Prediction of methane production from dairy cows using existing mechanistic models and regression equations

GrassGroTM simulation of pasture, animal performance and greenhouse emissions on low and high sheep productivity grazing systems: 1-year validation and 25-year analysis

Globally, there is a focus on reducing the absolute methane (CH4) and nitrous oxide emissions, and the emissions intensity (EI, kg CO2e/kg animal product) of livestock production. Increasing the productivity of mixed pasture systems has the potential to increase food (e.g., lamb) and textile fibre (e.g., wool) production while reducing the EI of those products from grazing livestock. The objective of this study was to quantify the differences in greenhouse gas (GHG) emissions and EI between sheep on Low (i.e., low sustainable stocking rate) and High (i.e., high sustainable stocking rate) productivity grazing systems (PGSs). Therefore, a replicated breeding-ewe trial on 18 paddocks was established across 2 - years. Three flocks on Low (3 × 16 ewes/flock) and High PGSs (3 × 32 ewes/flock) rotated across three land-classes and three paddocks per PGS. In year 1, the observed on-farm pasture quantity, quality, and botanical composition, together with lamb BW (kg), and daily CH4 production (DMP, g CH4/head per day) using Open Path Fourier Transformed Infrared (OP-FTIR) spectrometers data were measured. Subsequently, two simulations using GrassGroTM were conducted: (1) a 1-year GrassGroTM simulation that used the observed on-farm data to adjust parameters: date of mating, paddock fertility, and weight of mature ewes to validate GrassGroTM predictions to achieve accuracy and precision targets; and (2) a 25-year (1986-2011) simulation to analyse the effects of Low and High PGSs on sheep production and GHG emissions across a variable climate. The 1-year validation predictions fitted well with the observed on-farm data for: pasture biomass (kg/ha), DM digestibility (%), botanical composition (kg/ha), lamb (kg) product, and DMP (g CH4/head per day). The subsequent predicted results from the 25-year GrassGroTM simulation showed minimal effect of PGS on the mean DM intake (kg DM/day) or DMP for Low and High PGSs, but this was thought to be due to the biomass in both PGSs exceeding 1 500 kg DM/ha. The EI, over the 25-year simulation, on the High PGS was 16.5% lower than the Low PGS. Additional calculations of DMP were conducted using a recent global equation, giving estimates of DMP that closely matched the observed on-farm OP-FTIR DMP measurements, but these were lower than the GrassGroTM predictions and improved the accuracy and precision. It is concluded that in some pasture situations, managing pastures and stock numbers to intensify grazing systems can allow increased livestock production, without increasing daily CH4 emissions/head while substantially decreasing the EI of the animal products generated.

Describing the distribution type of DM intake for dairy cow pens based on pen characteristics

In practice, cows are fed by pen, but a diet is formulated to the nutrient requirements of a single cow. If the DM intake (DMI) of a pen were equal for all cows, this approach would have no error, but cows are grouped into pens on pregnancy and other management factors creating a distribution of DMI. The goal of precision feeding is to meet the requirements of individual animals to increase efficiency and reduce environmental impact but is not achieved when a group is fed as if the individuals have uniform requirements and the DMI distribution is not normal. The hypothesis of this work is that the DMI of cow pens are not normally distributed and the total DMI from the best-fit distribution shape for a cow pen will have lower percentage error to the observed DMI than a prediction of a single DMI that is fed at a uniform level and assumes a normal distribution. Our objective was to describe the distribution shape of DMI by week of lactation, and for different pen types. Pens were generated by randomly assorting cows by the week of lactation from a database into different categories of pen for size and lactation period. These pens were fitted to the best distribution type, and its parameters were used to randomly generate distribution plots that predict the total DMI for each pen. A second predictive model estimated the DMI of each pen using an empirical equation of DMI that was multiplied by the number of cows in the pen to represent feeding of a uniform DMI quantity. The percentage error for the distribution shape model was significantly lower than the empirical model with pen errors being less than 1%. The beta distribution type was the most common distribution to best represent the data of pen DMI. Describing the distribution and using it to predict a total pen DMI provides accurate estimates of feed quantity for a group. Reducing error by using the distribution of DMI for feed formulation, instead of the nutrient requirements of an individual animal can provide a precision nutrition approach to group feeding.

