Body reserves dynamics of suckling ewes across successive production cycles under outdoor and indoor contrasting farming system conditions

Abstract

Body reserves (BR) mobilization (BRM) and accretion (BRA) are crucial biological processes in ruminants that help them manage negative energy balance and adapt to changing environments. The BR dynamics (BRD) is affected by the interplay of key factors such as the farming system (FS) characteristics, physiological stage (PhySt), and parity (Par) or cohort (Coh) of the ewes, as well as litter size (LSi) at lambing and during suckling. This study aimed to evaluate the effects of contrasting FS (intensive, indoor (IND) vs. extensive, outdoor (OUT)) on the BRD of Romane ewes. Two flocks were monitored: 173 ewes in IND and 234 in OUT, belonging to 2 cohorts (Coh17/18). Ewes were monitored for body weight, body condition score (BCS), backfat thickness, back muscle thickness, non-esterified fatty acids (NEFA), β-hydroxybutyrate (BHB), triiodothyronine (T3), and insulin (INS) at 5 key PhySt (Mating, M; mid-pregnancy, P; before-lambing, bL; after-lambing, aL; and weaning, W) in primiparous (PRIM) or multiparous (MULT) ewes during successive production cycles. Data were analyzed using linear mixed models, with a significance threshold set at P ≤ 0.05. There was no isolated effect (P > 0.05) of FS on the BR traits evaluated. However, significant interactions were observed between FS and PhySt and Par (P < 0.001), LSi (P < 0.01), or Coh (P < 0.001) for all BR traits, indicating that FS may influence BR through factors such as feed availability, energy demands, or stress levels across different PhySt. MULT ewes showed better BR recovery than PRIM ewes regardless of FS, suggesting improved metabolic efficiency with maturity. Greater BRM and slower recovery were observed in ewes with larger LSi, regardless of FS, emphasizing the need for tailored nutritional strategies, and Coh18 ewes showed greater capacity to mobilize and rebuild BR than Coh17. BR dynamics were similar in both FS as indicated by comparable levels of BHB (but not NEFA), T3, and BCS around lambing. In both FS, BRM was observed from P until W, and BRA was observed from weaning until the next P. In conclusion, BRD in sheep is strongly shaped by PhySt, and to a lesser extent by environmental factors, influencing FS resilience and productivity. These insights support the importance of improving animal adaptive capacities through BR management to enhance sustainability in diverse FS, particularly in the face of climate variability and rising production costs.

Plasma biomarkers may be used as strong indicators for characterizing the dynamics of body reserve mobilization and accretion in sheep during successive production cycles either in indoor or outdoor farming systems.

Introduction

The strategic and efficient use of the 2 complementary phases of body reserves (BR) mobilization (BRM) and accretion (BRA) plays a crucial role in enhancing resilience in ruminants, particularly when facing negative energy balance (NEB; Bocquier and González-García, 2010; Destoumieux-Garzón et al., 2021). The NEB shown by BRM often arises due to fluctuations in feed availability and quality (Bocquier and González-García, 2010; Friggens et al., 2017) or during physiologically demanding stages (e.g., late pregnancy, early lactation, Chilliard et al., 2000). Contrarily, BR are rebuilt (BRA) when feed or energy shortage challenges disappear (e.g., biomass availability in rainy seasons) (Mysterud et al., 2001) or when physiological demands decrease (e.g., after weaning, at maintenance or dry-off).

In environments with variable resources, such as outdoor (OUT) farming systems (FS), BR dynamics (BRD) are essential for sustaining energy levels by mobilizing reserves during scarcity and rebuilding them when conditions improve (Colditz and Hine, 2016; Friggens et al., 2017). This adaptive mechanism ensures glucose availability for vital functions while also relying on lipid metabolism to provide alternative energy sources, reflecting an individual’s capacity to withstand nutritional or physiological challenges (Baumgard et al., 2017).

For sheep from a common breed and/or sharing some genetic background, reared in contrasting FS, BRD can vary significantly due to external factors like feed access, climate, and management practices (Colditz and Hine, 2016; Friggens et al., 2017). Animals raised in OUT may exhibit more pronounced BR fluctuations compared to those in indoor (IND) FS, highlighting the environmental impact on resilience (Friggens et al., 2017; Rust, 2019).

Key indicators such as body condition score (BCS, Bruckmaier et al., 1998; Kenyon et al., 2014) and plasma biomarkers (Shetty, 1990; Chilliard et al., 1998; Caldeira et al., 2007a, 2007b) are valuable for assessing BRD. During NEB, changes in lipid metabolism reflected in metabolite and hormone levels, provide insights into an animal’s energy status (Chilliard et al., 1998). For example, T3, an iodinated amino acid derivative, is primarily synthesized in the thyroid gland, with most produced by the deiodination of T4 in peripheral tissues (Furman, 2016). As the active form of thyroid hormone, Triiodothyronine (T3) regulates lipid metabolism by stimulating lipolysis through hormone-sensitive lipase (HSL) activation and enhancing mitochondrial β-oxidation for fatty acid utilization (Gagnon et al., 2010; Obregon, 2014). During NEB, elevated T3 levels accelerate lipid mobilization and fatty acid oxidation to meet energy demands, thereby reducing reliance on protein catabolism (Cicatiello et al., 2018).It is the primary metabolic hormone that drives metabolic and organ processes. It also controls flavin mononucleotide synthesis, which declines with reduced T3 production during severe malnutrition (Capo-chichi et al., 2000). Insulin metabolism maintains stable blood glucose levels by either mobilizing body stores for glucose synthesis and sparing glucose or storing excess nutrients (Brockman and Laarveld, 1986). As the primary hormonal regulator, insulin promotes growth and body gain when energy intake is high, with low concentrations having a catabolic effect (Brockman and Laarveld, 1986). In contrast, glucagon, epinephrine, and glucocorticoids mobilize energy during stress, with glucocorticoids also aiding glycogen repletion (Brockman and Laarveld, 1986). While growth hormone doesn’t regulate metabolism directly, it alters tissue sensitivity to insulin and counteracts insulin’s lipid synthesis effects, promoting lean growth and net protein synthesis (Brockman and Laarveld, 1986).

Monitoring these biomarkers across different environments can enhance our understanding of BRD and resilience (Chilliard et al., 1998), ultimately aiding in the development of management strategies that support sheep health and productivity under diverse farming conditions (González-García et al., 2015).

Thus, the characterization of indicators for BRD in farm animals remains a key issue in the context of the current agroecological transition, climate change, and the economic trade-offs associated with farm management strategies. The objective of this work was to understand the phenotypic variability of key biomarkers associated with BRD and how half-sib ewes (i.e., having half of their genetic background in common through sires) mobilize and rebuild their BR overtime in contrasting rearing FS (IND vs OUT). Our hypothesis was that difference in FS may affect the trajectories of plasma biomarkers over the whole production cycle in Romane ewes, in addition to better-known factors including physiological stage, parity, litter size, birth year (cohort), and genetic line.

Material and Methods

The experiments described here fully comply with applicable legislation on research involving animal subjects in accordance with the European Union Council directive (2010/63/UE). The researchers who carried out the experiments were certified by the relevant French governmental authority as well as the INRAE La Fage Experimental Farm (agreement number A-312031) and INRAE P3R experimental farm (agreement number C18-174-01). All experimental procedures were performed according to the guidelines for the care and use of experimental animals and approved by the French Ministry of Education and Scientific Research and the local ethics committees CEEA-019 (approval number APAFIS#9122-2017030117214997v3) and CEEA-115 (Science and Animal Health) (approval number APAFIS# 2016031819254696.V3).

