Effect of sex and milk replacer with or without supplemental carnitine and arginine on growth characteristics, carcass, and meat quality of artificially reared low-birth weight pigs.

This study compared milk replacer either remaining unsupplemented (CON) or supplemented with 0.5 g L-carnitine plus 16.7 g L-arginine/kg (CarArg) and fed to 48 low-birth weight (L-BtW) artificially reared piglets (24 per group) from days 7 to 28 of age. Eight farrowing series were needed to complete the study. On day 28, the lightest piglets were slaughtered, and the heaviest pigs were weaned. The heaviest pigs were weaned on day 28 and offered free access to a starter (weaning to 25 kg body weight [BW]), grower (25 to 60 kg BW), and finisher diet (60 to 96 kg BW on day 170 of age). After euthanization on days 28 and 170, blood was sampled for assessment of serum metabolite and hormone concentrations, and the semitendinosus muscle (STM) was weighed, and later subjected to enzyme activity analysis and assessment of myofiber characteristics. In the 170-d-old pigs carcass and meat quality traits were assessed. Growth data were analyzed accordingtoatwo-way analysis of variance (ANOVA), with dietary treatment and farrowing series as fixed effects, while remaining data were analyzed with dietary treatment, sex, their interaction, and farrowing series as main factors. Dietary treatments affected (P ≤ 0.049) muscle enzyme activity at both day 28, with greater citrate synthase (CS) and LDH activities and lower HAD:CS ratio in STM light portion, and lower LDH:CS ratio in STM dark portion, and 170 of age with lower HAD:CS ratio. In the starter period, CarArg pigs had greater average daily gain (P = 0.021) and average daily feed intake (P = 0.010). At slaughter, these pigs had lower (P = 0.013) glucose and greater (P = 0.022) urea serum concentrations. However, supplementing the milk replacer with carnitine and arginine had no long-term effects on growth performance, carcass composition, and meat quality of L-BtW pigs. In addition, muscle morphology and myofiber-related properties remained unaffected by the supplementation.

Breeding efforts to increase litter size in modern sows have inadvertently reduced the average birth weight of piglets, resulting in a higher number of piglets born with low-birth weight. These piglets are indeed vulnerable from birth and display relatively poor growth potential from a very early stage. For this reason, artificial rearing strategies are potentially a management option to improve the growth of these runt piglets. With an artificial rearing system, it is possible to provide specialized diets already during the suckling period, with inclusion of specific nutrients in certain concentrations suggested to improve the growth of runt piglets. Using an artificial rearing system allows for the provision of specialized diets during the suckling phase, which includes specific nutrients aimed at enhancing the growth of underdeveloped piglets. However, in the current experiment, the particular nutrients and their dosages did not significantly improve growth or other characteristics compared to the control group.

Evaluation of ruminal methane and ammonia formation and microbiota composition as affected by supplements based on mixtures of tannins and essential oils using Rusitec.

BACKGROUND: Dietary supplements based on tannin extracts or essential oil compounds (EOC) have been repeatedly reported as a promising feeding strategy to reduce the environmental impact of ruminant husbandry. A previous batch culture screening of various supplements identified selected mixtures with an enhanced potential to mitigate ruminal methane and ammonia formation. Among these, Q-2 (named after quebracho extract and EOC blend 2, composed of carvacrol, thymol, and eugenol) and C-10 (chestnut extract and EOC blend 10, consisting of oregano and thyme essential oils and limonene) have been investigated in detail in the present study with the semi-continuous rumen simulation technique (Rusitec) in three independent runs. For this purpose, Q-2 and C-10, dosed according to the previous study, were compared with a non-supplemented diet (negative control, NC) and with one supplemented with the commercial EOC-based Agolin® Ruminant (positive control, PC). RESULTS: From d 5 to 10 of fermentation incubation liquid was collected and analysed for pH, ammonia, protozoa count, and gas composition. Feed residues were collected for the determination of ruminal degradability. On d 10, samples of incubation liquid were also characterised for bacterial, archaeal and fungal communities by high-throughput sequencing of 16S rRNA and 26S ribosomal large subunit gene amplicons. Regardless of the duration of the fermentation period, Q-2 and C-10 were similarly efficient as PC in mitigating either ammonia (-37% by Q-2, -34% by PC) or methane formation (-12% by C-10, -12% by PC). The PC was also responsible for lower feed degradability and bacterial and fungal richness, whereas Q-2 and C-10 effects, particularly on microbiome diversities, were limited compared to NC. CONCLUSIONS: All additives showed the potential to mitigate methane or ammonia formation, or both, in vitro over a period of 10 d. However, several differences occurred between PC and Q-2/C-10, indicating different mechanisms of action. The pronounced defaunation caused by PC and its suggested consequences apparently determined at least part of the mitigant effects. Although the depressive effect on NDF degradability caused by Q-2 and C-10 might partially explain their mitigation properties, their mechanisms of action remain mostly to be elucidated.

