Search Results (130)

Intercropping systems involving sorghum, grasses, and legumes can enhance forage production and improve sustainability under no-tillage systems. In the context of agricultural systems, the effective selection of rotational species is essential, as they contribute to soil system dynamics and provide feed for livestock. In this study, the dry matter production of grain sorghum (GS: cultivar A 9902), forage sorghum (FS: cultivar Volumax), and dual-purpose sorghum (DPS: cultivar Rancheiro) intercropped with Urochloa brizantha and dwarf pigeon pea was evaluated at five sowing densities (0 to 24 seeds m⁻¹) over two growing seasons (2018 and 2019), conducted in a randomized complete block design under autumn growing conditions. Biometric and productive traits of sorghum were assessed, as well as the dry matter production of the companion species, in order to understand interspecific interactions within the system. Sorghum dry matter yield was not affected by pigeon pea density, indicating high stability of the main crop. Grain sorghum (GS) and forage sorghum (FS) showed higher production in the first season (20,428 and 18,210 kg ha⁻¹, respectively), whereas dual-purpose sorghum (DPS) performed best in the second season (25,388 kg ha⁻¹). GS exhibited the highest panicle production, exceeding the other cultivars by up to 55%. Increasing pigeon pea density enhanced its biomass production but reduced Urochloa production by up to 50%; however, Urochloa showed better performance when intercropped with GS and FS. Sorghum morphological traits were not affected, and overall, the intercropping system maintained sorghum productivity while increasing total biomass, demonstrating potential for silage production and pasture establishment. Full article

This study evaluated combinations of defoliation frequencies and intensities to identify grazing strategies that optimize forage accumulation and morphological composition in mixed pastures of Marandu palisadegrass (Urochloa brizantha cv. Marandu) with the legume Macrotyloma axillare. Treatments consisted of pre-grazing heights of 30 and 40 cm (defining defoliation frequency) combined with post-grazing heights of 15 and 20 cm (defoliation intensity), in a 2 × 2 factorial randomized block design with four repetitions. Forage accumulation rate, morphological component mass, and leaf area index (LAI) were evaluated under rotational stocking. The highest forage accumulation rates of grass and its stems occurred at a pre-grazing height of 30 cm. A taller pre-grazing height (40 cm) resulted in greater pre-grazing forage mass, leaf and stem mass of Marandu palisadegrass and LAI, but it also increased the amount of dead material and post-grazing stem mass. The greatest Macrotyloma forage accumulation occurred under grazing strategies of 30–20 cm and 40–15 cm. Lenient defoliation (20 cm post-grazing height) favored post-grazing leaf mass, whereas severe defoliation (15 cm) favored stem mass. Marandu palisadegrass showed higher LAI at 40 cm pre-grazing height (4.7) than at 30 cm (3.6), with slightly greater values under 20 cm (4.3) than 15 cm (4.1) post-grazing height, while Macrotyloma axillare exhibited low LAI. Across all grazing strategies, the legume mass decreased over time. Therefore, future studies should explore alternative grazing strategies and periodic reseeding of Macrotyloma axillare to maintain its presence in mixed tropical pastures. 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

Effective and sustainable livestock management within Mediterranean ecosystems depends heavily on accurate and timely monitoring of sward composition. Traditionally, this task has relied on human observers who must traverse large and often rugged areas to identify the distribution of grasses, legumes, shrubs, and other plant groups. However, this approach is not only labor-intensive and slow but also susceptible to substantial human error, especially when observations must be repeated frequently or carried out under difficult field conditions. In the present study, an alternative method that integrates wearable cameras with modern computer-vision techniques to automatically recognize pasture plant species through an edge device present in farm premises was investigated. Additionally, the feasibility of achieving reliable classification performance on resource-constrained edge devices was evaluated. To this end, five widely used pre-trained convolutional neural networks were compared against a lightweight custom model developed entirely from scratch. The results demonstrated that ResNet50 delivered the strongest classification accuracy, achieving a Matthews Correlation Coefficient (MCC) of 0.992. Nonetheless, the custom lightweight model proved to be a practical compromise for real-world field use, reaching an MCC of 0.893 while requiring only 6.24 MB of storage. The inference performance on Raspberry Pi 4, Raspberry Pi 5, and Jetson Orin Nano platforms was also evaluated, revealing that the Selective Search stage remains a major computational limitation for achieving real-time operation. The results obtained confirm the possibility of implementing a plant identification system in agricultural facilities without the need to transfer images to a cloud-based application. Full article