Animals, management, and experimental FS

Two flocks of Romane ewes, reared in 2 contrasting FS belonging to 2 INRAE experimental farms: La Fage (Causse du Larzac, Saint-Jean Saint-Paul, southern France, https://doi.org/10.15454/1.548325523466425E12) and P3R La Sapinière (Bourges, center of France, https://doi.org/10.15454/1.5483259352597417E12), were monitored each year between 2017 and 2021.

At La Fage, ewes (n = 234, initial average age = 7 mo old, body weight (BW) = 37 ± 4 kg, BCS = 3.05 ± 0.11) were reared exclusively outdoors on approximately 280 ha of rangelands (Macé et al., 2019) with targeted and strategic supplementation (but not ad libitum) provided prior to and around mating and during late pregnancy, to explore their ability to manage high seasonal variations in feed quantity and quality (González-García and Hazard, 2016). The detailed characteristics of the La Fage rangeland have been documented (Molénat et al., 2005; González-García et al., 2014; Macé et al., 2019). However, briefly, the native forage species included Bromus erectus, Stipa pennata, Carex humilis, Festuca duriscola, along with sparse shrubs and trees such as Buxus sempervirens and Juniperus communis. The 280-ha area is divided into paddocks (5 to 25 ha, averaging 15 ha) grazed once or twice annually. A small section (18 ha) was fertilized to enhance early spring forage for lactating ewes. Vegetation assessments, focusing on the green-to-senescent grass ratio and growth stage, were conducted visually during grazing. In autumn prior to mating in mid-November, ewes grazed on native and fertilized (6% of the entire rangeland) pasturelands. During the experimental years (2017 to 2021), the average seasonal temperatures were 7.9 °C in autumn, 11.06 °C in spring, 18.6 °C in summer, and 3.7 °C in winter. The average precipitation was highest in autumn (1901 mm) and winter (1533.5 mm), with spring at 1358.5 mm and summer being the driest at 684.5 mm. The average humidity ranged from 68.14% in summer to 88.70% in winter. The dry summer conditions limited biomass production, whereas higher rainfall in autumn and winter favored regrowth.

A detailed feeding regime for OUT has been described by González-García et al. (2014). For the present study, female lambs received feed supplementation with conserved feedstuffs, primarily locally produced high-quality hay (1.5 kg/ewe/d) provided before mating to prepare the maiden ewes for earlier first mating (i.e., at 7 mo of age). After mating, ewes received feed supplementation with hay (0.8 to 1 kg/ewe/d) and silage (reaching an average of 3 kg/ewe/d) during 3.5 mo when grazeable biomass was no longer available (i.e., the winter season). Thereafter during the last 1.5 mo of pregnancy, ewes received a combination of hay, silage, and barley gradually increasing until lambing (in early April). During this last period, ewes received an average of 4 kg of dry matter forage, consisting of 1 kg of fresh hay and 3 kg of fresh grass silage, along with barley supplementation, which peaked at 1.5 kg per ewe per day before-lambing (bL).

In OUT, there was a single mating phase each year at the end of the autumn, with the first mating occurring at 7 mo of age and next mating 1 yr later for the present study. In April, during the spring, lambing took place outdoors and ewes started grazing the fertilized pastureland for 4 wk after-lambing (aL). The ewes lambed an average of 1.8 live lambs and suckled 1.3 lambs, with lambs being weaned at 76.8 ± 10.6 d of age. Due to the prolonged drought period during the summer, the dried-off ewes usually grazed the aging biomass (Molénat et al., 2005; González-García et al., 2014; Macé et al., 2019). González-García et al. (2014) and González-García and Hazard (2016) have provided additional details on the meteorological conditions of the location and general management practices used in this system.

At La Sapinière farm, ewes (n= 173, initial average age = 10 mo, BW = 55 ± 4 kg, BCS = 3.7 ± 0.11) were reared IND in a barn. Although indoor conditions were not recorded, the farm’s external environment provides a reference for these conditions. Average daily seasonal humidity was highest in winter (83.9%) and autumn (83.8%), followed by spring (69.2%) and summer (60.7%). Daily temperature varied across seasons, averaging 6.06°C in winter, 9.99°C in autumn, 13.9°C in spring, and 20.8°C in summer. Seasonal average cumulated precipitation was greatest in autumn (1901.0 mm) and winter (1533.0 mm), followed by spring (1358.5 mm), with summer being the driest season (684.5 mm). The lighting corresponded to the natural lighting. This FS is characterized with high input management including feed tailored to the animals’ requirements (INRA, 2018). Ewes were ad libitum fed with a mixed diet (forage ad libitum + concentrate). Feed composition and estimated nutritive value of the ingredients used in diets for ewes-reared IND are presented in Table 1. In this FS, the first and second mating occurred at 10 and 18 mo of age, respectively. Lambing took place indoors during winter in February for the first lambing and in December during autumn for the second lambing (Fig. 1). Ewes lambed and suckled an average of 2.04 and 1.44 lambs, respectively. On average, lambs were weaned at 64.5 ± 11.1 d of age.

Table 1.

Feed composition and estimated nutritive value of the ingredients used for feeding ewes reared under indoor farming conditions

Item	M	P	bL	aL	W
Hay1	29.412	45.45	50.0	29.79	17.78
Silage3	58.82	45.45	28.57	48.94	71.11
Concentrate A	11.76	9.09	7.17	6.38	4.44
Concentrate B			14.29	14.89	6.67
Total quantity supplied, kg/ewe/d	1.7	2.2	2.8	4.7	4.5
Total forage DM, kg/ewe/d	0.93	1.4	1.56	2.57	2.32
UFL	0.82 (1024)	1.15 (124)	1.70 (113)	2.60 (100)	2.25 (98)
PDIN	84 (131)	151 (136)	216 (126)	353 (136)	228 (98)
PDIE	86 (134)	137 (123)	182 (106)	287 (111)	239 (103)
CI (UEM)	1.29 (65)	1.74 (87)	2.13 (107)	3.50 (127)	3.19 (94)

M= 2-wk before mating; P = mid-pregnancy; bL = 2-wk before-lambing; aL= 3- to 4-wk after-lambing; W= 2-d after weaning; DM, dry matter; UFL = net energy unit for lactation; PDIE = PDIA (dietary protein undegraded in the rumen which is digestible in the intestine) + PDIME (microbial protein that could be synthesized from the energy available in the rumen when degraded nitrogen is not limiting); PDIN =PDIA + PDIMN (microbial protein that could be synthesized from the degraded dietary N when energy is not limiting). Final PDI value of the diet is the minimum of PDIN or PDIE (INRA, 1988); CI (UEM) = Intake capacity; UFL, PDIN, PDIE and CI values are the quantities provided per ewe and per day.

¹Hay was produced on the farm and was mainly composed of Dactylis glomerata.

²Values give the percentage of each ingredient in feed composition.

³Silage was produced on the farm resulting from the wrapping of a forage field composed with exclusively Dactylis glomerata (M, P and W) or a mix of Dactylis glomerata and alfalfa (bL and aL). Concentrate A is a specific mixture of ingredients (i.e., cereals, pulses, vitamins, minerals and others) aiming to cover the ewes’ requirements during pregnancy; Concentrate B is a specific mixture of ingredients (i.e., cereals, pulses, vitamins, minerals and others) aiming to cover the ewes’ requirements during lactation.

⁴Values between parentheses give the percentage coverage of requirements. Nutritional requirements were estimated based on an average ewe weight of 70 kg for maintenance, a litter weight of 9 kg for pregnancy and an average daily gain for lamb growth of 500 g for suckling.

Figure 1.

