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# Clustering and genetic analysis of body weight and body condition score variations in meat sheep

Tiphaine Mace

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Tiphaine Mace. Clustering and genetic analysis of body weight and body condition score variations in meat sheep. 2017, 41 p. hal-01605227

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Master 2 - PRedictive and Integrative AniMal biology (PRIAM)

Clustering and genetic analysis of body
weight and body condition score
variations in meat sheep

Tiphaine Macé

Supervisor:
Dr Dominique Hazard
INRA UMR1388 GenPhyse
Génétique, Physiologie, Système d'Elevage
B.P. 52627 31326 Castanet-Tolosan
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# Clustering and genetic analysis of body weight and body condition score variations in meat sheep

## Abstract

The overall sustainability and innovative capacity of small ruminant sector in Europe have to be improved to cope with current and future economic, environmental and societal challenges. This involves improving robustness in sheep to cope with fluctuating environmental conditions, for instance to increase grazing. The ability of ewes to mobilize or to restore body reserves is a key mechanism to cope with fluctuations in feed resources. The purposes of this work were to characterize the different patterns of BW and BCS variations throughout productive cycles and to analyze the genetic variability of BW and BCS variations in meat sheep. Temporal changes in body reserves dynamics were investigated using Body Weight (BW) and Body Condition Score (BCS) measured frequently throughout repeated entire productive cycles in ewes reared exclusively outdoors in harsh conditions. Cluster analysis showed three different clusters for BCS variations whatever productive cycle and one to four clusters for BW variations depending on productive cycle. Major clusters were mainly characterized by body reserves accretion during dry-off period and early pregnancy and body reserves mobilization during mid-pregnancy and suckling. The heritability values were low to medium for BW variations (h²=0.11 to 0.19) and BCS variations (h²=0.06 to 0.15). Moderate to high genetic correlations between BW and BCS variations indicated both traits were genetically related. High negative genetic correlations for BCS and BW variations between dry-off period and pregnancy and/or suckling indicated body reserves mobilization and accretion were two genetically correlated processes. The present data demonstrated body reserves variations were heritable indicating such traits could be used in selection to increase robustness.

Key words: Sheep, Body reserves, Longitudinal analysis, Heritability, Quantitative genetic

## Introduction

To limit the utilization of feed inputs and the competition with agricultural surfaces used for human feeding, the livestock production sector needs to improve the efficiency and the robustness of farm animals. Ruminants will be particularly compelled to increase use of natural resources involving grazing in less controlled environments. Moreover, the extensive or pastoral systems represent more than 40 % of the sheep meat farms in France. Sheep husbandry in harsh conditions requires robust animals with specific adaptive characteristics to assure its adequate functioning and productivity in the fluctuating dynamics of those systems (Agreste, 2015). In most of such systems, the ability of the animal to mobilize or to restore their body reserves (BR) is a key physiological mechanism to respond to qualitative and quantitative variation in the available feedstuffs bases.
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Thus, the efficiency in the use of BR becomes crucial to ensure productivity, health, survival and reproduction whatever the production system (Russel *et al.*, 1971). A lack in BR can have an impact on reproduction at different steps of the dam's life: its puberty can be delayed, the ovulation suppressed and ewes with low Body Condition Score (BCS) at mating can have a higher incidence of embryonic loss. So, undernutrition can have an effect through all the physiological stages at the level of the reproductive function (Chilliard *et al.*, 1998; West *et al.*, 1989). Moreover, Molina *et al.* (1994) have demonstrated that fertility of ewes was affected by BCS and Gunn *et al.* (1991) have found that conception and lambing rates can be increased significantly if the BCS is around 2.5, but can decrease above this level. Blanc *et al.* (2006) have shown that the ability of the ewe to handle the demands of its offspring in harsh conditions depend on the role of its BR.

Feed efficiency including body reserves component can also have significant impacts on maternal behavior. Indeed, Dwyer *et al.* (2003) have shown that Blackface ewes that had low-intake were more aggressive towards their lambs and spent significantly less time licking them after parturition than high-intake ewes. The loss of BW and BCS can also lead to a decrease in milk yield (Lalman *et al.*, 2000).

One of the difficulties to measure this mechanism of mobilization and accretion of body reserves is to dispose of reliable and numerous data on these variations. The BCS has been defined by Murray (1919) and, associated with Body Weight (BW), proposed as a subjective measure for fat quantity stored by the animals (Waltner *et al.*, 1994). The BCS based on a five-point scale was elaborated by Jefferies (1961) and was assessed by palpations of the lumbar region. Russel *et al.* (1969) showed that BCS provides better estimates of the proportion of fat and hence energy reserves in the live animal than BW measures. It is generally assumed that a BCS of 3 is ideal to assure nutritional and metabolic welfare. If the BCS is above 3 or below 2, the metabolism may be unbalanced (Caldeira *et al.*, 2007).

The metabolic patterns can be used to characterize the BR dynamics. González-García *et al.* (2014) studied those patterns in Romane and showed that mobilization and accretion process of the body reserves were well illustrated by plasma concentration of some metabolites and metabolic hormones like Non Esterified Fatty Acids, β-hydroxybutyrate and triiodothyronine during last pregnancy, lambing and suckling periods.

The mobilization of BR can also be studied by using X-ray computed tomography (CT). It is a non-invasive method which allows accurate *in vivo* measures of tissue weights (Jones *et al.*, 2004). Lambe *et al.* (2003) have demonstrated that CT can show fat and muscle mobilization and accretion. The fat may also be measured on carcass animals.
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Numerous factors play a role in the variations of BW and BCS in ewes, such as the age or the parity of the ewe and the litter size. Overall, multiparous ewes are heavier than primiparous, and ewes with multiple litter sizes (i.e. twins or more) will loss more weight (and BR) mainly at the end of pregnancy, around lambing and during suckling. The year and/or climatic season may also impact the BW and BR status of the female. A recovering period normally occurs in positive energy balance periods (e.g. high availability of good quality forage, after weaning and before starting the following productive cycle). Thus, BW and BCS change according to the physiological stage of the ewe (González-García et al., 2014).

The estimation of BCS heritabilities for heifers ranged from 0.24 to 0.38 according to the breed and the physiological period (Koenen et al., 2001). For lactating cows, the heritability varies from 0.20 to 0.45 (Loker et al., 2011; Mao et al., 2004). For beef cows, BCS was generally moderately heritable with 0.30 (Arango et al., 2002). In sheep, Borg et al. (2005) found that heritabilities ranged from 0.10 to 0.16 for BCS measured in Targhee ewes in late pregnancy to post-weaning. This is less than what was found by Shackell et al. (2011) in intensive flocks in New Zealand with heritabilities that vary between 0.20 and 0.30. There is no genetic study for comparison between breeds in BCS.

For BW, Safari et al. (2005) found estimation of heritabilities that ranged from 0.30 to 0.41 for adult sheep. For Menz sheep, the heritabilities were between 0.34 and 0.56 (Gizaw et al., 2007).

The estimation of heritability for CT tissues ranged from 0.23 to 0.76 in Scottish Blackface sheep (Karamichou et al., 2006). There are no genetic parameters for metabolic patterns yet.

The adaptation of ewes to challenging husbandry constraints, such as the limitation of feed stuffs and other inputs, implies adding value to the biological capacity of using BR (i.e. lipid body reserves as storage compartment of energy to be used in case of imbalances).

The ultimate objective of this study was to investigate temporal changes and profiles of BR dynamics in Romane ewes reared in an extensive grazing system by using available individual BW and BCS data in a timespan of 13 years. We were able to monitor the progress of individual BW and BCS during several entire productive cycles of 1448 ewes which further allow us to characterize the different patterns of BW and BCS variation during each particular productive cycle and the whole productive life or career, and to analyze the genetic variability of BW and BCS of these meat ewes at key physiological stages. The current work could bring new insights into the characterization of genetic variability of BR probably to be used in future animal genetic selection programs.
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# Materials and methods

## *Experimental farm, animals, breeding system and climatic conditions*

The study was conducted with a database of historical individual BW and BCS registers generated from the Romane sheep flock of the INRA Experimental Farm *La Fage* (Causse du Larzac 43°54′54.52″N; 3°05′38.11″E; approximately 800 m.a.s.l, Roquefort-sur-Soulzon, Aveyron), south of France.

The flock was composed of 250 Romane ewes with a renewal rate of 30 % per year. There is one lambing per year and mating takes place in late October and November to obtain a peak of lambing at the beginning of spring (usually mid-April; González-García *et al.*, 2014). This breed come from an ancestral cross-bred of the Romanov (prolific breed and good maternal qualities) and the Berrichon-du-Cher (meat breed) (Ricordeau *et al.*, 1992). Romane is a hardy and prolific breed with in average 2.0 lambs born alive per lambing (Molénat *et al.*, 2005), good maternal qualities and a thick wool coat to protect them from harsh climatic conditions (Allain *et al.*, 2011, 2014). Before 2010, first mating of females was performed at 7 months of age for ewes with a weight around 40 kg and 19 months for the others. After 2010, the reproduction system changed and all females mated at 19 months of age. Pedigree of animals present in the study is known for a long time.

Ewes are reared exclusively outdoors with ewes and replacement lambs grazing during the year on about 280 ha of the so-called “Causse” rangeland. This rangeland is a calcareous plateau with thin soil which is covered by herbaceous vegetation. It is composed of about 25 % of shrubs and 75 % of grass such as *Bromus erectus*, *Stipa pennata*, *Carex humilis* and *Festuca duriscola*. The farm is divided in parks that include “natives” (non-fertilized) and fertilized parks (15 ha) used during spring (from lambing to weaning; Molénat *et al.*, 2005). During the second month of suckling, lambs have access to supplemented feeds. Depending on climatic conditions, the grass stops growing to some extent in summer (hot and drying). Generally, the rangeland vegetation re-greens in the beginning of autumn. During the winter (cold and wet), the flock is supplemented with conserved hay or silage mostly which correspond last two months of pregnancy.

The present study used a thirteen years historical database belonging to 1448 females descendant from 78 rams. Data were collected between 2002 and 2015 and saved in a data base for experimental data (Geedoc). A maximum of 3386 records per trait was registered over this period with 1448, 1170 and 765 ewes representing the productive cycles 1, 2 and 3 or more, respectively.

Number of ewes of 1 and 2 years of age, measured during productive cycle 1 was 768 and 680, respectively. Number of ewes of 2 and 3 years of age, measured during productive cycle 2 was 664 and 506, respectively. Number of ewes of age 3 and more, measured during productive cycles 3 or more was 765. 29 % of ewes in cycle 1 produced three lambs and more whereas 64 % and 74 % of ewes produced three lambs and more in cycle 2 and 3 respectively.
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## Measurements

Body weight and BCS used in this study have been measured regularly according to a physiological stages schedule during each productive cycle and during the whole productive life (or career) of the female. The BCS evaluation was performed using an adapted grid from the original grid described by Russel *et al.* (1969).

