Y Kondo

P Aimedmu

? M Pirre

t l{. I•AMAROSON, • W. l{ W A Matthews

Si-Ieldon

J L{ Benbrook

And A Iwata

Diurnal Variation of Nitric Oxide in the Upper Stratosphere

Two recent measurements of the temporal variation of nitric oxide at constant altitude near 40 km are reported. The observations were made at float altitude with a balloon-borne chemiluminescence detector together with in situ ozone measurements. The first measurement was made at 44øN on September 17, 1987, at an altitude of 40 km from before sunrise until 1000 LT. The second observation was made at the same latitude on June 18, 1988 at 39 km from 0800 to 1230 LT. The precision of the NO measurements is about 10% at these altitudes, obtained through a more accurate determination of the sample flow rate at pressures down to 2 mbar with a reduced ozone/oxygen flow rate. At an altitude of 40 km, nitric oxide was observed to start increasing very rapidly at sunrise when the solar zenith angle reached about 95 ø . After the rapid initial buildup, the rate of NO increase stabilized for 3 hours at about 1.2 ppbv/hour. Near 1100 LT at 39 km in summer the NO mixing ratio was observed to become nearly constant. These features of the diurnal variation of NO are in accord with the temporal variation expected from a time-dependent zero-dimensional photochemical model. Mixing ratios of NOy and N205 have been determined from the measured temporal variation of NO at 39.5 4-0.5 km, using the photochemical model of Pirre et al. (1989). These derived values of the NOy and N205 mixing ratios, 15.54-5.0 ppbv and 1.44-0.6 ppbv, respectively, are in good agreement with other recent measurements by balloon-borne and space-borne infrared experiments. C10 + NO -• C1 + NO2 which competes with the reaction (R•) C10 + O --• C1 + Reaction (R2), together with reaction (R2) Cl + O3 -• ClO + O2 (R3) completes the well-known catalytic cycle of ozone destruction. The role of reaction (R1) is pronounced in the upper stratosphere, where NO is relatively abundant. In the lower stratosphere, reaction NO2 + C10 + M -• C1ONO2 + M (R4)

INTRODUCTION

The catalytic reaction cycle of atomic oxygen and ozone with nitric oxide (NO) and nitrogen dioxide (NO2) is estimated to be the major loss process for ozone throughout the stratosphere below about 40 km [e.g., McElro•t and Salawitch, 1989]. In addition, these oxides of nitrogen (NO=) interact with reactive chlorine species in the following way: NO reacts with C10 through the reaction becomes important in temporarily sequestering both C10• and NO•. Since there is interaction between reactive species of chlorine and nitrogen, it is predicted that the rate of ozone destruction by an increase in stratospheric chlorine depends critically on the NO• or NO• concentration [e.g., [START_REF] Isaksen | Ozone perturbations by enhanced levels of CFCs, N20, and CH4: A two-dimensional diabatic circulation study including uncertainty estimates[END_REF]].

In the daytime upper stratosphere, nitric oxide (NO) be- Observations of the diurnal variation of NO in the upper stratosphere are very limited; however, at lower altitudes, more observations have been made, mainly with in situ chemiluminescence NO detectors [e.g., [START_REF] Ridley | Sunrise measurements of stratospheric nitric oxide[END_REF][START_REF] Kondo | Measurement of nitric oxide from 7 to 32 km and its diurnal variation in the stratosphere[END_REF][START_REF] Kondo | Di-urnal variation of nitric oxide at 32 km: Measurements and interpretation[END_REF]Kondo et al., , 1989a, b] (see [START_REF] Kondo | Di-urnal variation of nitric oxide at 32 km: Measurements and interpretation[END_REF] for a more complete set of references). [START_REF] Ridley | Stratospheric odd nitrogen: Nitric oxide measurements at 32 ø N in autumn[END_REF] measured the temporal variation of NO at 40 km near sunset. [START_REF] Roscoe | Simultaneous measurements of stratospheric NO and NO2 and their comparison with model predictions[END_REF] observed the temporal variation of NO between 32 and 43 km in the morning hours, as well as in the afternoon, using a balloon-borne infrared emission radiometer. To investigate the photochemistry of odd nitrogen, precise measurements of NO in the morning are very important because photodissociation of the various reservoir species, such as N2Os, occurs at that time.

comes
The NO measurements reported here are more accurate than previous determinations using the chemiluminescent technique.

22,513

We have significantly improved the precision of chemiluminescent NO measurements, mainly through a more precise determination of the flow rates relevant to our balloon-borne chemiluminescence detector [Kondo et al., 1989c]. Here the diurnal variation of NO at about 40 km from sumise through midafternoon is reported. Quantitative comparison of the observed results with a photochemical model is described. A detailed analysis of the observed and calculated diurnal NO variation allowed estimates to be made of the NO w and N2Os mixing ratios.

•i.•r o• •c• mm gi• •, •o• o• rs% •o• • [NO]/[NO•] mio, l•i.g •o •. •o• o• 2•% i. • [NO]/[NO4 •io.
[o•] •o [o] 30 [c•o] •o [HO2] 50
The NO• values discussed above are summarised in Table 4. In Figure 10 None of these measurements were made simultaneously, so the difference of 2-3 ppbv could be due to natural variability caused by dynamical transport.

In general, our direct NOy measurements agree with those of the ATMOS experiment, as can be seen in Figure 10 In conclusion, it can be said that the present measurement of NO is in reasonable agreement with the photo chemical model. An observed increase in NO during the early morning hours is somewhat larger than that predicted. However, the differences between the observed and predicted values are within the uncertainties in the measurements of NO, o•.one, and temperature, and in the reaction rate coefficient of RS. The and N205 mixing ratios, determined from the model using the present NO measurements, agree well with other recent observations.

  • concentration and the photochemical partitioning among the NO• species. The dominance of NO in the NOy family during the daytime is discussed later when the measurements are compared to the model calculations of Pirre et al. [1989]. Since NO• has a photochemical time constant of about a month in the upper stratosphere [Shirnazaki, 1984], it is constant on the time scale of a day in the same air mass. Consequently, the diurnal variation of NO is caused by change in the NO/NO• ratio which results from photodissociation of reservoir species such as N20• and C1ONO2, and by variation in the NO/NO2 ratio. In order to investigate the complex chemistry of odd nitrogen in the stratosphere under changing conditions of solar illumination, precise measurements of the diurnal variation of NO in the same air mass are needed.

  the instrument was modified to achieve better precision at 40 km. Detailed descriptions of the chemiluminescence NO detector used for the present experiments have been presented by KonHo et al. [1989a, b, c]. Fluctuations of the zero level of the instrument have been used to obtain an estimate of 25 pptv as the detection limit of the NO instrument for 10 s of operation. This value was confirmed during the nighttime flight in 1987, as described later. For NO mixing ratios above the detection limit the major sources of error in the NO measurements are the uncertainty in the mixing ratio of NO in the N2 gas used for calibration during flight, and the mass flow rates of the NO calibration gas and of the sample air. The fluctuation level of the signal is not significant during daytime NO measurements. One of our gas bottles with an NO concentration of 1.98 ppmv used for calibration has been evaluated recently at the NOAA Aeronomy Laboratory where this value was confirmed within an accuracy of 3% (D. W. Fahey, private communication, 1990). Estimates of the uncertainties in the NO measurements are given in Table 1. The precision of the NO measurement derived by adding these uncertainties is about 10%. For the second balloon experiment in 1988 the NO instrument was further modified to measure NO N by adding a gold converter [KonHo e• al., 1990]. A schematic diagram of the NO/NO w instrument is shown in Figure 1. The sample air is drawn through the inlet by a Roots pump directly to the reaction chamber for NO measurements. In an alternative measurement mode, sample air first passes through a gold converter heated to 300øC to reduce NO w into NO catalytically, upstream of the reaction chamber. In order to maintain a high conversion efficiency of NO w to NO, purified carbon monoxide (CO), passed through charcoal and Teflon filters, is mixed with

Fig. 4 .Fig. 5 .Fig. 6 .Fig. 8 .

 4568 Fig. 1. Schematic diagram of the NO/NOy instrument used for the flight on June 18, 1988. MLV, magnetic latch valve; MFC, mass flow controller; CPR, constant pressure regu-

  jot species of NOy other than NO= are HNO3 and N2Os, as indicated in Figure 9. Assuming 100% errors in estimating the co..c•..•io..• o• •o• •p•ci•. • [NO•]/[NO•] •io • •0 km at 0600 LT has an uncertainty of 11%, thus leading to an error of 30% in the estimate for NO•, including the 10% in NO• of 60% and 45%, respectively.

  these calculated NO• mixing ratios are shown together with our directly measured NOy concentrations during ascent on June 18, 1988. NO• mixing ratios from the Atmospheric Trace Molecule Spectroscopy (ATMOS) instrument [Russell et al., 1988] are also shown in Figure 10; the measurements were made on board Spacelab 3 at 30øN and 48øS in May. The ATMOS instrument determined each major species of the NO• family by infrared absorption spectra measurements through the llmb of the Earth's atmosphere. The NO• mixing ratio measured by the ATMOS instrument near 40 km at 30øN and 48øS in May are listed in Table 5; the average of these values at 39.5 km is 15.8 4-0.9 ppbv. This value is in very good agreement with our average NO• value of 15.5 4-5.0 ppbv for September and June; our June value of 16.5 ppbv at 44øN is especially close to the ATMOS May value of 16.7 ppbv at 30øN. The estimated error of the ATMOS data at 39.5 km is 13%. The agreement of our NOy values with the ATMOS measurements indicates that the model of Pirre et •1.[1989] presents a reasonable description of reactive nitrogen in the upper stratosphere at mid-latitudes.T'he A'rNlu• values of NO• agree with our direct measure-

  , although our values are somewhat smaller between 20 and 24 kin. In the lower stratosphere, extensive observations of NO• with a chemiluminescence NO detector combined with a gold catalytic converter have been made on board the ER 2 aircraft (D. M. Murphy et al., unpublished manuscript, 1990). In Figure 10 the range of values obtained at altitudes between 17 and 21 km and between latitudes 30 ø and 45øN is indicated as a large

Fig. 11 .Fig. 12 .

 1112 Fig. 11. Profile of the NOv/ozone ratio. Solid circles are the values listed in Table 1. Triangles are from the measured values of NO v and ozone during ascent on June 18, 1988. See text.

