Effects of different nitrogen doses and cultivars on fermentation quality and nutritive value of Italian ryegrass (Lolium multiflorum Lam.) silages

Objective

The fermentation profile and silage quality of 3 Italian ryegrass (Lolium multiflorum Lam.) cultivars (cvs. Devis, Hellen, and Trinova) treated with 5 nitrogen doses (0, 50, 100, 150, and 200 kg/ha) were evaluated.

Methods

The experiment was laid out in split plot in randomized complete block design with three replications. Annual ryegrass cultivars used in this study have been commonly grown in Turkey. Nitrogen doses were set in main plot and cultivars in split plot in the field. Plants were harvested at full-flowering stage with dry matter content about 220 g/kg for first cutting and 260 g/kg for second cutting. Harvested plants were chopped theoretically into 2 to 3 cm lengths for ensiling. Chopped fresh materials were ensilaged by compressing in 2 L plastic jars about 3±0.1 kg.

Results

Effects of N doses on dry matter, neutral detergent fiber, acid detergent fiber, dry matter digestibility, relative feed value, crude protein, pH, ammonia nitrogen, lactic acid, acetic acid, and lactic acid/acetic acid were statistically significant while water soluble carbohydrate, ash and organic matter were not statistically different. Ammonia nitrogen, crude protein, ash, organic matter, lactic acid, and lactic acid/acetic acid were affected by cultivars, but the other parameters were not. Increasing nitrogen applications positively affected the chemical composition of annual ryegrass silage. The significant increase in protein content was remarkable, however, silage fermentation properties were adversely affected by the increasing nitrogen dose.

Conclusion

It can be recommended 150 kg/ha nitrogen dose for annual ryegrass harvested at full blooming stage. Even though the silage fermentation properties of the used cultivars were similar, cv. Devis gave better results than the others in terms of silage pH and relative feed value.

Evaluation of Molly model predictions of ruminal fermentation, nutrient digestion, and performance by dairy cows consuming ryegrass-based diets

Several studies have been conducted to improve grazing management and supplementation in pasture-based systems. However, it is necessary to develop tools that integrate the available information linking the representation of biological processes with animal performance for use in decision making. The objective of this study was to evaluate the precision and accuracy of the Molly cow model predictions of ruminal fermentation, nutrient digestion, and animal performance by cows consuming pasture-based diets to identify model strengths and weaknesses, and to derive new digestive parameters when relevant. Model modifications for adipose tissue, protein synthesis in lean body mass and viscera representation were included. Data used for model evaluations were collected from 25 publications containing 115 treatment means sourced from studies conducted with lactating dairy cattle. The inclusion criteria were that diets contained ≥45% perennial ryegrass (Lolium perenne L.), and that dry matter intake, dietary ingredient composition, and nutrient digestion observations were reported. Animal performance and N excretion variables were also included if they were reported. Model performance was assessed before and after model reparameterization of selected digestive parameters, global sensitivity analysis was conducted after reparameterization, and a 5-fold cross evaluation was performed. Although rumen fermentation predictions were not significantly improved, rumen volatile fatty acids absorption rates were recalculated, which improved the concordance correlation coefficient (CCC) for rumen propionate and ammonia concentration predictions but decreased CCC for acetate predictions. Similar degradation rates of crude protein were observed for grass and total mixed ration diets, but rumen-undegradable protein predictions seemed to be affected by the solubility of the protein source as was the intestinal digestibility coefficient. Ruminal fiber degradation was greater after reparameterization, driven primarily by hemicellulose degradation. Predictions of ruminal and fecal outflow of neutral detergent fiber and acid detergent fiber, as well as total fecal output predictions, improved significantly after reparameterization. Blood urea N and urinary N excretion predictions resulted in similar accuracy using both sets of model parameters, whereas fecal N excretion predictions were significantly improved after reparameterization. Body weight and body condition score predictions were greatly improved after model modifications and reparameterization. Before reparameterization, yield predictions for daily milk, milk fat, milk protein, and milk lactose were greatly overestimated (mean bias of 61.0, 58.7, 73.7, and 64.6% of mean squared error, respectively). Although this problem was partially addressed by model modifications and reparameterization (mean bias of 3.2, 1.1, 1.7, and 0.4% of mean squared error, respectively), CCC values were still small. The ability of the model to predict grass digestion and animal performance in dairy cows consuming pasture-based diets was improved, demonstrating the applicability of this model to these productive systems. However, the failure to predict grass digestion based on standard model inputs without reparameterization indicates there are still fundamental challenges in characterizing feeds for this model.

