domain: sdv info shs

text:
J A A Pires
email: jose.pires@clermont.inra.fr

C Delavaud

† Y Faulconnier

† D Pomiès

Y Chilliard

Effects of body condition score at calving on indicators of fat and protein mobilization of periparturient Holstein-Friesian cows

Keywords: bovine, lactation, body reserve, mobilization

The objective was to study the effects of body condition score (BCS) at calving on dairy performance, indicators of fat and protein mobilization, and metabolic and hormonal profiles during the periparturient period of Holstein-Friesian cows. Twenty-eight multiparous cows were classed according to their BCS (0 to 5 scale) before calving as low (BCS ≤2.5; n = 9), medium (2.75 ≤ BCS ≤ 3.5; n = 10), and high (BCS ≥3.75; n = 9), corresponding to a mean of 2.33, 3.13, and 4.17 points of BCS, and preceding calving intervals of 362, 433, and 640 d, respectively. Cows received the same diets based on preserved grass to allow ad libitum feed intake throughout the study, and lactation diet contained 30% of concentrate (dry-matter basis). Measurements and sampling were performed between wk -4 and 7 relative to calving. No significant effects were observed of BCS group on dry matter intake (kg/d), milk yield, BCS loss, plasma glucose, and insulin concentrations. The high-BCS group had the lowest postpartum energy balance and the greatest plasma concentrations of leptin prepartum, nonesterified fatty acids and β-hydroxybutyrate postpartum, insulin-like growth factor 1, and milk fat content. Milk fat yield was greater for the high-than the low-BCS group (1,681 vs. 1,417 g/d). Low-BCS cows had the greatest concentration of medium-chain fatty acids (e.g., sum of 10:0 to 15:0, and 16:0), and the lowest concentration and secretion of preformed fatty acids (e.g., cis-9 18:1) in milk fat. Milk protein secretion was lowest in the low-BCS group, averaging 924, 1,051, and 1,009 g/d for low-, medium-, and high-BCS groups, respectively. Plasma 3-methylhistidine was greater in wk 1 and 2 postpartum compared with other time points, indicating mobilization of muscle protein. Plasma creatinine tended to be lower and the 3-methylhistidine: creatinine ratio was greater in low-compared with medium-and high-BCS cows, suggesting less muscle mass but more intense mobiliza-tion of muscle protein in lean cows. High-BCS cows were metabolically challenged during early lactation due to intense mobilization of body fat. Conversely, limited availability of body fat in low-BCS cows was associated with increased plasma indicators of body protein mobilization during the first weeks of lactation, and lower milk protein secretion. These results should be confirmed using an experimental approach where calving BCS variation would be controlled by design.

INTRODUCTION

The periparturient dairy cow experiences profound physiological and metabolic adaptations to support lactation, simultaneous with environmental challenges due to transitions of diet, housing, and social interactions. Physiological adjustments include short-term (homeostasis) and long-term (homeorhesis/teleophoresis) hormonal changes and altered tissue response to key hormones that lead to increased net lipolysis, enhanced utilization of mobilized FA by peripheral tissues and the mammary gland, and mobilization of the limited glucose and amino acid reserves (e.g., liver glycogen and muscle protein; [START_REF] Bell | Adaptations of glucose metabolism during pregnancy and lactation[END_REF][START_REF] Chilliard | Metabolic adaptations and nutrient partitioning in the lactating animal[END_REF]. Furthermore, ruminants depend on gluconeogenesis as the major source of glucose, but the increase in hepatic glucose output during early lactation is insufficient to meet requirements, and other adjustments of nutrient partitioning must take place to cope with this shortfall [START_REF] Doepel | Differences in splanchnic metabolism between late gestation and early lactation dairy cows[END_REF], namely by the establishment of insulin resistance in peripheral tissues [START_REF] Bell | Adaptations of glucose metabolism during pregnancy and lactation[END_REF].

Fat and labile protein reserves are mobilized during early lactation, but the ability to use body protein is limited in quantity and duration. For instance, estimates have ranged from 10 to 90 kg of fat, and up to 24 kg of protein [START_REF] Komaragiri | Factors affecting body tissue mobilization in early lactation dairy cows. 1. Effect of dietary protein on mobilization of body fat and protein[END_REF][START_REF] Chilliard | Metabolic adaptations and nutrient partitioning in the lactating animal[END_REF][START_REF] Chibisa | Effects of peripartum propylene glycol supplementation on nitrogen metabolism, body composition, and gene expression for the major protein degradation pathways in skeletal muscle in dairy cows[END_REF]. No further protein mobilization occurred after 5 wk of lactation, whereas utilization of body fat continued until at least 12 wk postpartum [START_REF] Komaragiri | Factors affecting body tissue mobilization in early lactation dairy cows. 1. Effect of dietary protein on mobilization of body fat and protein[END_REF]. Continuous genetic selection with emphasis on milk yield has led to chronic low BCS in modern dairy cows, especially in grass-based systems, and to a decline in reproductive performance, probably due to prioritization of nutrient partition toward milk secretion [START_REF] Chagas | Invited review: New perspectives on the roles of nutrition and metabolic priorities in the subfertility of high-producing dairy cows[END_REF][START_REF] Roche | Invited review: Body condition score and its association with dairy cow productivity, health, and welfare[END_REF]. Limited information exists on the mobilization of body reserves in cows with extreme BCS because most studies focus on animals in the medium range, and receiving conventional highenergy lactation diets ad libitum [START_REF] Broster | Body score of dairy cows[END_REF][START_REF] Roche | Invited review: Body condition score and its association with dairy cow productivity, health, and welfare[END_REF]. Furthermore, correlation between BCS and body fat may be weaker at the lower end of the BCS scale, and it has been speculated that BCS loss in lean cows may correspond to muscle mobilization [START_REF] Chagas | Invited review: New perspectives on the roles of nutrition and metabolic priorities in the subfertility of high-producing dairy cows[END_REF]. Additionally, cows may undergo extended lactations when insemination is voluntarily delayed to avoid periods of extreme negative energy balance (EBAL), as a result of reproductive failure, or to group calvings in a defined season [START_REF] Kolver | Extending lactation in pasture-based dairy cows: I. Genotype and diet effect on milk and reproduction[END_REF][START_REF] Kay | Extending lactation in pasture-based dairy cows. II: Effect of genetic strain and diet on plasma hormone and metabolite concentrations[END_REF]. Animals that experience extended lactations may increase their BCS over time [START_REF] Kolver | Extending lactation in pasture-based dairy cows: I. Genotype and diet effect on milk and reproduction[END_REF], especially if rations are not solely composed of pasture. Semi-mountain dairy production systems in central France are mostly grass based, and include a fall/winter indoor period and a spring/summer grazing season. Despite year-round milking, calvings are generally concentrated in the fall and winter seasons. Cows that are not inseminated or do not become pregnant during the breeding period may be maintained in extended lactations. Therefore, the diversity of intercalving intervals within a herd is likely to lead to BCS heterogeneity. The objective of this study was to assess the effects of BCS at calving on the adaptability of dairy cows to a moderate-energy diet based on preserved grass. Namely, to study how different calving BCS that resulted at least in part from variable calving intervals relate to dairy performance, changes in BW and BCS, plasma indicators of fat and protein mobilization, and overall metabolic and hormonal profile during the periparturient period. We expected that cows calving with high BCS would be metabolically challenged during early lactation due to mobilization of body fat. Conversely, we hypothesized that utilization of body reserves would be altered in low-BCS cows due to the limited availability of body fat, and that mobilization of body protein during the first weeks of lactation would be more intense in these animals.

MATERIALS AND METHODS

Animals, Diets, and Housing

The experiment was conducted in the Orcival farm of the Institut National de la Recherche Agronomique (INRA), located in a semi-mountain grassland area (1,000 m of altitude) in the Auvergne region (Puy-de-Dôme, France). Twenty-eight multiparous Holstein-Friesian cows were studied from 4 wk before expected calving until 7 wk postpartum. Cows were classed according to their BCS before calving as low (BCS ≤2.5; n = 9), medium (2.75 ≤ BCS ≤3.5; n = 10), and high (BCS ≥3.75; n = 9) using the French notation scale of 0 to 5 (Table 1; [START_REF] Bazin | Grille de notation de l'état d'engraissement des vaches pie-noires[END_REF]. Different BCS were due to differences in previous intercalving intervals (Table 1). In the North-American 1 to 5 scale [START_REF] Edmonson | A body condition scoring chart for Holstein dairy cows[END_REF], low-, medium-, and high-BCS groups would correspond to BCS ≤3.0, 3.2 ≤ BCS ≤3.8, and BCS ≥4.0, respectively, assuming a linear relationship between scoring systems. Cows received identical diets to allow ad libitum feed intake throughout the study (10% refusals). Diet ingredient, nutrient, and FA composition are presented in Table 2. The far-off dry period diet was mixed forage composed of 75% grass silage and 25% hay (DM basis) from permanent semi-mountain grasslands rich in Dactylis glomerata (orchardgrass). Starting 3 wk before the expected calving date, the forage mixture was top-dressed with 2 kg of the lactation concentrate. Lactation TMR was (DM basis) 46.5% grass silage, 22% hay, 30% concentrate, and 1.5% of a mineral and a vitamin premix. The lactation diet was initially formulated to meet 85% of energy and 100% of protein requirements to produce 32 kg/d of milk based on table values (INRA, 2007). Cows were housed in a freestall barn with individual feed bunks equipped with automatic gates, were milked at 0630 and 1600 h, and had free access to water throughout the study.

Sampling and Analyses

Blood samples were collected from the jugular vein after morning milking and before feeding at wk -4, -2, 1, 2, 3, 5, and 7 relative to calving date, corresponding to -28, -14, 4, 11, 18, 32, and 46 DIM, respectively (SD = 7 DIM prepartum and 1 DIM postpartum). Blood samples were drawn into evacuated tubes containing either EDTA (1.95 mg/mL) or Li-heparin (10 IU/mL; Terumo Europe NV, Leuven, Belgium). Plasma was separated by centrifugation at 1,400 × g for 15 min at 4°C and frozen at -20°C. Plasma (EDTA) was analyzed for glucose, NEFA, BHBA using enzymatic methods, and for insulin and leptin using RIA, as previously described [START_REF] Delavaud | Plasma leptin concentration in adult cattle: Effects of breed, adiposity, feeding level, and meal intake[END_REF]Lerch et al., 2012a). Plasma IGF1 concentration was determined by RIA (IGF-I RIA-CT; Mediagnost GmbH, Reutlingen, Germany) validated for the bovine. Heparinized plasma was analyzed for 3-methylhistidine (3-MH) by HPLC [START_REF] Adechian | Protein feeding pattern, casein feeding, or milk-soluble protein feeding did not change the evolution of body composition during a short-term weight loss program[END_REF], and creatinine was quantified in selected samples (wk -4, 1, 2, and 7) using an auto-matic analyzer (Jaffe method; Modular P800 analyzer; Roche Diagnostics, Mannheim, Germany). Intra-and interassay coefficients of variation were 1.8 and 2.8% for glucose, 2.8 and 7.7% for NEFA, 1.8 and 3.8% for BHBA, 0.7 and 2.3% for creatinine, 9 and 9.6% for insulin, 5.8 and 11.6% for leptin, and 4.6 and 15.0% for IGF-1, respectively. Feed and refusals were weighted 4 d per wk, and subsamples were collected weekly and pooled monthly to determine the DM content and calculate DMI. Forage and concentrate samples were collected monthly, stored at -20°C, and analyzed for nutrient composition, as previously described (Lerch et al., 2012a). Diet energy and protein truly digestible in the small intestine (PDI) content and energy and PDI balances were calculated according to the INRA ( 2007) system (Tables 2 and3). Milk yield was recorded daily, and milk samples were collected at 4 consecutive milkings each week and analyzed for components by near-infrared spectrometry (Lerch et al., 2012a). Milk FA composition was determined by gas-liquid chromatography in pooled samples from morning and evening milking collected on the same day of blood drawing, as previously described (Lerch et al., 2012b). Secretion of individual milk FA was calculated according to [START_REF] Glasser | Technical note: Estimation of milk fatty acid yield from milk fat data[END_REF]. Cows were weighed weekly and BCS [START_REF] Bazin | Grille de notation de l'état d'engraissement des vaches pie-noires[END_REF] was recorded once during the 2 wk before expected calving and weekly thereafter. All procedures were conducted in accordance with the French guidelines for experimental animal use.

