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.

LAP-MALDI MS Profiling and Identification of Potential Biomarkers for the Detection of Bovine Tuberculosis.

Detecting bovine tuberculosis (bTB) primarily relies on the tuberculin skin test, requiring two separate animal handling events with a period of incubation time (normally 3 days) between them. Here, we present the use of liquid atmospheric pressure (LAP)-MALDI for the identification of bTB infection, employing a three-class prediction model that was obtained by supervised linear discriminant analysis (LDA) and tested with bovine mastitis samples as disease-positive controls. Noninvasive collection of nasal swabs was used to collect samples, which were subsequently subjected to a short (<4 h) sample preparation method. Cross-validation of the three-class LDA model from the processed nasal swabs provided a sensitivity of 75.0% and specificity of 90.1%, with an overall classification accuracy of 85.7%. These values are comparable to those for the skin test, showing that LAP-MALDI MS has the potential to provide an alternative single-visit diagnostic platform that can detect bTB within the same day of sampling.

Transfer of polychlorinated, polybrominated and mixed-halogenated dioxins, furans and biphenyls, polychlorinated naphthalenes and alkanes, polycyclic aromatic hydrocarbons and chlorobenzenes to the milk of dairy cattle from controlled ingestion of industrial and municipal bioresources recycled to agricultural land.

Recycled bioresources (biosolids, compost-like-output, meat and bonemeal ash, poultry litter ash, paper sludge ash) were added to the feed of dairy cattle to simulate incidental ingestion from agricultural utilisation, to investigate the transfer of organic contaminants from the ingested materials to milk. The bioresources were blended with a loamy sand soil at agronomic rates to simulate a single application to land, which was added to the diet at 5 % of the total intake on a dry matter (DM) basis. Biosolids, and control treatments consisting of unamended soil, were also added directly to the feed at 5 % DM. The cattle were fed the bioresource amended diets for a target period of three to four weeks, depending on material, and monitoring continued for four weeks after treatment withdrawal. Milk samples were taken weekly with chemical analysis of selected samples for a range of organic contaminants including: polychlorinated, polybrominated and mixed-halogenated dioxins, furans and biphenyls, polychlorinated naphthalenes and alkanes (often called chlorinated paraffins), polycyclic aromatic hydrocarbons and chlorobenzenes. No statistically significant additional transfer of organic contaminants to the milk was detected due to the relatively low levels of contaminants present when the bioresources were incorporated with soil at agronomic rates. However, direct biosolids ingestion by cattle significantly increased the transfer of contaminants to milk in comparison to control animals. Although present in larger concentrations in biosolids than their chlorinated counterparts, the carry over rates and bioconcentration factors of brominated dioxins and furans were considerably smaller. Direct ingestion of biosolids resulted in most contaminants approaching, but not always completely reaching, steady state concentrations within the treatment feeding period, however, concentrations generally declined to control values within four-weeks after withdrawing the biosolids-amended diet.

Effects of esterification, saturation and amount of fatty acids infused into the rumen or abomasum in lactating dairy cows.

Our objective was to determine the effects of chemical structure, amount, and site of infusion of long-chain fatty acids (LCFA) in lactating dairy cows. Six multiparous Holstein cows were used in a 6 × 6 Latin square design with 21-d periods. During d 1 to 14, 250 g/d of LCFA and during d 15 to 21, 500 g/d of LCFA were infused continuously into either the rumen or abomasum. Treatments were 1) Control (CONT); 200 g/d of meat solubles plus 12 g/d of Tween 80 in 10 L of water, administered half in the rumen and half in abomasum; 2) control plus mostly saturated LCFA into the abomasum (SFAA); 3) control plus mostly saturated LCFA into the rumen (SFAR); 4) control plus soy (mostly unsaturated LCFA) free fatty acids (FFA) into the abomasum (UFAA); 5) control plus soy triglycerides (TG) into the abomasum (TGA); and 6) control plus soy TG into the rumen (TGR). The first 10 d of each period were for adaptation and washout from the previous treatment. The diet consisted of 30% (dry matter basis) corn silage, 20% alfalfa silage and 50% concentrate. Cows infused with UFAA had lower dry matter intake and milk yield than those infused with SFAA or TGA and reductions were greater at the higher infusion amount. Milk fat yield was decreased by UFAA relative to other treatments. Unsaturated LCFA decreased milk fat yield more than saturated LCFA. All LCFA treatments decreased short- and medium-chain FA in milk relative to CONT, with greatest decreases for UFAA. Apparent total tract digestibilities of nutrient fractions were decreased by UFAA compared with TGA and SFAA and tended to be lower at the higher infusion amount. Apparent digestibility of total fatty acids (FA) was greater for SFAR than for SFAA. Plasma glucagon-like peptide-1 was greater for cows infused with UFAA than SFAA or TGA and increased at the higher amount. Plasma cholecystokinin was greater for cows infused with LCFA compared with CONT. Postruminal unsaturated FFA reduced intake and digestibility of nutrients and FA compared with postruminal TG infusion; saturated FA did not decrease dry matter intake or disrupt nutrient digestion. Glucagon-like peptide-1 may be involved in regulation of feed intake by long-chain fatty acids.