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Dietary habits of pastoralists on the Tibetan plateau are influenced by remoteness and economic status.

In general, dietary habits of pastoralists are livestock-derived, but are also influenced by external food sources under globalization. We hypothesized that dietary habits of pastoralists would be influenced by their remoteness, and that changes from the traditional diet would result in deviations in the local ecological chain. To test this hypothesis, we determined the δ13C and δ15N values of soil, plants, and hair of animals and pastoralists (n = 885). The δ13C value in human hair reflects the proportions of protein originating from C3 and C4 plants; whereas, the δ15N value reflects the proportions of protein derived from plants and animals, with higher values indicating a greater consumption of meat. The isotopic signatures enabled us to estimate the variation in dietary habits of pastoralists across a socio-economic gradient of easily accessible to remote areas on the Tibetan plateau, and to determine the trophic transfer of the isotopes along an ecological chain. The trophic magnification factor (TMF) evaluated the trophic transfer of δ15N in the soil-plants-animals-pastoralists ecological chain. The high δ15N values in soil and plants were not recovered in animals and pastoralists in easily accessible and developed areas, indicating the use of external feed and food resources, and that they deviated from the ecological chain. The mean δ13C (-22.0 ‰) and δ15N values (6.9 ‰) of pastoralists indicated diets consisting mainly of local C3 plants and animal products. However, pastoralists in remote areas relied more on meat protein and on the local ecological chain than pastoralists in easily accessible areas, as their δ15N values and trophic magnification factor of δ15N in the ecological chain were greater. In addition to remoteness, per capita GDP influenced dietary changes in pastoralists, with richer pastoralists consuming more external food. We concluded that dietary changes of pastoralists in the easily accessible areas were due to external food resources and alterations in the local ecological chain of animals and plant-based foods available to the pastoralists.

Evaluation and quantification of associations between commonly suggested milk biomarkers and the proportion of grassland-based feeds in the diets of dairy cows.

This study is a first step approach towards the prediction of the proportion of grassland-based feeds (%GB) in dairy cow diets with the aid of three different groups of milk biomarkers. We aimed to evaluate and quantify the associations between biomarkers commonly suggested in the literature and %GB in individual cows as a hypothesis-generating stage for the prospective establishment of accurate %GB prediction models. Consumers and governments financially encourage sustainable, local milk production making grass-based feeding, in grassland-dominated regions, of major interest. Milk from grassland-fed cows differs from that of other feeding systems by inferential fatty acids (FA), ß-carotene content and yellow color; however, these biomarkers have not been evaluated together for their association with %GB. Using approved methods of parametric regression analysis, gas chromatography (GC), mid-infrared spectra (MIR) and color spectroscopy, we aimed to develop a first step towards an easy-to-implement, cost-effective milk-based control to estimate %GB in dairy cow diets. The underlying database was generated with 24 cows each fed one of 24 different diets gradually increasing in grass silage and decreasing in corn silage. Our results indicate that GC-measured α-linolenic acid, total n-3 FA and the n-6:n-3 ratio, MIR-estimated PUFA and milk red-green color index a* are robust milk biomarkers for constructing accurate prediction models to determine %GB. Based on simplified regression analysis, diets containing 75% GB should contain ≥ 0.669 and 0.852 g α-linolenic acid and total n-3 FA per 100 g total FA, respectively, and an n-6:n-3 FA ratio of < 2.02 measured with GC; estimated with MIR, polyunsaturated FA should be ≥ 3.13 g/100 g total FA. ß-carotene was not a good predictor for estimating %GB. Unexpectedly, the milk became greener with increasing %GB (negative a* values, ‒6.416 for 75% GB), suggesting the red-green color index, not yellow-blue, as a suitable biomarker.