TABLE 1 . Uncertainties in NO and NO w Measurements

 1

	Parameter	Uncertainty %
		NO	NO w
	Zero level	<0.1	5'
	Mixing ratio of
	NO cal gas	3	3
	Cal gas flow rate	2	2
	Sample air flow rate	5 $	20*
	Conversion efficiency	...	2
	Fluctuation of signal	1 •	3'

the sample air [Bollinger et al., 1983; Fahey et al., 1985]. The efficiency of conversion of NO2 into NO has been measured in the laboratory to be 0.994-0.02 at pressures between 7 and 60 mbar [Kondo et al., 1990]. The sensitivity of the NO w system for NO is calibrated during the flight periodically in the same way as for the NO measurement. The zero level of the NO w sys- tem is determined by adding compressed purified air near the entrance of the inlet tube. The NO w measurement system of this NO/NO w instrument is described in detail by Kondo et al. [1990]. The instrument used in the 1988 •ght can be switched by telecommand from the NO to the NO w measurement mode and vice versa during a flight. In 1988 it was operated in the NO w mode during ascent between 12 and 28 km and in the NO mode afterward. The sources of errors that affect the pre- cision of the NO w measurements and their estimated values are listed in Table 1. The uncertainty of the flow rate of the sample air for NO w measurements is significantly larger than that for NO measurements, due to a much reduced flow rate through the converter, especially at pressures lower than 30 mbar. By summing these uncertainties the precision of NO w measurement is estimated to be 35% at 28 km and 25% at 20 kin. In both flights, ozone was measured in situ on board the gondola with a UV photometer from the University of Houston [Robbins, 1985; Robbins et al., 1990] to an accuracy of about 4.5% and with a chemiluminescent ozone instrument from Ser- vice d'A•ronomie [Airnedieu and Barat, 1981; Airnedieu, 1983; Airnedieu et al., 1987] to an accuracy of about 4.10%.

TABLE 3 . Uncertainties in the Reaction Rate Coefficients and the Concentration of the Related Species Calculated for 40 kin.

 3

		Uncertainty %
	]NO2	30
	k3	40
	k4	20
	kx4	30
	kxs	20

TABLE 4 . Summary of the Derived NOy and N20• Mixing Ratio, and NOy/ozone Ratio, and the Measured Os Mixing Ratio

 4

			Date	Lat. Temp. A_lt.	NO•	N20•	Os	NO•/ozone
				øN	øK	km	ppbv	ppbv	ppmv	x 10 -s
		Sept. 17,1987 44	246	40.2 14.5 +4.3 1.2 +0.4 5.75 +0.3	2.52 +0.76
		June 18, 1988 44	259	39.1 16.5 +5.0 1.6 +0.6 5.40 +0.3	3.06 +0.92
		Oct. 4, 1985 44	227	32.5 18.5 +8.4 2.2 +1.2	7.0 +0.4	2.64 +1.19
		July 29, 1987 32	225	26.0 14.0 +8.5 1.9 +1.2	6.4 +0.3	2.19 +1.34
	40	--	I i
			i
		ß Calculated from NO data
		ß Measured June 18, 1985
		o ATMOS data May 19
			I
	30			0
		ß
	20
		o ER-2 data
	Iøo	I io			I 20

NO) (ppbv) Fig. 10. Comparison of NO• mixing ratio values from several sources. Solid circles are those obtained from the present NO observations (see Table 1), and the triangles are the direct measurements during ascent on June 18, 1988. Open circles are ATMOS data at 30øN in May [Ruzzell et al., 1988]. The large square labeled "ER 2 Data" indicates the range of values by D. M. Murphy et al. (unpublished manuscript, 1990). ments at 26 km, but NO• values estimated from the NO obser- vations (indirect values) at 32.5 and 26 km are 2-3 ppbv larger.

TABLE 5 . NO•, Mixing Ratio Altitude Mixing Ratio ppbv

 5

	km	30øN	48øS
	41.5	16.7 +2.3	12.9 +1.7
	39.5	16.7 +2.2	14.9 +1.2
	37.5	16.2 +2.1	16.3 +1.8
	From Ruzzell et al., [1988].

Acknowledgments. We wish to thank CNES for its excellent balloon operations and logistic support at Aire sur l'Adour and (3ap. The two balloon flights were supported by CNES with participation of Service d'A•ronomie, CNRS. S. Solomon generously provided the computed results of J•vo2 used in the present calculation. Thanks are due also to D. M. Murphy and D. W. Fahey, who kindly showed us their paper before publication. We wish to thank N. Toriyama,


Table of contents

List of tables Page

Table 1 strengths and weaknesses of in vivo methane measuring techniques in dairy cows .... 4

ACKNOWLEGEMENTS

I would like to thank my internship supervisors, Anne Ferlay and Cécile Martin, for giving me their trust, availability, explanations on the subject, but also for their kindness and great patience.

Thanks to Maguy Eugène and Jacques Rouel for their help in the creation of the data base, their constructive criticism and good humour! I thank Pierre Nozière and Jessie Guyader for their help in introducing me to Minitab statistical software and in analysing the data base.

Thanks are also due to Annie Royet for being my gateway to INRA not forgetting my MSc.

Supervisor Jan Dijkstra through whom coming to INRA became a reality.

I would also like to extend my profound gratitude to the European commission for the award of Erasmus Mundus Scholarship which has helped make me a better person. vi I did my internship in two teams: AGL and DIMA. I will therefore, limit my presentation on these two teams.

OVERVIEW OF INRA I. Background information on INRA

V. The team AGL

The team leader is Christine Leroux and co-leader is Anne Ferlay. The mission of the AGL team is to study the nutritional and physiological factors that regulate leptinemia, lipogenesis in adipose and udder tissue, and the fatty acid composition of milk in order to improve the nutritional and sensory qualities of milk and, performance of dairy ruminants. Their latest research projects have mainly focused on the short-and long-term effects of forage type (grazed or conserved grass), oilseed lipid supplements and their integration on the fatty acid (FA) content and composition of cow and goat milk. Parallel studies have investigated the effects of diet on lipogenic regulation (transcripts and enzyme activities) and gene expression profiles (transcriptomics analysis) in the mammary gland and adipose tissue in lactating ruminants. Another study reports the effects of low-input systems on metabolic and hormonal profiles in early lactating cows. The main methods used include the measurement of lipogenic enzyme activities, the analysis of gene expression (real-time RT-PCR, and transcriptome) in the mammary gland and in adipose tissue, gas-phase and high-performance liquid chromatography (GC/HPLC) on milk FA, plasma metabolic and hormonal profiling (radioimmunoassay), and the determination of adipocyte size and immunohistochemistry.

These methods are applied to animals under physiological and nutritional-controlled (diet trials) conditions.

VI. The team DIMA

The team leader is Morgavi Diego and co-leader is Pierre Nozière. The DIMA team studies digestive processes and their impact on production and the environment. The team analyzes the principles and mechanisms underlying ruminal digestion and digestive absorption. Special focus is given to the fine-grained characterization of the ruminal microbial ecosystem in terms of its hydrolysis, fermentation and detoxification functions. This team's main mission is to enable dietary control of the rumen microbial ecosystem and digestive flows of macronutrients and micronutrients in ruminants, for three core outcomes: 1) environmental protection and productive efficiency, 2) safeguarding animal heath, 3) improving the health status and nutritional quality of animal products for human consumption.

vii To implement these programmes, the DIMA team analyzes nutrient digestion and digestive absorption in ruminants via modelling, in vitro simulation of rumen function, and in vivo experiments on cattle or smaller ruminants in farmed or production (milk, meat) settings.

The team also works on mitigation of greenhouse gases (GHG) emissions and mainly on methane (CH 4 ). This is because CH 4 is the most important component of GHG emissions in ruminant production irrespective of the type of production system and it also represents a loss of energy for the animal. So the development of strategies to mitigate CH 4 emissions will have both environmental benefits for the planet and nutritional benefit for the animal. The originality of DIMA's approach is to study methanogenesis at different scales: the microbe, the animal and the farm scale with the following main objectives: i. To better understand methanogenesis in the rumen, ii. To quantify CH 4 emissions under different conditions iii. To propose mitigation options and iv.

To evaluate and predict the total GHG emissions at the farm scale viii

ABSTRACT

There is a need for the development of simple ways of measuring CH 4 emissions in dairy cows. The aim of this study was to conduct a meta-analysis in order to evaluate the relationship between CH 4 production and milk FA in dairy cows. A data base was built from 14 publications reporting 15 in vivo experiments with a total of 44 dietary treatments. The experiments included in the database recorded CH 4 emission and milk FA profile measured on the same group of animals. To determine the relationships between milk FA profile and CH 4 emissions, global correlations between these two parameters were first obtained. All milk FA that showed significant correlations (P< 0.05 and │r│≥0.4) with CH 4 were retained. This included three groups of milk FA: BCFA (isoC15, anteisoC15:0, isoC16 and anteisoC17) and C16:0 were positively related to CH 4 and intermediary products of rumen biohydrogenation (milk t16 + c14-C18:1, sum of all C18:2 and sum of PUFA) were negatively related to CH 4 .

A second approach was to explore the effect of experiment on the relationship between CH 4 and these milk FA and subsequently the effect of lipid or non-lipid supplementation in the diet. Correlations between CH 4 emissions and tested variables were stronger when compared with those obtained with all diets. This shows that the prediction of CH 4 by these milk FA is diet dependent. Milk FA can be considered as good predictors of CH 4 emission in dairy cows but more data are needed with a wide range of diets to confirm these predictions. Mots clés: Méta-analyse, CH 4 , AG, lait, vache laitière

INTRODUCTION

The production of greenhouse gases (GHG) from livestock and their impact on climate changes are a major concern worldwide [START_REF] Steinfeld | Livestock's long shadow: Environmental issues and options. Food and Agriculture Organization of the United Nations[END_REF]. Methane (CH 4 ) is one of the three main GHG, together with carbon dioxide (CO 2 ) and nitrous oxide (N 2 O). It has been reported that enteric CH 4 is the most important GHG emitted (50-60%) at the farm scale in ruminant production systems [START_REF] Ogino | Evaluating environmental impacts of the Japanese beef cow-calf system by the life cycle assessment method[END_REF]. CH 4 represents also a significant energy loss to the animal ranging from 2 to 12% of gross energy (GE) intake [START_REF] Johnson | Methane emissions from cattle[END_REF]. So, decreasing the production of enteric CH 4 from ruminants without altering animal production is desirable both as a strategy to reduce global GHG emissions and as a means of improving feed conversion efficiency [START_REF] Martin | Methane mitigation in ruminants: from microbe to the farm scale[END_REF].

Enteric CH 4 mitigation in ruminants is possible through various strategies e.g. reviews of [START_REF] Beauchemin | Nutritional management for enteric methane abatement: a review[END_REF][START_REF] Martin | Methane mitigation in ruminants: from microbe to the farm scale[END_REF][START_REF] Patra | Enteric methane mitigation technologies for ruminant livestock: a synthesis of current research and future directions[END_REF]. Actually, the feeding strategy approach is the most developed. Thus, evaluation and comparison among various dietary strategies could be necessary in order to define appropriate mitigation strategies and thereafter to quantify CH 4 emissions. However, accurate CH 4 measurements require intricate and expensive techniques which make their application under field conditions not feasible. For these reasons, mathematical models (empirical models and mechanistic models) have been developed for predicting CH 4 emissions. Mechanistic models are complex and require inputs which are not commonly measured [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF]. Although, empirical models represent a rather practical tool to estimate CH 4 emissions, their accuracy of prediction for a range of nutritional strategies is low due to overestimation [START_REF] Alemu | Estimation of enteric methane emissions trends (1990-2008) from Manitoba beef cattle using empirical and mechanistic models[END_REF]. Hence the use of biomarkers, as simple, non-invasive and attainable measurements to predict CH 4 emissions in ruminants may be useful as it is able to reveal changes caused by diet composition and/or physiological status.

