliare plots with an area of 0.04 m². Spectral data were obtained using a multispectral sensor (Red, Green, and NIR) mounted on a UAV, from which 45 vegetation indices were derived, in addition to a structural variable representing plant height (H95). Among these, H95, GDVI, GSAVI2, GSAVI, GOSAVI, GRDVI, and CTVI exhibited the strongest correlations with biomass. Following multicollinearity analysis, eight variables (R, G, NIR, H95, CVI, MCARI, RGR, and Norm G) were selected to train Random Forest (RF), Support Vector Machine (SVM), and XGBoost models. RF and XGBoost yielded the highest predictive performance, both achieving an R² of 0.80 for AGB—Fresh. Their superiority was maintained for AGB—Dry estimation, with R² values of 0.69 for XGBoost and 0.67 for RF. Although SVM produced higher estimation errors, it showed a satisfactory ability to capture variability, including extreme values. In modeling, the incorporation of plant height, combined with spectral data obtained from high spatial resolution imagery, makes AGB estimation models more reliable. The findings highlight the feasibility of integrating UAV-based remote sensing and machine learning algorithms for non-destructive biomass estimation in forage systems, with promising applications in pasture monitoring and agricultural land management in semi-arid environments. Full article

Pasture systems, typically composed of grasses, legumes, and forage crops, are vital livestock nutrition sources. The quality of these pastures depends on various factors, including species composition and growth stage, which directly impact livestock productivity. Remote sensing (RS) technologies offer powerful, non-invasive means for large-scale pasture monitoring and classification, enabling efficient assessment of pasture health across extensive areas. However, traditional supervised classification methods require labelled datasets that are often expensive and labour-intensive to produce, especially over large grasslands. This study explores unsupervised clustering as a cost-effective alternative for identifying pasture types without the need for labelled data. Leveraging spatiotemporal data from the Sentinel-2 mission, we propose a clustering framework that classifies pastures based on their temporal growth dynamics. For this, the pasture segments are first created with quick-shift segmentation, and spectral time series for each segment are grouped into clusters using time-series distance-based clustering techniques. Empirical analysis shows that the dynamic time warping (DTW) distance measure, combined with K-Medoids and hierarchical clustering, delivers promising pasture mapping with normalised mutual information (NMI) of 86.28% and 88.02% for site-1 and site-2 (total area of approx. 2510 ha), respectively, in New South Wales, Australia. This approach offers practical insights for improving pasture management and presents a viable solution for categorising pasture and grazing systems across landscapes. Full article

Although pastures cover nearly half of Brazil’s agricultural land and form the backbone of national livestock production, they have historically received limited attention regarding management and fertilization, resulting in widespread degradation. Sustainable intensification of these pasture-based systems is therefore essential to meet growing global demand for animal products while minimizing environmental impacts. This review highlights recent technological innovations in pasture fertilization in Brazil, with a particular focus on alternative phosphorus sources such as natural reactive phosphates, which offer slow-release nutrients at lower costs compared to conventional fertilizers. Efforts to enhance nitrogen use efficiency through nitrification and urease inhibitors show promise in reducing nutrient losses and greenhouse gas emissions, despite current cost constraints limiting adoption. The integration of grass-legume intercropping, especially with Arachis pintoi, has been shown to enhance forage quality and system persistence when appropriately managed. Moreover, plant growth-promoting microorganisms emerge as sustainable biotechnological tools for restoring degraded pastures and boosting forage productivity without adverse environmental consequences. Properly treated agro-industrial residues also present a viable nutrient source for pastures, provided environmental regulations are strictly followed to prevent pollution. Together, these innovations offer a comprehensive framework for enhancing the productivity and sustainability of Brazilian livestock systems, highlighting the pressing need for continued research and the adoption of advanced fertilization strategies. Full article

Donkey milk is highly regarded for its nutritional, immunological and hypoallergenic properties. In this context, the global demand is increasing, and the challenges of low production and milk hygiene need to be addressed. This study evaluated the effects of dietary and phytogenic supplementation on milk yield, nutrient digestibility, and milk quality in lactating jennies (Equus asinus). All donkeys had unrestricted access to natural pasture during the study. In addition to grazing, animals were divided into three groups (n = 10 per group) that differed only in the type of supplemental feed. The control group (CG) received pasture grass with a corn-based supplement; Group 1 (G1) received the same basal feed enriched with sunflower meal and a phytogenic blend of medicinal plants; and Group 2 (G2) received the same compound feed as G1 but without the phytogenic additives. Over an eight-week period, milk production, apparent digestibility coefficients (dry matter, protein, fibre, and ether extract), and microbiological quality were assessed. G1 demonstrated the highest milk yield (p < 0.001), improved nutrient digestibility (e.g., crude protein digestibility: 57.89 ± 4.21%), and a significant reduction in total viable counts (TVC) from 2.848 ± 0.265 to 1.898 ± 0.404 log₁₀ CFU/mL (p < 0.001), compared to CG and G2. The latter maintained relatively stable TVC values (2.930 ± 0.260 → 2.838 ± 0.196; p = 0.356641), accompanied by reduced interindividual variability, whereas CG exhibited a slight increase (2.922 ± 0.253 → 2.949 ± 0.323; p = 0.792259) and greater variability, suggesting a negative trend. Crude protein digestibility was 55.86 ± 6.66% in G2 and 45.26 ± 9.85% in CG, further supporting the superior nutrient utilization efficiency observed in G1. The phytogenic supplement stabilized milk chemical composition, suggesting potential galactagogues, immunomodulatory, and antimicrobial effects. These findings support the use of functional feed additives as a promising strategy to enhance productive performance and milk hygiene in sustainable donkey farming systems. Full article