A pilot investigation on the effect of induced saliva flow on digestive parameters in sheep, and a comparison with cattle.

Sheep with a relatively low methane yield were observed to have shorter fluid and particle mean retention times (MRT). Because the application of pilocarpine, a saliva stimulant, was successful in reducing retention times in ruminants in previous studies, we applied this substance to sheep, expecting a reduction in MRT and methane yield. Three non-pregnant sheep (74 ± 10 kg) were fed a hay-only diet in a 3 × 3 Latin square design with oral doses of 0, 2.5 and 5 mg pilocarpine/kg body weight and day. Measurements included feed and water intake, MRT of liquid and particulate phases in the reticulorumen (RR) and total gastrointestinal tract (GIT), ruminal microbial yield (via urinary purine bases and metabolic faecal nitrogen), total tract methane emission, apparent nutrient digestibility and rumen fluid parameters. Data were investigated for linear and quadratic effects using orthogonal polynomial contrasts. The MRT of liquid and small particles in the RR and total GIT, and the short-chain fatty acid concentration in rumen fluid, linearly declined with increasing pilocarpine dosage, while no quadratic relationship was detected. Intake of feed DM and water, apparent nutrient digestibility, methane yield and microbial yield were not affected by pilocarpine. When combining the sheep data with that of a similar experiment in cattle, we found that the MRT of the liquid phase was positively associated with estimated NDF digestibility and with methane production per digested NDF, but was not associated with microbial yield or the ratio of acetate to propionate. The ratio between MRT of the particulate and the liquid phase was smaller for sheep than that for cattle, and was not affected by treatment. Differences in this ratio might explain why species reacted differently to the saliva-inducing agent, which might help to explain the discrepancy between species in the effect of induced saliva flow on digestive parameters.

Effects of incremental increases in grass silage proportions from different harvest years on methane emissions, urinary nitrogen losses, and protein and energy utilisation in dairy cows.

Dairy cows, methane and global warming have become publicly related terms. However, appropriate dairy cow management may in fact be part of the climate solution when viewed as part of the biogenic carbon cycle. Accordingly, governments and consumers are encouraging more sustainable, locally produced, climate friendly dairy production that often includes grassland-based feeding. However, this system is presumed to result in greater methane emissions compared to corn silage- or concentrate-based diets. An increase in urine nitrogen, associated with increased ammonia and nitrous oxide emission potential, questions the environmental usefulness of this strategy pertaining to global warming. This study is the first to compare the effects of incremental increases of grass silage proportion on enteric methane production as well as N and energy losses in dairy cows. Twenty-four mid- to end-lactation dairy cows were each fed one of 24 different diets, from two different harvest years, gradually increasing in grassland-based feeds (grass silage and hay) from about 500 to 1000 g/kg and concomitantly decreasing in corn silage. Each cow underwent a 7-day total collection period and was housed for 48 h in respiration chambers. Incremental data were subjected to an approved parametric regression analysis approach. The dietary increase in grassland-based feeds did not impair milk yield, N and energy utilisation. Simplified regression equations revealed that, contrary to current assumptions, there was a decline in methane production from 373 to 303 g/day when increasing grassland-based feeds from 500 to 1000 g/kg diet, and there was a trend for a decline in emission intensity from 20.6 to 17.6 g/kg of energy-corrected milk. However, urine nitrogen emissions clearly increased even when related to nitrogen intake; the latter from 260 to 364 g/kg when increasing grassland-based feeds from 500 to 1000 g/kg. Methane and urine nitrogen emissions were not affected by year of harvest.

Individual differences in digesta retention and their relation to chewing in cattle-A pilot investigation.