Quantification of methane emitted by ruminants: A review of methods

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH₄), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH₄ emissions. This review documents existing methods and methodologies to measure and estimate CH₄ emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH₄ have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH₄ for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH₄ from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH₄ emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH₄ emissions from manure depends on the type of storage, animal housing, CH₄ concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH₄ emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH₄ in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

Comparison of Molly and Karoline models to predict methane production in growing and dairy cattle

Numerous empirical and mechanistic models predicting methane (CH₄) production are available. The aim of this work was to evaluate the Molly cow model and the Nordic cow model Karoline in predicting CH₄ production in cattle using a data set consisting of 267 treatment means from 55 respiration chamber studies. The dietary and animal characteristics used for the model evaluation represent the range of diets fed to dairy and growing cattle. Feedlot diets and diets containing additives mitigating CH₄ production were not included in the data set. The relationships between observed and predicted CH₄ (pCH₄) were assessed by regression analysis using fixed and mixed model analysis. Residual analysis was conducted to evaluate which dietary factors were related to prediction errors. The fixed model analysis showed that the Molly predictions were related to the observed data (± standard error) as CH₄ (g/d) = 0.94 (±0.022) × pCH₄ (g/d) + 31 (±6.9) [root mean squared prediction error (RMSPE) = 45.0 g/d (14.9% of observed mean), concordance correlation coefficient (CCC) = 0.925]. The corresponding equation for the Karoline model was CH₄ (g/d) = CH₄ (g/d) = 0.98 (±0.019) × pCH₄ (g/d) + 7.0 (±6.0) [RMSPE = 35.0 g/d (11.6%), CCC = 0.953]. Proportions of mean squared prediction error attributable to mean and linear bias and random error were 10.6, 2.2, and 87.2% for the Molly model, and 1.3, 0.3, and 98.6% for the Karoline model, respectively. Mean and linear bias were significant for the Molly model but not for the Karoline model. With the mixed model regression analysis RMSPE adjusted for random study effects were 10.9 and 7.9% for the Molly model and the Karoline model, respectively. The residuals of CH₄ predictions were more strongly related to factors associated with CH₄ production (feeding level, digestibility, fat concentrations) with the Molly model compared with the Karoline model. Especially large mean (underprediction) and linear bias (overprediction of low digestibility diets relative to high digestibility diets) contributed to the prediction error of CH₄ yield with the Molly model. It was concluded that both models could be used for prediction of CH₄ production in cattle, but Karoline was more accurate and precise based on smaller RMSPE, mean bias, and slope bias, and greater CCC. The importance of accurate input data of key variables affecting diet digestibility is emphasized.

Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows

This study determined the potential of computer vision systems, namely the whole-side carcass camera (HCC) compared to the rib-eye camera (CCC) and dual energy X-ray absorptiometry (DXA) technology to predict primal and carcass composition of cull cows. The predictability (R²) of the HCC was similar to the CCC for total fat, but higher for lean (24.0%) and bone (61.6%). Subcutaneous fat (SQ), body cavity fat, and retail cut yield (RCY) estimations showed a difference of 6.2% between both CVS. The total lean meat yield (LMY) estimate was 22.4% better for CCC than for HCC. The combination of HCC and CCC resulted in a similar prediction of total fat, SQ, and intermuscular fat, and improved predictions of total lean and bone compared to HCC/CCC. Furthermore, a 25.3% improvement was observed for LMY and RCY estimations. DXA predictions showed improvements in R² values of 26.0% and 25.6% compared to the HCC alone or the HCC + CCC combined, respectively. These results suggest the feasibility of using HCC for predicting primal and carcass composition. This is an important finding for slaughter systems, such as those used for mature cattle in North America that do not routinely knife rib carcasses, which prevents the use of CCC.

Challenges and opportunities to capture dietary effects in on-farm greenhouse gas emissions models of ruminant systems

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH₄) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N₂O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH₄ and/or N₂O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH₄ and N₂O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.

Evaluation of the Modified LIVestock SIMulator for Stall-Fed Dairy Cattle in the Tropics

Simple Summary

Models can play an important role in identifying and filling knowledge gaps related to sustainable resource use in (sub-)tropical livestock production systems. Yet, most simulation models used to study cattle production systems in the (Sub-)Tropics were developed using data that quantify and characterize biological processes of cattle kept in temperate regions, which may reduce the accuracy of predictions. Therefore, we adopted some published data that quantify and characterize biological processes of cattle kept in (sub-)tropical production systems to modify an existing ruminant livestock herd model. Then, the accuracy of predictions of feed intake and productive performance from the original and modified models were evaluated using meta data from (sub-)tropical stall-fed cattle. The modified model predicted voluntary dry matter intake and productive performance more accurately than the original model. Consequently, adopting relevant data that correctly describe the biological processes in (sub-)tropical cattle production systems is the way forward for improving simulation models for these systems.

