Methane mitigation in ruminants: from microbe to the farm scale

Decreasing enteric methane (CH4) emissions from ruminants without altering animal production is desirable both as a strategy to reduce global greenhouse gas (GHG) emissions and as a means of improving feed conversion efficiency. The aim of this paper is to provide an update on a selection of proved and potential strategies to mitigate enteric CH4 production by ruminants. Various biotechnologies are currently being explored with mixed results. Approaches to control methanogens through vaccination or the use of bacteriocins highlight the difficulty to modulate the rumen microbial ecosystem durably. The use of probiotics, i.e. acetogens and live yeasts, remains a potentially interesting approach, but results have been either unsatisfactory, not conclusive, or have yet to be confirmed in vivo. Elimination of the rumen protozoa to mitigate methanogenesis is promising, but this option should be carefully evaluated in terms of livestock performances. In addition, on-farm defaunation techniques are not available up to now. Several feed additives such as ionophores, organic acids and plant extracts have also been assayed. The potential use of plant extracts to reduce CH4 is receiving a renewed interest as they are seen as a natural alternative to chemical additives and are well perceived by consumers. The response to tannin- and saponin-containing plant extracts is highly variable and more research is needed to assess the effectiveness and eventual presence of undesirable residues in animal products. Nutritional strategies to mitigate CH4 emissions from ruminants are, without doubt, the most developed and ready to be applied in the field. Approaches presented in this paper involve interventions on the nature and amount of energy-based concentrates and forages, which constitute the main component of diets as well as the use of lipid supplements. The possible selection of animals based on low CH4 production and more likely on their high efficiency of digestive processes is also addressed. Whatever the approach proposed, however, before practical solutions are applied in the field, the sustainability of CH4 suppressing strategies is an important issue that has to be considered. The evaluation of different strategies, in terms of total GHG emissions for a given production system, is discussed.     

Variation in enteric methane emissions among cows on commercial dairy farms

Methane (CH₄) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH₄ emissions can be substantial. The extent of variation in CH₄ emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH₄ emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH₄ during milking on commercial dairy farms. Enteric CH₄ emissions from 1964 individual cows across 21 farms were measured for at least 7 days/cow using CH₄ analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH₄ emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH₄ emissions varied among farms. Considerable variation in CH₄ emissions was observed among cows after adjusting for fixed and random effects, with the CV ranging from 22% to 67% within farms. This study confirms that enteric CH₄ emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.     

Limited potential of harvest index improvement to reduce methane emissions from rice paddies

Rice is a staple food for nearly half of the world's population, but rice paddies constitute a major source of anthropogenic CH₄ emissions. Root exudates from growing rice plants are an important substrate for methane‐producing microorganisms. Therefore, breeding efforts optimizing rice plant photosynthate allocation to grains, i.e., increasing harvest index (HI), are widely expected to reduce CH₄ emissions with higher yield. Here we show, by combining a series of experiments, meta‐analyses and an expert survey, that the potential of CH₄ mitigation from rice paddies through HI improvement is in fact small. Whereas HI improvement reduced CH₄ emissions under continuously flooded (CF) irrigation, it did not affect CH₄ emissions in systems with intermittent irrigation (II). We estimate that future plant breeding efforts aimed at HI improvement to the theoretical maximum value will reduce CH₄ emissions in CF systems by 4.4%. However, CF systems currently make up only a small fraction of the total rice growing area (i.e., 27% of the Chinese rice paddy area). Thus, to achieve substantial CH₄ mitigation from rice agriculture, alternative plant breeding strategies may be needed, along with alternative management.     

Prediction of enteric methane emissions from cattle

Agriculture has a key role in food production worldwide and it is a major component of the gross domestic product of several countries. Livestock production is essential for the generation of high quality protein foods and the delivery of foods in regions where animal products are the main food source. Environmental impacts of livestock production have been examined for decades, but recently emission of methane from enteric fermentation has been targeted as a substantial greenhouse gas source. The quantification of methane emissions from livestock on a global scale relies on prediction models because measurements require specialized equipment and may be expensive. The predictive ability of current methane emission models remains poor. Moreover, the availability of information on livestock production systems has increased substantially over the years enabling the development of more detailed methane prediction models. In this study, we have developed and evaluated prediction models based on a large database of enteric methane emissions from North American dairy and beef cattle. Most probable models of various complexity levels were identified using a Bayesian model selection procedure and were fitted under a hierarchical setting. Energy intake, dietary fiber and lipid proportions, animal body weight and milk fat proportion were identified as key explanatory variables for predicting emissions. Models here developed substantially outperformed models currently used in national greenhouse gas inventories. Additionally, estimates of repeatability of methane emissions were lower than the ones from the literature and multicollinearity diagnostics suggested that prediction models are stable. In this context, we propose various enteric methane prediction models which require different levels of information availability and can be readily implemented in national greenhouse gas inventories of different complexity levels. The utilization of such models may reduce errors associated with prediction of methane and allow a better examination and representation of policies regulating emissions from cattle.     