Calculations and Statistical Analyses

Data were analyzed using PROC MIXED of SAS (version 9.1; SAS Institute Inc., Cary, NC) with repeated measures. The statistical model included the fixed effects of BCS group, parity (second or third and greater), time, and 2-way interactions with time, cow as a random effect, and Kenward-Rogers adjustment for calculation of denominator degrees of freedom.

Variance-covariance structures [spatial power, [AR(1)], compound symmetric (CS)] and heterogeneous variances were selected depending on which yielded the best fit according to the Schwarz Bayesian criterion. Nonsignificant 2-way interactions were removed from the model for all variables except for milk FA composition and secretion, for which the full statistical model and spatial power variance-covariance structure were kept.

Logarithmic transformation was used whenever needed to comply with the assumptions of normality and homoscedasticity of the residuals. When transformation was needed, least squares means and standard error of the mean were estimated from untransformed values, and P-values reflect statistical analyses of transformed data. Values reported are least squares means and standard error of the mean, unless otherwise stated. The significance level was predefined at P ≤ 0.05, and trends toward significance were considered at 0.05 < P ≤ 0.10. The SLICE option was used to compare treatment differences at individual time points when treatment or treatment by time interaction was significant. A contrast comparing plasma creatinine, 3-MH and the ratio 3-MH:creatinine of low-versus medium-and high-BCS groups was used to test the hypothesis of different muscle mobilization in lean cows. Means within a row with different superscript letters tend to differ (P = 0.06).

1 Parity during the current lactation.

2 Parity by time interaction was nonsignificant for all variables; BCS group by time interaction was nonsignificant for all variables except for postpartum BW (P < 0.05).

3 Weeks 3, 2, and 1 prepartum.

4 PDI = protein truly digestible in the small intestine.

5

Weeks 1 to 7 postpartum.

6

Scale from 0 = thin to 5 = fat.

RESULTS

BCS, Weight, and Intake

The BCS before calving was 2.33, 3.13, and 4.17 ± 0.10, and the preceding calving interval was 362, 433, and 640 d for low-, medium-, and high-BCS groups, respectively (P < 0.001; Table 1). All the cows in this study were multiparous and 17 out of the 28 were starting their second lactation. The number of lactations did not differ among the 3 BCS groups (Table 1). The differences in postpartum BCS were consistent with calving BCS classes (P < 0.001), but the BCS change during the first 7 wk of lactation did not differ across BCS groups (Table 3; Figure 1). Body weight was lowest for the low-BCS group throughout the study (P < 0.01), but it did not differ between medium-and high-BCS groups (Table 3; Figure 1). Body weight loss during the first 7 wk of lactation did not differ between high-and medium-BCS groups, but the low-BCS group lost less BW than the medium-BCS group (P < 0.05;

Table 3) and tended to lose less BW than the high-BCS group (P < 0.10; Table 3).

Prepartum DMI decreased gradually during the last 3 wk of gestation. It was 16.0, 15.4, and 14.3 kg/d (P < 0.001) at wk -3, -2, and -1, respectively, and did not differ among BCS classes (Table 3; Figure 1). Overall, DMI increased significantly during the first 3 wk postpartum, and tended to be greater in the medium-BCS group, averaging 19.5, 21.4, and 19.9 kg/d during the first 7 wk of lactation for low-, medium-, and high-BCS group, respectively, and no BCS group by time interaction was observed (Table 3; Figure 1).

Cows in second lactation had experienced a shorter prior calving interval (416 vs. 572 d; P < 0.01; Table 1), presented lower prepartum BCS (2.90 vs. 3.68 points; P < 0.001; Table 1) and BW (749 vs. 809 kg; P < 0.001; Table 3), and lost less BCS (1.14 vs. 1.57 points; P < 0.05; Table 3) and BW (98 vs. 133 kg; P < 0.01; Table 3) during the first 7 wk of lactation, compared with cows in the third-and-greater lactation.

Milk Production and Milk Composition

Milk yield was 33.4, 36.4, and 35.2 kg/d for low-, medium-, and high-BCS groups, respectively (Table 4; Figure 2) but did not differ statistically. Fat-corrected milk yield tended to differ across BCS groups (Table 4). Milk fat content was greatest for the high-BCS group (P < 0.01) and did not differ between medium-and low-BCS groups (Table 4; Figure 2). Milk fat yield was greater for the high-than low-BCS group (1,681 vs. 1,417 g/d;P < 0.05;Table 4;Figure 2). Milk protein yield was lowest for the low-BCS group (P < 0.05; Table 4; Figure 2), averaging 924, 1,051, and 1,009 g/d for low-, medium-, and high-BCS groups, respectively.

Milk yield tended to be lower, and milk fat content, fat yield, and FCM yield were lower for second-compared with third-and-greater-lactation cows (P < 0.05; Table 4). No parity effects were observed on milk protein content and yield.

Energy and Protein Balance

Prepartum EBAL remained positive but decreased from 26.7 MJ/d at wk -3 to 20.6 and 12.4 MJ/d at wk -2 and -1 (P < 0.001), respectively, and did not differ among BCS groups (Table 3; Figure 1). Postpartum EBAL was lowest (P < 0.01) for the high-BCS group, did not differ between low-and medium-BCS groups (Table 3; Figure 1), and averaged -23.9, -25.9, and -39.7 MJ/d for low-, medium-, and high-BCS groups, respectively (Table 3; Figure 1). None of the 3 BCS groups reached positive EBAL by wk 7 postpartum, when it was -11.0, -4.6, and -30.9 MJ/d for low-, medium-, and high-BCS groups, respectively (Figure 1).

Prepartum PDI balance did not differ across BCS groups and decreased from 539 to 363 g/d at wk -3 and -1, respectively (P < 0.001). Overall, PDI balance was negative during the first 7 wk postpartum, averaging -596, -350, -254, -178, -131, and -72 g/d at wk 2 to 7 (P < 0.001), respectively. Postpartum PDI balance was lower for the high-compared with the low-BCS group (P < 0.05), but the medium-BCS group did not differ from the other 2 BCS classes (Table 3). Postpartum EBAL was lower (-36.9 vs. -22.8 MJ/d; P < 0.001) and PDI balance tended to be lower (301 vs. -225 g/d; P = 0.10; Table 3) for third-and-greatercompared with second-lactation cows.

Plasma Metabolites and Hormones

Plasma Metabolites. Plasma glucose concentration did not differ across BCS groups (Table 5; Figure 3). Plasma NEFA concentrations were greatest for the high-BCS group (P < 0.05; Table 5), with significant effects postpartum (Figure 3). At wk 1 and 2, plasma NEFA was lowest for the low-BCS group, and the medium-BCS group did not differ from the high-BCS group. At wk 3 and 5, the medium-BCS group did not differ from the other 2. At wk 7, plasma NEFA was greatest in the high-BCS group (Figure 3). The high-BCS group presented the greatest BHBA at wk 5 and 7 postpartum (Figure 3), leading to a BCS group by time interaction (Table 5).

Plasma NEFA (P < 0.01) and BHBA (P < 0.05) concentrations were greater in third-and-greater-than second-lactation cows (Table 5). A parity by week interaction was observed for BHBA concentration because effects were significant only at wk 2, 3, and 7 postpartum (data not shown). A parity by week interaction was observed for plasma glucose concentration because third-and-greater-lactation cows had greater glucose concentrations at wk -4 (70.2 vs. 66.8; P < 0.05), but glucose concentration tended to be lower postpartum (55.9 vs. 59.4 mg/dL; P = 0.07) than in second-lactation cows (data not shown). Means within a row with different superscript letters differ (P < 0.05). 1 Parity during the current lactation. 2 Parity by time interaction was nonsignificant for all variables.

3 Weeks 1 to 7 of lactation. 4 Weeks 2 to 7 of lactation.

Plasma Indicators of Muscle Mobilization.

Plasma 3-MH concentrations increased postpartum but decreased quickly (Figure 4). Plasma 3-MH was greater in wk 1 and 2 postpartum compared with all other time points (P < 0.001), and it was lower than prepartum at wk 5 and 7 (P < 0.001; Figure 4). No BCS effects were observed on 3-MH concentration, despite numerically greater values in low-BCS cows (Table 5).

Plasma creatinine was analyzed at wk -4, 1, 2, and 7. Overall, creatinine concentration decreased from 71.0 μM at wk -4 to 56.9 μM at wk 7 (P < 0.001; Figure 4), and tended to be lower for low-compared with medium-and high-BCS cows (contrast: P = 0.10; Table 5; Figure 4). Cows in second lactation had greater creatinine concentration than cows in thirdand-greater lactation, and a parity by week interaction existed (Table 5) because differences disappeared after the first week postpartum (data not shown). The ratio of 3-MH:creatinine increased at wk 1 and 2 relative to wk 4 and 7 (P < 0.001), and was greater for low-BCS cows compared with animals with medium and high BCS (contrast: P < 0.05; Table 5; Figure 4).

Hormonal Profile. Plasma insulin decreased at calving, remained low postpartum, and did not differ across BCS classes (Figure 5; Table 5). A parity by week interaction was observed for plasma insulin Means within a row with different superscript letters differ (P < 0.05).

x,y

Means within a row with different superscript letters tend to differ (P = 0.07).

1 Parity during the current lactation.

2 Samples collected at wk -4, -2, 1, 2, 3, 5, and 7 relative to calving. concentration (P < 0.05) because insulin concentration was greater for second-compared with third-and-greater-lactation cows during the second week of lactation (14.7 vs. 11.9 μIU/mL; P < 0.01; data not shown).

Plasma IGF-1 concentrations were greatest for the high-BCS group throughout the study (P < 0.05; Table 5; Figure 5). At wk -4, IGF-1 concentration was 208, 156, and 150 ± 14 ng/mL for high-, medium-, and low-BCS cows (P < 0.001), respectively. Plasma IGF-1 concentrations decreased sharply postpartum and then increased gradually during the first 7 wk of lactation (P < 0.001; Figure 5). Postpartum IGF-1 concentration was 64, 50, and 45 ± 6.4 ng/mL for high-, medium-, and low-BCS cows (P < 0.05), respectively. A parity effect and parity by time interaction were observed on IGF-1 concentration, which was greater for secondthan third-and-greater lactation cows (Table 5), with significant differences postpartum (data not shown).