The original grid has been subdivided with a 1/10 scale instead of 1/4 scale (i.e. for example, 1.5; 1.6; 1.7... 3.2; 3.3 vs. the most conventional 1.5; 1.75; 2.0; 2.25...3.25). BCS measurements have been performed by two operators throughout the 14 years period comprised in this study, with regular training sessions for adjustments and calibration. Seven records of BW were used from mating to the dry-off i.e. 1) at mating (15 days before, November; **BW-M**), 2) at early pregnancy (40 +/- 15 DAM, December; **BW-Pa**), 3) at mid-pregnancy (80 +/- 14 DAM, January; **BW-Pb**), 4) at lambing (160 +/- 16 DAM, April; **BW-L**), 5) at the beginning of suckling (190 +/- 14 DAM, April; **BW-Sa**), 6) at weaning (250 +/- 16 DAM, June; **BW-W**) and 7) at the end of the dry-off period (310 +/- 12 DAM, August; **BW-D**).

Similarly, eight BCS records were used in the current study, from mating to the dry-off. The periods and nomenclatures are as follows: at mating (15 days before, November; **BCS-M**), 2) at early pregnancy (40 +/- 15 days after mating, DAM, in December; **BCS-Pa**), 3) at mid-pregnancy (80 +/- 14 DAM, January; **BCS-Pb**), 4) at lambing (160 +/- 16 DAM, April; **BCS-L**), 5) at the beginning of suckling period (190 +/- 14 DAM, April; **BCS-Sa**), 6) at the end of suckling (230 +/- 15 DAM, June; **BCS-Sb**), 7) at weaning (250 +/- 16 DAM, June; **BCS-W**) and 8) at the end of the dry-off period (310 +/- 12 DAM, August; **BCS-D**). Other measurements were also considered for the litter: the birth weight of the whole litter (**litterB**), the average lamb birth weight (**lambB**), the weight of the litter at weaning (**litterW**), and the average lamb BW at weaning (**lambW**).

Factor of variation litter size was organized in 4 classes: 1 lamb, 2 lambs, 3 lambs, 4 lambs and more. Factor of variation cycle of the ewe was organized in 3 classes: cycle 1, cycle 2, cycle 3 and more. Factor of variation age of the ewe was organized in 3 classes: 1 year, 2 years, 3 years and more.

Thus, for analyzing individual performances linked to mobilization of BR we used variations in BW and BCS at three periods whatever the number of productive cycle i.e. 1) between pregnancy (middle for BW and beginning for BCS) and lambing (**BW-Pb:L**; **BCS-Pa:L**), 2) between lambing and suckling (**BW-L:Sa**; **BCS-L:Sa**), 3) between pregnancy and weaning (**BW-Pb:W**; **BCS-Pa:W**). Similarly, for the analyses of performance during the BR reconstitution or accretion variations in BW and BCS at three periods whatever the number of productive cycle were calculated and used i.e. 1) between mating and pregnancy (**BW-M:Pb**; **BCS-M:Pa**), 2) between weaning and dry-off (**BW-W:D**; **BCS-W:D**), 3) between weaning and mating (**BW-W:M**; **BCS-W:M**).
<table><caption>Table 1. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) for the tested models to explain clusters found in cycle 1 for BW.</caption><thead><tr><th>Model</th><th>AIC</th><th>BIC</th><th>Order<sup>(c)</sup></th></tr></thead><tbody><tr><td>Complete<sup>(a)</sup></td><td>3703.676</td><td>3729.611</td><td></td></tr><tr><td>Without age effect<sup>(b)</sup></td><td>4161.496</td><td>4182.243</td><td>1</td></tr><tr><td>Without litter size effect<sup>(b)</sup></td><td>4049.453</td><td>4070.565</td><td>2</td></tr><tr><td>Without year effect<sup>(b)</sup></td><td>3701.853</td><td>3722.6</td><td>3</td></tr></tbody></table>

(a) Complete model corresponding to [I]; (b) Complete model without the factor indicated on the table; (c) Factor importance (1: more important to 3: less important)

<table><caption>Table 2. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) for the tested models to explain clusters found in cycle 2 for BW.</caption><thead><tr><th>Model</th><th>AIC</th><th>BIC</th><th>Order<sup>(c)</sup></th></tr></thead><tbody><tr><td>Complete<sup>(a)</sup></td><td>2247.17</td><td>2267.271</td><td></td></tr><tr><td>Without age effect<sup>(b)</sup></td><td>2260.758</td><td>2280.839</td><td>2</td></tr><tr><td>Without litter size effect<sup>(b)</sup></td><td>2447.113</td><td>2467.372</td><td>1</td></tr><tr><td>Without year effect<sup>(b)</sup></td><td>2257.611</td><td>2277.692</td><td>3</td></tr></tbody></table>

(a) Complete model corresponding to [I]; (b) Complete model without the factor indicated on the table; (c) Factor importance (1: more important to 3: less important)

<table><caption>Table 3. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) for the tested models to explain clusters found in cycle 1 for BCS.</caption><thead><tr><th>Model</th><th>AIC</th><th>BIC</th><th>Order<sup>(c)</sup></th></tr></thead><tbody><tr><td>Complete<sup>(a)</sup></td><td>1863.012</td><td>1888.946</td><td></td></tr><tr><td>Without age effect<sup>(b)</sup></td><td>1876.395</td><td>1897.143</td><td>2</td></tr><tr><td>Without litter size effect<sup>(b)</sup></td><td>2416.415</td><td>2437.527</td><td>1</td></tr><tr><td>Without year effect<sup>(b)</sup></td><td>1864.896</td><td>1885.643</td><td>3</td></tr></tbody></table>

(a) Complete model corresponding to [I]; (b) Complete model without the factor indicated on the table; (c) Factor importance (1: more important to 3: less important)

<table><caption>Table 4. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) for the tested models to explain clusters found in cycle 2 for BCS.</caption><thead><tr><th>Model</th><th>AIC</th><th>BIC</th><th>Order<sup>(c)</sup></th></tr></thead><tbody><tr><td>Complete<sup>(a)</sup></td><td>1768.517</td><td>1793.618</td><td></td></tr><tr><td>Without age effect<sup>(b)</sup></td><td>1782.224</td><td>1802.305</td><td>2</td></tr><tr><td>Without litter size effect<sup>(b)</sup></td><td>2038.86</td><td>2059.119</td><td>1</td></tr><tr><td>Without year effect<sup>(b)</sup></td><td>1766.697</td><td>1786.777</td><td>3</td></tr></tbody></table>

(a) Complete model corresponding to [I]; (b) Complete model without the factor indicated on the table; (c) Factor importance (1: more important to 3: less important)
## Database handling and statistical analyses

### Data description

Deviations from normality were tested using the Kolmogorov-Smirnov test (Univariate procedure of SAS; SAS Inst. Inc., Cary, NC). None of the raw variables were transformed.

All biologically and environmental relevant effects and interactions were tested using analyses of variance taking into account the repeated measures (MIXED procedure of SAS software, version 9.4; SAS Institute Inc, Cary, NC, USA) to identify factors of variation on BW and BCS. The age and litter size interaction and the cycle of the ewe and litter size interaction were tested.

The factors of variations (i.e. age of the ewe, cycle of the ewe, year of measurements and litter size) were also tested for the variations in BW and BCS and were taking into account if significant.

### Clustering of individual profiles

In addition to the description of average profiles for each factor of variation known, we performed a cluster analysis in order to investigate the variability in individual profiles of BW and BCS during each productive cycle. The objective was to understand the variability of the profiles, their variations and not having any *a priori* on the factors of variation. Clustering will also create profiles for BW and BCS different from those described for factors of variation. The R (version 3.3.1) packages Fdapace (Functional Data Analysis and Empirical Dynamics) and Rmixmod (Supervised, Unsupervised, Semi-Supervised Classification with MIXture MODelling) were used.

Individual profiles were smoothed on a define number of node (i.e. by default 51 nodes) from the seven measures of BW or the eight measures of BCS recorded for each individual (R package Fdapace). In our case, longitudinal data were used without taking into account the date of measurement but considering the seven or eight measurements were regularly spaced out throughout the productive cycle. Then a Functional Principal Component Analysis (FPCA) was performed on the individual splines (Principal Analysis by Conditional Estimation, Fdapace package, R software; Yao *et al.*, 2005). Clusters were obtained by fitting a mixture model to individual coordinates of the three first components of FPCA (Rmixmod package, R software). The algorithm used was the Expectation-Maximization (EM) algorithm. It provided an iterative computation of maximum likelihood estimation when the observed data are incomplete. Two to seven clusters were tested and the number of clusters was chosen using Akaike Information Criteria and Bayesian Information Criteria. The **AIC** (Akaike, 1974) was calculated as $AIC=2k-2×ln(L)$ with *k* the number of parameters to estimate and *L* the maximum likelihood of the model. The **BIC** (Schwarz, 1978) was known as $BIC=ln(n)×(k-2)×ln(L)$ with *k* the number of parameters and *n* the number of observations in the sample. The best model was the one with the lower criteria value, but they didn't determine the absolute adjustment quality of the model.
<table><caption>Table 5. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) for the tested models to explain clusters found in cycle 3 for BCS.</caption><thead><tr><th>Model</th><th>AIC</th><th>BIC</th><th>Order<sup>(c)</sup></th></tr></thead><tbody><tr><td>Complete<sup>(a)</sup></td><td>955.0286</td><td>975.3707</td><td></td></tr><tr><td>Without age effect<sup>(b)</sup></td><td>953.0819</td><td>969.3556</td><td>2</td></tr><tr><td>Without litter size effect<sup>(b)</sup></td><td>973.872</td><td>990.2463</td><td>1</td></tr><tr><td>Without year effect<sup>(b)</sup></td><td>953.075</td><td>969.3487</td><td>3</td></tr></tbody></table>

(a) Complete model corresponding to [I]; (b) Complete model without the factor indicated on the table; (c) Factor importance (1: more important to 3: less important)

<table><caption>Table 6. Means for Body Weight and Body Condition Score variables</caption><thead><tr><th>Physiological stages</th><th>Number of records</th><th>Mean (kg) (SD)</th></tr></thead><tbody><tr><td>BW-M</td><td>3382</td><td>49.58 (11.58)</td></tr><tr><td>BW-Pa</td><td>3092</td><td>52.13 (11.25)</td></tr><tr><td>BW-Pb</td><td>3076</td><td>57.27 (13.55)</td></tr><tr><td>BW-L</td><td>3137</td><td>55.09 (10.93)</td></tr><tr><td>BW-Sa</td><td>3137</td><td>54.82 (10.93)</td></tr><tr><td>BW-W</td><td>3189</td><td>54.71 (8.44)</td></tr><tr><td>BW-D</td><td>2352</td><td>54.36 (7.38)</td></tr><tr><td>BCS-M</td><td>3146</td><td>2.83 (0.24)</td></tr><tr><td>BCS-Pa</td><td>3382</td><td>2.91 (0.24)</td></tr><tr><td>BCS-Pb</td><td>3382</td><td>2.79 (0.24)</td></tr><tr><td>BCS-L</td><td>3377</td><td>2.63 (0.23)</td></tr><tr><td>BCS-Sa</td><td>3188</td><td>2.56 (0.25)</td></tr><tr><td>BCS-Sb</td><td>1200</td><td>2.63 (0.33)</td></tr><tr><td>BCS-W</td><td>2996</td><td>2.58 (0.28)</td></tr><tr><td>BCS-D</td><td>2136</td><td>2.65 (0.24)</td></tr></tbody></table>

SD: Standard Deviation; BW: Body Weight; BCS: Body Condition Score; M: Mating; Pa: Early pregnancy; Pb: Mid-pregnancy; L: Lambing; Sa: Beginning of Suckling; Sb: Middle of Suckling; W: Weaning; D: Dry-off.
The repeatability and the stability of the clusters were also tested by repeating the EM algorithm 3 to 4 times for each productive cycle. If the same ewes belonged to the same clusters, then they were validated.

