Grass and Forage Science最新文献

This research investigated the effect of intensive grazing on the annual and seasonal yield and botanical composition of three sward types: (1) Lolium perenne monoculture sward receiving 250 kg nitrogen (N) ha⁻¹ year⁻¹ (PRG 250 N); (2) Lolium perenne—Trifolium repens sward receiving 125 kg N ha⁻¹ year⁻¹ (PRGWC 125 N) and (3) a multispecies sward comprising eight species receiving 125 kg N ha⁻¹ year⁻¹ (MSS 125 N). Each sward type had its own farmlet of 20 paddocks and comprising 47 cows on 18.7 ha with each group of cows remaining on the same farmlet for the 2-year study. Total yield (13,015 kg ha⁻¹ year⁻¹ of dry matter forage) did not differ among the three sward types, despite a substantial difference in chemical N fertiliser between PRG 250 N and both PRGWC 125 N and MSS 125 N. Average botanical composition of PRG 250 N comprised 994 g kg⁻¹ grasses and 6 g kg⁻¹ weeds. The PRGWC 125 N sward had 864, 134 and 2 g kg⁻¹ of grasses, white clover and weeds, respectively, while the MSS 125 N had 671, 144, 180 and 5 g kg⁻¹ of grasses, legumes, forbs and weeds, respectively. Despite considerable variation in the component species, nutritive values were relatively unaffected by sward type. Lower organic matter digestibility was observed on MSS 125 N compared to PRG 250 N and PRGWC 125 N (788, 801 and 799 g kg⁻¹, respectively). These results suggest that increasing sward diversity and reducing the use of chemical N fertiliser can maintain grass yield and nutritive value.

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.