Prepartum plasma leptin concentration was greatest in the high-BCS group and did not differ between medium-and low-BCS classes (P < 0.05; Figure 5). A significant correlation existed between prepartum BCS and plasma leptin concentration in wk -2 (P < 0.001; R 2 = 0.31), but leptin variance was greater in the high-BCS group (Levene's test: P < 0.05) due to 3 high values in overconditioned cows (data not shown). Leptin concentrations decreased sharply at calving for all BCS groups, and postpartum levels remained lower than prepartum throughout the first 7 wk of lactation (P ≤ 0.05). No effect of BCS group was observed on postpartum leptin concentration (Table 5; Figure 5).

Milk FA Composition and Secretion

Milk FA composition and secretion of selected FA are presented in Table 6 and Figure 6. There was a significant time effect for milk content of all FA studied except cis-9 16:1. Milk fat concentration of most major FA (i.e., content ≥2%) was affected by calving BCS, except for 18:0, 6:0, 4:0, ∑trans FA, and ∑trans 18:1. Cows calving with low BCS had greater milk content of 10:0, 12:0, 14:0, 15:0, 16:0, 20:0, 20:3n-6, 22:0, ∑ SFA, ∑ odd-and branched-chain FA (OBCFA), and ∑ 10:0 to 15:0 than the high-BCS group, whereas cows calving with high BCS had greater milk content of cis-9 18:1, ∑ cis 18:1, cis-9,cis-12 18:2, cis-9,trans-11 conjugated linoleic acid, cis-11 20:1, ∑ MUFA, and ∑ PUFA. No statistical differences existed between highand medium-BCS groups for most FA. The exceptions were 15:0, cis-9 16:1, 18:3n-3, cis-9 20:1, 20:3n-6, and ∑OBCFA. The medium-BCS group had the greatest concentrations of 18:3n-3 and cis-9 20:1 of all 3 BCS classes. Only the concentration of 15:0 differed across the 3 calving BCS groups. Secretion of milk cis-9 18:1, cis-9,cis-12 18:2, and 18:3n-3 was lowest for cows calving with low BCS, and did not differ between the highand medium-BCS groups. No BCS class effects were observed on secretion of milk medium-chain FA in milk fat (Table 6). Cows in second lactation had greater concentrations of milk 6:0, 8:0, 10:0, cis-9 10:1, 12:0, 14:0, 15:0, trans-11 18:1, ∑ trans 18:1, cis-9,trans-11,cis-15 conjugated linolenic acid, 20:0, cis-9 20:1, 22:0, 22:5n-3 (+ 26:0), 24:0, ∑ SFA, ∑ OBCFA, ∑ trans FA, and ∑ 10:0 to 15:0, than cows in third-and-greater lactation. And the latter had greater concentrations of cis-9 18:1, ∑ cis 18:1, 18:3n-3, and ∑ MUFA. No parity effects were observed on milk 16:0 concentration. Cows in third-and-greater lactation had greater secretion of milk 16:0, 17:0, 18:0, cis-9 18:1, cis-9,cis-12 18:2, and 18:3n-3 than cows in second lactation. No parity effects were observed on secretion of C10 to C15 FA.

DISCUSSION

BCS, Dairy Performance, and EBAL

Semi-mountain dairy production systems in the Auvergne region of France are traditionally grass-based and calvings are concentrated in the fall and winter seasons. Cows may undergo extended lactations, and individual differences in calving interval contribute to promote herd BCS heterogeneity. This variability has to be managed, and a need exists to better understand the dynamics of body reserve mobilization. In the present study, the BCS differences at calving were interrelated with the duration of previous calving interval. The BCS before calving was 2.33, 3.13, and 4.17 on a 6-point (0 to 5) scale for low-, medium-, and high-BCS groups, respectively, and the average calving interval among the 3 BCS groups ranged from 362 to 640 d (Table 1).

Despite the differences in calving BCS, postpartum BCS loss did not differ among groups. Body condition scoring was not sensitive enough to detect differences in body reserve mobilization during early lactation. This may be due to the use of internal fat reserves, and the relatively short duration of the study. The literature shows a positive relationship between calving BCS and BCS loss postpartum, and the number of days to reach the nadir of BCS [START_REF] Broster | Body score of dairy cows[END_REF]Roche et al., 2009). The lack of differences in BCS loss in this study could be explained because it lasted only until 7 wk, whereas the nadir of BCS in fatter cows may occur after 12 wk postpartum [START_REF] Broster | Body score of dairy cows[END_REF]. When BCS loss is expressed as percentage of prepartum BCS, low-, medium-, and high-BCS groups lost respectively 53, 47, and 33% of BCS by wk 7, further suggesting that high-BCS cows were still mobilizing body fat at the end of the study. Body weight was lowest for low-BCS cows but it did not differ between medium-and high-BCS cows, which may be due to differences in body size or body composition between cows in medium-and high-BCS groups, which were not measured.

Cows with medium BCS tended to have greater postpartum DMI than low-and high-BCS groups. Some studies showed a negative association between calving BCS and postpartum DMI, but this is not always the case [START_REF] Broster | Body score of dairy cows[END_REF][START_REF] Roche | Invited review: Body condition score and its association with dairy cow productivity, health, and welfare[END_REF]. Deleterious effects of high BCS on DMI may not be de- Means within a row with different superscript letters tend to differ (P = 0.06). 1 Weeks 1 to 7 postpartum. 2 Weeks 2 to 7 postpartum. 3 Parity during the current lactation. 4 Parity by time interaction was nonsignificant for all variables except for concentration of cis-9 14:1, trans-11 18:1, cis-9,trans-1,cis-15 conjugated linolenic acid (CLnA), 20:3n-6 and 20:5n-3 (P < 0.05); BCS group by time interaction was nonsignificant for all variables except for concentration of cis-9 10:1, cis-9 14:1, 15:0, and ∑ odd-and branched-chain FA (OBCFA; P ≤ 0.05).

5

Contained trans-7,cis-9 and trans-8,cis-10 conjugated linoleic acid (CLA) as minor components.

tectable during the first weeks of lactation [START_REF] Broster | Body score of dairy cows[END_REF]. Additionally, the BCS effects on DMI may result from pre-and postpartum diets and their interactions. In the present study, the lactation TMR contained only 30% concentrate and 6.1 MJ of NE L /kg of DM (Table 1). Low-energy diets may affect the ability of lean cows to compensate for the limited availability of body fat via increased DMI, due to physical constraints imposed by bulkier feedstuffs [START_REF] Garnsworthy | The influence of body condition at calving and dietary-protein supply on voluntary food intake and performance in dairy cows[END_REF][START_REF] Chilliard | Physiological constraints to milk production: Factors which determine nutrient partitioning, lactation persistency, and mobilization of body reserves[END_REF]. Because all cows in this study received the same diets, interactions between pre-and postpartum diets on postpartum DMI are not likely. Nonetheless, we cannot exclude that the relatively short duration of the study postpartum may have precluded the detection of eventual BCS effects on DMI.

The literature shows a curvilinear relationship between BCS at calving and milk production [START_REF] Broster | Body score of dairy cows[END_REF][START_REF] Roche | Invited review: Body condition score and its association with dairy cow productivity, health, and welfare[END_REF]. In the current study, only numerical effects of BCS class were observed on milk yield during the first 7 wk of lactation, with lean cows producing 3 kg/d less than the medium-BCS group, and the latter producing 1.2 kg/d more than the high-BCS group. Cows with high BCS at calving produced milk with greater fat content than lean cows, reflecting the availability of body fat in the former. Enhanced mobilization of preformed FA in fatter cows is confirmed by plasma NEFA concentrations and milk FA profile and secretion, which is in agreement with the literature [START_REF] Garnsworthy | The effects of body condition of dairy cows at calving, food intake and performance in early lactation on blood composition of dairy cows given complete diets[END_REF]. Accordingly, cows in the high-BCS group had the lowest EBAL. Conversely, low-BCS cows had the lowest plasma NEFA concentrations during the first 2 wk of lactation and the lowest milk protein yield of the 3 BCS classes. Under low-energy diets, lean cows reduce milk and component yield to a greater extent than cows calving with moderate BCS, because lean animals have limited ability to rely on body reserves to sustain lactation [START_REF] Chilliard | Physiological constraints to milk production: Factors which determine nutrient partitioning, lactation persistency, and mobilization of body reserves[END_REF]. Effects of BCS at calving on composition of selected milk FA in early-lactation Holstein-Friesian cows. Milk samples were collected 4, 11, 18, 32, and 46 ± 1 d relative to calving. The BCS before calving was 2.33, 3.13, and 4.17 on a 6-point (0 to 5) scale for low-, medium (Med)-, and high-BCS groups, respectively. P-values of significant fixed effects in the statistical models are presented in Table 6. Significant BCS group differences within a week are indicated by * (panels A and B: P < 0.05; panel D: P < 0.01). Error bars indicate standard error of the mean. Color version available in the online PDF.

Plasma Indicators of Adipose and Muscle Mobilization

A sharp increase in plasma NEFA concentration is common at calving [START_REF] Doreau | Influence de la prise alimentaire sur les variations de différents constituants plasmatiques chez la vache en fin de gestation et en début de lactation[END_REF][START_REF] Reid | Effect of body condition at calving on tissue mobilization, development of fatty liver and blood chemistry of dairy cows[END_REF][START_REF] Vazquez-Añon | Peripartum liver triglyceride and plasma metabolites in dairy cows[END_REF]. It is concomitant with accumulation of triglycerides in the liver, which remain elevated during the first weeks postpartum and may alter hepatic function and lead to the onset of metabolic disorders [START_REF] Vazquez-Añon | Peripartum liver triglyceride and plasma metabolites in dairy cows[END_REF]. Overconditioning of dairy cows enhances plasma NEFA and liver triglyceride accumulation [START_REF] Rayssiguier | Influence de l'état corporel au vêlage et du niveau d'alimentation en début de lactation sur la stéatose hépatique chez la vache laitière[END_REF][START_REF] Reid | Effect of body condition at calving on tissue mobilization, development of fatty liver and blood chemistry of dairy cows[END_REF]. Furthermore, liver triglyceride infiltration is aggravated by feeding a low-energy diet postpartum [START_REF] Rayssiguier | Influence de l'état corporel au vêlage et du niveau d'alimentation en début de lactation sur la stéatose hépatique chez la vache laitière[END_REF]. In the present study, cows calving with high BCS had the greatest plasma NEFA and BHBA concentrations postpartum, but did not perform better than medium-BCS cows for milk and protein yield. In North-American conventional dairy systems, multiparous cows with plasma NEFA concentrations greater than 800 μM during wk 1 or 2 postpartum have 2 to 4 times greater risk of culling within first 60 DIM for cows [START_REF] Roberts | Metabolic parameters in transition cows as indicators for early-lactation culling risk[END_REF]. In the current study, cows calving with high BCS had plasma NEFA concentrations above this threshold, and plasma BHBA concentrations were greater than 1.2 mM after wk 1 postpartum, which indicates subclinical ketosis [START_REF] Roberts | Metabolic parameters in transition cows as indicators for early-lactation culling risk[END_REF].

Cows calving with medium BCS presented diverse production and metabolic profiles. They were closer to high-or low-BCS groups, depending on the variable studied. In some cases, such as for plasma NEFA concentration, the relationship changed over time because medium-BCS cows did not differ statistically from the high-BCS group during the first weeks after calving, but decreased to levels similar to low-BCS cows as lactation progressed. This diversity of responses found in the medium-BCS group probably reflects the diversity of individual adaptations during early lactation [START_REF] Hammon | Performance and metabolic and endocrine changes with emphasis on glucose metabolism in high-yielding dairy cows with high and low fat content in liver after calving[END_REF]. Furthermore, a potential confounding effect of frame size differences among groups cannot be excluded because BW did not differ between mediumand high-BCS cows, and could explain numerical effects on DMI and milk yield.

