Live yeast (Saccharomyces cerevisiae) products have the potential to increase milk yield of dairy cows in early lactation by improving rumen fermentation. Greater milk yields, however, are sometimes associated with poorer reproductive performance. This study aimed to assess the effect of a live yeast supplement on milk yield, methane emissions and reproduction indicators in high yielding dairy cows. Fifty Holstein cows were paired according to month of calving, parity and predicted milk yield, and allocated at random to either a Control diet or a diet containing live Yeast (Actisaf (R) Sc 47, 1 x 1010 cfu/g, Phileo by Lesaffre) supplying 1 x 1011 cfu/cow per day (10 g). Diets were fed to cows from 7 to 128 days in milk. Live yeast resulted in higher yields of milk (50.1 vs 47.5 kg/day), energy-corrected milk (ECM; 50.5 vs 47.7 kg/day), fat corrected milk (49.2 vs 46.3 kg/day) and milk fat (1 945 vs 1 823 g/day), compared with Control. There was no effect of treatment on DM intake (DMI), so cows fed on Yeast had greater feed efficiency (2.11 vs 1.98 kg ECM/kg DMI). Enhanced milk yield and feed efficiency were attributed to higher digestibility coefficients for DM (0.80 vs 0.77), NDF (0.66 vs 0.62) and gross energy (0.81 vs 0.78) in cows fed on Yeast compared with Control. Rumen pH, redox potential and volatile fatty acid concentrations, methane emissions, plasma metabolites and immunity indicators, and health events were not affected by treatment. There was no effect of treatment on days from calving to first milk progesterone rise above 3 ng/ml, days to first insemination, days to conception, conception rate, number of inseminations or incidence of atypical ovarian cycles. It was concluded that live yeast enhanced digestibility, milk yield and feed efficiency in high-yielding dairy cows, and that despite increased milk yield, methane emissions, reproduction and health indicators were maintained at the same levels as control cows. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Calcareous marine algae (CMA) products are included in dairy diets as buffers and sources of slow-release calcium. Studies have shown that milk yield, feed intake and rumen fermentation responses to CMA depend on baseline diet. In vitro studies suggest that CMA buffers might lower methane emissions, but this has never been tested in dairy cows. The objective of this study was to determine whether CMA products influence milk yield, feed intake, feed efficiency, rumen pH and volatile fatty acids (VFA), and methane emissions, in high-yielding dairy cows. Products investigated were Acid Buf and Acid Buf 10 (Celtic Sea Minerals, Carrigaline, Ireland), both derived from CMA, with additional Mg of marine origin in the latter. Two experiments were conducted, both involving partial-mixed rations (PMR) and concentrates fed during robotic milking. Experiment 1 used a Latin square design with four diets fed in four three-week periods to 48 cows. The control diet (CON1) was the normal farm diet containing limestone and MgO; these were replaced by Acid Buf for Diet AB1, by Acid Buf 10 for Diet AB+Mg1, and by Acid Buf plus garlic powder for Diet AB+GP. Experiment 2 used a randomised complete block design with three diets fed to 16 cows per treatment for 12 weeks. Diets CON2, AB2 and AB+Mg2 were the same as CON1, AB1 and AB+Mg1, except formulation of the baseline diet changed to allow for different forage composition. Energy-corrected milk yield was higher for AB+Mg1 and lower for AB+GP, compared with CON1. Milk yield was higher for AB2 compared with CON2. Diet did not affect feed intake in either experiment. Rumen pH was higher for AB+Mg1 compared with CON1 and was below pH 6.0 for fewer hours per day for AB+Mg1 and AB+GP. Rumen VFA were not affected by treatment in Experiment 1, but AB+Mg2 had higher total VFA and lower acetate to propionate ratio in Experiment 2. Methane production was lower for all CMA treatments. Methane yield was lower for AB1, AB+Mg1, AB+GP and AB+Mg2, and tended to be lower for AB2. These results suggest that CMA lower enteric methane synthesis by providing an alternative hydrogen sink. It is concluded that milk production and rumen fermentation responses were variable, but CMA products consistently lowered methane emissions in high-yielding dairy cows.