Schematic representation of the experimental design used for the individual monitoring of the dynamics of body reserves during successive physiological stages and productive cycles in Romane ewes connected to 2 feed efficiency genetic lines and reared in 2 contrasting FS (Indoor, IND or Outdoor, OUT). RFI, Residual feed intake; RFI-, low-feed efficiency; RFI+, high-feed efficiency; Coh = Cohort (Coh17 vs Coh18, ewes born in the year 2017 vs 2018, respectively); BW, body weight; BCS, body condition score; BFT, backfat thickness; BMT, back muscle thickness; NEFA, non-esterified fatty acids; BHB, β-hydroxybutyrate; T3, triiodothyronine; INS, insulin; M, 2 wk before mating; P, mid-pregnancy, 8 wk after mating; bL, 2 wk before-lambing; aL, 3 wk after-lambing; W, 2 d after weaning.

Breeding of ewes in the present study was conducted through natural mating in IND, using a ratio of 1 ram per 20 ewes over a 40-d period. Ewes in OUT were artificially inseminated. In both FS, the breeding season started with hormonal treatments used for estrus induction as classically used in such species. The lambing rate was 93% and 83% for IND in Coh17 and Coh18, respectively, while for OUT, it was 87% in Coh17 and 83% in Coh18. Ewes that lambed more than 2 live lambs had the third and extra lambs placed in artificial suckling. In both IND and OUT, the management practices were consistently applied each year from 2017 to 2021.

Year of birth (Cohort, Coh) was considered in the analysis as ewes were born in 2017 or 2018 (Coh17 and Coh18, respectively) in each FS. Each cohort of ewes was then monitored for 2 years corresponding to 2 production cycles, resulting in measures for primiparous (PRIM) and multiparous (MULT) ewes. A total of 87 and 86 ewes were in Coh17 and Coh18, respectively, in IND, whereas in the OUT 116 and 118 ewes were in Coh17 and Coh18, respectively (Fig. 1).

The experimental ewes were connected to a divergent selection program for feed efficiency (FE), which is conducted at La Sapinière farm. Two FE divergent lines are chosen according to their breeding value for residual feed intake (RFI; Tortereau et al., 2020). Experimental ewes used in the present study were the first generation of offspring from divergently selected rams for low or high RFI (Fig. 1). Ewes were allocated into 11 half-sib families, and each family was represented in both farms. Most of dams were not selected for RFI (i.e., except 25% of dams in IND).

Measured traits

All the ewes were individually and longitudinally monitored for their BRD through the following set of phenotypes: BW, BCS, subcutaneous backfat (BFT) or muscle thickness (BMT); plasma profiles for key metabolites and hormones linked to energy metabolism such as non-esterified fatty acids (NEFA), β-hydroxybutyrate (BHB), triiodothyronine (T3) and insulin (INS). Such monitoring was carried out on productive ewes reared in 2 contrasting FS (IND vs. OUT).

In both FS, all parameters were monitored regularly during the first 2 or 3 productive cycles with a frequency (schedule) determined by the PhySt. Therefore, ewes were phenotyped at mating (M, 2 wk before introduction of rams or AI), mid-pregnancy (P, 8 wk after introduction of rams or AI), bL (approximately 2 wk bL), aL (approximately 3 to 4 wk aL) and weaning (W, 2 d after weaning; Fig. 1). For ewes-reared OUT, BFT and BMT were measured only at 3 physiological stages (i.e., M, aL, and W) due to technical constraints in such extensive conditions. In addition, blood samples were only taken on ewes that suckled at least 1 lamb until weaning. Due to COVID-19 pandemic constraints, measurements of ewes-reared OUT planned in 2020 for phenotyping the second productive cycle in the second cohort (i.e., Coh18) were delayed until 2021, and then constituted data for the third cycle instead of the second one. Although the phenotyping of these ewes was delayed, the Coh18 ewes still lambed and reared young in the second cycle (2020), despite not being phenotyped that year. Their management and production were consistent with the protocols followed for the Coh17 OUT ewes. On average, per trait, 2,289 records from 234 ewes were collected in the OUT FS and 1,600 records from 173 ewes were collected in the IND FS.

Body condition trait measurements, blood sampling, hormone and metabolite assays

The BW measurements were performed using conventional scales with the help of a Combi clamp (Ritchie Agricultural, Angus, Scotland), at which time the BCS was assessed by a trained operator. The measurements of BCS were taken based on the original grid described by Russel et al. (1969) and adapted to consider variation from 1 (very thin) to 5 (obese) with a point increment of 0.25 or 0.1 for IND and OUT, respectively. In each FS, the same 1 or 2 operators recorded routinely the BCS and underwent regular calibration training sessions to assure the precision of repeated readings within an operator and the connection of measures between various operators (Macé et al., 2019). A real-time ultrasound system (Easi-Scan Linear portable scanner, BCF Ultrasound Australasia, Victoria, Australia) was used to quantify BFT and BMT on the 12th rib. The technicians responsible for assessing BCS also conducted BFT and BMT measurements and underwent regular training to ensure consistency and accuracy.

Blood was sampled by jugular venipuncture prior to first meal distribution in both systems at approximately 0800 hours on each sampling day in order to limit variations in blood concentrations due to biological circadian rhythm and the potential influence of feeding on hormone levels, such as insulin. At each sampling point, individual records were registered for plasma NEFA, BHB, T3, and INS. Plasma was sampled during the same dates to determine metabolic profiles of physiological traits associated with BRM and BRA. Two 9 mL blood samples were drawn from each ewe by a trained operator; 1 tube with 18 IU of lithium heparin per 1 mL blood, and another with 1.2 to 2 mg of potassium EDTA per 1 mL blood (Vacuette Specimen Collection System, Greiner Bio-One GmbH, Austria). Samples were immediately placed on ice before centrifugation at 3,600 × g for 20 min at 4 °C. The plasma was collected and stored at − 20 °C in individually identified aliquots of 3 μL until metabolite and hormone analyses. Individual concentrations of plasma metabolites and metabolic hormones were determined, in accordance with the procedures described by González-García et al. (2014).

Plasma NEFA were measured in duplicate using the commercially available Wako NEFA-HR2 R1 and R2 kit (Laboratoires Sobioda SAS, Montbonnot, Saint Martin, France) adapted for 96-well microplates. Intra- and inter-assay variation averaged 6.06% and 4.71%, respectively. Plasma concentrations of BHB were measured in duplicate using the enzymatic method proposed by Williamson and Mellanby (1974). Inter-assay variation averaged 1.72%. Plasma INS was measured in duplicate using the protocol of a commercially available RIA kit (Insulin-CT; MP Biomedicals–Orangeburg, New York, USA). Intra- and inter-assay variation averaged 6.74% and 5.73%, respectively. Total plasma T3 was measured in duplicate using the protocol of a commercially available RIA kit (Coat-A-Count Total T3; Siemens Healthcare Diagnostic Inc., Los Angeles, California, USA). Intra- and inter-assay variation averaged 2.79% and 7.38%, respectively. These assays were previously validated for use in sheep (González-García et al. (2014, 2015)

Statistical analyses

Analysis of variance

To evaluate the relevant fixed effects and interactions affecting the BR traits, analyses of variance (ANOVA) with repeated measurements were conducted using the linear mixed model package (lme4) (Bates et al., 2015) in R Software web-version 4.2.1 (R Core Team, 2023). The linear model for the ANOVA was:

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{y}=\mu +\mathrm{X}\beta +\mathrm{Z}\alpha +\mathrm{e},}\end{array}}$$ (1)

where y is the vector of BR phenotypic observations of the dependent variable (BW, BCS, BFT, BMT, NEFA, BHB, T3 or INS); μ is the population overall mean; β the vector of fixed effects and their interactions; α the vector of the random effect of the ewe nested within the FS with α ~ N (0, σ²_α); and e is the random residual error with e ~ N (0, σ²_e). Matrices X and Z relate phenotypes to fixed effects and animals (incidence matrices), respectively. The random effect of the ewe was used to take into account repeated measurements on the same animal.

