Models for predicting enteric methane emissions from dairy cows in North America,Europe, and Australia and New Zealand

There are several models in the literature for predicting enteric methane (CH₄) emissions. These models were often developed on region or country‐specific data and may not be able to predict the emissions successfully in every region. The majority of extant models require dry matter intake (DMI) of individual animals, which is not routinely measured. The objectives of this study were to (i) evaluate performance of extant models in predicting enteric CH₄ emissions from dairy cows in North America (NA), Europe (EU), and Australia and New Zealand (AUNZ) and (ii) explore the performance using estimated DMI. Forty extant models were challenged on 55, 105, and 52 enteric CH₄ measurements (g per lactating cow per day) from NA, EU, and AUNZ, respectively. The models were ranked using root mean square prediction error as a percentage of the average observed value (RMSPE) and concordance correlation coefficient (CCC). A modified model of Nielsen et al. (Acta Agriculturae Scand Section A, 63 , 2013 and 126) using DMI, and dietary digestible neutral detergent fiber and fatty acid contents as predictor variables, were ranked highest in NA (RMSPE = 13.1% and CCC = 0.78). The gross energy intake‐based model of Yan et al. (Livestock Production Science, 64 , 2000 and 253) and the updated IPCC Tier 2 model were ranked highest in EU (RMSPE = 11.0% and CCC = 0.66) and AUNZ (RMSPE = 15.6% and CCC = 0.75), respectively. DMI of cows in NA and EU was estimated satisfactorily with body weight and fat‐corrected milk yield data (RMSPE < 12.0% and CCC > 0.60). Using estimated DMI, the Nielsen et al. (2013) (RMSPE = 12.7 and CCC = 0.79) and Yan et al. (2000) (RMSPE = 13.7 and CCC = 0.50) models still predicted emissions in respective regions well. Enteric CH₄ emissions from dairy cows can be predicted successfully (i.e., RMSPE < 15%), if DMI can be estimated with reasonable accuracy (i.e., RMSPE < 10%).     

DM intake by feedlot beef cattle: factors that impact intake patterns

DM intake (DMI) for individual pens of cattle is recorded daily or averaged across each week by most commercial feedlots as an index of performance. Numerous factors impact DMI by feedlot cattle. Some are available at the start of the feedlot period (initial BW, sex), and others become available early in the feeding period (daily DMI during adaptation) or more continuously (daily DMI from the previous week). To evaluate the relative impact of these factors on daily DMI during individual weeks within the feedlot period, we employed a dataset compiled from 2009 to 2014 from one commercial feedlot, including 4 132 pens (485 458 cattle), which were split into two fractions: 80% were used to calculate DMI regressions on these factors to develop a prediction equation for mean DMI for each week of the feeding period, and 20% were reserved to test the adequacy of these prediction equations. Correlations were used to determine the relationship between all available variables with observed DMI. These variables were then included in the generalized least squares regression models. A veracity test of the model was performed against the reserved data. Daily DMI from previous week was the factor most highly correlated with daily DMI (P < 0.10) during from week 6 to week 31, accounting for approximately 70% of the variation, followed by mean daily DMI during adaptation period (weeks 1–4), including in the prediction model from weeks 5 to 12. Initial shrunk BW (ISBW) was the third most correlated factor, which was included in prediction equations from week 5 to week 20. Sex entered the prediction model only after week 8. Daily DMI for each test week within the feeding period was predicted closely (r² = 0.98) by these four factors (RMSE = 0.155 kg). In conclusion, the mean daily DMI during each week of the finishing period for a pen of cattle could be predicted closely based on mean daily DMI intake during the previous week plus other variables available early in a feedlot period (daily DMI during adaptation period, ISBW and sex).     

Monitoring and assessment of ingestive chewing sounds for prediction of herbage intake rate in grazing cattle

