Search Results (314)

Quinoa (Chenopodium quinoa Willd) is a nutrient-rich multipurpose crop. Its grains are used as a cereal, green leaves as a vegetable for humans, and the whole green plant as an alternate forage for livestock. Recently, whole-plant quinoa forage has been evaluated in several countries in Asia and Europe for its potential use as an alternative forage for livestock; however, this has not been performed in the United States. We investigated forage yield and related agronomic traits, nutritional composition, and feed quality-related traits in 60-day-old quinoa whole plants of four quinoa lines over a two-year period. The goal was to evaluate the feasibility of quinoa forage production in Missouri, a drought-prone midwestern state of the USA. Morphological traits (height and fresh and dry weight per plant), chemical composition (fiber contents), and nutritive quality (digestible nutrient contents) of forages were affected by quinoa genotype and year of planting. The crude protein content of quinoa forage averaged 16.23% and fiber 22.08%, which was similar to the values reported in Asia and Europe, but was slightly lower than that of alfalfa. Calcium (1.26%) and phosphorus (0.47% dry weight) were significantly higher than those reported in published quinoa forage results and are comparable to those in published alfalfa minerals. Lysine (0.98%) and methionine (0.25%) were higher than the published results for quinoa and alfalfa. Neutral detergent fiber (34.10%) and acid detergent fiber (25.01%) were lower than those of alfalfa, indicating better digestibility of the quinoa forage. The calculated digestible dry matter (69.40%), dry matter intake (3.56%), relative food value (192%), and total digestible nutrient (70.33%) were higher than those of alfalfa and comparable with published results for quinoa forage. Our preliminary results indicate that the quinoa lines evaluated in this study have excellent potential to be used as a non-traditional alternative forage, especially in environmentally stressed areas where the production of other forage crops is limited. Further research should explore the full multipurpose benefits of quinoa, including its use as grains, leafy green, and whole-plant forage. 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

Intensifying urbanization in Central Europe is increasingly pushing flood retention areas onto private farmland, yet the agronomic and socio-economic trade-offs remain poorly quantified. We conducted a narrative review of published field data and post-event assessments from 2014–2023 along the transboundary Sava River. Information was collected from research articles, case studies, and environmental monitoring reports, and synthesized in relation to national and EU regulatory thresholds to evaluate how floods altered soil functions and agricultural viability. Water erosion during floods stripped up to 30 cm of topsoil in torrential reaches, while stagnant inundation deposited 5–50 cm of sediments enriched with potentially toxic elements, occasionally causing food crops to exceed EU contaminant limits due to uptake from the soil. Flood sediments also introduced persistent organic pollutants: 13 modern pesticides were detected post-flood in soils, with several exceeding sediment quality guidelines. Waterlogging reduced maize, pumpkin, and forage yields by half where soil remained submerged for more than three days, with farm income falling by approximately 50% in the most affected areas. These impacts contrast with limited public awareness of long-term soil degradation, raising questions about the appropriateness of placing additional dry retention reservoirs—an example of nature-based solutions—on agricultural land. We argue that equitable flood-risk governance in the Sava River Basin requires: (i) a trans-boundary soil quality monitoring network linking agronomic, hydrological, and contaminant datasets; (ii) compensation schemes for agricultural landowners that account for both immediate crop losses and delayed remediation costs; and (iii) integration of strict farmland protection clauses into spatial planning, favoring compact, greener cities over lateral river expansion. Such measures would balance societal flood-safety gains with the long-term productivity and food security functions of agricultural land. Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

Vetch (Vicia sativa L.), an important forage legume, faces increasing drought stress due to climate change. This study evaluated drought responses in 26 genotypes using both in vitro and field trials. In vitro experiments analysed seedlings grown on culture media either with 20% polyethylene glycol (PEG) to simulate drought (C20) or without PEG as a control (C0), measuring root and shoot dry weights as well as proline content. Field trials under rainfed and drought conditions assessed 100 seed weight and seed weight per plant. All traits studied exhibited high variability, with elevated coefficients of variation and broad-sense heritability. Seedling roots grown in C20 had higher dry weight than those in C0, while shoots showed the opposite trend. In C20 medium, proline content increased significantly—by 118.1% in roots and 131.1% in shoots. However, proline concentration did not correlate with field yield traits, limiting its utility as a drought tolerance marker. Principal component analysis grouped genotypes based on biomass production and drought response. Importantly, in vitro root and shoot dry weights were positively correlated with field yield traits, indicating their value as early predictors of agronomic performance and offering a useful tool for selection in vetch breeding programmes. Full article