微生态制剂与反刍动物甲烷减排

反刍动物瘤胃中产甲烷菌可以利用氢气、甲醇和甲胺等物质生成甲烷,不仅造成饲料能量的浪费,同时甲烷作为一种主要的温室气体也对环境构成了严重的威胁。因此,众多学者都在寻找降低反刍动物甲烷排放的方法,其中有学者提出在饲粮中添加适量可直接饲喂的微生物(Direct-fed microbes,DFM)是一种很有潜力的方法,但目前还处于初步探索阶段。本文综述了几种重要的DFM,并介绍了其作用机制及应用效果。     

基于IPCC Tier 1层级的中国反刍家畜胃肠道甲烷排放格局变化分析

依据政府间气候变化委员会(IPCC)2006年公布的反刍家畜胃肠道CH4排放系数(奶牛61 kg CH4头-1a-1,肉役牛47 kg CH4头-1a-1,绵羊和山羊均为5 kg CH4头-1a-1)和IPCC Tier 1计算方法,结合《中国统计年鉴》和《中国畜牧业统计年鉴》,估算了中国以及各个省份1990—2010年CH4的排放总量,绘制出中国反刍家畜胃肠道CH4排放格局及历史变化图。研究结果表明:11990—2010年期间,中国反刍家畜胃肠道CH4排放量(5.90—7.65 Tg)总体呈现先上升(1995年最高)后下降的趋势,其中肉役牛(主要是黄牛和水牛)胃肠道CH4排放量(4.33 Tg)及其所占比例(65%)最大。奶牛胃肠道CH4排放量及其比例呈现逐年增加的趋势,2006年(0.83 Tg,12.7%)超过山羊和绵羊胃肠道CH4排放量及其比例,成为中国反刍家畜胃肠道CH4排放第二大源。但是,奶牛单位产奶量所排放的CH4量逐年降低,表明中国奶牛饲养技术与生产性能不断提高。2中国反刍家畜胃肠道CH4排放格局呈现区域集中特点,主要集中在中西部和北部的各个省份,其中四川、内蒙古、新疆、河南、西藏、山东、河北、黑龙江、云南和甘肃等10个省份的排放量占中国排放总量的50%以上。总之,1990—2010年间,中国反刍家畜胃肠道CH4排放总量为(6.77±0.46)Tg(肉役牛为主要排放源),随时间推移呈现先上升后下降的趋势;中国反刍家畜胃肠道CH4排放的格局呈区域集中分布,中西部和北部的各个省份占60%以上。     

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.     

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%).     

Diet supplementation with cinnamon oil,cinnamaldehyde, or monensin does not reduce enteric methane production of dairy cows

This study was conducted to evaluate the effect of dietary addition of cinnamon oil (CIN), cinnamaldehyde (CDH), or monensin (MON) on enteric methane (CH₄) emission in dairy cows. Eight multiparous lactating Holstein cows fitted with ruminal cannulas were used in a replicated 4×4 Latin square design (28-day periods). Cows were fed (ad libitum) a total mixed ration ((TMR); 60 : 40 forage : concentrate ratio, on a dry matter (DM) basis) not supplemented (CTL), or supplemented with CIN (50 mg/kg DM intake), CDH (50 mg/kg DM intake), or monensin (24 mg/kg of DM intake). Dry matter intake (DMI), nutrient digestibility, N retention, and milk performance were measured over 6 consecutive days. Ruminal degradability of the basal diet (with no additive) was assessed using in sacco incubations (0, 2, 4, 8, 16, 24, 48, 72 and 96 h). Ruminal fermentation characteristics (pH, volatile fatty acids (VFA), and ammonia (NH₃)) and protozoa were determined over 2 days. Enteric CH₄ emissions were measured over 6 consecutive days using the sulfur hexafluoride (SF₆) tracer gas technique. Adding CIN, CDH or MON to the diet had no effects on DMI, N retention, in sacco ruminal degradation and nutrient digestibility of the diet. Ruminal fermentation characteristics and protozoa numbers were not modified by including the feed additives in the diet. Enteric CH₄ emission and CH₄ energy losses averaged 491 g/day and 6.59% of gross energy intake, respectively, and were not affected by adding CIN, CDH or MON to the diet. Results of this study indicate that CIN, CDH and MON are not viable CH₄ mitigation strategies in dairy cows.     