The FS, PhySt, parity of the ewe (Par), Coh, genetic line of the sires (Line), and litter size class (LSi) were the fixed effects considered in the model (1). The FS effect were IND vs OUT, the PhySt effect considered the 5 key productive stages with the following measurements distribution (n indicating the average number of observations for each phenotype): M (OUT, n = 698; IND, n = 453), P (OUT, n = 398; IND, n = 260), bL (OUT, n = 398; IND, n = 315), aL (OUT, n = 397; IND, n = 317) and W (OUT, n = 398; IND, n = 227). The Par effect considered both primiparous ewes (PRIM, first lambing; OUT n = 1,060; IND n = 800) and multiparous (MULT, second or third lambing; OUT n = 1,229; IND n = 800) to account for differences in reproductive experience and minimize bias from the third cycle in Coh18 in OUT. We assume multiparous ewes behave similarly for BRD across the second and third cycle. The LSi effect took into account combination of the number of lambs born and suckled: ewes lambing single or more lambs but suckling no lambs (class 0; OUT, n = 119; IND, n = 73), ewes lambing and suckling single lamb from L to W (class 1; OUT, n = 783; IND, n = 260), ewes lambing 2 and suckling 1 (class 2; OUT, n = 517; IND n = 322), ewes lambing and suckling 2 (class 3; OUT, n = 506; IND, n = 543), ewes lambing more than 2 and suckling 1 (class 4; OUT, n = 78; IND, n = 107) and ewes lambing more than 2 and suckling 2 or more (class 5; OUT, n = 240; IND, n = 189). The class 0 of LSi factor was only considered for analyses of BW, BCS, BFT, and BMT, ewes in this class not being blood sampled. The Coh effect entailed animals born in the years 2017 (Coh17; OUT n = 1094; IND n = 798) or 2018 (Coh18; OUT, n = 1,195; IND n = 727) for both farms. The Line effect took into account the high (less efficient, RFI+; OUT, n = 1,278; IND, n = 795) and low (more efficient, RFI-; OUT, n = 994; IND, n = 805) FE lines.

The 2-way interactions between FS × PhySt, FS × Par, FS × Coh, FS × LSi, and FS × Line were tested on BR traits. Three-way interactions between FS × PhySt × Par, FS × PhySt × LSi, FS × PhySt × Coh, FS × Par × LSi, FS × LSi × Line, and FS × Coh × Line, were also investigated on BR traits, except FS × PhySt × Coh on T3 due to missing data for MULT ewes in Coh18 in the OUT FS. An effect of tested factors with a P ≤ 0.05 was deemed significant. Pair-wise comparisons of means were performed using a Tukey HSD post hoc test (P ≤ 0.05) by using the R package emmeans (Russell et al., 2024).

Results

The priority aim was to explore the dynamics of BR across PhySt in relation to FS, Par, LSi, and Coh. The results description focused on the effect of the FS, and the interactions of this factor with other key fixed effects i.e., FS × PhySt × Par, FS × PhySt × LSi, and FS × PhySt × Coh. These interactions were concomitant with our study objectives. Although interactions such as FS × PhySt × Line, FS × Par × LSi, FS × Par × Coh, FS × Par × Line, FS × LSi × Coh, FS × LSi × Line, and FS × Coh × Line were identified as potential sources of variation, we did not present detailed results or discussions for these interactions in this paper.

Progression through cycle of body condition traits and plasma biomarkers

The significance of fixed effects including FS, PhySt, Par, LSi, Coh, and Line, along with their interactions on BR traits measured repeatedly in ewes across different physiological stages under IND or OUT conditions, are presented in Table 2. There were no significant main effects of FS on BR variables (P > 0.05). However, its interaction with PhySt and Par was highly significant (P < 0.001) for all BR traits (Table 2). The Least squares means (LSMeans) from FS × PhySt × Par interaction for body condition traits and plasma biomarkers in IND and OUT environments are presented in Table 3. Overall, mean BW was consistently greater (P < 0.001) in ewes-reared IND compared to those reared OUT across all PhySt (Table 3). The BW increased (P < 0.001) from M to bL in both FS and parities, except in MULT ewes, where it increased (P < 0.001) from P to bL. In addition, BW declined from aL to W in both PRIM and MULT ewes. Moreover, in both FS, BW was greater (P < 0.001) in MULT ewes than in PRIM ewes across all the PhySt. For BCS, similar trends were observed in both IND and OUT ewes across all PhySt, with an increase (P < 0.001) in BCS from M to P followed by a decline (P < 0.001) from bL to W. Ewes-reared IND exhibited greater (P < 0.001) BCS from M to P, but these differences were no longer evident in the subsequent PhySt (bL to W), where both FS showed similar trends (Table 3). These differences were more pronounced in MULT than in PRIM ewes in both FS. Equally, in IND and OUT, BFT followed a similar trend across PhySt, with greater (P < 0.001) values observed in IND, particularly at M and bL, and a comparable trend at W (Table 3). BFT decreased from M to P (P < 0.001), with a more pronounced decline in IND ewes (P < 0.001). At M and bL, MULT ewes exhibited greater (P < 0.001) BFT levels than PRIM ewes. For BMT, ewes-reared IND generally showed greater (P < 0.001) levels than those reared OUT across PhySt. Moreover, MULT ewes had greater (P < 0.001) BMT levels than PRIM ewes, especially aL and/or at W regardless of FS. However, these comparisons were limited by the lack of BFT and BMT measurements at P and aL in the OUT FS (Table 3).

Table 2.

Significance levels for fixed effects and their interactions on BR phenotypes of Romane ewes reared under indoor (IND) or outdoor (OUT) FS conditions

Item	BW, kg	BCS, 1 to 5	BFT, mm	BMT, mm	NEFA, mmol/L	BHB, mg/L	T3, ng/mL	INS, µUI/mL
n = IND	173	172	141	141	141	141	141	141
OUT	192	193	190	190	193	193	193	170
Farming system (FS)	ns	ns	ns	ns	ns	ns	ns	ns
Physiological stage (PhySt)	***	***	***	***	***	***	***	***
Parity (Par)	***	***	***	***	***	***	***	**
Litter size (LSi)	*	***	*	***	ns	*	ns	*
Cohort (Coh)	ns	***	***	***	***	ns	***	***
Genetic line (Line)	*	ns	*	ns	ns	ns	ns	ns
FS × PhySt	ns	***	ns	ns	***	*	***	ns
FS × Par	***	ns	ns	ns	***	ns	ns	**
FS × LSi	*	***	ns	ns	*	ns	ns	ns
FS × Coh	***	***	ns	ns	ns	*	ns	ns
FS × Line	ns	ns	ns	ns	ns	ns	ns	ns
FS × PhySt × Par	***	***	***	***	***	***	***	***
FS × PhySt × LSi	***	***	***	***	***	***	**	**
FS × PhySt × Coh	***	***	***	***	***	***	***	***
FS × PhySt × Line	ns	ns	ns	ns	ns	ns	ns	***
FS × Par × LSi	*	ns	ns	ns	ns	ns	ns	ns
FS × Par × Coh	***	***	***	***	***	***	na	***
FS × Par × Line	***	ns	ns	ns	ns	ns	ns	ns
FS × LSi × Coh	*	***	ns	ns	ns	ns	ns	ns
FS × LSi × Line	ns	ns	ns	ns	ns	*	ns	*
FS × Coh × Line	ns	ns	ns	ns	ns	ns	ns	ns

BW = body weight; BCS = body condition score; BFT = back fat thickness; BMT = back muscle thickness; NEFA = plasma non-esterified fatty acids; BHB = plasma β-hydroxybutyrate; T3 = plasma triiodothyronine; INS = plasma insulin. The number of asterisks indicates the level of significance: *(P ≤ 0.05); **P < 0.01; ***P < 0.001; ns (non-significant) P > 0.05; na = no data assessed.