Gamba grass (Andropogon gayanus Kunth) is a promising forage alternative for Brazil’s Cerrado regions, attracting increasing research interest due to its potential to complement or replace widely planted species such as Urochloa and Megathyrsus. Despite the release of three cultivars, significant improvements in dry matter (DM) yield and forage quality are needed to fully realize its agronomic potential. This study aimed to evaluate genetic variability, estimate narrow sense heritability, and predict expected genetic gains for DM yield and key forage quality traits in two gamba grass populations derived from the cultivars BRS Sarandi and Planaltina. Trials were established in spring 2017 in Planaltina, DF, and evaluated during February–March 2018 and January–March 2019. Crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), cellulose (CEL), and hemicellulose (HEMIC) were quantified alongside DM yield. BRS Sarandi exhibited higher CP (12.3% vs. 9.8%) and lower NDF (57.1% vs. 63.4%), ADF (36.2% vs. 41.5%), CEL (20.8% vs. 23.7%), and HEMIC (20.9% vs. 21.9%) compared to Planaltina, while DM yield did not differ significantly between populations (4.57 t·ha⁻¹ vs. 4.50 t·ha⁻¹ per harvest, p > 0.05). Heritability estimates for individual harvests ranged from 0.31 to 0.68 for DM yield and 0.28 to 0.62 for quality traits, whereas multi-harvest models across years yielded lower estimates (0.07–0.27). Expected annual genetic gains were modest, with the highest predicted increase for CP (0.45% per year) and the largest decrease for NDF (−0.78% per year), reflecting the quantitative nature of trait inheritance and strong environmental influence. This study provides novel insights by simultaneously comparing two populations for multiple harvests and quantifying both yield and detailed forage quality traits, offering practical guidance for gamba grass breeding strategies. Results indicate that breeding programs should prioritize multiple selection cycles, precise phenotyping, genotypic and potentially genomic selection to accelerate improvement in both DM yield and forage quality, overcoming the constraints of low heritability and multi-trait selection. Full article

This study employs an integrated framework that combines field-based measurements, remote sensing, and Geographic Information Systems (GISs) to monitor vegetation dynamics and assess the suitability of a steppe range reserve for livestock grazing. Forty-three surface and subsurface soil samples were collected in April and November 2021 to capture seasonal variations. Above-ground biomass (AGB) measurements were recorded at five sampling locations across the reserve. Six Sentinel-2 satellite imageries, acquired around mid-March 2016–2021, were processed to derive time-series Normalized Difference Vegetation Index (NDVI) data, capturing temporal shifts in vegetation cover and density. The GIS-based Multi-Criteria Decision Analysis (MCDA) was employed to model the suitability of the reserve for livestock grazing. The results showed higher salinity, total dissolved solids (TDSs), and nitrate (NO₃) values in April. However, the percentage of organic matter increased from approximately 7% in April to over 15% in November. The dry forage productivity ranged from 111 to 964 kg/ha/year. On average, the reserve’s dry yield was 395 kg/ha/year, suggesting moderate productivity typical of steppe rangelands in this region. The time-series NDVI analyses showed significant fluctuations in vegetation cover, with lower NDVI values prevailing in 2016 and 2018, and higher values estimated in 2019 and 2020. The grazing suitability analysis showed that 13.8% of the range reserve was highly suitable, while 24.4% was moderately suitable. These findings underscore the importance of tailoring grazing practices to enhance forage availability and ecological resilience in steppe rangelands. By integrating satellite-derived metrics with in situ vegetation and soil measurements, this study provides a replicable methodological framework for assessing and monitoring rangelands in semi-arid regions. Full article