The land demands of ground-mounted PV systems raise concerns about competition with agriculture, particularly in regions with limited productive farmland. Agrivoltaics, which integrates solar energy generation with agricultural use, offers a potential solution. While agrivoltaics has been extensively studied, less is known about its feasibility and impacts in complex temperate maritime climates such as Aotearoa New Zealand, in particular, the effects of PV-induced shading on ground-level light availability and vegetation. This study modelled the spatial and seasonal distribution of ground-level irradiation and Photosynthetic Photon Flux Density (PPFD) beneath fixed-tilt PV arrays at the Tauhei solar farm in the Waikato region. It quantifies and maps PPFD to evaluate light conditions and its implications for vegetation growth. The results reveal significant spatial and temporal variation over a year. The under-panel ground irradiance is lower than open-field GHI by 18% (summer), 22% (spring), 16% (autumn), and 3% (winter), and this seasonal reduction translates into PPFD gradients. This variation supports a precision agrivoltaic strategy that zones land based on irradiance levels. By aligning crop types and planting schedules with seasonal light profiles, land productivity and ecological value can be improved. These findings are highly applicable in Aotearoa New Zealand’s pasture-based systems and show that effective light management is critical for agrivoltaic success in temperate maritime climates. This is, to our knowledge, the first spatial PPFD zoning analysis for fixed-tilt agrivoltaics, linking year-round ground-light maps to crop/pasture suitability. Full article

Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z_MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km² and a total loss of −74,117.06 km². Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z_MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km² and a total gain of +79,577.96 km², indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km². Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB. Full article

The number of donkeys in Europe has significantly declined in recent decades due to mechanization; however, recently, the demand for donkey milk and other purposes has led to a slight increase in their population. However, information on how they are kept and managed, and their welfare is limited. This review aimed to explore the husbandry, management, and welfare of donkeys (Equus asinus) across European Union member states, Switzerland, and the United Kingdom. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique was used. The search was conducted using Scopus and Web of Science, identifying 797 records that were screened using titles, keywords, and abstracts, resulting in 78 retained records. An additional 19 records were identified using snowballing and experts’ suggestions, bringing the total to 97. Dairy donkeys have been studied mainly in Italy, and there they are usually managed under extensive to semi-intensive husbandry systems. Donkeys involved in human intervention therapies are generally managed semi-intensively. Based on the literature, most donkeys are provided with shelter and outdoor access, and this can be with or without pasture, except the free-range donkeys that graze year-round. Health and management-related issues (e.g., obesity, dental disorders, and hoof disorders) could be overlooked, potentially compromising their welfare. The feeding management of donkeys is generally traditional and poorly studied, relying mainly on forages supplemented with concentrates. Most donkeys suffer from overweight/obesity except for lactating donkeys, which are often underweight. This may indicate unbalanced feeding practices. Improved understanding of housing and feeding management is essential for establishing evidence-based welfare guidelines tailored to the donkeys’ species-specific needs. Full article

The transformation of natural ecosystems into agroecosystems due to changes in land use/land cover (LULC) has been shown to significantly affect soil characterization and classification. The impact of LULC on soil taxonomy was assessed in a primary forest located in central–eastern Honduras, which had been deforested approximately forty years prior to the study. Morphological, physical, and physicochemical analyses were performed by describing 10 representative profiles, applying the Soil Taxonomy (ST) and World Reference Base for Soil Resources (WRB) nomenclatures. LULC resulted in physical degradation in agricultural areas, as evidenced by lighter-colored horizons (P02), reduced granular structure (P01, P02, P05), higher bulk densities (≤1.73 Mg m⁻³), and surface crusting (P02, P05); this phenomenon was also observed in pastures (P06–P09). SOC loss was 62% in croplands, 47–53% in agroforestry systems (P03) and fruit tree plantations (P04), and 25% in pastures. All profiles exhibited pH values between 6.5 and 8.4 and complete base saturation (BS), except for P08 and P09, which had pH values below 5.5, high levels of Al³⁺, and reduced BS (50–60%). Mollic epipedons and variability in the endopedons were also observed. According to the ST of the System of Soil Classification (SSC), the soils were classified as Mollisols, Entisols, Vertisols, and Alfisols; and as Phaeozems, Fluvisols, Gleysols, Anthrosols, Gypsisols, and Plinthosols by the WRB. We advocate for the inclusion of Anthropogenic Soils as a distinct Order within Soil Taxonomy (ST). The implementation of sustainable agricultural practices, in conjunction with the formulation of regulatory frameworks governing land use based on capacity and suitability, is imperative, particularly within the context of fragile tropical systems. Full article