Accurate measurement of herbage intake rate is critical to advance knowledge of the ecology of grazing ruminants. This experiment tested the integration of behavioral and acoustic measurements of chewing and biting to estimate herbage dry matter intake (DMI) in dairy cows offered micro-swards of contrasting plant structure. Micro-swards constructed with plastic pots were offered to three lactating Holstein cows (608±24.9 kg of BW) in individual grazing sessions (n=48). Treatments were a factorial combination of two forage species (alfalfa and fescue) and two plant heights (tall=25±3.8 cm and short=12±1.9 cm) and were offered on a gradient of increasing herbage mass (10 to 30 pots) and number of bites (~10 to 40 bites). During each grazing session, sounds of biting and chewing were recorded with a wireless microphone placed on the cows’ foreheads and a digital video camera to allow synchronized audio and video recordings. Dry matter intake rate was higher in tall alfalfa than in the other three treatments (32±1.6 v. 19±1.2 g/min). A high proportion of jaw movements in every grazing session (23 to 36%) were compound jaw movements (chew-bites) that appeared to be a key component of chewing and biting efficiency and of the ability of cows to regulate intake rate. Dry matter intake was accurately predicted based on easily observable behavioral and acoustic variables. Chewing sound energy measured as energy flux density (EFD) was linearly related to DMI, with 74% of EFD variation explained by DMI. Total chewing EFD, number of chew-bites and plant height (tall v. short) were the most important predictors of DMI. The best model explained 91% of the variation in DMI with a coefficient of variation of 17%. Ingestive sounds integrate valuable information to remotely monitor feeding behavior and predict DMI in grazing cows.     

Developing and validating a model to predict the dry matter intake of grazing lactating beef cows

Current techniques for measuring the dry matter intake (DMI) of grazing lactating beef cows are invasive, time consuming and expensive making them impractical for use on commercial farms. This study was undertaken to explore the potential to develop and validate a model to predict DMI of grazing lactating beef cows, which could be applied in a commercial farm setting, using non-invasive animal measurements. The calibration dataset used to develop the model was comprised of 94 measurements recorded on 106 beef or beef–dairy crossbred cows (maternal origin). The potential of body measurements, linear type scoring, grazing behaviour and thermal imaging to predict DMI in combination with known biologically plausible adjustment variables and energy sinks was investigated. Multivariable regression models were constructed for each independent variable using SAS PROC REG and contained milk yield, BW, parity, calving day and maternal origin (dairy or beef). Of the 94 variables tested, 32 showed an association with DMI (P < 0.25) upon multivariable analysis. These variables were incorporated into a backwards linear regression model using SAS PROC REG. Variables were retained in this model if P < 0.05. Five variables; width at pins, full body depth, ruminating mastications, central ligament and rump width score, were retained in the model in addition to milk yield, BW, parity, calving day and maternal origin. The inclusion of these variables in the model increased the predictability of DMI by 0.23 (R² = 0.68) when compared to a model containing milk yield, BW, parity, calving day and maternal origin only. This model was applied to data recorded on an independent dataset; a herd of 60 lactating beef cows two years after the calibration study. The R² for the validation was 0.59. Estimates of DMI are required for measuring feed efficiency. While acknowledging challenges in applicability, the findings suggest a model such as that developed in this study may be used as a tool to more easily and less invasively estimate DMI on large populations of commercial beef cows, and therefore measure feed efficiency.     

Meta-analysis of spineless cactus feeding to meat lambs: performance and development of mathematical models to predict dry matter intake and average daily gain

Spineless cactus is a useful feed for various animal species in arid and semiarid regions due to its adaptability to dry and harsh soil, high efficiency of water use and carbohydrates storage. This meta-analysis was carried out to assess the effect of spineless cactus on animal performance, and develop and evaluate equations to predict dry matter intake (DMI) and average daily gain (ADG) in meat lambs. Equations for predicting DMI and ADG as a function of animal and diet characteristics were developed using data from eight experiments. The dataset was comprised of 40 treatment means from 289 meat lambs, in which cactus was included from 0 to 75% of the diet dry matter (DM). Accuracy and precision were evaluated by cross-validation using the mean square error of prediction (MSEP), which was decomposed into mean bias, systematic bias and random error; concordance correlation coefficient, which was decomposed into accuracy (C_b) and precision (ρ); and coefficient of determination (R²). In addition, the data set was used to evaluate the predicting accuracy and precision of the main lamb feeding systems (Agricultural and Food Research Council, Small Ruminant Nutritional System, National Research Council and Institut National de la Recherche Agronomique) and also two Brazilian studies. The DMI, CP intake (CPI), metabolizable energy (ME) intake and ADG increased when cactus was included up to 499 g/kg DM (P<0.001). In contrast, animals fed high levels of cactus (>500 g/kg DM) had a decreased DMI, CPI and NDF intake, but increased feed efficiency (P<0.001) and similar ADG compared with those without cactus addition. The DMI was positively correlated with initial BW, final BW, concentrate and ADG, while it was negatively correlated with cactus inclusion and ME of the diet. On other hand, ADG was positively correlated with DMI, initial and mean BW and concentrate, and it was negatively correlated with cactus inclusion. The two developed equations had high accuracy (C_b of 0.95 for DMI and 0.94 for ADG) and the random error of MSEP was 99% for both equations. The precision of both equations was moderate, with R² values of 0.53 and 0.50 and ρ values of 0.73 and 0.71 for DMI and ADG, respectively. In conclusion, the developed equation to predict DMI had moderate precision and high accuracy, nonetheless, it was more efficient than those reported in the literature. The proposed equations can be a useful alternative to estimate intake and performance of lambs fed cactus.     

