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Dexcel Limited, Private Bag 3221, Hamilton, New Zealand
2 Corresponding author: chris.burke{at}dexcel.co.nz
| ABSTRACT |
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3 ng/mL with subsequent concentrations being consistent with an ovulatory cycle. Blood samples were collected before initiation of treatments, and at d –21, –14, –7, 0 (day of calving), 1, 2, 3, 4, 7, 14, 21, 28, and 35 in all cows. The PPAI was associated with body condition score, concentrations of plasma insulin and insulin-like growth factor-I, and growth hormone. Postpartum intake did not affect these metabolic hormones or PPAI, but yield of FCM during the first 35 d was reduced by 23% among cows on a restricted intake. No relationships were found between PPAI and milk production characteristics. These data demonstrate that when pasture is the sole dietary source during the calving transition period, PPAI may be influenced by prepartum intake levels, whereas postpartum intake influences milk yield, but not PPAI. The underlying mechanism(s) that associates the prepartum period to PPAI may involve the sensitivity of the growth hormone–insulin-like growth factor axis to dietary intake levels. Nonetheless, PPAI in grazing multiparous dairy cows appears largely unresponsive to intake levels during the calving transition period.
Key Words: postpartum anovulation metabolic hormones reproduction nutrition
| INTRODUCTION |
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The physiological effects of nutritional stress on ovarian follicular function and postpartum anestrus were reviewed (Jolly et al., 1995; Diskin et al., 2003). Although large dominant follicles reestablish on the ovaries within several days after calving (McDougall et al., 1995a), endocrine conditions required for final maturation and first ovulation postpartum are influenced by body fat reserves, energy balance, and metabolic state (Grainger et al., 1982; Butler and Smith, 1989). An understanding of how these nutritional considerations influence reproductive events is building (Robinson et al., 2006), but the mechanisms remain complex and inadequately understood.
The issue of calving transition (–3 wk to +3 wk of calving) management on fertility was reviewed (Formigoni and Trevisi, 2003; Rhoads et al., 2005), indicating that energy deficit coupled with a period of suppressed immune function is largely responsible for compromised reproductive performance. With the exception of some early studies involving cows with a 1970s genetic base (Grainger and Wilhelms, 1979; Grainger et al., 1982), there are no controlled studies that specifically investigate the effects of pre- and postpartum DMI levels, and their interaction, on PPAI of lactating dairy cows in a seasonal system. This study was designed and conducted to evaluate the effect of pre- and postpartum levels of pasture feeding on PPAI of seasonally managed lactating dairy cows. To better understand the mechanisms underlying these outcomes, an objective was to investigate the association between PPAI and metabolic indicators.
| MATERIALS AND METHODS |
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Experimental Design and Cow Management
At 29 ± 7.7 d (mean ± SD) prepartum, 68 multiparous Holstein-Friesian cows predicted to calve within the first 21 d of a seasonal calving period were randomly allocated either a high (PreH; 11.9 kg/d DM) or low pasture intake (PreL; 4.8 kg/d DM), while ensuring treatment groups were balanced for milk yield in the previous lactation, BW, BCS, age, and predicted calving date. At calving, half of the cows within each prepartum feeding treatment were randomly allocated to either a high (HH and LH; 13.5 kg/d DM) or low pasture intake (HL and LL; 8.6 kg/d DM) for 35 d, as a 2 x 2 factorial arrangement with 17 cows per treatment cell. At 35 d postpartum, all cows were grouped as a single herd on fresh pasture supplemented with pasture-silage (6.1 ± 1.7 kg/d DM) for 3 wk and thereafter managed solely on fresh pasture.