Most fat supplements added to dairy diets are derived from palm acid oil or palm fatty acid distillate. There are environmental concerns about palm oil production due to deforestation and high greenhouse gas (GHG) emissions. The objective of this study was to evaluate a palm-free fat supplement, Envirolac. Envirolac contains vegetable oils, marine oils and glycerine encapsulated within a cellulosic fibre-clay mineral carrier matrix. The carrier matrix has small particle size to facilitate rapid rumen passage and minimal interference with rumen digestion. Fifty cows were divided into two balanced groups of 25 cows. Each group received Control (calcium soap of palm fatty acid distilate, 0.5kg/d) and Envirolac (0.5kg/d) diets in a crossover design with two feeding periods of four weeks duration, so that each cow received both diets. Envirolac has a lower total fat concentration than the calcium soap, so the feeding rate delivered less total fat. When fed on Envirolac, cows yielded more milk 40.7v 40.1kg/d), energy-corrected milk (ECM; 42.9v 41.3kg/d) and milk components (1605v 1514g fat, 1239v 1199g protein, 1871v 1839 g lactose, per day), and produced milk with higher concentrations of fat (40.1 v 38.6g/kg) and protein (30.7v 30.1g/kg), than when fed on Control. When fed on Envirolac, cows produced milk with higher concentrations of fatty acids (FA) synthesised de novo in the mammary gland (25.5v 23.6g/100g total FA), lower concentration of palmitic acid (35.5v 37.2g/100g total FA), and higher concentrations of some long-chain fatty acids (C20:0, C20:1, C20:3n3, C21:0, C22:0, C22:6n3 and C23:0), than when fed on Control. There was no effect of treatment on dry matter intake (DMI; mean 23.4kg/d), so feed efficiency was higher (1.83v 1.76kg ECM/kg DMI) for Envirolac than Control. There was no effect of treatment on dry matter digestibility (0.73), methane production (433g/d), methane yield (19.3g/kg DMI) or methane intensity (10.9g/kg ECM). Feed carbon footprint of Envirolac was calculated to be 1028g CO2eq/kg DM, which is 0.46 of the value for a calcium soap (2830g CO2eq/kg DM), and reduced feed carbon footprint per kg ECM milk production by 11%. This study demonstrates that Envirolac can replace palm-based fat supplements in dairy diets to improve feed efficiency and reduce the carbon footprint of milk production.

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.

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

The aim of this study was to investigate the use of signal processing to detect eructation peaks in CH4 released by cows during robotic milking, and to compare recordings from three gas analysers (Guardian SP and NG, and IRMAX) differing in volume of air sampled and response time. To allow comparison of gas analysers using the signal processing approach, CH4 in air (parts per million) was measured by each analyser at the same time and continuously every second from the feed bin of a robotic milking station. Peak analysis software was used to extract maximum CH4 amplitude (ppm) from the concentration signal during each milking. A total of 5512 CH4 spot measurements were recorded from 65 cows during three consecutive sampling periods. Data were analysed with a linear mixed model including analyser × period, parity, and days in milk as fixed effects, and cow ID as a random effect. In period one, air sampling volume and recorded CH4 concentration were the same for all analysers. In periods two and three, air sampling volume was increased for IRMAX, resulting in higher CH4 concentrations recorded by IRMAX and lower concentrations recorded by Guardian SP (p < 0.001), particularly in period three, but no change in average concentrations measured by Guardian NG across periods. Measurements by Guardian SP and IRMAX had the highest correlation; Guardian SP and NG produced similar repeatability and detected more variation among cows compared with IRMAX. The findings show that signal processing can provide a reliable and accurate means to detect CH4 eructations from animals when using different gas analysers.