While information on individual differences in digesta mean retention time (MRT) might be interesting when selecting phenotypes for digestive efficiency, MRT measurements are prohibitively labour-intensive for large-scale application. Therefore, more easily measured proxies of MRT might be helpful. We used the opportunity of an experiment applying saliva stimulant in cattle to investigate the effect of different individual chewing behaviour on fluid and particle MRT with a consistent diet. Four non-lactating cattle (670-850 kg body mass [BM]) were used in a 4 × 4 Latin square design, treated with the saliva stimulant pilocarpine in dosages of 0, 1, 2.5 and 5 mg/kg BM per day. The cattle were fed hay with dry matter intake (DMI) assigned according to their metabolic body weight. MRT in the whole gastrointestinal tract (GIT), the reticulorumen (RR) and the distal tract were measured using Co-EDTA, Cr-mordanted fibre and La-mordanted fibre as markers representing fluid, small particles (2 mm) and large particles (1 cm), respectively. The chewing behaviour was measured via noseband pressure sensor and expressed as chewing frequency (chews per time) and chewing intensity (chews per DMI), both for total chewing (ingestion plus rumination) and rumination chewing alone. The animals differed considerably in chewing behaviour and MRT measures. BM did not show a significant effect on chewing behaviour and MRT measures, though it tended to negatively correlated to total chewing intensity. Chewing intensity exerted a significant negative influence on MRT of fluid and particles in the RR, which was not the case for chewing frequency. Chewing frequency showed a significant relationship with MRT of large particles in the GIT. We suggest that chewing behaviour could influence MRT in two ways: (i) by affecting saliva production via the masticatory-salivary reflex and subsequently, the fluid inflow to the RR; (ii) by contributing to particle size reduction. Should the link between chewing behaviour and MRT be corroborated in larger studies, chewing measures, with their large interindividual variation, could emerge as an easy-to-measure proxy for MRT characteristics.

Effect of induced saliva flow on fluid retention time, ruminal microbial yield and methane emission in cattle.

Both in vitro and animal studies indicated that a higher dilution rate is related to a more efficient microbial synthesis and a lower methane (CH4 ) yield. The latter could be a consequence of the former, as an increase in microbial cell synthesis offers an alternative hydrogen sink competing with methanogenesis. To test this assumption in live animals, we applied a saliva stimulant, pilocarpine, to modify liquid flow rate in cattle. Four non-lactating cows (750 ± 71 kg) were fed forage only (restricted to constant intake) in a 4 × 4 Latin square design with oral doses of 0, 1, 2.5 and 5mg pilocarpine/kg body weight and day. We quantified feed and water intake, ruminal and total tract mean retention time (MRT) of solute and particle markers, ruminal microbial yield (via urinary purine bases or metabolic faecal nitrogen), CH4 emission, digestibility, chewing behaviour, reticular motility and rumen fluid parameters. The effect of induced saliva flow was evident by visibly increased salivation and water intake. Increasing the pilocarpine dosages resulted in a linearly decreased MRT of fluid and small particles (p < 0.001 and p< 0.05, respectively) and methane yield as related to digested DM (p < 0.05), the latter at a magnitude of 5%. No effect of treatment was found on ruminal microbial yield estimated via purine derivates. Metabolic faecal N as an indicator of microbial growth linearly correlated with pilocarpine dosages (p < 0.05). No significant relationship was found between pilocarpine dosages and large particle MRT, nutrient digestibility, ruminal pH and short-chain fatty acids. In conclusion, different from some in vitro studies, there was little indication of a reciprocal effect of CH4 and microbial biomass production in cows fed a forage-only diet.

A High-Energy Diet and Spirulina Supplementation during Pre-Gestation, Gestation, and Lactation do Not Affect the Reproductive and Lactational Performance of Primiparous Sows.

Feeding high-energy (HED) or high-fat diets during gestation and lactation to pigs may help cover the energy requirements of high-prolific sows but may also adversely affect their reproductive performance. The microalga Arthrospira plantensis (Sp), rich in bioactive compounds, has been described to exert beneficial health effects. The present study investigated the effects of HED and Sp intake during gestation and lactation in pigs. Twenty-four primiparous crossbred sows were fed either a HED or a control diet. Half of the sows per group were supplemented with 20 g/day of Sp. Despite a higher gross energy intake, consuming the HED did not affect the sows' reproductive and lactational performance but significantly modified the colostrum fatty acid (FA) composition and tended to decrease the number of weaned piglets. The Sp supplementation did not affect the reproduction and lactation traits, but slightly affected the colostrum FA composition. A trend was observed for an interaction of diet and Sp in terms of offspring sex ratio with a 50% lower male-to-female ratio in the HED group compared to all other animals. These findings suggest that an HED and Sp intake hardly influence reproduction in sows. However, the HED modified the colostrum FA composition, whereas the Sp had only fewer effects, which may potentially affect offspring performance.

Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 °C target by 2030 but not 2050.

To meet the 1.5 °C target, methane (CH4) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute (ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies­namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio­decreased CH4 per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies­namely CH4 inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds­decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH4 emissions.

Genotype-by-Diet Interactions for Larval Performance and Body Composition Traits in the Black Soldier Fly, Hermetia illucens.

Further advancing black soldier fly (BSF) farming for waste valorisation and more sustainable global protein supplies critically depends on targeted exploitation of genotype-phenotype associations in this insect, comparable to conventional livestock. This study used a fully crossed factorial design of rearing larvae of four genetically distinct BSF strains (FST: 0.11-0.35) on three nutritionally different diets (poultry feed, food waste, poultry manure) to investigate genotype-by-environment interactions. Phenotypic responses included larval growth dynamics over time, weight at harvest, mortality, biomass production with respective contents of ash, fat, and protein, including amino acid profiles, as well as bioconversion and nitrogen efficiency, reduction of dry matter and relevant fibre fractions, and dry matter loss (emissions). Virtually all larval performance and body composition traits were substantially influenced by diet but also characterised by ample BSF genetic variation and, most importantly, by pronounced interaction effects between the two. Across evaluated phenotypes, variable diet-dependent rankings and the lack of generally superior BSF strains indicate the involvement of trade-offs between traits, as their relationships may even change signs. Conflicting resource allocation in light of overall BSF fitness suggests anticipated breeding programs will require complex and differential selection strategies to account for pinpointed trait maximisation versus multi-purpose resilience.

Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle.

Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH4 emissions and proxies for CH4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH4, root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling.

Digestion of bamboo compared to grass and lucerne in a small hindgut fermenting herbivore, the guinea pig (Cavia porcellus).

Bamboo is an enigmatic forage, representing a niche food for pandas and bamboo lemurs. Bamboo might not represent a suitable forage for herbivores relying on fermentative digestion, potentially due to its low fermentability. To test this hypothesis, guinea pigs (n = 36) were used as model species and fed ad libitum with one of three forages (bamboo, lucerne, or timothy grass) in a fresh or dried state, with six individuals per group, for 3 weeks. The nutrient composition and in vitro fermentation profile of bamboo displayed low fermentation potential, i.e. high lignin and silica levels together with a gas production (Hohenheim gas test) at 12 h of only 36% of that of lucerne and grass. Although silica levels were more abundant in the leftovers of (almost) all groups, guinea pigs did not select against lignin on bamboo. Dry matter (DM) intake was highest and DM digestibility lowest on the bamboo forage. Total short-chain fatty acid levels in caecal content were highest for lucerne and lowest for grass and bamboo. Bamboo-fed guinea pigs had a lower body weight gain than the grass and lucerne group. The forage hydration state did not substantially affect digestion, but dry forage led to a numerically higher total wet gut fill. Although guinea pigs increased DM intake on the bamboo diet, the negative effects on fermentation of lignin and silica in bamboo seemed overriding. For herbivores that did not evolutionary adapt, bamboo as an exclusive food resource can be considered as inadequate.

The rumen microbiome inhibits methane formation through dietary choline supplementation.

Enteric fermentation from ruminants is a primary source of anthropogenic methane emission. This study aims to add another approach for methane mitigation by manipulation of the rumen microbiome. Effects of choline supplementation on methane formation were quantified in vitro using the Rumen Simulation Technique. Supplementing 200 mM of choline chloride or choline bicarbonate reduced methane emissions by 97-100% after 15 days. Associated with the reduction of methane formation, metabolomics analysis revealed high post-treatment concentrations of ethanol, which likely served as a major hydrogen sink. Metagenome sequencing showed that the methanogen community was almost entirely lost, and choline-utilizing bacteria that can produce either lactate, ethanol or formate as hydrogen sinks were enriched. The taxa most strongly associated with methane mitigation were Megasphaera elsdenii and Denitrobacterium detoxificans, both capable of consuming lactate, which is an intermediate product and hydrogen sink. Accordingly, choline metabolism promoted the capability of bacteria to utilize alternative hydrogen sinks leading to a decline of hydrogen as a substrate for methane formation. However, fermentation of fibre and total organic matter could not be fully maintained with choline supplementation, while amino acid deamination and ethanolamine catabolism produced excessive ammonia, which would reduce feed efficiency and adversely affect live animal performance.