The amino acid 3-MH is a product of actin and myosin degradation that is not reused for protein synthesis. Urinary excretion of 3-MH around calving is an indicator of protein mobilization in early lactation [START_REF] Chibisa | Effects of peripartum propylene glycol supplementation on nitrogen metabolism, body composition, and gene expression for the major protein degradation pathways in skeletal muscle in dairy cows[END_REF]. Because creatinine excretion is proportional to body muscle mass, the urinary 3-MH:creatinine ratio may standardize muscle protein mobilization for total muscle mass [START_REF] Simmons | Protein and fat metabolism in cows given somavubove before parturition[END_REF]. Total urine collection is not feasible in most trials involving periparturient cows; therefore, plasma 3-MH concentration has been used as an indicator of muscle catabolism [START_REF] Blum | Variations of 3-methylhistidine in blood of dairycows[END_REF] and may discriminate cows receiving diets low in protein content during the periparturient period [START_REF] Doepel | Peripartum performance and metabolism of dairy cows in response to prepartum energy and protein intake[END_REF]. In the current experiment, plasma 3-MH presented the typical profile of the periparturient period and concentrations were within the ranges found by others [START_REF] Bell | Adaptations of glucose metabolism during pregnancy and lactation[END_REF][START_REF] Doepel | Peripartum performance and metabolism of dairy cows in response to prepartum energy and protein intake[END_REF]. Plasma 3-MH was significantly increased in wk 1 and 2, corresponding to samples collected on 4 and 11 ± 1 DIM, respectively. Therefore, the period of intense muscle protein mobilization was short, which is in agreement with previous estimates of protein mobilization postpartum [START_REF] Komaragiri | Factors affecting body tissue mobilization in early lactation dairy cows. 1. Effect of dietary protein on mobilization of body fat and protein[END_REF][START_REF] Chilliard | Metabolic adaptations and nutrient partitioning in the lactating animal[END_REF][START_REF] Chibisa | Effects of peripartum propylene glycol supplementation on nitrogen metabolism, body composition, and gene expression for the major protein degradation pathways in skeletal muscle in dairy cows[END_REF]. Plasma 3-MH concentrations on wk 5 and 7 were lower than prepartum (P < 0.001), as found in previous studies [START_REF] Kokkonen | Effect of body fatness and glucogenic supplement on lipid and protein mobilization and plasma leptin in dairy cows[END_REF], probably due to decreasing muscle mass during the first weeks of lactation. A numerically greater 3-MH concentration was observed in low-BCS cows, but interanimal variation of periparturient 3-MH profiles precluded the detection of statistical differences among BCS classes. Nonetheless, the uterus dry weight change during involution was estimated to be less than 2.5 kg, which may lead to a moderate contribution for plasma 3-MH in the dairy cow that may mobilize from near zero to 24 kg of protein during early lactation, as previously discussed [START_REF] Plaizier | Short communication: Effects of monensin on 3-methylhistidine excretion in transition dairy cows[END_REF]. Furthermore, because 3-MH is studied during the periparturient period for all cows, an eventual bias due to uterine involution would not affect the validity of group comparisons.

Plasma creatinine and the 3-MH:creatinine ratio was studied to assess and control for putative differences in muscle mass among BCS classes, because BCS may account for differences of muscle mass in addition to adiposity [START_REF] Reid | Effect of body condition at calving on tissue mobilization, development of fatty liver and blood chemistry of dairy cows[END_REF][START_REF] Kokkonen | Effect of body fatness and glucogenic supplement on lipid and protein mobilization and plasma leptin in dairy cows[END_REF]. Creatinine was analyzed at wk -4, 1, 2, and 7, which corresponded to baseline, intense mobilization, and postmobilization periods based on 3-MH data. Creatinine concentration at wk 7 of lactation was lower than prepartum (56.9 vs. 71.0 μM; P < 0.001). Previous studies showed a decrease in creatinine concentrations concomitant with a reduction in longissimus dorsi diameter during the periparturient period [START_REF] Bruckmaier | Longissimus dorsi muscle diameter, backfat thickness, body condition scores and skinfold values related to metabolic and endocrine traits in lactating dairy cows fed crystalline fat or free fatty acids[END_REF][START_REF] Kokkonen | Effect of body fatness and glucogenic supplement on lipid and protein mobilization and plasma leptin in dairy cows[END_REF]. The decrease in plasma creatinine concentration during early lactation could be partially explained by altered kidney function, because plasma creatinine concentration is a function of muscle mass and kidney glomerular filtration rate [START_REF] Perrone | Serum creatinine as an index of renal function: New insights into old concepts[END_REF]. Nonetheless, a study involving 6 cows at wk -4, 2, and 7 relative to calving showed no changes in filtration rate, despite increased urine volume at wk 7 [START_REF] Maltz | Kidney function and nitrogen balance of high yielding dairy cows at the onset of lactation[END_REF]. Low-BCS cows probably had less muscle mass because plasma creatinine concentration tended to be lower than in medium-and high-BCS groups (P = 0.10; Table 5), which is in agreement with previous observations of positive correlations between BCS and indicators of body muscle content, such as area of muscle fibers [START_REF] Reid | Effect of body condition at calving on tissue mobilization, development of fatty liver and blood chemistry of dairy cows[END_REF] and diameter of the longissimus dorsi muscle [START_REF] Kokkonen | Effect of body fatness and glucogenic supplement on lipid and protein mobilization and plasma leptin in dairy cows[END_REF]. The greater 3-MH:creatinine ratio observed in low-BCS cows indicates that lean cows experienced more intense mobilization of muscle protein at the onset of lactation, compared with animals with medium and high BCS (P < 0.05; Table 5), despite the trend for lower muscle mass, as indicated by plasma creatinine concentration. Previous research showed that cows that are underfed postpartum lose more body protein during the first 4 wk of lactation than cows allowed ad libitum intake (6.3 vs. 1 kg), and that the effect of undernutrition on protein mobilization is enhanced in higher-yielding animals, which mobilized 12.5 kg of body protein [START_REF] Chilliard | Mobilization of body proteins by early lactating cows measured by slaughter and D 2 O techniques[END_REF]. Therefore, the extent of protein mobilization during early lactation is influenced by environmental factors and interacts with the genetic background. In the current experiment, where all animals received the same diet, results suggest that low-BCS cows relied to a greater extent on mobilized muscle protein during the first weeks of lactation compared with animals that calved with more adipose reserves. The mechanisms regulating peripartum protein mobilization are not well characterized but may be mediated by changes in plasma insulin, growth hormone (GH), and IGF-1 concentrations [START_REF] Bell | Protein nutrition in late pregnancy, maternal protein reserves and lactation performance in dairy cows[END_REF], and upregulated expression of components of proteolytic pathways in the muscle [START_REF] Chibisa | Effects of peripartum propylene glycol supplementation on nitrogen metabolism, body composition, and gene expression for the major protein degradation pathways in skeletal muscle in dairy cows[END_REF]. Nonetheless, because BCS differences among groups derive, at least in part, from differences in the duration of the previous intercalving interval due to extended lactations and dry periods, potential confounding factors cannot be excluded, and these results should be confirmed under a controlled experimental setting.

Hormonal Profile

Plasma insulin and glucose followed patterns typical of the periparturient period, with a sharp decrease postpartum and sustained low concentrations during early lactation, reflecting the shortfall of glucogenic nutrients, negative EBAL, and preferential partitioning of nutrients toward milk production [START_REF] Chilliard | Physiological constraints to milk production: Factors which determine nutrient partitioning, lactation persistency, and mobilization of body reserves[END_REF][START_REF] Bell | Adaptations of glucose metabolism during pregnancy and lactation[END_REF]. No effects of BCS group were observed on plasma insulin and glucose concentrations, probably because DMI did not differ, and all cows received identical diets. Insulin concentration may not be a good indicator of EBAL during early lactation because it does not discriminate among cows with different BCS, EBAL, plasma NEFA concentration, and degree of liver triglyceride accumulation during the periparturient period [START_REF] Hammon | Performance and metabolic and endocrine changes with emphasis on glucose metabolism in high-yielding dairy cows with high and low fat content in liver after calving[END_REF].

Plasma IGF-1 concentrations decreased postpartum probably due to the uncoupling of the GH:IGF-1 axis. During the periparturient period, GH induction of hepatic IGF-1 production is impaired because low insulin concentrations downregulate the expression of GH receptor in the liver [START_REF] Lucy | Changes in the somatotrophic axis associated with the initiation of lactation[END_REF]. Plasma IGF-1 concentrations were greatest for the high-BCS group, which was unexpected because this group experienced the lowest EBAL due to greater milk fat yield. The IGF-1 concentrations contrast with previous studies where grazing cows calving with BCS ≥3 (1 to 5 scale) had greater prepartum IGF-1 concentration than cows with lower BCS, but did not differ postpartum [START_REF] Meikle | Effects of parity and body condition at parturition on endocrine and reproductive parameters of the cow[END_REF]. Complex relationships existed among postpartum IGF-1 concentration, BCS, EBAL, and week of lactation in the current study. Postpartum IGF-1 was negatively correlated with plasma NEFA and BHBA concentrations, and absolute value of BCS loss (P < 0.001; -0.31 ≤ r ≤ -0.28; n = 138), and positively correlated with week of lactation, postpartum BCS, DMI, EBAL, and glucose concentration (P < 0.05; 0.18 ≤ r ≤0.35; 110 ≤ n ≤ 136; data not shown). These results are in agreement with other studies reporting negative correlations of IGF-1 with NEFA and BHBA concentrations [START_REF] Vandehaar | Effect of energy and protein density of prepartum diets on fat and protein metabolism of dairy cattle in the periparturient period[END_REF][START_REF] Meikle | Effects of parity and body condition at parturition on endocrine and reproductive parameters of the cow[END_REF][START_REF] Wathes | Differences between primiparous and multiparous dairy cows in the inter-relationships between metabolic traits, milk yield and body condition score in the periparturient period[END_REF], and positive correlations between BCS and IGF-1 concentration in multiparous cows during wk 4 and 7 postpartum [START_REF] Wathes | Differences between primiparous and multiparous dairy cows in the inter-relationships between metabolic traits, milk yield and body condition score in the periparturient period[END_REF]. A putative effect of BCS on postpartum plasma IGF-1 concentration deserves further investigation because it may be associated with the declining fertility that has been observed in the Holstein breed.