The aim of this study was to investigate variability in enteric CH4 emission rate and emissions per unit of milk across lactations among dairy cows on commercial farms in the UK. A total of 105,701 CH4 spot measurements were obtained from 2206 mostly Holstein-Friesian cows on 18 dairy farms using robotic milking stations. Eleven farms fed a partial mixed ration (PMR) and 7 farms fed a PMR with grazing. Methane concentrations (ppm) were measured using an infrared CH4 analyser at 1s intervals in breath samples taken during milking. Signal processing was used to detect CH4 eructation peaks, with maximum peak amplitude being used to derive CH4 emission rate (g/min) during each milking. A multiple-experiment meta-analysis model was used to assess effects of farm, week of lactation, parity, diet, and dry matter intake (DMI) on average CH4 emissions (expressed in g/min and g/kg milk) per individual cow. Estimated mean enteric CH4 emissions across the 18 farms was 0.38 (s.e. 0.01) g/min, ranging from 0.2 to 0.6 g/min, and 25.6 (s.e. 0.5) g/kg milk, ranging from 15 to 42 g/kg milk. Estimated dry matter intake was positively correlated with emission rate, which was higher in grazing cows, and negatively correlated with emissions per kg milk and was most significant in PMR-fed cows. Mean CH4 emission rate increased over the first 9 weeks of lactation and then was steady until week 70. Older cows were associated with lower emissions per minute and per kg milk. Rank correlation for CH4 emissions among weeks of lactation was generally high. We conclude that CH4 emissions appear to change across and within lactations, but ranking of a herd remains consistent, which is useful for obtaining CH4 spot measurements.

Ruminants digest plant biomass more efficiently than monogastric animals due to their symbiotic relationship with a complex microbiota residing in the rumen environment. What remains unclear is the relationship between the rumen microbial taxonomic and functional composition and feed efficiency (FE), especially in crossbred dairy cattle (Holstein x Gyr) raised under tropical conditions. In this study, we selected twenty-two F1 Holstein x Gyr heifers and grouped them according to their residual feed intake (RFI) ranking, high efficiency (HE) ( n = 11) and low efficiency (LE) ( n = 11), to investigate the effect of FE on the rumen microbial taxa and their functions. Rumen fluids were collected using a stomach tube apparatus and analyzed using amplicon sequencing targeting the 16S (bacteria and archaea) and 18S (protozoa) rRNA genes. Alpha-diversity and beta-diversity analysis revealed no significant difference in the rumen microbiota between the HE and LE animals. Multivariate analysis (sPLS-DA) showed a clear separation of two clusters in bacterial taxonomic profiles related to each FE group, but in archaeal and protozoal profiles, the clusters overlapped. The sPLS-DA also revealed a clear separation in functional profiles for bacteria, archaea, and protozoa between the HE and LE animals. Microbial taxa were differently related to HE (e.g., Howardella and Shuttleworthia ) and LE animals (e.g., Eremoplastron and Methanobrevibacter) , and predicted functions were significatively different for each FE group (e.g., K03395—signaling and cellular process was strongly related to HE animals, and K13643—genetic information processing was related to LE animals). This study demonstrates that differences in the rumen microbiome relative to FE ranking are not directly observed from diversity indices (Faith’s Phylogenetic Diversity, Pielou’s Evenness, Shannon’s diversity, weighted UniFrac distance, Jaccard index, and Bray–Curtis dissimilarity), but from targeted identification of specific taxa and microbial functions characterizing each FE group. These results shed light on the role of rumen microbial taxonomic and functional profiles in crossbred Holstein × Gyr dairy cattle raised in tropical conditions, creating the possibility of using the microbial signature of the HE group as a biological tool for the development of biomarkers that improve FE in ruminants.

Dried distillers’ grains with solubles (DDGS) from bioethanol production can replace soya in diets for dairy cows, but the optimum inclusion level of European wheat DDGS (wDDGS) is unknown. Two batches of wDDGS from different UK bioethanol plants were fed to 44 (Experiment 1) and 40 (Experiment 2) cows in a Latin square design. Each wDDGS replaced soya and rapeseed at four inclusion levels (g/kg of diet dry matter (DM): 0, 80, 160 and 240—Experiment 1; 0, 75, 150 and 225—Experiment 2). Diets were balanced for metabolisable energy (ME) and protein (MP), and for minimum starch and saturated fat in Experiment 2. In Experiment 1, DM intake (29 kg/day) and milk yield (42.3 kg/day) were unaffected by wDDGS inclusion up to 160 g/kg but were lower than control with 240 g/kg inclusion, which was attributed to the low proportion of solubles in this wDDGS batch. In Experiment 2, DM intake (22.4 kg/day) and milk yield (32.1 kg/day) were unaffected by wDDGS inclusion up to 225 g/kg. ME content of wDDGS, determined in vivo (MJ/kg DM) was 12.1 (Experiment 1) and 13.4 (Experiment 2). It is concluded that the optimum inclusion level of wDDGS is at least 225 g/kg DM in diets balanced for minimum starch and saturated fat as well as ME and MP supplies.

