Grass and Forage Science最新文献

Climate change and associated risks of extreme weather has led to growing interest in drought-tolerant species such as lucerne (Medicago sativa L.), tall fescue (Festuca arundinacea Schreb.) and cocksfoot (Dactylis glomerata L.) in mixed grass-legume forage production in North-Western Europe. Lucerne and grasses have distinct nutritive value that can be combined when grown in mixtures. The extent of a ‘net mixture effect’ (NE) on the nutritive value, that is the deviations of the mixture quality from the expected one derived from the pure stands, has not been studied in any depth and requires further investigation in the context of climate change. The present study was conducted at four sites during two main cropping years with the aim of comparing potentially drought-tolerant mixtures against pure stands. With significant sward type × site × year interactions, nutritive value of the mixed swards often differed significantly from pure stands, ranging intermediate between the component pure stand nutritive value. The concentration of water-soluble carbohydrate (WSC) did not differ between mixtures and lucerne pure stands. Significant NE were found, with larger measured concentrations in mixtures compared to those predicted, for neutral and acid detergent fibre and the crude protein:WSC ratio. The concentrations of WSC and metabolizable energy were smaller than expected. The NE was not influenced by sward type or species number, except for WSC at a few sites. The results show that improved knowledge of positive mixing effects could be used to specifically enhance the nutritive value in grasses-lucerne mixtures irrespective of sward diversity.

Accurate extraction of grassland sample coverage is crucial for regional ecological environment monitoring. Due to the strong feature learning capability, high flexibility, and scalability of deep learning methods, they have great potential in grassland sample extraction modelling. However, we still lack a model that can achieve both lightweight structure and effective performance for small object segmentation to considering the small target characteristics of grassland vegetation and the requirements for model deployment in later stages. Here, we combined the UNet model, which performs well in small target segmentation, with the lightweight network Shufflenetv2 model, proposing an improved UNet neural network, Shufflenetv2UNet, for grassland sample coverage extraction. The core of Shufflenetv2UNet is the removal of maximum pooling and double-layer convolution modules from downsampling in the UNet neural network. In addition, the Inverted Residual Block structure module from Shufflenetv2 was added to achieve a lightweight model and improved extraction accuracy. The Shufflenetv2UNet achieves an accuracy of 98.23%, with a parameter size of 50.74 M, and a model inference speed of 0.004 s. Compared to existing extraction methods, this model has advantages in prediction accuracy, parameter size, and model inference speed. Moreover, Shufflenetv2UNet achieved different types of grassland sample coverage extractions, with good robustness, generalization, and universality, enabling investigators to quickly and accurately obtain grassland sample coverage. This allows more dynamic and accurate ground measurement data for regional grassland environmental monitoring.

Sustainable intensification of livestock systems implies greater efficiency in resource utilization resulting in greater output of products and other ecosystem services per unit of resource input. Integrating forage legumes into livestock systems is a viable way to reduce the input of industrial N fertilizer, reducing the use of fossil fuels and helping to mitigate global warming, a major problem during the Anthropocene. Some forage legumes have greater concentrations of secondary compounds, such as condensed tannins, that might reduce the emission of greenhouse gases (GHG) from ruminant eructation and excreta. Furthermore, forage legumes might enhance cattle performance because of greater nutritive value, resulting in greater production per unit of GHG released. Shortening the production cycle and improving cattle reproductive efficiency could have a major impact on reducing the overall carbon footprint of the system. Grazing systems with more diversified plant species are typically more resistant and resilient, adapting to current climate changes during the Anthropocene. Novel technologies might accelerate the development of future grazing systems using forage legumes as a key component. Breeding efforts for the next-generation legumes must focus on adaptation and potential use for mitigation of negative environmental impacts. There are examples of successful integration of forage legumes into livestock systems in different regions of the world, with a major reduction in off-farm inputs and maintaining the system productive. These successful examples could be used to increase adoption and improve the efficiency of current livestock systems.

More than 70% of Switzerland's agricultural area is covered by grasslands that often exhibit highly diverse species compositions and heterogeneous growth patterns. An essential requirement for efficient and effective pasture management is the regular estimation of herbage biomass. While various methods exist for estimating herbage biomass, they are often time-consuming and may not accurately capture the variability within pastures. This highlights the need for more efficient, accurate estimation techniques. To help improve herbage biomass estimation, we present estiGrass3D+, a Random Forest model. This model predicts pasture biomass using a digital terrain model (DTM) derived from a digital surface model (DSM) for sward height modelling, along with vegetation indices and agronomic variables from UAV images only. The model was successfully evaluated with independent test data from different sites on the Swiss central plateau, including both grazed and mown areas. Model performance on an independent validation dataset achieved a NRMSE of 20.3%, while the training dataset had an NRMSE of 21.5%. These consistent results confirm that estiGrass3D+ is both transferable and applicable to unseen data while maintaining accuracy and reliability across different datasets. The wide applicability of our method demonstrates its practicality for predicting herbage biomass under different pasture management scenarios. Additionally, our method of deriving a DTM directly from a DSM simplifies the measurement of grass sward height by UAVs, eliminating the need for prior ground control point (GCP) marking and subsequent aligning, enhancing the efficiency of herbage biomass estimation.