A two-year (2023–2024) multifactorial field study was conducted under the agro-climatic conditions of Northern Kazakhstan, with the objective of refining cultivation practices for hayfields of perennial legumes and grasses, including alfalfa (Medicago sativa L.), smooth brome (Bromus inermis Leyss.), and sainfoin (Onobrychis arenaria Kit). The elements targeted for optimization included the species composition and component ratios in the mixtures, as well as the regimes of pre-sowing and foliar applications of growth regulators (AminoMax, Black Jak, Miller Start, Lider-S). The integrated experimental design accounted for laboratory and field germination, biometric parameters (plant height, leafiness), phenophase dynamics, autumn survival and overwintering, indicators of photosynthetic activity, as well as yields of green biomass and dry matter, and chemical composition (crude protein, fiber, ash, fat, and nitrogen-free extract). Grass–legume mixtures ensured more stable progression of phenophases, improved overwintering, and enhanced protein value compared to monocultures; the inclusion of sainfoin contributed to improved forage quality without compromising yield. Growth regulators promoted accelerated initial plant development and enhanced the intensity of net photosynthetic productivity. The greatest effect of application was observed in the grass component with Miller Start, whereas in the legume species it was most pronounced with AminoMax. The results of the study revealed that the optimal proportion of legumes in the forage mixtures is 30–40%. Under contrasting hydrothermal conditions, the yield of fresh and dry matter ranged from 4.19 to 4.81 t ha⁻¹ and 1.27–1.51 t ha⁻¹ (2023) to 10.43–14.46 t ha⁻¹ and 3.05–4.63 t ha⁻¹ (2024). The greatest effect was observed with Miller Start and AminoMax treatments (p < 0.05), whereas the action of Black Jak and Lider-S was moderate, confirming differences in their mechanisms of action under contrasting weather conditions. Full article

Accurate estimation of aboveground biomass (AGB) is essential for monitoring forage availability and guiding sustainable management in high-altitude pastures, where grazing sustains livelihoods but also drives ecological degradation. Although remote sensing has advanced biomass modeling in rangelands, applications in Andean–Amazonian ecosystems remain limited, particularly using UAV-based structural and spectral data. This study evaluated the potential of UAV LiDAR and multispectral imagery to estimate fresh and dry AGB in ryegrass (Lolium multiflorum Lam.) pastures of Amazonas, Peru. Field data were collected from subplots within 13 plots across two sites (Atuen and Molinopampa) and modeled using Random Forest (RF), Support Vector Machines, and Elastic Net. AGB maps were generated at 0.2 m and 1 m resolutions. Results revealed clear site- and month-specific contrasts, with Atuen yielding higher AGB than Molinopampa, linked to differences in climate, topography, and grazing intensity. RF achieved the best accuracy, with chlorophyll-sensitive indices dominating fresh biomass estimation, while LiDAR-derived height metrics contributed more to dry biomass prediction. Predicted maps captured grazing-induced heterogeneity at fine scales, while aggregated products retained broader gradients. Overall, this study shows the feasibility of UAV-based multi-sensor integration for biomass monitoring and supports adaptive grazing strategies for sustainable management in Andean–Amazonian ecosystems. Full article

This study evaluated a sustainable strategy for Para grass (Brachiaria mutica) forage using composted cow manure in the Mekong Delta, Vietnam. At Nam Can Tho Experimental Farm (January–September 2023), a completely randomized design with three replications and three harvest cycles tested five topdressing rates: 0, 2.5, 5.0, 7.5, and 10 t/ha/year (TDM0–TDM10). Tiller emergence, plant height, forage quality, biomass yield, and cost–benefit were measured. Tiller counts were unaffected (p > 0.05), but plant height rose significantly with manure rate. Forage quality remained optimal (CP 7.10–7.85%, NDF 60.5–63.8%). Average fresh biomass yield (FBM, t/ha) increased linearly: y = 0.788x + 14.9 (R² = 0.937), where x is manure rate (t/ha/year). TDM10 yielded 50% more fresh forage (22.6 t/ha) and 48% more dry matter (4.43 t/ha) than the control (15.0 and 2.98 t/ha; p = 0.001), with crude protein up 56% (0.347 t/ha) and neutral detergent fiber up 41% (2.68 t/ha). Total cost increased slightly (from 521 to 552 USD/ha), but per-ton cost dropped 30% (from 34.7 to 24.4 USD). At 10 t/ha/year, manure optimized yield, profitability, circular nutrient use, and reduced fertilizer dependence, providing a scalable model for tropical smallholder livestock feed. Full article