Recently, attention has been directed towards the use of milk fatty acids (FA) as biomarkers for prediction of CH 4 emissions in dairy ruminants. Milk FA are targeted because the precursors for both the synthesis of CH 4 and mammary de novo synthesis of even saturated FA (≤C16) arise in the rumen (Mohammed et al., 2011), explaining the positive correlation between de novo FA (particularly C8:0, r= 0.90 and r=0.37) and CH 4 emissions reported by [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF] and Mohammed et al. (2011), respectively. Moreover, [START_REF] Vlaeminck | Milk odd and branched-chain fatty acids to predict ruminal methanogenesis in dairy cows[END_REF] suggested a higher potential of milk odd-and branched-chain FA (OBCFA) to predict CH 4 emissions due to their strong link to individual molar proportions of volatile fatty acids (VFA) in the rumen. However, differences exist across studies in the main milk FA used as CH 4 predictors [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF][START_REF] Montoya | Relationships between odd-and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle[END_REF] [START_REF] Vlaeminck | Milk odd and branched-chain fatty acids to predict ruminal methanogenesis in dairy cows[END_REF].

Despite the fact that milk FA present high potential to predict rumen CH 4 emissions, models developed in dairy cows are restricted to diets varying in nature and amount of FA [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF][START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF]Mohammed et al., 2011) which limits their application under various feeding conditions. Although [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF] considered 3 experiments;

the dietary treatments were limited to fat sources. Thus a relatively broader range of diets and physiological conditions is required to explore a more general potential of milk FA as predictors of CH 4 emissions in dairy cows.

LITERATURE REVIEW 2.1 Mechanisms of CH 4 production in the rumen

Most of the enteric CH 4 produced by ruminants has its origin in the rumen (~90% vs. 10% at mean in the large intestine) [START_REF] Murray | Rates of production of methane in the rumen and large intestine of sheep[END_REF]. Microbial digestion of feed components in the rumen under anaerobic conditions results in the production of volatile fatty acids (VFA), mainly acetate, propionate and butyrate used by the animal as source of energy, and the production of gases (CO 2 and CH 4 ) eliminated through eructation [START_REF] Murray | Rates of production of methane in the rumen and large intestine of sheep[END_REF][START_REF] Boadi | Mitigation strategies to reduce enteric methane emissions from dairy cows: update review[END_REF]. Rumen microbes find the energy they need in the form of ATP through dehydrogenation reactions releasing hydrogen (H 2 ) in the rumen. H 2 produced is used by methanogenic archaea, a microbial group distinct from Eubacteria, to reduce CO 2 into CH 4 [START_REF] Martin | Methane mitigation in ruminants: from microbe to the farm scale[END_REF] [START_REF] Bannink | Modeling approaches to link various levels of organization in animal physiology[END_REF]. The quantity of CH 4 produced per unit of fermented feed is proportional (CH 4 = 0.45 acetate -0.275 propionate + 0.40 butyrate) to the pattern of the VFA produced [START_REF] Moss | Methane production by ruminants: its contribution to global warming[END_REF]. Therefore, a lower acetate and butyrate production results in lower CH 4 emission (and vice versa) while propionate formation serves as a competitive pathway for H 2 use in the rumen.

Though VFA are the main driving variables for excess H 2 and methanogenis, other factors of minor importance exist (Figure 1): the use of amino acids as nitrogen (N) source for microbial biomass synthesis results in net production of H 2, whereas that of ammonia (NH 3 ) induces a net consumption H 2 [START_REF] Bannink | Modeling approaches to link various levels of organization in animal physiology[END_REF] Adapted from [START_REF] Bannink | Modeling approaches to link various levels of organization in animal physiology[END_REF]

Measurement of Methane Emission from Dairy Cattle

To properly evaluate mitigation strategies, it must be possible to quantify cattle emissions under diverse conditions. Various techniques used to measure CH 4 emissions in vivo have been reported in literature. These include the use of gold standard reference method (respiration chambers; [START_REF] Frankenfield | On heat, respiration, and calorimetry[END_REF]), SF 6 tracer technique [START_REF] Storm | Methods for measuring and estimating methane emission from ruminants[END_REF] and the Green-Feed (C-lock Inc., USA) system [START_REF] Hammond | Methane emissions from growing dairy heifers estimated using an automated head chamber (GreenFeed) compared to respiration chambers or SF6 techniques[END_REF] and the novel approaches such as laser CH 4 detector [START_REF] Chagunda | Opportunities and challenges in the use of the Laser Methane Detector to monitor enteric methane emissions from ruminants[END_REF]. Their strengths and weaknesses have been reported by [START_REF] Storm | Methods for measuring and estimating methane emission from ruminants[END_REF] and [START_REF] Chagunda | Opportunities and challenges in the use of the Laser Methane Detector to monitor enteric methane emissions from ruminants[END_REF]. Generally, the high costs involved and the complexity of these techniques limit their applicability on a large scale (Table 1). Also it is important to examine critically the measurement methods used when evaluating mitigation dietary strategies.

Lipid metabolism in the rumen

VFA (mainly acetate, propionate and butyrate) produced in the rumen are then absorbed through the rumen epithelium into blood plasma for utilisation by the periphery tissues and organs [START_REF] Boadi | Mitigation strategies to reduce enteric methane emissions from dairy cows: update review[END_REF]. During absorption through the rumen epithelium, butyric acid is predominantly converted to β-hydroxybutyrate [START_REF] Bauman | MAMMARY GLAND, MILK BIOSYNTHESIS AND SECRETION | Milk Fat[END_REF] and used in combination with acetic acid as precursors for de novo fatty acid synthesis in the mammary gland [START_REF] Månsson | Fatty acids in bovine milk fat[END_REF].

Dietary lipids are essentially composed of triacylglycerides (TAG), phospholipids, and glycolipids [START_REF] Shingfield | Role of trans fatty acids in the nutritional regulation of mammary lipogenesis in ruminants[END_REF]. After ingestion, they are hydrolysed to non-esterified fatty acids (NEFA) by the action of bacteria lipases and released in the rumen where they are adsorbed onto feed particles and biohydrogenated or incorporated into bacterial lipids. [START_REF] Shingfield | Role of trans fatty acids in the nutritional regulation of mammary lipogenesis in ruminants[END_REF]. Ruminant diets often are poor in lipids but contain a high proportion of unsaturated fatty acids (UFA); mainly cis-9 C18:1 (oleic acid, OA), cis-9 cis-12 C18:2 (linoleic acid, LA), cis-9 cis-12 cis-15 C18:3 (linolenic acid, LNA) [START_REF] Buccioni | Lipid metabolism in the rumen: New insights on lipolysis and biohydrogenation with an emphasis on the role of endogenous plant factors[END_REF]).

The biohydrogenation of LA and LNA begins with the isomerisation of the cis-12 double bond, reduction to trans-11 C18:1 (vacenic acid, VA) and finally to C18:0 (stearic acid, SA) [START_REF] Shingfield | Role of trans fatty acids in the nutritional regulation of mammary lipogenesis in ruminants[END_REF]. Ruminal biohydrogenation of LA and LNA in most diets is 70% to 95% and 85% to 100% respectively, indicating that SA is the major FA leaving the rumen [START_REF] Glasser | Digestion of fatty acids in ruminants: A meta-analysis of flows and variation factors: 2. C18 fatty acids[END_REF]. However, inclusion of fish or marine oils rich in polyunsaturated FA (PUFA) inhibits the complete biohydrogenation of C18 unsaturated FA leading to an increase in the amount of trans C18 intermediates leaving the rumen [START_REF] Shingfield | Role of trans fatty acids in the nutritional regulation of mammary lipogenesis in ruminants[END_REF].

Metabolism of OA results in the formation of oxygenated fatty acids (10-OH 18:0 and 10-O 18:0) and numerous trans-C18:1 intermediates with double bonds at position delta-6 to 16 [START_REF] Mckain | Metabolism of conjugated linoleic acids and 18: 1 fatty acids by ruminal bacteria: products and mechanisms[END_REF]. Also biohydrogenation of OA to SA has been reported by [START_REF] Harfoot | Lipid metabolism in the rumen. The rumen microbial ecosystem[END_REF]; but this is lower (from 58% to 87%) than that observed for LA and LNA [START_REF] Loor | Relationship among trans and conjugated fatty acids and bovine milk fat yield due to dietary concentrate and linseed oil[END_REF][START_REF] Doreau | Effect of linseed fed as rolled seeds, extruded seeds or oil on fatty acid rumen metabolism and intestinal digestibility in cows[END_REF].

Beside the biohydrogenation of dietary PUFA, [START_REF] Vlaeminck | Factors affecting odd-and branched-chain fatty acids in milk: a review[END_REF] outlined in detail microbial formation of OBCFA in the rumen. The precursors for linear odd-chain FA are propionate or valerate, whereas those of branched FA (iso and anteiso FA) are branched chain amino acids (isovaline, leucine and isoleucine) and branched short chain carboxylic acids (isobutyric, isovaleric and 2-methyl butyric acids, [START_REF] Fievez | Milk odd-and branched-chain fatty acids as biomarkers of rumen function-An update[END_REF]. Variations in OBCFA concentrations leaving the rumen reflect changes in the relative abundance and/or activities of specific bacterial population in the rumen [START_REF] Vlaeminck | Factors affecting odd-and branched-chain fatty acids in milk: a review[END_REF]. Thus an increased proportion of cellulotytic bacteria results in high iso-FA while an increased proportion of amylolytic bacteria enhance anteiso and linear odd-chain FA [START_REF] Tajima | Dietdependent shifts in the bacterial population of the rumen revealed with real-time PCR[END_REF].

Lipid metabolism in the mammary gland

In ruminants, FA secreted into milk have double origins: they are derived from either preformed FA from peripheral circulation (from adipose tissue mobilization or dietary origin)

or FA synthesis in mammary secretory epithelial cells also known as de novo synthesis [START_REF] Shingfield | Role of trans fatty acids in the nutritional regulation of mammary lipogenesis in ruminants[END_REF][START_REF] Bauman | MAMMARY GLAND, MILK BIOSYNTHESIS AND SECRETION | Milk Fat[END_REF]. Mammary epithelial cells use acetate and βhydroxybutyrate as substrates for de novo synthesis of even saturated FA (from 4 to 16 carbons) [START_REF] Månsson | Fatty acids in bovine milk fat[END_REF]. All of C4:0 to C12:0, about 95% of C14:0 and about half of C16:0 secreted in milk emanate from de novo FA synthesis whereas the remaining C16:0 and longer chain fatty acids (≥ 18 atoms of carbon) are derived from peripheral circulation [START_REF] Chilliard | Ruminant milk fat plasticity: nutritional control of saturated, polyunsaturated, trans and conjugated fatty acids[END_REF].