C, N, and P concentrations and stoichiometric ratios of the leaf are important for adaptive plant growth and nutrient utilization. However, our knowledge of how these traits change with N fertilization in intercropping systems remains limited. Hence, a 2-year experiment was conducted with four cropping patterns, including two-row common vetch (Vicia sativa L.) intercropping with one-row oat (Avena sativa L.), one-row common vetch intercropping with two-row oat as well as sole oat and common vetch cropping with 0, 50, and 100 kg N/ha fertilization. Leaf total nitrogen (LTN) and phosphorus (LTP) concentrations and C:N:P stoichiometric ratios were measured and their linkages with soil nutrient availability were analysed. N fertilization significantly increased LTN, N:P, and C:P, while significantly decreasing LTP and C:N. Intercropping affected leaf nutrient concentrations and stoichiometric ratios, which were affected by N fertilization, basic soil fertility, and crop species. Intercropping and N fertilization affected soil nitrate nitrogen (SNN), available phosphorus (SAP) content, and dry matter (DM) yield. There were strong correlations between the leaf nutritional traits of oats and SNN, SAP, and DM, but few correlations were observed with common vetch. In conclusion, intercropping led to contrasting changes in leaf nutrient concentrations and stoichiometric ratios, which varied with N fertilization and crop species. We failed to reveal solid and direct effects of intercropping ratio on leaf nutritional traits. These findings contribute to a better understanding of crop nutritional traits and the link between leaves and soil in response to intercropping and N fertilization.

Six seed mixtures differing in number of species and their proportion of timothy (Phleum pratense L.) were tested during three/four production (ley) years in replicated field experiments at three climatically different sites in Norway; one a mountainous inland site at 61° N (Løken) and two in coastal environments, at 61° N (Fureneset) and 65° N (Tjøtta). There were significant differences in forage accumulation (FA) and digestible forage accumulation (DFA) between the three sites. There was a significant FA decline from the third to the fourth ley year for mixtures containing timothy, but not for mixtures without timothy. Estimated interannual FA- stability was higher for timothy-based seed mixtures than for mixtures without timothy at the inland site, but FA-stability was lower at the coastal sites. In the third-year herbage of timothy-based mixtures at the inland site consisted almost solely of timothy, whereas at the coastal sites meadow fescue (Festuca pratensis Huds.) and especially tall fescue (F. arundinacea Schreb.) dominated. In seed mixtures without timothy, cocksfoot (Dactylis glomerata L.) suppressed other species at the inland site, whereas at the coastal sites, tall fescue and ryegrasses (Lolium spp.) were the dominant species in the third-year herbage. Length of growing season and site-specific growing conditions were important drivers for the observed species changes. Timothy can thus be recommended for ley establishment at sites where the growing season is short (<4 months) and plant growth is intensive, but under conditions with a longer growing season it needs to be sown in mixtures with grass species that surpass the regrowth capacity of timothy.

Reduced reliance on inputs such as fertilizer is fundamental to sustainable grazing systems. This two-year study compared four sward types, including multispecies swards (MSS), for herbage dry matter (DM) production, species contribution to DM, and herbage nutritive value under grazing. The systems were: (1) Lolium perenne L. monoculture (PRG; 170 kg N ha⁻¹ year⁻¹); (2) permanent pasture (PP; 135 kg N ha⁻¹ year⁻¹), (3) six species sward consisting of two grasses, two legumes and two herbs (6S; 70 kg N ha⁻¹ year⁻¹), (4) twelve species sward consisting of three grasses, four legumes and five herbs (12S; 70 kg N ha⁻¹ year⁻¹). Herbage samples were collected for DM yield, botanical composition, and nutritive value. Mean annual DM production for PRG, PP, 6S, and 12S was 11,374, 8526, 13,783, and 13,338 kg DM ha⁻¹ respectively. Herb proportions decreased in 6S and 12S from 2020 to 2021 while grass proportions increased. Mean crude protein levels were similar across all systems (p > 0.05), with higher ash content in 6S and 12S compared to PRG (p < 0.001). Organic matter digestibility was lowest in PP compared to PRG (p < 0.001) while neutral detergent fibre content of PP and PRG were greater than 6S and 12S (p < 0.001). Water soluble carbohydrate content was highest in PRG (p < 0.0001). Over 2 years, MSS delivered increased herbage DM yield and nutritive quality relative to PRG and PP swards, from reduced N inputs. However, maintenance of the herb component of MSS is a challenge.

Integrating forage legumes into grasslands offers numerous ecosystem services. However, the management of grass-legume pastures is challenging because the interaction between the defoliation frequency and severity may affect the legume persistence and forage intake by grazing animals. This 2-year study evaluated the most effective grazing intensity to increase forage intake while maintaining a balanced legume proportion in tropical pastures consisting of Brachiaria brizantha and Arachis pintoi. Three treatments (grazing intensities) were assessed: severe, moderate, and low, corresponding to stubble heights of 10, 15, and 20 cm, respectively, and a pre-grazing height of 25 cm. Two purebred Tabapua heifers were used for each treatment, and additional put-and-take animals were introduced, as required. Canopy structure, grazing behaviour, nutritive value, and forage intake were evaluated. The legume proportion in herbage mass remained consistent across all intensities, averaging 35% pre- and 32% post-grazing. The grazing duration remained consistent across treatments on the first and third days, with an average grazing time of 390 and 440 min, respectively. Under post-grazing conditions, low-intensity grazing had a 52% greater intake rate than severe-intensity grazing, which was attributed to a greater biting rate (37.9 bites/min) and bite weight (0.9 g DM/bite). Moderate grazing intensity (stubble height of ~15 cm associated with a pre-grazing height of ~25 cm) is recommended to manage a mixture of B. brizantha and A. pintoi when the objective is to couple high forage intake with a balanced legume proportion in the pasture.