Managed disturbances and their consequences for plant community structure, productivity, and foliar nutrients in the habitat of the endangered Eld’s deer remain inadequately characterized. We assessed the effects of prescribed fire (PF), mechanical mowing (MM), and their combination (PF_MM) on plant communities in the Datian National Nature Reserve of Hainan, China. Our findings demonstrated that the PF_MM treatment produced the greatest number of species (38 species, representing increases of 26.6% and 72.7% compared to PF and MM, respectively) and diversity indexes, indicating enhanced structural stability relevant to ecological conservation. In contrast, MM yielded the highest aboveground biomass (AGB) and the highest foliar nitrogen (N, 14.28 g kg⁻¹), phosphorus (P, 2.08 g kg⁻¹), and potassium (K, 3.61 g kg⁻¹) concentrations, but concurrently promoted shrub dominance, potentially risking long-term nutrient depletion and functional group imbalance. Legume (Fabaceae) richness was negatively associated with foliar P and K, which is consistent with the nutrient dilution effect often observed in more diverse plant communities. Structural equation modeling indicated that treatment effects on AGB were mediated by the importance value of Fabaceae, whereas treatment effects on foliar N and P were expressed both directly and indirectly via the richness of Fabaceae and other families. Consequently, no single management approach can simultaneously enhance all desired metrics or indices. New management strategies or technologies should be explored to balance biodiversity conservation with improved pasture quality, thereby further supporting the recovery of Eld’s deer habitat while maintaining ecosystem health. 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

Alfalfa (Medicago sativa L.), a drought-tolerant legume, significantly influences carbon and nitrogen cycling in arid and semi-arid regions. This study investigated carbon and nitrogen storage and sequestration dynamics in alfalfa pastureland cultivated for 2–7 years under temperate continental arid climate conditions (110–190 mm annual precipitation). Overall, the biomass, carbon and nitrogen sequestration in alfalfa pasture, and carbon and nitrogen storage and sequestration in soil exhibited a quadratic pattern with planting years. The above-ground biomass peaked at 19.28 t·hm⁻², with carbon and nitrogen sequestration reaching the highest level at 10.18 t·hm⁻² and 0.511 t·hm⁻², respectively, in year 5. Both annual carbon and nitrogen sequestration of the below-ground vegetation exhibited an increase, reaching a peak before decreasing with planting year, and from Y3 to Y7, the sequestration values were consistently higher than those in Y2. Soil carbon and nitrogen sequestration peaked in year 3. Compared to the adjacent fallow lands, alfalfa pasturelands maintained positive soil carbon sequestration until year 6 but became negative (−8.03 t·hm⁻²) by year 7. From years 2–6, alfalfa pasture fixed carbon and nitrogen at comparable rates but returned disproportionately less carbon than nitrogen to the soil. To optimize sustainability, we recommend (1) rotating alfalfa after 6 years to prevent soil nutrient depletion and (2) applying carbon-rich fertilizers post-year 3 to balance nutrients and prolong productivity in arid climates. 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