Once the optimal number of clusters was found, each cluster was characterized depending on biological and environmental known effects. The best fitting general linear model to explain a cluster was analyzed with:

$$Y = age + litter\ size + year \ [I]$$

where Y is the number of cluster, age the effect of the ewe's age, litter size the effect of the litter size and year the effect of the year of measurement. The significance of the effects added in the model was estimated with AIC and BIC criteria (table 1, 2, 3, 4 and 5).

The animal repartition into clusters was analyzed according to the cycle. The number of animals present in each cluster was expressed in percentage of ewes.

## Genetic analyses

The variance components for variations in BW and BCS were estimated by restricted maximum likelihood methodology applied to an animal model using ASREML 1.0 software (Gilmour et al., 2002). All biologically relevant effects and interactions were tested (MIXED procedure of the SAS software) to find an appropriate fixed effects model. Age, parity of the ewe, litter size and year of measurement were used as fixed effects. Random effects included additive genetic and permanent environmental effects of the ewe. Analyses assumed a repeatability model with measurements across productive cycles considered to be unique traits with constant variances. Univariate analyses were done to estimate variances for each dependent variable using all data and separately for each productive cycle. Bivariate models also were used to estimate covariances between dependent variables.

The following animal mixed model was fitted:

$$Y = X\beta + Z_{a}a + Z_{c}c + e \ [II]$$

where Y is the vector of variations in BW or BCS; β is the vector of fixed effects; and a and c are vectors of random ewe additive genetic and permanent environmental effects with incidence matrices X, Za, and Zc, respectively, and e is the vector of residual effects.

From the variance components, three parameters were defined: 1) heritability, h² = σ²_a/(σ²_a + σ²_c + σ²_e); 2) fraction of permanent environmental variance, c² = σ²_c/(σ²_a + σ²_c + σ²_e); and 3) fraction of temporary environmental variance, e² = σ²_e/(σ²_a + σ²_c + σ²_e). In addition repeatability (r) was defined as the sum of h² and c².
<table><caption>Table 7. Significance probabilities for fixed effects</caption><thead><tr><th>Variables</th><th>Age</th><th>Litter size</th><th>Year</th><th>Parity</th></tr></thead><tbody><tr><td>BW-M</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>BW-Pa</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>BW-Pb</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>BW-L</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>BW-Sa</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>BW-W</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>BW-D</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>BCS-M</td><td>***</td><td>NS</td><td>***</td><td>***</td></tr><tr><td>BCS-Pa</td><td>***</td><td>NS</td><td>***</td><td>NS</td></tr><tr><td>BCS-Pb</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>BCS-L</td><td>***</td><td>***</td><td>***</td><td>NS</td></tr><tr><td>BCS-Sa</td><td>***</td><td>***</td><td>***</td><td>NS</td></tr><tr><td>BCS-Sb</td><td>NS</td><td>***</td><td>***</td><td>NS</td></tr><tr><td>BCS-W</td><td>NS</td><td>***</td><td>***</td><td>NS</td></tr><tr><td>BCS-D</td><td>***</td><td>***</td><td>***</td><td>NS</td></tr></tbody></table>

<table><caption>Table 8. Least squared means for Body Weight and Body Condition Score variables depending on parity.</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2"></th><th colspan="3">Parity</th></tr><tr><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td rowspan="2">BW-M</td><td>Number of ewes</td><td>1447</td><td>1170</td><td>765</td></tr><tr><td>Lsmeans(SEM)</td><td>45.19 (0.43)</td><td>48.92 (0.48)</td><td>55.58 (0.62)</td></tr><tr><td rowspan="2">BW-Pa</td><td>Number of ewes</td><td>1226</td><td>1101</td><td>765</td></tr><tr><td>Lsmeans(SEM)</td><td>47.48 (0.47)</td><td>51.51 (0.50)</td><td>58.19 (0.63)</td></tr><tr><td rowspan="2">BW-Pb</td><td>Number of ewes</td><td>1226</td><td>1101</td><td>749</td></tr><tr><td>Lsmeans(SEM)</td><td>49.12 (0.51)</td><td>53.87 (0.55)</td><td>61.01 (0.69)</td></tr><tr><td rowspan="2">BW-L</td><td>Number of ewes</td><td>1352</td><td>1069</td><td>716</td></tr><tr><td>Lsmeans(SEM)</td><td>49.50 (0.48)</td><td>55.45 (0.55)</td><td>61.64 (0.70)</td></tr><tr><td rowspan="2">BW-Sa</td><td>Number of ewes</td><td>1352</td><td>1069</td><td>716</td></tr><tr><td>Lsmeans(SEM)</td><td>52.14 (0.11)</td><td>58.49 (0.13)</td><td>65.23 (0.25)</td></tr><tr><td rowspan="2">BW-W</td><td>Number of ewes</td><td>1341</td><td>1118</td><td>730</td></tr><tr><td>Lsmeans(SEM)</td><td>48.50 (0.50)</td><td>54.58 (0.56)</td><td>61.09 (0.72)</td></tr><tr><td rowspan="2">BW-D</td><td>Number of ewes</td><td>1266</td><td>611</td><td>475</td></tr><tr><td>Lsmeans(SEM)</td><td>52.51 (0.09)</td><td>58.46 (0.15)</td><td>63.25 (0.25)</td></tr><tr><td rowspan="2">BCS-M</td><td>Number of ewes</td><td>1368</td><td>1083</td><td>695</td></tr><tr><td>Lsmeans(SEM)</td><td>2.90 (0.01)</td><td>2.72 (0.01)</td><td>2.10 (0.01)</td></tr><tr><td rowspan="2">BCS-Pa</td><td>Number of ewes</td><td>1448</td><td>1169</td><td>765</td></tr><tr><td>Lsmeans(SEM)</td><td>2.99 (0.01)</td><td>2.84 (0.01)</td><td>2.80 (0.01)</td></tr><tr><td rowspan="2">BCS-Pb</td><td>Number of ewes</td><td>1448</td><td>1170</td><td>764</td></tr><tr><td>Lsmeans(SEM)</td><td>2.80 (0.00)</td><td>2.74 (0.00)</td><td>2.75 (0.00)</td></tr><tr><td rowspan="2">BCS-L</td><td>Number of ewes</td><td>1444</td><td>1168</td><td>765</td></tr><tr><td>Lsmeans(SEM)</td><td>2.60 (0.01)</td><td>2.61 (0.01)</td><td>2.63 (0.01)</td></tr><tr><td rowspan="2">BCS-Sa</td><td>Number of ewes</td><td>1368</td><td>1095</td><td>725</td></tr><tr><td>Lsmeans(SEM)</td><td>2.43 (0.01)</td><td>2.45 (0.02)</td><td>2.47 (0.02)</td></tr><tr><td rowspan="2">BCS-Sb</td><td>Number of ewes</td><td>525</td><td>379</td><td>296</td></tr><tr><td>Lsmeans(SEM)</td><td>2.54 (0.00)</td><td>2.60 (0.00)</td><td>2.58 (0.00)</td></tr><tr><td rowspan="2">BCS-W</td><td>Number of ewes</td><td>1269</td><td>1070</td><td>657</td></tr><tr><td>Lsmeans(SEM)</td><td>2.50 (0.00)</td><td>2.54 (0.00)</td><td>2.57 (0.00)</td></tr><tr><td rowspan="2">BCS-D</td><td>Number of ewes</td><td>1196</td><td>560</td><td>386</td></tr><tr><td>Lsmeans(SEM)</td><td>2.65 (0.01)</td><td>2.66 (0.02)</td><td>2.63 (0.03)</td></tr></tbody></table>

Lsmeans: Least Squared Means; SEM: standard error of the mean.

<footnote>*** p<0.001; NS: Non Significant.</footnote>
# Results

## Descriptive statistics

### Mean profiles for BW and BCS

Although there were no significant effects of the physiological stage, some variations could be seen. The lower weight was measured at BW-M (49.58 ±11.58 kg) whereas the higher was found to be at BW-Pb (57.27 ±13.55 kg) (Table 6). When analyzing the whole timespan of a productive cycle (from mating to dry-off), ewes recovered weight from BW-M to BW-Pb and lost weight from BW-Sa to BW-D. The gain of BW was more important through BW-W to BW-D and through BW-W to BW-M. This gain decreased with the increase of the parity. There are very few variations of BW between lambing and weaning.

For BCS, the lower score was at BCS-Sa (2.56 ± 0.01) whereas the highest was observed at BCS-Pa (2.91 ± 0.01). Thus, ewes increased their BCS from BCS-M to BCS-Pa and from weaning to dry-off (BCS-W, BCS-D). Contrarily, ewes lost BCS in the period going from BCS-Pa to BCS-Sa and from BCS-Sb to BCS-W. The gain in BCS was more important from BCS-W to BCS-D and from BCS-W to BCS-M. The loss of BCS was mostly between BCS-Pa and BCS-W and more precisely between BCS-Pa to BCS-L.

### Factor of variations for BW and BCS

Body weight at the different physiological stages were affected by the age of the ewe, the litter size, the year the measurements were realized and by the parity of the ewe (Table 7). BCS were affected by the year at all physiological stages. The age effect was significant for all BCS variables except for BCS-Sb and BCS-W and the litter size was also significant for all BCS measurements except for BCS-M and BCS-Pa. The parity of the ewe only affected BCS at BCS-M and at BCS-Pb. The interactions between fixed effects are not significant for age and litter size and for parity and litter size.

Advancing in parity induced a significant increase in BW at all physiological stages (Table 8). For primiparous ewes, average BW is around 48 kg whereas for the multiparous ewes (3 years or more), average BW is around 61 kg. Similarly, advances in parity implied a decrease in BCS at BCS-M, BCS-Pa and BCS-Pb between cycle 1 and 2, an increase in BCS at BCS-Sa, BCS-Sb between cycle 1 and 2 and BCS-W and a stabilization at BCS-Pb between cycle 2 and 3, BCS-L, BCS-Sb between cycle 2 and 3 and BCS-D.
<figure><img src="image_8.png" /><figcaption>Figure 1. Cluster profiles for Body Weight. A. Productive cycle 1; Proportion of animals in cluster A1: 44%, A2: 23%, A3: 19%, A4: 14%. B. Productive cycle 2; Proportion of animals in cluster B1: 64%, B2: 19%, B3: 17%. C. Productive cycle 3 and more.</figcaption></figure>

* Composition of each cluster at productive cycle n is given by indicating proportion of animals from clusters found at previous cycle n-1.
Cluster analysis for BW and BCS

Cluster analysis for BW resulted in 4, 3 and 1 clusters for productive cycle 1, 2 and 3 respectively (Figure 1). Clusters A1, A2, A3 and A4 represented 44, 23, 19 and 14 % of ewes, respectively. Clusters A2 and A4 showed similar profile during cycle 1 and only differed in the magnitude of the BW value at each point (i.e. A4 consistently heavier than A2). A similar "parallelism" was observed when comparing clusters A1 and A3. Clusters A2 and A4 showed that these females gained weight until BW-Pb (55 kg and 65 kg respectively) and then lost weight until BW-W (49 kg and 57 kg respectively). Then, ewes gained weight until BW-D. Clusters A1 and A3 showed that ewes gained weight between BW-M and BW-L (+5 kg and + 8 kg respectively) and the weight stabilized until the end of cycle 1.

