Charles-Henri Moulin

Harry Archimède

René Poccard-Chapuis

Eliel González García

Fabien Stark

Considering crop-livestock integration flows in terms of diversity and complexity: a certain regard on resilience and mixed farming systems properties

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Considering crop-livestock integration flows in terms of diversity and complexity: a certain regard on resilience and mixed farming systems properties C.-H. Moulin 1 , H. Archimède 2 , R. Poccard-Chappuid 1 , E. González-García 1 , F. Stark 1,2,3 1 UMR SELMET, Montpellier SupAgro, Place Pierre Viala, 34060 Montpellier, France, 2 URZ, INRA, Centre INRA Antilles Guyane, Prise d'Eau, 97170 Petit Bourg, Guadeloupe; 3 IAVFF-Agreenium, 42 rue Scheffer, 75016 Paris, France ; charles-henri.moulin@supagro.fr From an agroecological perspective, mixed crop-livestock systems (MCLS) can be characterized according to two main properties: i) diversity of production as a structural property and, ii) croplivestock integration (CLI) as a functional property of the farming system. The objective of this work was to assess the resilience of a range of CLI configurations at system level. We conducted this analysis on three contrasting socioeconomic territories in the humid tropics: Guadeloupe, Brazilian Amazonia and Cuba, thus looking to cover a spectrum of CLI configurations. In order to analyze CLI in a holistic way, we used the ecological network analysis (ENA) method to study the flow networks of nutrients. We applied ENA to a sample of seventeen MCLS farms. We modelled them as a network of flows, using nitrogen as the "currency". We then characterized CLI in terms of intensity and organization of flows, and examined the resilience of the flow networks. The results show that both the intensity and organization of CLI flows are consistent in characterizing CLI in a range of situations i.e. from low CLI intensity based on few flows to high CLI intensity based on complex flows. The CLI is especially effective in promoting resilience, viewed as the stability provided by the distribution of flow networks among the farming system compartments. This study proposes a suitable framework to analyze complex farming systems helping with a methodological support to the objectives of agroecological transition in agriculture.


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text:
Marwa Al-Jammas

Jacques Agabriel

Jean Vernet

Isabelle Ortigues

Effects of diet composition on carcass fat in beef cattle: a meta-analysis


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text:
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Genetic parameters of white striping in relation to body weight, carcass composition and meat quality traits in broiler chickens

Nabeel Alnahhas, Cécile Berri, Marie Chabault-Dhuit Chabault, Pascal P. Corresponding author: nabeel.alnahhas@tours.inra.fr

White striping (WS) is an emerging meat quality defect of increasing importance for the industry. The genetic determinism of this defect is still unknown so, the purpose of the present study was to shed some light on the heritability (h2) of WS and its genetic correlations (rg) with other traits of interest. The analyzed database contained 10451 records measured on two lines of broilers selected divergently on the ultimate pH (pHu) of breast muscle (pHu+, pHu-) for 6 generations. WS scores were recorded only on the last two generations (n=1348) as 0 (NORM), 1 (MOD) and 2 (SEV). The incidence in the measured population was 36.7% of MOD and 14% of SEV totaling to 50.7% and it was higher in the pHu+ line than in the pHu-line (65.8 vs. 35.15% respectively, P < 0.0001). A strong genetic determinism for WS (h2 = 0.65±0.08) was evidenced. WS was significantly correlated with body weight (rg = 0.33±0.15), breast meat yield (0.68±0.06), but not with the percentage of thigh + drumstick or abdominal fat. In both lines, increased body weight, muscle mass and yield were significantly associated with increased severity of WS. Significant rg were observed between WS and breast (0.21±0.08) and thigh (0.32±0.11) pHu and cooking loss (0.30±0.15) but not with drip loss and curing cooking yield. Globally, severely affected breast fillets exhibited a paler color and higher drip and cooking losses. In conclusion, genetics seems to be a major determinant of WS. This condition appears to be more affected by the rate of muscular development and meat yield than by body weight. In addition, its impact on meat quality is limited but not negligible.

Keywords: Genetic parameters, white striping, broiler

  Chartrin, Maryse Boulay, Marie-Christine Bourin, Elisabeth Le Bihan-Duval To cite this version: Nabeel Alnahhas, Cécile Berri, Marie Chabault-Dhuit Chabault, Pascal P. Chartrin, Maryse Boulay, et al.. Genetic parameters of white striping in relation to body weight, carcass composition and meat quality traits in broiler chickens. 22th European Symposium on the Quality of Poultry Meat -16th European Symposium on the Quality of Eggs and Egg Products, May 2015, Nantes, France. 190 p. 2, 3, 4, 7) INRA, UR83 Recherches Avicoles, F-37380 Nouzilly, France.; (5) SYSAAF, Centre INRA Val de Loire, Unité de Recherches Avicoles, F-37380 Nouzilly, France.; (6) ITAVI, Centre INRA Val de Loire, F-37380 Nouzilly, France.


Magdalena Kristiawan

Della Guy

Kamal Valle

Amadou Kansou

Bruno Ndiaye

Chantal Vergnes

David

A phenomenological model of starch expansion by extrusion

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.


Lucile Herve

Hélène Quesnel

Sandra Wiart

Philippe Lamberton

Colette Mustiere

Vanessa Lollivier

Marion Boutinaud

Myoepithelial cell contraction participates in mammary epithelial cell exfoliation in cow milk

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Myoepithelial cell contraction participates in mammary epithelial cell exfoliation in cow milk

L. Herve A varying proportion of mammary epithelial cells (MEC) is exfoliated through shedding from the mammary epithelium into milk, which might influence MEC number in the udder and thus milk yield. We hypothesized that myoepithelial cell contraction induced by oxytocin (OT) release at milking might stimulate MEC exfoliation through mechanical forces. Therefore, to investigate the role of myoepithelial cell contraction in MEC exfoliation, we inhibited or induced myoepithelial cell contraction by injecting the OT receptor antagonist Atosiban (Ato) or exogenous OT, respectively. Eight multiparous cows were assigned to 2 treatments during 2 milkings according to a crossover experimental design of 2 periods: 1) Control + OT and 2) Ato + OT. For the Control + OT treatment, cows were first milked to recover standard milk and then received an i.v. injection of OT (5 IU) to collect residual milk through a second milking. For the Ato + OT treatment, cows were injected with Ato (50 µg/kg of BW), milked to recover the cisternal milk, and then received 5 IU of OT before a milking to collect the alveolar milk. For each type of collected milk, MEC were purified using an immuno-magnetic method and the percentage of apoptotic MEC was assessed by flow cytometry. Tight junction opening was assessed by plasma lactose concentration determination. Data were analyzed using an ANOVA procedure. Inhibiting myoepithelial cell contraction by Ato injection induced a decrease in MEC number in milk (P<0.01). In contrast, OT injection after standard milking increased the concentration of MEC in milk (P<0.05) and decreased the percentage of apoptotic MEC in milk (P<0.01) while OT injection during Ato + OT treatment increased the number of MEC in milk (P<0.01). During the milking after the Ato injection, plasma lactose concentration was on average lower than during standard milking whereas in both treatments, OT injections increased the lactose concentration in plasma (P<0.001). These results together showed that myoepithelial cell contraction associated with a loss of mammary epithelium integrity stimulates MEC exfoliation into milk.

  12 , H. Quesnel 12 , S. Wiart 12 , P. Lamberton 12 , C. Mustière 12 , V. Lollivier 12 , M.

	Boutinaud 12
	1 INRA, UMR1348 PEGASE, F 35590 Saint Gilles, France
	2 Agrocampus Ouest, UMR1348 PEGASE, F 35000 Rennes, France


Ambre David

Anaïs Boura

Aleksandar Rankovic

Yvan Y Kraepiel

Jean Christophe Lata

Sébastien Barot

Luc Abbadie

Jérome J Ngao

Long term impact of climate on tree-growth patterns in Paris street trees and its consequences on tree cooling potential: a

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Long term impact of climate on tree-growth patterns in Paris street trees and its consequences on tree cooling potential: A dendroclimatic approach

David, Ambre 1 -Boura, Anaïs 2 -Rankovic, Aleksandar 3 -Kraepiel, Yvan 4 -Lata, Jean-Christophe 5 -Barot, Sébastien 6 -Abbadie, Luc 7 -Ngao, Jérôme 8

Water availability is widely recognized as being an essential factor for tree survival, growth and for maximizing their ability in mitigating urban heat islands (UHI) through evapotranspiration. In urban areas, where the ground surface is highly impervious and the trees are not regularly irrigated, the reduction of precipitation infiltration into the soils may put the trees face an increased water stress 1,2 . It is also generally predicted that trees in urban sites have higher water losses than trees in natural forests due to increased evapotranspiration demand 3 . There is currently insufficient data to generalize the physiological responses of trees to the complex urban environment, where both climatic and management factors are entangled. Especially, few information is available on the effect of water stress on tree health, and their consequences on ecosystem services such as UHI mitigation. Furthermore, on-going climatic changes make it all the more necessary to anticipate the potential trajectories of urban trees, in terms of (i) risk assessment of tree survival, and (ii) their potential ability cooling services. In this context, a retrospective approach of the long-term relations of trees to urban climate can thus provide a way to both enhance our understanding of current urban tree hydric state and gain insights on future levels of water stress levels under new climates.

It is well known that there is a close relationship between tree growth and climate. Indeed, the size and the state of tree-rings are affected by the yearly sequence of favourable and unfavourable climates 4 . In turn, climate phenomena can be identified and reconstructed through ring-width sequences 5 . Thus, dendrochronology and study of wood structure can be used as informative tools in order to understand the long-term influence of past climate on urban trees growth. Consequently, understanding the past trajectory of trees under past climates can provide insights on their answer to future climate projections. Since tree cooling potential is tightly linked to water availability, negative feedback of water stress to tree cooling potential can be expected.

To further investigate these issues, we are implementing a 3 years project, financed by CDC-Biodiversité and we tested this approach by studying 95 urban Tilia tomentosa Moench individuals in Paris (France), selected along a 10-120-year age gradient. Tree age was estimated by using wood core samples, and the health status of each tree was visually assessed according to the "Visual Tree Assessment". Number of tree-rings and ring-width were measured using a Lintab measurement table and compared to meteorological data at local and regional scales (Paris, Île-de-France region). The xylem vessel diameter and density were used as a proxy of hydraulic conductance efficiency, embolism vulnerability and health status of trees 6,7,8 . The sampling design will enable a comparative approach of trees of different ages, which is used to assess whether chronic water stress is influenced by both tree age and climate history.