The triglycerides (TAG) are transported to the mammary gland into chylomicrons and very low-density lipoproteins (VLDL) through the blood [START_REF] Shingfield | Role of trans fatty acids in the nutritional regulation of mammary lipogenesis in ruminants[END_REF]. Within the capillary wall in the mammary gland, the enzyme lipoprotein lipase hydrolyses the VLDL TAG into glycerol and NEFA which are taken up by the mammary epithelial cell [START_REF] Bauman | MAMMARY GLAND, MILK BIOSYNTHESIS AND SECRETION | Milk Fat[END_REF]. Saturated FA entering into mammary epithelial cells can be desaturated by stearyol-CoA desaturase (delta-9 desaturase) [START_REF] Palmquist | Biosynthesis of conjugated linoleic acid in ruminants and humans[END_REF]. Stearoyl and palmitoyl-CoA are the preferred substrates for delta-9 desaturase but C18:0 is the predominant precursor with OA as the corresponding product [START_REF] Bernard | Expression and nutritional regulation of lipogenic genes in the ruminant lactating mammary gland[END_REF]. Also this desaturation is the principal source of cis-9 trans-11 Congugated LA (Rumenic acid, RA) in milk [START_REF] Mosley | Cis-9, trans-11 conjugated linoleic acid is synthesized directly from vaccenic acid in lactating dairy cattle[END_REF].

Furthermore, endogenous chain elongation of propionyl-CoA as precursor leads to the formation of C5:0, C7:0, C9:0, and C11:0 in milk and these add up to the odd-chain FA C13:0, C15:0 and C17:0 transferred from the duodenum [START_REF] Fievez | Milk odd-and branched-chain fatty acids as biomarkers of rumen function-An update[END_REF]. These oddchain FA can further be desaturated by delta-9 desaturase, but only the conversion of C17:0 to cis-9 C17:1 seems quantitatively important [START_REF] Fievez | Milk odd-and branched-chain fatty acids as biomarkers of rumen function-An update[END_REF].

Thus the different pathways (de novo FA synthesis, uptake from circulation of long chain FA and desaturation) contribute to the FA pool of milk [START_REF] Chilliard | Ruminant milk fat plasticity: nutritional control of saturated, polyunsaturated, trans and conjugated fatty acids[END_REF]. [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF] were the first authors to predict CH 4 emissions (g/d) using individual milk FA concentrations. These authors relied on 32 paired observations derived from 8 dairy cows, used according to 4 x 4 Latin square design, and receiving 4 dietary treatments which consisted of a maize silage-based control diet and 3 diets supplemented with linseed under different forms (whole crude seed, extruded seed and oil) to evaluate relations between milk FA and measured CH 4 emissions. Stepwise multiple regression analysis was performed to describe CH 4 emissions from a combination of dietary intake components, milk yield and composition, and milk FA concentrations. Two high predictive equations were proposed and containing milk FA concentrations (cis-9 C14:1, C16:0, trans-16+cis-14 C18:1, and C18:2n-6) and forage intake (Table 2). The forage intake estimates the part of the organic matter fermented in the rumen that leads to the acetate-CH 4 pathway. The milk C16:0 is a main de novo synthesised FA (acetate being the precursor). Moreover, trans-16+cis-14C18:1 is an intermediate of LA ruminal biohydrogenation, and C18:2n-6 is the main dietary FA of maize silage. However, the contribution of cis-9 C14:1 was rather unexpected [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF]. A second equation (equation 2, Table 2) was thus developed excluding the latter two FA, and this slightly decreased the R 2 value of the prediction compared with the first equation (0.953 to 0.931).

Relationships between milk fatty acid profiles and CH 4 production in the rumen

Although [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF] 2) and milk FA combined with DM intake, and rumen endodiniomorph concentrations, (equation 6, Table 2). The authors found a negative relationship between CH 4 and cis-9 C17:1, cis-11 C18:1 and sum of trans-C18:1 while a positive relationship was found between CH 4 and trans, trans CLA and anteiso C15:0.

The negative relationship between cis-9 C17:1 and CH 4 were expected as it is a product from the desaturation of C17:0 (Microbial origin) whose precursor is propionate and propionogenesis is negatively related to CH 4 production [START_REF] Fievez | Use of principal component analysis to investigate the origin of heptadecenoic and conjugated linoleic acids in milk[END_REF]. The inclusion of cis-11 C18:1, sum of trans-C18:1 and trans, trans CLA in the equation can also be ascribed to biohydrogenation intermediates of PUFA.

The best equation developed by Mohammed et al. (2011) was the one including milk FA, and rumen protozoa concentration (equation 5, Table 2) in the analysis. The positive correlation observed between CH 4 emissions and total entodionomorph concentration was likened to the symbiotic association between methanogens and protozoa [START_REF] Tokura | Methanogens associated with rumen ciliates[END_REF]. Thus, a lower CH 4 production would be associated to lower total protozoal counts. However, in the experiments from which this relationship was observed [START_REF] Beauchemin | Crushed sunflower, flax, or canola seeds in lactating dairy cow diets: effects on methane production, rumen fermentation, and milk production[END_REF], sunflower seed diet induced lower protozoa concentration than flaxseed diet (-38% and -23% relative to control diet, respectively) but higher CH 4 emissions (18% and 10% relative to control diet, respectively). In a recent meta-analysis approach Guyader et al., (2014), 21% of the experiments used reported variations in CH 4 emission without changes in protozoa concentration. This indicates that rumen protozoa concentration is not a strong predictor of CH 4 emissions under conditions where a decrease in protozoa concentration is not associated with a corresponding decrease in CH 4 emissions. Mohammed et al. (2011) also estimated CH 4 using both equations 1 and 2 of [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF] and found both equations to over-predict CH 4 emissions in dairy cows by 61% and 21% respectively. This low prediction result in this study is attributed to the lack of correlations between CH 4 and the milk FA used by [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF] in their equations. [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF] developed a multivariate model using data from 3 experiments with a total of 10 dietary treatments and 50 observations. Dietary treatments were supplemented with calcium fumarate, diallyldisulfide, caprylic acid, capric acid, lauric acid, myristic acid, extruded linseed, linseed oil and yucca powder.

The positive relationship between anteiso C17:0 and CH 4 (equation 3, table 2) was attributed to the negative and positive correlations of this FA with dietary crude protein (CP) and fibre, respectively [START_REF] Cabrita | Nitrogen supplementation of corn silages. 2. Assessing rumen function using fatty acid profiles of bovine milk[END_REF]. The fermentation of protein is associated with lower CH 4 production, whereas the inverse was observed with fibre fermentation [START_REF] Bannink | Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall[END_REF].

Thus a higher milk anteiso C17:0 concentrations imply more CH 4 emission. The milk trans-10+11 C18:1 are a biohydrogenation intermediate of LA. The concentration of trans10 C18:1 is increased if the starch content in the diet is high. The isomers cis (11 and 13) of C18:1 are originated from ruminal biohydrogenation from dietary PUFA.

When Mohammed et al., (2011) used the CH 4 prediction equation developed by [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF] involving the variables 17:0 anteiso, t10-+ t11-18:1, c11-18:1 and c13-18:1, this equation resulted in 19% over-prediction of CH 4 emissions in this study. This could be attributed to the lack of correlations between CH 4 and the variables described above in the study of Mohammed et al. (2011). Although the number of data (n = 50) from 3 studies in the data analysis of [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF] is relatively important, there is not enough variations in composition of diet, proportions of dietary forage and concentrate, type of supplements which may allow application of the equation to other diets. Thus more data are needed to confirm relationships between milk FA concentrations and CH 4 production for a wide range of dietary conditions.

Finally, [START_REF] Weill | Method for evaluating the amount of methane produced by a dairy ruminant and method for decreasing and controlling this amount. Google Patents[END_REF] reported a predictive equation (equation 7, Table 2) for CH 4 emissions based on the sum of FA shorter than 16 carbons (FA ≤ C16). Knowing that milk FA ≤ C16 are de novo synthesized in the mammary gland from acetate (Bauman and Griinari, 2003), de novo synthesized FA seem be a good predictor of CH 4 emissions since acetate is positively related to enteric CH 4 production;

All of the above studies showed relationship between measured CH 4 emissions and milk FA.

On the other hand, [START_REF] Vlaeminck | Milk odd and branched-chain fatty acids to predict ruminal methanogenesis in dairy cows[END_REF] developed a predictive model to relate milk OBCFA to estimated CH 4 emissions. They found a higher potential of milk OBCFA to predict CH 4 emissions due to their strong link to individual molar proportions of VFA in the rumen. In their model, milk concentrations of C15:0 and iso C15:0 were found to be negatively and positively related to calculated CH 4 proportions, respectively. In addition to these two FA, Montaya et al. (2011) included iso C14:0 and iso C16:0 (positively related), C15:0 and sum of C17:0 and C17:1 cis-9 (negatively related) as relevant predictors of calculated CH 4 proportions in a three model approach (R² ranged from 0.63 to 0.66). Conversely, [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF] found no relationship between C15:0 and CH 4 emissions while C17:0 tended to be positively related to CH 4 emissions. Differences across studies can be related to variation in size and unevenness of datasets.

Objectives of Internship

Among studies relating milk FA to direct enteric CH 4 measurements, [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF] were the only ones that considered multiple experiments with diverse dietary treatments. This is however, limited to diets differing only in nature and amount of FA. Therefore the aim of this work was to evaluate relationships between CH 4 emission and milk FA in dairy cows under various nutritional conditions and to develop a more general predictive equation from multiple studies of literature. This was done in silico by: i. Creating a database from the bibliography that reported measured CH 4 emission and milk FA composition in the same group of dairy cows.

ii. Analysing the dataset of the database in order to validate new and existing models for predicting CH 4 emissions under various nutritional situations.

MATERIALS AND METHODS

Selection of publications

Only publications reporting in vivo data of both CH 4 emission and milk FA profile on the same group of animals were included in the database. Publications were obtained from bibliographical databases of editorial platforms (ISI Web of Science, PubMed and Google scholar) using "in vivo'', "methane" and "milk fatty acids" as keywords. An experiment was defined as one control treatment and at least one experimental treatment testing one or more dietary CH 4 mitigation strategies with the same basal diet. Publications obtained included experiments on both dairy goats and dairy cows. With the view that there are some differences in between the concentrations of milk FA in dairy goats and cows (Chilliard et al., 2007) and due to the big differences observed in CH 4 emissions (g/d) between cows and goats, data from goats were separated from the others The final curated database contained fourteen publications on dairy cows, comprising 15 experiments (Nexp) with a total of 44 treatments (Nt).

Creating the data base

Following the selection of publications, the information collected was organised and grouped under different headings. These included quantitative data on: animal (days in milk, body weight), diet (chemical and ingredient composition), rumen fermentation (pH and VFA concentrations), CH 4 production (g/d, MJ/d, g/kg DMI, g/kg milk, and %GEI), apparent total tract digestibility, milk production (yield, fat and protein percents) and milk fatty acids concentrations. Qualitative data on experimental protocol (design and treatment), animal species and techniques for measuring CH 4 emissions and milk FA, were also added to the data base.