Abstract

Ruminant livestock systems in the (Sub-)Tropics differ from those in temperate areas. Yet, simulation models used to study resource use and productive performance in (sub-)tropical cattle production systems were mostly developed using data that quantify and characterize biological processes and their outcomes in cattle kept in temperate regions. Ergo, we selected the LIVestock SIMulator (LIVSIM) model, modified its cattle growth and lactation modules, adjusted the estimation of the animals’ metabolizable energy and protein requirements, and adopted a semi-mechanistic feed intake prediction model developed for (sub-)tropical stall-fed cattle. The original and modified LIVSIM were evaluated using a meta-dataset from stall-fed dairy cattle in Ethiopia, and the mean bias error (MBE), the root mean squared error of prediction (RMSEP), and the relative prediction error (RPE) were used to assess their accuracy. The modified LIVSIM provided more accurate predictions of voluntary dry matter intake, final body weights 140 days postpartum, and daily milk yields than the original LIVSIM, as shown by a lower MBE, RMSEP, and RPE. Therefore, using data that quantify and characterize biological processes from (sub-)tropical cattle production systems in simulation models used in the (Sub-)Tropics can considerably improve their accuracy.

Prediction of methane emissions from dairy cows using multiple regression analysis

The UK is bound by the UN Framework Convention on climate change to reduce methane emissions to below 1990 levels by the year 2000. The Kyoto protocol requires a further cut of 12.5% by 2010. Ruminants are estimated to produce 74 Tg of methane per year (Benchar et al. 1998) which represents about 15% of total emissions (Crutzen et al., 1986). Therefore any reduction in the release of methane gas by enteric fermentation from the dairy herd is environmentally important. The objective of this study was to use data obtained from calorimetry trials to generate multiple regression equations predicting the levels and variability of methane emissions from dairy cows.

Daily methane emissions and emission intensity of grazing beef cattle genetically divergent for residual feed intake

As daily methane production (DMP; g CH4/day) is strongly correlated with dry matter intake (DMI), the breeding of cattle that require less feed to achieve a desired rate of average daily gain (ADG) by selection for a low residual feed intake (RFI) can be expected to reduce DMP and also emission intensity (EI; g CH4/kg ADG). An experiment was conducted to compare DMP and EI of Angus cattle genetically divergent for RFI and 400-day weight (400dWT). In a 6-week grazing study, 64 yearling-age cattle (30 steers, 34 heifers) were grazed on temperate pastures, with heifers and steers grazing separate paddocks. Liveweight (LW) was monitored weekly and DMP of individual cattle was measured by a GreenFeed emission monitoring unit in each paddock. Thirty-nine of the possible 64 animals had emission data recorded for 15 or more days, and only data for these animals were analysed. For these cattle, regression against their mid-parent estimated breeding value (EBV) for post-weaning RFI (RFI-EBV) showed that a lower RFI-EBV was associated with higher LW at the start of experiment. Predicted dry matter intake (pDMI), predicted DMP (pDMP) and measured DMP (mDMP) were all negatively correlated with RFI-EBV (P < 0.05), whereas ADG, EI, predicted CH4 yield (pMY; g CH4/kg DMI) were not correlated with RFI-EBV (P > 0.1). Daily CH4 production was positively correlated with animal LW and ADG (P < 0.05). The associations between ADG and its dependent traits EI and pMY and predicted feed conversion ratio (kg pDMI/kg ADG) were strongly negative (r = –0.82, –0.57 and –0.85, P < 0.001) implying that faster daily growth by cattle was accompanied by lower EI, MY and feed conversion ratio. These results show that cattle genetically divergent for RFI do not necessarily differ in ADG, EI or pMY on pasture and that, if heavier, cattle with lower RFI-EBV can actually have higher DMP while grazing moderate quality pastures.