Relationships between methane emission of Holstein Friesian dairy cows and fatty acids,volatile metabolites and non-volatile metabolites in milk

This study investigated the relationships between methane (CH₄) emission and fatty acids, volatile metabolites (V) and non-volatile metabolites (NV) in milk of dairy cows. Data from an experiment with 32 multiparous dairy cows and four diets were used. All diets had a roughage : concentrate ratio of 80 : 20 based on dry matter (DM). Roughage consisted of either 1000 g/kg DM grass silage (GS), 1000 g/kg DM maize silage (MS), or a mixture of both silages (667 g/kg DM GS and 333 g/kg DM MS; 333 g/kg DM GS and 677 g/kg DM MS). Methane emission was measured in climate respiration chambers and expressed as production (g/day), yield (g/kg dry matter intake; DMI) and intensity (g/kg fat- and protein-corrected milk; FPCM). Milk was sampled during the same days and analysed for fatty acids by gas chromatography, for V by gas chromatography–mass spectrometry, and for NV by nuclear magnetic resonance. Several models were obtained using a stepwise selection of (1) milk fatty acids (MFA), V or NV alone, and (2) the combination of MFA, V and NV, based on the minimum Akaike’s information criterion statistic. Dry matter intake was 16.8±1.23 kg/day, FPCM yield was 25.0±3.14 kg/day, CH₄ production was 406±37.0 g/day, CH₄ yield was 24.1±1.87 g/kg DMI and CH₄ intensity was 16.4±1.91 g/kg FPCM. The observed CH₄ emissions were compared with the CH₄ emissions predicted by the obtained models, based on concordance correlation coefficient (CCC) analysis. The best models with MFA alone predicted CH₄ production, yield and intensity with a CCC of 0.80, 0.71 and 0.69, respectively. The best models combining the three types of metabolites included MFA and NV for CH₄ production and CH₄ yield, whereas for CH₄ intensity MFA, NV and V were all included. These models predicted CH₄ production, yield and intensity better with a higher CCC of 0.92, 0.78 and 0.93, respectively, and with increased accuracy (C_b) and precision (r). The results indicate that MFA alone have moderate to good potential to estimate CH₄ emission, and furthermore that including V (CH₄ intensity only) and NV increases the CH₄ emission prediction potential. This holds particularly for the prediction model for CH₄ intensity.     

Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters

The objectives of the present study were to examine relationships between methane (CH4) output and animal and dietary factors, and to use these relationships to develop prediction equations for CH4 emission from beef cattle. The dataset was obtained from 108 growing-to-finishing beef steers in five studies and CH4 production and energy metabolism data were measured in indirect respiration calorimeter chambers. Dietary forage proportion ranged from 29.5% to 100% (dry matter (DM) basis) and forages included grass silage, fresh grass, dried grass and fodder beet. Linear and multiple regression techniques were used to examine relationships between CH4 emission and animal and dietary variables, with the effects of experiment or forage type removed. Total CH4 emission was positively related to live weight (LW), feeding level and intake of feed (DM and organic matter) and energy (gross energy (GE), digestible energy (DE) and metabolisable energy (ME)) (P < 0.001), while CH4/DM intake (DMI) was negatively related to energy digestibility and ME/GE (P < 0.05 or less). Using LW alone to predict CH4 emission produced a poor relationship when compared to DMI and GE intake (GEI) (R2 = 0.26 v. 0.68 and 0.70 respectively). Adding feeding level, dietary NDF concentration and CP/ME or feeding level, energy digestibility and ME/GE to support LW resulted in a R2 of 0.66 or 0.84. The high R2 (0.84) was similar to that obtained using DMI or GEI together with energy digestibility and ME/GE as predictors. Further inclusion of dietary forage proportion and ADF and NDF concentration to the multiple relationships using GEI as the primary predictor resulted in a R2 of 0.87. These equations were evaluated through internal validation, by developing a range of similar new equations from two-thirds of the present data and then validating these new equations with the remaining one-third of data. The validation indicated that addition of energy digestibility and ME/GE to support LW with feeding level, DMI and GEI considerably increased the prediction accuracy. It is concluded that CH4 emission of beef steers can be accurately predicted from LW plus feeding level, DMI or GEI together with energy digestibility and ME/GE. The dataset was also used to validate a range of prediction equations for CH4 production of cattle published elsewhere.     