Macromineral and trace element concentrations in milk from Finnish Ayrshire cows fed microalgae (Spirulina platensis) and rapeseed (Brassica napus).

Given the lack of research regarding the effect of microalgal supplementation in dairy cows on milk mineral concentrations, this study investigated the effect of feeding different protein supplements in dairy cow diets on milk, feces, and blood plasma mineral concentrations, associated milk and blood plasma transfer efficiencies, and apparent digestibility. Lactating Finnish Ayrshire cows (n = 8) were allocated at the start of the trial to 4 diets used in a replicated 4 × 4 Latin square design experiment: (1) control diet (CON), (2) a pelleted rapeseed supplement (RSS; 2,550 g/d), (3) a mixture of rapeseed and Spirulina platensis (RSAL; 1,280 g of RSS + 570 g of S. platensis per day), and (4) S. platensis (ALG; 1,130 g of S. platensis per day). In each of the 4 experimental periods, a 2-wk adaptation to the experimental diets was followed by a 7-d sampling and measurement period. Feed samples were composited per measurement period, milk, and feed samples (4 consecutive days; d 17-20), and blood plasma samples (d 21) were composited for each cow period (n = 32). Data were statistically analyzed using a linear mixed effects model with diet, period within square, square and their interaction as fixed factors, and cow within square as a random factor. Cows fed ALG were not significantly different in their milk or blood plasma mineral concentrations compared with CON, although feeding ALG increased fecal concentrations of macrominerals (Ca and Mg) and trace elements (Co, Cu, Fe, I, Mn, and Zn), and reduced their apparent digestibility, compared with CON. When compared with CON and ALG, milk from cows fed RSAL and RSS had lower milk I concentrations (-69.6 and -102.7 µg/kg of milk, respectively), but total plasma I concentrations were not affected significantly. Feeding S. platensis to dairy cows did not affect mineral concentrations in cows' blood or milk, but care should be taken when rapeseed is fed to avoid reducing milk I concentrations which may in turn reduce consumers' I intake from milk and dairy products.

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.

LAP-MALDI MS coupled with machine learning: an ambient mass spectrometry approach for high-throughput diagnostics.

Large-scale population screening for early and accurate detection of disease is a key objective for future diagnostics. Ideally, diagnostic tests that achieve this goal are also cost-effective, fast and easily adaptable to new diseases with the potential of multiplexing. Mass spectrometry (MS), particularly MALDI MS profiling, has been explored for many years in disease diagnostics, most successfully in clinical microbiology but less in early detection of diseases. Here, we present liquid atmospheric pressure (LAP)-MALDI MS profiling as a rapid, large-scale and cost-effective platform for disease analysis. Using this new platform, two different types of tests exemplify its potential in early disease diagnosis and response to therapy. First, it is shown that LAP-MALDI MS profiling detects bovine mastitis two days before its clinical manifestation with a sensitivity of up to 70% and a specificity of up to 100%. This highly accurate, pre-symptomatic detection is demonstrated by using a large set of milk samples collected weekly over six months from approximately 500 dairy cows. Second, the potential of LAP-MALDI MS in antimicrobial resistance (AMR) detection is shown by employing the same mass spectrometric setup and similarly simple sample preparation as for the early detection of mastitis.

Equations to predict nitrogen outputs in manure, urine and faeces from beef cattle fed diets with contrasting crude protein concentration.