Table 3.

Least squares means (LSMeans) of body reserve phenotypes at key physiological stages (PhySt) in Romane ewes reared indoor (IND) or outdoor (OUT), across different parities

Trait	Farming system	PRIM					SEM	MULT					SEM
		M	P	bL	aL	W		M	P	bL	aL	W
BW, kg	IND	54.5a	62.5b	73.0c	62.9b	59.0d	± 4.19	67.8a	67.7a	80.0b	74.2c	68.1a	± 4.28
	OUT	37.1a	44.4b	51.7c	45.7b	43.0b	± 4.18	52.3a	54.2b	63.2c	56.8d	53.6ab	± 4.27
BCS, 1 to 5	IND	3.70a	3.85b	3.21c	2.48d	2.27d	± 0.11	2.80a	3.14b	2.95c	2.10d	2.14d	± 0.11
	OUT	3.05a	2.99a	2.70b	2.48c	2.21d	± 0.11	2.74a	2.90b	2.81ab	2.49c	2.38c	± 0.11
BFT, mm	IND	7.64a	6.14b	5.49c	4.79d	4.17e	± 0.59	5.54a	5.57a	5.15b	4.91b	4.93b	± 0.59
	OUT	5.35a	na	4.26b	na	4.61b	± 0.59	4.55a	na	4.02b	na	4.85a	± 0.59
BMT, mm	IND	26.9a	26.3a	24.7b	23.5c	23.2c	± 3.93	27.0a	26.5a	26.4a	24.8b	25.2b	± 3.93
	OUT	22.5a	na	19.0b	na	18.7c	± 3.92	20.0a	na	19.50a	na	19.9a	± 3.92
NEFA, mmol/L	IND	0.60a	0.44b	0.55a	0.57a	0.64a	± 0.11	0.33a	0.38a	0.62b	0.69b	0.56c	± 0.11
	OUT	0.55a	0.40b	0.32b	0.31b	0.78c	± 0.11	0.31a	0.21b	0.14b	0.35a	0.71c	± 0.11
BHB, mg/L	IND	25.8a	20.7a	38.0b	36.6b	20.5a	± 3.53	19.2a	20.4a	24.7a	30.7b	18.5a	± 3.61
	OUT	34.0ad	26.3b	40.1c	39.2ac	33.2d	± 3.59	20.5a	18.7a	35.6b	36.7b	27.1c	± 3.50
T3, ng/mL	IND	1.14a	0.92b	0.95b	1.17a	0.92b	± 0.48	0.80ac	0.87bc	0.68a	0.87c	0.90c	± 0.48
	OUT	0.90a	0.91a	0.88a	1.26b	0.68c	± 0.52	0.56a	0.70ac	0.61ac	0.95b	0.75c	± 0.52
INS, µUI/mL	IND	5.02a	5.12a	4.85a	8.05b	4.33a	± 1.45	4.02a	3.89a	5.45b	6.74c	3.68a	± 1.45
	OUT	2.65a	2.80a	2.75a	3.50a	1.33b	± 1.45	2.66ac	1.43bc	3.30a	3.07a	1.09bd	± 1.48

M = 2-wk before mating; P = mid-pregnancy; bL = 2-wk before-lambing; aL = 3- to 4-wk after-lambing; W = 2-d after weaning; BW = body weight; BCS = body condition score; BFT = backfat thickness; BMT = back muscle thickness; NEFA = non-esterified fatty acids; BHB = Beta-hydroxybutyrate; T3 = triiodothyronine; INS = insulin. ± SEM = overall standard error of the mean. The effect of the interaction of farming system, physiological stage and parity was significant (P < 0.001) for all the BR traits. LSMeans with different letters in the superscript in the same row within parity class are statistically significantly different (P ≤ 0.05). na = no data assessed.

Overall, for NEFA, a similar trend (P < 0.001) was observed in both IND and OUT FS across all PhySt, except for OUT ewes at bL, which exhibited lower (P < 0.001) NEFA levels (Table 3). In general, NEFA levels declined (P < 0.001) until P or bL then increased (P < 0.001) until W in IND and OUT, respectively. The levels were comparable from M to P in both FS, but NEFA levels were greater (P < 0.001) around lambing in ewes-reared IND compared to those reared OUT. At W, NEFA levels were elevated in both FS, but with greater (P < 0.001) levels expressed OUT than IND. Overall, regardless of the FS, NEFA levels were greater (P < 0.001) in PRIM ewes compared to MULT ewes across all PhySt. Moreover, in PRIM ewes-reared IND, high NEFA concentration at M was not significantly different (P > 0.05) from those found from bL until W. Indeed, the greatest (P < 0.001) mean NEFA concentrations were found from bL until W and at W in ewes-reared IND or OUT, respectively, in both PRIM and MULT ewes. For BHB, similar (P < 0.001) tendencies were observed in both IND and OUT FS across all PhySt. BHB levels peaked around lambing in both FS and then declined from W until P, with a more pronounced drop (P < 0.001) in IND ewes compared to OUT. Overall, PRIM ewes exhibited greater (P < 0.001) BHB levels than MULT ewes across all PhySt, a difference especially notable at M, where PRIM ewes in the OUT system showed more elevated (P < 0.001) levels than those in IND (Table 3). Regardless of the FS, T3 levels followed a consistently high trend across all PhySt. However, peaks were more pronounced at M in PRIM ewes-reared IND and at aL in PRIM ewes in both IND and OUT FS. Regardless of the FS, INS values were consistently greater (P < 0.001) in IND compared to OUT across all PhySt. Overall, INS levels remained relatively stable from M to bL in both IND and OUT with a sharp peak observed at aL in IND, while the stable trend continued in OUT until aL. At W, INS levels declined (P < 0.001) sharply in both FS, with the decrease (P < 0.001) being more pronounced in OUT. Moreover, in the IND FS, INS levels were consistently greater (P < 0.001) in PRIM ewes, whereas in OUT, values were comparable between PRIM and MULT ewes throughout the PhySt (Table 3).

The interaction among FS, PhySt, and Coh was highly significant (P < 0.001) across all BR traits (Table 2), indicating that the effect of FS (IND vs. OUT) on BR traits depended on both the PhySt and Coh of the ewe. The LSMeans for body condition traits and plasma biomarkers for this interaction are shown in Figs. 2 and 3, respectively. The LSMeans with overlapping error bars are not statistically different (P > 0.05), whereas those with non-overlapping error bars are considered statistically different at P ≤ 0.05.

Figure 2.

Least Squares Means (LSMeans) for BW, body weight; BCS, body condition score; BFT, backfat thickness; BMT, back muscle thickness for the interaction between farming system (Indoor, IND vs Outdoor, OUT), cohort (17 vs 18, ewes born in the year 2017 vs 2018, respectively) and physiological stages (M, 2 wk before mating; P, mid-pregnancy, 8 wk after mating; bL, 2 wk before-lambing; aL, 3 wk after-lambing; W, 2 d after weaning) in Romane ewes. Error bars that overlap suggest no significant differences (P > 0.05), while non-overlapping error bars indicate statistically significant differences (P ≤ 0.05).

Figure 3.