Estimating daily methane production in individual cattle with irregular feed intake patterns from short-term methane emission measurements

Spot measurements of methane emission rate (n = 18 700) by 24 Angus steers fed mixed rations from GrowSafe feeders were made over 3- to 6-min periods by a GreenFeed emission monitoring (GEM) unit. The data were analysed to estimate daily methane production (DMP; g/day) and derived methane yield (MY; g/kg dry matter intake (DMI)). A one-compartment dose model of spot emission rate v. time since the preceding meal was compared with the models of Wood (1967) and Dijkstra et al. (1997) and the average of spot measures. Fitted values for DMP were calculated from the area under the curves. Two methods of relating methane and feed intakes were then studied: the classical calculation of MY as DMP/DMI (kg/day); and a novel method of estimating DMP from time and size of preceding meals using either the data for only the two meals preceding a spot measurement, or all meals for 3 days prior. Two approaches were also used to estimate DMP from spot measurements: fitting of splines on a ‘per-animal per-day’ basis and an alternate approach of modelling DMP after each feed event by least squares (using Solver), summing (for each animal) the contributions from each feed event by best-fitting a one-compartment model. Time since the preceding meal was of limited value in estimating DMP. Even when the meal sizes and time intervals between a spot measurement and all feeding events in the previous 72 h were assessed, only 16.9% of the variance in spot emission rate measured by GEM was explained by this feeding information. While using the preceding meal alone gave a biased (underestimate) of DMP, allowing for a longer feed history removed this bias. A power analysis taking into account the sources of variation in DMP indicated that to obtain an estimate of DMP with a 95% confidence interval within 5% of the observed 64 days mean of spot measures would require 40 animals measured over 45 days (two spot measurements per day) or 30 animals measured over 55 days. These numbers suggest that spot measurements could be made in association with feed efficiency tests made over 70 days. Spot measurements of enteric emissions can be used to define DMP but the number of animals and samples are larger than are needed when day-long measures are made.     

Methane emissions from two breeds of beef cows offered diets containing barley straw with either grass silage or brewers’ grains

Increasing the concentration of dietary lipid is a promising strategy for reducing methane (CH₄) emissions from ruminants. This study investigated the effect of replacing grass silage with brewers’ grains on CH₄ emissions of pregnant, non-lactating beef cows of two breeds. The experiment was a two×two factorial design comprising two breeds (LIMx, crossbred Limousin; and LUI, purebred Luing) and two diets consisting of (g/kg diet dry matter (DM)) barley straw (687) and grass silage (301, GS), or barley straw (763) and brewers’ grains (226, BG), which were offered ad libitum. Replacing GS with BG increased the acid-hydrolysed ether extract concentration from 21 to 37 g/kg diet DM. Cows (n=48) were group-housed in equal numbers of each breed across two pens and each diet was allocated to one pen. Before measurements of CH₄, individual dry matter intake (DMI), weekly BW and weekly body condition score were measured for a minimum of 3 weeks, following a 4-week period to acclimatise to the diets. CH₄ emissions were subsequently measured on one occasion from each cow using individual respiration chambers. Due to occasional equipment failures, CH₄ measurements were run over 9 weeks giving 10 observations for each breed×treatment combination (total n=40). There were no differences between diets for daily DMI measured in the chambers (9.92 v. 9.86 kg/day for BG and GS, respectively; P>0.05). Cows offered the BG diet produced less daily CH₄ than GS-fed cows (131 v. 156 g/day: P<0.01). When expressed either as g/kg DMI or kJ/MJ gross energy intake (GEI), BG-fed cows produced less CH₄ than GS-fed cows (13.5 v. 16.4 g/kg DMI, P<0.05; 39.2 v. 48.6 kJ/MJ GEI, P<0.01). Breed did not affect daily DMI or CH₄ expressed as g/day, g/kg DMI or kJ/MJ GEI (P>0.05). However, when expressed as a proportion of metabolic BW (BW^0.75), LUI cows had greater DMI than LIMx cows (84.5 v. 75.7 g DMI/kg BW^0.75, P<0.05) and produced more CH₄ per kg BW^0.75 than LIMx cows (1.30 v. 1.05 g CH₄/kg BW^0.75; P<0.01). Molar proportions of acetate were higher (P<0.001) and propionate and butyrate lower (P<0.01) in rumen fluid samples from BG-fed compared with GS-fed cows. This study demonstrated that replacing GS with BG in barley straw-based diets can effectively reduce CH₄ emissions from beef cows, with no suppression of DMI.     