Body fat mobilization in early lactation influences methane production of dairy cows

Long-chain fatty acids mobilized during early lactation of dairy cows are increasingly used as energy substrate at the expense of acetate. As the synthesis of acetate in the rumen is closely linked to methane (CH4) production, we hypothesized that decreased acetate utilization would result in lower ruminal acetate levels and thus CH4 production. Twenty heifers were sampled for blood, rumen fluid and milk, and CH4 production was measured in respiration chambers in week -4, +5, +13 and +42 relative to first parturition. Based on plasma non-esterified fatty acid (NEFA) concentration determined in week +5, animals were grouped to the ten highest (HM; NEFA > 580 μmol) and ten lowest (LM; NEFA < 580 μmol) mobilizing cows. Dry matter intake (DMI), milk yield and ruminal short-chain fatty acids did not differ between groups, but CH4/DMI was lower in HM cows in week +5. There was a negative regression between plasma NEFA and plasma acetate, between plasma NEFA and CH4/DMI and between plasma cholecystokinin and CH4/DMI in week +5. Our data show for the first time that fat mobilization of the host in early lactation is inversely related with ruminal CH4 production and that this effect is not attributed to different DMI.

Ruminal pH predictions for beef cattle: Comparative evaluation of current models

This study evaluated 8 empirical models for their ability to accurately predict mean ruminal pH in beef cattle fed a wide range of diets. Models tested that use physically effective fiber (peNDF) as a dependent variable were Pitt et al. (1996, PIT), Mertens (1997, MER), Fox et al. (2004, FOX), Zebeli et al. (2006, ZB6), and Zebeli et al. (2008, ZB8), and those that use rumen VFA were Tamminga and Van Vuuren (1988, TAM), Lescoat and Sauvant (1995, LES), and Allen (1997, ALL). A data set of 65 published papers (231 treatment means) for beef cattle was assembled that included information on animal characteristics, diet composition, and ruminal fermentation and mean pH. Model evaluations were based on mean square prediction error (MSPE), concordance correlation coefficient (CCC), and regression analysis. The prediction potential of the models varied with low root MSPE (RMSPE) values of 4.94% and 5.37% for PIT and FOX, RMSPE values of 9.66% and 12.55% for ZB6 and MER, and intermediate RMSPE values of 5.66% to 6.26% for the other models. For PIT and FOX, with the lowest RMSPE, approximately 96% of MSPE was due to random error, whereas for ZB6 and MER, with the highest RMSPE, 15.85% and 23.42% of MSPE, respectively, was due to linear bias, and 37.19% and 60.12% of the error, respectively, was due to deviation of the regression slope from unity. The CCC was greatest for PIT (0.67) and FOX (0.62), followed by 0.60 for LES and TAM, 0.52 for ZB8, 0.39 for MER, 0.34 for ALL, and 0.22 for ZB6. Residuals plotted against model-predicted values showed linear bias (P < 0.001) for all models except PIT (P = 0.976) and FOX (P = 0.054) and mean bias (P < 0.001) except for FOX (P = 0.293), LES (P = 0.215), and TAM (P = 0.119). The study showed that the empirical models PIT and FOX, based on peNDF, and LES and TAM, based on VFA, are preferred over the others for prediction of mean ruminal pH in beef cattle fed a wide range of diets. Several animal (BW and intake), diet (forage and OM contents), and ruminal (ammonia and acetate concentrations) factors were (P < 0.001) related to the residuals for each model. We conclude that the accuracy of prediction of mean ruminal pH was relatively low for all extant models. Consideration of factors in addition to peNDF and total VFA, as well as the use of data from studies with continuous measurement of ruminal pH over 24 h or more, would be useful in the development of improved models for predicting ruminal pH in beef cattle.

Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions

This review examines research aimed at reducing enteric methane emissions from the Australian dairy industry. Calorimeter measurements of 220 forage-fed cows indicate an average methane yield of 21.1 g methane (CH4)/kg dry matter intake. Adoption of this empirical methane yield, rather than the equation currently used in the Australian greenhouse gas inventory, would reduce the methane emissions attributed to the Australian dairy industry by ~10%. Research also indicates that dietary lipid supplements and feeding high amounts of wheat substantially reduce methane emissions. It is estimated that, in 1980, the Australian dairy industry produced ~185 000 t of enteric methane and total enteric methane intensity was ~33.6 g CH4/kg milk. In 2010, the estimated production of enteric methane was 182 000 t, but total enteric methane intensity had declined ~40% to 19.9 g CH4/kg milk. This remarkable decline in methane intensity and the resultant improvement in the carbon footprint of Australian milk production was mainly achieved by increased per-cow milk yield, brought about by the on-farm adoption of research findings related to the feeding and breeding of dairy cows. Options currently available to further reduce the carbon footprint of Australian milk production include the feeding of lipid-rich supplements such as cottonseed, brewers grains, cold-pressed canola, hominy meal and grape marc, as well as feeding of higher rates of wheat. Future technologies for further reducing methane emissions include genetic selection of cows for improved feed conversion to milk or low methane intensity, vaccines to reduce ruminal methanogens and chemical inhibitors of methanogenesis.