The communication will show the current results, and we will use them to discuss the ways future climate change could further impact tree-growth and thus potential tree cooling effects in Paris City.

Key references

1

  Whitlow and Bassuk (1987) A 3-year study of water relations of urban street trees. Journal of applied ecology 29: 436-450 2 Clark and Kjelgren (1990) Water as a limiting factor in the development of urban trees. Journal Gricar et al. (2009) Number of cells in xylem, phloem and dormant cambium in silver fir (Abies alba), in trees of different vitality. IAWA journal 30(2): 121-133 7 Gricar et al. (2013) Anatomical characteristics and hydrologic signals in tree-rings oaks (Quercus robur L.). Trees 27: 1669-1680 8 Tulik (2010) Diminished vessel diameter as a possible factor in the decline of European ash (Fraxinus excelsior L.). Annals of Forest Science 67: 103

	of Arboriculture 16: 203-208
	3 McCarthy and Pataki (2010) Drivers of variability in water use of native and non-native urban
	trees in the greater Los Angeles area. Urban ecosystems 13(4): 393-414
	4 Fritts (1976) Tree rings and climate. The Blackburn press p567
	5 Hughes (2002) Dendrochronology in climatology -the state of the art. Dendrochronologia 20(1-
	2):95-116
	6


Sébastien Roux

Samuel Buis

Patrice Loisel

Output, 2016, Reunion Island

Introduction

This work is devoted to the analysis of models having functional inputs and is motivated by the intensive use of climatic variables in crop models. The main output of these models is the crop yield, which is estimated, among others, from daily-sampled climatic variables (Temperature, Rain, Radiation, Evapotranspiration). We want to test to what extent this fine temporal resolution is mandatory to generate accurate predictions and quantify how much a priori simplifications, such as lowering the temporal resolution, would affect the model results. This may lead to a better understanding of model behavior as well as to a simplification of the model and/or of the acquisition of its input variables. To this aim, we introduce the use of filtering operators into Global Sensitivity Analysis using switching factors [1]. Low pass filters are used to reduce the temporal resolution of climatic variables. GSA is required because we want to explore the impact of this input simplification in a global exploration of model inputs. Switching factors have been proposed [1], [2] in the context of spatially distributed inputs and further analyzed in [3]. They were initially introduced to assess the sensitivity to the presence of stochastic errors in spatial functional inputs. We use them here to test the sensitivity of a model to simplifications of the temporal structure of its climatic inputs.

Methodology

The method is presented for a model f having two independent inputs: one functional (X) and one scalar (p). The scalar output Y is written as Y = f (X, p). Let g be an operator that transforms X into g(X). We introduce a switching factor η and a modified model f g such as:

f g (η, X, p) = f (X, p) if η = 0 f (g(X), p) if η = 1
A map-labeling scheme [4] is used to perform a GSA using n X samples of X. This leads to a GSA on the independent factors (η, l, p) of model fg defined as fg (η, l, p) = f g (η, X l , p), with X l denoting the sample of X with index l. As we are in a factor fixing context, we focus on Total Sensitivity Indices (TSI) with the perspective of getting small indices for the switching factor η.

Application to a simplified crop model

We tested the method with a simplified crop model that couples a simple water balance with a radiation-driven biomass growth. The crop growth has two limiting factors: High temperature and water stress. The model has the typical input structure of classical crop models (4 climatic variables at a daily time-step). The use of a simplified definition allows for fast computations and qualitative validation of sensitivity results. We applied the proposed method using n x = 42 climatic years and with 3 other factors. TSI of the 4 switching factors, of the climate label and of the 3 others factors were computed using a Sobol algorithm from the R package "Sensitivity". We used two different filtering operators: an inter-annual mean g 1 (X)(t) = The results presented in Fig. 1 show that the application of the inter-annual filter g 1 does not provide small TSI for both the 4 switching variables (T SI(η rain ) > 0.2). It means that simplification by inter-annual mean is not acceptable. This was however not the case when excluding the rain input from the simplification: In that case, the TSI of the 3 remaining switching factors are simultaneously small. Hence the simplification of these 3 variables by their inter-annual mean seems acceptable. Using filter g 2 (d) we also showed that rain input can be simplified in terms of temporal resolution even with large d, but only for model settings corresponding to low run-off soil conditions. This property was expected from the model definition.

Conclusion

Combining switching factors with filtering operators to analyze models with climatic inputs seems to be a promising method. This approach can easily be extended to other input transformations in order to test the effect of other a priori modifications of the model input structure: An example would be a low pass filtering preserving high value applied to the rain input. One difficulty lies in the definition of such relevant transformation: It requires relevant a priori knowledge on the model behavior. A next step of this work is to apply the method to a more complex crop model sharing a comparable structure of model inputs.

Figure 1 :

 1 Figure 1: Left: Application of the inter-annual mean operator g1 (in red) on the climatic variable (year 1..4 in black); Right: Total sensitivity indices when applying the filter to the 4 climatic variables (top) and to 3 of them when excluding Rain (bottom).


Maria Inès

Cécile Fariello Rico

Cécile Arnould

Simon Boitard

Patrice Dehais

Thomas Faraut

Elisabeth Duval

Christine Leterrier

Sylvie Mercier

Frederique Pitel

Julien Recoquillay

M I Fariello

E Lebihan

D Robelin

G Salin

M Sancristobal
email: magali.san-cristobal@toulouse.inra.fr

Local Score Based Method On Pool-Sequenced Behavior-Divergent Quail Lines Precisely Detected Selection Signatures Related To Autism

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

[1] Introduction

Detecting genomic regions that have evolved under selection has received much interest these last years. Linkage disequilibrium (LD) leads to the persistence of a footprint of selection around positively selected mutations, so the selection signature is not limited to a single causal mutation, but generally extends to a wider genomic interval including this mutation. Consequently, detection power is expected to increase when searching for such intervals rather than considering markers independently of each other. Haplotypic methods (e.g. Voight et al. (2006), Sabeti et al. (2007), Fariello et al. (2013)) require individual genotype data (for inferring haplotypes) and are rather computationally demanding. On the other hand, single marker statistics have a lot of variability. Sliding window approaches do not require individual genotypes and run faster than haplotype-based methods. However, they are also less powerful and imply to choose a window size, which is usually done arbitrarily. To overcome this problem, alternative approaches to find clusters of high Fst values were proposed by Myles et al. (2008) and Johansson et al. (2010).

The objective of this study is to present a new strategy for detecting footprints of selection, which runs very fast and is suited to the case where only allele frequencies are available. We demonstrate that this novel approach performs well by detecting genes associated to social reinstatement behavior in quail, using unpublished pooled NGS data from two quail lines that have been divergently selected for this trait. This strategy aims at accumulating selection signals via small p-values of single-marker tests, (or equivalently large values of -log10(p-value), further defined as a score) in an automatic manner, using the statistical theory of local scores (e.g. [START_REF] Mercier | Applied Probability Trust[END_REF]). The local score approach plays an important role in bio-informatics, where it is used for computing sequence alignment scores (Karlin and Altschul (1990)).

[2] Materials and Methods

Quail data

Two divergent lines produced and maintained at the INRA experimental unit 1295 (UE PEAT, F-37380 Nouzilly, France) were used in the experiment. These lines show high (HSR) and low social reinstatement behavior (LSR) (e.g. [START_REF] Mills | [END_REF], Schweitzer et al. (2011)). Ten individuals from generation 50 of each quail line were pooled and sequenced (paired-ends, 100 bp) on a HiSeq 2000 sequencer (Illumina). In the absence of an available genome sequence for the quail, the reads of the two divergent lines were mapped to the chicken genome assembly (GallusWU2.58). Allele frequencies and then F-LK values (i.e. Fst corrected for population structure, Bonhomme et al. ( 2010)) were finally computed at all SNPs.

Model

We propose to start from results of single marker tests along the genome and to highlight segments of adjacent loci with small p-values. More precisely, our strategy is to consider high values of the "score" X = -log10(p-value) (up to an additive constant) and to accumulate them over adjacent loci. The Lindley process h=(h_1,…h_l,…h_L) is defined as h_l=max (0,h_(l-1)+X_l ) and h_0=0. The local score is the maximum of the Lindley process...

[3] Results

On the quail data, all methods gave unclear patterns, except the local score that clearly pinpointed 4 short genomic regions (see Figure 2 for GGA1). One to two genes laid in each candidate region. Some of them are known to be related to behavior traits in Human, in particular related to autism, suggesting that the detected regions are not false positives.

Figure 2 :

 2 Figure 2: Selection footprints for Quail data on GGA1. Top: windowed Fst (sliding window of 10kb), bottom: local score (Lindley process) of score function -log10(pvalue(Fst))-1.


Laura Pascual

Christopher Sauvage

Guillaume Bauchet

Nelly Desplat

Jean-Paul Bouchet

Dominique D Brunel

Marie-Christine Le Paslier

Mathilde M Causse

Approaches of the genetic control of tomato fruit composition in the resequencing era

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Identifying the genes controlling the variation of quantitative traits is a key goal for geneticists. Genetic loci underlying quantitative trait variation (QTL) have been mapped by traditional linkage mapping for years and positional cloning identified several QTLs. However linkage mapping is limited to the analysis of traits differing between two lines and the impact of genetic background on QTL effect has been underlined. Thanks to the increase in molecular markers, genome-wide association studies were then proposed to circumvent QTL limitations. ln tomato, a self-pollinated crop, we have shown that association studies are possible, using the admixed nature of cherry tomato genomes that limits the impact of population structure in such an approach. Nevertheless, the results might be restricted by linkage disequilibrium and allele frequency, which vary greatly along the genome. Multi-allelic Advanced Generation Inter-Cross (MAGIC) populations allow a wide range of variability to be analyzed and avoid dealing with population structure. We have constructed a MAGIC population by crossing 8 tomato lines, representing a wide range of genetic diversity within the species. The large range of phenotypic variability represented by these lines was then characterized at different scales, metabolomic, proteomic and transcriptomic. The variation of a few genes/proteins, involved in primary metabolism and stress response, was related to metabolite content by network reconstruction.