Use of short-term breath measures to estimate daily methane production by cattle

Methods to measure enteric methane (CH₄) emissions from individual ruminants in their production environment are required to validate emission inventories and verify mitigation claims. Estimates of daily methane production (DMP) based on consolidated short-term emission measurements are developing, but method verification is required. Two cattle experiments were undertaken to test the hypothesis that DMP estimated by averaging multiple short-term breath measures of methane emission rate did not differ from DMP measured in respiration chambers (RC). Short-term emission rates were obtained from a GreenFeed Emissions Monitoring (GEM) unit, which measured emission rate while cattle consumed a dispensed supplement. In experiment 1 (Expt. 1), four non-lactating cattle (LW=518 kg) were adapted for 18 days then measured for six consecutive periods. Each period consisted of 2 days of ad libitum intake and GEM emission measurement followed by 1 day in the RC. A prototype GEM unit releasing water as an attractant (GEM water) was also evaluated in Expt. 1. Experiment 2 (Expt. 2) was a larger study based on similar design with 10 cattle (LW=365 kg), adapted for 21 days and GEM measurement was extended to 3 days in each of the six periods. In Expt. 1, there was no difference in DMP estimated by the GEM unit relative to the RC (209.7 v. 215.1 g CH₄/day) and no difference between these methods in methane yield (MY, 22.7 v. 23.7 g CH₄/kg of dry matter intake, DMI). In Expt. 2, the correlation between GEM and RC measures of DMP and MY were assessed using 95% confidence intervals, with no difference in DMP or MY between methods and high correlations between GEM and RC measures for DMP (r=0.85; 215 v. 198 g CH₄/day SEM=3.0) and for MY (r=0.60; 23.8 v. 22.1 g CH₄/kg DMI SEM=0.42). When data from both experiments was combined neither DMP nor MY differed between GEM- and RC-based measures (P>0.05). GEM water-based estimates of DMP and MY were lower than RC and GEM (P<0.05). Cattle accessed the GEM water unit with similar frequency to the GEM unit (2.8 v. 3.5 times/day, respectively) but eructation frequency was reduced from 1.31 times/min (GEM) to once every 2.6 min (GEM water). These studies confirm the hypothesis that DMP estimated by averaging multiple short-term breath measures of methane emission rate using GEM does not differ from measures of DMP obtained from RCs. Further, combining many short-term measures of methane production rate during supplement consumption provides an estimate of DMP, which can be usefully applied in estimating MY.     

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.     

Differential effects of oilseed supplements on methane production and milk fatty acid concentrations in dairy cows

It is known that supplementing dairy cow diets with full-fat oilseeds can be used as a strategy to mitigate methane emissions, through their action on rumen fermentation. However, direct comparisons of the effect of different oil sources are very few, as are studies implementing supplementation levels that reflect what is commonly fed on commercial farms. The objective was to investigate the effect of feeding different forms of supplemental plant oils on both methane emissions and milk fatty acid (FA) profile. Four multiparous, Holstein-Friesian cows in mid-lactation were randomly allocated to one of four treatment diets in a 4×4 Latin square design with 28-day periods. Diets were fed as a total mixed ration with a 50 : 50 forage : concentrate ratio (dry matter (DM) basis) with the forage consisting of 75 : 25 maize silage : grass silage (DM). Dietary treatments were a control diet containing no supplemental fat, and three treatment diets containing extruded linseed (EL), calcium salts of palm and linseed oil (CPLO) or milled rapeseed (MR) formulated to provide each cow with an estimated 500 g additional oil/day (22 g oil/kg diet DM). Dry matter intake (DMI), milk yield, milk composition and methane production were measured at the end of each experimental period when cows were housed in respiration chambers for 4 days. There was no effect of treatment diet on DMI or milk protein or lactose concentration, but oilseed-based supplements increased milk yield compared with the control diet and milk fat concentration relative to control was reduced by 4 g/kg by supplemental EL. Feeding CPLO reduced methane production, and both linseed-based supplements decreased methane yield (by 1.8 l/kg DMI) and intensity (by 2.7 l/kg milk yield) compared with the control diet, but feeding MR had no effect on methane emission. All the fat supplements decreased milk total saturated fatty acid (SFA) concentration compared with the control, and SFA were replaced with mainly cis-9 18:1 but also trans FA (and in the case of EL and CPLO there were increases in polyunsaturated FA concentration). Supplementing dairy cow diets with these oilseed-based preparations affected milk FA profile and increased milk yield. However, only the linseed-based supplements reduced methane production, yield or intensity, whereas feeding MR had no effect.     