In the current experiment, the high-BCS group had the greatest plasma leptin concentration prepartum, which can be explained only by differences in adiposity, because EBAL and insulin concentration did not differ prepartum across BCS groups. Therefore, the effects of high BCS on prepartum leptin concentrations are in agreement with the concept that adiposity is the major determinant of leptinemia in nonlactating ruminants [START_REF] Chilliard | Leptin expression in ruminants: Nutritional and physiological regulations in relation with energy metabolism[END_REF]. Others have found positive correlations between BCS and leptin 1 wk before calving in multiparous cows [START_REF] Wathes | Differences between primiparous and multiparous dairy cows in the inter-relationships between metabolic traits, milk yield and body condition score in the periparturient period[END_REF], during the periparturient period of primiparous cows [START_REF] Meikle | Effects of parity and body condition at parturition on endocrine and reproductive parameters of the cow[END_REF] and in late lactation [START_REF] Ehrhardt | Development of a specific radioimmunoassay to measure physiological changes of circulating leptin in cattle and sheep[END_REF]. Leptin concentrations decreased sharply postpartum and remained lower than prepartum for all BCS groups throughout the first 7 wk of lactation. No BCS effects were observed on postpartum leptin concentration, as previously observed [START_REF] Holtenius | Effects of feeding intensity during the dry period. 2. Metabolic and hormonal responses[END_REF]. One study reported a positive correlation between postpartum BCS and plasma leptin, but only in fatter cows (BCS at calving ≥3) and during the first 2 wk of lactation [START_REF] Meikle | Effects of parity and body condition at parturition on endocrine and reproductive parameters of the cow[END_REF]. The decrease in leptin concentration at calving is associated to the changes in EBAL that occur during this period, and may be mediated by decreased insulin concentrations and impaired insulinmediated glucose utilization [START_REF] Block | Demonstration of a role for insulin in the regulation of leptin in lactating dairy cows[END_REF][START_REF] Chilliard | Leptin expression in ruminants: Nutritional and physiological regulations in relation with energy metabolism[END_REF], and enhanced β-adrenergic stimulation [START_REF] Chilliard | Leptin in ruminants. Gene expression in adipose tissue and mammary gland, and regulation of plasma concentration[END_REF], which are all typical of the periparturient period.

Milk FA Composition and Secretion

Week of lactation, EBAL, and BCS at calving were the main factors influencing milk FA composition in the present study because all animals received the same diet. Short-and medium-chain FA are mainly synthesized de novo by the epithelial cells of the mammary gland, and part of 16:0 and longer-chain FA are preformed and derived from blood circulation, and may originate directly from the diet or ruminal metabolism, or be mobilized from fat depots (Chilliard et al., 2000b). In the present experiment, milk fat content of ∑ 10:0 to 15:0 increased and 18:0 and cis-9 18:1 decreased as lactation progressed, as previously reported [START_REF] Gross | Milk fatty acid profile related to energy balance in dairy cows[END_REF]. Nonetheless, FA composition of samples collected during the first week (corresponding to 4 ± 1 DIM) suggest a lesser contribution of preformed FA compared with samples from the second week of lactation (Figure 6). A similar time pattern was previously reported for FA composition of colostrum [START_REF] Santschi | Colostrum and milk fatty acids of dairy cows as influenced by extruded linseed supplementation during the transition period[END_REF].

Cows calving with low BCS had greater milk content of medium-chain FA (e.g., ∑ 10:0 to 15:0 and 16:0), and lower content and secretion of cis-9 18:1 compared with medium-and high-BCS groups. This was probably due to limited availability of body fat in lean cows, in agreement with their lowest plasma NEFA concentrations during early lactation. The main FA circulating in the nonesterified fraction in ruminants are 18:1 (45%), 16:0 (30%), and 18:0 (15%; [START_REF] Scalia | In vitro effects of nonesterified fatty acids on bovine neutrophils oxidative burst and viability[END_REF], which reflects the composition of bovine fat. Decreased content in medium-chain FA during early lactation may be due to lower availability of precursor substrates arising from DMI, to inhibition of FA synthesis in the mammary gland by C18 FA, or result from a dilution effect by increased flow of preformed FA (Chilliard et al., 2000b). Because the intensity of lipolysis postpartum depends, at least in part, on the degree of body fatness (Chilliard et al., 2000a), the effects of BCS on milk fat secretion and FA composition are likely due to the lower body fat content and mobilization in lean cows.

No statistical differences existed between high-and medium-BCS groups for the milk content and secretion of most FA. This may be explained by the lack of differences in plasma NEFA concentration during the first 5 wk of lactation, despite the lower EBAL in the high-BCS group.

Parity Effects

All cows in this study were multiparous; nonetheless, parity was controlled in the statistical analyses because second-lactation cows have not reached their mature body size and production potential. Calving interval, calving BCS, BW, and BCS loss were greater for thirdand-greater-compared with second-lactation cows. A trend existed for greater milk production and significantly greater milk fat content and yield for third-andgreater-lactation cows, leading to lower EBAL, greater plasma NEFA and BHBA concentrations, lower IGF-1 concentration, and a trend for lower glucose concentration postpartum in third-and-greater-lactation cows. The milk fat content of preformed FA such as 18:1 was greater and the content of short-and medium-chain FA was lower in third-and-greater-compared with secondlactation cows, which is in agreement with differences in calving BCS (3.7 vs. 2.9 points), and metabolic, hormonal, and production profiles, indicating enhanced use of mobilized body fat in fatter and older cows.

CONCLUSIONS

Cows calving with high BCS presented elevated plasma NEFA and BHBA concentrations. Unexpectedly, high-BCS cows also had the greatest concentrations of IGF-1 despite their lowest EBAL postpartum. Low-BCS cows produced less milk fat than the high-BCS group, and had the lowest milk protein yield of the 3 BCS groups. Overall, metabolite data show that lean cows mobilized less body fat during the first weeks of lactation, but may have experienced more intense muscle protein catabolism, as indicated by their greatest plasma 3-MH:creatinine ratio. These results support the initial hypothesis that lean cows have limited availability of body reserves and may rely to a greater extent on body protein to meet nutritional needs of early lactation, compared with animals with higher BCS associated with extended lactations. Low body reserve status during early lactation decreased the productivity of dairy cows fed a conventional winter diet based on preserved grass, in practical conditions of a semi-mountain dairy system. Because the calving BCS of cows in this study was related to previous intercalving intervals, confounding factors not controlled by the experimental design cannot be excluded. Therefore, these results should be confirmed using an experimental approach where calving BCS variation would be controlled by design.

Figure 1 .

 1 Figure 1. Effects of BCS at calving on changes in body condition (A), weight (B), DMI (C), and energy balance (D) of periparturient Holstein-Friesian cows. The BCS before calving was 2.33, 3.13, and 4.17 on a 6-point (0 to 5) scale for low-, medium (Med)-, and high-BCS groups, respectively. P-values of significant fixed effects in the statistical models are presented in Table 3. Significant BCS group differences within a week are indicated by * (in panel A: P < 0.01; panel B: P < 0.001; panel D: P ≤ 0.05). Error bars indicate standard error of the mean. Color version available in the online PDF.

Figure 2 .

 2 Figure 2.Effects of BCS at calving on milk production and composition of early-lactation dairy cows. The BCS before calving was 2.33, 3.13, and 4.17 on a 6-point (0 to 5) scale for low-, medium (Med)-, and high-BCS groups, respectively. P-values of significant fixed effects in the statistical models are presented in Table4. Significant BCS group differences within a week are indicated by * (panels B, C, and E: P < 0.05).Error bars indicate standard error of the mean. Color version available in the online PDF.

  wk -4, 1, 2, and 7 relative to calving.

Figure 3 .

 3 Figure 3. Effects of BCS at calving on plasma indicators of fat mobilization and energy metabolism of periparturient Holstein-Friesian cows. Panel A: glucose; panel B: NEFA; panel C: BHBA. The BCS before calving was 2.33, 3.13, and 4.17 on a 6-point (0 to 5) scale for low, medium (Med)-, and high-BCS groups, respectively. P-values of significant fixed effects in the statistical models are presented in Table 5. Significant BCS group differences within a week are indicated by * (panels B and C: P ≤ 0.05). Error bars indicate standard error of the mean. Color version available in the online PDF.

Figure 4 .

 4 Figure 4. Effects of BCS at calving on plasma indicators of muscle mobilization of periparturient Holstein-Friesian cows. Panel A: 3-methylhistidine (3-MH); panel B: creatinine; panel C: 3-MH:creatinine ratio. The BCS before calving was 2.33, 3.13, and 4.17 on a 6-point (0 to 5) scale for low-, medium (Med)-, and high-BCS groups, respectively. P-values of significant fixed effects in the statistical models are presented in Table 5. Contrasts of low versus Med and high BCS: panel B: P = 0.10; panel C: P < 0.05. Error bars indicate standard error of the mean. Color version available in the online PDF.

Figure 5 .

 5 Figure 5. Effects of BCS at calving on plasma concentrations of insulin (A), IGF-1 (B), and leptin (C) in periparturient Holstein-Friesian cows. The BCS before calving was 2.33, 3.13, and 4.17 on a 6-point (0 to 5) scale for low-, medium (Med)-, and high-BCS groups, respectively. P-values of significant fixed effects in the statistical models are presented in Table 5. Significant BCS group differences within a week are indicated by * (panels B and C: P < 0.05). Error bars indicate standard error of the mean. Color version available in the online PDF.

Figure 6 .

 6 Figure 6.Effects of BCS at calving on composition of selected milk FA in early-lactation Holstein-Friesian cows. Milk samples were collected 4, 11, 18, 32, and 46 ± 1 d relative to calving. The BCS before calving was 2.33, 3.13, and 4.17 on a 6-point (0 to 5) scale for low-, medium (Med)-, and high-BCS groups, respectively. P-values of significant fixed effects in the statistical models are presented in Table6. Significant BCS group differences within a week are indicated by * (panels A and B: P < 0.05; panel D: P < 0.01). Error bars indicate standard error of the mean. Color version available in the online PDF.

Table 1 .

 1 Allotment of dairy cows by BCS group and parity 1

		Parity 2		BCS group			P-value
	Item	2	3+	High	Medium	Low	Parity	BCS group
	n Calving BCS 3 Lactation number 2 Age at calving 2 (mo) Calving interval 4 (d) Dry period length 4 (d)	1 7 2.90 ± 0.17 -41.0 ± 1.49 416 ± 32 73 ± 5	1 1 3.68 ± 0.20 -78.2 ± 5.97 572 ± 40 86 ± 6	9 4.17 ± 0.10 a 2.78 ± 0.48 60.6 ± 7.30 640 ± 51 a 91 ± 6 a	1 0 3.13 ± 0.09 b 3.1 ± 0.46 54.2 ± 7.70 433 ± 24 b 68 ± 6 b	9 2.33 ± 0.10 c 2.67 ± 0.48 51.4 ± 7.70 362 ± 7 b 76 ± 6 ab	-<0.001 -<0.001 <0.01 0.09	-<0.001 0.80 0.68 <0.001 0.04
	a-c Means within a row with different superscript letters differ (P < 0.05).
	1 Values are LSM ± SEM.
	2 During the current lactation.
	3 Scale from 0 = thin to 5 = fat, recorded during the last 2 weeks of gestation.

4

Prior to the current lactation.

Table 2 .

 2 Diet ingredient, nutrient, and FA composition

	Item	Far-off dry period 1	Close-up dry period 2	Lactation
	Ingredient (% of DM) Grass silage 3 Hay 3 Concentrate 4 Mineral and vitamin mix 5	75 25 --	66 22 1 2 -	46.5 22 3 0 1.5
	Nutrient composition (% of DM)
	Net energy (MJ/kg of DM)	5.7	5.9	6.1
	CP PDI 6	14.1 7.4	14.6 7.8	15.4 8.3
	NDF	59.6	56.6	52.0
	ADF	35.8	32.7	28
	Fat	4.5	4.4	4.1
	Ash	9.9	9.5	9.0
	FA composition (g/100 g of FA)
	C16:0	17.35	17.05	16.61
	C18:0	1.92	1.90	1.86
	cis-11 C18:1	0.45	0.86	1.50
	cis-9 C18:1	4.57	8.05	13.34
	C18:2n-6	23.86	25.44	27.84
	C18:3n-3	45.56	40.83	33.64
	Other	6.28	5.86	5.22

Table 3 .