Clusters B1, B2 and B3 represented 64 %, 19 % and 17 % of ewes respectively. Clusters B1 and B2 showed paralleled profiles during cycle 2 just differing in the BW values (i.e. profile B2 higher than B1). Cluster B3 was stable throughout the whole productive cycle (around 54 kg). Clusters B1 and B2 showed that ewes gained weight from BW-M to the BW-Pb (+5 kg and + 6 kg respectively), lost weight until BW-L, gained weight or stabilized it from BW-L to BW-W and then lost weight until the end of cycle 2. The ewes included in the B1 cluster came from A1 (51 %), A2 (31 %), A3 (13 %) and A4 (5 %). The majority of the ewes included in B2 cluster (68 %) corresponded to those appearing in the A1 cluster and then 15 % came from the A4, 12 % from the A2 and 5 % from the A3. Principally, the ewes included in cluster B3 came mainly from A1 (44 %) and A4 (34 %); the other 22 % came from A2 and A3.

Ewes representing the cycle 3 showed the same profile, i.e. they gained BW from BW-M to BW-L, lost weight until BW-Sa (-5 kg) and then lost around 4 kg from the BW-Sa to the end of cycle 3.

Clusters analysis for BCS resulted in 3 clusters for productive cycle 1, 2 and 3 (Figure 2). Clusters A1, A2 and A3 represented 64, 25 and 11 % of ewes, respectively. Clusters A1 and A2 showed paralleled profiles during cycle 1, just differing in the level of BCS (A1 > A2). Cluster A1 showed that ewes had a better BCS than ewes in cluster A2 (+0.2). Clusters A1 and A2 showed that ewes gained BCS until BCS-Pa, lost BCS until BCS-L and then the BCS stabilized until the end of cycle 1. Cluster A3 showed ewes that gained BCS from BCS-M to BCS-Pa, then lost it until BCS-L (-0.2), gained BCS from BCS-L to BCS-Sb (+0.3) and then lost BCS around weaning and after stabilized until the end of cycle 1 (-0.1).
<figure><img src="image_9.png" /><figcaption>Figure 2. Cluster profiles for Body Condition Score. A. Productive cycle 1; Proportion of animals in cluster A1: 64%, A2: 25%, A3: 11%. B. Productive cycle 2; Proportion of animals in cluster B1: 50%, B2: 43%, B3: 6%. C. Productive cycle 3 and more; Proportion of animals in cluster C1: 74%, C2: 14%, C3: 13%.</figcaption></figure>

* Composition of each cluster at productive cycle *n* is given by indicating proportion of animals from clusters found at previous cycle *n-1*.
Clusters B1, B2 and B3 represented 50 %, 43 % and 6 % of ewes respectively. Clusters B1 and B2 showed paralleled profiles during cycle 2, just differing in the level of BCS. Cluster B2 showed that ewes had a better BCS than ewes in cluster B1 (+0.3). Clusters B1 and B2 showed that ewes gained BCS until BCS-Pa, lost BCS until BCS-Sa (-0.2), stabilized until BCS-W and then recovered BCS until the end of cycle 2 (+0.1). Cluster B3 showed ewes that gained body condition from BCS-M to BCS-Pa, stabilized it until BCS-Sa, gained body condition until BCS-Sb (+0.1), lost BCS from BCS-Sb to BCS-W (-0.1) and then gained body condition until the end of cycle 2.

Ewes in cluster B1 came from cluster A1 in majority (61 %), from cluster A2 with 37 % and then from A3 with just 2 %. Ewes included in B2 also came from A1 in majority (73 %) and then from A2 and A3 with 27 %. Ewes in cluster B3 were mainly from cluster A1 (66 %), cluster A3 (27 %) and then from cluster A2 (7 %).

Clusters C1, C2 and C3 represented 74 %, 14 % and 13 % of ewes respectively. Clusters C1 and C3 showed paralleled profiles during cycle 3 and just differed in the level of BCS. Cluster C1 showed that ewes had a better BCS than ewes in cluster C3 (+0.2). Clusters C1 and C3 showed that ewes gained BCS until BCS-Pa, lost BCS until BCS-Sb (-0.2) and then gained it until the end of cycle 3 (+0.1). Cluster C2 showed ewes that had a stabilized BCS from BCS-M to BCS-L, lost BCS from BCS-L to BCS-Sa, gained body condition until BCS-Sb (+0.2) and then lost body condition until the end of cycle 3.

Ewes in cluster C1 were in clusters B1 and B2 in cycle 2 (52 and 45 % respectively) and only 3 % were from cluster B3. Ewes associated with cluster C2 mainly came from B1 (90 %) and the rest of them were from B2. Ewes included in cluster C3 came basically from cluster B2 (60 %), cluster B1 (22 %) and finally from cluster B3 (18 %).

Overall, clusters in cycle 1, cycle 2 and cycle 3 showed paralleled profiles.

In order to characterize each cluster, a generalized linear model was used [1]. Effects of the age of the ewe, the litter size, the year of the measurements and parity of the ewe were proven to be significant (Tables 1, 2, 3, 4, 5). They can explain the repartition in clusters. The most significant effect for BW in cycle 1 is the age of the ewe, followed by the litter size (Table 1). The year of the measurements was the least significant effect. The effect that is the most significant for BW in cycle 2 and BCS for cycles 1, 2 and 3 was the litter size, followed by the age of the ewe (Tables 2, 3, 4, 5). The year of measurements was the least significant effect.
<table><caption>Table 9. Estimates (± standard errors) of variance components for BW and BCS variations.</caption><thead><tr><th>Variables</th><th>h<sup>2</sup></th><th>c<sup>2</sup></th><th>e<sup>2</sup></th><th>r</th><th>σ<sup>2</sup><sub>p</sub></th></tr></thead><tbody><tr><td>BW-M:Pb</td><td>0.19 (0.02)</td><td>0.00 (0.00)</td><td>0.81 (0.02)</td><td>0.19 (0.02)</td><td>10.45 (0.28)</td></tr><tr><td>BW-Pb:W</td><td>0.13 (0.03)</td><td>0.02 (0.02)</td><td>0.85 (0.02)</td><td>0.15 (0.02)</td><td>17.64 (0.46)</td></tr><tr><td>BW-Pb:L</td><td>0.18 (0.02)</td><td>0.00 (0.00)</td><td>0.82 (0.02)</td><td>0.18 (0.02)</td><td>17.45 (0.48)</td></tr><tr><td>BW-L:Sa</td><td>0.11 (0.02)</td><td>0.02 (0.02)</td><td>0.89 (0.02)</td><td>0.13 (0.02)</td><td>13.76 (0.35)</td></tr><tr><td>BW-W:D</td><td>0.11 (0.02)</td><td>0.00 (0.00)</td><td>0.89 (0.02)</td><td>0.11 (0.02)</td><td>8.62 (0.24)</td></tr><tr><td>BW-W:M</td><td>0.16 (0.03)</td><td>0.00 (0.00)</td><td>0.84 (0.03)</td><td>0.16 (0.03)</td><td>10.42 (0.39)</td></tr><tr><td>BCS-M:Pa</td><td>0.06 (0.01)</td><td>0.00 (0.00)</td><td>0.94 (0.01)</td><td>0.06 (0.01)</td><td>0.04 (0.001)</td></tr><tr><td>BCS-Pa:W</td><td>0.14 (0.02)</td><td>0.00 (0.00)</td><td>0.86 (0.02)</td><td>0.14 (0.02)</td><td>0.06 (0.001)</td></tr><tr><td>BCS-Pa:L</td><td>0.09 (0.02)</td><td>0.00 (0.00)</td><td>0.91 (0.02)</td><td>0.09 (0.02)</td><td>0.06 (0.001)</td></tr><tr><td>BCS-L:Sa</td><td>0.07 (0.02)</td><td>0.00 (0.00)</td><td>0.93 (0.02)</td><td>0.07 (0.02)</td><td>0.04 (0.001)</td></tr><tr><td>BCS-W:D</td><td>0.15 (0.02)</td><td>0.00 (0.00)</td><td>0.85 (0.02)</td><td>0.15 (0.02)</td><td>0.04 (0.001)</td></tr><tr><td>BCS-W:M</td><td>0.11 (0.04)</td><td>0.03 (0.06)</td><td>0.86 (0.05)</td><td>0.14 (0.05)</td><td>0.04 (0.002)</td></tr></tbody></table>

h²: heritability; c²: proportion of total phenotypic variance due to ewe permanent environmental effect;

e²: proportion of total phenotypic variance due to temporary environmental effects; r: repeatability;

σ²p: total phenotypic variance.

<table><caption>Table 10. Phenotypic and genetic correlations (± standard errors) for BW variations.</caption><thead><tr><th>Variables</th><th>BW-M:Pb</th><th>BW-Pb:W</th><th>BW-Pb:L</th><th>BW-L:Sa</th><th>BW-W:D</th><th>BW-W:M</th></tr></thead><tbody><tr><td>BW-M:Pb</td><td>-</td><td>NS</td><td>NS</td><td>NS</td><td>-0.27(0.12)</td><td>NS</td></tr><tr><td>BW-Pb:W</td><td>-0.46(0.02)</td><td>-</td><td>NS</td><td>0.44 (0.11)</td><td>-0.59 (0.10)</td><td>-0.54 (0.14)</td></tr><tr><td>BW-Pb:L</td><td>-0.40(0.02)</td><td>0.44 (0.02)</td><td>-</td><td>-0.47 (0.09)</td><td>NS</td><td>NS</td></tr><tr><td>BW-L:Sa</td><td>NS</td><td>0.16 (0.02)</td><td>-0.36 (0.02)</td><td>-</td><td>-0.48 (0.13)</td><td>NS</td></tr><tr><td>BW-W:D</td><td>-0.06(0.02)</td><td>-0.53 (0.01)</td><td>NS</td><td>-0.11 (0.02)</td><td>-</td><td>0.87 (0.06)</td></tr><tr><td>BW-W:M</td><td>NS</td><td>-0.43(0.02)</td><td>NS</td><td>-0.10(0.03)</td><td>0.70(0.01)</td><td>-</td></tr></tbody></table>

Phenotypic correlations below the diagonal; genetic correlations above the diagonal; NS: Non Significant.