Animal board invited review: Grassland-based livestock farming and biodiversity

Grasslands dominate land cover nationally and globally, and their composition, structure and habitat value are strongly influenced by the actions of domestic and wild grazing animals that feed on them. Different pastures are characterised by varying opportunities for selective feeding by livestock; agronomically improved, sown swards generally consist of a limited range of plant species whereas longer-term leys and semi-natural grasslands are characterised by a more diverse mixture of plants. In the case of botanically diverse permanent pastures/grazing lands, the dietary preferences of different grazers have a more pronounced effect on the botanical composition of the sward in the longer term. Selection of a dominant species within the sward can give less abundant components a chance to compete, increasing community evenness and species richness. Conversely, the selection of minor components reduces sward compositional heterogeneity and hence plant species richness and evenness. Body size, gut type (foregut vs hindgut fermentation), physiological status (growing, pregnant, lactating), metabolic status (extent of body reserves) and environmental conditions all influence the nutrient requirements of a given animal and related foraging priorities. The diet selected is also strongly influenced by the availability of preferred food items, and their vertical and horizontal distribution within the sward. In general, larger animals, such as cattle and horses, are less selective grazers than smaller animals, such as sheep and goats. They are quicker to switch to consuming less-preferred sward components as the availability of preferred resources declines due to their greater forage demands, and as a result can be very effective in controlling competitive plant species consistently avoided by more selective grazers. As a result, low-intensity mixed grazing of cattle and sheep has been shown to improve the diversity and abundance of a range of taxa within grazed ecosystems. Mixed/co-species grazing with different animals exploiting different grassland resources is also associated with increased pasture use efficiency in terms of the use of different sward components and related improvements in nutritional value. In situations where cattle are not available, for example if they are not considered commercially viable, alternative species such as goats, ponies or South American camelids may offer an opportunity to diversify income streams and maintain productive and biodiverse pastures/grazing lands. Stocking rate and timing of grazing also have a considerable role in determining the impact of grazing. Regardless of the species grazing or the pasture grazed, grazing systems are dynamic since selective grazing impacts the future availability of sward components and subsequently dietary choices. New technologies under development provide opportunities to monitor plant/animal interactions more closely and in real time, which will in future support active management to deliver targeted biodiversity gains from specific sites.     

Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources

Milk fatty acid (FA) profile has been previously used as a predictor of enteric CH₄output in dairy cows fed diets supplemented with plant oils, which can potentially impact ruminal fermentation. The objective of this study was to investigate the relationships between milk FA and enteric CH₄ emissions in lactating dairy cows fed different types of forages in the context of commonly fed diets. A total of 81 observations from three separate 3×3 Latin square design (32-day periods) experiments including a total of 27 lactating cows (96±27 days in milk; mean±SD) were used. Dietary forages were included at 60% of ration dry matter and were as follows: (1) 100% corn silage, (2) 100% alfalfa silage, (3) 100% barley silage, (4) 100% timothy silage, (5) 50 : 50 mix of corn and alfalfa silages, (6) 50 : 50 mix of barley and corn silages and (7) 50 : 50 mix of timothy and alfalfa silages. Enteric CH₄output was measured using respiration chambers during 3 consecutive days. Milk was sampled during the last 7 days of each period and analyzed for components and FA profile. Test variables included dry matter intake (DMI; kg/day), NDF (%), ether extract (%), milk yield (kg/day), milk components (%) and individual milk FA (% of total FA). Candidate multivariate models were obtained using the Least Absolute Shrinkage and Selection Operator and Least-Angle Regression methods based on the Schwarz Bayesian Criterion. Data were then fitted into a random regression using the MIXED procedure including the random effects of cow, period and study. A positive correlation was observed between CH₄ and DMI (r=0.59,P<0.001), whereas negative associations were observed between CH₄ and cis9-17:1 (r=−0.58, P<0.001), and trans8, cis13-18:2 (r=−0.51,P<0.001). Three different candidate models were selected and the best fit candidate model predicted CH₄ with a coefficient of determination of 0.84 after correction for cow, period and study effects and was: CH₄ (g/day)=319.7−57.4×15:0−13.8×cis9-17:1−39.5×trans10-18:1−59.9×cis11-18:1−253.1×trans8, cis12-18:2−642.7×trans8, cis13-18:2−195.7×trans11, cis15-18:2+16.5×DMI. Overall and linear prediction biases of all models were not significant (P>0.19). Milk FA profile and DMI can be used to predict CH₄emissions in dairy cows across a wide range of dietary forage sources     