Applying a mechanistic fermentation and digestion model for dairy cows with emission and nutrient cycling inventory and accounting methodology

In mitigating greenhouse gas (GHG) emissions and reducing the carbon footprint of dairy milk, the use of generic estimates in inventory and accounting methodology at farm level largely ignores variation of on-farm GHG emissions. The present study aimed to implement results of an extant dynamic, mechanistic Tier 3 model for enteric methane (CH4) (applied in Dutch national GHG inventory) in order to capture variation in enteric CH4 emission, and in faecal N and organic matter (OM) digestibility, ultimately required to predict manure CH4 and ammonia emission. Tier 3 model predictions were translated into calculation rules that could easily be implemented in an annual nutrient cycling assessment tool including GHG emissions, which is currently used by Dutch dairy farmers. Calculations focussed on (1) enteric CH4 emission, (2) apparent faecal OM digestibility and (3) apparent faecal N digestibility. Enteric CH4 was expressed in CH4 yield indicated with the term emission factor (EF; g CH4/kg DM) for individual dietary components and feedstuffs. Factors investigated to cover predicted variation in EF value included the level of feed intake, the type of roughage fed (proportions of grass silage and maize silage) and the quality of roughage fed. A minimum number of three classes of roughage type (i.e. 0. 40% and 80% maize silage in roughage DM) appeared necessary to obtain correspondence between interpolated EF values from EF lists and Tier 3 model predictions. A linear decline in EF value with 1% per kg increase in DM intake is adopted based on model simulations. The quality of roughage was represented by the effect of maturity of harvested grass or of the whole plant maize at cutting, based on a survey of modelling as well as experimental work. Also, predictions were assembled for apparent faecal OM digestibility which could be used in national inventory and in farm accounting. Apparent faecal N digestibility (as a major determinant of predicted urinary N excretion) was predicted, to support current Dutch national ammonia emission inventory and to correct the level of N digestibility in farm accounting. Compared to generic values or values retrieved from the Dutch feeding tables, predicted OM and N digestibility and enteric CH4 are better rooted in physiological principles and better reflect observed variation under experimental conditions. The present results apply for conditions with fairly intensive grassland management in temperate regions.

Quantifying effects of grassland management on enteric methane emission

Data on the effect of grassland management on the nutritional characteristics of fresh and conserved grass, and on enteric methane (CH4) emission in dairy cattle, are sparse. In the present study, an extant mechanistic model of enteric fermentation was evaluated against observations on the effect of grassland management on CH4 emission in three trials conducted in climate-controlled respiration chambers. Treatments were nitrogen fertilisation rate, stage of maturity of grass and level of feed intake, and mean data of a total of 18 treatments were used (4 grass herbage treatments and 14 grass silage treatments). There was a wide range of observed organic matter (OM) digestibility (from 68% to 84%) and CH4 emission intensity (from 5.6% to 7.3% of gross energy intake; from 27.4 to 36.9 g CH4/kg digested OM; from 19.7 to 24.6 g CH4/kg dry matter) among treatment means. The model predicted crude protein, fibre and OM digestibility with reasonable accuracy (root of mean square prediction errors as % of observed mean, RMSPE, 6.8%, 7.5% and 3.9%, respectively). For grass silages only, the model-predicted CH4 correlated well (Pearson correlation coefficient 0.73) with the observed CH4 (which varied from 5.7% to 7.2% of gross energy intake), after predicted CH4 was corrected for nitrate consumed with grass silage, acting as hydrogen sink in the rumen. After nitrate correction, there was a systematic under-prediction of 18%, which reduced to 9% when correcting the erroneously predicted rumen volatile fatty acid (VFA) profile (RMSPE 15%). Although a small over-prediction of 3% was obtained for the grass herbages, this increased to 14% when correcting VFA profile. The model predictions showed a systematic difference in CH4 emission from grass herbages and grass silages, which was not supported by the observed data. This is possibly related to the very high content of soluble carbohydrates in grass herbage (an extra 170 g/kg dry matter compared with grass silages) and an erroneous prediction of its fate and contribution to CH4 in the rumen. Erroneous prediction of the VFA profile is likely to be due to different types of diets included in the empirical database used to parameterise VFA yield in the model from those evaluated here. Model representations of feed digestion and VFA profile are key elements to predict enteric CH4 accurately, and with further evaluations, the latter aspect should be emphasised in particular.