<table><caption>Table 11. Phenotypic and genetic correlations (± standard errors) for BCS variations.</caption><thead><tr><th>Variables</th><th>BCS-M:Pa</th><th>BCS-Pa:W</th><th>BCS-Pa:L</th><th>BCS-L:Sa</th><th>BCS-W:D</th><th>BCS-W:M</th></tr></thead><tbody><tr><td>BCS-M:Pa</td><td>-</td><td>-0.44 (0.14)</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>BCS-Pa:W</td><td>-0.41(0.02)</td><td>-</td><td>0.48 (0.09)</td><td>0.49 (0.12)</td><td>-0.71 (0.08)</td><td>-0.73(0.14)</td></tr><tr><td>BCS-Pa:L</td><td>-0.42(0.01)</td><td>0.59 (0.01)</td><td>-</td><td>NS</td><td>-0.46 (0.12)</td><td>-0.48(0.21)</td></tr><tr><td>BCS-L:Sa</td><td>NS</td><td>0.17 (0.02)</td><td>-0.31 (0.02)</td><td>-</td><td>-0.45 (0.14)</td><td>-0.52(0.18)</td></tr><tr><td>BCS-W:D</td><td>NS</td><td>-0.49 (0.02)</td><td>-0.09 (0.02)</td><td>-0.08 (0.02)</td><td>-</td><td>0.75(0.10)</td></tr><tr><td>BCS-W:M</td><td>NS</td><td>-0.46(0.02)</td><td>-0.14(0.03)</td><td>-0.07(0.03)</td><td>0.59(0.02)</td><td>-</td></tr></tbody></table>

Phenotypic correlations below the diagonal; genetic correlations above the diagonal; NS: Non Significant.
## Genetic analysis for BW and BCS variations

### Variance components

Estimates of variance components for BW and BCS are presented in Table 9. Direct heritabilities for BW variations ranged between 0.11 ± 0.02 and 0.19 ± 0.02. Highest heritabilities were found for BW-M:Pb and BW-Pb:L (0.19 +/- 0.02 and 0.18 ± 0.02, respectively) and lowest heritabilities were found for BW-L:Sa and BW-W:D (0.11 ± 0.02 and 0.11 ± 0.02, respectively). Medium heritabilities values were found for BW-Pb:W and BW-W:M (0.13 ± 0.03 and 0.16 ± 0.03 respectively). Repeatability estimates were 0.19, 0.18, 0.11, 0.11, 0.15 and 0.16 BW-M:Pb, BW-Pb:L, BW-L:Sa, BW-W:D, BW-Pb:W, BW-W:M, respectively. Estimates for the fraction of permanent environmental variance were null except for BW-Pb:W and BW-L:Sa with 0.02. Estimates for the fraction due to temporary environmental effect were high and ranged from 0.81 ± 0.02 to 0.89 ± 0.02. The phenotypic variance were high for BW-Pb:W, BW-Pb:L and BW-L:Sa and lower for BW-M:Pb, BW-W:D and BW-W:M.

Direct heritabilities for BCS variations ranged between 0.06 ± 0.01 and 0.15± 0.02. Highest heritabilities were found for BCS-W:D and BCS-Pa:W (0.15 ± 0.02 and 0.14 ± 0.02, respectively). The lowest heritabilites were found for BCS-M:Pa and BS-L:Sa (0.06 ± 0.01 and 0.07 ± 0.01 respectively). Medium heritabilities were found for BCS-Pa:L and BCS-W:M. Repeatability estimates were 0.15, 0.14, 0.06, 0.07, 0.09 and 0.14 respectively. Estimates for the fraction of permanent environmental variance were null except for BCS-W:M with 0.03. Estimates for the fraction due to temporary environmental effect of the ewe were high and ranged from 0.85 ± 0.02 to 0.94 ± 0.01. The phenotypic variance were high for BCS-Pa:W and BCS-Pa:L and lower for BCS-M:Pa, BCS-L:Sa, BCS-W:D and BCS-W:M.

## Genetic and phenotypic correlations for BW

High negative genetic correlations were found between BW-Pb:W and BW-W:D, BW-W:M (Table 10). Moderate negative genetic correlations were found between BW-Pb:L and BW-L:Sa (-0.47 ± 0.09) and between BW-L:Sa and BW-W:D (-0.48 ± 0.13). Low genetic correlation was found between BW-M:Pb and BW-W:D. Moderate positive genetic correlations was found between BW-Pb:W and BW-L:Sa. High positive genetic correlation was found between BW-W:D and BW-W:M.

Phenotypic correlations followed the same variations than genetic correlations but were lower. The highest positive phenotypic correlation was found between BW-W:M and BW-W:D and the highest negative phenotypic correlation was found between BW-W:D and BW-Pb:W.
<table><caption>Table 12. Genetic correlations (± standard errors) between BW and BCS variations</caption><thead><tr><th>Variables</th><th>BW-M:Pb</th><th>BW-Pb:W</th><th>BW-Pb:L</th><th>BW-L:Sa</th><th>BW-W:D</th><th>BW-W:M</th></tr></thead><tbody><tr><td>BCS-M:Pa</td><td>0.45 (0.14)</td><td>-0.33 (0.17)</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>BCS-Pa:W</td><td>NS</td><td>0.45 (0.09)</td><td>NS</td><td>0.34 (0.11)</td><td>-0.60 (0.11)</td><td>-0.44 (0.15)</td></tr><tr><td>BCS-Pa:L</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>-0.28 (0.14)</td><td>NS</td></tr><tr><td>BCS-L:Sa</td><td>NS</td><td>0.23 (0.14)</td><td>NS</td><td>NS</td><td>-0.66 (0.13)</td><td>NS</td></tr><tr><td>BCS-W:D</td><td>-0.26 (0.13)</td><td>-0.46 (0.11)</td><td>-0.21 (0.12)</td><td>-0.32 (0.13)</td><td>0.57 (0.13)</td><td>0.56 (0.15)</td></tr><tr><td>BCS-W:M</td><td>-0.49 (0.21)</td><td>NS</td><td>NS</td><td>-0.43 (0.21)</td><td>0.62 (0.16)</td><td>0.67 (0.15)</td></tr></tbody></table>

NS: Non Significant

<table><caption>Table 13. Phenotypic correlations (± standard errors) between BW and BCS variations</caption><thead><tr><th>Variables</th><th>BW-M:Pb</th><th>BW-Pb:W</th><th>BW-Pb:L</th><th>BW-L:Sa</th><th>BW-W:D</th><th>BW-W:M</th></tr></thead><tbody><tr><td>BCS-M:Pa</td><td>0.13 (0.02)</td><td>-0.05 (0.02)</td><td>NS</td><td>NS</td><td>0.07 (0.02)</td><td>NS</td></tr><tr><td>BCS-Pa:W</td><td>-0.04 (0.02)</td><td>0.43 (0.02)</td><td>0.17 (0.02)</td><td>0.08 (0.02)</td><td>-0.22 (0.02)</td><td>-0.16 (0.03)</td></tr><tr><td>BCS-Pa:L</td><td>-0.05 (0.02)</td><td>0.26 (0.02)</td><td>0.33 (0.02)</td><td>NS</td><td>-0.11 (0.02)</td><td>-0.12 (0.03)</td></tr><tr><td>BCS-L:Sa</td><td>NS</td><td>0.12 (0.02)</td><td>-0.05 (0.02)</td><td>0.14 (0.01)</td><td>-0.09 (0.02)</td><td>NS</td></tr><tr><td>BCS-W:D</td><td>-0.04 (0.02)</td><td>-0.17 (0.02)</td><td>-0.06 (0.02)</td><td>-0.07 (0.02)</td><td>0.33 (0.02)</td><td>0.16 (0.03)</td></tr><tr><td>BCS-W:M</td><td>NS</td><td>-0.18 (0.03)</td><td>-0.10 (0.03)</td><td>NS</td><td>0.22 (0.03)</td><td>0.26 (0.03)</td></tr></tbody></table>

NS: Non Significant.

<table><caption>Table 14. Genetic correlations (± standard errors) between variations in BW or BCS and weight of the litter or average weight of the lamb at birth and at weaning</caption><thead><tr><th>Variables</th><th>litterB</th><th>lambB</th><th>litterW</th><th>lambW</th></tr></thead><tbody><tr><td>BW-M:Pb</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>BW-Pb:W</td><td>-0.27 (0.11)</td><td>-0.22 (0.11)</td><td>NS</td><td>NS</td></tr><tr><td>BW-Pb:L</td><td>NS</td><td>NS</td><td>NS</td><td>0.37 (0.12)</td></tr><tr><td>BW-L:Sa</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>BW-W:D</td><td>NS</td><td>NS</td><td>0.28 (0.14)</td><td>NS</td></tr><tr><td>BW-W:M</td><td>0.41 (0.14)</td><td>0.41 (0.13)</td><td>NS</td><td>0.28 (0.14)</td></tr><tr><td>BCS-M:Pa</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>BCS-Pa:W</td><td>NS</td><td>NS</td><td>-0.31 (0.14)</td><td>NS</td></tr><tr><td>BCS-Pa:L</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>BCS-L:Sa</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>BCS-W:D</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>BCS-W:M</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr></tbody></table>

litterB: weight of the litter at birth; lambB: average weight of the lamb at birth; litterW: weight of the litter at weaning; lambW: average weight of the lamb at weaning; NS: Non Significant.
## Genetic and phenotypic correlations for BCS

High negative genetic correlations were found between BCS-Pa:W and BCS-W:D, BCS-W:M (Table 11). Moderate negative genetic correlations were found between BCS-M:Pa and BCS-Pa:W, between BCS-Pa:L and BCS-W:D, BCS-W:M and between BCS-L:Sa and BCS-W:D, BCS-W:M. Moderate positive genetic correlations were found between BCS-Pa:W and BCS-Pa:L, BCS-L:Sa. High positive genetic correlation was found between BCS-W:D and BCS-W:M.

Phenotypic correlations followed the same variations than genetic correlations but were lower. The highest positive phenotypic correlation values were found between BCS-Pa:L and BCS-Pa:W and between BCS-W:M and BCS-W:D. The highest negative phenotypic correlation was found between BCS-W:D and BCS-Pa:W.

## Genetic and phenotypic correlations between BW and BCS variations

High positive genetic correlations were found between BW-W:M and BCS-W:M and between BW-W:D and BCS-W:D (Table 12). Moderate positive genetic correlations were found between BW-M:Pb and BCS-M:Pa and between BW-Pb:W and BCS-Pa:W. No significant genetic correlations were found between BW-Pb:L and BCS-Pa:L and between BW-L:Sa and BCS-L:Sa. High negative genetic correlations were found between BW-W:D and BCS-Pa:W, BCS-L:Sa. Moderate negative genetic correlations were found between BW-M:Pb and BCS-W:M, between BW-Pb:W and BCS-W:D, between BW-L:Sa and BCS-W:M and between BW-W:M and BCS-Pa:W. Low negative genetic correlations were found between BW-M:Pb and BCS-W:D, between BW-Pb:W and BCS-M:Pa, between BW-Pb:L and BCS-W:D, between BW-L:Sa and BCS-W:D and between BW-W:D and BCS-Pa:L. Low positive genetic correlations were found between BW-Pb:W and BCS-L:Sa and between BW-L:Sa and BCS-Pa:W. High positive genetic correlations were found between BW-W:D and BCS-W:M and between BW-W:M and BCS-W:D.

Phenotypic correlations followed the same variations than genetic correlations but were lower (Table 13). The highest positive phenotypic correlation was found between BCS-W:M and BW-W:M. The highest negative phenotypic correlation was found between BCS-L:Sa and BW-W:D.