Least Squares Means (LSMeans) for non-esterified fatty acids (NEFA), β-hydroxybutyrate (BHB), triiodothyronine (T3), insulin (INS) for the interaction between farming system (Indoor, IND vs Outdoor, OUT), cohort (17 vs 18, ewes born in the year 2017 vs 2018, respectively) and physiological stages (M, 2 wk before mating; P, mid-pregnancy, 8 wk after mating; bL, 2 wk before-lambing; aL, 3 wk after-lambing; W, 2 d after weaning) in Romane ewes. Error bars that overlap suggest no significant differences (P > 0.05), while non-overlapping error bars indicate statistically significant differences (P ≤ 0.05).

Generally, ewes-reared IND had greater (P ≤ 0.05) BW and BMT (Fig. 2) across all PhySt compared to those in OUT, regardless of Coh. However, ewes from Coh18 were heavier than those from Coh17 at P in the OUT FS (P ≤ 0.05), while in the IND FS, ewes in Coh17 were heavier at P (P ≤ 0.05). Regarding BMT, overall, ewes in Coh17 exhibited consistently greater (P ≤ 0.05) levels at several PhySt, irrespective of the FS. For BCS and BFT, a similar tendency was observed across all PhySt in both Coh17 and Coh18, regardless of the FS (P > 0.05). In both Coh, ewes raised in IND generally had greater (P ≤ 0.05) BCS levels than those in OUT from M to bL, except for Coh18 IND at bL, where BCS levels were comparable to those in OUT (P > 0.05). Interestingly, from aL to W, BCS levels remained stable in both FS for Coh17 and Coh18, except for Coh18 IND, which showed a notable decline in BCS levels from aL to W (P ≤ 0.05). Additionally, Coh17 ewes in IND consistently had greater (P ≤ 0.05) BFT levels than Coh18 ewes, while no significant differences (P > 0.05) in BFT were observed between the Coh in the OUT FS (Fig. 2).

Regardless of Coh and FS, NEFA concentrations were similar (P > 0.05) from M to P, except at P for ewes-reared OUT, where Coh17 showed lower (P ≤ 0.05) NEFA levels (Fig. 3). Overall, at bL and W, Coh17 had greater (P ≤ 0.05) NEFA levels in both IND and OUT, and specifically in IND at W. Overall, across PhySt, BHB levels were comparable (P > 0.05) between Coh17 and Coh18 (Fig. 3). However, at bL, Coh18 had greater (P ≤ 0.05) levels in OUT, while at aL, it had greater (P ≤ 0.05) levels in IND. At W, Coh17 showed greater (P ≤ 0.05) BHB levels in both FS. For T3, regardless of FS, Coh17 consistently showed greater (P ≤ 0.05) levels across PhySt in both IND and OUT. Overall, across PhySt, INS levels were greater (P ≤ 0.05) in Coh18 compared to Coh17 in both IND and OUT, except at P, where Coh17 had greater (P ≤ 0.05) levels in OUT.

The interactions among FS, PhySt, and LSi (Table 2) significantly affected all BR traits. The LSMeans for BCS, BW, BFT, and BMT in this interaction are shown in Fig. 4. LSMeans with overlapping error bars are not statistically different (P > 0.05), whereas those with non-overlapping error bars are considered statistically different at P ≤ 0.05. Overall, BW, BCS, BFT, and BMT levels were greater in ewes kept IND compared to those kept OUT (P ≤ 0.05), regardless of LSi and across all PhySt, except for BCS at aL and W and BFT at W (Fig. 4). Generally, irrespective of FS, greater BW and BMT was observed in ewes with larger LSi from M until bL (P ≤ 0.05), however, from aL to W, BW was greater IND in ewes that had smaller LSi (P ≤ 0.05) and this difference was not observed in ewes-reared OUT. Overall, ewes with larger LSi had greater BCS and/or BFT levels from M to P (P ≤ 0.05), regardless of FS. However, this pattern reversed from bL to W, where ewes with larger LSi lost more BCS than their counterparts, a trend more pronounced in those reared IND (P ≤ 0.05). Overall, from M to aL, NEFA levels were consistently greater in ewes with larger LSi reared in IND (P ≤ 0.05), while this difference was not evident in OUT (P > 0.05), except at bL (Fig. 5). BHB levels were generally comparable across all LSi and PhySt in both FS (P > 0.05), except from bL to W, where they were elevated in ewes with larger LSi (P ≤ 0.05). Overall, T3 and INS levels were similar (P > 0.05), across all LSi and PhySt; however, at bL, ewes with smaller LSi had greater INS levels in both FS (P ≤ 0.05). At aL, INS levels were lowest (P ≤ 0.05) in OUT for ewes with the largest LSi (class 5) and highest (P ≤ 0.05).in IND for LSi class 4 (Fig. 5).

Figure 4.

Least Squares Means (LSMeans) for BW, body weight; BCS, body condition score; BFT, backfat thickness; BMT, back muscle thickness for the interaction between farming system (Indoor, IND vs Outdoor, OUT), litter size class and physiological stages (M, 2 wk before mating; P, mid-pregnancy, 8 wk after mating; bL, 2 wk before-lambing; aL, 3 wk after-lambing; W, 2 d after weaning) in Romane ewes. Error bars that overlap suggest no significant differences (P > 0.05), while non-overlapping error bars indicate statistically significant differences (P ≤ 0.05).

Figure 5.

Least Squares Means (LSMeans) for non-esterified fatty acids (NEFA), β-hydroxybutyrate (BHB), triiodothyronine (T3), insulin (INS) for the interaction between farming system (Indoor, IND vs Outdoor, OUT), litter size class and physiological stages (M, 2 wk before mating; P, mid-pregnancy, 8 wk after mating; bL, 2 wk before-lambing; aL, 3 wk after-lambing; W, 2 d after weaning) in Romane ewes. Error bars that overlap suggest no significant differences (P > 0.05), while non-overlapping error bars indicate statistically significant differences (P ≤ 0.05).

The FS × Par × Coh interaction (Table 2) significantly affected all BR traits analyzed. Only BW was significantly affected by FS × Par × Line (P < 0.001) and FS × Par × LSi (P ≤ 0.05). The FS × LSi × Coh interaction significantly affected BW and BCS (P < 0.001), while FS × LSi × Line affected only BHB and INS (P ≤ 0.05). The FS × Coh × Line interaction did not significantly affect any BR traits (P > 0.05) whereas the interaction among FS, PhySt, and line had a significant effect only on INS (P < 0.001).

Discussion

Previous studies highlight the importance of BRD in enabling ruminants to overcome NEB (Bauman and Bruce Currie, 1980; González-García et al., 2014; Macé et al., 2019) making it a promising trait for designing resilient livestock systems in the climate change context. This study investigated key body condition traits and plasma biomarkers to characterize BRD and associated phenotypic variability in how half-sib Romane ewes (i.e., having half of their genetic background in common through sires) raised under 2 contrasting FS (IND vs. OUT). Most studies on BRM/BRA, hormone concentrations, and nutrient intake have focused on dairy breeds, with limited data available for meat breeds, which limited the possibility of making direct comparisons. However, comparisons have been made with dairy breeds, as some physiological mechanisms governing body reserve dynamics have been reported to be similar (González-García et al.; 2014, 2015).

Farming system effect on body reserve dynamics

The environment significantly influences BRD and resilience in sheep (Rust, 2019), particularly when comparing animals from contrasting environments with common genetic backgrounds (Colditz and Hine, 2016; Friggens et al., 2017). Precisely, variation in nutrition, climate, and housing conditions in contrasting rearing environments affect BR traits, including fat stores and muscle mass, which indicate an animal’s adaptive capacity and physiological stability under stress (Friggens et al., 2017).