Data entry and verification

The information collected from all publications under the different headings was entered into a Microsoft Office Excel 2010 file. In order to obtain more information as possible it was necessary to perform some calculations. For instance, in some experiments where chemical compositions of only diet components were given, calculations were done to obtain the chemical composition of the diet. Also unit conversion of certain measurements (e.g. CH 4 g/d = CH 4 L/d * 0.714) were done to ensure homogeneity in data. Sums of milk FA were calculated from individual FA given in each publication to ensure that the same FA were included in the different sums. Also to understand and verify the data entered, a supplementary excel sheet was created for each publication showing how the calculated values were obtained.

Coding of experiments

First, each publication was coded as "reference" with unique whole numbers from 1 to 15. In the case where a publication presented more than one experiment, a number was (1, 2, etc…) added in order to properly differentiate them.

A second level of encoding was to classify experiments into two categories according to lipid and non-lipid supplementation. The group "lipid" (Nexp = 11, Nt =24) consisted of experiments testing effects of various fatty acids (lauric acid, myristic acid, stearic acid) and fatty acid sources (algal meal, linseed, coconut oil, soya bean oil, sunflower seeds, flax seeds, cotton seeds and canola seeds ) on CH 4 emission. The group "non-lipid" (Nexp = 6, Nt =17) consisted of experiments testing essential oils (Origanum vulgare leaves), chemical additives (monensin), yeast (Saccharomyces cerevisiae), and forage type (maize silage and grass silage). One experiment that tested grape marc containing both lipid and non-lipid compounds was included in both categories.

Experiments were further coded according to the classes of lipid and non-lipid supplements.

The group "lipid-saturated fatty acids" (Nexp = 5, Nt =14) tested lauric acid, myristic acid, stearic acid, coconut oil, and a combination of lauric acid, myristic and linseed oil. The group "lipid-unsaturated fatty acids" (Nexp = 7, Nt =22) supplemented algal meal, linseed, soya bean oil, sunflower seeds, flax seeds, cotton seeds, canola seeds and grape marc. One experiment that tested a mixture of both saturated (lauric acid, myristic acid) and unsaturated fatty acid (linseed oil) in equal proportions was classified under both groups. Experiments under "non-lipid chemical" (Nexp = 3, Nt =7) tested chemical compounds (monensin and grape marc) whereas the group "non-lipid other" (Nexp = 3, Nt =10) consisted of experiments testing essential oils (Origanum vulgare leaves), yeast (Saccharomyces cerevisiae), and type of forage (maize silage and grass silage).

Meta-analysis

The data created was subsequently exported to Minitab (version 15) for analysis. In order to explore the relationships between CH 4 and milk fatty acids, various approaches were taken.

Three units of CH 4 measurements (CH 4 g/d, CH 4 g/kg DMI and CH 4 g/kg milk) were chosen for this. Firstly, global correlation was calculated using all treatments irrespective of the experiment. Secondly, correlations that were significant (P < 0.05) and with an absolute value of 0.4 and above (|-0.4| ≤ r ≤ |-1|) where kept for further analysis. Descriptive statistics of the remaining data were obtained and when the number of treatments is below 10, the data was discarded. Thirdly, with the remaining data, scatter plot diagrams with CH 4 on the ordinate and individual milk FA concentration (% of milk FA) on abscissa were made. From the scatter plot, one experiment [START_REF] Moate | Effects of feeding algal meal high in docosahexaenoic acid on feed intake, milk production, and methane emissions in dairy cows[END_REF] was excluded as it presented a high leverage effect and then the above steps were repeated.

An analysis of variance-covariance with general linear model (GLM) was performed in order to first relate the within experiment variations of CH 4 and milk FA as a function of the identifier "reference" established during coding. Graphs were also plotted to show this relationship. This step was repeated but this time with the experimental factor ("lipid" or "non-lipid") as a function.

RESULTS

Descriptive statistics for the database using milk FA that showed significant correlation with CH 4 emissions are given in Table 3. and "non-lipid supplementation" respectively. For BCFA, correlation between milk-isoC16: 0 and CH 4 in g/kg milk (r= 0.79, P= 0.000) was the strongest while the weakest correlation (r= 0.51, P= 0.025) was observed between milk-isoC15:0 and CH 4 emission in g/d (Table 4). By integrating the experiment effect and lipid supplementation factors, milk-anteisoC15: 0, had the highest correlation coefficient (r= 0.93, P= 0.000 and r= 0.94, P= 0.001, respectively)

with CH 4 (g/d). milk FA in dairy cows used these group of milk FA in their predictive equations (Dijkstra et al., 2011;Mohammed et al., 2011). The positive correlations between isoC15:0, isoC16:0 and CH 4 emissions corroborate earlier reports [START_REF] Vlaeminck | Milk odd and branched-chain fatty acids to predict ruminal methanogenesis in dairy cows[END_REF][START_REF] Montoya | Relationships between odd-and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle[END_REF]. Similarly in the best equation developed by Mohammed et al. (2011), milk isoC16:0 contributed to the prediction of CH 4 emissions (equation 5, Table 2). This supports the explanation that there is a strong relationship between calculated CH 4 emissions and milk OBCFA [START_REF] Vlaeminck | Milk odd and branched-chain fatty acids to predict ruminal methanogenesis in dairy cows[END_REF]. It is well established that cellulolytic bacteria are enriched in high iso-FA and their activities are more favoured with a higher F:C (from 0.5 to 0.9 forage proportion) in the diet [START_REF] Vlaeminck | Factors affecting odd-and branched-chain fatty acids in milk: a review[END_REF] which results in higher iso-FA in milk. Consequently the fibre degradation by cellulolytic bacteria produces more acetate as VFA and so more CH 4 hence the positive relation.

Milk anteisoC17:0 tended to be related to CH 4 emissions when experiment effect was integrated but showed significant relation with CH 4 emissions for diets with lipid supplementation. This shows that prediction of CH 4 emissions from this milk FA depends on lipid supplementation and slightly on basal diet. Indeed, [START_REF] Dijkstra | Relationships between methane production and milk fatty acid profiles in dairy cattle[END_REF] reported anteisoC17:0 as the major CH 4 predictor in their predictive equation (equation 3 et al., 2003). Stoichiometrically, fermentation of fibre is associated with higher CH 4 production relative to protein fermentation [START_REF] Bannink | Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall[END_REF]; which explains the positive relationship between this FA and CH 4 .

Milk C16:0

Although milk C16:0 showed only a slight positive correlation with CH 4 g/kg when all treatments were used, a special attention is made for this FA because its concentration in milk is important and it was also included in the predictive equations of [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF].

When the experiment and lipid supplementation factor effects were incorporated, it had much larger correlation coefficients across all units of measurements of CH 4 emissions. This shows that the prediction of CH 4 emissions from milk C16:0 is diet dependent. In all cases milk C16:0 was positively related to CH 4 emissions. Similarly [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF] reported milk C16:0 as a good predictor of CH 4 emissions in their equations (equations 1 and 2, Table 2).

This FA is a main product from mammary de novo synthesis which is based primarily on acetate produced in the rumen during digestion of fibre [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF]. Acetate is known to be positively related to CH 4 [START_REF] Moss | Methane production by ruminants: its contribution to global warming[END_REF] which justifies the positive relationship between this milk FA and CH 4 emissions.

Intermediates of rumen biohydrogenation

The correlation coefficients of milk t16 + c14-C18: 1, sum of all C18: 2, sum of PUFA were improved when the effects of experiment, lipid and non-lipid supplementation were integrated in the data analysis. This shows the prediction of CH 4 from these FA is diet dependent. (protozoa, cellulolytic bacteria) and using H 2 (methanogens) [START_REF] Martin | Methane mitigation in ruminants: from microbe to the farm scale[END_REF]. In addition, the CH 4 -suppressing effect of PUFA is due to intermediary products (such as isomers of milk C18:1 and C18:2) resulting from biohydrogenation of these FA in the rumen (Mohammed et al., 2011). This effect explains the negative relations observed between milk t16+ c14-C18: 1, sum of all C18: 2, sum of PUFA and CH 4 emissions.

CONCLUSION

Three groups of milk FA: BCFA, C16:0 and several intermediates of rumen biohydrogenation showed significant relationships with CH 4 emissions in dairy cattle. BCFA and C16:0 were positively related while the FA arising from ruminal biohydrogenation were negatively related to CH 4 emissions. Milk FA better predicted CH 4 emissions when experiment effect and lipid supplementation factors were taken into account which means that prediction of CH 4 from these FA is diet dependent. Milk FA can be considered as good predictors of CH 4 production in dairy cattle but more data are needed in the database with a wide range of diets to confirm these predictions.

From the groups of milk FA above, C16:0 can be measured easily with infra-red spectrometry already implemented in laboratories of Milk Recording Organisation whereas BCFA and products of ruminal biohydrogenation analyses requires more time in research laboratories implemented with chromatographic analysis. This presents a setback for the use of these groups of milk FA in predicting CH 4 emissions.

In this study, a first statistical analysis was made but needs to be completed by taking into account interfering factors such as milk production, forage intake, nature of forage, type of lipid supplementation, protozoa concentrations, and VFA. This would enable all significant FA to be put in the same equation in order to class them in terms of importance.

  1. INTRODUCTION .............................................................................................................. 1 2 LITERATURE REVIEW ................................................................................................... 2 2.1 Mechanisms of CH 4 production in the rumen ............................................................. 2 2.2 Measurement of Methane Emission from Dairy Cattle ............................................... 3 2.3 Lipid metabolism in the rumen .................................................................................... 4 2.4 Lipid metabolism in the mammary gland .................................................................... 6 2.5 Relationships between milk fatty acid profiles and CH 4 production in the rumen ..... 7 2.6 Objectives of Internship ............................................................................................. 11 3 MATERIALS AND METHODS ...................................................................................... 11 3.1 Selection of publications ........................................................................................... 11 3.2 Creating the data base ................................................................................................ 12 3.3 Data entry and verification ........................................................................................ 12 i 3.4 Coding of experiments ............................................................................................... 12 3.5 Meta-analysis ............................................................................................................. 13 4 RESULTS ......................................................................................................................... 14 5 DISCUSSION ................................................................................................................... 17 5.1 Milk branchedchain fatty acids (BCFA) ............................................................... 17 5.2 Milk C16:0 ................................................................................................................. 18 5.3 Intermediates of rumen biohydrogenation ................................................................. 19 6 CONCLUSION ................................................................................................................. 19 Publications used for the meta-analysis ................................................................................... 24 APPENDIX .............................................................................................................................. 26