Ubiquitous parasites drive a 33% increase in methane yield from livestock

Of anthropogenic methane emissions, 40% can be attributed to agriculture, the majority of which are from enteric fermentation in livestock. With international commitments to tackle drivers of climate change, there is a need to lower global methane emissions from livestock production. Gastrointestinal helminths (parasitic worms) are globally ubiquitous and represent one of the most pervasive challenges to the health and productivity of grazing livestock. These parasites influence a number of factors affecting methane emissions including feed efficiency, nutrient use, and production traits. However, their effects on methane emissions are unknown. This is to our knowledge the first study that empirically demonstrates disease-driven increases in methane (CH₄) yield in livestock (grams of CH₄ per kg of dry matter intake). We do this by measuring methane emissions (in respiration chambers), dry matter intake, and production parameters for parasitised and parasite-free lambs. This study shows that parasite infections in lambs can lead to a 33% increase in methane yield (g?CH₄/kg DMI). This knowledge will facilitate more accurate calculations of the true environmental costs of parasitism in livestock, and reveals the potential benefits of mitigating emission through controlling parasite burdens.     

Grazed grass herbage intake and performance of beef heifers with predetermined phenotypic residual feed intake classification

Data were collected on 85 Simmental and Simmental × Holstein–Friesian heifers. During the indoor winter period, they were offered grass silage ad libitum and 2 kg of concentrate daily, and individual dry matter intake (DMI) and growth was recorded over 84 days. Individual grass herbage DMI was determined at pasture over a 6-day period, using the n-alkane technique. Body condition score, skeletal measurements, ultrasonic fat and muscle depth, visual muscularity score, total tract digestibility, blood hormones, metabolites and haematology variables and activity behaviour were measured for all heifers. Phenotypic residual feed intake (RFI) was calculated for each animal as the difference between actual DMI and expected DMI during the indoor winter period. Expected DMI was calculated for each animal by regressing average daily DMI on mid-test live weight (LW)^0.75 and average daily gain (ADG) over an 84-day period. Standard deviations above and below the mean were used to group animals into high (>0.5 s.d.), medium (±0.5 s.d.) and low (<0.5 s.d.) RFI. Overall mean (s.d.) values for DMI (kg/day), ADG (kg), feed conversion ratio (FCR) kg DMI/kg ADG and RFI (kg dry matter/day) were 5.82 (0.73), 0.53 (0.18), 12.24 (4.60), 0.00 (0.43), respectively, during the RFI measurement period. Mean DMI (kg/day) and ADG (kg) during the grazing season was 9.77 (1.77) and 0.77 (0.14), respectively. The RFI groups did not differ (P > 0.05) in LW, ADG or FCR at any stage of measurement. RFI was positively correlated (r = 0.59; P < 0.001) with DMI during the RFI measurement period but not with grazed grass herbage DMI (r = 0.06; P = 0.57). Low RFI heifers had 0.07 greater (P < 0.05) concentration of plasma creatinine than high RFI heifers and, during the grazed herbage intake period, spent less time standing and more time lying (P < 0.05) than high RFI heifers. However, low and high RFI groups did not differ (P > 0.05) in ultrasonic backfat thickness or muscle depth, visual muscle scores, skeletal size, total tract digestibility or blood hormone and haematology variables at any stage of the experiment. Despite a sizeable difference in intake of grass silage between low and high RFI heifers during the indoor winter period, there were no detectable differences between RFI groupings for any economically important performance traits measured when animals were offered ensiled or grazed grass herbage.     

