Animal Feed Science and TechnologyAssociations among nutrient concentration, silage fermentation products, in vivo organic matter digestibility, rumen fermentation and in vitro methane yield in 78 grass silages

Milk production and methane emissions from dairy cows fed silages from different grassland species and harvesting frequencies

The aim of this study was to examine how silages from different grassland species and harvesting frequencies affect feed intake, milk production, and methane (CH4) emission in dairy cows. We hypothesized that cows consuming silages of more frequent harvest, grass species with greater OM digestibility and legumes with lower NDFom concentration would have greater silage DMI and milk yield and thereby lower CH4 yield and intensity. Forty Norwegian Red cows were allocated to 5 treatments in a cyclic changeover design with four 21-d periods (14 d of adaptation, 7 d of data collection). The 5 treatments evaluated were silages produced from timothy (Phleum pratense L.) in a 3-cut system (T3), timothy in a 2-cut system (T2), perennial ryegrass (Lolium perenne L.) in a 3-cut system (PR3), red clover (Trifolium pratense L.) in a 3-cut system (RC3) and a mix of T3 and RC3 (50:50 on DM basis; T3/RC3). The treatments were prepared by mixing silages from each crop over the growing season, proportional to the harvested DM yield of each cut. Cows were offered the mixed silages ad libitum supplemented with a fixed level of concentrate. Gas emissions were measured using 2 Greenfeed units. Milk yield was recorded in the milking robot at each visit, and milk samples were collected at 3 consecutive milkings during the last 7 d of each period. Cows were weighed after each milking, and total-tract digestibility of each diet was estimated using acid insoluble ash as internal marker in fecal grab samples. The data were analyzed using the MIXED procedure of SAS with block, period, and treatment as fixed effects and animal within block as random effect. Silage and total DMI did not differ between T3 and T2, but total DMI was lower for PR3 than for T3. There was a quadratic effect of increased proportion of red clover, with highest intakes of T3/RC3 and lower intakes of RC3 than of T3. Energy-corrected milk yield was lower for T2 than T3, and for PR3 than T3. There was a quadratic effect of increased proportion of red clover, with highest ECM yield in T3/RC3 and lower in RC3 than in T3. Organic matter digestibility was lower for T2 than T3, but it did not differ between T3 and PR3. Including red clover in the diet linearly decreased OM digestibility. Methane production (g/d) did not differ between T3 and T2, but CH4 intensity (g/kg ECM) was greater for T2 than for T3. There was no difference between T3 and PR3 for CH4 production but yield and intensity were greater for PR3 than T3. Including red clover in the diet linearly increased CH4 production, yield and intensity with greatest intensity in the 100% red clover diet. In conclusion, changing harvesting frequency for timothy from 2 to 3 harvests per year did not affect CH4 production or yield, but CH4 intensity was reduced. Replacing timothy with perennial ryegrass and increased inclusion rate of red clover both increased CH4 yield and intensity.

Development of an Alternative In Vitro Rumen Fermentation Prediction Model

The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R2 ≥ 0.83). Additionally, the root mean square errors (RMSEs) were ≤1.85 mL/g in the rumen methane model and ≤2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models' capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency.

The Effect of Inorganic Nitrogen Fertilizers on the Quality of Forage Composed of Various Species of Legumes in the Northern Part of a Temperate Climate Zone

This study focuses on the effect of inorganic nitrogen fertilizers on the quality of perennial grasses. Both grasses and legumes are important in swards, and each type of grass has different biological and ecological properties. Legumes in multi-species swards, especially in their early ages, benefit other Poaceae grasses by improving their growth. When evaluating individual cuts over a three-year period, it was determined that the quality indicators of the forage were significantly influenced by the year of use, N fertilizer application, and the different species compositions of the swards. In many cases, N fertilizers significantly reduced the CP content while tending to increase MADF and NDF. Monoculture grass swards had the highest WSC content; in most cases, N fertilizers increased the WSC content in the forage. DMD was the lowest in the first year of use, specifically in the first cut. Our three-year experiment, which investigated twelve swards with different species compositions, demonstrated that legume grasses improved the quality indicators of forage and contributed to maintaining a more stable overall forage yield over the years. As the climate continues to become warmer, there is a growing need to study a wide range of plant species and different varieties suitable for local growth conditions.

Evaluation of in vitro digestibility of Aspergillus oryzae fungal biomass grown on organic residue derived-VFAs as a promising ruminant feed supplement

BACKGROUND

As demand for high quality animal feed continues to raise, it becomes increasingly important to minimize the environmental impact of feed production. An appealing sustainable approach to provide feed fractions is to use organic residues from agro-food industry. In this regard, volatile fatty acids (VFAs) such as acetic, propionic and butyric acids, derived from bioconversion of organic residues can be used as precursors for production of microbial protein with ruminant feed inclusion potential. This study aims to investigate the in vitro digestibility of the Aspergillus oryzae edible fungal biomass cultivated on VFAs-derived from anaerobic digestion of residues. The produced fungal protein biomass, along with hay clover silage and rapeseed meal were subjected to various in vitro assays using two-stage Tilley and Terry (TT), gas, and bag methods to evaluate and compare its digestibility for application in ruminant feed.