Accurately predicting nitrogen (N) outputs in manure, urine and faeces from beef cattle is crucial for the realistic assessment of the environmental footprint of beef production and the development of sustainable N mitigation strategies. This study aimed to develop and validate equations for N outputs in manure, urine and faeces for animals under diets with contrasting crude protein (CP) concentrations. Measurements from individual animals (n = 570), including bodyweight, feed intake and chemical composition, and N outputs were (i) analysed as a merged database and also (ii) split into three sub-sets, according to diet CP concentration (low CP, 84-143 g/kg dry matter, n = 190; medium CP, 144-162 g/kg dry matter, n = 190; high CP, 163-217 g/kg dry matter, n = 190). Prediction equations were developed and validated using residual maximum likelihood analysis and mean prediction error (MPE), respectively. In low CP diets the lowest MPE for N outputs in manure, urine and faeces was 0.244, 0.594 and 0.263, respectively; diet CP-specific equations improved accuracy in certain occasions, by 4.9% and 18.3% for manure N output and faeces N output respectively, while a reduction by 5.7% in the prediction accuracy for urinary N output was noticed. In medium CP diets the lowest MPE for N outputs in manure, urine and faeces was 0.227, 0.391 and 0.394, respectively; diet CP-specific equations improved accuracy by 13.2%, 41.2% and 16.8% respectively. In high CP diets the lowest MPE for N outputs in manure, urine and faeces was 0.120, 0.154 and 0.144, respectively; diet CP-specific equations improved accuracy in certain occasions by 5.8%, 9.1% and 6.3% respectively. This study demonstrated that for improved prediction accuracy of N outputs in manure, urine and faeces from beef cattle, the use of dietary CP concentration is essential while dietary starch, fat, and metabolisable energy concentrations can be used to further improve accuracy. In beef cattle fed low CP concentration diets, using diet CP-specific equations improves prediction accuracy when feed intake or dietary CP concentration are not known. However, in beef cattle fed medium or high CP concentration diets, using equations that have been developed from animals fed similar CP concentration diets, substantially improves the prediction accuracy of N outputs in manure, urine and faeces in most cases.

Beneficial effects of multi-species mixtures on N2O emissions from intensively managed grassland swards.

In a field experiment, annual nitrous oxide (N2O) emissions and grassland yield were measured across different plant communities, comprising systematically varying combinations of monocultures and mixtures of three functional groups (FG): grasses (Lolium perenne, Phleum pratense), legumes (Trifolium pratense, Trifolium repens) and herbs (Cichorium intybus, Plantago lanceolata). Plots received 150 kg ha-1 year-1 nitrogen (N) (150 N), except L. perenne monocultures which received two N levels: 150 N and 300 N. The effect of plant diversity on N2O emissions was derived from linear combinations of species performances' in monoculture (species identity) and not from strong interactions between species in mixtures. Increasing from 150 N to 300 N in L. perenne resulted in a highly significant increase in cumulative N2O emissions from 1.39 to 3.18 kg N2O-N ha-1 year-1. Higher N2O emissions were also associated with the legume FG. Emissions intensities (yield-scaled N2O emissions) from multi-species mixture communities around the equi-proportional mixture were lowered due to interactions among species. For N2O emissions scaled by nitrogen yield in forage, the 6-species mixture was significantly lower than L. perenne at both 300 N and 150 N. In comparison to 300 N L. perenne, the same N yield or DM yield could have been produced with the equi-proportional 6-species mixture (150 N) while reducing N2O losses by 63% and 58% respectively. Compared to 150 N L. perenne, the same N yield or DM yield could have been produced with the 6-species mixture while reducing N2O losses by 41% and 24% respectively. Overall, this study found that multi-species grasslands can potentially reduce both N2O emissions and emissions intensities, contributing to the sustainability of grassland production.

Speciation and milk adulteration analysis by rapid ambient liquid MALDI mass spectrometry profiling using machine learning.

Growing interest in food quality and traceability by regulators as well as consumers demands advances in more rapid, versatile and cost-effective analytical methods. Milk, as most food matrices, is a heterogeneous mixture composed of metabolites, lipids and proteins. One of the major challenges is to have simultaneous, quantitative detection (profiling) of this panel of biomolecules to gather valuable information for assessing food quality, traceability and safety. Here, for milk analysis, atmospheric pressure matrix-assisted laser desorption/ionization employing homogenous liquid sample droplets was used on a Q-TOF mass analyzer. This method has the capability to produce multiply charged proteinaceous ions as well as highly informative profiles of singly charged lipids/metabolites. In two examples, this method is coupled with user-friendly machine-learning software. First, rapid speciation of milk (cow, goat, sheep and camel) is demonstrated with 100% classification accuracy. Second, the detection of cow milk as adulterant in goat milk is shown at concentrations as low as 5% with 92.5% sensitivity and 94.5% specificity.

Concentrations of organic contaminants in industrial and municipal bioresources recycled in agriculture in the UK.