  The Institut National de la Recherche Agronomique (INRA) also known as the French National Institute for Agricultural Research is a public body establishment under the dual supervision of the Ministry of Higher Education and Research and the Ministry of Agriculture, Food and Fisheries. Although it is the second largest public research institute in France, INRA ranks the number one agricultural institute in Europe and number two in the world. It was set up in 1946 after World War II in response to pressing demands from society to feed France. Today, it plays a key role in overcoming global challenges such as climate change, nutritional status of populations, ecological equilibrium of rural areas, competitiveness and sustainability of agricultural production. Approximately 9000 people work at INRA in one of 13 research departments (made up of 200 research and experimental units) and 17 regional research centres (on over 150 sites) throughout France. These include 4100 researchers and engineers and 4500 technicians and administrative staff. In addition, the institute welcomes over 1700 doctoral students, 1000 foreign researchers and 1900 interns each year. II. Missions of INRA The missions of INRA are: 1. to produce and disseminate scientific knowledge 2. to develop innovations and know-how for the benefit of society 3. through its expertise, to inform decision-making by public and private sector players 4. to develop scientific and technical culture and participate in the science/society debate 5. to contribute to training in and through research. III. Clermont-Ferrand-Theix-Lyon Research Centre The centre of Clermont-Ferrand-Theix-Lyon consists of 27 research and experimental units situated on four sites in Auvergne region (Theix, Crouël, Aurillac and Monts d'Auvergne), one in Rhône-Alpes region (Lyon) and one in Limousin region (Limoges). The centre develops multidisciplinary research themes which relate to animal and animal product sector, as well as plant improvement and human nutrition. It represents a strong potential for INRA, as it brings together 8% of the total workforce of the Institute and is the third of the 17 research centres. The Centre is a major player in research in the Auvergne region, representing 15% of the regional scientific forces. INRA in Auvergne with an annual budget of €60 million has been able to achieve between 2008 and 2012, 26 projects funded by the National Research Agency, 30 European projects and 1600 published scientific articles in peer-reviewed reading. IV. Herbivores Joint Research Unit Herbivores Joint Research Unit (UMR1213) is a joint research unit associating INRA with VetAgro Sup which is an institute of higher education and research in food, animal health, agriculture and environmental sciences. Within INRA, UMR1213 is attached to two departments (PHASE for Physiologie et système d'élevage and SAE2 for Sciences sociales, agriculture et alimentation, espace et environnement). It contributes to the design of sustainable farming systems for herbivores that seek to reconcile production efficiency, product quality and socioeconomic viability with environmental protection and valuation, and animal welfare. The unit is composed of 143 permanent staff (includes 75 researchers and engineers) and 100 temporary staff (made up of 20 PhD students and post-doctoral fellows). With an annual operating budget of €3.5 million (which 1/3 comes from contracts) it produces 320 papers each year. The unit has produced and co-produced two computer-aided decision-making software packages (INRAtion and Prévalim 200 users) and five current patents. UMR1213 comprises seven research teams and two support teams (Figure 1).

Figure 1

 1 Figure 1 Organigram of Herbivores Joint Research Unit UMR1213

  Key words: Meta-analysis, CH 4 , FA, milk, dairy cow RÉSUMÉ Il est nécessaire pour le développement de moyens simples de mesurer les émissions de CH 4 des vaches laitières. Le but de cette étude était de procéder à une méta-analyse afin d'évaluer la relation entre la production de CH 4 et les acides gras (AG) du lait. Une base de données a été construite à partir de 14 publications portant sur 15 expériences in vivo avec un total de 44 traitements alimentaires. Les expériences utilisées dans la base de données rapportaient les émissions de CH 4 et le profil en AG du lait mesurés sur les mêmes animaux. Pour déterminer les relations entre le profil d'AG du lait et les émissions de CH 4 , les corrélations globales entre ces 2 paramètres ont d'abord été obtenues. Tous les AG du lait qui ont montré des corrélations significatives (P< 0.05 et │r│≥0.4) avec les émissions de CH 4 ont été retenus. Cela concernait trois groupes d'AG du lait: les AG à chaine ramifiée (isoC15, anteisoC15:0, isoC16 et anteisoC17) et le C16:0 étaient positivement corrélés au CH 4 alors que les produits ix intermédiaires de biohydrogénation du rumen (t16+c14-C18:1, somme de tous les C18:2 et somme des AG polyinsaturés) étaient négativement liés au CH 4 . Une deuxième approche a consisté à étudier la relation entre CH 4 et ces AG du lait en considérant toutes les expériences de la base de données puis en distinguant les expériences avec ou sans supplémentation lipidique dans l'alimentation. Les corrélations ainsi obtenues étaient améliorées par rapport à celles obtenues avec tous les régimes confondus. Ceci montre que la prédiction de CH 4 par ces AG du lait dépend de l'alimentation. Les AG du lait peuvent être considérées comme de bons prédicteurs des émissions de CH 4 chez la vache laitière, mais plus de données sont nécessaires avec une large gamme de régimes pour confirmer ces prévisions.

Figure 2

 2 Figure 2 Schematic representation of the main driving variables for H 2 excess and methanogenesis.

Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15

 56789101112131415 Figure5Global approach for the relation between CH 4 emission (g/d) and milk FA content in isoC15:0 and anteisoC15:0 (g/100g of total FA)

Table 2

 2 General linear model results for the relation between CH 4 emissions and milk FA content taking into account experimental effect....................................................................... 15 Table 6 General linear model results for the relation between CH 4 emission and milk FA content for experiments with lipid supplementation of the diet ............................................... 16 Table 7 General linear model results for the relation between CH 4 emission and milk FA content for experiments without lipid supplementation of the diet .......................................... 17 Figure 1 Organigram of Herbivores Joint Research Unit UMR1213 ....................................... v Figure 2 Schematic representation of the main driving variables for H 2 excess and methanogenesis. ......................................................................................................................... 3 Figure 3 Global approach for the relation between CH 4 emission (g/d, g/kg DMI and g/kg milk) and milk FA content in isoC16:0, anteisoC17:0 and sum of BCFA (g/100g of total FA) .................................................................................................................................................. 26 Figure 4 Global approach for the relation between CH 4 emission (g/d and g/kg DMI) and milk FA content (g/100g of total FA) arising from rumen biohydrogenation ......................... 26 Figure 5 Global approach for the relation between CH 4 emission (g/d) and milk FA content in isoC15:0 and anteisoC15:0 (g/100g of total FA) ..................................................................... 27

Different predictive equations for CH 4 emission in dairy cows ................................. 10 Table 3 Summary descriptive statistics of selected milk FA in the database used for analysis .................................................................................................................................................. 14

Table 4 Correlation coefficients 1 between milk FA content and the CH 4 emissions expressed in g/d, g/kg DMI and g/kg milk ................................................................................................ 15 Table 5

Figure 6 Global approach for the relation between CH 4 emission (g/kg milk) and milk C16:0 content (g/100g of total FA) ..................................................................................................... 27 Figure 7 Relation between CH 4 emission (g/d) and milk FA content (g/100g of total FA) taking into account experimental effect ................................................................................... 28 Figure 8 Relation between CH 4 emission (g/d) and milk FA content (g/100g of total FA) for experiments with dietary lipid supplementation ...................................................................... 28 Figure 9 Relation between CH 4 emission (g/d) and milk FA content (g/100g total milk FA) for experiments without supplementation of lipids in the diet ................................................. 29 Figure 10 Relation between CH 4 emission (g/kg DMI) and milk FA content (g/100g of total FA) taking into account experimental effect ............................................................................ 29 Figure 11 Relation between CH 4 emission (g/kg DMI) and milk FA content (g/100g of total FA) for experiments with dietary lipid supplementation ......................................................... 30 Figure 12 Relation between CH 4 emission (g/ kg DMI) and milk FA content (g/100g total milk FA) for experiments without supplementation of lipids in the diet ................................. 30 Figure 13 Relation between CH 4 emission (g/kg milk) and milk FA content (g/100g of total FA) taking into account experimental effect ............................................................................ 31 Figure 14 Relation between CH 4 emission (g/kg milk) and milk FA content (g/100g of total FA) for experiments with dietary lipid supplementation ......................................................... 31 Figure 15 Relation between CH 4 emission (g/ kg DMI) and milk FA content (g/100g total milk FA) for experiments without supplementation of lipids in the diet ................................. 32 iii

  . Biohydrogenation of long chain unsaturated FA and some chemical compounds (e.g. sulphate, nitrate, ferric iron, manganese and fumarate) though quickly reduced and exhaust[START_REF] Mcallister | Redirecting rumen fermentation to reduce methanogenesis[END_REF][START_REF] Van Zijderveld | Dietary strategies to reduce methane emissions from ruminants[END_REF][START_REF] Jeyanathan | The use of direct-fed microbials for mitigation of ruminant methane emissions: a review[END_REF]) also serve as alternative H 2 sink.

Table 1

 1 strengths and weaknesses of in vivo methane measuring techniques in dairy cows

	Type of technique	Strengths	Weaknesses
	Gold standard reference method: respiration chambers	Ambient environment can be	Animal behaviour not natural;
	Storm et al. (2012)	controlled; reliability and stability of	results cannot be applied to free
		instruments can be measured;	ranging animals; expensive so low
		controlled feed intake;	replication.
		measurements in kinetics.
	SF 6 tracer technique	Ability to investigate energy	Determination of CH 4 emission
	Storm et al. (2012)	efficiency in free ranging animals;	dynamics is debatable; variable SF 6
		SF 6 gas is cheap and has extremely	release rate from permeation tubes;
		low detection limit is simple to	within and between animal
		analyse; Nearly all aspects of	variations; Needs a lab and high
		feeding and nutrition (e.g. level of	laboratory skills.
		feeding, different additives and
		grazing) can be investigated.
	GreenFeedTM (C-lock Inc., USA)	Can measure CH 4 production on a	Only measures CH 4 emissions when
	(Source INRA document)	large number of cows (N = 20) in	the animals have their heads in the
		loose housing simultaneously over a	feeder and are eating. At least 50
		period of several weeks or months.	green feed are available in the world
		The system is mobile and can be	now.
		used in experimental or commercial
		farms. Real-time measurements
		useful on farm scale.
	Laser CH 4 detector	Detects CH 4 within a mix of other	As analysis is based on real-time
	Chagunda (2013)	gases with high specificity; non-	breath it is not clear whether
		invasive and non-contact technique;	fluctuations that arise are due to
		real-time measurements useful on	biomechanics of breathing or
		farm scale; portable piece of	equipment fluctuations; in grazing
		equipment and can be used at the	environment wind speed, wind
		farm scale in theory.	direction, relative humidity and
			pressure have significant (P<0.0001)
			effects on resultant concentration of
			CH 4 . Calculations to estimate CH 4
			emissions from CH 4 concentrations
			are not clear. More tests needed as
			there is only one publication.