A mathematical model to describe the diurnal pattern of enteric methane emissions from non-lactating dairy cows post-feeding

Enteric methane emission is not only a source of energy loss in ruminants, but also a potent contributor to greenhouse gas production. To identify the nature and timing of interventions to reduce methane emissions requires knowledge of temporal kinetics of methane emissions during animal husbandry. Accordingly, a mathematical model was developed to investigate the pattern of enteric methane emissions after feeding in dairy cows. The model facilitated estimation of total enteric methane emissions (V, g) produced by the residual substrate (V₁, g) and newly ingested feed (V₂, g). The model was fitted to the 10 h methane emission patterns after morning feeding of 16 non-lactating dairy cows with various body weights (BW), and the obtained parameters were used to predict the kinetics of 24 h methane emission for each animal. The rate of methane emission (g/h) reached a maximum within 1 to 2 h after feeding, followed by a gradual post-prandial decline to a basal value before the next feeding. The model satisfactorily fitted curves for each cow according to the criterion of goodness-of-fit, and provided biological descriptions for fluctuations in methane emissions based on basal V₁ and feeding V₂ in response to the changes in BW and dry matter intake (DMI) of different dairy cows. The basal V₁ and feeding V₂ are probably maintained by slow- and readily-degradable substrates, respectively. The former contributed at least 0.6 of methane production. In summary, the model provides a means to separate basal V₁ and feeding V₂ within V, and can be used to predict 24 h emission from a single feeding period.

Climatic warming and the future of bison as grazers

Climatic warming is likely to exacerbate nutritional stress and reduce weight gain in large mammalian herbivores by reducing plant nutritional quality. Yet accurate predictions of the effects of climatic warming on herbivores are limited by a poor understanding of how herbivore diet varies along climate gradients. We utilized DNA metabarcoding to reconstruct seasonal variation in the diet of North American bison (Bison bison) in two grasslands that differ in mean annual temperature by 6 °C. Here, we show that associated with greater nutritional stress in warmer climates, bison consistently consumed fewer graminoids and more shrubs and forbs, i.e. eudicots. Bison in the warmer grassland consumed a lower proportion of C₃ grass, but not a greater proportion of C₄ grass. Instead, bison diet in the warmer grassland had a greater proportion of N₂-fixing eudicots, regularly comprising >60% of their protein intake in spring and fall. Although bison have been considered strict grazers, as climatic warming reduces grass protein concentrations, bison may have to attempt to compensate by grazing less and browsing more. Promotion of high-protein, palatable eudicots or increasing the protein concentrations of grasses will be critical to minimizing warming-imposed nutritional stress for bison and perhaps other large mammalian herbivores.

Feed intake of sheep when allowed ad libitum access to feed in methane respiration chambers

The patterns of feed intake when animals are allowed ad libitum access to feed in a respiration chamber is not known, nor are the potential effects of the artificial environment of chambers on voluntary feed intake. The objectives of the study were to describe the pattern of hourly feed intake of sheep when fed for ad libitum intake in respiration chambers and determine the repeatability of this pattern and the correlation between feed intake and methane production calculated at hourly intervals. Daily and hourly measurements of methane production and feed intake of 47 Merino wethers were measured in respiration chambers twice, 4 wk apart. We found that hourly feed intake of sheep with ad libitum access to feed in respiration chambers showed a repeatable pattern over the 2 measurement periods (r = 0.89, P < 0.001). During both measurements, sheep ate continuously throughout the 23 h period, but most of the eating occurred during the first 8 h in the respiration chambers. There was a significant linear correlation (r = 0.22) between hourly feed intake and hourly methane production (P < 0.001). An unexpected result from this study was that despite using an accepted and published acclimatization procedure to habituate the animals to the respiration chambers, sheep had 15 to 25% lower feed intake in the respiration chambers compared with their feed intake during the previous week in the animal house pens. In addition, daily feed intake in the respiration chamber was not correlated with feed intake in any of the 7 d before entering the chamber (P > 0.05). Future methane research may consider using feed intake and changes in intake levels as a quantitative indicator of habituation to the methane measurement procedure and environment, which, given the tight association between feed intake and methane production, will be crucial in providing accurate values for methane production.