## Genetic and phenotypic correlations between BW or BCS variations and litter parameters

Low negative genetic correlations were found between BW-Pb:W and litterB, lambB and between BCS-Pa:W and litterW (Table 14). Low positive genetic correlations were found between BW-W:D and litterW and between BW-W:M and lambW. Moderate positive genetic correlations were found between BW-Pb:L and lambW, between BW-W:M and litterB, lambB.
<table><caption>Table 15. Phenotypic correlations (± standard errors) between variations in BW or BCS and weight of the litter or average weight of the lamb at birth and at weaning</caption><thead><tr><th>Variables</th><th>litterB</th><th>lambB</th><th>litterW</th><th>lambW</th></tr></thead><tbody><tr><td>BW-M:Pb</td><td>0.11 (0.02)</td><td>NS</td><td>0.10 (0.02)</td><td>0.14 (0.02)</td></tr><tr><td>BW-Pb:W</td><td>-0.17 (0.02)</td><td>-0.12 (0.02)</td><td>-0.15 (0.02)</td><td>-0.11 (0.02)</td></tr><tr><td>BW-Pb:L</td><td>NS</td><td>0.09 (0.02)</td><td>0.04 (0.02)</td><td>0.06 (0.02)</td></tr><tr><td>BW-L:Sa</td><td>-0.06 (0.02)</td><td>-0.07 (0.02)</td><td>-0.12 (0.02)</td><td>-0.12 (0.02)</td></tr><tr><td>BW-W:D</td><td>NS</td><td>-0.06 (0.02)</td><td>0.09 (0.02)</td><td>0.08 (0.02)</td></tr><tr><td>BW-W:M</td><td>0.09 (0.03)</td><td>0.09 (0.03)</td><td>0.10 (0.03)</td><td>0.09 (0.03)</td></tr><tr><td>BCS-M:Pa</td><td>NS</td><td>0.04 (0.02)</td><td>0.04 (0.02)</td><td>NS</td></tr><tr><td>BCS-Pa:W</td><td>-0.10 (0.02)</td><td>-0.05 (0.02)</td><td>-0.11 (0.02)</td><td>-0.11 (0.02)</td></tr><tr><td>BCS-Pa:L</td><td>-0.08 (0.02)</td><td>0.10 (0.02)</td><td>NS</td><td>NS</td></tr><tr><td>BCS-L:Sa</td><td>-0.08 (0.02)</td><td>-0.13 (0.02)</td><td>-0.09 (0.02)</td><td>-0.07 (0.02)</td></tr><tr><td>BCS-W:D</td><td>NS</td><td>NS</td><td>0.09 (0.02)</td><td>0.08 (0.03)</td></tr><tr><td>BCS-W:M</td><td>0.08 (0.03)</td><td>NS</td><td>0.09 (0.03)</td><td>0.08 (0.03)</td></tr></tbody></table>

litterB: weight of the litter at birth; lambB: average weight of the lamb at birth; litterW: weight of the litter at weaning; lambW: average weight of the lamb at weaning; NS: Non Significant.
Phenotypic correlations followed the same variations than genetic correlations but were lower (Table 15). The phenotypic correlations were low with a maximum at -0.17 ± 0.02 between BW-Pb:W and litterB.

## Discussion

Very few studies are available in the literature regarding genetic determinism of body reserves dynamics (mobilization/accretion). However, body reserves dynamics is now considered by the livestock industry as an important mechanism for improving the robustness and resilience of farm animals to different rearing conditions, especially when livestock, particularly sheep, can be produced in a wide range of farming environments and has to cope with high fluctuations in feed resources. The aims of the present study were therefore i) to characterize body reserves variations during production by cluster analysis, ii) to investigate genetic variability of body reserves variations in sheep. We focused on body condition score and body weight measured in ewes reared in extensive conditions.

### BW and BCS

Averages of BW and BCS considered separately at the different physiological stages of production cycle indicated that body reserves mobilization occurred during mid-pregnancy and the beginning of suckling, as indicated by a decrease in BCS and BW. During the first month of suckling, there was a loss of body condition despite the use of fertilized parks ensuring availability of grass (Molénat et al., 2005). These body conditions losses were probably due to higher energy requirements than energy available during this specific period of suckling. The rest of the suckling period was characterized by stabilization in BCS that could be related to a decrease in the energetic needs of the ewe for suckling in relation with the feed diversification of the lambs. The state recovery started at weaning (BCS) and last until the beginning of the next pregnancy. This increase in body reserves was linked to the low energetic needs of the ewe and began as of July whereas this season was the dry season. Body reserves accretion continued in autumn through grazing of new growth again available in native paddocks and lasted until the beginning of the next pregnancy, despite the winter, through the complementation in feed that started at mid-pregnancy (see overall feeding system schedule in González-García et al., 2014). These results are in agreement with a previous report regarding the variations in BW and BCS during a productive cycle in similar environmental conditions but on a small number of ewes (González-García et al. 2014). Increase in body weight was concomitant to increase in BCS at early pregnancy but while BW continued to increase during pregnancy, BCS decreased at mid-pregnancy, as described by González-García et al. (2014). Romane ewes gained weight between mating and mid-pregnancy and then lost weight until weaning of lambs.

Significant biological and environmental effects were found for BCS and BW. Multiparous ewes were heavier compared to primiparous ewes in agreement with results found by González-García et al. (2014) in Romane ewes.
<figure><img src="image_10.png" /></figure>
Interestingly, multiparous ewes showed lower BCS during body reserves accretion and slightly higher BCS during body reserves mobilization period, suggesting the range of BCS variation was lower in multiparous than primiparous ewes. In addition, ewes having singletons exhibited higher BW and BCS than ewes having twins or triplets suggesting lower energy requirements for small litter size. The parity, age and litter size of ewes played an important role in the level of BW and BCS.

## Cluster analysis

In order to characterize longitudinally and without any *a priori* on source of variation, all profiles describing variability in BW and BCS changes, a cluster analysis was performed at the scale of the productive cycle.

During the productive cycle 1, the four clusters for BW varied according to the level of BW and the described profiles. The major cluster (cluster A1) was characterized by the lowest weights throughout the productive cycle and an increase in BW during pregnancy and quite stability in BW after lambing. This cluster corresponded to primiparous ewes performing first mating regime at 7 months old and a small litter size (less than 1.5 lambs per ewe). A second cluster (cluster A3) showing a similar profile but characterized by higher weights corresponded to primiparous ewes performing first mating at 18 months of age with 2 or more lambs.

A third profile (cluster A4) showing the higher weights during pregnancy and a decrease in BW until weaning corresponded to primiparous ewes performing first mating at 7 or 18 months of age with a small litter size (1.5 lambs per ewe). The last profile (cluster A2), showing smallest changes in BW, corresponded to primiparous ewes performing first mating at 18 months of age with a litter size of 2.2 lambs per ewe. Thus, age effect influenced the tendency of the shape during cycle 1. Additionally to the age effect, litter size effect explained difference in level between clusters.

In productive cycle 2, the most important effect that explained the difference in level between clusters for BW was the litter size. Ewes included in cluster showing higher weight (cluster B2) mostly had 1 to 2 lambs whereas ewes included in the others clusters had a higher prolificacy (more than 2 lambs). Differences in tendency of the shape between the two clusters of cycle 2 showing lower BW compared to the third cluster were due to the age effect. Indeed, ewes that were still gaining weight during the cycle 2 (cluster B3) were 2 years old, starting their first mating at 7 months, and the ewes in the last profile (cluster B1) were the 3 years old ewes. Surprisingly, heavier ewes showed lower litter size which is the inverse of what was reported by Michels *et al.* (2000) with the positive genetic correlation found between BW and prolificacy in Merino ewes. Only one cluster was found in productive cycle 3 and changes characterizing profile of this cluster were similar to those of the major cluster found in productive cycle 2 but level of weight was higher.
<figure><img src="image_11.png" /></figure>
The individual repartition of ewes in clusters varied between productive cycles. Indeed, ewes that were included in the same cluster in cycle 1 weren't included together in the same cluster in cycle 2 but distributed in different clusters. This may be due to the fact that the most important effects that modeled the clusters were the litter size and the age. For the three clusters found for BCS changes, they were very similar across the three productive cycles but the internal population varied. It appeared that ewes with singletons were mostly included in the minority cluster that presented an increase during suckling for cycle 1 and 2 (clusters A3 and B3). The two others clusters for cycle 1 and 2, had both a similar profile characterized by decrease in BCS during part of the pregnancy and suckling but differed with the level of BCS. The cluster profile showing the lowest BCS in cycle 1 and 2 included ewes with the highest litter size (clusters A2 and B1). Surprisingly, in cycle 3, the cluster with the highest and most stabilized level of BCS (cluster C2) was composed of ewes having more than 3 lambs while ewes having in average smallest litter size were included in the cluster with the lowest BCS (cluster C3). The last and major cluster (cluster C1) of cycle 3 was represented by ewes with intermediate litter size of 3 lambs in average.

## Genetic parameters

The variations of BW and BCS for the genetic study were chosen according to key physiological stages that were determined with the clusters. Three variations were chosen during a BR accretion phase (i.e. between mating and mid-pregnancy, between weaning and dry-off and between weaning and mating) and three variations were chosen representing a phase with BR mobilization (i.e. between mid-pregnancy and lambing, between mid-pregnancy and weaning and between lambing and suckling).

Genetic parameters have been estimated for BW or BCS changes between key physiological stages for BR mobilization or accretion, as described in cluster analysis. Estimated heritabilities for BW and BCS variations were low to medium (from 0.06 to 0.19). Estimates for such traits have not been described previously. The highest heritability was obtained for the BW increase between mating and mid-pregnancy.

The heritabilities for BW variations were lower than those estimated for BW that ranged between 0.44 and 0.58 (Supplemental data 1) even if BW traits were highly correlated (i.e. close to 1, supplemental data 2) whatever the physiological stages. Heritability estimates for BW were consistent with previous studies investigating genetic variability for BW in sheep (Safari *et al.*, 2005; Gizaw *et al.*, 2007). Similarly, the heritabilities found for BCS variations were lower than those found for BCS ranging between 0.26 and 0.37 (Supplemental data 1) even if BCS traits were highly correlated (i.e. close to 0.9, Supplemental data 3). Heritabilities for BCS found in our study were in agreement with previous studies investigating genetic parameters for BCS in cows and sheep. Reported estimates ranged from 0.10 to 0.45 (Koenen *et al.*, 2001; Mao *et al.*, 2004; Borg *et al.*, 2005; Shackell *et al.*, 2011). Borg *et al.* (2005) estimated lower heritabilities that ranged from 0.10 to 0.16 in Targhee ewes during late pregnancy to post-weaning period.
<figure><img src="image_12.png" /></figure>
Heritabilities found in the present study for BW and BCS variations suggested that the biological capacities determining the nature of BR accretion and mobilization processes are heritable and could be transmitted across generations.

The proportion of total phenotypic variance for BCS and BW variations due to ewe permanent environmental effect was very low and close to zero. Therefore, the repeatability values for BCS and BW variations were close to the heritability. This result contrasted with the higher proportion of total phenotypic variance due to the ewe permanent environmental effect for BCS and BW traits (Supplemental data 1).