Animals form an integral part of our planetary ecosystem but balance is critical to effective ecosystem functioning as demand for livestock products has increased, greater numbers of domesticated livestock have created an imbalance and hence had a negative impact on a number of ecosystem services which means that life as we know it will become unsustainable. Policies and technology advances have helped to manage the impact but more needs to be done. The aim of this paper is to highlight ways in which better knowledge of animal science, and other disciplines, can both harness technology and inform policy to work towards a sustainable balance between livestock and the environment. Effective policies require simple, quantifiable indicators against which to set targets and monitor progress. Indicators are clear for water pollution, but more complex for biodiversity. Hence, more progress has been made with the former. It is not yet possible to measure the impacts of changes in livestock management on greenhouse gas emissions per se at a farm level and progress has been slower, although new technologies are emerging. With respect to land use, the simple indicator of area has been used, but total area is oversimplistic. Our analysis of land suitability and use highlights a relatively overlooked role of livestock in acting as a 'buffer' to use by-products and grains which do not meet the standards for processing by industry during years of inclement weather, which in the past has provided an 'insurance policy' for farmers. Since extreme weather events are increasing in frequency with climate change, this role for livestock may be more important in future. The conclusions of the review with respect to strengthening the links between research and policy are i) to encourage animal scientists to identify the relevant environmental indicators, work with the cutting edge experts developing technologies to measure these cost-effectively and across a range of relevant livestock systems and ii) to work with the feed industry to optimize diets not just in terms of least cost financially but also least 'cost' in terms of global carbon flux and engage in dialogue with the food industry and policy makers on regulations for grain quality.

There are environmental, social and economic pressures to reduce the use of soya bean meal in ruminant diets by using alternative protein sources, such as those derived from rapeseed. A new protected form of rapeseed (NovaPro) has been developed to provide similar quantities of digestible undegradable protein (DUP) compared to soya bean meal. NovaPro is hot pressed expelled rapeseed (no hexane solvent used), treated with a specific wood derived xylose-rich lignosulphonate in the presence of elevated moisture and heat to increase DUP. The objective of this study was to evaluate NovaPro as a protein supplement for high yielding dairy cows. Four diets were formulated to supply similar quantities of metabolisable energy and protein but containing different dominant protein sources. The main protein sources were: Control - soya bean and rapeseed meals; NP1 - NovaPro and wheat dried distillers grains with solubles (DDGS); PR - protected solvent-extracted rapeseed meal and wheat-DDGS; NP2 NovaPro and SoyPass. Diets were fed to 44 cows using a Latin square design with four feeding periods of 28 days each. Milk yield was significantly higher when cows were fed on rapeseed treatment diets (mean 42.7 kg/d) than when fed on the control diet (mean 41.1 kg/d), as was energy-corrected milk (ECM) yield (mean 43.2 versus 41.7 kg/d). Dry matter intake was higher when cows were fed on NP1 and NP2 (mean 25.0 kg/d) than when they were fed on the control diet (mean 23.9 kg/d); dry matter intake for PR was intermediate (mean 24.4 kg/d). Concentrations of milk fat and protein reflected differences in milk yield, and there was no difference between treatments in fat or protein yield, although fat plus protein yield was higher when cows were fed on rapeseed treatment diets (mean 2.84 kg/d) than when fed on the control diet (mean 2.72 kg/d). Differences in rumen fluid and blood composition were commensurate with differences in diet composition, nutrient intake and milk yield. Retrospective calculation of metabolisable energy and protein supplies showed that these were within 3% of requirements for observed responses. Calculation of amino acid profiles suggested that profiles, particularly methionine, were better for the rapeseed treatment diets. Results of this study support the hypothesis that cows fed on NovaPro and other rumen protected rapeseed proteins will have similar or improved milk production compared to a control (soya-based) diet. Improved milk yield was accompanied by increased dry matter intake, but it is likely that intake was driven by milk yield rather than vice versa. The most likely explanation for improved milk yield when cows were fed on the rapeseed treatment diets is that amino acid balance was improved compared to control.