Backfat thickness and longissimus dorsi real-time ultrasound measurements in light lambs

The aim of this study was to assess the accuracy of ultrasound measurements for predicting carcass traits in 124 Spanish pascual-type lambs (13 to 16 kg carcass weight). Ultrasound images were taken transversal and longitudinal to the vertebral column and at thoracic (TV; between 12th and 13th ribs) and lumbar (LV; between first and second lumbar vertebrae) locations. Skin thickness, subcutaneous backfat thickness (BFT), and depth (DLD), width (WLD), and area (ALD) of longissimus dorsi were obtained with ImageJ 1.42q software. After slaughter, BFT (TV, 2.30 ± 0.06 mm; LV, 2.46 ± 0.06 mm), DLD (TV, 2.47 ± 0.03 cm; LV, 2.48 ± 0.03 cm), WLD (TV, 4.50 ± 0.04 cm; LV, 4.60 ± 0.04 cm), and ALD (TV, 9.96 ± 0.12 cm(2); LV, 10.19 ± 0.13 cm(2)) were directly measured on the lamb carcass. Correlations between ultrasound and direct carcass measurements were greater than 0.61 for DLD, WLD, and ALD (P < 0.05) whereas they fluctuated between 0.32 and 0.60 for BFT (P < 0.05); moreover, correlations were significantly (P < 0.05) greater for transversal than for longitudinal views. In a similar way, linear regression analyses suggested a moderate underestimation for BFT and lumbar DLD when using real-time ultrasound technologies whereas WLD, ALD, and thoracic DLD suffered from under- and overestimation for small and large values of carcass traits, respectively. After decomposing the mean square prediction error (MSPE) for the different ultrasound measurements, we found that the error due to disturbance contributed most to the MSPE followed by the error of central tendency and the error due to regression. The SE of prediction (SEP) was also calculated as an additional precision indicator, obtaining estimates less than that in previous studies with larger lambs. In conclusion, transversal ultrasound measurements at the thoracic and lumbar levels could be a useful tool for predicting DLD, WLD, and ALD in light lambs, perhaps suffering from worse prediction properties when focusing on BFT. This information could be of special relevance for light lamb producers worldwide, with a special emphasis in the Mediterranean basin where this kind of production system accounts for a large percentage of the sheep industry.

Effect of high-sugar grasses on methane emissions simulated using a dynamic model

High-sugar grass varieties have received considerable attention for their potential ability to decrease N excretion in cattle. However, feeding high-sugar grasses alters the pattern of rumen fermentation, and no in vivo studies to date have examined this strategy with respect to another environmental pollutant: methane (CH(4)). Modeling allows us to examine potential outcomes of feeding strategies under controlled conditions, and can provide a useful framework for the development of future experiments. The purpose of the present study was to use a modeling approach to evaluate the effect of high-sugar grasses on simulated CH(4) emissions in dairy cattle. An extant dynamic, mechanistic model of enteric fermentation and intestinal digestion was used for this evaluation. A simulation database was constructed and analysis of model behavior was undertaken to simulate the effect of (1) level of water-soluble carbohydrate (WSC) increase in dietary dry matter, (2) change in crude protein (CP) and neutral detergent fiber (NDF) content of the plant with an increased WSC content, (3) level of N fertilization, and (4) presence or absence of grain feeding. Simulated CH(4) emissions tended to increase with increased WSC content when CH(4) was expressed as megajoules per day or percent of gross energy intake, but when CH(4) was expressed in terms of grams per kilogram of milk, results were much more variable due to the potential increase in milk yield. As a result, under certain conditions, CH(4) (g/kg of milk) decreased. The largest increases in CH(4) emissions (MJ/d or % gross energy intake) were generally seen when WSC increased at the expense of CP in the diet and this can largely be explained by the representation in the model of the type of volatile fatty acid produced. Effects were lower when WSC increased at the expense of NDF, and intermediary when WSC increased at the expense of a mixture of CP and NDF. When WSC increased at the expense of NDF, simulated milk yield increased and, therefore, CH(4) (g/kg of milk) tended to decrease. Diminished increases of CH(4) (% gross energy intake or g/kg of milk) were simulated when DMI was increased with elevated WSC content. Simulation results suggest that high WSC grass, as a strategy to mitigate N emission, may increase CH(4) emissions, but that results depend on the grass composition, DMI, and the units chosen to express CH(4). Overall, this project demonstrates the usefulness of modeling for hypothesis testing in the absence of observed experimental results.