This study aimed to assess whether the fertilizing effects of compost (Com) and vermicompost (VCom) applied to a preceding wheat crop, either alone or in combination with microbial biofertilizers (MBF; arbuscular mycorrhizal fungi and nitrogen-fixing bacteria), could sustain forage maize yield across contrasting soil textures. A split–split plot trial was conducted in 2023 in sandy, loamy, and clay soils. Treatments included Com, VCom, standard mineral nitrogen fertilization, and unfertilized control, each tested with or without MBF inoculation. Maize was harvested at the milk–dough stage and assessed for biomass yield, dry matter partitioning, chemical composition, and in vitro digestibility. Interactions among factors were frequent, particularly with soil texture, but overall, Com and VCom sustained biomass yield and forage quality, especially when combined with MBF. Notably, in loamy soil, VCom coupled with MBF (38.4 t ha⁻¹) outperformed mineral fertilization (32.9 t ha⁻¹). Across soils, loam produced the highest dry matter yield (27.0 t ha⁻¹) and sand the lowest (23.7 t ha⁻¹), while clay showed variable responses depending on the amendment–MBFs combination. All plots treated with the MBFconsistently exhibited higher yields compared to their respective controls, with an average increase of 52.6% across texture and fertilization strategies. Fertilization strategy and soil texture slightly yet significantly affected maize chemical composition, while digestibility remained largely preserved. Crude protein concentration peaked under mineral fertilization in loamy soil (8.3% dry matter). These findings highlight the potential of bio-based fertilizers, especially when integrated with microbial inoculants, to reduce mineral nitrogen dependency and support the sustainable intensification of forage maize. Full article

Climate change and livestock expansion have affected forage supply in the dry tropics. Therefore, optimizing planting methods adapted to adverse tropical environments is essential for establishment and yield. The objective of this study was to evaluate the effect of different planting methods on the establishment rate, morphology, yield, and nutritional composition of Cuba OM-22 under the soil and climate conditions of the dry tropics of Peru, using a block design with four replicates and five methods for propagation by cuttings. The S4 (two-node cuttings, 25 cm in length; horizontal position 180°, parallel to the soil surface; fully buried at 8 cm depth; no spacing between cuttings along the furrow) method offered the best balance between yield and quality, with higher establishment rate (55.93%), height (182.15 cm; higher than S1 and S5), and more tillers (surpassing S1 and S2 by 16.97% and 18.86%). In addition, it obtained good green forage yields (137.43 t ha⁻¹) and was better than all planting methods in dry matter yield (37.45 t ha⁻¹). In nutritional composition, S4 ranked among the highest averages for nitrogen-free extract (NFE) (43.22%) and ash (11.06%). However, protein, crude fiber, and fat content did not differ between methods. On the other hand, planting methods showed negative correlations between the number of tillers and ash content (p = 0.006; r = −0.79), ash and NFE (p = 0.000; r = −0.92), and protein with crude fiber (p = 0.029; r = −0.68). These findings highlight S4 as a key strategy for optimizing establishment, yield, and quality in Cuba OM-22 in the dry tropics. Full article

Studying the effects of grazing pressure on species composition, beta diversity and yields is important for conservation purposes as well as for grassland management. The case study area in Hortobágy, which is one of the largest continuous grassland areas in Europe, has been managed for centuries by grazing of Hungarian grey cattle. The effect of grazing pressure was investigated in terms of distance from the livestock enclosure (50 m, 250 m, 500 m, 1000 m, and 1700 m) and in an ungrazed control area on dry and mesic alkaline grasslands in spring and autumn of 2024. In both types of grasslands at each distance, species composition and mean plant height were recorded in six 4 × 4 m plots. Overall, in both seasons the control areas were the poorest in terms of species richness. Among the grazed areas in both grassland types the ones at 1700 m distance had the lowest number of species. The species richness of mesic grassland decreased linearly with distance. The dry grassland showed a polynomial trend and was more species-rich at all distances than the mesic grassland. Green yield was the highest in the dry grassland at 250 m in spring and at 50 m in autumn, while in the mesic grassland it was highest at 1700 m in spring and between 500 and 1700 m in autumn. Forage quality in dry grassland was lowest at 50 m and highest between 500 and 1000 m. In mesic grassland, this parameter was equalized at all distances. The highest Simpson diversity was found at a distance of 500–1000 m from the livestock enclosure in both types. It is advisable to evaluate separately the spring and autumn characteristics of the alkaline grasslands, as there may be significant differences between them. Overall, it can be concluded that alkaline dry grasslands are particularly suitable for grazing because of their species composition and their good tolerance to grazing. Alkaline mesic grasslands are poorer in species and more sensitive to grazing; consequently, mowing or mixed utilization should be considered. Full article