Many types of bioresource materials are beneficially recycled in agriculture for soil improvement and as alternative bedding materials for livestock, but they also potentially transfer contaminants into plant and animal foods. Representative types of industrial and municipal bioresources were selected to assess the extent of organic chemical contamination, including: (i) land applied materials: treated sewage sludge (biosolids), meat and bone meal ash (MBMA), poultry litter ash (PLA), paper sludge ash (PSA) and compost-like-output (CLO), and (ii) bedding materials: recycled waste wood (RWW), dried paper sludge (DPS), paper sludge ash (PSA) and shredded cardboard. The materials generally contained lower concentrations of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (PCBs) relative to earlier reports, indicating the decline in environmental emissions of these established contaminants. However, concentrations of polycyclic aromatic hydrocarbons (PAHs) remain elevated in biosolids samples from urban catchments. Polybrominated dibenzo-p-dioxins/dibenzofurans (PBDD/Fs) were present in larger amounts in biosolids and CLO compared to their chlorinated counterparts and hence are of potentially greater significance in contemporary materials. The presence of non-ortho-polychlorinated biphenyls (PCBs) in DPS was probably due to non-legacy sources of PCBs in paper production. Flame retardent chemicals were one of the most significant and extensive groups of contaminants found in the bioresource materials. Decabromodiphenylether (deca-BDE) was the most abundant polybrominated diphenyl ether (PBDE) and may explain the formation and high concentrations of PBDD/Fs detected. Emerging flame retardant compounds, including: decabromodiphenylethane (DBDPE) and organophosphate flame retardants (OPFRs), were also detected in several of the materials. The profile of perfluoroalkyl substances (PFAS) depended on the type of waste category; perfluoroundecanoic acid (PFUnDA) was the most significant PFAS for DPS, whereas perfluorooctane sulfonate (PFOS) was dominant in biosolids and CLO. The concentrations of polychlorinated alkanes (PCAs) and di-2-ethylhexyl phthalate (DEHP) were generally much larger than the other contaminants measured, indicating that there are major anthropogenic sources of these potentially hazardous chemicals entering the environment. The study results suggest that continued vigilance is required to control emissions and sources of these contaminants to support the beneficial use of secondary bioresource materials.

A computer vision approach to improving cattle digestive health by the monitoring of faecal samples.

The digestive health of cows is one of the primary factors that determine their well-being and productivity. Under- and over-feeding are both commonplace in the beef and dairy industry; leading to welfare issues, negative environmental impacts, and economic losses. Unfortunately, digestive health is difficult for farmers to routinely monitor in large farms due to many factors including the need to transport faecal samples to a laboratory for compositional analysis. This paper describes a novel means for monitoring digestive health via a low-cost and easy to use imaging device based on computer vision. The method involves the rapid capture of multiple visible and near-infrared images of faecal samples. A novel three-dimensional analysis algorithm is then applied to objectively score the condition of the sample based on its geometrical features. While there is no universal ground truth for comparison of results, the order of scores matched a qualitative human prediction very closely. The algorithm is also able to detect the presence of undigested fibres and corn kernels using a deep learning approach. Detection rates for corn and fibre in image regions were of the order 90%. These results indicate the potential to develop this system for on-farm, real time monitoring of the digestive health of individual animals, allowing early intervention to effectively adjust feeding strategy.

Liquid Atmospheric Pressure Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Adds Enhanced Functionalities to MALDI MS Profiling for Disease Diagnostics.

A liquid matrix-assisted laser desorption/ionization (liquid MALDI) method has been developed for high-throughput atmospheric pressure (AP) mass spectrometry (MS) analysis of the molecular content of crude bioliquids for disease diagnostics. The presented method is rapid and highly robust, enabling its application in environments where speed and low-cost high-throughput analyses are required. Importantly, because of the creation of multiply charged analyte ions, it provides additional functionalities that conventional solid MALDI MS profiling is lacking, including the use of high-performance mass analyzers with limited m/z range. The concomitant superior MS/MS performance that is achieved similar to ESI MS/MS adds greater analytical power and specificity to MALDI MS profiling while retaining the advantages of a fast laser-based analysis system and off-line large-scale sample preparation. The potential of this MALDI MS profiling method is demonstrated on the detection of dairy cow mastitis, which is a substantial economic burden on the dairy industry with losses of hundreds of dollars per diseased cow per year, equating to a total annual loss of billions of dollars, as well as leading to the use of large quantities of antibiotics, adding to the proliferation of antimicrobial resistance. Only small amounts of aliquots obtained from the daily farm milking process were prepared for liquid MALDI MS profiling using a simple one-pot/two-step analyte extraction. Automated analysis was performed using a custom-built AP-MALDI ion source, enabling the simultaneous detection of lipids, peptides, and proteins. Diagnostic, multiply charged, proteinaceous ions were easily sequenced and identified by MS/MS experiments. Samples were classified according to mastitis status using multivariate analysis, achieving 98.5% accuracy (100% specificity) determined by "leave 20% out" cross-validation. The methodology is generally applicable to AP-MALDI MS profiling on most commercial high-resolution mass spectrometers, with the potential for expansion into hospitals for rapid assessment of human and other biofluids.