 3 Effects of BCS at calving and parity on changes in BW and BCS, intake, and nutritional balance of periparturient Holstein-Friesian cows

		Parity 1	BCS group				P-value 2
	Item	2	3+	High	Medium	Low	SEM	Parity	BCS group	Time
	Prepartum 3 BW (kg)	749	809	809 a	818 a	711 b	17	<0.001	<0.01	<0.05
	DMI (kg/d)	14.9	15.5	14.7	16.1	14.9	0.54	NS	NS	<0.001
	Energy balance (MJ/d) PDI balance 4 (g/d)	19.7 448	20.3 463	15.8 405	23.3 503	20.8 458	3.0 39	NS NS	NS NS	<0.001 <0.001
	Postpartum BCS 5,6 BCS loss 6,7 BW 5 (kg) BW loss 7 (kg) DMI 5 (kg/d) Energy balance 8 (MJ/d) PDI balance 8 (g/d)	2.35 -1.14 660 -98 20.2 -22.8 -225	2.43 -1.57 693 -133 20.3 -36.9 -301	3.28 a -1.37 706 a -121 ab,x 19.9 -39.7 b -333 b	2.21 b -1.47 703 a -129 a 21.4 -25.9 a -270 ab	1.68 c -1.23 621 b -97 b,y 19.5 -23.9 a -187 a	0.11 0.15 14 8.9 0.61 3.0 38	NS <0.05 <0.05 <0.01 NS <0.001 <0.10	<0.001 NS <0.001 <0.05 <0.10 <0.01 <0.05	<0.001 -<0.001 -<0.001 <0.001 <0.001
	a-c Means within a row with different superscript letters differ (P < 0.05).
	x,y

Table 4 .

 4 Effects of BCS at calving and parity on milk production and composition of early-lactation Holstein-Friesian cows

		Parity 1		BCS group					P-value 2
									BCS		BCS group
	Item	2	3+	High	Medium	Low	SEM	Parity	group	Time	× time
	Milk yield 3 (kg/d) 4% FCM 4 (kg/d) Milk fat 4 (%) Milk fat yield 4 (g/d) Milk protein 4 (%) Milk protein yield 4 (g/d) Log 10 SCC 4	33.8 35.2 4.04 1,414 2.78 974 4.46	36.2 39.9 4.41 1,657 2.71 1,014 4.56	35.2 39.8 4.59 a 1,681 a 2.76 1,009 a 4.41	36.4 37.7 4.00 b 1,509 ab 2.80 1,051 a 4.69	33.4 35.2 4.08 b 1,417 b 2.68 924 b 4.43	1.25 1.5 0.13 72 0.05 34 0.12	<0.10 <0.01 <0.05 0.01 NS NS NS	NS <0.10 <0.01 <0.05 NS <0.05 NS	<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 NS	NS <0.10 <0.10 0.01 NS <0.05 NS
	a,b

Table 5 .

 5 Effects of BCS at calving and parity on plasma metabolite and hormone concentrations of periparturient Holstein-Friesian cows

		Contrast low vs.	medium + high
		BCS group	× time
	P-value	Parity	× time
			Time
			BCS group
			Parity
			SEM
			Low
	BCS group		Medium
			High
			3+
	Parity 1
			2
				(μM)
			Item	NEFA 2

Table 6 .

 6 Effects of BCS at calving and parity on milk FA composition 1 and secretion 2 of early-lactation Holstein-Friesian cows

		Parity 3	BCS group				P-value 4
	Item	2	3+	High	Medium	Low	SEM	Parity	BCS group	Time
	FA composition (g/100 g of FA)
	4:0	3.85	3.72	3.83	3.69	3.83	0.07	0.11	0.21	<0.001
	6:0	2.16	1.90	1.89	2.00	2.20	0.09	<0.05	0.06	<0.001
	8:0 10:0	1.08 2.00	0.89 1.57	0.88 1.51 b	0.99 1.77 ab	1.10 2.07 a	0.07 0.14	<0.05 <0.05	0.08 <0.05	<0.001 <0.001
	cis-9 10:1 12:0 14:0	0.17 2.18 8.73	0.13 1.74 7.59	0.12 1.66 b 7.33 b	0.16 1.95 ab 7.85 b	0.17 2.29 a 9.30 a	0.01 0.16 0.40	0.01 <0.05 <0.05	0.06 <0.05 <0.01	<0.001 <0.001 <0.001
	cis-9 14:1 15:0 16:0 cis-9 16:1	0.55 0.96 25.8 1.42	0.52 0.80 25.9 1.75	0.55 0.79 c 25.28 b 1.90 a	0.54 0.89 b 24.88 b 1.49 b,y	0.52 0.96 a 27.29 a 1.36 b,x	0.036 0.02 0.48 0.052	0.51 <0.001 0.90 <0.001	0.80 <0.001 <0.01 <0.001	<0.001 <0.001 <0.001 0.27
	17:0	0.74	0.76	0.73	0.77	0.74	0.02	0.29	0.31	<0.001
	18:0 cis-9 18:1	13.91 23.26	13.73 26.23	13.38 27.08 a	13.99 25.47 a	14.087 21.68 b	0.42 0.91	0.71 <0.01	0.44 <0.001	<0.001 <0.001
	trans-10 18:1	0.22	0.20	0.22	0.22	0.19	0.01	0.11	0.08	<0.001
	trans-11 18:1 cis-9,cis-12 18:2 cis-9,trans-11 CLA 5 18:3n-3	1.73 1.58 0.57 0.84	1.57 1.64 0.52 0.92	1.59 1.63 a 0.55 ab 0.87 b	1.75 1.71 a 0.60 a 0.93 a	1.60 1.47 b 0.50 b 0.84 b	0.07 0.048 0.03 0.025	<0.05 0.27 0.17 0.01	0.14 <0.01 0.05 <0.05	<0.001 <0.001 <0.001 0.001
	cis-9,trans-11,cis-15 CLnA 20:0 cis-9 20:1 cis-11 20:1 20:3n-6	0.074 0.19 0.11 0.056 0.049	0.057 0.17 0.10 0.059 0.044	0.062 0.17 b 0.10 b 0.065 a 0.042 b	0.074 0.18 ab 0.11 a 0.059 a 0.051 a	0.063 0.19 a 0.10 b 0.050 b 0.046 ab	0.007 0.005 0.003 0.003 0.003	<0.05 <0.01 0.001 0.34 0.14	0.34 0.01 <0.05 0.001 <0.05	<0.001 <0.01 <0.001 <0.05 <0.001
	20:4n-6	0.10	0.091	0.097	0.10	0.092	0.006	0.17	0.55	<0.001
	20:5n-3 22:0	0.090 0.063	0.088 0.052	0.086 0.053 b	0.090 0.058 ab	0.091 0.062 a	0.004 0.002	0.70 <0.001	0.59 <0.05	<0.001 <0.001
	22:5n-3 + 26:0	0.18	0.16	0.16	0.18	0.18	0.006	<0.01	0.09	<0.001
	24:0 ∑ SFA ∑ MUFA ∑ PUFA ∑ OBCFA ∑ trans FA 6	0.044 63.31 30.71 3.93 3.70 4.02	0.036 60.29 33.78 3.96 3.48 3.67	0.037 58.94 b 35.11 a 3.97 a 3.35 b 3.81	0.039 60.64 b 33.09 a 4.21 a 3.67 a 4.10	0.044 65.83 a 28.54 b 3.64 b 3.75 a 3.64	0.002 0.99 0.95 0.11 0.05 0.14	<0.05 0.01 0.01 0.81 0.001 <0.05	0.09 <0.001 <0.001 <0.01 <0.001 0.06	<0.001 <0.001 <0.001 0.09 <0.001 0.001
	∑ Unidentified FA ∑ 10:0 to 15:0 ∑ >C16 7	1.19 15.37 47.62	1.15 13.01 51.02	1.17 12.59 b 51.07 a	1.19 13.89 b 50.42 a	1.14 16.08 a 46.46 b	0.038 0.77 1.14	0.32 0.01 <0.05	0.57 0.01 <0.05	<0.05 <0.001 <0.001
	FA secretion (g/d)
	10:0	26.22	23.58	23.14	25.34	26.20	2.05	0.27	0.55	<0.01
	12:0	28.33	25.55	24.94	27.41	28.47	2.15	0.27	0.49	<0.001
	14:0	114.8	113.4	112.6	111.2	118.5	6.27	0.86	0.66	<0.05
	15:0	12.79	12.35	12.26	12.97	12.48	0.61	0.54	0.67	0.36
	16:0	340.0	391.0	389.2	351.7	355.7	17.60	<0.05	0.25	<0.001
	17:0	9.67	11.74	11.15	10.95	10.01	0.63	<0.01	0.39	<0.001
	18:0 cis-9 18:1 cis-9,cis-12 18:2 18:3n-3	184.7 321.3 21.64 11.25	217.6 426.7 26.07 14.31	208.5 436.8 a 26.13 a 13.64 a	203.7 378.4 a 25.16 a 13.53 a	191.1 306.8 b 20.27 b 11.17 b	10.56 26.00 1.25 0.66	0.01 <0.01 <0.01 0.001	0.48 <0.01 <0.01 <0.05	<0.001 <0.001 <0.001 <0.001
	∑ 10:0 to 15:0	202.8	195.7	193.9	198.3	205.5	11.72	0.60	0.77	<0.01
	a-c Means within a row with different superscript letters differ (P < 0.05).

x,y

From drying-off to 3 wk before expected calving.

Introduced

wk before expected calving; duration was 3.4 ± 1.2 wk. 3 Permanent semi-mountain grasslands rich in Dactylis glomerata (orchardgrass).

Concentrate contained 7 MJ and 104 g of protein truly digestible in the small intestine/kg of DM, 18.6% CP, 34.1% NDF, and 3.2% fat; composed of 24% wheat bran, 15% dehydrated distillers grains by-product (<7% starch), 15% triticale, 12% wheat, 10% beet pulp, 10% dehydrated distillers barley rootlets, 6% corn cobs, 2% sunflower meal, 2% rapeseed meal, 2% sugar cane molasses, 1% rapeseed grains, 0.5% wheat shorts, 0.25% calcium carbonate, and 0.25% sodium chloride (on a DM basis).

Mineral and a vitamin mix contained 3% P, 22% Ca, 5% Mg, 2.5% Na, 1% S, 375,000 IU of vitamin A/kg, 110,000 IU of vitamin D 3 /kg, 1,500 IU of vitamin E/kg, 1 g of Cu/kg,

g of Zn/kg, 2.5 g of Mn/kg, 100 mg of I/kg, 40 mg of Co/kg, and 24 mg of Se/kg (Galaphos Midi ensil; CCPA, Aurillac, France).6 Protein truly digestible in the small intestine(INRA, 2007).

Journal of Dairy Science Vol. 96 No. 10, 2013

Change from prepartum until wk 7 postpartum.

Calculated from wk 2 to 7 postpartum.

Sum of FA containing at least 1 trans double bond.

Sum of FA with more than 16 carbons.