Although FS alone did not affect BRD in this study, its interactions with PhySt, Par, LSi, and Coh suggest that FS may impact BR through factors such as feed availability, energy demands, or stress levels across different PhySt, likely contributing to the observed variation in BRD (Colditz and Hine, 2016; Friggens et al., 2017). Phenotypic differences can arise among genetically closest sheep raised in different environments; for instance, sheep in resource-rich IND systems may manage BR differently than those in challenging OUT conditions (Colditz and Hine, 2016; Friggens et al., 2017).

Overall BR mobilization and accretion

Using ewes, from a common breed and genetically connected, in 2 contrasting environments, we expected different patterns of BRM and BRA due to environmental factors such as nutritional availability, climate, and physical activity. As anticipated, the harsher and more unpredictable conditions in OUT typically require greater BRM to maintain homeostasis and meet the animals’ energy demands (Atti et al. 2004; Jouven et al., 2010; Friggens et al., 2013; González-García et al., 2014; Snoj et al., 2014). However, BRM and BRA were similar in both FS as indicated by comparable levels of plasma BHB (but not NEFA), T3, and the BCS around lambing. To meet the EU iSAGE project goals, specific feed management was implemented in the OUT system, particularly to prepare young ewes for the first mating, which may have contributed to similar BR management across both FS (McNamara et al., 2003). Additionally, targeted supplementation with high-quality hay and barley during late pregnancy likely improved the energy balance in ewes reared in this FS (Cavestany et al., 2008; Delany et al., 2010; Catunda et al., 2013; González-García et al., 2014). While nutritional strategies influence energy balance, Friggens et al. (2017) reported that BR mobilization is genetically driven, occurring regardless of feed availability suggesting that genetic factors regulate BRM as an adaptive mechanism, rather than it being solely a response to nutritional status. Blanc et al. (2006) further stressed that physiological adaptations supporting lipid metabolism, including increased lipolysis in adipose tissue, enhanced hepatic fatty acid oxidation, and endocrine regulation of insulin and leptin sensitivity, promote BRM. These adaptations are driven by gene expression changes primarily in adipose tissue and the liver, with HSL and adipose triglyceride lipase (ATGL) facilitating lipolysis, while peroxisome proliferator-activated receptors and carnitine palmitoyl transferase 1 regulate fatty acid oxidation. Additionally, Macé et al. (2022) reported leptin receptor gene (LEPR) as a key gene associated with BR levels and dynamics in ewes, suggesting its potential role in BR mobilization.

In both FS, BRM was observed from mid-pregnancy until weaning. Indeed, the decrease in back fat reserves, as indicated by simultaneous decreases in BCS and BFT from mid-pregnancy, or from bL until weaning, together with the kinetics of BW, indicated that ewes were mobilizing their BR during the second half of pregnancy and suckling, whatever the FS. Specifically, high BRM rates were most pronounced around lambing in ewes-reared IND, and from aL to weaning in those reared OUT, as indicated by elevated plasma concentrations of NEFA and/or BHB during these periods, peak T3 levels aL, and a decline in INS levels from aL. Overall, this observed BRM in both FS could be attributed to insufficient feed intake to cover energy requirements resulting from advancing pregnancy, an imbalance caused by fetus growth and an indication to cover the lamb’s requirements during suckling (Ingvartsen and Andersen, 2000; Nielsen et al., 2003; González-García et al., 2014; Smith et al., 2017). Additionally, Macé et al. (2019) reported BRM from mid-pregnancy to suckling and BRA from after weaning until the onset of the next pregnancy in extensively reared Romane ewes.

The observed high plasma NEFA levels exhibited around lambing in ewes-reared IND could be the result of their greater fat reserves, as indicated by BCS and/or BFT at mating and mid-pregnancy as animals with high prepartum-fat reserves have been reported to lose more fat during suckling, producing more NEFA than they can metabolize during BRM (Atti et al., 1995; Adewuyi et al., 2005). Indeed, greater BCS loss was reported in fat goats than thin ones (Kharrat and Bocquier, 2010) and in sheep, the ewes that were fatter at lambing lost more BW and BCS during suckling (Atti et al., 1995; Yagoubi and Atti, 2020), likely because they had more lipid tissues to be mobilized during harsh and/or energy-demanding periods. Additionally, the rise in average NEFA levels around lambing reflects increased lipolysis to meet the high energy demands of this critical period (Chilliard, 1999; Ingvartsen and Andersen, 2000). During early lactation, ewes remained in NEB until feed intake capacity increased, prompting further mobilization of lipid reserves (Bell, 1995). Notably, however, ewes-reared in OUT showed an unexpected pattern with declining NEFA levels during the latter half of pregnancy and a peak at weaning, observed in both PRIM and MULT ewes. The lower NEFA levels bL in ewes OUT may indicate slow BRM, likely linked to lower fat reserves as reflected by low BCS in OUT ewes. With limited fat tissue, these ewes may have struggled to meet energy demands, further evidenced by low INS levels during mating and mid-pregnancy. This underscores the need for targeted nutritional support earlier in gestation to mitigate energy deficits later on.

Although INS profiles have been reported to be negatively correlated with NEB (Chilliard et al., 1998) and NEFA profile, around lambing, ewes IND showed greater INS (and NEFA) levels than those reared OUT, an indication of potential INS resistance (Snoj et al., 2014). During pre-partum, greater fat reserves can lead to increased lipolysis (raising NEFA) and reduced INS sensitivity, causing elevated INS levels to maintain glucose homeostasis (González-García et al., 2014; Snoj et al., 2014).

As expected, BRA was observed from weaning until next mid-pregnancy as indicated by lower concentrations in INS until bL and post-weaning. INS plays a critical role in the homeostatic mechanisms (energy status), and its profile is known to be negatively correlated with NEB (Chilliard et al., 1998), thus with NEFA and BHB profiles. Increases in backfat reserves (BCS and BFT) and concurrent declines in NEFA, BHB, and T3 from weaning to mid-pregnancy, regardless of FS, further support BRA likely due to reduced energy requirements (Chilliard et al., 1998). BRA peaked at mid-pregnancy, likely suggesting an adaptive mechanism that enables ewes to prioritize and protect their reproductive investment during pregnancy (Friggens, 2003). Overall, recovery from energy deficits was more efficient in IND than OUT FS, as evidenced by greater INS levels and lower BHB levels at weaning, although BCS and BFT did not differ significantly between FS. However, recovery was slower in ewes with the largest LSi (LSi 5) across both systems, reflected in lower BCS, BFT and/or INS levels in this class. Our findings are in agreement with those reported by González-García et al. (2014, 2015) who demonstrated greatest BRM occurring around lambing and suckling, and accretion occurring after weaning and late lactation in suckler and dairy ewes, respectively. They noted low INS levels during the BRM phase (around lambing and suckling), which contrasts with present findings showing that INS levels were generally highest aL, regardless of FS. This difference could be attributed to the feed supplementation around lambing since feed supplementation, especially around lambing, is essential for regulating metabolic hormones like INS, affecting energy balance during lactation (González-García et al., 2014).