The whole genomes of the 8 founder lines were resequenced identifying more than 4 millions SNPs when mapped onto the reference genome. A set of 1536 SNPs markers was then selected to genotype the MAGIC population and used to construct a linkage map. A large increase in recombination frequencies compared to bi-parental populations was shown. QTLs for fruit quality traits were mapped and related to the variations detected at the genome sequence and expression levels in the parental lines. The advantages and limits of the three types of population, in the context of the available genome sequence and resequencing facilities, will be presented.

  1 , C. Sauvage \ G. Bauchet \ N. Desplat \ J.P. Bouchet '. D. Brunei 2, M.C. Le Paslier 2, M. Causse 1

	1 INRA, UR 1052, GAFL, BP94,F-84143 Montfavet, France
	2 INRA, EPGV, Centre de Génotypage, Evry, France
	Email: mathilde.causse@avignon.inra.fr


domain: sdv info shs

text:
Alex N Hristov

Ermias Kebreab

Z T Yu

Cécile Martin

Maguy Eugène

David Yanez Ruiz

Kevin John Shingfield

S Ahvenjärvi

Padraig O'kiely

Chris K Reynolds

D R Yáñez -Ruiz

J Dijkstra

A Bannink

A Schwarm

M Kreuzer

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements

Keywords: livestock, methane, nitrous oxide, mitigation, database

d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Global network for the development of nutrition-related strategies for mitigation of methane and nitrous oxide emissions from ruminant livestock

for future research priorities, methodologies, and science-based GHG mitigation solutions to governments and non-governmental organizations, advisory/extension networks, and the ruminant livestock sector. Animal scientists with an interest in GHG mitigation research are encouraged to contact members of the consortium to identify areas and opportunities for future collaboration and contribution of data.


Estellé, J Liang Xiao

Jordi Estellé

Pia Kiilerich

Yuliaxis Ramayo-Caldas

Zhongkui Xia

Qiang Feng

Anniøyan Pedersen

Niels Jørgen Kjeldsen

Emmanuelle Maguin

Joël Doré

Nicolas Pons

Emmanuelle Le Chatelier

Lise Madsen

Jun Wang

Stanislav D Ehrlich

Karsten Kristiansen

Claire Rogel-Gaillard

The Pig's other genome: a reference gene catalogue of the gut microbiome as a new resource for deep studies of the interplay between the host and its microbiome

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The Pig's other genome: a reference gene catalogue of the gut microbiome as a new resource for deep studies of the interplay between the host and its microbiome

Liang Xiao, Jordi Estellé, Pia Kiilerich, Yuliaxis Ramayo-Caldas, Zhongkui Xia, Qiang Feng, Niels Jørgen Kjeldsen, Emmanuelle Maguin, Joel Dore, Nicolas Pons, et al.

To cite this version:

Liang Xiao, Jordi Estellé, Pia Kiilerich, Yuliaxis Ramayo-Caldas, Zhongkui Xia, et al. Office, Guy's Hospital, United Kingdom.

Up to 400 words (360 words)

The pig is a major species for livestock production and is also extensively used for biomedical research. A reference catalogue of gut microbial genes would clearly complement the recently established pig genome sequence. We established a comprehensive catalogue of gut microbial genes from 287 pigs from France, Denmark and China. Deep sequencing of fecal DNA samples generated 1,758 Gigabases (Gb) of high quality data with an average of 6.125 Gb per sample. The dataset allowed us to identify 7,685,872 non-redundant (NR) genes with an average contigN50 length of 1.89 Kilobases, together with 719 metagenomic species (MGS).

50% of the NR genes could be taxonomically classified, and of these more than 98% could be assigned to the Bacteria super kingdom. At the phylum level, Firmicutes was the most abundant followed by Bacteroidetes. At the genus level, Prevotella was the most abundant followed by Bacteroides, Clostridium, Ruminococcus and Eubacterium. We identified a common set of 4,430 NR genes, 36 MGS and 3,463 related annotated functions shared by

Comment citer ce document : Xiao, L., Estellé, J., Kiilerich, P., Ramayo-Caldas, Y., Xia, Z., Feng, Q., Pedersen, A., Kjeldsen, N. J., Maguin, E., Doré, J., Pons, N., Le Chatelier, E., Madsen, L., Wang, J., Ehrlich, S.

D., Kristiansen, K., Rogel Gaillard, C. ( 2016). The Pig's other genome: a reference gene catalogue of the gut microbiome as a new resource for deep studies of the interplay between the host 100% of the 287 pig samples, suggesting the existence of a core of genes, species, and functions in the gut microbiome in pigs. The pig and human catalogues share 12.6 % and 9.3 % of their genes, respectively, a higher proportion than that shared between the mouse and human catalogues. Importantly, 78 % and 96% of the functional pathways are shared, underscoring the potential use of pigs for biomedical research. The pig metagenome exhibited a higher alpha diversity than both the human and mouse microbiomes, and lower beta diversity than the human metagenome at the gene, genus, and KEGG levels. We show that gender, age and host genetics markedly influence the pig gut microbiome. We report a common set of antibiotic resistance genes (ARGs) found in all pigs, regardless the country of origin or the supplementation with antibiotics, but show a significantly greater ARG load in animals continuously fed antibiotics. Thus, our data confirmed the efficiency of eliminating antibiotics from animal diet to reduce the risk of dissemination associated with farming systems. The pig microbiome gene catalogue reported here provides a straightforward new resource for metagenomics-based research in biomedicine and for sustainable knowledgebased pig farming.


Hervé Dakpo
email: kherve.dakpo@rennes.inra.fr

Laure Latruffe

K Hervé

Agri-environmental subsidies and French suckler cow farms' technical efficiency accounting for GHGs

Keywords: by-production, GHG emissions, agri-environmental subsidies, livestock D24, O47, Q10, Q50

Agri-environmental subsidies and French suckler cow farms' technical efficiency accounting for GHGs

K Hervé Dakpo, Laure Latruffe

Introduction

Till the late eighties agriculture in developed countries was characterised mainly by productivity increase and farming intensification without little mention to environmental management or outputs (either positive or negative). In the early nineties the emergence of multifunctionality and sustainability concepts have given rise to new strands of thought emphasising environmental concerns (landscape, biodiversity, water pollution, pesticides use, atmospheric pollution, erosion…) in policy design [START_REF] Bohman | The use and abuse of multifunctionality[END_REF]. In the European Union (EU) farmers are subsidised by the Common Agricultural Policy (CAP), whose objective has gradually shifted from supporting farmer's income and modernising the sector, to enhancing farms' competitiveness and promoting a sustainable use of resources (Cooper et al., 2009 p85). Among the different CAP policy instruments that embed the challenges of environmental protection, agri-environmental and cross compliance measures are the main ones. Other instruments have a more indirect impact on environmental output provision, such as farm modernisation support, training and advice measures, and payments for location in disadvantaged regions, the so-called Less Favoured Areas (LFA) subsidies. ( See Cooper et al., 2009 pp86-88 for more details).

Agri-environmental measures (AEMs) are the most direct measures for which the 'provision of public goods is the primary rationale' (Cooper et al., 2009 p86). They are also the 'most significant both in terms of its spatial coverage and the financial resources allocated to it' (Cooper et al., 2009 p89). AEMs are examples of payments for environmental services (PES), a generic instrument used to pay farmers for mitigating (respectively increasing) the production of negative (respectively positive) externalities from agricultural activities [START_REF] Baylis | Agri-environmental policies in the EU and United States: A comparison[END_REF]. The effectiveness of AEMs has been debated in the literature regarding the variability (and contrastability) in terms of impact results [START_REF] Kleijn | How effective are European agri-environment schemes in conserving and promoting biodiversity[END_REF], Oréade-Brèche, 2005[START_REF] Kleijn | Mixed biodiversity benefits of agri-environment schemes in five European countries[END_REF][START_REF] Scheper | Environmental factors driving the effectiveness of European agri-environmental measures in mitigating pollinator lossa meta-analysis[END_REF]. We contribute here to studies on the impact of these measures on farm performance, and focus more precisely on greenhouse gases (GHGs) and farm technical efficiency. Although AEMs are not explicitly designed for GHGs mitigation, some specific measures can directly affect the level of atmospheric pollution. For instance actions towards the reduction of nitrous oxide and inputs' usage (e.g. fertilisers), and towards the preservation of water quality can affect the levels of GHGs releases (Oréade-Brèche, 2005). Land management initiatives can also increase the potential of carbon storage in soils. However, there is a quasi-inexistence of ex-post scientific studies that assess the potential relation between AEMs and GHG emissions. This may be explained by limits on data availability, and by the only recent growing interest on GHG emissions in the agricultural sector. Our paper contributes to this gap by providing an analysis of the impact of AEMs on farms' technical efficiency in a French case study, when technical efficiency is measured with and without accounting for atmospheric pollution, and more precisely GHGs and carbon sequestration in grasslands. This can provide insights for the low carbon and resource efficient economy objective of the EU (EEA, 2010).

Numerous researches have been conducted on the impact of subsidies on farms' technical efficiency (see [START_REF] Minviel | Meta-regression analysis of the impact of agricultural subsidies on farm technical efficiency[END_REF]'s meta-analysis). The most frequent finding is a negative impact, suggesting that farms that receive more subsidies are less technically efficient. This is explained by the possibility that farmers reduce their managerial effort (based on [START_REF] Martin | The Impact of Subsidies on X-Efficiency in LDC Industry: Theory and an Empirical Test[END_REF]'s suggestion for firms) or change their risk attitudes (suggested for the agricultural sector by [START_REF] Serra | Farms' technical inefficiencies in the presence of government programs*[END_REF]) when they receive these additional certain payments. The case of the impact of subsidies from AEMs, that is to say agrienvironmental subsidies, on farms' technical efficiency has been less studied than other types of subsidies. The particularity of agri-environmental subsidies is that they are provided to farmers who voluntary enrol in AEMs aimed at promoting environmental-friendly practices.

Hence, when contracting such schemes, farmers may modify their practices and increase their input use in order to comply with the scheme requirements, e.g. labour increase in order to plant hedges or land increase in order to become more extensive. However, this input increase may not be accompanied by an increase in the output, implying that one would conclude to a negative impact of agri-environmental subsidies on technical efficiency. But the conclusion may change if environmental non-marketed goods are included in the computation of technical efficiency. If AEMs effectively lead to an increase in the environmental good (or a decrease in the environmental bad) produced by the farms, then farms implementing environmental-friendly practices (and receiving the subsidies) may have a better environmentally-adjusted-technical efficiency than other farms. Our paper is the first one to assess the effect of subsidies on such pollution-adjusted efficiency, the literature having so far been restricted to the classic technical efficiency that do not account for environmental goods.

In the past few years the literature has integrated environmental bads in the computation of technical efficiency, with an improvement of the available methods along the years (see the review by [START_REF] Dakpo | Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric framework[END_REF]. In this paper we use the most recent approach suggested by [START_REF] Dakpo | On modeling pollution-generating technologies: a new formulation of the by-production approach[END_REF], the 'extended by-production' approach, to incorporate GHGs in the calculation of pollution-adjusted technical efficiency for a sample of beef cattle farms located in grassland areas in central France. Carbon sequestration in grasslands is also accounted for.

The period studied is 1993 to 2013, which encompasses the very first period of AEMs' implementation in France (the effective implementation started in 1995) as well as the following two rural development programming (RDP) periods (2001-2006 and 2007-2013) in which AEMs are included.

At the crossroads of many international debates (as for instance the COP21 held in Paris, 2015), it is widely admitted that anthropogenic GHG releases in the atmosphere are responsible for the acceleration of the global warming phenomena. In light of the expected consequences, mitigation actions need to be implemented in all sectors of human activities.

Livestock farming is no exception to this, since, according to several FAO reports, this sector is responsible for 13 to 18% of the total GHG emissions, mainly through methane emissions but also carbon dioxide and nitrous oxide [START_REF] Steinfeld | Livestock's long shadow[END_REF][START_REF] Gerber | Tackling climate change through livestock -A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO)[END_REF]. This confirms the relevance of including GHG emissions as bad outputs in an assessment of pollution-adjusted technical efficiency for livestock farming.

In summary, the contributions of our paper are twofold. Methodologically, we extend the byproduction model in an innovative way by using an order-m approach for robustness purposes (Cazals et al., 2002, Daraio and[START_REF] Daraio | Advanced robust and nonparametric methods in efficiency analysis: Methodology and applications[END_REF]. Empirically, we provide a first understanding of the impact of AEMs on farmers' technical efficiency when one considers atmospheric pollution in the shape of GHG emissions.

The rest of the paper is structured as follows. Section 2 is an overview of the implementation of the AEMs in France. Section 3 presents the methodology and Section 4 describes the data.

Section 5 explains the results and Section 6 concludes.

Overview of AEMs in France

France adopted of the EU regulation 797/1985 in 1991 making this country one of the latest applicants of article 19 of this regulation [START_REF] Desjeux | Agricultural policies in France: from EU regulation to national design[END_REF]. As put forward in Buller et al. (2000 p9) 'France appeared to regard agri-environmental debate as an almost quaint, essentially British, obsession with wildlife that had little in common with the reality of French farming culture and with French rural environmental concerns'. This apparent reluctance may be explained by the fact that for many agricultural organisations, AEMs are impediments to the traditional productivism concept, due to extensification and land set-aside measures. From another perspective, the existence of extensive systems (pastures, mountain farming) with high environmental value have strengthened the perception that agriculture is already a producer of environmental outputs without the need to resort to AEMs to encourage the maintenance of this activity in such areas [START_REF] Buller | Agri-environmental Policy in the European Union[END_REF].

Regulation 2078/93 1 has taken three forms in France: first, support for extensive rearing activities, with the creation of the grassland premium ('prime à l'herbe'); second, design of various local agri-environmental programs ('programmes agri-environnementaux' -PAE); and third, implementation of sustainable development plans ('plans de développement durable' -PDD). Grassland premiums are aimed at encouraging de-intensification and restraining the reduction of (permanent) grasslands areas. 2 Regarding local agrienvironmental programs, they are more localised (regions, sub-regions or smaller areas) and include for example measures for water protection (through inputs' reduction, reconversion of croplands to grasslands, long term -20 yearsleys, protection of threatened local breeds, farmers' training programmes), support to conversion into organic farming, incentives for extensive suckler/sheep farming. As for sustainable development plans (which appeared around 1996), they relate to the three sustainability pillars, namely economic, environmental and social aspects. They are based on the production system itself (i.e. the farm), by integrating economic and environmental data. In summary regulation 2078/93 set two objectives in France: the reduction of agriculture's polluting impact, and the maintenance of natural spaces.

In 2000 (with EU regulation 1257/99) a new scheme including contracts based on regional farming practices ('contrat territorial d'exploitation' -CTE) was enforced under the RDP [START_REF] Buller | Agri-environmental Policy in the European Union[END_REF]-2006[START_REF] Baschet | Evaluation of Agri-environmental Policies implemented in France from 2000 to 2002 under the CTE farm contract scheme[END_REF]. These contracts aimed at encouraging the adoption of environmental-friendly cropping and rearing practices. Similarly to any AEM, their subsidy amount is based on the estimation of the farmer's foregone income and includes also an 1 Regulation 2078/93 has extended the EU co-funding to a minimum of 50%. It also implies that AEMs are not only aimed at fragile natural zones.

2 A farmer must commit to keep at least 75% of the farm's total agricultural area in grasslands, and to maintain the stocking rate (number of livestock unit per hectare) lower than 1.4. The farmer must also comply with the maintenance of grasslands' hedgerows.

incentive (up to 20% as much as the previous generation payments). With these contracts, farmers are committed for a period of five years (more details on the difference between these contracts and previous instruments are discussed in Desjeux et al. (2007 pp32-33)). However, these contracts were suspended in August 2002 after the change of the parliamentary majority, and were removed in October 2003. The same year they were replaced by another instrument coined 'contrat d'agriculture durable' (CAD), which was more devoted to agrienvironmental matters than previous contracts.

During the CAP Health Check and discussions on the RDP for years 2007-2013 (EU regulation 1698/2005), the existing AEMs were revised and reinforced to strengthen their environmental impact. The main change is the creation of territorialised AEMs, that are more specific and targeted towards pre-identified territories. In France AEMs represent 30% of the CAP RDP expenses, i.e. 5% of total CAP budget in 2011, which makes them still marginal. In 2009 only 12% of the French utilised agricultural area were under AEMs (compared to 91% in Finland). 3 The 2014-2020 CAP reform has reinforced the focus on territorialised AEMs with climatic and agri-environmental measures, which stress more on the whole farm system commitments rather than plots' specific environmental stakes.

Methodology

Let's define by the vectors of respectively the farm's inputs, good outputs, bad outputs and environmental factors; the latter are exogenous variables that play a role on technical efficiency. is the total number of decision making units (DMUs) on the sample and represents each period of time.

Non-parametric robust efficiency measures

Classically, the production technology (free of bad outputs) can be defined as:

(1) .

In the non-parametric DEA framework, a (good) output efficiency score for assuming variable returns to scale (VRS) [START_REF] Banker | Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis[END_REF] can be computed as follows:

(2) .

where is the efficiency score of , and and are scalars.

Due to its non-parametric nature, DEA can be very sensitive to outliers and extreme observations especially when data are plagued by measurement errors. In this non-parametric framework robust versions have been proposed: order-m and order-quantile frontiers [START_REF] Cazals | Nonparametric frontier estimation: a robust approach[END_REF][START_REF] Aragon | Nonparametric frontier estimation: A conditional quantile-based approach[END_REF]. These partial or robust frontiers not only overcome the drawback associated to extreme points, but also offer the advantage of limiting the curse of dimensionality inherent to the non-parametric approaches, given that they have the same rate of convergence as parametric estimators. Operationally, these partial frontiers allow a percentage of the cloud of observations to lie above the frontiers. In this paper we use the order-m robust frontier. The partial frontiers have mostly been discussed in relation to the free convex hull technology, or free disposal hull (FDH), where the convexity assumption is not maintained [START_REF] Deprins | Measuring labor-efficiency in post offices[END_REF]). Yet, as underlined in Daraio and Simar (2007b p13), 'convexity has always been assumed in mainstream production theory and general equilibrium. In addition, in many empirical applications, the convexity assumption can be reasonable and sometimes natural'. Following this, all the developments carried out in this paper are related to convex technologies.

In the case of the partial version of convex technologies, the robust equivalent of the output efficiency score in equation (2) can be obtained by using Monte-Carlo simulation, as follows:

[1] Given an input level , draw with replacement observations among such that . The obtained sample can be denoted as ;

[2] Solve the following linear program:

(3) .

[3] Do again steps [1] to [2] for where is a large number;

[4] Compute the robust efficiency score as .

The quality of the estimation can be improved by increasing . In this paper we chose a very large . 4 Regarding the choice of , this can be guided by the elbow property which states in this case to retain the value of for which the proportion of observations above the frontier is stable [START_REF] Simar | Detecting outliers in frontier models: A simple approach[END_REF]. In this case one can argue that the obtained attainable set is a 'full' frontier robust to the presence of outliers.

Regarding the inclusion of undesirable outputs in the production technology, we rely on the model proposed by [START_REF] Dakpo | On modeling pollution-generating technologies: a new formulation of the by-production approach[END_REF]. This approach enables representing adequately a multiware technology when outputs may not all be substitutable and may not all be produced by the same inputs. More precisely, it models two types of sub-technologies, one for the good output and one for the bad outputs, and links both sub-technologies. This linkage is an extension of the classic by-production model proposed by [START_REF] Murty | On modeling pollution-generating technologies[END_REF]. It allows for interconnectedness between the different production processes present in a DMU, while in [START_REF] Murty | On modeling pollution-generating technologies[END_REF] independence is maintained. To define the technology, inputs are split into two categories: non-polluting inputs and pollution-generating inputs .

The by-production technology can be represented as follows:

where and (4) .

4 As noticed in [START_REF] Daraio | Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach[END_REF], the model in (3) assumes local convexity, and a global convex technology can be estimated. However, in our paper we only consider local convexity (global convexity can be easily estimated).

where is the good output production function and is the bad output production function. [START_REF] Murty | On modeling pollution-generating technologies[END_REF] proposed the following DEA representation:

(5) .

where and are the intensity variables given weights to each observation in the reference set.

In model (5) the two sub-technologies are represented by two distinct intensity variables which represent the weights given to each DMU in the benchmark of an evaluated observation. In model (5) independence between the sub-technologies is maintained, and [START_REF] Dakpo | On modeling pollution-generating technologies: a new formulation of the by-production approach[END_REF] added the following dependence constraints to overcome this:

(6) .

As underlined in [START_REF] Coelli | Environmental efficiency measurement and the materials balance condition[END_REF] physical processes are ruled by materials balance principles, i.e. the amount of pollution generated is proportional to the levels of polluting inputs consumed. Under this circumstance one can consider constant returns to scale (CRS)

for the bad output sub-technology (by removing the convexity constraint ). In terms of efficiency evaluation there are two situations possible: first, good output efficiency is estimated under the fixed levels of inputs and bad outputs; second, a pollution-adjusted technical efficiency can be measured where good and bad outputs are respectively maximised and minimised. Here we consider this latter situation. However, given the materials balance laws, bad outputs cannot be minimised by holding the levels of polluting inputs fixed. One strategy is to minimise those inputs along with the bad outputs. Here, in order to capture allocation inefficiency, we propose to measure the efficiency under the free choice of polluting inputs. The efficiency program therefore assumes endogenous levels of polluting inputs, as expressed in (7): [START_REF] Aragon | Nonparametric frontier estimation: A conditional quantile-based approach[END_REF] .

where , , and are scalars.

In model (7) the inclusion of bad outputs in the objective function implies endogeneising the levels of polluting inputs. The program that we propose in (7) is a fractional program which can be easily linearised using [START_REF] Charnes | Programming with linear fractional functionals[END_REF]'s transformation. To our knowledge, robust frontiers are designed for single system technologies. However, our byproduction model here is made of two unified sub-technologies. Hence, we propose an extension of the order-m to assess efficiency in [START_REF] Aragon | Nonparametric frontier estimation: A conditional quantile-based approach[END_REF]. The robust version needs to account for the endogenous levels of polluting inputs. We therefore propose the following algorithm:

[1] Given that polluting inputs imply two distinct orientations under good and bad outputs sub-technologies, it may seem intuitive to consider the draw of two samples. 5 However, for consistency, since in our model in (7) polluting inputs are endogeneised, the draw of a sample may neglect these polluting inputs. Hence, for a given level of non-polluting input , we draw a sample of size with replacement among such that . Let's denote the sample by ;

[2] Solve the following fractional program:

(8) .

5 One can refer to conditional free and cost disposability discussed in [START_REF] Murty | On the properties of an emission-generating technology and its parametric representation[END_REF] for a discussion on these two orientations.

In (8) the two sub-technologies are considered independently but the dependence constraints link them. and can be greater or less than one:

[3] Do again steps [1] to [2] for where is a large number;

[4] Compute the robust efficiency score as . As explained previously, the choice of here also follows the elbow property (proportion of DMUs for which ).

The models above show two types of efficiency scores: the classic one obtained in model (2)

and the pollution-adjusted one obtained in model (7).

Role of AEMs on technical efficiency

We investigate the role of AEMs on farms' technical efficiency in a second stage using econometric procedures. This is investigated according to two questions:

(1) Does the adoption of AEMs raise farms' efficiency?

(2) Does the level of subsidies received when contracting AEMs, affect farms' efficiency?

To shed light on the first question, we assess the effect of adoption of AEMs on farm technical efficiency using a binary variable for AEMs and considering the whole sample; this is our econometric Model A. To answer the second question, we investigate whether there is a subsidy effect on farm technical efficiency among those farmers who adopted AEMs, namely whether the level of AEM subsidies (as a continuous variable) plays a role on the efficiency of those farmers who have contracted AEMs; this is our econometric Model B.

Model A is equivalent to the estimation of equation (9):

(9) .
where is the farm efficiency score, is a dichotomous variable which equals if the farmer has adopted AEMs and otherwise, is a vector that includes the other variables explaining efficiency (the environmental factors mentioned above), is a specific function, and is an error term.

Equation (9) can be estimated using a simple ordinary least squares (OLS) since is the robust version of the efficiency score and therefore is not bounded by one. However, though

for EU Member States it is compulsory to design a policy programme for AEMs, the latter are voluntary-based for farmers. In impact evaluation this creates a typical problem known as self-selection, where the decision of farmers to adopt AEMs is not randomly distributed but is rather associated to a number of observable and unobservable features [START_REF] Clougherty | Correcting for Self-selection Based Endogeneity in Management Research: Review, Recommendations and Simulations[END_REF]. Self-selection is a problem of omitted variables which can potentially affect both the level of efficiency and the AEM adoption variable , this latter variable being deemed endogenous in equation (9). To correct for this endogeneity we estimate in a first step the latent variable associated to using a probit regression:

(10) .

where can be viewed here as instrumental variables which are correlated to but do not explain ; is a specific function and is an error term. (10) can be viewed as the selection equation. The deterministic part of (10) is then used as a prediction for which can be considered as an estimated instrument for the endogenous variable . Using this instrument in a classic two-stage least squares enables correcting for the endogeneity problem associated to variable . Since here we use an estimated instrument, we also include cross terms and squared variables in the selection equation in (10). This strategy for estimating an instrument to correct for the endogeneity problem is particularly useful when the endogenous variable is dichotomous as in (9). In the case of a continuous endogenous variable as in Model B below, this is not necessary.

Model B is equivalent to the estimation of equation (11):

(11) .
where where is the farm technical efficiency score for farms having contracted AEMs;

is the amount of subsidies received by the farms through AEMs (agri-environmental subsidies); is a specific function and is an error term. Here also there is an endogeneity issue associated to the variable , since farmers may decide simultaneously on the level of and the level of inputs and outputs. The use of instrumental variables allows correct for this.

The instrumentation equation that is estimated using a classic instrumental variable approach (two-stage least squares) is as follows:

(12) .
where is a specific function and is an error term.

Data description

The application is to a sample of beef cattle farms located in central France in the Massif Central region, an area with grasslands. The sample is unbalanced: around 78 farms are included in the sample each year, making a total number of 1,651 farm-year observations over the 21 year period . The good output considered in the DEA model is the meat production in tons of live weight. The four inputs used are the fodder area (in hectares) for beef cattle production, labour (in full-time equivalent working units) devoted to beef production, herd size (in livestock units) and beef production-related costs (in 2005 Euros).

These production costs include operational and structural costs, and more precisely on and off farm feed costs; veterinary and rearing expenses; costs related to fertilisers, seeds, fuel, electricity, water, equipment and buildings (depreciation and maintenance); and all other expenses associated to the production activity.

As for the bad output, is includes GHGs released into the atmosphere. Three gases are generated in livestock farming: carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O). We quantified these GHGs using Life Cycle Analysis (LCA). This methodology helps evaluating the environmental impacts associated to products during their whole life cycle [START_REF] Finnveden | Recent developments in life cycle assessment[END_REF]. Here the product considered is meat production in livestock farms and the boundary is from cradle to the farm gate. 6 The LCA methodology has been adapted to the particular case of French suckler cows using the French tools GES'TIM [START_REF] Gac | Greenhouse Gases and Carbon Sequestration -Contributions for the environmental assessment of the agricultural activities[END_REF] and Dia' terre® (ADEME, 2011) which are grounded on Tier 1 and Tier 2 methodologies of IPCC. Methane is mainly related to enteric fermentation and a small share of it arises from manure stocking and spreading. Nitrous oxide is associated to nitrogen fertilisers and animal excreta. Carbon dioxide comes from fossil fuels' burning, and from manufacturing and transportation of animal feeds, fertilisers, seeds, pesticides, machineries, buildings, veterinary products and other farm inputs. The total GHGs emissions are computed by summing the three gases using their global warming potential (GWP) related to carbon dioxide. 7 The total GHG emissions are expressed in carbon dioxide equivalent.

To account for carbon sequestration in grassland areas, we adapted the tools developed in the national expertise conducted for the French Institute for Agricultural Research (INRA) by [START_REF] Arrouays | Contribution à la lutte contre l'effet de serre(stocker du carbone dans les sols agricoles de France?)[END_REF]. In this study the authors report equations to estimate the quantities for carbon sequestration by taking into account rotation between cash crops and grasslands.

Carbon sequestration is also expressed in carbon dioxide equivalent.

Descriptive statistics of inputs and outputs for our sample are provided in Table 1. Over the whole period 1993-2013 farms in the sample operated on average 149.9 hectares of total agricultural area, among which 118.3 hectares of fodder area, and the stocking rate was 1.27 livestock unit per hectare of fodder area. More than three quarter of the observations (78%) display a stocking rate below 1.4, a threshold which characterises more extensive farms. The average quantity of GHGs emitted was 14.5 kg of carbon dioxide equivalent per kg of live meat. This result falls within the large range of emissions intensity found in the literature (de Vries and de Boer, 2010, [START_REF] Desjardins | Carbon Footprint of Beef Cattle[END_REF][START_REF] De Vries | Comparing environmental impacts of beef production systems: A review of life cycle assessments[END_REF]. When accounting for carbon sequestration in soils, the pollution intensity is decreased on average by a little more than 12%.

6 Several studies have used LCA to assess the environmental impacts of meat production in livestock systems and in particular GHG emissions [START_REF] Wiedemann | Environmental impacts and resource use of Australian beef and lamb exported to the USA determined using life cycle assessment[END_REF][START_REF] Cardoso | Impact of the intensification of beef production in Brazil on greenhouse gas emissions and land use[END_REF]. 1.8 0.14 Notes: Sample size: 1,651 farm-year observations. The livestock unit is a reference unit used for the aggregation of different types of animals on the basis of their nutritional or feed requirement; one livestock unit corresponds to one dairy cow which produces about 3,000 litres of milk per year. Net GHG emissions implies that carbon sequestration is accounted for. CO 2 -eq: carbon dioxide equivalent.

The exogenous variables (environmental factors) used in the regression analysis are presented in Table 2. The agri-environmental subsidies considered here are mainly grassland premiums, and also include extensification and other related subsidies. The subsidies were provided to farmers who committed to keep a large share of permanent grasslands on their farm and long temporary leys (five years). They aim at limiting the stocking rate (i.e. the number of livestock units per hectare of fodder area) on farms, improving nitrogen management, reducing the quantity of fertiliser spread on land, and increasing carbon sequestration in land.

In other words, these subsidies create incentives towards extensive farming and a more efficient input consumption. They can thus directly or indirectly affect the levels of GHGs.

Over the whole period 64% of the farm-year observations of the sample received agrienvironmental subsidies. The subsidy variables are incorporated in the regression as a ratio of the amount in Euros per livestock unit, in order to control for size effects. On average farms that have adopted AEMs received 44.9 Euros of agri-environmental subsidies per livestock unit. All farms in our sample are conventional farms (no organic farms). Several explanatory variables are included in the regressions of technical efficiency scores, based on the literature review: farm total land area (in hectares); capital to labour ratio, as the value of assets (in Euros) related to the number of working units; share of hired labour in total labour; debt to asset ratio; stocking rate as the number of livestock units per hectare of fodder area; share of permanent grassland in farm total area; numerical productivity as the number of live-weaned calves born per cow multiplied by 100; quantity of concentrates per cow (in kg); feed autonomy in percentage, which represents the share of animal feed that is produced on farm; the proportion of land that is rented in; subsidies received when the farm is located in LFA; other subsidies, that is to say excluding agri-environmental subsidies and LFA subsidies; the quantity of nitrogen spread per hectare of fodder area.

Empirical results

Technical efficiency

In a first stage we compute technical efficiency of farms (including or excluding GHGs), and in a second-stage we investigate with econometric models (Models A and B) the impact of AEMs (adoption and subsidies) on this technical efficiency. For the first stage, we considered four different DEA models, thereby four different robust efficiency scores, depending on whether GHGs are included or excluded and depending on the assumptions regarding inputs.

The first DEA model, Model (i), is related to the case where we do not consider GHG emissions (as in (3)). The second DEA model, Model (ii), is the one where gross GHG emissions are considered, and herd size and production-related costs are endogenous in the optimisation program (as in (8)). In the third DEA model, Model (iii), net GHG emissions are considered, i.e. carbon sequestration in soil is accounted for. Herd size and production-related costs are endogenous in the optimisation program as in Model (ii). The fourth DEA model, Model (iv), is the same as Model (iii), except that in addition to herd size and productionrelated costs, fodder area is also treated as endogenous in the maximisation program. Fodder area is linked to the level of carbon sequestration in grasslands. This endogenous variable is also present in the technology that generates net GHG emissions (i.e. the dependence constraints account for three variables: herd, other production-related costs and land). We considered a pooled frontier for the estimation of the efficiency scores, that is to say one single frontier is estimated for the whole period. We retained this strategy in order to increase the number of observations under analysis and limit the curse of dimensionality inherent to non-parametric estimation. In addition, due to the unbalance nature of the data, this approach makes the different years more comparable. Descriptive statistics of the four robust technical efficiency scores are shown in Table 3. Table 3 shows that Model (i) displays the highest good output efficiency score average, of 94.3%. Such high score compared to the three models incorporating GHGs, suggests that inefficiency in these three models is mainly due to bad output inefficiency. This is confirmed by decomposing, for the three models incorporating GHGs, the global pollution-adjusted efficiency into good and bad outputs efficiency components. The figures, presented in Table 4, show that most farms are super-efficient in terms of good output production (efficiency scores greater than one). The average robust good output efficiency score is greater than 1.20 for all three models. By contrast, the potential inefficiency ranges from 25% to 34% in the case of the bad output. The distribution of the robust efficiency scores compared to the non-robust version (see Tables A.1 and A.2 in Appendix) confirms the presence of potential outliers and the necessity to compute robust efficiency scores. Results in Table 3 also show that pollution-adjusted efficiency is lower when considering carbon sequestration (i.e. net GHG emissions) than not (i.e. gross GHG emissions). This may reveal heterogeneous practices of farmers in terms of carbon sequestration in soils. Model (iii) in Table 3 yields the lowest average pollutionadjusted efficiency score, namely 78.6%. Though Models (ii), (iii) and (iv) exhibit similar descriptive statistics of efficiency in Table 3, the non-parametric Kolmogorov, Smirnov and Wilcoxon rank sum tests are significant at less than 5%, indicating that the farms are not ranked similarly in the three models. The same is true for the good and bad output efficiency scores whose descriptive statistics are shown in Table 4.

In terms of time evolution, the annual averages of the pollution-adjusted efficiency scores are displayed on Figure 1. There is a slight increasing trend, though from year to year there is large variability. This may reveal the sensitivity of this livestock sector to environmental conditions. For instance the drop in 2003 may be due to the drought that has occurred the region at that time and that has affected farmers differently.

Econometric results

Table 5 presents the regression results from the second stage of Model A, namely equation (9), where the dependent variables are in turn the four efficiency scores (Models (i) to (iv))

and where the main exogenous variable of interest is a dummy variable that captures whether or not the farmer has adopted AEMs. The other exogenous variables are those presented in Table 2, as well as year and regional dummies (23 years and 5 regions in total). Given the 6. In our case of two-stage least squares for panel data (with farm fixed or random effects), we used the Balestra-Varadharajan-Krishnakumar's transformation. (good) efficiency, while capital to labour ratio, the debt to asset ratio and the nitrogen quantity per hectare of fodder area have a negative impact. When pollution is included in the analysis, in all Models ((ii), (iii), (iv)) capital to labour ratio, share of hired labour and nitrogen quantity per hectare of fodder area have a significant negative influence on pollution-adjusted technical efficiency, whereas numerical productivity, feed autonomy and other subsidies have a significant positive impact. For robustness check we replicated the analysis with a balanced panel. More precisely, we balanced the panel over the whole period 1993-2013 (with a total of 52 farms per year) and computed the robust efficiency scores on this new sample. In terms of evolution and averages of efficiency scores, the results are very similar to the unbalanced panel case described in the previous section. However, as shown in Table 7, the results of the econometric analyses are different. Compared to the unbalanced panel case (that was shown in Table 5), in the balanced panel case (Table 7) the adoption of AEMs has no effect on the distribution of farms' technical efficiency except when efficiency is obtained from Model (iv), that is to say where GHGs are incorporated and where the three variable inputs (herd size, productionrelated costs, fodder area) are endogenously considered. In this model the adoption of AEMs has a significant positive effect on technical efficiency. As regard the other subsidies (excluding agri-environmental subsidies and LFA subsidies), they also clearly exhibit a positive significant impact. Note: *** p < 0.001, ** p < 0.01, * p < 0.05. Results for year and regional dummies are not shown.

Figures in

When we consider only those farmers who have adopted AEMs (Model B), the regression results of the effect of agri-environmental subsidies (equation (11)) are presented in Table 8 (for the unbalanced case). Here we also treated the potential endogeneity associated to agrienvironmental subsidies (equation (12)) using a panel two-stage least squares estimation with the instruments of Table 6 introduced as such (no cross terms nor square terms). Results show a significant positive impact of agri-environmental subsidies for those farms that have adopted AEMs on technical efficiency obtained from all Models (i), (ii), (iii), (iv), that is to say whatever the type of technical efficiency (including or excluding GHGs). Results also show a significant positive impact of stocking rate and numerical productivity, and a significant negative impact of LFA subsidies on all four types of technical efficiency. Again, for robustness check we conducted the same estimations using a balanced panel sample extracted from our data (with a total of 52 farms per year). Results, shown in Table 9, confirm the significant positive impact of AEMs subsidies on farm technical efficiency, except for the case when technical efficiency is calculated with Model (iii) where this impact is insignificant. Note: *** p < 0.001, ** p < 0.01, * p < 0.05. Results for year and sub-region dummies are not shown.

Conclusion

We investigated in this paper the impact of the adoption of AEMs and the impact of subsidies received in this frame, on farms' technical efficiency when the latter includes or excludes GHGs emissions as a bad output, and considers carbon sequestration or not. The application was for the specific case of beef cattle farming in French grassland areas during 1993-2013.

Our results do not confirm the general finding of the literature, namely a negative effect of public subsidies on farms' technical efficiency. On the contrary, we found here that among farmers who had adopted AEMs, agri-environmental subsidies received had a positive impact on farms' technical efficiency either with or without the inclusion of GHGs. However, the adoption of AEMs itself does not lead to a significant increase in technical efficiency.

From a policy point of view, this indicates that what matters for technical efficiency is the level of subsidies received by farmers when contracting AEMs. It also suggests that the AEMs designed during the period considered here were adequate to enhance farms' technical efficiency, whether it is the classic technical efficiency or the efficiency adjusted for GHG pollution.

The approach undertaken in this paper assumes that the exogenous variables do not influence the level of the technology attainable by inefficient farms. This strong assumption of separability between exogenous variables and the technology could be relaxed using conditional frontier estimation, which can also allow assess the effect of these exogenous conditions on technical efficiency distribution. Another avenue for future research is to investigate the impact of AEM adoption and subsidies on the components of pollutionadjusted efficiency scores (namely good output efficiency and bad output efficiency), which can provide further insights on the effect on AEMs on farms' technical efficiency.

Figure 1 :Figure 2 :

 12 Figure 1: Evolution of annual averages of pollution-adjusted efficiency scores over the whole period

Figure 2 :

 2 Figure 2: Evolution of annual averages of good and bad outputs efficiency scores over the whole period

(

  unbalanced) panel structure of the data, we included individual (farm) effects, fixed or random depending on the Hausman test results. To correct for the endogeneity associated to the AEM adoption variable, we used two instruments (W in equation (10)): beef price and farm revenue per labour unit, both in constant Euros. These two instruments, as well as their cross terms and their squared values, are firstly used in a selection equation (equation (10)) in order to construct a latent variable which is used as an estimated instrument in a classic twostage least squares (equation (9)). Descriptive statistics of the instruments are shown in Table

Table 1 :

 1 Descriptive statistics of inputs and outputs used for the whole period

		Mean	Minimum	Maximum	Standard deviation	Relative standard deviation
	Fodder area (hectares)	118.3	32.3	442.2	51.9	0.44
	Labour (working units)	1.7	0.5	4.6	0.6	0.36
	Herd size (livestock units)	149.8	42.6	465.0	70.9	0.47
	Production-related costs
	(thousands 2005 Euros)	73.2	8.4	329.3	39.4	0.54
	Meat production (tons of live
	weight)	46.9	7.4	173.9	24.4	0.52
	GHG emissions (tons)	669.1	158.9	2,589.4	344.2	0.51
	Pollution intensity (kg CO 2 -
	eq/kg of live meat)	14.5	9.6	28.0	2.0	0.14
	Net GHG emissions (tons)	591.8	126.4	2,356.4	316.4	0.53
	Net pollution intensity (kg
	CO 2 -eq/kg of live meat)	12.7	7.8	24.3

Table 2 :

 2 Descriptive statistics of the exogenous variables used for the whole period(1993- 2013)

	Mean	Minimum Maximum	Standard deviation	Relative standard deviation

Table 3 :

 3 Descriptive statistics of the robust efficiency scores given different DEA models for the whole period

						Proportion of
	DEA models	Mean	Minimum	Maximum	Standard deviation	super-efficient observations
						(%)
	Model (i): No GHG emissions in the model	0.943	0.050	1.250	0.107	37.8
	Model (ii): gross GHG emissions are
	considered and herd size and production-related costs are	0.815	0.406	1.192	0.099	2.4
	endogenous in the maximisation
	Model (iii): net GHG emissions are
	considered and herd size and production-related costs are	0.786	0.395	1.229	0.101	1.5
	endogenous in the maximisation
	Model (iv): net GHG emissions are
	considered and herd size, production-related costs and fodder area are	0.798	0.409	1.230	0.103	1.6
	endogenous in the maximisation

Table 4 :

 4 Robust pollution-adjusted efficiency scores' components (good and bad outputs) given different DEA models for the whole period

	Models	Efficiency components	Mean	Minimum Maximum	Standard deviation	Proportion of super-efficient observations (%)
	Model (ii): gross GHG emissions are considered and herd size and	Good output efficiency	1.234	0.524	2.860	0.344	79.0
	production-related costs are endogenous in the maximisation	Bad output efficiency	0.697	0.264	1.289	0.165	3.8
	Model (iii): net GHG emissions are considered and herd size and	Good output efficiency	1.266	0.531	2.943	0.353	81.6
	production-related costs are endogenous in the maximisation	Bad output efficiency	0.663	0.240	1.529	0.176	3.6
	Model (iv): net GHG emissions are considered and herd size,	Good output efficiency	1.206	0.299	3.454	0.469	65.1
	production-related costs
	and fodder area are endogenous in the maximisation	Bad output efficiency	0.750	0.213	2.297	0.266	15.0

  Table 5 indicate a significant negative effect of the binary variable representing the adoption of AEMs on technical efficiency in the case where GHGs are not considered only, that is to say when (good) technical efficiency is calculated with Model (i). Still in the case of Model (i), total agricultural area, stocking rate, numerical productivity, quantity of concentrates per livestock unit and other subsidies have a significant positive impact on

Table 5 :

 5 Regression results of the effect of adoption of AEMs on farms' technical efficiency (Model A) for the whole period

		Efficiency	Efficiency	Efficiency	Efficiency
	Variables	calculated	calculated	calculated	calculated
		with Model	with Model	with Model	with Model
		(i)	(ii)	(iii)	(iv)
	Dummy variable which captures whether or not	-0.03891 *	0.00436	0.00033	0.00929
	farmers received agri-environmental subsidies
	Total farm land area (hectares)	0.00046 ***	0.00001	-0.00003	0.00008
	Capital to labour ratio (thousands 2005 Euros)	-0.00010 *	-0.00010 ***	-0.00010 **	-0.00011 **
	Share of hired labour in total labour (%)	-0.00033	-0.00069 ***	-0.00071 ***	-0.00063 ***
	Debt to asset ratio	-0.00065 ***	0.00018 *	0.00022 *	0.00009
	Stocking rate (livestock units per hectare of fodder	0.12987 ***	0.07352 ***	-0.02394	-0.06253 ***
	area)
	Share of permanent grassland in farm total area (%)	-0.00005	0.00018	0.00011	0.00016
	Numerical productivity	0.00211 ***	0.00224 ***	0.00201 ***	0.00200 ***
	Quantity of concentrates per cow (kg)	0.00002 *	0.00001 *	0.00002 *	0.00001
	Feed autonomy (%)	0.00063	0.00190 **	0.00194 ***	0.00158 *
	Proportion of land rented in (%)	-0.00023	0.00010	0.00015	0.00010
	LFA subsidies per livestock unit (2005 Euros)	-0.00009	0.00024 *	0.00015	0.00015
	Other subsidies per livestock unit (2005 Euros)	0.00020 **	0.00024 ***	0.00023 ***	0.00016 **
	Nitrogen quantity per hectare of fodder area (kg)	-0.00062 ***	-0.00180 ***	-0.00178 ***	-0.00194 ***
	R 2	0.168	0.244	0.470	0.258
	Number of observations	1,651	1,651	1,651	1,651
	Note: *** p < 0.001, ** p < 0.01, * p < 0.05. Results for year and regional dummies are not shown.

Table 6 :

 6 Descriptive statistics of instrumental variables for the whole period

		Mean	Minimum	Maximum	Standard deviation	Relative standard deviation
	Beef price (2005 Euros per kilogram)	1.95	1.34	2.93	0.22	0.11
	Revenue per labour (2005 Euros)	4,449	-11,940	24,200	2,728	0.61

Table 7 :

 7 Regression results of the effect of adoption of AEMs on farms' technical efficiency (Model A) for the whole period with balanced panel data

		Efficiency	Efficiency	Efficiency	Efficiency
	Variables	calculated with Model	calculated with Model	calculated with Model	calculated with Model
		(i)	(ii)	(iii)	(iv)
	Dummy variable which captures whether or not	-0.03081	0.03299	0.02529	0.05074**
	farmers received agri-environmental subsidies
	Total farm land area (hectares)	0.00005	-0.00004	-0.00009	0.00009
	Capital to labour ratio (thousands 2005 Euros)	0.00005	-0.00001	-0.00002	-0.00001
	Share of hired labour in total labour (%)	0.00035	-0.00047*	-0.00042	-0.00036
	Debt to asset ratio	-0.00080***	-0.00045*	-0.00047*	-0.00045*
	Stocking rate (livestock units per hectare of fodder	0.15789***	0.14766***	0.06079*	0.13311***
	area)
	Share of permanent grassland in farm total area (%)	0.00016	0.00038*	0.00050**	0.00036*
	Numerical productivity	0.00183***	0.00288***	0.00283***	0.00295***
	Quantity of concentrates per cow (kg)	0.00002*	0.00002**	0.00002	0.00003**
	Feed autonomy (%)	-0.00002	0.00168*	0.00137	0.00147*
	Proportion of land rented in (%)	-0.00014	0.00006	0.00006	0.00007
	LFA subsidies per livestock unit (2005 Euros)	-0.00009	0.00003	0.00005	-0.00007
	Other subsidies per livestock unit (2005 Euros)	0.00022**	0.00018**	0.00016*	0.00019**
	Nitrogen quantity per hectare of fodder area (kg)	-0.00039*	-0.00159***	-0.00179***	-0.00151***
	R 2	0.155	0.222	0.237	0.194
	Number of observations	1,092	1,092	1,092	1,092

Table 8 :

 8 Regression results of the effect of agri-environmental subsidies on farms' technical efficiency (Model B) for the whole period (1993-2013) ** p < 0.001, ** p < 0.01, * p < 0.05. Results for year and sub-region dummies are not shown.

		Efficiency	Efficiency	Efficiency	Efficiency
	Variables	calculated with Model	calculated with Model	calculated with Model	calculated with Model
		(i)	(ii)	(iii)	(iv)
	Amount of agri-environmental subsidies per livestock unit (2005 Euros)	0.00656***	0.00394***	0.00415***	0.00427***
	Total farm land area (hectares)	0.00060***	0.00005	0.00005	0.00022*
	Capital to labour ratio (thousands 2005 Euros)	-0.00016	-0.00015*	-0.00016*	-0.00017*
	Share of hired labour in total labour (%)	-0.00028	-0.00055*	-0.00038	-0.00047
	Debt to asset ratio	0.00008	0.00002	-0.00004	-0.00003
	Stocking rate (livestock units per hectare of fodder area)	0.38153***	0.22147***	0.12707*	0.12503*
	Share of permanent grassland in farm total area (%)	-0.00023	-0.00030	-0.00012	-0.00008
	Numerical productivity	0.00240***	0.00315***	0.00299***	0.00320***
	Quantity of concentrates per cow (kg)	0.00002	0.00005***	0.00003**	0.00003**
	Feed autonomy (%)	-0.00187	0.00145	0.00039	0.00038
	Proportion of land rented in (%)	0.00007	0.00019	0.00015	0.00016
	LFA subsidies per livestock unit (2005 Euros)	-0.00128***	-0.00091***	-0.00089*** -0.00092***
	Other subsidies per livestock unit (2005 Euros)	-0.00006	0.00016	0.00012	0.00009
	Nitrogen quantity per hectare of fodder area (kg)	0.00030	-0.00125***	-0.00143*** -0.00140***
	R2	0.146	0.258	0.182	0.159
	Number of observations	1,049	1,049	1,049	1,049
	Note:

*

Table 9 :

 9 Regression results of the effect of agri-environmental subsidies on farms' technical efficiency (Model B) for the whole period with the balanced panel data

		Efficiency	Efficiency	Efficiency	Efficiency
	Variables	calculated with	calculated with	calculated with	calculated with
		Model (i)	Model (ii)	Model (iii)	Model (iv)
	Amount of agri-environmental
	subsidies per livestock unit	0.00813**	0.00444*	0.00364	0.00418*
	(2005 Euros)
	Total farm land area (hectares)	0.00092*	0.00001	-0.00015	0.00018
	Capital to labour ratio (thousands 2005 Euros)	-0.00050*	-0.00035*	-0.00031*	-0.00034**
	Share of hired labour in total labour (%)	-0.00069	-0.00065	-0.00041	-0.00063
	Debt to asset ratio	-0.00045	-0.00066	-0.00089*	-0.00069
	Stocking rate (livestock units per hectare of fodder area)	0.34014**	0.20621**	0.07206	0.20011**
	Share of permanent grassland in farm total area (%)	-0.00035	-0.00021	-0.00013	-0.00017
	Numerical productivity	0.00211*	0.00253***	0.00249***	0.00272***
	Quantity of concentrates per cow (kg)	0.00006*	0.00005**	0.00004*	0.00005**
	Feed autonomy (%)	0.00194	0.00261	0.00191	0.00281*
	Proportion of land rented in (%)	0.00013	-0.00009	-0.00006	-0.00006
	LFA subsidies per livestock unit (2005 Euros)	-0.00019	-0.00031	-0.00035	-0.00035
	Other subsidies per livestock unit (2005 Euros)	0.00012	0.00021	0.00019	0.00020
	Nitrogen quantity per hectare of fodder area (Kg)	-0.00081	-0.00202***	-0.00221***	-0.00197***
	R2	0.09675	0.38353	0.42352	0.39366
	Number of observations	692	692	692	692

http://ec.europa.eu/eurostat/statistics-explained/index.php/Agri-environmental_indicator_-_commitments.