Methane yields from Brahman cattle fed tropical grasses and legumes

In the national greenhouse inventory, methane emissions from the Australian tropical beef herd are derived from cattle fed two diets. In the experiments reported here, methane production was measured by open-circuit gas exchange from 13 Brahman cattle offered 22 diets from combinations of five tropical grass species and five legumes, with a minimum of three steers per diet. All diets were offered daily ad libitum, with the exception of three legume diets fed without grass and leucaena (Leucaena leucocephala) mixed with grass, which were offered at 15 g dry matter per kg liveweight. Diets were fed as long-chopped dried hay, with the exception of leucaena, which was harvested and fed within 2 days. For the data from cattle fed diets of grass and grass mixed with legumes, methane production could be predicted as 19.6 g/kg forage dry matter intake (residual standard deviation 12.3). Observed methane yields were not predictable from a stoichiometry, which used volatile fatty acid proportions in rumen fluid. Mean methane emission rates across all diets were equivalent to 8.6–13.4% of digestible energy intake, and 5.0–7.2% of gross energy intake. The latter values are comparable to IPCC (2006) recommendations (5.5–7.5%) for large ruminants fed low-quality crop residues and by-products. Methane yields per unit of ingested dry matter or digested organic matter were variable across diets but were related to digestibility and contents of fibre and protein. These results constitute a significant downward revision of the methane emissions attributable to the northern Australian beef herd grazing tropical pastures.

Estimation of enteric methane emissions trends (1990–2008) from Manitoba beef cattle using empirical and mechanistic models

Alemu, A. W., Ominski, K. H. and Kebreab, E. 2011. Estimation of enteric methane emissions trends (1990–2008) from Manitoba beef cattle using empirical and mechanistic models. Can. J. Anim. Sci. 91: 305–321. The objective of this study was to estimate and assess trends in enteric methane (CH4) emissions from the Manitoba beef cattle population from the base year of 1990 to 2008 using mathematical models. Two empirical (statistical) models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis), and two dynamic mechanistic models: MOLLY (v3) and COWPOLL were used. Beef cattle in Manitoba were categorized in to 29 distinct subcategories based on management practice, physiological status, gender, age and production environment. Data on animal performance, feeding and management practices and feed composition were collected from the literature as well as from provincial and national sources. Estimates of total enteric CH4 production from the Manitoba beef cattle population varied between 0.9 and 2.4 Mt CO2 eq. from 1990 to 2008. Regardless of the type of models used, average CH4 emissions for 2008 were estimated to be 45.2% higher than 1990 levels. More specifically, CH4 emissions tended to increase between 1990 and 1996. Emissions were relatively stable between 1996 and 2002, increased between 2003 and 2005, but declined by 13.2% between 2005 and 2008, following the same trend as that observed in the beef cattle population. Models varied in their estimates of CH4 conversion rate (Ym, percent gross energy intake), emission factor (kg CH4 head−1 yr−1) and CH4 production. Total CH4 production estimates ranged from 1.2 to 2.0 Mt CO2 eq. for IPCC Tier 2, from 0.9 to 1.5 Mt CO2 eq. for Ellis, from 1.3 to 2.1 Mt CO2 eq. for COWPOLL and from 1.5 to 2.4 Mt CO2 eq. for MOLLY. The results indicate that enteric CH4 estimates and emission trends in Manitoba were influenced by the type of model and beef cattle population. As such, it is necessary to use appropriate models for reliable estimates for enteric CH4 inventory. A more robust approach may be to integrate different models by using mechanistic models to estimate regional Ym values, which may then be used as input for the IPCC Tier 2 model.