Animal board invited review: Widespread adoption of genetic technologies is key to sustainable expansion of global aquaculture

Aquaculture production comprises a diverse range of species, geographies, and farming systems. The application of genetics and breeding technologies towards improved production is highly variable, ranging from the use of wild-sourced seed through to advanced family breeding programmes augmented by genomic techniques. This technical variation exists across some of the most highly produced species globally, with several of the top ten global species by volume generally lacking well-managed breeding programmes. Given the well-documented incremental and cumulative benefits of genetic improvement on production, this is a major missed opportunity. This short review focusses on (i) the status of application of selective breeding in the world’s most produced aquaculture species, (ii) the range of genetic technologies available and the opportunities they present, and (iii) a future outlook towards realising the potential contribution of genetic technologies to aquaculture sustainability and global food security.     

Animal board invited review: Germplasm technologies for use with poultry

Over the last century, several reproductive biotechnologies beyond the artificial incubation of eggs were developed to improve poultry breeding stocks and conserve their genetic diversity. These include artificial insemination (AI), semen storage, diploid primordial germ cell (PGC) methodologies, and gonad tissue storage and transplantation. Currently, AI is widely used for selection purposes in the poultry industry, in the breeding of turkeys and guinea fowl, and to solve fertility problems in duck interspecies crosses for the production of mule ducklings. The decline in some wild game species has also raised interest in reproductive technologies as a means of increasing the production of fertile eggs, and ultimately the number of birds that can be raised. AI requires viable sperm to be preserved in vitro for either short (fresh) or longer periods (chilling or freezing). Since spermatozoa are the most easily accessed sex cells, they are the cell type most commonly preserved by genetic resource banks. However, the cryopreservation of sperm only preserves half of the genome, and it cannot preserve the W chromosome. For avian species, the problem of preserving oocytes and zygotes may be solved via the cryopreservation and transplantation of PGCs and gonad tissue. The present review describes all these procedures and discusses how combining these different technologies allows poultry populations to be conserved and even rapidly reconstituted.     

Effects of dietary addition of cellulase and a Saccharomyces cerevisiae fermentation product on nutrient digestibility,rumen fermentation and enteric methane emissions in growing goats

This study was designed to assess the effectiveness of dietary cellulase (243 U/g, derived from Neocallimastix patriciarum) and a Saccharomyces cerevisiae fermentation product (yeast product) on ruminal fermentation characteristics, enteric methane (CH₄) emissions and methanogenic community in growing goats. The experiment was conducted in a 5 × 5 Latin square design using five Xiangdong black wether goats. The treatments included a Control and two levels of cellulase (0.8 g and 1.6 g/kg dry matter intake (DMI), i.e. 194 U/kg and 389 U/kg DMI, respectively) crossed over with two levels (6 g or 12 g/kg DMI) of the yeast product. There were no significant differences regarding feed intake, apparent digestibility of organic matter, neutral detergent fibre and acid detergent fibre among all the treatments. In comparison with the Control, the ruminal ammonia N concentration was decreased (p = 0.001) by cellulase and yeast product addition. The activities of carboxymethylcellulase and xylanase were decreased after cellulase addition. Moreover, dietary cellulase and yeast product addition led to a significant reduction (p < 0.05) of enteric CH₄ emissions although the diversity and copy numbers of methanogens among treatments were not dissimilar. The present results indicate that the combination of cellulase and yeast fermentation product can reduce the production of CH₄ energy and mitigate the enteric CH₄ emissions to a certain degree.     