Implications of dairy systems on enteric methane and postulated effects on total greenhouse gas emission

The effects of feeding total mixed ration (TMR) or pasture forage from a perennial sward under a management intensive grazing (MIG) regimen on grain intake and enteric methane (EM) emission were measured using chambers. Chamber measurement of EM was compared with that of SF₆ employed both within chamber and when cows grazed in the field. The impacts of the diet on farm gate greenhouse gas (GHG) emission were also postulated using the results of existing life cycle assessments. Emission of EM was measured in gas collection chambers in Spring and Fall. In Spring, pasture forage fiber quality was higher than that of the silage used in the TMR (47.5% v. 56.3% NDF; 24.3% v. 37.9% ADF). Higher forage quality from MIG subsequently resulted in 25% less grain use relative to TMR (0.24 v. 0.32 kg dry matter/kg milk) for MIG compared with TMR. The Fall forage fiber quality was still better, but the higher quality of MIG pasture was not as pronounced as that in Spring. Neither yield of fat-corrected milk (FCM) which averaged 28.3 kg/day, nor EM emission which averaged 18.9 g/kg dry matter intake (DMI) were significantly affected by diet in Spring. However, in the Fall, FCM from MIG (21.3 kg/day) was significantly lower than that from TMR (23.4 kg/day). Despite the differences in FCM yield, in terms of EM emission that averaged 21.9 g/kg DMI was not significantly different between the diets. In this study, grain requirement, but not EM, was a distinguishing feature of pasture and confinement systems. Considering the increased predicted GHG emissions arising from the production and use of grain needed to boost milk yield in confinement systems, EM intensity alone is a poor predictor of the potential impact of a dairy system on climate forcing.     

Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system

Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R²_val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R²_val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R²_val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R²_val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.     

Predicting feed intake and feed efficiency in lactating dairy cows using digesta marker techniques

Direct measurement of individual animal dry matter intake (DMI) remains a fundamental challenge to assessing dairy feed efficiency (FE). Digesta marker, is currently the most used indirect technique for estimating DMI in production animals. In this meta-analysis we evaluated the performance of marker-based estimates against direct or observed measurements and developed equations for the prediction of FE (g energy-corrected milk (ECM)/kg DMI). Data were taken from 29 change-over studies consisting of 416 cow-within period observations. Most studies used more than one digesta marker. So, for each observed measurement of DMI, faecal dry matter output (FDMO) and apparent total tract dry matter digestibility (DMD), there was one or more corresponding marker estimate. There were 924, 409 and 846 observations for estimated FDMO (eFDMO), estimated apparent total tract DMD (eDMD) and estimated DMI (eDMI), respectively. The experimental diets were based mainly on grass silage, with soya bean or rapeseed meal as protein supplements and cereal grains or by-products as energy supplements. Across all diets, average forage to concentrate ratio on a dry matter (DM) basis was 59 : 41. Variance component and repeatability estimates of observed and marker estimations were determined using random factors in mixed procedures of SAS. Between-cow CV in observed FDMO, DMD and DMI was, 10.3, 1.69 and 8.04, respectively. Overall, the repeatability estimates of observed variables were greater than their corresponding marker-based estimates of repeatability. Regression of observed measurements on marker-based estimates gave good relationships (R²=0.87, 0.68, 0.74 and 0.74, relative prediction error =10.9%, 6.5%, 15.4% and 18.7%for FDMO, DMD, DMI and FE predictions, respectively). Despite this, the mean and slope biases were statistically significant (P<0.001) for all regressions. More than half of the errors in all regressions were due to mean and slope biases (52.4% 87.4%, 82.9% and 85.8% for FDMO, DMD, DMI and FE, respectively), whereas the contributions of random errors were small. Based on residual variance, the best model for predicting FE developed from the dataset was FE (g ECM/kg DMI)=1179(±54.1) +38.2(±2.05)×ECM(kg/day)−0.64(±0.051)×BW (kg)−75.6(±4.39)×eFDMO (kg/day). Although eDMD was positively related to FE, it only showed a tendency to reduce the residual variance. Despite inaccuracy in marker procedures, eFDMO from external markers provided a reliable determination for FE measurement. However, DMD estimated by internal markers did not improve prediction of FE, probably reflecting small variability.     