## Genetic and phenotypic correlations

Up to date, correlations between BW and BCS have not been extensively studied and not at all correlations between these traits throughout several physiological stages. Phenotypic correlations were at the same level for BW or BCS variations: -0.46 ± 0.02 or -0.41 ± 0.02 for the correlations between mid-pregnancy and weaning and mating and mid-pregnancy variations for BW and BCS, respectively.

High positive genetic correlation for BW and BCS variations between the beginning of dry-off period and the whole dry-off period suggested that the anabolic BR accretion processes was predominant during this period. Surprisingly, we did not find any genetic correlation for BW and BCS variations between the dry-off period and early pregnancy whereas increases in BW and BCS indicated that BR accretion continued at early pregnancy. High negative genetic correlations found for BCS and BW variations between the beginning of dry-off period and the period including late pregnancy and suckling suggested that mechanism implied either in BR mobilization and/or accretion processes were genetically related. The large BR mobilization period from pregnancy to weaning was correlated to shorter periods (i.e. from pregnancy to lambing; from lambing to the beginning of suckling period) in the same mobilization period, either considering BW or BCS variations. Surprisingly, the variations between early pregnancy to lambing and lambing to beginning of suckling was not correlated for BCS variations and negatively correlated for BW variations whereas those variations follow the same direction whatever the cluster considered.

High positive genetic correlations were found between BCS and BW variations at the same physiological period. Such correlations were similar to those found by Berry *et al.* (2003) for the comparison between BCS and BW at the same days in milk in dairy sheep. This association between BW and BCS variations suggested that changes in body condition score and body weight were concomitant and genetically linked. Surprisingly no correlation was found between BW and BCS variations at mid-pregnancy whereas most of clusters indicated that both BW and BCS decreased during this period. Nevertheless, a few clusters showing increase in BW or little variation in BCS at mid-pregnancy could decrease correlation between BW and BCS. Similarly, no genetic correlations were found between BCS and BW at the beginning of suckling. This could be explained by very little variations in BW in most of the clusters.
<figure><img src="image_13.png" /></figure>
Weight recovery and body condition recovery were strongly and positively correlated whatever the considered interval during the dry-off period. This could be explained by the recovery observed during this period in both BW and BCS profiles whatever the productive cycle. Body weight recovery during dry-off period was negatively correlated to the loss of body condition in late pregnancy, in beginning of suckling and to all mobilization period. The highest negative correlations were found in the beginning of dry-off period whereas few BW variations were observed at that moment. Similarly, whatever the considered interval during the dry-off period, body condition recovery was negatively correlated to weight decrease during mid-pregnancy and suckling. However, body condition recovery was negatively correlated to the gain of weight in early pregnancy. This may be surprising because both BW and BCS profiles showed similar increase. BW and BCS are two traits that were genetically linked between the accretion phase and the mobilization phase of body reserves. This negative relationship was still found even if the cluster profiles for BW did not show similar changes (during mid-pregnancy and suckling period).

We found a positive genetic relationship between increase in BW during dry-off period and weight of lambs at birth or at weaning. This is in agreement with moderate and positive correlation between birth weight of lambs and ewe weight at post-weaning described by Borg et al. (2009). However, any relationship was found in the present study between increase in BCS of ewes and lamb weight, whatever the period of body reserves accretion considered, while Borg et al. (2009) described genetic relationship between BCS and lamb weight. Absence of significant correlation between BCS of ewes and lamb weight could be due in our case to a small population used compared to the large population used by Borg et al. (2009) (12 154 lambs from 2 930 dams and 200 rams). Another hypothesis may be that in our study, the variability of BCS variations was not important enough to obtain a significant effect on lambs. These moderate correlations could be used in further selections to obtain heavier lambs with a better care of the ewe feed during the weaning-mating stage.

Conclusion

Body condition score and BW changes were analyzed during productive cycles at different physiological stages (mating, pregnancy, lambing, suckling, weaning and dry-off) in order to characterize the different patterns of BR variations (mobilization/accretion) and to analyze the genetic variability of BR variations in meat sheep. Major clusters found for BCS variations were mainly characterized by BR accretion during dry-off and early pregnancy and BR mobilization during late pregnancy and suckling. These clusters were mainly influenced by litter size and age effects. Moderate heritabilities were found for both accretion and mobilization processes indicating both mechanisms can be transmitted. Accretion and mobilization processes were highly genetically correlated, suggesting common genetic determinism. These results show a possibility in selecting animals genetically for BR accretion/mobilization to improve robustness. These results also allow another view of the accretion and mobilization dynamics in regard with parity and physiological stages that are
<figure><img src="image_14.png" /></figure>
usually neglected. It highlights the importance of an adequate maternal nutrition during post-weaning period until mating that could influence the future weight of the offspring.

## Acknowledgments

I would like to thank INRA and GenPhySe in particular for welcoming me during this internship opportunity. I would also like to thank Dominique Hazard, who supervised my work, for the time he awarded to me, his advices and his help during the internship and the writing of this report.

I also thank Eliel González-García for his advices during the internship and the writing of this report.

My thanks also go to all the members of INRA, the interns and Ph.D. students and especially to Juliette Bouvier-Muller, Anais Morisset, Aude Rolland, Chloé Espinosa and Audrey Combasteix for making this internship so pleasant.

The authors would like to thank the INRA Divisions of Animal Genetics for their financial support over the years in providing salaries, experimental costs, and computational facilities. The authors are indebted to Didier Foulquié, Fabien Carrière, Sébastien Douls and all the staff on the farm of “La Fage” for their active role in collecting experimental data and for managing the experimental flock.

## References

* Akaike, H. (1974). A new look at the statistical model identification. *IEEE transactions on automatic control*, 19(6), 716-723.

* Allain, D., Pena, B., Foulquié, D., Bourdillon, Y., François, D. (2011). Genetic variability of fleece shedding in the Martinik hair, Romane sheep breeds and their crossbreds. In *Fibre production in South American camelids and other fibre animals*, 15-24. Wageningen Academic Publishers.

* Allain, D., Foulquié, D., Autran, P., François, D., Bouix, J. (2014). Importance of birthcoat for lamb survival and growth in the Romane sheep breed extensively managed on rangelands. *Journal of animal science*, 92(1), 54-63.

* Agreste. 2015. Productions animales. Graphagri, 145-163.

* Arango, J. A., Cundiff, L. V., Van Vleck, L. D. (2002). Genetic parameters for weight, weight adjusted for body condition score, height, and body condition score in beef cows. *Journal of Animal Science*, 80(12), 3112-3122.

* Blanc, F., Bocquier, F., Agabriel, J., D'hour, P., Chilliard, Y. (2006). Adaptive abilities of the females and sustainability of ruminant livestock systems. A review. *Animal Research*, 55(6), 489-510.
Berry, D. P., Buckley, F., Dillon, P., Evans, R. D., Rath, M., Veerkamp, R. F. (2003). Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models. *Journal of dairy science*, 86(11), 3704–3717.

Borg, R. C., Notter, D. R., Kott, R. W., Kuehn, L. A. (2005). Genetic factors influencing body weights and condition scores in adult Targhee ewes. *Journal of Dairy Science*, 88, 102–103.

Borg, R. C., Notter, D. R., Kott, R. W. (2009). Phenotypic and genetic associations between lamb growth traits and adult ewe body weights in western range sheep. *Journal of Animal Science*, 87(11), 3506–3514.

Caldeira, R. M., Belo, A. T., Santos, C. C., Vazques, M. I., Portugal, A. V. (2007). The effect of body condition score on blood metabolites and hormonal profiles in ewes. *Small Ruminant Research*, 68(3), 233–241.

Chilliard, Y., Bocquier, F., Doreau, M. (1998). Digestive and metabolic adaptations of ruminants to undernutrition, and consequences on reproduction. *Reproduction Nutrition Development*, 38(2), 131-152.

Dwyer, C. M., Lawrence, A. B., Bishop, S. C., Lewis, M. (2003). Ewe-lamb bonding behaviours at birth are affected by maternal undernutrition in pregnancy. *British Journal of Nutrition*, 89(01), 123–136.

Fdapace. https://cran.r-project.org/web/packages/fdapace/index.html.

Geedoc. https://germinal.toulouse.inra.fr/_mcbatut/GEEDOC/.

Gizaw, S., Lemma, S., Komen, H., Van Arendonk, J. A. (2007). Estimates of genetic parameters and genetic trends for live weight and fleece traits in Menz sheep. *Small Ruminant Research*, 70(2), 145–153.

González-García, E., De Figuereido, V. G., Foulquie, D., Jousserand, E., Autran, P., Camous, S., Tesniere, A., Bocquier, F. Jouven, M. (2014). Circannual body reserve dynamics and metabolic profile changes in Romane ewes grazing on rangelands. *Domestic animal endocrinology*, 46, 37–48.

Gunn, R. G., Smith, W. F., Senior, A. J., Barthram, E., Sim, D. A., Hunter, E. A. (1991). Premating herbage intake and the reproductive performance of North Country Cheviot ewes in different levels of body condition. *Animal Production*, 52(01), 149–156.

Jefferies, B. C. (1961). Body condition scoring and its use in management. *Tasmanian Journal of Agriculture*, 32, 19–21.

Jones, H. E., Lewis, R. M., Young, M. J., Simm, G. (2004). Genetic parameters for carcass composition and muscularity in sheep measured by X-ray computer tomography, ultrasound and dissection. *Livestock Production Science*, 90(2), 167–179.

Karamichou, E., Richardson, R. I., Nute, G. R., McLean, K. A., Bishop, S. C. (2006). Genetic analyses of carcass composition, as assessed by X-ray computer tomography, and meat quality traits in Scottish Blackface sheep. *Animal Science*, 82(02), 151–162.
Koenen, E. P. C., Veerkamp, R. F., Dobbelaar, P., De Jong, G. (2001). Genetic analysis of body condition score of lactating Dutch Holstein and Red-and-White heifers. *Journal of Dairy Science*, 84(5), 1265-1270.

Lalman, D. L., Williams, J. E., Hess, B. W., Thomas, M. G., Keisler, D. H. (2000). Effect of dietary energy on milk production and metabolic hormones in thin, primiparous beef heifers. *Journal of Animal Science*, 78(3), 530-538.

Lambe, N. R., Young, M. J., Brotherstone, S., Kvame, T., Conington, J., Kolstad, K., Simm, G. (2003). Body composition changes in Scottish Blackface ewes during one annual production cycle. *Animal Science*, 76(02), 211-219.

Loker, S., Bastin, C., Miglior, F., Sewalem, A., Schaeffer, L. R., Jamrozik, J., Osborne, V. (2011). Short communication: Estimates of genetic parameters of body condition score in the first 3 lactations using a random regression animal model. *Journal of dairy science*, 94(7), 3693-3699.

Mao, I. L., Sloniewski, K., Madsen, P., Jensen, J. (2004). Changes in body condition score and in its genetic variation during lactation. *Livestock Production Science*, 89(1), 55-65.

Michels, H., Decuypere, E., Onagbesan, O. (2000). Litter size, ovulation rate and prenatal survival in relation to ewe body weight: genetics review. *Small Ruminant Research*, 38(3), 199-209.