Scenarios of climate change, extensive land use, soil degradation, the loss of native forest cover due to monoculture expansion, and pasture scarcity pose new challenges to livestock farming worldwide. Associated crops emerge as an alternative to mitigate these factors; however, selecting compatible species that do not generate competition and optimize the attributes of the forage is a necessity. Therefore, this study evaluated the effect of a maize and bean association, and cutting time on the morphological variables, yield, and nutritional composition of forage. A randomized complete block design (RCBD) with a 3A × 3C factorial arrangement and three blocks was used. Factor A (associations) had three levels: INIA-604-Morocho maize monoculture (M), M+PER1003544 chaucha bean association (M+F1), and M+PER1003551 chaucha bean association (M+F2). Factor C (maize cutting stage) had three levels: R2 (blister grain), R3 (milky grain), and R4 (pasty grain). A total of 27 experimental units were established. No silage was made; the nutritional quality was evaluated as the raw material for silage. The treatments modulated key attributes for silage. In R4, the M+F2 association (INIA-604-Morocho + PER1003551) showed a higher percentage of dry matter in the system (32.36%) and better mixture quality due to a lower NDF and ADF (48.22% and 23.29%) and higher digestibility and protein values (62.10% and 9.53%). In addition, dry matter yields increased compared with R2 in M+F1 (134.16%), M+F2 (90.56%), and M (138.48%). Although R3 maximized green forage, R4 offered the best combination of quantity and quality for silage (as raw material), reducing the risk of deterioration and improving forage use efficiency. In general, combining maize with beans and adjusting the cut to R4 optimizes the production and quality of the raw material for silage, with the criterion that these findings pertain to pre-ensiled material and should be validated in future studies. Full article

Waterlogging (WL) is an abiotic stress that severely limits crop yield. However, limited research has addressed the effects of long-term WL stress at different developmental stages on the yield and physiological responses of forage maize. In this study, forage maize plants were subjected to 14-day WL stress at the emergence (E), four-leaf (V4), eleven-leaf (V11), and tasseling (VT) stages. Plant height significantly decreased by 60% at the E stage and 48% at the V4 stage when exposed to 14-day WL. Leaf area decreased by 79% at the E stage, and the number of green leaves decreased most significantly at the VT stage. Chlorophyll fluorescence (Fv/Fm) and the relative chlorophyll content index (RCI) decreased most significantly at the V4 stage. The lysigenous aerenchyma formation rate of the roots increased significantly after 14-day WL at the V4 stage, whereas the number of adventitious roots increased most significantly at the V11 stage. The hydrogen peroxide (H₂O₂) and malondialdehyde (MDA) contents, which are indicative of the root oxidation state, exhibited the highest increase at the E stage. In addition, at the E and V4 stages, the expression of genes related to energy metabolism and lysigenous aerenchyma formation in the roots was upregulated after 14-day WL. The total dry matter (DM) of maize after harvest decreased most significantly when exposed to 14-day WL at the V4 stage, while acid detergent fiber (ADF) and neutral detergent fiber (NDF) increased with the developmental stages. Consequently, total digestible nutrients (TDNs) and the relative feed value (RFV) decreased with advancing developmental stages, with the highest decrease at the VT stage. These results demonstrate that effective drainage management during the early developmental stage (V4) is more important to prevent forage maize yield loss due to prolonged WL stress, which is expected to increase in frequency due to climate change, and management during the later developmental stage (VT) is critical to prevent decreases in feed values. These findings provide valuable insights into the physiological responses of forage maize to WL stress. Full article