This study aimed to estimate the response to selection through different selection indices between methane production and milk production and its components in specialized tropical, dual-purpose, and family dairy systems. Methane emissions were sampled during milking using the Guardian-NG gas monitor; milk samples were collected individually during methane sampling. DNA was extracted from the hair follicles of all the animals included in this study. The variance and covariance components were estimated using the mixed model methodology. Due to the incomplete genealogical information, molecular markers were used to build the genomic relationship matrix (Matrix G). The estimated heritability for methane emissions during milking was 0.18 and 0.32 for the univariate and bivariate analysis, respectively. The genetic correlation between the milk fat and protein percentages and methane emissions during milking was negative, -0.09 and -0.18, respectively. The response to selection, estimated through selection indices, demonstrated that it is feasible to reduce methane emissions up to 0.021 mg/L during milking in five generations without detriment to milk components.

The objective of this study was to evaluate short-term variations of trans fatty acids (TFA) in plasma lipoproteins and ruminal fermentation parameters of non-lactating cows subjected to ruminal pulses of vegetable oils. Three non-lactating, non-pregnant Holstein cows, each with a ruminal cannula, were arranged in a 3 × 3 Latin square design with three-day pulsing periods and four-day washout intervals between treatments. Cows were treated with single ruminal pulses of: (1) control (skimmed milk (SM); 500 mL); (2) soybean oil (SO; 250 g/d in 500 mL of SM) and (3) partially-hydrogenated vegetable oil (PHVO; 250 g/d in 500 mL of SM). Time changes after infusion in TFA contents were only observed for plasma C18:1 trans-4, trans-5 and trans-12, and high-density lipoprotein fraction C18:1 trans-9. After ruminal pulses, concentration of acetate decreased linearly; molar concentrations of propionate and valerate increased linearly; molar concentrations of butyrate and isovalerate changed quadratically and were greater at 1 h than at other times. There was an accumulation of several C18:1 TFA in plasma and lipoproteins, especially on the third day of pulsing. Overall, naturally occurring C18:1 TFA isomers (produced during ruminal biohydrogenation of SO) and preformed TFA (supplied by PHVO) elicited differential TFA partitioning and transport in plasma and lipoproteins.

El objetivo de este trabajo fue estimar la respuesta a la selección a través de diferentes índices de selección entre producción de metano y producción y componentes de la leche en los sistemas de lechería tropical especializada, doble propósito y lechería familiar. El muestreo de las emisiones de metano se realizó durante la ordeña mediante el equipo Guardian-NG. Se tomaron muestras de leche de manera individual durante el muestreo de metano. La extracción de ADN se realizó de folículos pilosos de todos los animales incluidos en el estudio. La estimación de los componentes de varianza y covarianza se realizó mediante la metodología de modelos mixtos. Se utilizaron los marcadores moleculares para construir la matriz de relaciones genómicas (Matriz G), debido a que no se contaba con información genealógica completa. La heredabilidad estimada para las emisiones de metano durante la ordeña fue de 0.18 y 0.32 para los análisis univariados y bivariados, respectivamente. Los resultados de las correlaciones genéticas entre porcentaje de grasa y proteína en leche con las emisiones de metano durante la ordeña fueron negativas, -0.09 y -0.18 respectivamente. La respuesta a la selección estimada mediante los índices de selección demostró que es factible obtener reducciones de hasta 0.021 mg/l de emisiones de metano durante la ordeña en cinco generaciones; lo anterior sin detrimento en los componentes de la leche.

This study analyzed effects of vegetable oils fed to dairy cows on abundance of genes related to lipid metabolism in milk somatic cells (MSC). During 63 days, 15 cows were allocated to 3 treatments: a control diet with no added lipid the same diet supplemented with olive oil (OO, 30 g/kg DM) or hydrogenated vegetable oil (HVO, 30 g/kg DM). On days 21, 42 and 63, MSC were obtained from all cows. Relative abundance of genes involved in lipid metabolism in MSC from cows fed control on days 42 and 63 was compared with relative abundance at day 21 to evaluate fold-changes. Those genes without changes over the time were selected to analyze effects of OO and HVO. Compared with control, on day 42, PLIN2 and THRSP were upregulated by OO. Compared with control, on day 21, HVO up regulated ACACA, down regulated FABP3, and on day 63 THRSP and FABP4 were down regulated. Dietary oil supplementation (3% DM) had a modest nutrigenomic effect on different biological functions such as acetate and FA activation and intra-cellular transport, lipid droplet formation, and transcription regulation in MSC.