Molénat, G., Foulquie, D., Autran, P., Bouix, J., Hubert, D., Jacquin, M., Bibe, B. (2005). Pour un élevage ovin allaitant performant et durable sur parcours: un système expérimental sur le Causse du Larzac. *Productions Animales Paris Institut National de la Recherche Agronomique*, 18(5), 323.

Molina, A., Gallego, L., Torres, A., Vergara, H. (1994). Effect of mating season and level of body reserves on fertility and prolificacy of Manchega ewes. *Small Ruminant Research*, 14(3), 209-217.

Murray, J. A. (1919). Meat production. *The Journal of Agricultural Science*, 9(02), 174-181.

Ricordeau, G., Tchamitchian, L., Brunel, J. C., Nguyen, T. C., François, D. (1992). La race ovine INRA 401: un exemple de souche synthétique. *INRA Productions animales*, (hs), 255-262.

Rmixmod. https://cran.r-project.org/web/packages/Rmixmod/index.html.

Russel, A. J. F., Doney, J. M., & Gunn, R. G. (1969). Subjective assessment of body fat in live sheep. *The Journal of Agricultural Science*, 72(03), 451-454.

Russel, A. J. F., Doney, J. M., Gunn, R. G. (1971). The distribution of chemical fat in the bodies of Scottish Blackface ewes. *Animal Production*, 13(03), 503-509.
Safari, E., Fogarty, N. M., Gilmour, A. R. (2005). A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. *Livestock Production Science, 92*(3), 271–289.

Schwarz, G. (1978). Estimating the Dimension of a Model. *The Annals of Statistics*, 6(2), 461–464.

Shackell, G. H., Cullen, N. G., Greer, G. J. (2011). Genetic parameters associated with adult ewe liveweight and body condition. In *Proceedings of the Association for the Advancement of Animal Breeding and Genetics conference, Perth*, 103–106.

Waltner, S. S., McNamara, J. P., Hillers, J. K., Brown, D. L. (1994). Validation of indirect measures of body fat in lactating cows. *Journal of dairy science*, 77(9), 2570–2579.

West, K. S., Meyer, H. H., Sasser, R. G. (1989). Ewe body condition and nutrition effects on embryonic loss. *Journal of Animal Science*, 67(1), 424.

Yao, F., Mueller, H.G., Wang, J.L. (2005). Functional Linear Regression Analysis for Longitudinal Data (Dense data). *Annals of Statistics* 33, 2873–2903.
<table><caption>Supplemental data 1. Estimates (± standard errors) of variance components for BW and BCS.</caption><thead><tr><th>Variables</th><th>h<sup>2</sup></th><th>c<sup>2</sup></th><th>e<sup>2</sup></th><th>r</th><th>σ<sup>2</sup><sub>p</sub></th></tr></thead><tbody><tr><td>BW-M</td><td>0.45 (0.04)</td><td>0.19 (0.04)</td><td>0.37 (0.02)</td><td>0.63 (0.02)</td><td>23.98 (0.90)</td></tr><tr><td>BW-Pa</td><td>0.57 (0.04)</td><td>0.15 (0.04)</td><td>0.28 (0.01)</td><td>0.72 (0.01)</td><td>28.73 (1.22)</td></tr><tr><td>BW-Pb</td><td>0.57 (0.04)</td><td>0.13 (0.03)</td><td>0.29 (0.01)</td><td>0.71 (0.01)</td><td>36.38 (1.54)</td></tr><tr><td>BW-L</td><td>0.52 (0.04)</td><td>0.15 (0.04)</td><td>0.33 (0.02)</td><td>0.67 (0.02)</td><td>36.90 (1.48)</td></tr><tr><td>BW-Sa</td><td>0.44 (0.04)</td><td>0.19 (0.04)</td><td>0.37 (0.02)</td><td>0.63 (0.02)</td><td>39.71 (1.46)</td></tr><tr><td>BW-W</td><td>0.52 (0.04)</td><td>0.15 (0.03)</td><td>0.33 (0.02)</td><td>0.67 (0.02)</td><td>37.69 (1.48)</td></tr><tr><td>BW-D</td><td>0.58 (0.05)</td><td>0.14 (0.04)</td><td>0.28 (0.02)</td><td>0.72 (0.02)</td><td>29.36 (1.28)</td></tr><tr><td>BCS-M</td><td>0.35 (0.04)</td><td>0.03 (0.03)</td><td>0.62 (0.02)</td><td>0.38 (0.02)</td><td>0.04 (0.001)</td></tr><tr><td>BCS-Pa</td><td>0.37 (0.04)</td><td>0.04 (0.03)</td><td>0.60 (0.02)</td><td>0.40 (0.02)</td><td>0.05 (0.001)</td></tr><tr><td>BCS-Pb</td><td>0.32 (0.03)</td><td>0.04 (0.03)</td><td>0.64 (0.02)</td><td>0.36 (0.02)</td><td>0.05 (0.001)</td></tr><tr><td>BCS-L</td><td>0.26 (0.04)</td><td>0.10 (0.03)</td><td>0.64 (0.02)</td><td>0.36 (0.02)</td><td>0.04 (0.001)</td></tr><tr><td>BCS-Sa</td><td>0.27 (0.03)</td><td>0.04 (0.03)</td><td>0.69 (0.02)</td><td>0.31 (0.02)</td><td>0.04 (0.002)</td></tr><tr><td>BCS-Sb</td><td>0.26 (0.05)</td><td>0.05 (0.05)</td><td>0.69 (0.04)</td><td>0.31 (0.04)</td><td>0.05 (0.003)</td></tr><tr><td>BCS-W</td><td>0.33 (0.04)</td><td>0.05 (0.03)</td><td>0.61 (0.02)</td><td>0.39 (0.02)</td><td>0.05 (0.001)</td></tr><tr><td>BCS-D</td><td>0.33 (0.04)</td><td>0.06 (0.03)</td><td>0.61 (0.02)</td><td>0.39 (0.02)</td><td>0.04 (0.001)</td></tr></tbody></table>

h²: heritability; c²: proportion of total phenotypic variance due to ewe permanent environmental effect;

e²: proportion of total phenotypic variance due to temporary environmental effects; r: repeatability;

σ²p: total phenotypic variance.

<table><caption>Supplemental data 2. Phenotypic and genetic correlations (± standard errors) for BW.</caption><thead><tr><th>Variables</th><th>BW-M</th><th>BW-Pa</th><th>BW-Pb</th><th>BW-L</th><th>BW-Sa</th><th>BW-W</th><th>BW-D</th></tr></thead><tbody><tr><td>BW-M</td><td>-</td><td>0.97 (0.01)</td><td>0.96 (0.01)</td><td>0.92 (0.01)</td><td>0.94 (0.01)</td><td>0.93 (0.01)</td><td>0.97 (0.01)</td></tr><tr><td>BW-Pa</td><td>0.90 (0.01)</td><td>-</td><td>0.97 (0.01)</td><td>0.90 (0.02)</td><td>0.92 (0.02)</td><td>0.93 (0.01)</td><td>0.95 (0.01)</td></tr><tr><td>BW-Pb</td><td>0.86 (0.01)</td><td>0.87 (0.01)</td><td>-</td><td>0.93 (0.00)</td><td>0.96 (0.01)</td><td>0.95 (0.01)</td><td>0.96 (0.01)</td></tr><tr><td>BW-L</td><td>0.77 (0.01)</td><td>0.77 (0.01)</td><td>0.77 (0.01)</td><td>-</td><td>0.96 (0.01)</td><td>0.89 (0.02)</td><td>0.90 (0.02)</td></tr><tr><td>BW-Sa</td><td>0.75 (0.01)</td><td>0.73 (0.01)</td><td>0.73 (0.01)</td><td>0.82 (0.01)</td><td>-</td><td>0.97 (0.01)</td><td>0.94 (0.01)</td></tr><tr><td>BW-W</td><td>0.75 (0.01)</td><td>0.75 (0.01)</td><td>0.73 (0.01)</td><td>0.74 (0.01)</td><td>0.77 (0.01)</td><td>-</td><td>0.96 (0.01)</td></tr><tr><td>BW-D</td><td>0.80 (0.01)</td><td>0.81 (0.01)</td><td>0.77 (0.01)</td><td>0.76 (0.01)</td><td>0.75 (0.01)</td><td>0.83 (0.01)</td><td>-</td></tr></tbody></table>

Phenotypic correlations below the diagonal; genetic correlations above the diagonal.

<table><caption>Supplemental data 3. Phenotypic and genetic correlations (± standard errors) for BCS.</caption><thead><tr><th>Variables</th><th>BCS-M</th><th>BCS-Pa</th><th>BCS-Pb</th><th>BCS-L</th><th>BCS-Sa</th><th>BCS-Sb</th><th>BCS-W</th><th>BCS-D</th></tr></thead><tbody><tr><td>BCS-M</td><td>-</td><td>0.93 (0.02)</td><td>0.87 (0.03)</td><td>0.82 (0.04)</td><td>0.75 (0.05)</td><td>0.72 (0.07)</td><td>0.82 (0.04)</td><td>0.92 (0.03)</td></tr><tr><td>BCS-Pa</td><td>0.61 (0.01)</td><td>-</td><td>0.91 (0.03)</td><td>0.83 (0.04)</td><td>0.75 (0.05)</td><td>0.74 (0.07)</td><td>0.77 (0.05)</td><td>0.87 (0.03)</td></tr><tr><td>BCS-Pb</td><td>0.51 (0.02)</td><td>0.54 (0.01)</td><td>-</td><td>0.89 (0.03)</td><td>0.79 (0.04)</td><td>0.72 (0.07)</td><td>0.82 (0.04)</td><td>0.86 (0.04)</td></tr><tr><td>BCS-L</td><td>0.41 (0.02)</td><td>0.41 (0.02)</td><td>0.54 (0.01)</td><td>-</td><td>0.90 (0.03)</td><td>0.88 (0.05)</td><td>0.84 (0.04)</td><td>0.77 (0.05)</td></tr><tr><td>BCS-Sa</td><td>0.35 (0.02)</td><td>0.36 (0.02)</td><td>0.45 (0.02)</td><td>0.63 (0.01)</td><td>-</td><td>0.87 (0.05)</td><td>0.90 (0.03)</td><td>0.71 (0.05)</td></tr><tr><td>BCS-Sb</td><td>0.34 (0.02)</td><td>0.37 (0.02)</td><td>0.36 (0.02)</td><td>0.48 (0.02)</td><td>0.50 (0.02)</td><td>-</td><td>0.93 (0.04)</td><td>0.71 (0.08)</td></tr><tr><td>BCS-W</td><td>0.38 (0.02)</td><td>0.37 (0.02)</td><td>0.42 (0.02)</td><td>0.48 (0.02)</td><td>0.58 (0.01)</td><td>0.57 (0.02)</td><td>-</td><td>0.82 (0.04)</td></tr><tr><td>BCS-D</td><td>0.45 (0.02)</td><td>0.45 (0.02)</td><td>0.43 (0.02)</td><td>0.47 (0.02)</td><td>0.51 (0.02)</td><td>0.49 (0.02)</td><td>0.58 (0.02)</td><td>-</td></tr></tbody></table>

Phenotypic correlations below the diagonal; genetic correlations above the diagonal.