Opposing seasonal temperature dependencies of CO2 and CH4 emissions from wetlands

Wetlands are critically important to global climate change because of their role in modulating the release of atmospheric greenhouse gases (GHGs) carbon dioxide (CO₂) and methane (CH₄). Temperature plays a crucial role in wetland GHG emissions, while the general pattern for seasonal temperature dependencies of wetland CO₂ and CH₄ emissions is poorly understood. Here we show opposite seasonal temperature dependencies of CO₂ and CH₄ emissions by using 36,663 daily observations of simultaneous measurements of ecosystem-scale CO₂ and CH₄ emissions in 42 widely distributed wetlands from the FLUXNET-CH₄ database. Specifically, the temperature dependence of CO₂ emissions decreased with increasing monthly mean temperature, but the opposite was true for that of CH₄ emissions. Neglecting seasonal temperature dependencies may overestimate wetland CO₂ and CH₄ emissions compared to the use of a year-based static and consistent temperature dependence parameter when only considering temperature effects. Our findings highlight the importance of incorporating the remarkable seasonality in temperature dependence into process-based biogeochemical models to predict feedbacks of wetland GHG emissions to climate warming.     

高寒草地土壤温室气体排放对放牧的响应研究进展

高寒草地生态系统具有独特的地理环境和气候特征,对放牧干扰十分敏感,在全球温室气体通量中贡献突出,研究高寒草地放牧对土壤温室气体排放的影响机制具有重要意义。本文总结高寒草地温室气体源/汇特征、不同放牧方式对土壤微环境和微生物群落结构的影响,发现高寒草地主要是CO₂源、CH₄汇、N₂O源。放牧通过家畜选择性采食、践踏和排泄物返还等多重机制作用于地上植物、土壤结构、温度、湿度和养分,进而影响地下微生物及温室气体通量。本文旨在为高寒草地生态系统健康发展和管理及缓解全球气候变化提供科学依据,并对未来研究方向进行展望。     

Animal board invited review: Genomic-based improvement of cattle in response to climate change

Climate change brings challenges to cattle production, such as the need to adapt to new climates and pressure to reduce greenhouse emissions (GHG). In general, the improvement of traits in current breeding goals is favourably correlated with the reduction of GHG. Current breeding goals and tools for increasing cattle production efficiency have reduced GHG. The same amount of production can be achieved by a much smaller number of animals. Genomic selection (GS) may offer a cost-effective way of using an efficient breeding approach, even in low- and middle-income countries. As climate change increases the intensity of heatwaves, adaptation to heat stress leads to lower efficiency of production and, thus, is unfavourable to the goal of reducing GHG. Furthermore, there is evidence that heat stress during cow pregnancy can have many generation-long lowering effects on milk production. Both adaptation and reduction of GHG are among the difficult-to-measure traits for which GS is more efficient and suitable than the traditional non-genomic breeding evaluation approach. Nevertheless, the commonly used within-breed selection may be insufficient to meet the new challenges; thus, cross-breeding based on selecting highly efficient and highly adaptive breeds may be needed. Genomic introgression offers an efficient approach for cross-breeding that is expected to provide high genetic progress with a low rate of inbreeding. However, well-adapted breeds may have a small number of animals, which is a source of concern from a genetic biodiversity point of view. Furthermore, low animal numbers also limit the efficiency of genomic introgression. Sustainable cattle production in countries that have already intensified production is likely to emphasise better health, reproduction, feed efficiency, heat stress and other adaptation traits instead of higher production. This may require the application of innovative technologies for phenotyping and further use of new big data techniques to extract information for breeding.     

Development of methane emission factors for enteric fermentation in cattle from Benin using IPCC Tier 2 methodology

The objective of this study was to develop emission factors (EF) for methane (CH₄) emissions from enteric fermentation in cattle native to Benin. Information on livestock characteristics and diet practices specific to the Benin cattle population were gathered from a variety of sources and used to estimate EF according to Tier 2 methodology of the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories. Most cattle from Benin are Bos taurus represented by Borgou, Somba and Lagune breeds. They are mainly multi-purpose, being used for production of meat, milk, hides and draft power and grazed in open pastures and crop lands comprising tropical forages and crops. Estimated enteric CH₄ EFs varied among cattle breeds and subcategory owing to differences in proportions of gross energy intake expended to meet maintenance, production and activity. EFs ranged from 15.0 to 43.6, 16.9 to 46.3 and 24.7 to 64.9 kg CH₄/head per year for subcategories of Lagune, Somba and Borgou cattle, respectively. Average EFs for cattle breeds were 24.8, 29.5 and 40.2 kg CH₄/head per year for Lagune, Somba and Borgou cattle, respectively. The national EF for cattle from Benin was 39.5 kg CH₄/head per year. This estimated EF was 27.4% higher than the default EF suggested by IPCC for African cattle with the exception of dairy cattle. The outcome of the study underscores the importance of obtaining country-specific EF to estimate global enteric CH₄ emissions.     

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle

Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations.Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.