Table 2

 2 Different predictive equations for CH 4 emission in dairy cows

	Predictive equations		Diets tested	Statistical analysis	N R²	Reference
	1 CH 4 (g/d) = (9.46 × milk C16:0 -(97.6 ×	Maize silage, grass hay	Stepwise multiple	32 0.953	Chilliard et al.
	milk trans-16+cis-14 C18:1) + (13.3 ×	concentrates containing	regression of SAS		(2009)
	forage intake) -(78. × milk cis-9	either, crude linseed,
	C14:1) + (77.4 × C18:2n-6 ) -21.2		extruded linseed or linseed
	2 CH 4 (g/d) = (-100.8 × milk trans-16+cis-	Maize silage, grass hay	Stepwise multiple	32 0.931 Chilliard et al.
	14C18:1) + (6.78 × milk 16:0) + (13.1	concentrates containing	regression of SAS		(2009)
	× forage intake) + 80.1		either, crude linseed,
			extruded linseed or linseed
	(Individual FA in g/100g milk FA,
	Forage intake in g/kg DM)
	3 CH 4 (g/kg DM) = 24.6 + (8.74 × milk	Grass silage, maize silage,	Mixed model	50	0.73 Dijkstra et al.
	anteiso C17:0) -(1.97 × milk trans-	wheat straw, calcium	univariate		(2011)
	10+11 C18:1) -(9.09 × milk cis-11	fumarate, diallyldisulfide,	regression, then
	C18:1) + (5.07 × milk cis-13 C18:1)		caprylic acid, capric acid,	GLM multivariate
			lauric acid, myristic acid,	model
			extruded linseed, linseed
	(Individual FA in g/100g milk FA)		oil and yucca powder.
	4 CH 4 (g/d) = (-486.2 × milk cis-9 C17:1)	Barley silage, barley grain	Stepwise regression	32	0.74 Mohammed et
	-(122.7 × milk cis-11 C18:1) + (2220	steam rolled, and either	analysis		al. (2011)
	× milk trans trans-CLA) -(11.76 ×	calcium salts of long chain
	milk Σtrans-C18:1) + (260.1× milk	FA, crushed sunflower
	anteiso C15:0) + 272.4		seeds, flaxseeds or canola
			seeds
	5 CH 4 (g/d) = (-910.8 × milk cis-9 C17:1)	Barley silage, barley grain	Stepwise regression	16	0.90 Mohammed et
	+ (331.2 × milk iso C16:0) + (0.0001 ×	steam rolled, and either	analysis		al. (2011)
	Σentodionomorphs)		calcium salts of long chain
	+ 242.5		FA, crushed sunflower
			seeds, flaxseeds or canola
			seeds
	6 CH 4 (g/d) = (-124.9 × milk cis-11	Barley silage, barley grain	Stepwise regression	32	0.83 Mohammed et
	C18:1) + (2115.9 milk trans trans-	steam rolled, and either	analysis		al. (2011)
	CLA) -(10.04 × milk Σtrans-C18:1) +	calcium salts of long chain
	(455.5 × milk anteiso C15:0) -(305.9×	FA, crushed sunflower
	milk C17:0) + (7.83× DMI) 104.8		seeds, flaxseeds or canola
			seeds
	(Individual FA in g/100g FAME;
	Σentodionomorphs as ×10 5 /mL)
	7 CH 4 (g/L milk) = milk ΣFA≤C16 × 11.368 × [milk production (L/year) -0.4274 ]	n.a	n.a	n.a 0.953 Weill et al. (2009)
	(ΣFA≤C16 in g/100g in g/100g milk
	FA )
	8 CH 4 (g/d) = 257±51.7+ (129±42.3) ×milk	Crushed wheat, alfalfa	n.a	30	0.22 Moate et al.
	C8:0		hay, and either dried or			(2014)
			ensiled grape marc
	9 CH 4 (g/d) = 536±18.2-(3.8±0.54) ×milk	Crushed wheat, alfalfa	n.a	61	0.63 Moate et al.
	totalC18		hay, dried or ensiled grape			(2014)
			marc
	Individual FA in g/100g milk FA
	n.a = not available	N=number of observations

Table 3

 3 Summary descriptive statistics of selected milk FA in the database used for analysis

	Milk FA (g/100g FA)	Nexp	Nt	Mean	range
	isoC15:0	6	19	0.208	0.21
	anteisoC15:0	6	19	0.480	0.41
	C16:0	13	38	28.19	27.45
	isoC16:0	5	17	0.264	0.38
	anteisoC17:0	6	19	0.381	0.54
	t16+c14C18:1	3	12	0.501	1.320
	BCFA	6	19	1.773	1.560
	Sum of all C18:2	13	38	3.294	6.53
	Sum of PUFA	13	38	3.832	7.181
	Nexp= number of experiment	Nt= number of treatments	t= trans c= cis	BCFA= sum of branched-chain FA

Table 4

 4

	reports the correlation coefficients between these milk FA and CH 4 emissions
	expressed in g/d, g/kg DMI and g/kg milk. The milk isoC15:0 (P= 0.025) and anteisoC15:0
	(P= 0.008) were only positively correlated with CH 4 g/d. Milk C16:0 only showed significant
	and slight positive correlation (P= 0.021) with CH 4 g/kg milk. Milk isoC16:0 showed the
	strongest positive correlation whatever the units of CH 4 emissions used (Table 4). Milk
	anteisoC17:0 had also positive correlation coefficients (r ≥ 0.60) with CH 4 emissions
	(expressed as g/d, g/kg DMI or g/kg milk). Milk t16+c14C18:1 had the highest negative
	correlation coefficient with CH 4 emissions (g/d or g/kg DMI). The sum of all C18:2 FA and
	the sum of PUFA were negatively correlated with CH 4 g/d (P= 0.012 and 0.005, respectively)
	and CH 4 g/kg DMI (P= 0.008 and 0.003, respectively).

Table 5

 5 General linear model results for the relation between CH 4 emissions and milk FA Tables 5, 6 and 7 show the GLM results for the relation between CH 4 emissions and milk FA taking into account the effects of experiment, experimental factors "lipid supplementation"

	content taking into account experimental effect
	Milk FA (g/100g	Nexp	Nt	CH 4 g/d (Y)	r	P
	FA) (X)
	isoC15:0	6	19 Y = (2.72±65.46) + (1381.7±313.5) × X	0.83 0.001
	anteisoC15:0	6	19 Y= (-58.16±73.20) + (742.8±155.9) × X 0.93 0.000
	C16:0	13	38 Y= (99.02±66.83) + (8.20±2.35) × X	0.89 0.002
	isoC16:0	5	17 Y= (78.35 ±48.60) + (728.3±173.7) × X	0.91 0.002
	anteisoC17:0	6	19 Y = (68.7±118.5) + (587±315.9) × X	0.83 0.088
	t16+c14C18:1	3	12 Y= (397.88±14.76) -(199.96±25.41) ×	0.95 0.000
			X
	BCFA	6	19 Y= (-103.53±78.4) + (226.48±45.08) ×	0.93 0.000
			X
	Sum of all C18:2	13	38 Y= (419.95±33.7) -(28.62±10.21) × X	0.87 0.010
	Sum of PUFA	13	38 Y= (427.72±38.64) -(26.68±10.15) × X	0.87 0.015
				CH 4 g/kg DMI (Y)
	C16:0	13	38 Y= (7.65 ±2.62) + (0.35±0.09) × X	0.96 0.001
	isoC16:0	5	17 Y= (6.4 ±1.93) + (34.37±6.89) × X	0.97 0.000
	anteisoC17:0	6	19 Y= (7.12±5.29) + (25.62±14.11) × X	0.92 0.094
	t16+c14C18:1	3	12 Y= (20.50±0.66) -(8.12±1.14) × X	0.95 0.000
	BCFA	6	19 Y= (-1.29±3.23) + (10.34±1.86) × X	0.97 0.000
	Sum of all C18:2	13	38 Y= (21.37±1.35) -(1.21±0.41) × X	0.95 0.007
	Sum of PUFA	13	38 Y= (21.68±1.55) -(1.12±0.41) × X	0.95 0.011
				CH 4 g/kg milk (Y)
	C16:0	13	38 Y= (2.54±2.56) + (0.39±0.09) × X	0.97 0.000
	isoC16:0	5	17 Y= (4.27 ±1.58) + (33.34±5.63) × X	0.98 0.000
	anteisoC17:0	6	19 Y= (6.45±5.14) + (18.18±13.71) × X	0.92 0.209
	BCFA	6	19 Y= (-2.90±3.15) + (9.34±1.82) × X	0.97 0.000

Table 6

 6 General linear model results for the relation between CH 4 emission and milk FA content for experiments with lipid supplementation of the diet

	Milk FA unit(g/100g FA)	Nexp Nt	CH 4 g/d (Y)	r	P
	(X)
	isoC15:0	5	15 Y= (-10.29±74.61) + (1497.9±379.9) × X	0.92 0.003
	anteisoC15:0	5	15 Y= (-77.48±71.82) + (839.6±167) × X	0.94 0.001
	C16:0	10	30 Y= (109.83±72.66) + (7.96±2.58) × X	0.88 0.006
	isoC16:0	4	13 Y= (60.55 ±57.42) + (760.1±207.8) × X	0.91 0.006
	anteisoC17:0	5	15 Y= (-109.7±128.6) + (1167.2±383.5) × X	0.89 0.014
	t16+c14C18:1	2	8 Y= (403.25±16.75) -(202.97±24.13) × X	0.97 0.000
	BCFA	5	15 Y= (-97.30±81.78) + (238.29±51.23) × X	0.94 0.001
	Sum of all C18:2	10	30 Y= (418.80±38.58) -(27.18±11.30) × X	0.86 0.026
	Sum of PUFA	10	30 Y= (427.61±44.34) -(25.57±11.25) × X	0.85 0.035
				CH 4 g/kg DMI (Y)
	C16:0	10	30 Y= (5.52 ±2.24) + (35.85±8.11) × X	0.94 0.003
	isoC16:0	4	13 Y= (5.52 ±2.24) + (35.85±8.11) × X	0.97 0.002
	anteisoC17:0	5	15 Y= (-0.396±5.86) + (50.16±17.48) × X	0.95 0.018
	t16+c14C18:1	2	8 Y= (20.34±0.75) -(8.19±1.09) × X	0.96 0.001
	BCFA	5	15 Y= (-0.799±3.38) + (10.83±2.11) × X	0.97 0.001
	Sum of all C18:2	10	30 Y= (21.17±1.54) -(1.15±0.45) × X	0.93 0.019
	Sum of PUFA	10	30 Y= (21.53±1.77) -(1.08±0.45) × X	0.93 0.027
				CH 4 g/kg milk (Y)
	C16:0	10	30 Y= (3.76±2.73) + (0.38±0.10) × X	0.97 0.001
	isoC16:0	4	13 Y= (4.12 ±1.87) + (34.70±6.77) × X	0.98 0.001
	antisoC17:0	5	15 Y= (-0.228±6.01) + (40.40±17.92) × X	0.94 0.059
	BCFA	5	15 Y= (-2.70±3.08) + (10.09±1.93) × X	0.98 0.001
	Milk anteisoC17:0 tended to be positively related to CH 4 in g/d (P=0.088) and CH 4 in g/kg
	DMI (P=0.094) when experiment effect was integrated in the model (Table 5) but showed
	significant relation to CH 4 in g/d (P= 0.014) and CH 4 in g/kg DMI (P = 0.018) when the
	experimental factor "lipid supplementation" was taken into account. Generally correlations

Table 2

 2

	) in

  The negative relationship between these group of FA and CH 4 emission is consistent with the findings of[START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF] equations 1 and 2 Table 2), Dijkstra et al. (2011; equation 3 Table 2) and Mohammed et al. (2011; equations 4 and 6 Table 2). Diets rich in PUFA contribute to a decrease in CH 4 emissions through toxic effects on microbes producing H 2

List of figures

DISCUSSION

The best correlations between CH 4 emissions and milk FA were obtained with BCFA (iso15 anteiso15, iso16 and anteiso17), C16:0 and intermediates of ruminal biohydogenation (t16 + c14-C18: 1, sum of all C18: 2, sum of PUFA).

