The objective of this meta-regression was to evaluate the influence of ruminant species, sex, and climatic regions on the metabolizable energy (ME) requirements for maintenance (MEm) and weight gain (MEg) in growing small ruminants raised for meat production across different climatic regions. Data included 655 and 337 treatment means from 173 and 99 studies on sheep and goats, respectively. Metabolizable energy intake (MEI; MJ/kg(<^>0.75)) was regressed against average daily gain (ADG; g/kg(<^>0.75)), with the study included as a random effect. The analysis found that MEm was not affected by species (P = 0.50), but MEg (MJ/g ADG) was significantly different between species (P = 0.02), with sheep requiring 0.032 (+/- 0.002) and goats 0.026 (+/- 0.002) MJ/g ADG. Sex did not affect MEm in either species (P >= 0.32). However, in goats, intact males had a greater MEg (P = 0.02) than females (0.030 +/- 0.003 vs. 0.013 +/- 0.006 MJ/g ADG). MEm was lower (P = 0.03) in small ruminants raised in subtropical regions (0.497 +/- 0.046 MJ/kg(<^>0.75)) compared to those in semi-arid (0.600 +/- 0.038 MJ/kg(<^>0.75)) and tended to be lower than those in arid regions (0.529 +/- 0.050 MJ/kg(<^>0.75)). However, these differences disappeared when adjusting for maturity, diet composition, digestibility, or altitude. MEg was significantly higher (P < 0.05) in animals raised in arid regions (0.032 +/- 0.006 MJ/g ADG) compared to those in Mediterranean (0.009 +/- 0.004 MJ/g ADG) or semi-arid regions (0.009 +/- 0.004 MJ/g ADG) after adjusting for diet composition and digestibility. Similarly, ruminants in Mediterranean regions had lower MEg (0.019 +/- 0.004 and 0.009 +/- 0.004 MJ/g ADG) than those in tropical regions (0.033 +/- 0.002 and 0.024 +/- 0.002 MJ/g ADG), respectively after adjusting for maturity, diet composition, and digestibility. MEg in semi-arid regions was consistently lower than in tropical regions, regardless of the covariates tested. For predictive purposes, the global model exhibited the best accuracy (CCC = 0.57 and RSR = 0.79), comparable to the model derived specifically for the tropical region (CCC = 0.58 and RSR = 0.80). This meta-analysis provides a comprehensive evaluation of species-specific differences in ME requirements in small ruminants while recognizing the challenges posed by confounding effects and climatic variability inherent in global datasets. The analysis suggests that animals raised in tropical conditions may have lower MEm than current feeding systems that use data from temperate climates.

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d-1) and yield [g kg-1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.

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

In civil engineering, bentonite-water mixtures, commonly referred to as drilling muds, are intensively used in order to lubricate tools, consolidate walls, and help extracting cuttings during drilling operations. The efficiency of drilling muds in each of these tasks lies in their rheological properties depending mainly on the amount of clay materials. During the field works, drilling muds are mixed with the excavated soil materials (e.g., sand, clay, organic matter) that may change drastically the rheological properties of mixtures. With the aim of under-standing better the rheology of field drilling muds mixed with other clays, rheological measurements on mono -and binary-clay suspensions were performed using a rotational rheometer equipped with coaxial cylinders, for which the type of clay materials (i.e., bentonite, kaolin and illite), the total clay volume fraction phi(t )and the bentonite to clay volume ratio R-b were varied. The contribution of this work is twofold: (i) to highlight the major role of phi(t) and R-b independently on the rheology of binary-clay suspensions and (ii) to provide phenomenological models to quantify the dependency of the yield stress on both phi(t) and R-b that would be particularly useful for industrial applications.

Manure N from cattle contributes to nitrate leaching, nitrous oxide, and ammonia emissions. Measurement of manure N outputs on commercial beef cattle operations is laborious, expensive, and impractical; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were to 1) collate an international dataset of N excretion in feces and urine based on individual observations from beef cattle; 2) determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and 3) develop robust and reliable N excretion prediction models based on individual observation from beef cattle consuming various diets. A meta-analysis based on individual beef data from different experiments was carried out from a raw dataset including 1,004 observations from 33 experiments collected from 5 research institutes in Europe (n = 3), North America (n = 1), and South America (n = 1). A sequential approach was taken in developing models of increasing complexity by incrementally adding significant variables that affected fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models with experiment as a random effect. Simple models including dry matter intake (DMI) were better at predicting fecal N excretion than those using only dietary nutrient composition or body weight (BW). Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI. A model including DMI and dietary component concentrations led to the most robust prediction of fecal and urinary N excretion, generating root mean square prediction errors as a percentage of the observed mean values of 25.0% for feces and 25.6% for urine. Complex total manure N excretion models based on BW and dietary component concentrations led to the lowest prediction errors of about 14.6%. In conclusion, several models to predict N excretion already exist, but the ones developed in this study are based on individual observations encompassing larger variability than the previous developed models. In addition, models that include information on DMI or N intake are required for accurate prediction of fecal, urinary, and total manure N excretion. In the absence of intake data, equations have poor performance as compared with equations based on intake and dietary component concentrations.

Methane (CH4) produced from enteric fermentation in ruminants has a noticeable impact on climate change. Prediction models are an alternative to current laborious and costly in vivo CH4 measurement techniques. The objectives of this study were to: (1) collate a database of individual sheep records from CH4 emission studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g/d) and CH4 yield [g/kg of dry matter intake (DMI)]; (3) develop and cross-validate these newly developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories in the LAC region. After removing outliers, the final database retained 219 individual sheep records from 11 studies, 48.2% of the original database. Models were developed using a sequential approach, by incrementally adding different variables with increasing complexity. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. The predictive ac-curacy of fitted CH(4 )prediction models was evaluated using a leave-one-out cross-validation. Overall, increasing model complexity improved the predictive performance of CH4 production and yield equations. Feed intake was the most important predictor of sheep CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the growing lambs and mature sheep subsets, whereas they performed slightly worse in the complete subset. Methane yield can be predicted using dietary forage content only, or with an increased complexity model combining body weight, feeding level, and dietary forage content. The use of the newly-developed models rather than IPCC Tier 2 equations can substantially improve the accuracy of GHG inventories from LAC countries.

The experiment reported in this research paper aimed to evaluate the effects of high-starch or starch and oil-supplemented diets on rumen and faecal bacteria, and explore links between the structure of bacterial communities and milk fatty acid (FA) profiles. We used four Holstein dairy cows in a 4 × 4 Latin square design. Cows were fed a diet rich in cereals (high-starch diet with 23% starch content on dry matter (DM) basis), a diet supplemented with saturated FA from Ca salts of palm oil + 18% DM starch, a diet with high content of monounsaturated FA (from extruded rapeseeds) + 18% DM starch or a diet rich in polyunsaturated FA (from extruded sunflower seeds) + 17% DM starch. At the end of each experimental period, cows were sampled for rumen and faecal contents, which were used for DNA extraction and amplicon sequencing. Partial least squares (PLS) regression analysis highlighted diet-related changes in both rumen and faecal bacterial structures. Sparse PLS discriminant analysis was further employed to identify biologically relevant operational taxonomical units (OTUs) driving these differences. Our results show that Butyrivibrio discriminated the high-starch diet and linked positively with higher concentrations of milk odd- and branched-chain FA. YS2-related OTUs were key taxa distinguishing diets supplemented with Ca salts of palm oil or sunflower seeds and correlated positively with linoleic acid in milk. Similarly, diets modulated faecal bacterial composition. However, correlations between changes in faecal and rumen bacteria were poor. With this work, we demonstrated that high-starch or lipid-supplemented diets affect rumen and faecal bacterial community structure, and these changes could have a knock-on effect on milk FA profiles.

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Mixtures of clay and sand suspended in water are complex systems encountered in many industrial and environmental applications, but their understanding and modelling are still limited and require further investigations. In this way, the rheological behaviour of pure non-thixotropic clay and coarse-grained clay suspensions is investigated with a rotational rheometer equipped by an inclined blade vane-in-cup, where the type and the volume fraction of both fine clay and coarse materials are varied. The Herschel–Bulkley model τ=τy+Kγ̇n is used to describe successfully the flow curves of suspensions relating the shear stress τ to the shear rate γ̇, from which the yield stress τy, the consistency K and the index n are deduced. The main contributions of this study are (i) to validate the inclined blade vane-in-cup for the estimation of the rheological behaviour of polymer microgels, pure clay suspensions, and coarse-grained clay suspensions, (ii) to conclude on the most appropriate model type to predict the yield stress of pure clay suspensions over a wide range of fine clay volume fractions, and (iii) to highlight how frictional contacts of coarse grains may play a role on the bulk yield stress of coarse-grained clay suspensions. We believe that this work would be useful for improving the rheological description of cohesive suspensions in geophysical and industrial applications.

Context Dairy consumption has the ability to provide nutrient dense food in low-income countries. However, cows in the tropics may not be able to reach their full potential due to poor nutrition. In tropical regions, milk is mostly produced by Bos taurus × Bos indicus crossbred cattle for which no nutrient requirement tables have been fully developed. Although many novel feeds and feed additives have been tested, nutrient requirements specifically targeting energy and protein for these livestock need to be estimated accurately for milk production to increase sustainably. Aims To determine the net energy for lactation (NEL) requirement for maintenance and efficiency of utilisation of metabolisable energy intake (MEI) for milk production (kL) of Bos taurus × Bos indicus crossbred dairy cows in the tropics. Methods A meta-analysis using 141 observations from 38 independent studies in tropical regions with crossbred dairy cows was conducted. The energy produced in milk corrected for zero energy balance (EL0) was regressed by MEI including other covariates. This meta-regression analysis was conducted by frequentist inference via optimisation in RStan. Key results The best-fit model contained only MEI as a covariate. This model predicted a net energy for lactation value at maintenance of 0.323 MJ/kg BW0.75.day (s.e. = 0.0004) with variations for each specific study. The efficiency with which MEI is used for milk production was estimated to be 0.554 (s.e. = 0.00008), which was common for all studies. Conclusion The key energy parameters estimated in this study should replace commonly used values derived from Bos taurus breeds when formulating diets for crossbred tropical cattle. Implications Nutritional requirement tables need to be estimated specifically for Bos taurus × Bos indicus crossbred dairy cows as their requirements differ from Western breeds. Using appropriate nutritional requirements of crossbred cattle would lead to better nutrition and increased production as determined by their genetic merit.

This research compared the response of dairy cows and goats in terms of digestive processes (enteric methane [CH4] emissions, ruminal fermentation, total tract digestibility), and of feeding behavior to diets supplemented with various lipids known to modulate differently milk fat con-centration between species. Four Holstein cows and four Alpine goats were fed a diet based on grassland hay and concentrate in a 45:55 ratio (on DM basis) containing no additional lipid (control), or supplemented with corn oil (5% dry matter intake [DMI]) and wheat starch (COS), marine algae powder (MAP; 1.5 % DMI), or hydrogenated palm oil (HPO; 3% DMI) in a replicated 4 x 4 Latin square design. Each experimental period lasted 28 days. Dairy performances (intake, milk production and composition), digestive parameters (total tract digestibility, fermentation), and feeding behavior were measured from day 19 to day 24, during which cows and goats stayed in individual respiration chambers for quantification of enteric CH4 emissions. Compared to the control, milk fat concentration differed between all diets in cows, being the highest with HPO (+10.7 %), intermediate with MAP (-16.0 %), and the lowest with COS (-44.4 %), whereas only MAP decreased the milk fat concentration in goats (-18.0 %). Goats had more eating bouts per day, spent less time eating and ruminating (min/[g DMI/kg body weight]). Cows also differed from goats in their ruminal fermentation parameters: a higher total volatile fatty acid concen-tration with more acetate and less butyrate, whereas a higher ammonia (NH3) concentration was observed for goats which was in agreement with a 7 times larger total protozoal population. Enteric CH4 yield (expressed per unit of intake), CH4 intensity (expressed per unit of milk) and total tract digestibility did not differ in dairy cows and goats fed diets supplemented with various lipids. Only COS significantly reduced CH4 yield and CH4 intensity in both species (overall average, -28 % in g/kg DMI, -27 % in % gross energy intake, and -31 % in g/kg milk) compared to other diets. The lower CH4 yield for COS is in agreement with the higher ruminal propionate proportion (+50.0 % and +22.4 % than control for cows and goats, respectively). The mitigating effect on methanogenesis and changes in ruminal fermentation parameters and total tract digestibility of fiber observed for both species with COS was accompanied in goats by an adaptation of their feeding behavior that counterbalanced the negative digestive interaction of the lipid- and starch-rich diet inducing milk fat depression in cows. In conclusion, cows and goats specific behavior contribute to the observed differences in dairy performances to milk fat depressing diets.

Novel laboratory experiments of fluidized granular flows entering water are reported, for the purpose of investigating tsunamis generated by pyroclastic flows. Qualitatively, the impact of a fluidized granular flow into water leads to (i) an initial vertical granular jet over water, (ii) a leading and largest wave, and (iii) a turbulent mixing zone forming a turbidity current. The present study focuses on the leading wave features in the near-field region, as a function of the mass flux per width q(m) and the volume per width nu of the flow, the maximum water depth H-o, and the slope angle theta of the inclined plane. The obtained waves are of Stokes and cnoidal types, for which the generation is mostly controlled by q(m) and nu. By contrast, H-o plays no role on the wave generation that occurs in the shallowest region. Moreover, a comparison between fluidized granular, dry (nonfluidized) granular, and water flows entering water is addressed under similar flow conditions. The dimensionless amplitude scales as A/H-o = f(zeta), where zeta = FrSM sin theta is a dimensionless parameter depending on the Froude number Fr, the relative slide thickness S, the relative mass M, and the slope angle theta. Data of fine fluidized granular, fine dry granular, and water flows collapse on a master curve, which implies that the nature of the flowing material is of lesser importance in the current setup. By contrast, coarse granular flows generate lower amplitude waves, which is attributed to the penetration of water into the porous granular medium.

There is a need to quantify methane (CH4) emissions with alternative methods. For the past decade, milk fatty acids (MFA) could be used as proxies to predict CH4 emissions from dairy cows because of potential common rumen biochemical pathways. However, equations have been developed based on a narrow range of diets and with limited data. The objectives of this study were to (1) construct a set of empirical models based on individual data of CH4 emissions and MFA from a large number of lactating dairy cows fed a wide range of diets; (2) further increase the models' level of complexity (from farm to research level) with additional independent variables such as dietary chemical composition (organic matter, neutral detergent fiber, crude protein, starch, and ether extract), dairy performance (milk yield and composition), and animal characteristics (days in milk or body weight); and (3) evaluate the performance of the developed models on independent data sets including measurements from individual animals or average measurements of groups of animals. Prediction equations based only on MFA [C10:0, iso C17:0 + trans-9 C16:1,cis-11 C18:1, and trans-11,cis-15 C18:2 for CH4 production (g/d); iso C16:0, cis-11 C18:1, trans-10 C18:1, and cis-9,cis-12 C18:2 for CH4 yield (g/kg of dry matter intake, DMI); and iso C16:0, cis-15 C18:1, and trans-10 + trans-11 C18:1 for CH4 intensity (g/kg of milk)] had a root mean squared error of 65.1 g/d, 2.8 g/kg of DMI, and 2.9 g/kg of milk, respectively, whereas complex equations that additionally used DMI, dietary neutral detergent fiber, ether extract, days in milk, and body weight had a lower root mean squared error of 46.6 g/d, 2.6 g/kg of DMI, and 2.7 g/kg of milk, respectively). External evaluation with individual or mean data not used for equation development led to variable results. When evaluations were performed using individual cow data from an external data set, accurate predictions of CH4 production (g/d) were obtained using simple equations based on MFA. Better performance was observed on external evaluation with individual data for the simple equation of CH4 production (g/d, based on MFA), whereas better performance was observed on external evaluation mean data for the simple equation of CH4 yield (g/kg of DMI). The performance of evaluation of the models is dependent on the domain of validity of the evaluation data sets used (individual or mean).