A Basic Model to Predict Enteric Methane Emission from Dairy Cows and Its Application to Update Operational Models for the National Inventory in Norway.

The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Development of basic models utilized information that is available only from feeding experiments. Basic models were developed using a database with 63 treatment means from 19 studies and were evaluated against an external database (n = 36, from 10 studies) along with other extant models. In total, the basic model database included 99 treatment means from 29 studies with records for enteric CH4 production (MJ/day), dry matter intake (DMI) and dietary nutrient composition. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH4 emissions than extant models. In order to propose country-specific values for the CH4 conversion factor Ym (% of gross energy intake partitioned into CH4) and thus to be able to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Ym over a wide range of feeding situations. A simulated operational database containing CH4 production (predicted by the basic model), feed intake and composition, Ym and gross energy intake (GEI), in addition to the predictor variables energy corrected milk yield and dietary concentrate share were used to develop an operational model. Input values of Ym were updated based on the results from the basic models. The predicted Ym ranged from 6.22 to 6.72%. In conclusion, the prediction accuracy of CH4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH4 in Norway.

Transfer of Lauric and Myristic Acid from Black Soldier Fly Larval Lipids to Egg Yolk Lipids of Hens Is Low.

Implementing insects, such as the black soldier fly larvae (BSFL), as animal feed commonly includes the previous removal of substantial amounts of fat. This fat may represent an as yet underutilized energy source for livestock. However, transfer of lauric and myristic acid, prevalent in BSFL fat and undesired in human nutrition, into animal-source foods like eggs may limit its implementation. To quantify this, a laying hen experiment was performed comprising five different diets (10 hens/diet). These were a control diet with soybean oil and meal and a second diet with soybean oil but with partially defatted BSFL meal as protein source. The other three diets were based on different combinations of partially defatted BSFL meal and fat obtained by two different production methods. Lauric acid made up half of the BSFL fat from both origins. Both BSFL fats also contained substantial amounts of myristic and palmitic acid. However, in the insect-based diets, the net transfer from diet to egg yolk was less than 1% for lauric acid, whereas the net transfer for myristic and palmitic acid was about 30% and 100%, respectively. The net transfer did not vary between BSFL originating from production on different larval feeding substrates. The results illustrate that hens are able to metabolize or elongate very large proportions of ingested lauric acid and myristic acid, which are predominant in the BSFL lipids (together accounting for as much as 37 mol%), such that they collectively account for less than 3.5 mol% of egg yolk fatty acids.

Effects of full-time v. part-time grazing on seasonal changes in milk coagulation properties and fatty acid composition.

For this research communication our objective was to investigate to what extent milk coagulation properties and milk fatty acid (FA) composition were affected by different feeding systems, season and their interaction. Eighteen cows in total were subjected to one of three different feeding system treatments: full-time grazing or part-time grazing combined with indoor feeding of fresh grass with low or high concentrate supplementation. Milk was sampled in spring, summer and autumn. Milk coagulation time was 15.0, 19.0 and 17.7 min, coagulation dynamics 1.67, 3.41 and 1.79 min, and curd firmness 52.7, 32.4 and 47.0 mm in spring, summer and autumn, respectively. Thus, milk coagulation properties of the milk were lower during summer. There were strong seasonal effects on milk FA proportions, but there were not always changes with progressing season, or changes were different with respect to the impact of the feeding systems (system × season interaction). The milk fat was favourably rich in oleic acid, conjugated linoleic acid and α-linolenic acid and had a low n-6/n-3 fatty acid ratio in all systems. Factors like seasonal variations in grass composition and the energy balance of the cows were considered relevant for the milk FA composition. Overall, seasonal variations in milk quality were less pronounced with part-time grazing with fresh grass indoors as compared to full-time grazing without concentrate.

Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid-infrared spectra.

BACKGROUND: A robust proxy for estimating methane (CH4 ) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid-infrared (FT-MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d-1 ) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT-MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS: Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d-1 ) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross-validation statistics: R2 = 0.68 and standard error = 57 g CH4 d-1 ). CONCLUSIONS: The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large-scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry.