Keywords: Genotype by environment interaction, Creole cattle, meat production traits, intensive fattening, pasture

Creole cattle is the beef cattle farmed in Guadeloupe for meat production. Although its remarkable adaptation to the Guadeloupean environment, its production is not sufficient to fulfill the meat demand of the Guadeloupean population. A major reason for this is the lack of a structured breeding plan to improve its meat production and effort to design it is ongoing at INRA Antilles. Due to the variety of farming systems in Guadeloupe, genotype by environment interaction (GEI) affecting the growth and meat production traits could be a limiting factor and its significance must be estimated in order to design an appropriate breeding plan. We estimated GEI affecting growth traits (liveweight, average daily gain, yearling weight) and carcass traits (slaughter weight, hot carcass weight, muscles weight, adipose tissue weight, empty digestive tract weight) by implementing character state models whose response variables were the phenotypes recorded on groups of half-sibs split between two fattening systems: intensive system and pasture. We also estimated the heritabilities and genetic correlations between all traits within each environment by implementing bivariate mixed models. This study proofs that GEI affecting growth and meat production traits in Creole cattle is significant while their genetic parameters do not differ between environments.

Results and discussion

• Summary statistics

• Genetic parameters within environments

•

Genetic parameters between environments  Bivariate character state models between environments  Bivariate random regression model between environments

Conclusions

INTRODUCTION

Creole cattle. The Creole cattle of Guadeloupe originated from the bovines imported into the Caribbean islands after the discovery of the American continent. Many breeds appear to have contributed to the genetic pool of Creole cattle [START_REF] Naves | Caracterisation et gestion d'une population bovine locale de la zone tropicale : le bovin creole de la Guadeloupe[END_REF]. The exact dynamics of these importations are not easy to trace back because of the nature of the trades in that period, however it is agreed upon that the first breeds to be imported into the Caribbean islands came from Spain, Portugal and the Canary islands. Breeds from west Africa, India and American continent were imported during the 19 th century, as well as more importations from Europe occurred later during the 20 th century. Further crossings also occurred between the Caribbean breeds themselves. Due to this complex history, genetic characterization of the Creole cattle of Guadeloupe by genetic markers and molecular markers resulted indeed in the conclusion that the Creole cattle of Guadeloupe represents an original genetic resource [START_REF] Naves | Caracterisation et gestion d'une population bovine locale de la zone tropicale : le bovin creole de la Guadeloupe[END_REF].

Farming systems in Guadeloupe. Creole cattle is farmed for meat production mainly and in some contexts it is still used for drought power also. A special interest for breeding Creole cattle in Guadeloupe is due to its tolerance to the stressors featuring cattle farming in this island. Namely, the main limiting factors for meat production in Guadeloupe are: climatic conditions, parasites, vector-born diseases and farming conditions. All of these factors, but the farming conditions, will be described in more detail in the materials and methods section. The farming conditions in Guadeloupe features two main typologies: the mixed farming system and the specialized pasture system. The mixed farming system is the most diffused one and it features both animal and vegetal productions within the farm, with a great variety of management conditions. Traditionally, cattle are kept on pastures tethered with a chain fixed to the ground with a picket. They are moved once or twice a day, when they consumed all the grass available within the reach of their chain, and receive water. A few specialized pasture system also exist. It is diffused only among the biggest farms and features fenced fields where bovines are free to graze on savanna. Even when pastures are not sufficient to fulfill the food requirement of the animals due to adverse climatic conditions, complementation is seldom practiced. It is based mostly on sugar cane sub-products and rarely on concentrates. Creole cattle was shown to tolerate the stressors characterizing the Guadeloupean environment better than exotic cattle breeds and crossbreeds between Creole and exotic breeds. Indeed the attempts to import purebred highly producing cows from temperate regions normally fail, while crossbred (between Creole cattle and Limousine, for example) shown lower adaptation capacity to the guadeloupean environment then purebred Creole cattle. [START_REF] Berbigier | Performances de croissance et d'abbatage de taurillons limousins x creoles et Créoles élevés au soleil et à l'ombre en guadeloupe (antilles françaises) rev[END_REF][START_REF] Bru | Estimation of Sweat Rate and Thermal Tolerance of Pure Creole and of Limousin x Creole Crossbred Growing Bulls in Guadeloupe (French West Indies)[END_REF][START_REF] Barré | Les tiques des ruminants dans les Petites Antilles: biologie, importance economique, principes de lutte[END_REF][START_REF] Mahieu | Incidence of Toxocara vitulorum in Creole calves of Guadeloupe[END_REF]. However, even if Creole cattle is the most adapted and best performing breed in the Guadeloupean environment, its productivity is still not sufficient to fulfill the meat demand of the Guadeloupean population and they highly rely on meat importation. A major reason for this productive gap is that the Creole cattle population of Guadeloupe have never been object of any structured breeding

plan. An effort to establish a breeding plan for the genetic improvement of Creole cattle population in Guadeloupe is ongoing at the experimental herd of INRA in Domaine de Gardel.

Genotype by environment interaction Due to the variety of farming systems in Guadeloupe, a major limiting factor for the genetic gain of the Guadeloupean Creole cattle population could be the occurrence of genotype by environment interaction, between animals managed in grazing systems with forages only, or in intensive fattening systems. Statistically speaking, an interaction between two factors in a model is significant when their effects depends on each other, that is: the effect of one factor o the response variable depends on the level of the other and vice versa [START_REF] Ott | An introduction to statistical methods and data analysis[END_REF]. In the context of genetic improvement of livestock this interaction is referred to as Genotype by environment interaction (GEI), that is: the effect of the environment on the phenotype depends on the genotype and vice versa [START_REF] Lynch | Genetics and analysis of quantitative traits[END_REF]. Hence, when GEI is significant each genotype reacts to the environmental change in a specific way and the effect of the environmental change on the estimated breeding value (EBV) is not the same for all the genotypes (figure 1). This causes a re-ranking of the reproducers in each environment and finally: if a breeding plan neglects a significant GEI affecting the traits included in the EBV and is applied to a population of animals which are raised across different environments, the genetic gain of the population will be very likely to mismatch its expectation [START_REF] Falconer | The Problem of Environment and Selection Am[END_REF][START_REF] Robertson | The Sampling Variance of the Genetic Correlation Coefficient[END_REF].

Aim of this study. This study is a part of the effort to establish a structured breeding plan for Creole cattle in Guadeloupe and focuses on the estimation of GEI affecting growth traits and meat production traits expressed within half sibs groups fattened either on pasture or in intensive fattening system. An estimate of the significance of GEI affecting meat production and growth traits of Creole cattle fattened across two contrasted environments is needed to decide whether or not GEI must be taken into account in the estimation of EBVs used to implement a breeding plan for Creole cattle in Guadeloupe. (C) Re-ranking effect of Genotype × environment interaction.

(D) Combined scaling and re-ranking effect of Genotype × environment interaction

(1) genotype 1 shows very weak plasticity and is called a robust phenotype. [START_REF] Lynch | Genetics and analysis of quantitative traits[END_REF].

MATERIALS AND METHODS

Experimental device. Where lwgtn th and lwgt1 st are the weight at the last weighting and at the first one, respectively; agen th and age1 st are the ages, expressed in days, at the last weighting and at the first one, respectively. YRWGT was computed as follows: if the animal had no LWGT at 365 days, the average daily gain between the nearest observations on LWGT around 365 days was computed and the expected weight at 365 days was obtained according to it. If the first LWGT was after 365 days, the first two lwgts were used to compute the average daily gain and YRWGT was derived according to it.

Carcass traits Most of the males have been slaughtered, while the females were kept or sold for reproduction,. Animals were sacrificed when they reached an acceptable commercial weight for the local market (around 320 Kg). Due to the difference in growth rate between INT and PAST, animals were slaughtered between 14 and 17 month in INT, and between 17 and 21 month in PAST. The traits measured on the carcasses were: slaughter weight (SLWGT), weight of the empty digestive tract (DTW), hot carcass weight (HCW), weight of the muscular tissue (MTW), weight of the adipose tissue (ATW) . SLWGT was the weight of the animal as measured just before being slaughtered and after one day of starving.

DTW was the weight of the empty digestive tract. HCW was the weight of the carcass at the end of the slaughter chain. MTW was the weight of the skeletal muscular tissue in the carcass and was computed according to the following equation (Naves, 1996) (r²=0.93). Where ckf is the cold kidney fat.

Statistical analysis

The dataset was analyzed by using, SAS ® , ASREML ® [START_REF] Gilmour | Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models[END_REF] and MATLAB ® software. Data editing and exploration was performed by using SAS ® software, the linear mixed models were implemented by using ASREML ® software and, for random regression models only, the genetic parameters were computed by using MATLAB ® software. Several linear mixed models were implemented: bivariate animals models to estimate the genetic correlations between traits within each fattening system; bivariate character states models [START_REF] Falconer | The Problem of Environment and Selection Am[END_REF] to estimate the genotype by environment interaction affecting each trait expressed across the two fattening systems; a bivariate random regression model between environments (Kirkpatrik and Heckman, 1989) in order to estimate the genetic parameters of LWGT and the significance of GEI affecting it.

Bivariate animal models within each environment

The bivariate animal models read as follows:

µ µ 1 2               +                             1 1 1 1 1 1 2 2 2 2 2 2 y b u e X 0 Z 0 = + + 0 X 0 Z y b u e
Where y 1 is the vector collecting observations on one trait expressed within one fattening environment and y 2 is the vector collecting the observations on a different trait expressed within the same fattening environment. μ 1 and μ 2 are the means of trait one and two, respectively. b 1 and b 2 are the vectors of fixed effects for trait one and two, respectively; the fixed effects included in the model were a class factor coding for the contemporary groups and the following covariables: weaning age (WAGE), weaning weight (WWGT) and, for the carcass traits only, age at slaughter (SLAGE). X 1 and X 2 are the incidence matrices connecting each observation to its set of fixed effects. u 1 and u 2 are the vectors of breeding values for trait one and two, respectively. Z 1 and Z 2 are the incidence matrices connecting each observation to its set of random effects. The only random effect fitted in the model was the animal effect.

e 1 and e 2 are the vectors of residuals for trait one and two, respectively. The variance structure of all these models was:

⊗ ⊗     ⊗ ⊗     =           1 2 A A y A A y
Where: g 11 and g 22 are the genetic variances for trait one and two, respectively. g 12 is the genetic covariance between the two traits. A is the genetic relationships matrix between the animals in the pedigree. r 11 and r 22 are the residual variances of trait one and two, respectively. r 12 is the residual covariance between the two traits.

The heritability of each trait was computed as genetic variances of trait one and two, respectively [START_REF] Mrode | Linear models for the prediction of aimal breeding values 2 nd edn[END_REF].

Bivariate character state models between environments

The character state models implemented to estimate GEI read as follows:

µ µ 1 2               +                             1 1 1 1 1 1 2 2 2 2 2 2 y b u e X 0 Z 0 = + + 0 X 0 Z y b u e
Where y 1 is the vector collecting the observations on one trait expressed within INT and y 2 is the vector collecting the observations on the same traits expressed within PAST.

g g g g r r ⊗ ⊗     ⊗ ⊗     =           1 2 A A y A A y
We used the genetic correlation between the same trait expressed across INT and PAST as measure of GEI. This parameter was computed as: [START_REF] Mrode | Linear models for the prediction of aimal breeding values 2 nd edn[END_REF].

Bivariate random regression model between environments lwght was analyzed by

implementation of a bivariate random regression models with Legendre polynomials of order 2 for the fixed and animal effect, and Legendre polynomials of order 1 for the individual permanent environmental effects. Its expression in matrix notation reads as follows:

1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2                                        y b a pe e X 0 Z 0 Q 0 = + + + 0 X 0 Z 0 Q y b a pe e
Where y 1 and y 2 are the vectors collecting observations on LWGT in INT and in PAST, respectively. X 1 and X 2 are the incidence matrices relating each weighting to its set of fixed regression coefficients for trait one and two, respectively. b 1 and b 2 are the column vectors of fixed regression coefficients for trait one and two, respectively. The fixed effects fitted in this model were: the contemporary group (defined as the group of animals weighted on the same day, of the same gender, fattened within the same fattening system and which entered the experiment on the same year), the weaning age and the weaning weight. Z 1 and Z 2 are the incidence matrices relating each observation to its random regression coefficient for animal effect for trait one and trait two, respectively. a 1 and a 2 are the column vectors containing the random regressions coefficients for animal effects for trait one and two, respectively. Q 1 and Q 2 are the index matrices relating each observation to its random regression coefficient for permanent environmental effect for trait one and two, respectively. pe 1 and pe 2 are the column vectors of random regressions coefficients for permanent environmental effects for trait one and two respectively, respectively. The variance structure of these model was: σ is the genetic variance of trait i at age j, t j is j th the row vector of the Nage by 3 (Nage is the number of ages at which weightings were performed) Legendre polynomials matrix. t' j is the transpose of t j .

1 2 var e e ⊗ ⊗     ⊗ ⊗     ⊗   =     ⊗             11
Similarly, the genetic covariance between trait one and two at each age was computed as:

12 jj σ = j 12 j t G t' . While the genetic covariance of trait i between age j and k was computed as: jk i σ = j 12 k t G t' . From these point estimates of genetic parameters, we estimated the heritabilities of each trait at each age and the genetic correlations between traits at each age as well as the genetic correlation of each trait at different ages by applying the same formulas described for the animal models. The EBVs of animal i at age j for trait k (EBV ijk ) were computed as ijk EBV = ik j u t' . Where u ik is the 1 by 3 row vector of random regression coefficients of animal i for trait k and t' j is the 3 by 1 column vector of Legendre polynomials for age j. The correlation between the EBV for trait one and the EBV for trait two of all animals was computed at each age using the same formula described for the character state model. [START_REF] Mrode | Linear models for the prediction of aimal breeding values 2 nd edn[END_REF]) 2, which shows the parabolic trend of the plot with downward concavity. The number of observations on LWGT was low until 300 days and then decreased again after 500 days, consequently the graphs of its parameter estimates were considered more reliable between 300 and 500 days. Due to the resources available for this research, both the number of observations on each trait and the number of animals included in the pedigree were small. Indeed the standard errors of all the estimated genetic parameters were big, singularities occurred in the average information matrices of some models and their convergence was not easy to be reached (Hill and Thompson, 1978;[START_REF] Wang | The dilemma of negative analysis of variance estimators of intraclass correlation[END_REF][START_REF] Reverter | The role of different pedigree structures on the sampling variance of heritability estimates[END_REF]. Further, while the number of observations per fattening treatment, sex and year was quite balanced, the number of offspring per sire was not. However, since all genetic parameters were estimated by restricted maximum likelihood, this should not cause too much bias on their estimates (Harville, 1977;[START_REF] Swallow | Monte Carlo Comparison of ANOVA, MIVQUE, REML, and ML Estimators of Variance Components[END_REF]Wang et Al., 1991;[START_REF] Liu | Sampling distributions, biases, variances, and confidence intervals for genetic correlations[END_REF].

RESULTS AND DISCUSSION

Summary statistics

Table 1 summarizes the observed mean of each trait within each fattening system. The means of YRWGT, ADG, HCW and ATW were higher in INT than in PAST. The means of SLWGT, MTW, weaning weight and weaning age did not differ between the two fattening systems. The means of DTW, btw and age at slaughter were higher in PAST than in INT. Due to the higher nutritional level of INT treatment with respect to PAST treatment, the mean of the traits related to growth and tissue synthesis was higher in INT than in PAST. SLWGT did not differ between treaments because the animals were slaughtered after having reached a commercial weight around 300 Kg, indeed SLAGE was higher in INT than in PAST. While WAGE and WWGT did not differ between treatments because the animals were randomly assigned to receive one of them. We expected MTW to be higher in INT that in PAST as well, but the analysis did not confirm this expectation. DTW was higher in PAST than in INT, we expected this result because the fiber content of the food used was higher in PAST than INT and it induces the digestive tract to increase its capacity. [START_REF] Kay | adaptation of the ruminant digestive tract to the diet[END_REF]Hoffman, 1989). and HCW, in PAST as response variables were not considered very reliable because the models converged after many iterations and required the constraining of their Average information matrices to be positive definite. This result was probably related to the heritabilities obtained for those traits, which were lower in PAST than in INT (Hill and Thompson, 1978). The models including HCW and SLWGT, DTW and MTW (in both systems) and the models including ATW and DTW, ATW and SLWGT (in PAST) as response variables gave the same problems although their heritability estimates in PAST were similar or higher than the corresponding values in INT. The phenotypic correlations of the models which required the constraining of the average information matrices were considered as indicators of the corresponding genetic correlations (Lynch and Walsh, 1998;Koots and Gibson, 1998). The estimates of genetic correlations between YRWGT, ADG, SLWGT and HCW were in accordance with the estimates reported in previous similar studies (Barwick et Al., 2006;Bouquet et Al., 2010) and were all high and positive. Given the standard errors associated with the genetic correlation estimates, most the values obtained in INT were considered equal to the corresponding estimates obtained in PAST. The largest differences between the values estimated in INT and in PAST were observed between the genetic and phenotypic correlations of DTW with the other traits, which were higher in PAST than in INT. This result together with the result we obtained for the average of DTW in PAST and the literature referring to the effect of diet on the development of the digestive tract of ruminants [START_REF] Kay | adaptation of the ruminant digestive tract to the diet[END_REF]Hoffman, 1989) suggests the hypothesis that the overall development of the digestive tract plays an important role for meat production performance in pasture. It is worth to note that the genetic correlations between ATW and the other traits are positive in both systems. These results suggests two hypotheses. The efficiency of the digestive system of Creole cattle such that it can extract from low energy foodstuffs enough resources to be allocated simultaneously both to production traits and to its own energy deposits (De Jong and Van Noordwijk, 1994). This efficiency could be related to the higher weight of the digestive tract observed in pasture respect to its weight in intensive fattening system, the genetic correlation of DTW with growth and carcass traits are indeed higher in PAST than in INT.

However, no literature was found concerning the genetic parameters of tdw. Table 2. Genetic correlations (above diagonal) and phenotypic correlations (below diagonal) between the traits analyzed. The heritability of each trait is on the bold diagonal. The values obtained in intensive fattening system are in the upper table (INT), the values obtained in pasture are in the lower table (PAST). YRWGT is yearling weight. ADG is average daily gain. DTW is the weight of the empty digestive tract. SLWGT is slaughter weight. HCW is hot carcass weight. MTW is the weight of the muscles in the carcass. ATW is the weight of the adipose tissue in the carcass. * means that the model required the constrain of the average information matrix and converged after many iterations.

Genetic parameters between environments

Bivariate character state models between environments. The results from the character state models are summarized in table 3. While the heritabilities obtained from this models were included in the computation of the average heritabilities shown in the diagonal of table 2. The heritabilities estimates spanned from 0.15 to 0.49 in INT and from 0.17 to 0.85 in PAST.

Their standard errors spanned from 0.11 to 0.22 in INT and from 0.12 to 0.25 in PAST. The estimates of the heritabilities obtained for YRWGT, ADG SLWGT, HCW and ATW in this study were similar to the values reported on previous studies performed within the tropics for the same traits or related traits [START_REF] Marshall | Breed differences and genetic parameters for body composition traits in beef cattle[END_REF][START_REF] Mourão | Genetic parameters for growth traits of a Brazilian Bos taurus x Bos indicus beef composite[END_REF]Boligon et Al, 2009;).

The genetic correlation between any trait expressed within INT and the same trait expressed in PAST were all lower than 0.8 except for DTW. The standard errors of these correlations spanned from 0.26 to 0.43. Genetic correlations estimates below 0.8 were considered as indicators of significant GEI [START_REF] Robertson | The Sampling Variance of the Genetic Correlation Coefficient[END_REF]. The genetic correlation between MTW expressed in INT and MTW expressed in PAST was 0.84, which is slightly higher than the threshold of significance we defined. This result could be related to the fact that the average of MTW in PAST did not differ from the average of MTW in INT. However, given its high correlation with traits affected by significant GEI, this value was considered indicator of significant GEI. The results obtained for YRWGT, ADG, SLWGT, HCW, and MTW agreed with previous literature about GEI affecting growth and carcass traits in the tropics [START_REF] Mello De Alencar | Evidências de Interação Genótipo x Ambiente sobre Características de Crescimento em Bovinos de Corte R[END_REF]Texeira et Al, 2006;[START_REF] Barwick | Genetics of steer daily and residual feed intake in two tropical beef genotypes, and relationships among intake, body composition, growth and other post-weaning measures[END_REF]. GEI affecting DTW was not significant, this result proofs the effect of the fattening environment on the weight of DTW is the same for all the genotypes. No literature was found about this.

The correlations between the EBVs of all animals for any trait expressed in INT and the EBVs of all animals for the same trait expressed in PAST were higher than the corresponding genetic correlations and followed the same trend, but their standard errors were much smaller.

The R 2 -adjusted of the regressions spanned from 0.27 to 0.99 and supported the results obtained for the genetic correlations and the correlations between EBVs.

-   = -   - -  22 G
The estimates of the genetic parameters for LWGT at each age are shown by graphs due to the dimension of the matrices. All the estimated genetic parameters were considered more reliable after 300 days because of the higher number of observations available. Figure 3 summarizes the phenotypic and genetic variances of LWGT within each fattening system and the genetic covariance between LWGT expressed in INT and LWGT expressed in PAST.

Both the genetic and phenotypic variances tend to increase with the age in both systems, with a more steady increase in PAST and an overall higher increase in INT. While the genetic covariance is quite constant. the same value between the range of 0.3 and 0.7, following the curves observed in the graph of its variances. Being higher than heritability in PAST until 450 and lower after that day.

Figure 5 and 6 show the genetic correlation of LWGT between ages estimated in INT and in PAST, respectively. In INT, the graph of genetic correlation across ages displays a highly correlated surface between 300 and 450 days of age which is wider than the corresponding surface in PAST. While the highly correlated surface from 450 days on is wider in PAST than in INT. These results are in accordance with the trajectories of the sire's EBVs estimated within each fattening system (which are shown in figure 7 and 8, respectively). In INT, most of the EBVs trajectories are parabolic with downward concavity, some of them have upward concavity and a few linear trajectories occur as well. The range of the EBV estimates span from -40 to 40, and most of the curves reach their maximum (or minimum) between 350 and 500 days. In PAST, most of the EBVs trajectories in PAST are parabolic with downward concavity, some have upward concavity and some are linear. However, the concavity of the parabolas are not very pronounced. Between 350 and 450 days, most of the parabolas reach their maximum or minimum, within a range of 20 and -20. After that day, they diverge constantly. The EBVs trajectories show more variation in INT than they do in PAST and their concavity is much more pronounced in INT than in PAST. These graphs together with figure 9, showing the EBV of each sire for the whole growth trajectory estimated in INT and in PAST, proof that the effect of the fattening system on the EBV is not the same for all the sires but it depends on the genotype of the sire itself. Indeed, the difference between the EBV of each sire estimated in INT and its EBV estimated in PAST at each age (figure 10) are not the same for all the sires. This graph is in accordance with the plot of r g and r bv along the ages shown in figure 4. Indeed, the differences between the EBVs estimated in each system are smaller at the ages featuring higher correlations and vice versa.

The graphs showing the dependency between EBV and fattening system together with the plot of the overall EBV of each sire in each fattening system and the graph showing that the genetic correlations between LWGT expressed in INT and LWGT expressed in PAST at the same age are always lower than 0.8, proof that GEI affects LWGT significantly during the whole growth trajectory. These results are in accordance with previous investigations on GEI affecting growth traits in the tropics (Menendez and Mandonnet, 2006;Pegolo et Al., 2008).

CONCLUSIONS

Significant genotype by environment interaction affects yearling weight, average daily gain, slaughter weight, hot carcass weight, muscular tissue weight and adipose tissue weight expressed across intensive fattening system and pasture. Genotype by environment interaction was not significant for empty digestive tract weight. These results indicate that genotype by environment interaction is not negligible in the design of a breeding plan for improving growth and meat production traits of Creole cattle in Guadaloupe. However, the fattening systems had no significant effect on the genetic correlations' estimates between yearling weight, average daily gain, slaughter weight, hot carcass weight, muscular tissue weight and adipose tissue weight. Hence the correlated responses to selection for these traits in each fattening environment won't differ between systems. This results suggests the hypothesis that the efficiency of the digestive system of Creole cattle is such that it can extract sufficient resources to allocate both to production traits and its own energy deposits simultaneously.

Further, the higher genetic correlations of the empty digestive tract weight with the other traits and the higher average weight of the empty digestive tract observed in pasture suggest a relationship between the digestive efficiency and the weight of the digestive tract.

AKNOWLEDGEMENTS

I'm grateful to Alberto Menendez Buscadera for his help with the random regression analysis, to Jean-Luc Goudrine for having written the code we used implemented in Matlab and to Michel Naves for its supervision.

Figure 1 .

 1 Figure 1. Reaction norms of three genotypes in response to two environments. (A) No Genotype × environment interaction: all genotypes respond equally to the environmental change (B) Scaling effect of genotype × environment interaction.

  μ 1 and μ 2 are the means of the trait for INT and for PAST, respectively. b 1 and b 2 are the vectors of fixed effects in INT and in PAST, respectively; the fixed effects included in the model were: a class factor (coding for the contemporary groups), wage, WWGT and, for the carcass traits only, slage. X 1 and X 2 are the incidence matrices connecting each observation to its set of fixed effects. u 1 and u 2 are the vectors of breeding values for INT and PAST, respectively. Z 1 and Z 2 are the incidence matrices connecting each observation to its random effects. The only random effect fitted in the model was the animal effect. e 1 and e 2 are the vectors of residuals in INT and PAST, respectively. Using the same notation as for the variance structure on the bivariate animal models, the variance structure of the character state models reads as follows:

  and G 22 are the 3 by 3 matrices containing the random regression coefficients modeling the genetic variance function of trait one and two, respectively. G 21 is the 3 by 3 matrix containing the random regression coefficients modeling the genetic covariance function between trait one and two , respectively. G 12 is the transpose of G 21. A is the 961 by 961 genetic relationships matrix. P 1 and P 2 are the 2 by 2 matrices containing the random regression coefficients modeling the variance of permanent environmental effects for trait one and two respectively. e 1 and e 2 are the residual variances of trait one and two respectively. The genetic variance of trait i at age j was computed as 2

Figure 2 .

 2 Figure 2. Plot of the number of observations on live weight (frequency count) at collected at each age.

Figure 4

 4 Figure 4 summarizes the estimates at each age of heritability in INT, of heritability in PAST, of the genetic correlation between LWGT expressed in INT and LWGT expressed in PAST (r g ) and of the correlation between the EBVs of all animals estimated from the observations collected within INT and the EBVs of all animals estimated from the observations collected within PAST (r bv ). The trajectories of the heritabilities agree with the trend of their genetic and phenotypic variances shown in figure 3: from 300 days on the trajectory of the genetic and phenotypic variances in PAST are quite linear and the trajectory of heritability in PAST is constant on the value of 0.5. While the trajectory of the heritability in INT oscillates around

Figure 3 .

 3 Figure 3. Plot of the variances obtained for live weight at each age. Auge VarP and Auge VarG are the phenotypic and genetic variances in intensive fattening system, respectively. PAST VarP and PAST VarG are the phenotypic and genetic variances in pasture, respectively. CovG is the genetic covariance between live weight expressed in intensive fattening system and liveweight expressed in pasture.

Figure 4 .

 4 Figure 4. Plot of the genetic parameters obtained for live weight at each age. Auge h2 and PAST h2 are the heritabilities obtained in intensive fattening system and pasture, respectively. CorG is the genetic correlation between liveweight expressed in intensive fattening system and pasture. CorEBV is the correlation of the breeding of all the animals estimated at each age in intensive fattening system with their breeding values estimated at the same age in pasture.

Figure 5 .

 5 Figure 5. Contour plot of the genetic correlation between live weight expressed at each age in intensive fattening system. aage1 and aage2 are the ages. agcor is the genetic correlation.

Figure 6 .

 6 Figure 6. Contour plot of the genetic correlation between live weight expressed at each age in pasture. page1 and page2 are the ages. pgcor is the genetic correlation.

Figure 7 .

 7 Figure 7. Plot of the EBV for live weight of each sire estimated in intensive fattening system at each age. Each line represent one of the sires.

Figure 8 .

 8 Figure 8. Plot of the EBV for live weight of each sire estimated in pasture at each age. Each line represent one of the sires.

Figure 9 .

 9 Figure 9. Plot of the EBV for live weight of each sire for the whole growth trajectory in each system. INT and PAST are the EBVs of each sire in intensive fattening system and pasture, respectively. Each line represent one of the sires.

Figure 10

 10 Figure 10Plot of the difference between the EBV for live weight of each sire at each age estimated in intensive fattening system and its EBV for live weight at the same age estimated in pasture. each line represents one of the sires.

  The experimental device was located at Domaine de Gardel in

	observations on the weight of each animal at different ages. LWGT was used to compute
	ADG according to the following formula:	( adg lwgtn lwgt 1 ) / ( th st = -	1 ) st agen age th	.	-
	Grande-Terre, Guadeloupe, French West Indies, featuring the following general
	environmental conditions. Weather varying between two seasons: the dry season, from
	December to May, featuring temperatures between 22°C and 28°C; and the rain season, from
	June to November, featuring temperatures between 25°C and 31°C. The humidity is always
	75% or higher. Parasitic pathogen agents, the most prevalent being strongyles (Toxocara
	vitulorum) and ticks (Ambylomma variegatum, Boophilus spp). The latter is also the vector of
	Cowdria ruminantium, the etiologic agent of Cowdriosis, and predisposing factor for the
	dermatophilosis caused by Dermatophilus congolensis. Phenotypic observations were
	recorded on several growth and carcass traits expressed by 33 half sibs groups of Creole
	calves. The calves were born in Creole cows herds managed outdoor and grazed in either
	natural savanna or improved pasture. After weaning, the calves were drenched against
	parasites, and followed an adaptation period of 40 days on average. The members of each
	group were then randomly assigned to receive one out of two fattening treatments in a
	roughly balanced fashion. All animals were Creole bovines and were produced either by
	artificial insemination or natural mating. The number of offspring per sire varied from 2 to 57
	and the offspring were born from 138 dams in intensive system and 139 dams in pasture. The
	pedigree of the whole population included 961 animals and spanned 6 generations. The
	experimental device featured two fattening treatments: an intensive one (INT) and an
	extensive one (PAST). The former treatment featured keeping the animal in feedlots,
	sheltered from sun and precipitations and fed with unifeed plus a supplementation of
	concentrates. Namely the food provided in INT treatment was composed as follows: cropped
	grass ad libitum and concentrates (corn, middling, soybean meal, limestone, urea, minerals
	and vitamins). The concentrate was distributed using a automatic concentrate distributor, to a
	quantity tabulated according to the liveweight of each animal, in order to cover 60 % of the
	intakes capacity of the animal Whereas the latter featured free grazing on pasture as the only
	food source and no shelter either from sun nor precipitations. The stocking rate was between
	1500 Kg/ha and 2500 Kg/ha for the pasture system. Acaricide treatment was given every two
	weeks.
	Growth performance traits The traits analyzed to measure growth performance were:
	live weight (LWGT), average daily gain (ADG) and yearling weight (YRWGT). The
	observations on LWGT were collected in a longitudinal dataset including repeated

  The second method relied on the two vectors of EBVs obtained for each trait analyzed by the character state model: one vector collected the EBVs of all animals estimated from the phenotypic observations on the trait expressed within INT, whereas the other vector collected the EBVs of the same animals estimated from the phenotypic observations on the same trait expressed in PAST. The correlation between the elements of these two vectors was computed for each trait as a measure of GEI. This correlation was of the EBVs estimated within INT and PAST, respectively. As a third measure of GEI we estimated the r 2 adjusted of the following regression model implemented by proc reg of SAS ® software:EBVp are the EBVs of animal i estimated from the observations on a trait expressed within INT and PAST, respectively. µ is the mean of the EBVs estimated within INT.

	computed as:	, INT PAST EBV r	=	σ	, INT PAST EBV	/	2 EBV INT	*	σ	2 EBV PAST	σ . Where	, INT PAST EBV r	is the correlation
	between the EBVs of all animals estimated from the observations in INT and PAST.
	σ	, INT PAST EBV	is the covariance between the EBVs estimated in INT and PAST.	σ	2 EBV INT	and
	σ	2 EBV PAST	are the variances i EBVi	=	µ	i EBVp e +	i	+ . Where
														g r	, INT PASTt	=	σ	, INT PAST g	/	2 INT g	*	σ	2 PAST g	σ . Where
	g r	, INT PAST	is the genetic correlation between the observations on the same trait expressed across
	INT and PAST.	σ	2 INT g	and	σ	2 PAST g	are the genetic variances of the trait expressed within INT and
	PAST, respectively.

, INT PAST g σ is the genetic covariance between the observations on the same trait expressed across INT and PAST. Further analyses were implemented in order to estimate other measures of GEI. i EBVi and i i e is the residual

  Observations on growth performance traits were available on each of the 718 animals (376 on INT and 342 on PAST), while observations on carcass traits were

	available on 293 animals only (171 in INT and 122 in PAST). The total number of
	observations on LWGT was 13163, which was the sum of 6819 observations on LWGT of
	376 animals within INT and 6344 observations on LWGT of 342 animals within PAST. The
	number of observations on LWGT at each age are plotted in Figure

Table 1 .

 1 Summary statistics of the traits analyzed. The table reports the mean, standard error (se) and number of observations (n) obtained for each trait in each system. p is the p value of the two sided t test for the difference between the mean observed in innt and the mean observed in PAST for each trait (*** is p<0.0001; **is 0.0001<p<0.025; N.S. is p>0.025).YRWGT is yearling weight. ADG is average daily gain. SLWGT is slaughter weight. HCW is hot carcass weight. MTW is the weight of the muscles in the carcass. ATW is the weight of the adipose tissue in the carcass. DTW is the weight of the empty digestive tract. WAGE is the weaning age. WWGT is the weaning weight. SLAGE is the age at slaughter.Genetic parameters within environmentsThe heritability estimates, the genetic correlations and phenotypic correlations between traits within the same fattening system are given in table 2 together with their standard errors. The difference between the genetic correlations obtained in INT and the cprresponding values obtained in PAST spanned from -0.53 to 0.44 and difference between the phenotypic correlations spanned from -0.22 to 0.18.

			INT			PAST
		mean	se	n	mean	se	n	p
	YRWGT	243.56	48.70 376	201.57	36.16 342	***
	ADG	682.84 166.32 376	421.04 148.37 342	***
	SLWGT	319.99	38.23 171	315.71	44.39 122	N.S.
	HCW	180.01	24.25 171	172.15	28.87 122	**
	MTW	121.12	19.34 171	118.19	24.60 122	N.S.
	ATW	28.83		6.26 171	23.88	4.88 122	***
	DTW	16.39		2.11 171	17.79	2.30 122	***
	WAGE	211.50	16.30 376	211.31	16.85 342	N.S.
	WWGT	156.35	29.81 376	158.54	27.69 342	N.S.
	SLAGE	15.65		1.59 171	19.51	2.01 122	***

Table 3 .

 3 This table shows the genetic covariance (Cov) and the genetic correlation (r g ) between each trait expressed within the intensive fattening system and the same trait expressed within pasture. r BV is the correlation between the breeding values of all the animals for each trait estimated in intensive fattening system (INT) and their EBVs estimated in pasture (PAST). Reg is the r 2 adjusted of the regression model where the EBV of each animal estimated in INT is regressed on its EBV estimated in PAST. YRWGT is yearling weight. ADG is average daily gain. DTW is the weight of the empty digestive tract. SLWGT is slaughter weight. HCW is hot carcass weight. MTW is the weight of the muscles in the carcass.ATW is the weight of the adipose tissue in the carcass.

		YRW GT ± se	ADG ± se	DTW ± se	SLW GT ± se	HCW ± se	MTW ± se	ATW ± se
	Cov	49 ±31.03	889 ±988	1.36 ±0.71	229 ±167.11	81.1 ±61.91	69.34 ±42.80	4.54 ±4.32
	r g	0.59 ±0.35	0.31 ±0.34	0.93 ±0.40	0.69 ±0.39	0.66 ±0.41	0.85 ±0.43	0.43 ±0.38
	r BV	0.81 ±0.01	0.52 ±0.02	0.99 ±0.00	0.83 ±0.01	0.82 ±0.01	0.97 ±0.00	0.62 ±0.02
	Reg	0.66	0.27	0.99	0.69	0.68	0.94	0.38

Acknowledgments References


H Davi

U Vilhar

M Cater

T Grebenc

H Kraigher

T Levanic

P Simoncic

EFFECT OF MANAGEMENT ON FOREST CARBON BUDGET: A MODELLING STUDY ON VIRGIN AND MANAGED SLOVENIAN BEECH STANDS

Introduction

Forest ecosystems play a dominant role in controlling terrestrial carbon sinks (Janssens et al., 2003). In order to predict more accurately the response of the carbon balance of forest ecosystems to atmospheric and climate change, the effect of management needs to be more clearly understood. Thornton et al. (2002) had analysed the effects of disturbance history on carbon budgets in evergreen needle-leaf forests. Their results suggested that there was a consistent pattern of early carbon source followed by strong and gradually diminishing sink during recovery from a major disturbance.

Carbon stocking in virgin forests were few investigated in temperate climate (Knohl et al., 2003). In their review, Hyvönen et al. (2007) found that unmanaged stands were either close to neutral or weak carbon sinks. Hollinger et al. (1994) used the eddy-correlation technique to investigate the exchange of CO 2 between an undisturbed old-growth forest and the atmosphere. But they neither gave annual estimate of fluxes nor assess of carbon stocks in soil or wood. More recently, Knohl et al., (2003), found that a virgin beech forest was a large carbon sink over 2 years, with 494 g C m -2 per year in 2000 and 490g C m -2 per year in 2001.

One way to study the effects of management on carbon fluxes is to compare carbon fluxes by modelling or eddy flux measurements on several sites following various management modes or other types of disturbance (Thornton et al., 2002). Another solution is to use process based models and simulates the effect of various modes of thinning (Loustau et al., 2005). The purpose of this study was to compare two beech ecosystems in similar conditions but with different management modes (old unmanaged forest and a managed forest) using both measurements and modelling. The first objective was to assess by the comparison of the two sites the effect of management both on stock and carbon fluxes. The second objective was to provide an estimation of the carbon fluxes in a virgin forest, which are few studied in European countries.

Material and Methods

The sites

The virgin and managed silver-fir-beech forests investigated are located in the northern part of the Dinaric Alps in SE Slovenia (45°20N, 14°30E, 860-890 m). The bedrock is Cretaceous limestone and the soil generally shallow (leptosolic). The climate of the region is typical of Dinaric mountains, with annual precipitation up to 1,500 mm and a mean annual air temperature of 8.4°C. The Rajhenavski Rog virgin forest was officially declared as a virgin forest in the first forest management plan of the area in 1894, but is more precisely defined as a secondary virgin forest.

CASTANEA model and measurements

CASTANEA is a physiological process-based model simulating the carbon and the water balance in forest stands. The canopy is assumed to be homogeneous horizontally and is vertically subdivided into a variable number of layers (i.e. multi-layer canopy model). The main simulated output variables are the canopy photosynthesis, maintenance and growth respiration, growth of organs, soil heterotrophic respiration, transpiration, and evapotranspiration.

The radiation extinction and diffusion are based on the SAIL model. Half-hourly rates of gross canopy photosynthesis are calculated based on Farquhar et al. (1980) coupled with a stomatal conductance model of [START_REF] Ball | Progress in photosynthesis research[END_REF]. Maintenance respiration depends on temperature and nitrogen content of the various organs (Ryan, 1991), while growth respiration depends on biochemical composition of organs. After subtraction of maintenance respiration requirements, the remaining assimilates are allocated to the growth of various plant tissues using a priority rule, which varies with the season. The heterotrophic respiration is calculated using a Soil Organic Carbon (SOC) model (Epron et al., 2001) derived from CENTURY (Parton et al., 1987). Phenological stages (budburst, end of leaf growth, and start of leaf yellowing) and leaf growth depend on degree days (i.e. heat amount accumulated). The bigleaf Penman-Monteith equation is used to calculate both transpiration (Tr) and evaporation (EP). The soil water balance is calculated using a bucket model with three layers. During water stress periods, the slope of the relationship between leaf assimilation and stomata conductance (g 1 ) is assumed to decrease. Complete description and validation of the model were given in Dufrêne et al. (2005).

In the two sites, in 2005 and 2006, measurements of soil extractable water, leaf photosynthesis, and stand carbon biomass (soil and aboveground) were used to calibrate the model. Soil water content, soil respiration and tree ring increment were used to validate the model.

Results and discussion

The model was applied from 1997 to 2006 to estimate the various carbon fluxes in the two ecosystems: canopy photosynthesis, autotrophic and heterotrophic respirations, wood growth and Net Ecosystem Exchange. The CASTANEA model reproduced the soil water dynamics in 2005 and 2006 both in managed and virgin forest. Stand growth simulated in managed and virgin forest, were compared to tree ring increment measured in the managed forest from 1997 to 2006 (Fig. 1). The canopy photosynthesis was found globally high compared to others studies, respectively 2040 g C m -2 y -1 in the virgin forest and 1960 g C m -2 y -1 in the managed forest. The higher photosynthesis in the virgin forest (Fig. 2) was counterbalanced by a much higher heterotrophic respiration (+14%), due to higher soil carbon content and a higher wood respiration (+11%) caused by the regeneration. The result is that the managed forest showed a higher stock (+2%: 56 vs 55 t C m -2 ), but a lower flux of carbon (-60%: 132 vs 211 g C m -2 y -1 ).

Figure 1 :

 1 Figure 1: Stand growth from 1997 to 2006 measured from tree ring increment and simulated by CASTANEA in managed forest and simulated in virgin forest.

Figure 2 :

 2 Figure 2: Summarize of average annual carbon fluxes simulated by CASTANEA from 1997 to 2006 in virgin and managed forests.


1 Evaluación de las ventajas de la liberalización: las consecuencias de la volatilidad del precio

Jean-Marc Boussard

Preparado para el IId Taller sobre modelización en el sector agropecuario

El problema :

• El comercio obtiene beneficios Sería sin embargo absurdo descuidarlas….

…..si existieran

Si existieran…

• ¿Cómo se las calcula? Los modelos de equilibrio general • Las ecuaciones básicas -(1) Oferta = demanda -(2) Precio de consumo = utilidad marginal -(3) Uso de los factores <=disponibilidad -(4) Precios = productividad marginal -(5) Gastos de consumo+ ahorro =renta -(6) Ahorro = Gastos de inversión -( 7)Capital(t)= Capital(t-1) -depreciación (t-1)+ inversión(t-1) -( 8) Suma (X -M) = 0

• Tantas ecuaciones como factores : va bene! • ¿Pero estas ecuaciones corresponden a la realidad?

(4) Precios = productividad marginal Las modificaciones en la ecuación (4)

• El costos marginales igualan al equivalente de certeza de la demanda • Se calcula con la varianza del precio :

• Con : • Y: • Donde: ( ) 2 1 1 2 ˆ--- = t t p p p σ ) ( ˆ1 1 1 - - - - + = t t t t p p p p α t t t t q A p p 2 σ - =
A: coeficiente de aversión al riesgo α: elasticidad de anticipación ^ : indica una anticipación t : tiempo ; Q: cantidad ; P : precios • La dificultad :

-En los modelos normales el capital pasa de una producción a otra sin límites -No se puede hacer, no es posible transformar una segadora-trilladora en central eléctrica -Realmente, el capital es específico a cada sector • La solución « normal bis» :

-Una elasticidad para asignar el nuevo capital • Nuestra solución:

-el nuevo ahorro se invierte según un modelo de selección de portafolio

Un modelo de selección de portafolio

• Los datos -Se define la esperanza de rendimiento del capital en cada producción, por medio de los precios de los productos -Se define la varianza-covarianza de similar manera • Entonces, la asignación del nuevo ahorro entre los factores se hace maximizando: El "modelo CIRAD" : una generalización de este tipo de modelos

j ij i j i j i j s s A r s U σ ∑∑ ∑ -
Las lecciones políticas:

• El riesgo es una falla de mercado • Es necesario encontrar métodos para coordinar los intercambios que no pasan solamente por el mercado • Es contrario a la ideología actualmente desarrollada por la OMC, pero no inevitablemente contraria a los objetivos fundamentales de esta institución • Pruebas existen de los efectos positivos de los precios garantizados

Los efectos positivos de los precios garantizados

•σ

  fracción del ahorro invertido en la actividad j r j : rentabilidad esperada del capital en la actividad σ ij : Varianza-covarianza de la rentabilidad Es cierto que con el comercio se gana eficacia • Es cierto también que la volatilidad de los precios hace perder eficacia • Si no es posible el comercio sin aumentar la volatilidad de los precios, los beneficios del comercio se vuelven imprevisibles • Todos los productos no están en esta situación, pero los productos agrícolas sí lo están Todos los productos no están en esta situación Los productos agrícolas sí lo están el caso de la agricultura Convergencia (|α/a |<1) Períodico (|α/a |=1) Divergencia (|α/a |>1)Fuerza e insuficiencias del cobweb• Sirvió para justificar la decisión del Presidente Roosevelt de cortar los vínculos entre la agricultura y el mercado • No da cuenta de la existencia de fluctuaciones de larga duración que no son períodicas, que no divergen y no convergen • Es posible con el riesgo fabricar un cobweb mejorado que presenta estas características Anticipación de la varianza del precio; A: coeficiente de aversión al riesgo

Fuentes año Ganancias de PBI países pobres (Mil mill. $) Ganancias de PBI países ricos (Mil mill. $) Ganancias de PBI Mundo (Mil mill. $) GTAP 6 [1] 2005 22 62 84 GTAP 4 2002 108 146 254 LINKAGE 2002 [2] 2005 189 196 385 LINKAGE [3] 2003 86 201 287 Carnegie

	Estas ganancias son más modestas en La misma situación… • Los $/per capita
	beneficios Source • Las ventajas de la liberalización son Année Paises pobres Paises ricos Total Mundo de la GTAP 2005 (1) 2005 4,26 64,58 13,70 tentadoras para un hombre político liberali-GTAP 2002(1) 2002 20,89 152,08 41,44
	• Es en el momento de la implementación zación LINKAGE (1) 2005 17,41 205,21 46,82 según los que los problemas aparecen, y obligan a LINKAGE(1) 2003 104,26 274,79 135,73 modelos volver de nuevo al proteccionismo y a la de moda OCDE(2) 2003 --28,30
	administración de la economía OCDE(3) 1999 53,0 64,58		54,98
	GTAP 5 and GTAP6 : Ackermann (2005). • Ahora, la misma situación parece darse de [4] [1] FUENTES: 2006 30,1 28,5 58,6
	[2] Linkage : Van [3 Anderson, Martin and Van nuevo Mensbrugghe, D. and J.C. Beghin (2005) y	CEPII [5]	2005	274	62	337
	der Mensbrugghe (2005).
	[4] Carnegie : Polaski, 2006
	, table 3.1
	[5] CEPII : Bouet et al.
	(2004)

  • Rendimientos del trigo, Francia y EEUU, durante un siglo • La ruptura de 1936(US) y 1945(F) • Inconveniencia: no hay límite a la producción • La razón para un control de la producción

	Para terminar…
	Rendimiento del trigo, USA y France, 1866-2001
	0 20 40 60 80 100 • Eso es lo que estudiamos actualmente 1866 1876 1886 1896 1906 1916 1926 1936 1946 1956 1966 1976 1986 USA France con el modelo del CIRAD • Esperamos demostrar el beneficio del rendimiento(Qx/h comercio regulado mediante cuotas 1996 Tiempo • Este principio de las cuotas no resuelve
	todos los problemas (dificultad para
	establecerlas)
	• Pero, es una pista para la reflexión…

Una pista de investigación

Sin cuotas Con cuotas

Con cuotas

Las cuotas de producción pueden estabilizar series caóticas. En esta caso, el papel del mercado es solamente marginal


• Climate (T°, radiation, humidity, precipitations)

Temporal variability

Campaigns :

• LAI Automatic :

• Eddy covariance (Net CO 2 Ecosystem Exchange)

Why Volatile Carbon Soil ?

• Solve eddy covariance measurement problems

• Improve soil carbon modelling

  Technical support (Isotope, microscope, mineral analysis (C. Brechet)GIS (D. Maurice)Flux and budget of carbon, water and nitrogen in interaction with climate and edaphic conditions Ecosystem carbon cycle :Ability of the forest to store the atmospheric carbon :

•δ

  Explain stable isotope fluctuations of soil CO 2 efflux Why Soil Respiration ? • Partitioning NEE between components fluxes (Rs second flux, Rs = 60-70% of TER) • Explain temporal variability of TER • Explain spatial variability of TER When low turbulence = quite nights EC fluxes Total ecosystem respiration (30% of the nighttimes) profile in canopy air below EC system We need [CO 2 ] soil measurements for soil CO 2 storage estimation Correction CO 2 stored in the canopy air and in the soil boxes » : Soil carbon budget & photosyntates allocation Soil carbon model : "Century" soil carbon pools Site specific soil CO 2 efflux function Fc (t°, SWC) Multi-layers model : CO 2 production profile depending on layer features on carbon sequestration Temporal variability of isotopic signature of soil efflux Carbon has two main isotopes ( 12 C and 13 C) Isotopic composition δ 13 C (‰) δ 13 C atm -8 ‰ δ 13 C SOM -21 → -30 ‰ Discrimination (modification of the δ 13 C value) during the biophysical processes (photosynthesis, CO 2 diffusion) Measurements of δ 13 C soil respiration Contribution of the heterotrophic respiration sources (litter/old SOM) Contribution of the plant organs (soluble sugar and amino acids, storage carbohydrate and proteins) in autotrophic soil respiration Separation of NEE between GPP-Reco Input for inverse carbon cycle model Determination of terrestrial & oceanic fluxes from [CO 2 ] atm and δ atm low frequency measurements (Keeling plots) but temporal variability of δ 13 C Rs !!!! Daily and seasonal δ 13 C Rs variations ?Which factors control these variations ?

Roots + Rhizosphère

To separate the impact of physical from biological processes Isotope workpackage of the project Two/one time(s) per month air sampling ( depths) IRMS analyses for 13 CO 2 in soil Keeling plot 13 CO 2 of soil CO2 efflux


Chi-Hua Huang

Frédéric Darboux

Frederic Darboux

How soil roughness affects runoff and sediment production ?

 Soil erosion is a surface boundary process occurring in a multiple range of spatial and temporal scales.

 Spatial and temporal variability in erosion is caused by the combined effects of variable surface conditions, hillslope positions and rainfall pattern.

"A proper quantification of soil erodibility should include the transient surface boundary effects"

Evolving Concepts in Soil Erodibility:

--Soil erodibility = Fn (soil properties, surface condition, ….) --Change in surface condition causes spatial and temporal variations in soil erosion.

--Soil erodibility controls the dominant erosion process.

Surface condition

Soil erodibility Erosion process

Surface Conditions Affecting Erosion:

• Microtopography or surface roughness.

• Seepage and hillslope hydrology.

• Macroporosity and bulk density.

• Wet consolidation.

• Drying and cracking due to shrinkage.

• Prior rain history.

• Freeze and thaw cycle.

• Biological activities and plant materials.

Seepage/drainage effects on erosion Seepage/drainage effects on erosion

• Erosive forces on the surface are controlled by topography.

• Changes in surface topography reflect the dominant erosion processes on the surface.

• Dissipation of flow energy is caused by surface roughness and treated as a residual term in energy balance.

• Microtopography affects both soil erodibility and erosivity.

• Both surface and sub-surface hydrology are controlled by topography (at all scales).

Why interested in studying soil roughness?

• "Single-valued roughness index"

• "Roughness decay with rain"

Topography (macro/micro) and Erosion

Untangling the Myth of Soil Roughness

Quiz 1:

Will rainfall increase or decrease soil roughness?

Quiz 2:

Will an increased soil roughness increase or decrease soil erosion? Current erosion models predict a reduced sediment production with an increased soil roughness

Assumptions: Fully submerged flow Flow depth >> roughness height Roughness elements dissipate flow energy

In partially inundated flow, soil microtopography can either converge or diverge the surface flow, hence cause both increased and decreased erosion at the same time but at different locations on the surface.

Hypothesis: Surface depressions and mounds may affect runoff and sediment production differently

  know the answer by now. Otherwise, I will be happy to repeat this presentation! Thank You

Implications

If the surface is dry or rainfall is insufficient to produce a full runoff, the delayed runoff from surface depressions will cause reduced total runoff, hence, total sediment yield from rough surfaces.

Once the depressions are filled, or under moist conditions, the net roughness effect becomes uncertain due to complex interactions between mounds and depressions as well as other factors affecting soil erodibility (for example, seepage vs. drainage).


domain: sdv info

text:
Wessam Galia

Clarisse Perrin

Nawara Jameh

Magali Genay

Annie Dary

Variability of the proteolytic properties in Streptococcus thermophilus

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

The prtS gene was detected in 23 out of 30 strains. So in most cases, the PrtS -phenotype of some S. thermophilus strains does not result from an absence of the corresponding gene.

• Sequencing of prtS gene in strains presenting a PrtS activity (PrtS + ) or not (PrtS -)

The prtS gene was sequenced in one PrtS + strain and four PrtS -strains.

The ORF shared 100% identity with the corresponding one of strain LMD-9 and 95% identity with that of strain CNRZ385, so the PrtS -phenotype of the strains having the prtS gene result from an alteration of its expression.

Streptococcus thermophilus requires exogenous source of amino acids or peptides for its optimal growth and must utilize caseins as amino acids source in milk via the action of a Cell-Envelope Proteinase (CEP). Strains possessing an active proteolytic system could present a high growth rate in milk leading to high production of lactic acid. The aim of this work was to evaluate the variability of the proteolytic properties in a collection of 30 S. thermophilus strains.

Does a relationships exist between acidifying and Does a relationships exist between acidifying and Does a relationships exist between acidifying and Does a relationships exist between acidifying and proteolytic proteolytic proteolytic

proteolytic potential? potential? potential? potential?

•

  PERRIN, Nawara JAMEH, Magali GENAY and Annie DARY Unité de Recherche sur l'Animal et les Fonctionnalités des Produits Animaux, équipe Protéolyse-Biofonctionnalité des Protéines et Peptides Nancy-Université, Faculté des Sciences et Techniques de l'UHP Nancy1, BP239, 54506 Vandoeuvre-lès-Nancy cedex, France Research of the prtS gene in the different S. thermophilus strains:

In conclusion, all of the H-Strains were PrtS + , and M-L-Strains were PrtS +/-or PrtS -. The PrtS +/-or PrtS -phenotype of the strains having prtS gene seems rather result from an alteration of its expression than from a mutation of this gene.

•The 30 S. thermophilus strains could be clustered into 3 groups on the basis of their acidifying properties in milk medium We determined whether the absence of PrtS activity in a strain possessing a functional prtS gene (souche PB18O) result from an alteration of its transcription. In all growth phases, a prtS mRNA was detected by RT-PCR in this strain as in the prtS + strain PB302 used as a control. The next step will consist in comparing the quantity of prtS transcript in the two strains.


Véronique Decognet

Marc Bardin

Claire Troulet

Philippe C Nicot

Geographic structure of Botrytis cinerea populations from vegetable greenhouses in southern France

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

Decognet, V., Bardin, M., Troulet, C., Nicot, P., 2006. Geographic structure of Botrytis cinerea populations from vegetable greenhouses in southern France, 2. Botrytis genome workshop, Versailles, France, 2006/10/05-07 Decognet Véronique, Bardin Marc, Troulet Claire, Nicot Philippe INRA-Unité de Pathologie Végétale, Avignon, France Veronique.decognet@avignon.inra.fr Botrytis cinerea can cause severe losses in sheltered tomato crops. Chemical control is the main way to control gray mould, together with preventive cultural methods and climatic control. Information on the genetic structure of the pathogen's population could lead to a better understanding of epidemics and the development of better strategies of disease control. A total of 1800 isolates of B. cinerea were collected from commercial tomato greenhouses in 2002, 2003 and 2004 at different geographical scales in Southern France. Samples in 2002 were taken once, at the end of the growing season, from two adjacent greenhouses in each of two sites located 300 km apart. Their characterization with 9 SSR markers described by Fournier et al. (2002, Molec. Ecol. Notes 2:253-255) revealed the presence of one or several dominant haplotypes in each individual greenhouse, combined with an extreme diversity of the remaining isolates. The two adjacent greenhouses of a given site shared most of their dominant haplotypes together with several rare haplotypes. Similar results were obtained for isolates collected from sporulating lesions on the plants or from the air spora (ca 100 from each source in each greenhouse). In contrast, a strong geographic structure was suggested at a higher scale by the fact that the sampling sites shared few common haplotypes (7 out of a total of 74), and that none of those dominant on one site was dominant on the other. In 2003 and 2004, a smaller geographic scale was considered. Isolates were collected on tomato plants over a four-month period in one of the glasshouses examined in 2002 and in two other glasshouses located within a 50 km radius. Again, a few dominant haplotypes together with a high diversity of other isolates were found on each site. Shared haplotypes among sites were also observed at that scale. In the one glasshouse that was sampled each year, only one haplotype was common to the three successive tomato crops, with highly different frequencies. In that glasshouse, where tomato and lettuce were consecutively produced in a yearly rotation, a few isolates (36) were also collected from diseased lettuce in 2002-2003 and in 2003-2004. Contrary to the situation in the tomato crops, no dominant haplotypes were found among the isolates collected from lettuce, and no haplotypes were shared with isolates from tomato. The comparison of haplotype frequencies at different geographic and temporal scales suggests that migration and exchange of inoculum are frequent among neighbouring greenhouses (even though they are usually considered as confined). In addition, the systematic occurrence of dominant haplotypes in all greenhouses suggests that endogenous secondary inoculum (produced on diseased plants) plays an essential role in the development of grey mould epidemics in commercial tomato greenhouses. Finally, differences in populations sampled from lettuce and tomato suggest a possible host specialization of B. cinerea. These findings have a direct impact for the management of grey mould in vegetable greenhouses.