Biological and environmental factors affect indicators for BR monitoring traits

Biological and environmental factors can substantially affect the variability of body condition traits, as well as plasma metabolite and hormone levels. Accounting for FS, PhySt, Par, LSi, or Coh is essential for accurately predicting the energy status of ewes in relation to BRM and BRA. In both FS, the dynamics of plasma metabolites and hormones during the BRM period (from mid-pregnancy until weaning) were comparable in PRIM and MULT ewes, but concentrations differed. Overall, in both FS the MULT ewes demonstrated a better recovery in response to BRM, which was best illustrated by lower NEFA and BHB from weaning to mid-pregnancy (BR replenishment phase), suggesting that Romane ewes, in this study, were more adaptive in their BR use and buildup with increase in parity, a ‘biological adaptation’ acquired with maturity in age regardless of the rearing environment. However, the elevated NEFA, BHB, and T3 levels in PRIM than MULT ewes from mid-pregnancy until weaning, lead to speculation that PRIM ewes were probably mobilizing more BR during this period than MULT ewes in both FS. This could be due to greater fat tissue rates but also to lower intake capacity, concurrent growth requirements, nutrient partitioning for biological functions (Meikle et al., 2004; Mekuriaw, 2023), and challenges related to social hierarchy (Walter et al., 2022) in PRIM ewes. Additionally, greater relative energy demands during their first pregnancy and lambing, smaller BW, and limited physiological adaptation likely contribute to increased BRM (Berry et al., 2006; Mezzetti et al., 2021). In agreement, Berry et al. (2006) reported that first-parity cows had lower BW and net energy intake, lost more BCS, and experienced prolonged NEB due to the combined energy demands of growth and lactation. In Lacaune dairy sheep, González-García et al. (2015) reported greater NEFA and BHB values in PRIM than in MULT ewes consistent with the results in the present study, but no significant difference in mean T3 according to parity. At the end of suckling until 1 wk after weaning, these authors also found greater T3 concentrations in PRIM ewes that nursed single lambs in Lacaune dairy breed (González-García et al., 2015).

The interaction between FS, PhySt, and LSi, significantly affected BRM and accretion in both FS. In both FS, BRM was more pronounced in ewes with larger LSi, as indicated by overall lower BFT levels, greater NEFA and/or BHB levels, and lower INS levels (except for class 4 LSi). Indeed, BW dynamics highlight metabolic trade-offs across FS and LSi as shown by greater BW in IND ewes than OUT ewes, except from aL to W, where differences narrowed. Similarly, in IND, ewes with larger LSi had higher BW from M to bL. However, aL, those with smaller LSi maintained higher BW, while no difference was found in the OUT ewes. This suggests that larger LSi contributed to greater BW gain bL, whereas post-lambing, higher lactation demands led to greater BW loss in IND ewes with larger LSi. In contrast, reduced BW loss aL in OUT ewes could be linked with reduced metabolic demands due to the lower BW and/or lower litter size in OUT, or potentially due to supplemental feeding. Overall, ewes with larger LSi lost more BR due to increased energy demands from larger litters and greater lactation costs. This greater BRM resulted in slower recovery, as indicated by lower INS levels at weaning and until the next mid-pregnancy, reflecting delayed metabolic recovery. The energy required for milk production in these ewes limits the restoration of body condition. Our results are in agreement with Macé et al. (2018, 2019) and González-García et al. (2014) who reported lower BCS and greater BW in Romane ewes with greater litter size. González-García et al. (2015) and Pesántez-Pacheco et al. (2019) also reported high NEFA and BHB levels in ewes carrying multiple lambs, and greater T3 levels in ewes with single lambs in dairy ewes, and greater INS in ewes carrying single lambs in both PRIM and MULT ewes (González-García et al., 2015). The litter size directly impacts the ewe’s energy requirements, leading to greater mobilization of BR (such as fat and muscle) when dietary intake alone cannot meet these demands (González-García et al., 2015).

The interplay between FS, Coh, and PhySt influenced BRM in ewes. IND ewes, particularly Coh17, had higher BW, BCS, and BFT compared to OUT ewes, likely due to their slightly greater birth weights (4.15 kg for IND vs. 4.09 kg for OUT in Coh17 and 3.92 kg for IND vs. 3.88 kg for OUT in Coh18), as well as improved nutrition, lower energy expenditure, and more stable environments. Greater NEFA, BHB, and T3, alongside lower INS levels in Coh17, suggest more BRM and slower recovery compared to Coh18, possibly driven by differences in metabolic demands and/or use of different sires, and social grouping. Additionally, in the OUT FS, this disparity could suggest that ewes in Coh17 could have experienced more variable feed availability, leading to greater reliance on BR, especially during critical periods such as late pregnancy and suckling. Overall, the disparity between Coh could be associated with the individual differences resulting from the use of different sires and/or the year effect (precisely in OUT conditions) including quality of the feeding regime (feed quality, or management adjustments). Epigenetics effects during time that ewes were in utero cannot be excluded to affect BRD in adult ewes, but this was probably limited in our experiment because pregnancy in OUT started in a more favorable season (i.e., autumn) and feed supplementation was provided during pregnancy.

Conclusion

This study underscores the critical role of BR dynamics in the adaptive capacities of Romane ewes raised either indoor or outdoor. Interactions of biological and environmental factors such as FS, physiological stage, litter size at lambing and suckling, parity, and cohort influenced the BR traits. In both FS, the expressed BR mobilization and accretion profiles were largely similar as reflected by comparable plasma levels of BHB (but not NEFA), T3, and BCS around lambing. The mobilization phase was observed from mid-pregnancy until weaning, while the reconstitution of reserves was observed from weaning until next mid-pregnancy in both systems. The multiparous ewes showed better recovery than primiparous ewes in both farms, suggesting improved metabolic efficiency with maturity. Larger litter size regardless of the rearing system induced greater BR mobilization and slower recovery, emphasizing the need for tailored nutritional strategies.

In general, BR management in sheep is shaped by biological and environmental factors, influencing resilience and productivity. These insights support the development of adaptive livestock management practices, crucial for enhancing sustainability in diverse FS, particularly in the face of climate variability.

Exploring the best combination of biochemical and zootechnical criteria and the most relevant physiological stages will aid in developing a phenotyping strategy suited to commercial farms. Future research should also focus on the genetic variability of BRD and its links to reproductive and production traits. Genomic analyses to identify quantitative trait loci associated with BRD phenotypes will be key to understanding its genetic basis and refining breeding strategies for improved ewe performance.

Glossary

Abbreviations

aL

after-lambing

bL

before-lambing

BCS

body condition score

BFT

back fat thickness

BHB

β-hydroxybutyrate

BMT

back muscle thickness

BR

body reserves

BRA

body reserves accretion

BRD

body reserves dynamics

BRM

body reserves mobilization

BW

body weight

Coh

cohort

FE

feed efficiency

FS

farming system

HSL

hormone-sensitive lipase

IND

indoor

INS

insulin

Line

genetic line of the sires

LSi

litter size

M

mating

MULT

multiparous

NEB

negative energy balance

NEFA

non-esterified fatty acids

OUT

outdoor

P

mid-pregnancy

Par

parity

PhySt

physiological stage

PRIM

primiparous

RFI

residual feed intake

T3

triiodothyronine

W

weaning

Funding sections

This research received partial funding from the European Union’s Horizon 2020 Research and Innovation program through the Innovation for Sustainable Sheep and Goat Production in Europe (iSAGE) project (grant agreement No. 679302). Agnes Nyamiel was supported by a Ph.D. grant jointly funded by Région Occitanie and Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE). The funding sources played no role in the study’s design, data collection, analysis, interpretation, writing of the manuscript, or the decision to publish the findings.

Conflict of interest statement

The authors confirm that there are no conflicts of interest to declare.

Author contributions

Agnes Nyamiel (Data curation, Formal analysis, Writing—original draft, Writing—review & editing), Dominique Hazard (Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing—review & editing), Didier Marcon (Investigation, Writing—review & editing), Christian Durand (Investigation, Writing—review & editing), Sébastien Douls (Investigation, Writing—review & editing), Gaetan Bonnafe (Investigation, Writing—review & editing), Flavie Tortereau (Investigation, Methodology, Writing—review & editing), Anne Tesnière (Investigation, Writing—review & editing), and Eliel González-García (Conceptualization, Supervision, Writing—review & editing)