Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America.

The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.

Epiphytic microbiota in Swedish grass-clover herbage and the effect of silage additives on fermentation profiles and bacterial community compositions of the resulting silages.

AIMS: To investigate the epiphytic microbiota in grass-clover herbage harvested at different sites and occasions and to explore the effect of different silage additives on the resulting silage microbiota. METHODS AND RESULTS: Herbage was harvested from grass-clover leys at geographically distributed sites in a long-term field experiment in Sweden, in early and late season of two consecutive years. Different silages were made from the herbage using: (1) no additive, (2) acid-treatment, and (3) inoculation by starter culture. Herbages were analysed for botanical and chemical composition, and the resulting silages for products of fermentation. Bacterial DNA was extracted from herbage and silage samples, followed by sequencing using Illumina 16S rRNA amplicon sequencing. Herbage microbiota showed no clear correlation to site or harvesting time. Silage additives had a major effect on the ensiling process; inoculation resulted in well fermented silages comprising a homogenous microbiota dominated by the genera Lactobacillus and Pediococcus. A minor effect of harvest time was also observed, with generally a more diverse microbiota in second-harvest silages. Untreated silages showed a higher relative abundance (RA) from non-lactic acid bacteria compared to acid-treated silages. In most silages, only a few bacterial amplicon sequence variants contributed to most of the RA. CONCLUSIONS: The epiphytic microbiota in grass-clover herbage were found to be random and not dependent on site. From a microbial point of view, the most predictable and preferable silage outcome was obtained by inoculation with a starter culture. Acid-treatment with formic- and propionic acid surprisingly resulted in a less preferable silage. Silage making without additives cannot be recommended based on our results.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries.

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Feed intake, methane yield, and efficiency of utilization of energy and nitrogen by sheep fed tropical grasses.

Forage allowance impacts dry matter (DM) intake and the use of nutrients by ruminants. The efficient use of protein and energy from pasture is related to better livestock performance and lower environmental impacts. The aims of this study were to evaluate the effect of forage allowance levels on intake, digestibility, nitrogen (N) and energy balance, and methane (CH4) emissions by lambs fed fresh pearl millet [Pennisetum americanum (L.) Leeke]. An indoor trial was performed using lambs in a completely randomized design with four treatments [forage allowance at 1.5, 2.0, 2.5 kg DM/100 kg of live weight (LW), and ad libitum allowing 20% of refusals] and four replicates (lambs). Forage intake, digestibility, total urine and feces excretion, and CH4 emission were measured to calculate N and energy balances. An increase in forage allowance resulted in a linear increase in lamb forage intake, N retention, and metabolizable energy intake. Moreover, lamb CH4 emission (g/day) also increased with greater forage allowance, while CH4 yield decreased linearly as forage allowance increased. Our results indicate that maximizing forage intake improves N and energy use efficiency and mitigates CH4 yield and decreases CH4 conversion factor (Ym) by lambs fed pearl millet forage. Thus, management strategies that optimize intake of tropical forages by ruminants improve the use of nutrients ingested and mitigates negative impacts to the environment.

Methane Production in Dairy Cows Correlates with Rumen Methanogenic and Bacterial Community Structure.

Methane (CH4) is produced as an end product from feed fermentation in the rumen. Yield of CH4 varies between individuals despite identical feeding conditions. To get a better understanding of factors behind the individual variation, 73 dairy cows given the same feed but differing in CH4 emissions were investigated with focus on fiber digestion, fermentation end products and bacterial and archaeal composition. In total 21 cows (12 Holstein, 9 Swedish Red) identified as persistent low, medium or high CH4 emitters over a 3 month period were furthermore chosen for analysis of microbial community structure in rumen fluid. This was assessed by sequencing the V4 region of 16S rRNA gene and by quantitative qPCR of targeted Methanobrevibacter groups. The results showed a positive correlation between low CH4 emitters and higher abundance of Methanobrevibacter ruminantium clade. Principal coordinate analysis (PCoA) on operational taxonomic unit (OTU) level of bacteria showed two distinct clusters (P < 0.01) that were related to CH4 production. One cluster was associated with low CH4 production (referred to as cluster L) whereas the other cluster was associated with high CH4 production (cluster H) and the medium emitters occurred in both clusters. The differences between clusters were primarily linked to differential abundances of certain OTUs belonging to Prevotella. Moreover, several OTUs belonging to the family Succinivibrionaceae were dominant in samples belonging to cluster L. Fermentation pattern of volatile fatty acids showed that proportion of propionate was higher in cluster L, while proportion of butyrate was higher in cluster H. No difference was found in milk production or organic matter digestibility between cows. Cows in cluster L had lower CH4/kg energy corrected milk (ECM) compared to cows in cluster H, 8.3 compared to 9.7 g CH4/kg ECM, showing that low CH4 cows utilized the feed more efficient for milk production which might indicate a more efficient microbial population or host genetic differences that is reflected in bacterial and archaeal (or methanogens) populations.