Milk branchedchain fatty acids (BCFA)

Most of the milk FA that were related to CH 4 emissions were branched-chain FA (isoC15:0, anteisoC15:0, isoC16:0 and anteisoC17:0) and their sum (BCFA). Generally correlations between CH 4 emissions and test variables were stronger with the integration of experiment and lipid supplementation effects when compared to the global approach. This shows that the prediction of CH 4 emissions by these FA is diet dependent. However, these relations were removed when taking into account non-lipid supplementation because only one experiment with non-lipid supplementation reported these FA (Appendix figures 9, 12 and 15). [START_REF] Vlaeminck | Milk odd and branched-chain fatty acids to predict ruminal methanogenesis in dairy cows[END_REF] were the first to demonstrate the potential of milk OBCFA in predicting CH 4 emissions and this was subsequently affirmed by (Montaya et al., 2011) but in these two publications estimated CH 4 emissions were calculated from rumen VFA APPENDIX Figure 3 Global approach for the relation between CH 4 emission (g/d, g/kg DMI and g/kg milk) and milk FA content in isoC16:0, anteisoC17:0 and sum of BCFA (g/100g of total FA) Figure 4 Global approach for the relation between CH 4 emission (g/d and g/kg DMI) and milk FA content (g/100g of total FA) arising from rumen biohydrogenation


Jean Clency

Fabien Bibi

Nathalie N Gontard

Carole Guillaume

Brice Sorli

Brice Sorli Intelligent

Intelligent food packaging : RFID bio-based sensing label to monitor food shelf life

Vegetal protein: Wheat Gluten

• "bio" material and can be coated onto a substrate.

• displays sensitivity to gases and vapors (considered as food quality markers).

• exhibits electrical properties and dielectric properties (Dipoles, charges, charged molecular chains). Increase of permittivity with increase in RH.

C   C   H  F  H  H 
-More polarizations because of water (dipole).

-Increase mobility of molecular chain and dipoles.

(Electromagnetic properties: Dielectric properties of food)

Increase in dielectric loss with increase in RH.

-Increase mobility of charges in the network. (J.Ahmed, 2007).

-Lossy medium. (S.Ryyniinen,1995).

Effects of relative humidity (RH) on wheat gluten

  Wireless system for transferring data from a tag attached to an object = Volatile markers

  charge. Energy stored (dipoles, polarization): rep. by permittivity (Ɛ'). Energy loss (conduction, friction): rep. by dielectric loss (

•

  Effects of humidity on wheat gluten (permittivity and dielectric loss) => Modification of electrical property of RFID antenna.• Modification in reflected power.• Modification in reading distance.RFID tag with wheat gluten layer deposited -Impact on reading distanceWG-RFID tag WG-RFID tag + moisture WG-RFID tag + moisture + drying -2 m


Jaap Van Milgen

Raquel Melchior

Patrice Roger

Josselin Delamarre

Hervé Demay

Bernard Carissant

Georges Guillemois

Yolande Yaguelin

Agnès Starck

Alain Machais

Nadine Mézière

Colette Mustiere

Context Objective M&M Results Conclusions

M&M Results Conclusions

.04



  Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs VSP CVB UFV GfE BSAS FEDNA System SID SID SID AID SID SID SID SID Country EU FR DK NL BR DE UK ES Year 2011 -2012 -2011 2006 2003 2013 Lys 100 Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs van Milgen et al., 2012: Meta-analysis of the Ile response in growing pigs The Ile concentration and the response criteria were standardized avoiding differences Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs This system allows to design low protein diets and to account for the associated energy sparing effect By formulating to each EAA and using the NE system, the formulation cost can be reduced while keeping the same performance 20 / 6 / 2014 Alberto Conde-Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Context Objective M&M Results Conclusions .014  Tryptophane  In piglets 22% SID Trp:Lys (Simongiovanni et al., 2011)  25-65 kg LW 20% SID Trp:Lys (Simongiovanni et al., 2013)  >65 kg LW 19% SID Trp:Lys (Simongiovanni et al., 2013)  Trp regulates feed intake and is involved in immune function pathways (Bulletin 30) Second limiting amino acids 20 / 6 / 2014 Alberto Conde-Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Isoleucine  Without blood cells 53% SID Ile:Lys (Fu 2005)  With blood cells 63% Ile:Lys (Fu 2005, Kendall 2004)  Different types of interactions between AA (BCAA and others) Second limiting amino acids 20 / 6 / 2014 Alberto Conde-Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Context Objective M&M Results Conclusions  In piglets 70% SID Val:Lys (Gloaguen et al., 2013)  Growing % SID Val:Lys (a minimum 65%) Finishing  Information G&F pigs is scarce .A.: Essential Amino Acid, N.E.A.A.: non-Essential Amino Acid FEED 20 / 6 / 2014 Alberto Conde-Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs

  1 L-lysine, L-threonine, DL-methionine and L-tryptophan.  Canh et al., 1998: castrates; 52 to 104 kg body weight  Performance & carcass composition 20 / 6 / 2014 Alberto Conde-Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs study the animal response to different protein and/or Lys and Val contents on the performance of growing and finishing pigs = Val is limiting in low protein diets 20 / 6 / 2014 Alberto Conde-Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs 3Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs

  Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs

Context Objective M&M Results Conclusions .018 Low protein diet consequences Crude protein, % 16.5 14.5 + AA 1 12.5 + AA 1 Effect

	Digestible lysine g / MJ NE	0.76	0.76	0.76	-
	Weight gain, g/d	805	805	797	ns
	Backfat, mm	15.2	15.4	15.9	ns
	Muscle, mm	56.9	56.5	57	ns
	Lean meat, %	57.2	57.1	56.7	ns

Context Objective M&M Results Conclusions 15 % Lys 30 % Val Low protein diet consequences

	Diet	A	B	C	D	Diet	A	B	C	D
	Lys	100	100	100	100	Lys	100	100	100	100
	Thr	75	75	65	65	Thr	75	75	66	65
	Met	41	41	36	30	Met	41	41	36	30
	Cys	28	28	24	31	Cys	69	69	61	65
	Met+Cys	69	69	60	61	Met+Cys 69	69	60	61
	Trp	24	24	21	21	Trp	24	24	21	21
	Val	58	80	70	70	Val	58	80	70	70
	Ile	60	60	52	53	Ile	60	60	53	53
	Leu	126	126	110	130	Leu	129	129	113	153
	His	34	34	30	38	His	35	35	31	42
	Phe	57	57	49	63	Phe	58	58	51	67
	Tyr	45	45	39	49	Tyr	45	45	39	54
	Phe+Tyr 102	102	89	111	Phe+Tyr 102	102	90	121



Conde-Aguilera et al., 2014 (unpublished)

.023

Context Objective M&M Results Conclusions Low protein diet consequences

		Val	Lys	-N +
	Item	Diet A	Diet B	Diet C	Diet D	RSD	P-value
	Initial BW, kg	87.3	88.4	87.4	88.4	8.4	0.97
	Final BW, kg	107 a	110 b	111 c	109 abc	4	0.03
	Hot carcass weight, kg	85.7	87.5	88.9	87.3	3.0	0.06
	ADG, g/d	1014 a	1144 b	1232 c	1131 abc	190	0.03
	ADFI, kg/d	3.56 a	4.03 b	4.13 b	3.69 a	0.52	0.01
	G:F, g/g	0.28	0.29	0.30	0.31	0.03	0.14
							.030



Conde-Aguilera et al., 2014 (unpublished)

AA Finishing pig diets 20 / 6 / 2014 Alberto Conde-Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs Context Objective M&M Results Conclusions Low protein diet consequences



Conde-Aguilera et al., 2014 (unpublished)

/ 6 / 2014 Alberto Conde-Aguilera / Reducing the crude protein content in the diet of growing and finishing pigs

 Low proteins should be formulated :

 with adjusted levels of digestible lys to net energy  with optimal ratios of essential AA relative to lys (ideal protein)

 using AA standardized ileal digestibility coefficients.


est destinée au dépôt

Coffee grown under shades of diverse natural shade tree species ("rustic" systems) has potential to provide numerous ESs. However, coffee systems in Costa Rica have gone through transformation that involved sparse or absence of shade and intensive production systems with higher external input, favouring short term financial benefits over long-term generation of ESs.

The objective of the study was to understand how coffee producing households´ characteristics, related to decisions on resources endowments and management, interact with field characteristics to generate key ESs: yield and regulation of important coffee pests and diseases. We carried-out household and management surveys, measurements of coffee field characteristics and monitoring of ESs in Llano Bonito watershed, Costa Rica for the coffee production year 2013-2014. To understand household characteristics, resource endowments and key ESs management within coffee production systems, a structured questionnaire survey was employed to 40 sample households. 97 plots were sampled in coffee fields of surveyed households and measurements were carried out to characterise site, coffee, and shade tree, and to quantify coffee yield. These plots were further resampled and 69 plots were selected to monitor variations of locally important coffee pests and diseases along 4 time series. After the final coffee harvest, a second household level survey was carried-out to record coffee management practices for the sampled plot for the production year.

Households and fields were categorised into 4 and 3 categories respectively with the aid of Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC).

Households were categorised based on their functional characteristics, which included their orientation to produce coffee and state of coffee management whereas fields were categorised based on their biophysical characteristics, which included their suitability for generating ESs. Features of each category and interactions between household and field categories are summarised. The method shows how diversities of decision and biophysical contexts can be taken into account while assessing and promoting ESs from agroecosystems. The results demonstrate feasibility to evaluate variations in decision and biophysical contexts of agro-ecosystems for understanding ESs, and have potential to contribute to understanding and thus promotion of ESs.

  Interaction between household and field characteristics in generation of ecosystem services from coffee agro-ecosystem of Llano Bonito, Costa Rica Sanjeeb Bhattarai, Clementine Allinne, Jacques Avelino, Christian C. Gary, Walter Rossing, Pablo Tittonell, Bruno Rapidel To cite this version: Sanjeeb Bhattarai, Clementine Allinne, Jacques Avelino, Christian C. Gary, Walter Rossing, et al.. Interaction between household and field characteristics in generation of ecosystem services from coffee agro-ecosystem of Llano Bonito, Costa Rica. 7. Conference of the Ecosystem Services Partnership (ESP) : "Local action for the common good", Sep 2014, San José, Costa Rica. 2 p. hal-02796712