RESULTS

The produced fungal biomass contained a higher crude protein (CP) (41%-49%) and rather similar neutral detergent fiber (NDF) (41%-56%) compared to rapeseed meal. The rumen in vitro dry matter digestibility (IVDMD) of the fungal biomass in the TT method ranged from 82% to 88% (statistically similar to that of the gas method (72% to 85%)). The IVDMD of fungal biomass were up to 26% and 40% greater than that of hay clover silage and rapeseed meal, respectively. The type of substrate and bag method had pronounced effect on the fermentation products (ammonium-N (NH4+-N), total gas and VFAs). Fungal biomass digestion resulted in the highest release of NH4+-N (340-540 mg/L) and the ratio of acetate to propionate ratio (3.5) among subjected substrates.

CONCLUSION

The results indicate that gas method can be used as a reliable predictor for IVDMD as well as fermentation products. Furthermore, the high IVDMD and fermentation product observed for Aspergillus oryzae fungal biomass digestion, suggest that the supplementation of fungal biomass will contribute to improving the rumen digestion by providing necessary nitrogen and energy to the ruminant and microbiota.

Advances in microbial pretreatment for biorefining of perennial grasses

Perennial grasses are potentially abundant sources of biomass for biorefineries, which can produce high yields with low input requirements, and many added environmental benefits. However, perennial grasses are highly recalcitrant to biodegradation and may require pretreatment before undergoing many biorefining pathways. Microbial pretreatment uses the ability of microorganisms or their enzymes to deconstruct plant biomass and enhance its biodegradability. This process can enhance the enzymatic digestibility of perennial grasses, enabling saccharification with cellulolytic enzymes to produce fermentable sugars and derived fermentation products. Similarly, microbial pretreatment can increase the methanation rate when the grasses are used to produce biogas through anaerobic digestion. Microorganisms can also increase the digestibility of the grasses to improve their quality as animal feed, enhance the properties of grass pellets, and improve biomass thermochemical conversion. Metabolites produced by fungi or bacteria during microbial pretreatment, such as ligninolytic and cellulolytic enzymes, can be further recovered as added-value products. Additionally, the action of the microorganisms can release chemicals with commercialization potential, such as hydroxycinnamic acids and oligosaccharides, from the grasses. This review explores the recent advances and remaining challenges in using microbial pretreatment for perennial grasses with the goal of obtaining added-value products through biorefining. It emphasizes recent trends in microbial pretreatment such as the use of microorganisms as part of microbial consortia or in unsterilized systems, the use and development of microorganisms and consortia capable of performing more than one biorefining step, and the use of cell-free systems based on microbial enzymes. KEY POINTS: • Microorganisms or enzymes can reduce the recalcitrance of grasses for biorefining • Microbial pretreatment effectiveness depends on the grass-microbe interaction • Microbial pretreatment can generate value added co-products to enhance feasibility.

Effect of grassland cutting frequency, species mixture, wilting and fermentation pattern of grass silages on in vitro methane yield

Mitigating enteric methane (CH4) emissions is crucial as ruminants account for 5% of global greenhouse gas emissions. We hypothesised that less frequent harvesting, use of crops with lower WSC concentration, ensiling at low crop dry matter (DM) and extensive lactic acid fermentation would reduce in vitro CH4 production. Timothy (T), timothy + red clover mixture (T + RC) or perennial ryegrass (RG), cut either two or three times per season, was wilted to 22.5% or 37.5% DM and ensiled with or without formic acid-based additive. Silages were analysed for chemical composition and fermentation products. In vitro CH4 production was measured using an automated gas in vitro system. Methane production was, on average, 2.8 mL/g OM lower in the two-cut system than in the three-cut system (P < 0.001), and 1.9 mL/g OM lower in T than in RG (P < 0.001). Silage DM did not affect CH4 production (P = 0.235), but formic acid increased CH4 production by 1.2 mL/g OM compared to the untreated silage (P = 0.003). In conclusion, less frequent harvesting and extensive silage fermentation reduce in vitro CH4 production, while RG in comparison to T resulted in higher production of CH4.

Rumen Fermentation Parameters Prediction Model for Dairy Cows Using a Stacking Ensemble Learning Method

Volatile fatty acids (VFAs) and methane are the main products of rumen fermentation. Quantitative studies of rumen fermentation parameters can be performed using in vitro techniques and machine learning methods. The currently proposed models suffer from poor generalization ability due to the small number of samples. In this study, a prediction model for rumen fermentation parameters (methane, acetic acid (AA), and propionic acid (PA)) of dairy cows is established using the stacking ensemble learning method and in vitro techniques. Four factors related to the nutrient level of total mixed rations (TMRs) are selected as inputs to the model: neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM). The comparison of the prediction results of the stacking model and base learners shows that the stacking ensemble learning method has better prediction results for rumen methane (coefficient of determination (R2) = 0.928, root mean square error (RMSE) = 0.968 mL/g), AA (R2 = 0.888, RMSE = 1.975 mmol/L) and PA (R2 = 0.924, RMSE = 0.74 mmol/L). And the stacking model simulates the variation of methane and VFAs in relation to the dietary fiber content. To demonstrate the robustness of the model in the case of small samples, an independent validation experiment was conducted. The stacking model successfully simulated the transition of rumen fermentation type and the change of methane content under different concentrate-to-forage (C:F) ratios of TMR. These results suggest that the rumen fermentation parameter prediction model can be used as a decision-making basis for the optimization of dairy cow diet compositions, rapid screening of methane emission reduction, feed beneficial to dairy cow health, and improvement of feed utilization.