The application of dolomitic limestone is a recommended practice for improving pastures established on acidic soils. On the other hand, pasture availability should determine the adjustment of the biotic load. The aim of this study is to evaluate the potential of pasture plant community composition as an indicator to assess the effects of intensification strategies in the Montado ecosystem, specifically soil pH correction and/or increasing animal stocking rate. Forty-eight sampling areas of a biodiverse pasture were monitored on a 4-ha plot located at the Mitra farm (Évora district; southern Portugal). The experimental design included four treatments: with and without limestone application (respectively, DL and WDL) × traditional low stocking rate (LSR, 7 sheep ha⁻¹) and high stocking rate (HSR, 18 sheep ha⁻¹). Floristic composition, structural parameters, and diversity metrics were recorded and analyzed using multivariate statistical tools. Pasture diversity was assessed through the computation of richness indices, with plant species identified as ecological indicators representative of each study area. The results showed Rumex pulcher, Trifolium subterraneum, Plantago lanceolata, and Lolium rigidum as botanical indicators of the four treatments of this study, respectively, LSR in untreated soil, HSR in untreated soil, HSR in treated soil, and LSR in treated soil. The results also show that soil amendment led to a more distinct and stable pasture floristic composition (PFC) compared to untreated areas. Conversely, the stocking rate (SR) played a secondary but still ecologically relevant role. Notably, HSR appears to reduce the need for lime application to achieve a balanced floristic composition and desirable plant community structure, potentially lowering soil amendment costs without compromising pasture quality. In LSR areas, the application of lime was essential to significantly improve the floristic richness, the vegetation cover, and the presence of legumes. Full article

The aims of the study were to evaluate the effectiveness of audio detection for identifying feeding sounds in free grazing sheep and to assess whether the recognition of these sounds could be influenced by pasture characteristics. Twelve Valle del Belice dry ewes were grazed on two mixed swards: on 10 May, grass-rich sward (G); on 13 May, legume-rich sward (L). Each ewe was fitted with a collar equipped with a point of view (POV) camera. All audio files (without viewing the videos) were listened to and sounds recognized as herbage prehension and rumination activity were highlighted. Time spent eating and ruminating was then calculated. To validate the audio file analysis, all video files were subjected to observation of the same behavioral aspects detected with audio. The regression between the prehensions number estimated using sound alone and the actual values recorded through video was significant (r² 0.743; p < 0.001). No differences were found in recognizing grazing behavior between data obtained by listening or watching the videos and between the two swards. The acoustic analysis of the single bites on grass and legume forages reveals significant differences between the two forage classes (p ≤ 0.001) particularly in terms of energy, temporal structure, and spectral features. Since sheep showed a strong selective activity towards legumes even in the grass-rich sward (selectivity index 3.1), this may have reduced acoustic differences between swards. Full article

Regenerative agricultural practices emphasize the use of diverse pasture species within sustainable agriculture production systems. The inclusion of a range of legume species in diverse pasture swards is likely to increase biological N fixation (BNF) across seasons, reducing the system’s reliance on synthetic N inputs. The present field study aims to quantify BNF in selected legume species within diverse pasture (combining 9 species) and standard pastures (ryegrass and clover combination) and assess their performance to identify the potential for improving N supply while maintaining year-round pasture quality. A year-round seasonal BNF was assessed by evaluating soil N status, nodulation patterns, plant composition, and conducting ¹⁵N natural abundance studies. The results revealed that the diverse pasture sward produced 5.4% more dry matter compared to the standard pasture, while soil mineral N (NO₃⁻, NH₄⁺) remained statistically similar between the two treatments. Nitrogen yield was 9.3% higher in the diverse pasture than in the standard pasture. ¹⁵N natural abundance analysis assessment revealed no substantial variation in BNF rates across treatments throughout the study. However, in contrast to standard pasture, the BNF rate in diverse pasture experienced a 3-fold increase from winter to summer, while the standard pasture exhibited a 1.5-fold increase. In both pasture systems, BNF increased with clover proportion up to 30%, indicating optimal fixation at moderate clover levels. The findings underscore the potential of diverse pastures when strategically managed to enhance seasonal BNF while sustaining pasture productivity. Full article