Prediction of Lignin Content in Ruminant Diets and Fecal Samples Using Rapid Analytical Techniques.

The measurement of lignin content in ruminant diet and fecal samples is important for digestibility studies, but it is typically time-consuming and costly. The work reported involves correlation of traditional wet chemistry data with those from three rapid instrumental techniques, Fourier transform infrared spectroscopy (FTIR), conventional thermogravimteric analysis (TGA), and high-resolution TGA (MaxRes TGA) to predict the lignin content of diets and feces from digestibility trials. Calibration and performance data indicate that the FTIR model is acceptable for screening, while the conventional and MaxRes TGA predictions are high accuracy for quantitative analysis. Cross validation and model performance data reveal that MaxRes TGA provides the best-performing predictive model. This work shows that MaxRes TGA can accurately predict lignin content in ruminant diet and fecal samples with distinct advantages over traditional wet chemistry: namely, the requirement of small sample size, ease of sample preparation, speed of analysis, and high sample throughput at considerably lower cost.

Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.

Enteric methane (CH4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

Influence of ruminal methane on digesta retention and digestive physiology in non-lactating dairy cattle.

Enteric methane (CH4) production is a side-effect of herbivore digestion, but it is unknown whether CH4 itself influences digestive physiology. We investigated the effect of adding CH4 to, or reducing it in, the reticulorumen (RR) in a 4×4 Latin square experiment with rumen-fistulated, non-lactating cows, with four treatments: (i) control, (ii) insufflation of CH4 (iCH4), (iii) N via rumen fistula, (iv) reduction of CH4 via administration of bromochloromethane (BCM). DM intake (DMI), apparent total tract digestibility, digesta mean retention times (MRT), rumen motility and chewing activity, spot breath CH4 emission (CH4exhal, litre/kg DMI) as well as CH4 dissolved in rumen fluid (CH4RRf, µg/ml) were measured. Data were analysed using mixed models, including treatment (or, alternatively, CH4exhal or CH4RRf) and DMI relative to body mass0·85 (rDMI) as covariates. rDMI was the lowest on the BCM treatment. CH4exhal was highest for iCH4 and lowest for BCM treatments, whereas only BCM affected (reduced) CH4RRf. After adjusting for rDMI, CH4RRf had a negative association with MRT in the gastrointestinal tract but not in the RR, and negative associations with fibre digestibility and measures of rumination activity. Adjusting for rDMI, CH4exhal had additionally a negative association with particle MRT in the RR and a positive association with rumen motility. Thus, higher rumen levels of CH4 (CH4exhal or CH4RRf) were associated with shorter MRT and increased motility. These findings are tentatively interpreted as a feedback mechanism in the ruminant digestive tract that aims at mitigating CH4 losses by shortening MRT at higher CH4.

The impact of obesity-related SNP on appetite and energy intake.

An increasing number of studies have reported a heritable component for the regulation of energy intake and eating behaviour, although the individual polymorphisms and their 'effect size' are not fully elucidated. The aim of the present study was to examine the relationship between specific SNP and appetite responses and energy intake in overweight men. In a randomised cross-over trial, forty overweight men (age 32 (sd 09) years; BMI 27 (sd 2) kg/m2) attended four sessions 1 week apart and received three isoenergetic and isovolumetric servings of dairy snacks or water (control) in random order. Appetite ratings were determined using visual analogue scales and energy intake at an ad libitum lunch was assessed 90 min after the dairy snacks. Individuals were genotyped for SNP in the fat mass and obesity-associated (FTO), leptin (LEP), leptin receptor (LEPR) genes and a variant near the melanocortin-4 receptor (MC4R) locus. The postprandial fullness rating over the full experiment following intake of the different snacks was 17·2 % (P= 0·026) lower in A carriers compared with TT homozygotes for rs9939609 (FTO, dominant) and 18·6 % (P= 0·020) lower in G carriers compared with AA homozygotes for rs7799039 (LEP, dominant). These observations indicate that FTO and LEP polymorphisms are related to the variation in the feeling of fullness and may play a role in the regulation of food intake. Further studies are required to confirm these initial observations and investigate the 'penetrance' of these genotypes in additional population subgroups.