The aim of the study was to compare the effect of fiber- or starch-rich diets based on grass silage, supplemented or not with bicarbonate, on CH4 emissions and milk fatty acid (FA) profile in dairy cows. The experiment was conducted as a 4 × 4 Latin square design with a 2 × 2 factorial arrangement: carbohydrate type [starch- or fiber-rich diets with dietary starch level of 23.1 and 5.9% on a dry matter basis, respectively], without or with bicarbonate addition [0 and 1% of the dry matter intake, respectively]. Four multiparous lactating Holstein cows were fed 4 diets with 42% grass silage, 8% hay, and 50% concentrate in 4 consecutive 4-wk periods: (1) starch-rich diet, (2) starch-rich diet with bicarbonate, (3) fiber-rich diet, and (4) fiber-rich diet with bicarbonate. Intake and milk production were measured daily and milk composition was measured weekly; CH4 emission and total-tract digestibility were measured simultaneously (5 d, wk 4) when animals were in open-circuit respiration chambers. Sensors continuously monitored rumen pH (3 d, wk 4), and fermentation parameters were analyzed from rumen fluid samples taken before feeding (1 d, wk 3). Cows fed starch-rich diets had less CH4 emissions (on average, -18% in g/d; -15% in g/kg of dry matter intake; -19% in g/kg of milk) compared with fiber-rich diets. Carbohydrate type did not affect digestion of nutrients, except starch, which increased with starch-rich diets. The decrease in rumen protozoa number (-36%) and the shift in rumen fermentation toward propionate at the expense of butyrate for cows fed the starch-rich diets may be the main factor in reducing CH4 emissions. Milk of cows fed starch-rich diets had lower concentrations in trans-11 C18:1, sum of cis-C18, cis-9,trans-11 conjugated linoleic acid (CLA), and sum of CLA, along with greater concentration of some minor isomers of CLA and saturated FA in comparison to the fiber-rich diet. Bicarbonate addition did not influence CH4 emissions or nutrient digestibility regardless of the carbohydrate type in the diet. Rumen pH increased with bicarbonate addition, whereas other rumen parameters and milk FA composition were almost comparable between diets. Feeding dairy cows a starch-rich diet based on grass silage helps to limit the negative environmental effect of ruminants, but does not lead to greater milk nutritional value because milk saturated FA content is increased.

Methane (CH4) is a potent greenhouse gas coming from the anaerobic microbial fermentation of the diet in the rumen. One of the main current challenge for the dairy sector is to find CH4 mitigation strategies (diets or genetics) without altering animal performance. Enteric methane measurement methods are costly and very difficult to apply on a large scale on field. Thus, there is a need to develop alternative measurement methods, such as equations based on proxies to predict CH4 emissions. Milk fatty acids (FA) have been identified as potential predictors of the methanogenesis in dairy cattle, but the prediction ability of extant published CH4 equations must be improved, and their domain of applicability must be enlarged to a wide range of diets. The objective of this PhD thesis was to confirm the potential of milk FA as proxies to predict enteric CH4 emissions in dairy cows fed a wide range of diets. Two databases (based on individual and mean data, respectively) were built thanks to an international collaboration, and gathered data on CH4, milk FA composition, dairy performances, diet and animal characteristics. Two in vivo experiments were conducted with the aim to study the effect of dietary strategies poorly documented, on methanogenesis and milk FA. The data from these experiments were included in the created database. Firstly, simple CH4 prediction equations were developed [g/d, g/kg of DMI (DMI), and g/kg of milk] based only on milk FA, and secondly other variables related to cow intake or characteristics, and dairy performance were added and constituted complex equations. Relationships between CH4 and several milk FA (C10:0, iso C17:0 + trans-9 C16:1, iso C16:0, cis-11 C18:1, cis-15 C18:1, cis-9,cis-12 C18:2, and trans-11,cis-15 C18 :2) were found, confirming common rumen metabolic pathways between methanogenesis and lipid metabolism. Equations were also closely related to the diets included in the database used for their development. Simple equations were less accurate than complex ones (prediction error of 58.6 g/d, 2.8 g/kg DMI and 3.7 g/kg milk vs 42.8 g/d, 2.5 g/kg DMI and 3.3 g/kg milk, respectively). A minimum difference of 16% in CH4 emissions between mitigating strategies can be evidenced with the best prediction equation developed in this PhD. Methane prediction equations based on milk FA well determined by infrared spectrometry methods need to be developed in order to be used on a routine basis and on a large scale. These prediction equations would allow studying the effect of novel mitigation strategies of enteric CH4 emissions in dairy cows.

Optimizing milk production efficiency implies diets allowing low methane (CH4) emissions and high dairy performance. We hypothesize that nature of energy (starch v. lipids) and lipid supplement types (monounsaturated fatty acid (MUFA) v. polyunsaturated fatty acid (PUFA) mitigate CH4 emissions and can induce low milk fat content via different pathways. The main objective of this experiment was to study the effects of starch-rich or lipid-supplemented diets that induce milk fat depression (MFD) on rumen biohydrogenation (RBH) of unsaturated fatty acids (FA) and enteric CH4 emissions in dairy cows. Four multiparous lactating Holstein cows (days in milk=61±11 days) were used in a 4×4 Latin square design with four periods of 28 days. Four dietary treatments, three of which are likely to induce MFD, were based (dry matter basis) on 56% maize silage, 4% hay and 40% concentrates rich in: (1) saturated fatty acid (SFA) from Ca salts of palm oil (PALM); (2) starch from maize grain and wheat (MFD-Starch); (3) MUFA (cis-9 C18:1) from extruded rapeseeds (MFD-RS); and (4) PUFA (C18:2n-6) from extruded sunflower seeds (MFD-SF). Intake and milk production were measured daily. Milk composition and FA profile, CH4 emissions and total-tract digestibility were measured simultaneously when animals were in open-circuit respiration chambers. Fermentation parameters were analysed from rumen fluid samples taken before feeding. Dry matter intake, milk production, fat and protein contents, and CH4 emissions were similar among the four diets. We observed a higher milk SFA concentration with PALM and MFD-Starch, and lower milk MUFA and trans-10 C18:1 concentrations in comparison to MFD-RS and MFD-SF diets, while trans-11 C18:1 remained unchanged among diets. Milk total trans FA concentration was greater for MFD-SF than for PALM and MFD-Starch, with the value for MFD-RS being intermediate. Milk C18:3n-3 content was higher for MFD-RS than MFD-SF. The MFD seems more severe with MFD-SF and MFD-RS than PALM and MFD-Starch diets, because of a decrease in milk SFA concentration and a stronger shift from trans-11 C18:1 to trans-10 C18:1 in milk. The MFD-SF diet increased milk trans FA (+60%), trans-10 C18:1 (+31%), trans-10,cis-12 CLA (+27%) and PUFA (+36%) concentrations more than MFD-RS, which explains the numerically lowest milk fat yield and indicates that RBH pathways of PUFA differ between these two diets. Maize silage-based diets rich in starch or different unsaturated FA induced MFD with changes in milk FA profiles, but did not modify CH4 emissions.

Nitrogen excreted in dairy manure can be potentially transformed and emitted as NH3, which can create livestock and human respiratory problems and be an indirect source of N2O. The objectives of this study were to: (i) investigate environmental factors influencing NH3 emissions from dairy housing; and (ii) identify key explanatory variables in the NH3 emissions prediction from dairy housing using a meta-analytical approach. Data from 25 studies were used for the preliminary analysis, and data from 10 studies reporting 87 treatment means were used for the meta-analysis. Season and flooring type significantly affected NH3 emissions. For nutritional effect analysis, the between-study variability (heterogeneity) of mean NH3 emission was estimated using random-effect models and had a significant effect (P < 0.01). Therefore, random-effect models were extended to mixed-effect models to explain heterogeneity regarding the available dietary and animal variables. The final mixed-effect model included milk yield, dietary crude protein, and dry matter intake separately, explaining 45.5% of NH3 emissions heterogeneity. A unit increase in milk yield (kg d(-1)) resulted in a 4.9 g cow(-1) d(-1) reduction in NH3 emissions, and a unit increase in dietary crude protein content (%) and dry matter intake (kg d(-1)) resulted in 10.2 and 16.3 g cow(-1) d(-1) increases in NH3 emissions, respectively, in the scope of this study. These results can be further used to help identify mitigation strategies to reduce NH3 emissions from dairy housing by developing predictive models that could determine variables with strong association with NH3 emissions.