Evaluation and application potential of an accelerometer-based collar device for measuring grazing behavior of dairy cows

The commercially available collar device MooMonitor+ was evaluated with regards to accuracy and application potential for measuring grazing behavior. These automated measurements are crucial as cows feed intake behavior at pasture is an important parameter of animal performance, health and welfare as well as being an indicator of feed availability. Compared to laborious and time-consuming visual observation, the continuous and automated measurement of grazing behavior may support and improve the grazing management of dairy cows on pasture. Therefore, there were two experiments as well as a literature analysis conducted to evaluate the MooMonitor+ under grazing conditions. The first experiment compared the automated measurement of the sensor against visual observation. In a second experiment, the MooMonitor+ was compared to a noseband sensor (RumiWatch), which also allows continuous measurement of grazing behavior. The first experiment on n = 12 cows revealed that the automated sensor MooMonitor+ and visual observation were highly correlated as indicated by the Spearman’s rank correlation coefficient (r_s) = 0.94 and concordance correlation coefficient (CCC) = 0.97 for grazing time. An r_s-value of 0.97 and CCC = 0.98 was observed for rumination time. In a second experiment with n = 12 cows over 24-h periods, a high correlation between the MooMonitor+ and the RumiWatch was observed for grazing time as indicated by an r_s-value of 0.91 and a CCC-value of 0.97. Similarly, a high correlation was observed for rumination time with an r_s-value of 0.96 and a CCC-value of 0.99. While a higher level of agreement between the MooMonitor+ and both visual observation and RumiWatch was observed for rumination time compared to grazing time, the overall results showed a high level of accuracy of the collar device in measuring grazing and rumination times. Therefore, the collar device can be applied to monitor cow behavior at pasture on farms. With regards to the application potential of the collar device, it may not only be used on commercial farms but can also be applied to research questions when a data resolution of 15 min is sufficient. Thus, at farm level, the farmer can get an accurate and continuous measurement of grazing behavior of each individual cow and may then use those data for decision-making to optimize the animal management.     

The influence of natural diet composition, food intake level, and body size on ingesta passage in primates

An important component of digestive physiology involves ingesta mean retention time (MRT), which describes the time available for digestion. At least three different variables have been proposed to influence MRT in herbivorous mammals: body mass, diet type, and food intake (dry matter intake, DMI). To investigate which of these parameters influences MRT in primates, we collated data for 19 species from trials where both MRT and DMI were measured in captivity, and acquired data on the composition of the natural diet from the literature. We ran comparative tests using both raw species values and phylogenetically independent contrasts. MRT was not significantly associated with body mass, but there was a significant correlation between MRT and relative DMI (rDMI, g/kg(0.75)/d). MRT was also significantly correlated with diet type indices. Thus, both rDMI and diet type were better predictors of MRT than body mass. The rDMI-MRT relationship suggests that primate digestive differentiation occurs along a continuum between an "efficiency" (low intake, long MRT, high fiber digestibility) and an "intake" (high intake, short MRT, low fiber digestibility) strategy. Whereas simple-stomached (hindgut fermenting) species can be found along the whole continuum, foregut fermenters appear limited to the "efficiency" approach.     

Prediction of water intake and excretion flows in Holstein dairy cows under thermoneutral conditions

The increase in the worldwide demand for dairy products, associated with global warming, will emphasize the issue of water use efficiency in dairy systems. The evaluation of environmental issues related to the management of animal dejections will also require precise biotechnical models that can predict effluent management in farms. In this study, equations were developed and evaluated for predicting the main water flows at the dairy cow level, based on parameters related to cow productive performance and diet under thermoneutral conditions. Two datasets were gathered. The first one comprised 342 individual measurements of water balance in dairy cows obtained during 18 trials at the experimental farm of Méjussaume (INRA, France). Predictive equations of water intake, urine and fecal water excretion were developed by multiple regression using a stepwise selection of regressors from a list of seven candidate parameters, which were milk yield, dry matter intake (DMI), body weight, diet dry matter content (DM), proportion of concentrate (CONC) and content of crude protein (CP) ingested with forage and concentrate (CPf and CPc, g/kg DM). The second dataset was used for external validation of the developed equations and comprised 196 water flow measurements on experimental lots obtained from 43 published papers related to water balance or digestibility measurements in dairy cows. Although DMI was the first predictor of the total water intake (TWI), with a partial r² of 0.51, DM was the first predictive parameter of free water intake (FWI), with a partial r² of 0.57, likely due to the large variability of DM in the first dataset (from 11.5 to 91.4 g/100 g). This confirmed the compensation between water drunk and ingested with diet when DM changes. The variability of urine volume was explained mainly by the CPf associated with DMI (r.s.d. 5.4 kg/day for an average flow of 24.0 kg/day) and that of fecal water was explained by the proportion of CONC in the diet and DMI. External validation showed that predictive equations excluding DMI as predictive parameters could be used for FWI, urine and fecal water predictions if cows were fed a well-known total mixed ration. It also appeared that TWI and FWI were underestimated when ambient temperature increased above 25°C and possible means of including climatic parameters in future predictive equations were proposed.