ACKNOWLEDGMENTS

The authors thank M. Barbet, N. Bioret, and the staff at the experimental unit Monts d'Auvergne of Orcival (INRA, UE1296, Puy-de-Dôme, France) for animal care and sample collection; D. Bany, E. Tixier, and M. Tourret (INRA, UMR 1213, Saint-Genès-Champanelle, France) for laboratory analyses; N. Hafnaoui and D. Remond (INRA, UMR 1019, Saint-Genès-Champanelle, France) for the quantification plasma 3-MH; and S. Lerch (INRA, UMR 1213) for insightful discussions on milk FA profiles. This research was funded by the Department of Animal Physiology and Livestock Production Systems (PHASE; Nouzilly, France) of INRA.


Bogdan Pavkovic
email: pavkovic@imag.fr

Won-Joo Hwang
email: ichwang@inje.ac.kr

Fabrice Theoleyre
email: theoleyre@unistra.fr

Cluster-Directed Acyclic Graph Formation for IEEE 802.15.4 in Multihop Topologies

IEEE 802.15.4 represents the future standard for multihop Wireless Sensor Networks. However, this protocol is based on a cluster-tree which performs poorly in multihop. In the same way, scheduling the superframes to avoid collisions represents a key challenge. We propose here a simple greedy scheduling algorithm, integrated with the IEEE 802.15.4 MAC mechanisms. This scheduling permits to modify the Cluster-Tree into a Directed Acyclic Graph which reduces the delay while improving robustness, removing the single point of failure problem of the original structure. Simulations proved that these mechanisms optimize the MAC layer for multihop topologies.

I. INTRODUCTION & MOTIVATIONS

The IEEE has proposed a standard to govern the medium access in Wireless Sensor Networks [1]. The standard supports two main operating modes. In non-beacon mode, all the nodes use an unslotted CSMA-CA solution to access the medium. In beacon-enabled mode, the standard introduces the concept of superframes: a coordinator delimits an active part during which the other nodes use a slotted CSMA-CA to transmit their frames. Once active part is over, all the nodes may go to sleep until the next beacon periodically transmitted by the coordinator.

IEEE 802.15.4 was initially designed mainly for single hop networks: the PAN coordinator is directly connected to the end-devices, forming a star topology. Then, in a mesh topology, any pair of IEEE 802.15.4 nodes may communicate directly. Finally, in a cluster-tree, nodes forward packets along a tree rooted at the PAN coordinator. Possibly, some nodes (Reduced Function Devices) may refuse to forward packets, forming a leaf in the cluster-tree.

We will here focus on the beacon-enabled mode with a cluster-tree since this is the only way to deploy a multihop wireless sensor network while saving energy in IEEE 802.15.4. All the nodes wake-up synchronously at the beginning of the superframe: collisions between beacons are frequent. Besides, nodes buffer their packet during the sleeping period. Thus, they try to send their packets synchronously, increasing the number of collisions. Finally, the cluster-tree is a single-pointof-failure topology: a single link disconnection may degrade globally the performances.

Cuomo et al. [START_REF] Cuomo | Performance analysis of ieee 802.15.4 wireless sensor networks: An insight into the topology formation process[END_REF] investigated the properties of the clustertree when a node associates to the first available parent. Although this study gives an overview of what we may obtain, the approach does not present a method to find the best structure. Abbagnale et al. [START_REF] Abbagnale | A case study for evaluating IEEE 802.15.4 wireless sensor network formation with mobile sinks[END_REF] studied the impact of mobility on the topology formation process, creating some convergence problems.

Pavkovic et al. [START_REF] Pavkovic | IEEE 802.15.4 and RPL cross-optimization for reliable opportunistic routing in wsn[END_REF] proposed an opportunistic RPL version which implements QoS mechanisms. Since this scheme relies on a Directed-Acyclic Graph, the authors implemented a centralized coloring scheme to create this structure. We propose to focus here on constructing this DAG in a distributed manner to reduce the multihop problems:

1) we propose to maintain a Directed Acyclic Graph (DAG) at the MAC layer. This DAG structure optimizes both the robustness and the delay; 2) we use the Beacon-Only Period while scheduling properly the superframes to reduce collisions while limiting bandwidth wastage; 3) we present a simple localized scheduling solutions, assigning collision-free slots in a self-stabilizing manner.

II. RELATED WORK

A. IEEE 802.15.4

In a star, the PAN coordinator acts as gateway and collects all the packets of sensors. The mesh topology acts in a similar way, leading on a routing protocol to forward packets. Only the cluster-tree permits multihop communications: the nodes relay the packets along a tree rooted at the gateway. Zigbee may be used for instance to assign addresses hierarchically routing the packets automatically along the tree.

In non-beacon mode, all the nodes use CSMA-CA to transmit their frames: since no synchronization is required, no node can sleep. In beacon-enabled mode, IEEE 802.15.4 uses the concept of superframes. The PAN coordinator begins to send periodically beacons to delimit the beginning of its superframe. Then, all the nodes participating to its superframe can access the medium with a slotted CSMA-CA during the Contention Access Period (CAP). When the superframe is finished, all the nodes may sleep until the next beacon.

In a cluster-tree, all the non-leaf nodes must maintain a superframe to exchange packets with their children. The active part lasts for Superframe Duration (SD) and a coordinator transmits a beacon every Beacon Interval (BI). SD and BI are respectively obtained from Superframe Order (SO) and Beacon Order (BO) parameters values. The duty cycle is given by (BO -SO) -1 .

An unassociated node must discover a coordinator either passively by waiting a beacon or actively by transmitting a request. The node transmits an association-request during the CAP, acknowledged by the coordinator. The node has then to retrieve its association-reply after macResponseWaitTime. A coordinator starts to transmit beacons as soon as it is associated.

B. Limits of IEEE 802.15.4 in multihop WSN

For a node, the superframe of its parent is designated as incoming and the superframe maintained by the node itself as outgoing. The standard suggests the superframe of one child and its parent are interspaced by StartT ime. If the StartT ime value is constant, coordinators with the same depth start synchronously their superframes. Practically, beacons collide, making the protocol inefficient.

In the literature exist two main approaches to reduce the number of collisions. In the Beacon Only Period (BOP) solution, nodes implement a TDMA approach to send their beacons: at the beginning of each superframe a few slots are dedicated to beacons [START_REF] Koubâa | Tdbs: a time division beacon scheduling mechanism for zigbee cluster-tree wireless sensor networks[END_REF]. While collisions are avoided during the BOP, data frames may still collide in the second part since the data part keeps on overlapping. Simulations proved performances quickly degrade since hidden terminals are frequent [START_REF] Anastasi | The mac unreliability problem in ieee 802.15.4 wireless sensor networks[END_REF].

A second solution consists in using a variable StartT ime: two nodes that have the same parent should for instance not use the same StartT ime so that their superframes do not overlap [START_REF][END_REF]. If we assume that all the nodes use the same BO and SO values, finding the adequate StartT ime for all of them is equivalent to scheduling the superframes with a TDMA approach. Villaverde et al. [START_REF] Villaverde | Experimental evaluation of beacon scheduling mechanisms for multihop ieee 802.15.4 wireless sensor networks[END_REF] have proved experimentally this approach leads to the best performances.

Koubaa et al. [START_REF] Koubâa | Tdbs: a time division beacon scheduling mechanism for zigbee cluster-tree wireless sensor networks[END_REF] proposed a centralized algorithm to schedule the superframes with variable superframe duration (this corresponds to a classical knapsack problem). Muthukumaran et al. propose rather a greedy distributed algorithm to pick the first free slot [START_REF] Panneer Selvan Muthukumaran | Meshmac: Enabling mesh networking over ieee802.15.4 through distributed beacon scheduling[END_REF]. Rhee et al [START_REF] Rhee | Drand: Distributed randomized tdma scheduling for wireless ad hoc networks[END_REF] presented a distributed slot assignment for a TDMA MAC in wireless sensor networks. The authors use a localized greedy algorithm to pick a free slot for the transmission. We will inspire from this approach to assign slots distributively.

Pavkovic et al have proposed an opportunistic RPL version which implements QoS mechanisms [START_REF] Pavkovic | IEEE 802.15.4 and RPL cross-optimization for reliable opportunistic routing in wsn[END_REF]. Since this scheme relies on a Directed-Acyclic Graph, the authors implemented a centralized coloring scheme to create this structure. The authors proposed also a distributed version, not evaluated in the paper. This version does not work well with large densities: beacons begin to collide with each other and no re-rescheduling was proposed to cope with bad decisions.

Although most propositions focus on the MAC mechanisms, few attention has been given to the cluster-tree construction itself. Indeed, the standard does not specify how the parent should be selected, although it greatly impacts the performances. We will explore this problem in the rest of this paper.

III. CLUSTER-DAG FOR IEEE 802.15.4

It is well known that a simple tree is inaccurate for wireless multihop networks: the death of one single node may render non-operational the part or whole network for a long time. We propose to use rather a Directed Acyclic Graph structure: each node may have several parents, we just forbid the apparition of loops. In a DAG, several redundant paths may exist toward the PAN coordinator. Thus, we improve the robustness while not introducing any instability problem during the convergence.

We aim at minimizing the energy consumption. Thus, we should use shortest paths routing to reduce the number of transmissions. We chose to preserve a route length stretch factor of 1: the routes length through the DAG has to be the same as in the original graph.

We will now expose how we can modify IEEE 802.15.4 to lead on a cluster-Directed Acyclic Graph structure.

A. Cluster-tree Construction

We implement the passive discovery method of IEEE 802.15.4: a node waits for beacons to become aware of already associated neighboring coordinators .

A node has to choose as coordinator one of the neighbors which are the closest to the PAN coordinator. Thus, we insert in the IEEE 802.15.4 beacons a field denoting the depth, coded in 6 bits since a diameter of 63 hops seems a realistic upper bound for an IEEE 802.15.4 network.

When a node receives a beacon and is not yet associated, it chooses the source as parent. We privilege in this way the convergence time. Besides, a coordinator which has a smaller depth in the cluster-tree often transmits first its beacons.

We then apply the classical association procedure of IEEE 802.15.4: the node sends an association-request during the CAP, acknowledged by the coordinator. The node have to wait before transmitting a data-request during the next CAP, and the coordinator replies by an ack followed by a association-reply specifying its short address (16 bits).

B. Cluster-DAG

We now aim at authorizing multiple parents. After the node transmits its association-request, it does not go to sleep, it keep on listening other beacons to find alternative candidates.

A node N receiving a beacon applies the following rules: 1) the beacon is received from an already associated parent, but its depth is strictly larger than the minimum depth of the rest of its associated parents: N initiate a disassociation since we aim at using shortest hop routes; 2) the beacon comes from a non-associated parent and its depth is strictly smaller than the minimum depth of all my associated parents: N engages an association by sending immediately an association-request. When the corresponding ack is received, the source is inserted in the parents list, tagged as on-going association; 3) the beacon comes from a non-associated parent and its depth is strictly equal to the minimum depth of all my associated and non-associated parents: N engages an association. In this way, we avoid associating to a new parent if an association is already on-going with a better parent: we should reduce the number of disassociations.

In conclusion, a node always maintain a list of parents which are strictly closer to the PAN coordinator than itself. In other words, we forbid any loop by maintaining shortest hops paths.

A sub-optimal parent is removed and a disassociation procedure is engaged only when the association with a better parent is concluded. Indeed, we can intermittently receive beacons through a bad link: the association will be successful only if the quality is sufficient (a too large Packet Error Rate means in IEEE 802.15.4 the association never terminates in time).