Mixed legume–grass pastures may enhance nitrogen recycling via litter and excreta compared to unfertilized grass monocultures. This study evaluated litter biomass, litter deposition rate, and the chemical and isotopic composition of Urochloa decumbens litter in monoculture and mixed pasture intercropped with Arachis pintoi cv. Belmonte at five planting spacings (0.40, 0.50, 0.60, 0.70, and 0.80 m) in a Ferralsol. Additionally, isotopic analysis of sheep feces under grazing was conducted across the dry season. The experiment was conducted according to a split-plot scheme, with spacings in the plots and the periods or years in the subplots, in a randomized block design, with four replications. Litter biomass was not significantly influenced by planting spacing; however, the litter deposition rate was substantially greater in mixed pastures, reaching up to 77.2 kg ha⁻¹ day⁻¹ in the second year. Isotopic analysis revealed that up to 39% of the litter carbon was derived from C3 plants (Arachis pintoi), while nitrogen concentration ranged from 8.3 g kg⁻¹ in monoculture to 12.9 g kg⁻¹ at 0.40 m spacing. Spatial arrangement was critical for optimizing nutrients dynamic. Narrower planting spacings (0.40–0.50 m) increased the proportion of Arachis pintoi and enhanced litter deposition rates, improving nitrogen inputs and cycling within mixed Urochloa decumbens. Full article

This study evaluated the effects of reduced nitrogen fertilization and the intercropping of annual ryegrass (Lolium multiflorum Lam.) with forage legumes—common vetch (Vicia sativa L.) and red clover (Trifolium pratense L.)—on milk production and enteric methane emissions in grazing dairy cows. Twelve Holstein × Jersey cows were assigned to a crossover design involving two treatments: ryegrass monoculture (RG) or ryegrass—legume mixture (RG + Leg). Methane emissions were measured using GreenFeed systems; grazing behavior, milk yield and composition, and organic matter digestibility were also assessed. Legume inclusion contributed ~9% of the pre-grazing biomass, and cows grazing RG + Leg pastures had lower herbage mass (−214 kg DM/ha) and lower herbage allowance (−6 kg DM/cow/day) than cows on monoculture ryegrass. Energy-corrected milk (ECM), methane emissions (g/day and g/kg ECM), and grazing behavior were not significantly affected by treatment. These results suggest that, under subtropical grazing conditions, reducing nitrogen fertilization combined with the modest inclusion of vetch and red clover does not mitigate enteric methane emissions nor enhance animal performance. Enhanced strategies to increase legume proportion in mixed swards are needed to unlock their potential for sustainable intensification of pasture-based dairy systems. Full article

Vegetation and soil indices are able to indicate patterns of gradual plant growth. Therefore, productivity data may be used to predict performance in the development of pastures prior to grazing, since the morphology of the pasture follows repetitive cycles through the grazing of animals. Accordingly, in recent decades, much attention has been paid to the monitoring and development of vegetation by means of remote sensing using remote sensors. The current study seeks to determine the differences between three remote sensing products in the monitoring and development of white clover and perennial ryegrass ratios. Various grass and legume associations (perennial ryegrass, Lolium perenne, and white clover, Trifolium repens) were evaluated in different proportions to determine their yield and relationship through vegetation and soil indices. Four proportions (%) of perennial ryegrass and white clover were used, being 100:0; 90:10; 80:20 and 70:30. Likewise, to obtain spectral indices, a Spectral Evolution PSR-1100 spectroradiometer was used, and two UAVs with a MAPIR 3W RGNIR camera and a Parrot Sequoia multispectral camera, respectively, were employed. The data collection was performed before and after each cut or grazing period in each experimental unit, and post-processing and the generation of spectral indices were conducted. The results indicate that there were no significant differences between treatments for yield or for vegetation indices. However, there were significant differences in the index variables between sensors, with the spectroradiometer and Parrot obtaining similar values for the indices both pre- and post-grazing. The NDVI values were closely correlated with the yield of the forage proportions (R² = 0.8948), constituting an optimal index for the prediction of pasture yield. Full article