Measurement and prediction of enteric methane emission

The greenhouse gas (GHG) emissions from the agricultural sector account for about 25.5% of total global anthropogenic emission. While CO(2) receives the most attention as a factor relative to global warming, CH(4), N(2)O and chlorofluorocarbons (CFCs) also cause significant radiative forcing. With the relative global warming potential of 25 compared with CO(2), CH(4) is one of the most important GHGs. This article reviews the prediction models, estimation methodology and strategies for reducing enteric CH(4) emissions. Emission of CH(4) in ruminants differs among developed and developing countries, depending on factors like animal species, breed, pH of rumen fluid, ratio of acetate:propionate, methanogen population, composition of diet and amount of concentrate fed. Among the ruminant animals, cattle contribute the most towards the greenhouse effect through methane emission followed by sheep, goats and buffalos, respectively. The estimated CH(4) emission rate per cattle, buffaloe, sheep and goat in developed countries are 150.7, 137, 21.9 and 13.7 (g/animal/day) respectively. However, the estimated rates in developing countries are significantly lower at 95.9 and 13.7 (g/animal/day) per cattle and sheep, respectively. There exists a strong interest in developing new and improving the existing CH(4) prediction models to identify mitigation strategies for reducing the overall CH(4) emissions. A synthesis of the available literature suggests that the mechanistic models are superior to empirical models in accurately predicting the CH(4) emission from dairy farms. The latest development in prediction model is the integrated farm system model which is a process-based whole-farm simulation technique. Several techniques are used to quantify enteric CH(4) emissions starting from whole animal chambers to sulfur hexafluoride (SF6) tracer techniques. The latest technology developed to estimate CH(4) more accurately is the micrometeorological mass difference technique. Because the conditions under which animals are managed vary greatly by country, CH(4) emissions reduction strategies must be tailored to country-specific circumstances. Strategies that are cost effective, improve productivity, and have limited potential negative effects on livestock production hold a greater chance of being adopted by producers. It is also important to evaluate CH(4) mitigation strategies in terms of the total GHG budget and to consider the economics of various strategies. Although reductions in GHG emissions from livestock industries are seen as high priorities, strategies for reducing emissions should not reduce the economic viability of enterprises.

Factors affecting methane production and mitigation in ruminants

Methane (CH(4)) is the second most important greenhouse gas (GHG) and that emitted from enteric fermentation in livestock is the single largest source of emissions in Japan. Many factors influence ruminant CH(4) production, including level of intake, type and quality of feeds and environmental temperature. The objectives of this review are to identify the factors affecting CH(4) production in ruminants, to examine technologies for the mitigation of CH(4) emissions from ruminants, and to identify areas requiring further research. The following equation for CH(4) prediction was formulated using only dry matter intake (DMI) and has been adopted in Japan to estimate emissions from ruminant livestock for the National GHG Inventory Report: Y = -17.766 + 42.793X - 0.849X(2), where Y is CH(4) production (L/day) and X is DMI (kg/day). Technologies for the mitigation of CH(4) emissions from ruminants include increasing productivity by improving nutritional management, the manipulation of ruminal fermentation by changing feed composition, the addition of CH(4) inhibitors, and defaunation. Considering the importance of ruminant livestock, it is essential to establish economically feasible ways of reducing ruminant CH(4) production while improving productivity; it is therefore critical to conduct a full system analysis to select the best combination of approaches or new technologies to be applied under long-term field conditions.

The effect of level of intake and forage quality on methane production by sheep

In an experiment to determine the effect of level and quality of forage intake on methane (CH4) emissions, 16 wether lambs were allocated over two periods to two dietary treatments consisting of ryegrass at two stages of physiological maturity: an advanced stage of flowering and seeding (reproductive phase) and before flowering (vegetative phase). Additionally, in each period the lambs were divided into four groups and fed differing levels of food, from three-quarters maintenance to twice maintenance, to ensure a range of dry matter intakes amongst lambs. Apparent in vivo digestibility was measured and the mean values were 62.5% and 75.3% (s.e.d. = 0.84) for reproductive and vegetative ryegrass, respectively. Methane emissions were measured with the sulfur hexafluoride tracer technique. Daily methane emission was highly correlated with the amount of dry matter intake (DMI) (R2 = 0.83) and the regression was similar for both types of feed. Mean CH4 emissions per unit of DMI were 23.7 and 22.9 g/kg DMI (s.e.d. = 0.59) for reproductive and vegetative phases of ryegrass, respectively. The CH4 emissions per unit of DMI were not related to either level of DMI or diet quality.