We may also integrate here a quality metric before deciding to associate with a parent: the node will estimate the packet error rate of the radio links either passively (by measuring collisions and packet delivery ratio of beacons) or actively (by sending probes). A node would select the source as parent only if the quality is sufficient.

C. Illustration

We represent in figure 1 the cluster-DAG we obtained. We simulated IEEE 802.15.4 with 10 nodes, distributed on a random square. One node is randomly selected to act as PAN coordinator.

We can remark that the DAG structure permits to introduce more redundancy, except for the neighbors of the PAN coordinator: by definition, no other node is closer to the PAN coordinator.

IV. SUPERFRAME ORGANIZATION

We adopt the approach depicted in figure 2: 1) a Beacon-Only-Period is reserved at the beginning of each superframe. When several coordinators interfere but only one has children, they may use different BOP slots in the superframe; 2) we schedule the superframes such that two interfering coordinators with children do not maintain their superframe simultaneously, i.e. they use different superframe slots.

With this solution, a node without children can share its superframe with another coordinator, it just has to maintain a different BOP slot. In this way, we waste only one BOP slot and not a whole superframe.

Our solution is distributed and self-stabilizing. The algorithm converges to a legal state whatever the initial state is. By legal state, we mean a structure without loop and in which each coordinator has at least one path to the PAN coordinator;

Finally, the solution reacts automatically to interference (self-healing property): when two interfering coordinators have children and share the same superframe / BOP slots, data packets may collide. At least one of them chooses another slot to avoid collisions among data frames.

We will now explain how we can assign BOP and superframe slots to obtain these properties.

A. Information collect

We aim at assigning the BOP and superframe slots while avoiding collisions. Thus, we should maintain the list of interfering nodes and their slot choice.

In IEEE 802.15.4, a node is deaf to the superframes it does not participate to. Thus, we propose that node MUST follow all the superframes maintained by neighboring coordinator, whatever its role is (parent, child, simple neighbor). As soon as a node has received the beacon, it may go to sleep if it does not aim at participating to this superframe.

We include in the IEEE 802.15.4 beacons:

• the depth: distance in hops to the PAN coordinator;

• the number of children;

• a list of 1-neighbors with for each node its short address, the BOP and superframe slots they use. To track topology changes, a node MUST periodically scan the medium to receive beacons from unknown neighbors. We may adopt the trickle timer [START_REF] Levis | The trickle algorithm[END_REF] approach: the period increases when a node does not detect a new neighbor during the scanning phase (i.e. the network has converged). In this way, we limit the energy consumption due to this scanning phase.

B. BOP slot Assignment

A node selects its BOP slot in the same way as [START_REF] Koubâa | Tdbs: a time division beacon scheduling mechanism for zigbee cluster-tree wireless sensor networks[END_REF]: a new coordinator selects randomly one BOP slot, and listens to the medium to detect a coordinator already owner of this BOP slot. If it is free, it transmits its beacon, else it chooses another BOP slot.

C. Superframe slot Assignment

We propose two different algorithms to assign superframe slots: random and greedy.

In the random approach, a node chooses randomly one slot among the least loaded slots and discard the slots used by its parents to avoid collisions.

For the greedy solution, the node N applies the following rules:

1) if several slots with no interfering node exist, N chooses randomly one of them; 2) if no slot is empty, N sorts them according to the number of interfering nodes with children. Then, it chooses randomly among the least loaded slots. Even if this algorithm tries to limit collisions, two nodes might choose the same superframe slot at the same time. If they have not chosen the same BOP slot, they would be able to receive their respective beacons. If such collision is detected, each coordinator selects another superframe slot with a probability of 50%.

If both nodes have the same superframe and BOP slots, the beacons will collide. Thus, no child will be able to associate to these coordinators. We chose that each coordinator without child re-selects another superframe slot (this coordinator did not receive any association-request and has no association-reply in its buffer). This reassignment has no consequence since no child is following this coordinator. When a coordinator starts its superframe, it may decide to change its superframe slot. In that case, it sends a beacon with its new slot: a receiver knows consequently the coordinator has changed its superframe slot. The receiver will update its neighborhood table and will follow the coordinator during its new slot: a node listens to the beacons of a coordinator even if it is not a child since it must maintain a neighborhood table.

D. Illustration

Let examine the scheduling obtained in figure 1. We assume BO=7 and SO=2 (i.e. we have 2 5 = 32 superframe slots) and 5 BOP slots. To each vertex, the label X(Y, Z) denotes for X the id, Y the superframe slot and Z its BOP slot. For instance, the sink (address 0) has the superframe slot 0 and BOP slot 0. We can verify that although slots have been assigned distributively, no collision occurs if we assume only 2-neighbors can interfere.

V. PERFORMANCE EVALUATION

We have implemented the beacon-enabled mode of IEEE 802.15.4 in the WSNet simulator [START_REF] Chelius | Worldsens: development and prototyping tools for application specific wireless sensors networks[END_REF]. We use a fixed radiorange and nodes are placed randomly in a disk to limit side effects. Default parameters are represented in tab. I. We simulated a duty-cycle comprised between 1% (2 9-2 ) and 25% (2 4-2 ). We evaluated low-density topologies since we consider a power control solution should limit interference. A larger density would mean a larger topology redundancy, and thus more possibilities to choose the parents. Besides, our solutions uses the Beacon-Only Period to reduce interferences among coordinators. Thus, a small density stresses our solution.

We measured mainly the Packet Delivery Ratio (ratio between the number of transmitted packets and the number of received packets), the end-to-end delay and the superframe collision ratio (the ratio of coordinators which have an interfering coordinator sending a superframe at the same instant). We use the energy consumption values of the popular CC1100 chipset (31mA in TX, 15mA in RX and 400nA when sleeping).

We implemented 3 solutions for comparison: 1) 802.15.4: the slot used by a coordinator follows directly thus of its parent; 2) random: a coordinator chooses a random slot, except the slot(s) used by its parent(s); 3) greedy: a coordinator selects a slots not used in its neighborhood and tries to detect and solve collisions, as highlighted in section IV-C.

A. Traffic model & routing

We model a bidirectional traffic: a node generates one packet for the PAN coordinator every T interpk . Inversely, the PAN coordinator generates packets toward a random destination in the network with a rate T interpk nb nodes-1 to obtain the same rate in the upload and download directions.

Each node maintains two buffers. Tthe first one is a FIFO, dedicated to packets toward the PAN coordinator. A packet is pulled from the buffer when the node is in the idle state, during the CAP of the superframe of its parent. The second buffer is dedicated to the download direction: packets are extracted from the buffer after the reception of a data-request from the destination. The packets for the same destination follow also the FIFO strategy (the oldest packet is transmitted first). A procedure periodically removes packets that exceeded their timeout (macTransactionPersistenceTime as defined in IEEE 802.15.4). A node which receives a beacon notifying pending packets in the coordinator MUST send a data-request before transmitting the data packets present in its upload buffer.

We implemented a centralized routing solution: packets are routed along the IEEE 802.15.4 structure. If IEEE 802.15.4 leads on a DAG, we chose to implement an anycast strategy: the node picks a packet in its upload buffer whatever the current parent is, balancing the load among all its parents.

B. Cluster-DAG properties

We first evaluated in figure 3 the structure itself. The Directed Acyclic Graph authorizes to maintain multiple parents for each node. We can remark in fig. 3 obviously increases when we increase the number of parents. However, it reaches quickly a maximum of 2 parents on average, the density being low.

Then, we measured the time required before the last node becomes associated (it has a valid parent and it gets a short 16 bits address). Since we maintain the density constant, the diameter increases with the number of nodes: the association time is consequently larger (fig. 3(b)). However, it is similar whatever the superframe scheduling algorithm is.

We have finally measured the impact of the number of Beacon-Only-Period slots (fig. 3(c)). While the random and greedy strategies are not impacted by the number of slots, IEEE 802.15.4 requires at least 4 slots to reduce the superframe collisions. Too many BOP slots mean a larger overhead, and not enough space for data packets.

We also measured the impact for choosing several parents on the energy consumption (fig. 5). We can verify a node which listens to several parents does not consume significantly more energy: most of the energy consumption is concentrated in maintaining its own superframe.

C. Impact of the BO/SO values

We measured the impact of the number of slots in the scheduling (= 2 BO-SO ) in fig. 4. When we have only 4 slots (BO=4), the diversity is not sufficient to avoid collisions between superframes: the random and greedy algorithms perform worse than the standard. However, as soon as the diversity is sufficient, greedy and random perform better. The packet delivery ratio decreases when BO increases: the duty cycle decreases, letting less time to transmit and receive packets. We can also verify that the greedy algorithm significantly increases the network capacity, i.e. its ability to forward more packets.

We may also remark the delay increases with BO: a coordinator has on average more time to wait before being in the active part of its superframe. Finally, we can verify that maintaining a DAG in the IEEE 802.15.4 layer helps to reduce the delay: a coordinator has on average less time to wait before entering in the active part of any of its parents.

D. Scalability

Finally, we measured the scalability (impact of the network size on the behavior). We measured the PDR in fig. 6: it decreases when the number of nodes increases. However, we can remark the greedy superframe selection achieves the best performances: it succeeds to reduce collisions. Inversely, the original IEEE 802.15.4 scheduling performs poorly. random solution avoids collisions with parents and improves

We also measured the superframe collision ratio: the proportion of coordinators which may suffer from collisions from an interfering coordinator. Reducing this ratio means that we reduce the collisions because of interferences. IEEE 802.15.4 performs very poorly: because the superframe slot depends directly on the depth, almost all the frames collide. This means performances will quickly drop when traffic increases. The significantly the diversity. However, the greedy solution performs better: we reduce the number of collisions by one half compared to a random strategy. Thus, all packet drops will mainly be caused by the IEEE 802.15.4 MAC mechanisms (e.g. too many CCA) and not by interferences: active parts of the superframes are not overlapping for interfering coordinators.

VI. CONCLUSION & PERSPECTIVES

We proposed here to modify the topology of IEEE 802.15.4: adopting a Directed Acyclic Graph structure. A DAG permits to improve the robustness and the delay since a node may choose to have more parents simultaneously. This way it can adopt an opportunistic routing approach: it forwards the packets to the next awake parent. Besides, combining the Beacon-Only Period and the superframe scheduling reduces the collisions while limiting bandwidth wastage when some coordinators have no child in a superframe. Finally, our slot assignment algorithm is very simple, localized, and converges quickly to a stable and accurate assignment. Simulations have proved that modifying the topology and the way IEEE 802.15.4 uses it reduces the beacons and data collisions.

In the future, we plan to explore the impact of realistic experimental collisions on such superframe/BOP slots assignment. We conjecture our algorithm detecting reactively the collisions will be robust to complex interference patterns. We also plan to study how such MAC structure impacts the routing strategy: should the next hop be chased opportunistically or does a multipath scheme improve the reliability and the delay?

Figure 1 .

 1 Figure 1. Illustration of the topology constructed by IEEE 802.15.4-original and DAG improved version

Figure 2 .

 2 Figure 2. Beacon-Only-Period and superframe scheduling combination

Figure 3 .

 3 Figure 3. Structure of IEEE 802.15.4 -BO=7, SO=2

Figure 4 .

 4 Figure 4. Impact of the duty-cycle -SO=2

Figure 5 .

 5 Figure 5. Energy consumption

  Figure 6. Scalability