Differential effects of dairy snacks on appetite, but not overall energy intake.

Dietary regulation of appetite may contribute to the prevention and management of excess body weight. The present study examined the effect of consumption of individual dairy products as snacks on appetite and subsequent ad libitum lunch energy intake. In a randomised cross-over trial, forty overweight men (age 32 (sd 9) years; BMI 27 (sd 2) kg/m2) attended four sessions 1 week apart and received three isoenergetic (841 kJ) and isovolumetric (410 ml) servings of dairy snacks or water (control) 120 min after breakfast. Appetite profile was determined throughout the morning and ad libitum energy intake was assessed 90 min after the intake of snacks. Concentrations of amino acids, glucose, insulin, ghrelin and peptide tyrosine tyrosine were measured at baseline (0 min) and 80 min after the intake of snacks. Although the results showed that yogurt had the greatest suppressive effect on appetite, this could be confounded by the poor sensory ratings of yogurt. Hunger rating was 8, 10 and 24 % (P < 0·001) lower after the intake of yogurt than cheese, milk and water, respectively. Energy intake was 11, 9 and 12 % (P < 0·02) lower after the intake of yogurt, cheese and milk, respectively, compared with water (4312 (se 226) kJ). Although there was no difference in the postprandial responses of hormones, alanine and isoleucine concentrations were higher after the intake of yogurt than cheese and milk (P < 0·05). In conclusion, all dairy snacks reduced appetite and lunch intake compared with water. Yogurt had the greatest effect on suppressing subjective appetite ratings, but did not affect subsequent food intake compared with milk or cheese.

Associations between dairy consumption and body weight: a review of the evidence and underlying mechanisms.

As the incidence of obesity is reaching 'epidemic' proportions, there is currently widespread interest in the impact of dietary components on body-weight and food intake regulation. The majority of data available from both epidemiological and intervention studies provide evidence of a negative but modest association between milk and dairy product consumption and BMI and other measures of adiposity, with indications that higher intakes result in increased weight loss and lean tissue maintenance during energy restriction. The purported physiological and molecular mechanisms underlying the impact of dairy constituents on adiposity are incompletely understood but may include effects on lipolysis, lipogeneis and fatty acid absorption. Furthermore, accumulating evidence indicates an impact of dairy constituents, in particular whey protein derivatives, on appetite regulation and food intake. The present review summarises available data and provides an insight into the likely contribution of dairy foods to strategies aimed at appetite regulation, weight loss or the prevention of weight gain.

Abomasal infusion of casein, starch and soybean oil differentially affect plasma concentrations of gut peptides and feed intake in lactating dairy cows.

The effects of specific nutrients on secretion and plasma concentrations of gut peptides (glucagon-like peptide-1((7-36)) amide (GLP-1), glucose-dependent insulinotropic polypeptide (GIP), and cholecystokinin-8 (CCK)) differ across species, but are not reported for cattle. Our objective was to determine acute (hours) and chronic (1 week) effects of increased abomasal supply of protein, carbohydrate, or fat to the small intestine on dry matter intake (DMI) and plasma concentrations of GLP-1, GIP, CCK, and insulin. Four mid-lactation Holstein cows were used in a 4 x 4 Latin square design experiment. Treatments were 7-day abomasal infusions of water, soybean oil (500 g/d), corn starch (1100 g/d), or casein (800 g/d). Jugular vein plasma was obtained over 7h at the end of the first and last day of infusions. Oil infusion decreased DMI on day 7, but total metabolizable energy (ME) supply (diet plus infusate) did not differ from water infusion. Casein and starch infusion had no effect on feed DMI; thus, ME supply increased. Decreased DMI on day 7 of oil infusion was accompanied by increased plasma GLP-1 concentration, but decreased plasma CCK concentration. Increased plasma GIP concentration was associated with increased ME supply on day 7 of casein and starch infusion. Casein infusion tended to increase plasma CCK concentration on both days of sampling, and increased plasma GLP-1 and insulin concentration on day 1 of infusion. The present data indicate a sustained elevation of plasma concentration of GLP-1, but not CCK, may contribute to the reduced DMI observed in dairy cows provided supplemental fat.