The GWP equals 25 for methane and 298 for nitrous oxide[START_REF] Forster | Changes in atmospheric constituents and in radiative forcing[END_REF].

INRA Egeé team of UMRH, Clermont-Ferrand, France, for providing access to their data. They acknowledge financial support from the FACCE-JPI project INCOME.

Appendix

Table A.1: Non-robust efficiency scores given different DEA models for the whole period


Weight measurement at the bottom of a cylindrical grain column

The well-known Janssen's experiment (1895) 2 From a certain height of bed, any powder addition does not make any variation of mass weighed at the bottom Hydrostatic pressure rgh Weight measured at the bottom of the column Weight poured in the column Janssen's model 3 Pressure at the bottom of a cylindrical grain column 3 hypotheses: • Lateral uniformity of the vertical stress (layer model) • The horizontal stress is proportional to the vertical stress • Slipping at the wall (Coulomb criterion) l = D/4µK Janssen's model Weight deflection into lateral sides of the column 3 Pressure at the bottom of a cylindrical grain column 3 hypotheses: • Lateral uniformity of the vertical stress (layer model) • The horizontal stress is proportional to the vertical stress • Slipping at the wall (Coulomb criterion) Hydrostatic pressure rgh P =rgl(1-e -h/l )

1 What is the local stress distribution in an ensiled granular media ?

Applications:

• Stability of silos • Initial state before powder flowing (mixing processing…)

•••

  local vertical stress in an ensiled granular media? Effect of the particle size? Measurement of the vertical stress at the bottom of the cell 0 < z ≤ 14 cm (height of the bed) Measurement of the vertical stress in the granular bed 0 < x ≤ 4 cm & 0 < z ≤ 14 cm (position in the bed) 2D-cartography of the local vertical stress Native powder : durum wheat semolina (pasta, noodle, couscous…) Characterisation of the local vertical stress profiles Effect of the size and the structure on the local vertical stress profiles Depth of the probe (cm) l b » 2 cm l h » 4 cm II • 3 zones • 2 characteristic lengths: l h » 4 cm and l b » 2 cm • Jannsen value at the bottom of the cell vertical stress profiles in the center of the cell Janssen & local vertical stress profiles in the center of the cell Local approach -x=0 Depth of the probe (cm) l b » 2 cm l h » 4 cm II • 3 zones • 2 characteristic lengths: l h » 4 cm and l b » 2 cm • Jannsen value at the bottom of the • 2 characteristic lengths: l h » 4 cm and l b » 2 cm • Jannsen value at the bottom of the cell Zone I: Lateral uniformity of the vertical stress • Zone II & III: Lateral inohomogeneity of the vertical stress Þ Janssen's hypothesis not checked for semolina Generic device implementation for measuring 2D-cartography of the vertical stress in ensiled granular powders.• Non-equivalence between global and local vertical stress measurements except at the bottom of the cell (semolina).• Particle size affects the intensity of the local vertical stress but not the shape of the vertical stress profile and the property and inter-particle force characterization * duri@supagro.inra.fr 21 th May 2014Thank you for you attention


Mahmoud Fakih
email: mahmoud.fakih@cirad.fr

Jean-Yves Delenne

Thierry Fourcaud

Farhang

J.-Y Delenne

F Radjai

Development of a discrete element method to estimate the Force distributions probed by a growing root in a granular soil

come     Development of a discrete element method to estimate the Force distributions probed by a growing root in a granular soil

We used a numerical discrete-element [START_REF] Radjai | Discrete element modeling of granular materials[END_REF] model to simulate the mechanical interactions between the soil and a growing root. The simulations allow characterizing the reorganization of grains under the action of root forces. By return, the forces experienced by the root during its growth can be estimated numerically at the root cap. Sensitivity analyses of this force signal to root stiffness and granular properties have been performed. For all values of root stiffness, we find the same broad distribution of forces normalized by the mean force, indicating that the force experienced by the root reflects the distribution of contact forces inside the soil. Interestingly, we find that the mean force increases with root stiffness up to a threshold. For all values of packing fraction, we get a general scaling by normalizing the mean force by soil hardness obtained for an infinitely rigid root. In other words, for root growth the soil characteristics are fully captured by their effect on the hardness, which appears to be the only control parameter of root growth.

Acknowledgement

This project is supported by Agropolis Fondation under the reference ID 1202-073 through the "Investissements d'avenir" program (Labex Agro: ANR-10-LABX-001-01).


Nils Paulhe

Christophe Duperier

Daniel Jacob

Étienne Thévenot

Jean-Francois Martin

Claire Lopez

Franck Giacomoni

METABOHUB'S SPECTRAL DATABASE: FROM DATA MODEL TO WEB INTERFACE

Keywords:

NMR peak NMR peak pattern Coupling Constant UV spectre peaks users putative compound compound name

For the MetaboHUB's Spectral Database code architecture, we use modelling concepts and objects-objects relations to represent mass / nuclear magnetic resonance (NMR) spectrometry complexity. Manipulated entities and their links are based on data / metadata used by chemists in laboratory routine. Our work is split in three levels with data-model building, users services designing and code engineering.

Still in early development phase, some functional blocks are already being tested.

The first challenge was to validate this data-model and the use cases with chemists. We are currently testing the chemical library module with data provided by MetaboHUB partners.

Code architecture: the MVC pattern is better for code maintainability, team development and security.

Light view of the data-model with main entities: both reference and putative compounds are linked to spectra. A specific entity, metadata, is dedicated to link spectra by their shared properties. The user entity is independent: it is used only for web-portal access control, not for spectra or compounds data ownership.

Introduction

MetaboHUB is an infrastructure of metabolomics and fluxomics that provides tools to research teams and partners; a full workpackage focuses on bioinfomatics tools design and development, including one called "MetaboHUB's Spectral Database". This tool is created to gather and share knowledge and data from both Mass and NMR Spectra.

The core of the database called "data-model", is a computational representation of each entity involved in spectra analysis and chemical compounds annotation. Link spectra to compounds: spectra match 4 types of compounds, common properties or relations given by a virtual "parent" object in the data-model. We provide reference compounds via chemical libraries manager. Putative compounds are annotated with an interactive identification tool and stored in the database. Three different entities of each reference compound allow us to match putative compounds with missing properties (like the 3D structure).

NMR spectra specificity: the data-model must support one and multidimensional spectra (schema 4) and specific entities like peaks patterns and coupling constants (schema 3). There are two ways to access the data: though a web-portal or a webservice. Each service on the web-portal gets his own/specific interface, with user control access. Additional interfaces, like spectral annotation tools, will be implemented later as plugin.

The core of the database is split in stand-alone blocks, which allow the community to re-use them in others projects. Each block is specific to one function.

It was designed with spectrometry experts knowledge; their experience will also be required to build an interactive identification interface for spectral analysis. This database will be available through a web-portal and open for each partner of the MetaboHUB consortium in a first time, to the whole community later.

Two milestones are coming: a first to provide a mechanism to import spectral data in the data-model and feed the database, a second to define metadata around spectral analysis.

We use a user-friendly framework (twitter bootstrap) to design web interfaces. Interfaces and user access: some database queries are available for all users (get data), others require an authentification (add or modify elements, data curation).

"Spectral annotation tools" building is an aim of the MetaboHUB's Spectral Database. We have to create the structure to be used to host and link all data provided by chemists.

  -(+)-α-pineneCode architecture and frameworks are an originality of this project. Developers use java frameworks and common generic toolbox to keep to the time scheduled and interoperability. There are three levels of architectures: the spectral database core (figure2), implemented in both webservices and the web-portal model-view-controller (MVC) (schema 5).

  Webservices are the way to open with bots data stored in the spectral database, submitting massives requests and hudge computing. We use popular technologies like REST and Json. user services computing To store data in the database, we use the Hibernate framework. We keep the model relations and constrains, with an abstraction of technologies used for SQL requests. data model Removing the complexity of the full data-model allows us a better understanding of it; the first schema proposes a light view of relation entities, others are focused on parts of it. Parts of the data-model are specific for spectra technics (like MS-MS), others are created for futur developments (like biological matrix spectra annotation).

  figure 1: three basic interface available in the web-portal


Hélène Romé

Amandine Varenne

Frédéric Herault

Hervé Chapuis

Christophe Alleno

Patrice Dehais

Alain Vignal

Thierry Burlot

Pascale Le Roy

GWAS analysis identifies 138 QTL for egg quality variance in layers receiving various diets

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

GWAS analysis identifies 138 QTL for egg quality variance in layers receiving various diets Hélène Romé1,2, Amandine Varenne3, Frédéric Hérault1,2, Hervé Chapuis4, Christophe Alleno5, Patrice Dehais6, Alain Vignal6, Thierry Burlot3, Pascale Le Roy1,2* The variance of traits is more often studied, due to the wish to canalize some traits. At this time only one study has identified QTLs for the variance of egg quality trait. The development of a high density chip for chicken by Affymetrix, the 600K Affymetrix® Axiom® HD genotyping array, allows today to narrow the localization of QTL with a higher accuracy. Furthermore, this high density chip should also allow going beyond the classical hypothesis of additivity, between QTL effects or between QTL and environmental effects. Thus, the aim of the present study was to search for QTL influencing the variance of egg quality traits using the 600k SNP chip and to observe if the QTL detected could be different according to various environments. For this, a population of 438 sires from a pure line was genotyped. Their 31,381 F1 crossbred daughters were phenotyped for egg quality traits at 50 and 70 weeks of age. Moreover, to observe the putative existence of QTL by environment interactions, these layers were divided into 2 groups, fed ad libitum with two different diets. The egg shell color and its components (redness, yellowness, and lightness), the egg shell strength, stiffness and shape, the short length of egg and the egg weight were recorded. GWAS analysis was performed using a linear regression model. Thus, 138 QTL for the variance of egg quality traits, spread over all marked chromosomes except the chromosome 16 have been detected. Only two QTL was common to a previous study on mean of traits. Variances and mean are two different traits, which should be considered separately. Among them, 94 QTL had a significant difference of effect depending on diet. The correlation between estimates of SNP effects for both diets was between -0.06 and 0.25.To sum up, this study showed that numerous loci influence variance of quality traits. These traits seem to be highly dependent on diets, more than the mean. This shows the capacity of laying hens to adapt to their environment.