Detailed descriptions of grazing management, measurements of pasture intakes, and milk production characteristics have been published (Roche, 2007). Briefly, animals were rotationally grazing through 56 paddocks, with a fresh grazing area offered twice daily. Pasture consisted mostly (
85%) of perennial ryegrass (Lolium perenne L.). Treatment groups grazed within the same paddock at all times, but within a defined grazing area to achieve differential intakes. Pre- and postgrazing herbage masses and grazing area allocations were used to control average pasture intakes for cows within treatments. Herbage masses were assessed using a Rising Plate Meter (Farmworks, Palmerston North, New Zealand), which measures compressed pasture height. These measurements (100 per treatment before and after grazing) were calibrated to DM yields from cut and dried pasture samples (20 x 0.125 m2 quadrats per treatment) thrice weekly. On each of these occasions, a "hand-plucked" sample to grazing height was collected. These samples were bulked at 2 weekly intervals and analyzed for DM content and nutrient composition. Average (± SD) ME estimates of pasture for PreH and PreL were 11.6 ± 0.31 and 11.7 ± 0.09 MJ/kg of DM, respectively. Respective values after calving for high and low pasture intake treatments were 12.1 ± 0.21 and 12.3 ± 0.07 MJ/kg of DM. A detailed description of botanical and nutrient composition of the pasture fed during the current study is in Roche (2007).
Individual BW and BCS (1 to 10 scale; Roche et al., 2004) were measured weekly throughout the study. Individual milk yields were measured at each milking and compositional characteristics were determined twice weekly until 35 d postpartum.
Blood Sampling Regimen
Blood samples were collected from all cows by coccygeal venipuncture into evacuated tubes containing sodium heparin before treatment allocation on d –21, –14, –7, 0 (day of calving), 1, 2, 3, 4, 7, 14, 21, 28, 35, and weekly thereafter for individuals until confirmed as cycling. Sampling time was approximately 0730 h. Plasma was harvested after centrifugation (1,120 x g for 12 min) and stored at –17°C.
PPAI Determination
Progesterone was measured in a composite of milk samples collected at the Monday p.m.–Tuesday a.m. and Thursday p.m.–Friday a.m. milking each week from calving until cows were confirmed as cycling. Progesterone content was determined using an ELISA kit (Ridgeway Sciences, Gloucestershire, UK) validated for use in cattle (Sauer et al., 1986). The PPAI was defined as the interval from calving to the first day that progesterone concentration increased to
3 ng/ mL, with subsequent concentrations being consistent with the onset of ovulatory cycles. The PPAI was not measured in 6 cows (last sampled 51 to 77 d postpartum; HH, 2; LH, 3; HL, 0; LL, 1), but PPAI estimates were generated using the CENSOR procedure (GenStat 5.4.1, GenStat for Windows, 9th ed., VSN International Ltd., Hemel Hempstead, UK). These cows had not ovulated before onset of the seasonal breeding period, at which time they received a progesterone-based treatment for anestrus.
Plasma Metabolite Measurements
Plasma from each cow was analyzed for concentrations of NEFA (mmol/L; colorimetric method using a commercial kit (Wako, Osaka, Japan), BHBA (mmol/ L; BHBA dehydrogenase assay based on formation of acetoacetate and NADH after addition of NAD), glucose (mmol/L; hexokinase method based on formation of NADPH), albumin (g/L; citrate buffer reagent), urea (mmol/L; urease hydrolysis method), Ca++ (mmol/L; ocresolphthalein complexone method) and Mg++ (mmol/ L; xylidyl blue reaction). All assays were performed at 30°C using an Hitachi 717 analyzer (Roche, Basel, Switzerland) by Alpha Scientific Limited, Hamilton, New Zealand. The inter- and intraassay coefficients of variation (CV) were 5% or less for all assays.
Plasma Hormone Measurements
All plasma samples were analyzed in duplicate for growth hormone (GH), IGF-I, and insulin using double-antibody RIA (Chagas et al., 2006). Intra- and interassay CV for GH were 8.4 and <10.7%, respectively; sensitivity of the GH assay was 0.19 ng/mL. Intra-and interassay CV for IGF-I were 8.8 and <10.5%, respectively; sensitivity of the IGF-I assay was 1 ng/ mL. Intra- and interassay CV for insulin were 10.3 and 12.4%, respectively; sensitivity of the insulin assays was 2 µIU/mL.
Statistical Analyses
Data were analyzed using REML in GenStat 5.4.1 (VSN International Ltd.) with fixed effects of treatment in a 2 x 2 factorial arrangement and cow as the random effect. Repeated measures through time were modeled separately for pre- and postpartum periods using spline models within the linear mixed model framework (Verbyla et al., 1999). The log10 PPAI was analyzed using the CENSOR procedure in GenStat to test treatment effects, but the untransformed least squares means (LSM) are presented in results.
Associations between measured variables pooled within treatment and PPAI at individual time points or the mean value for each cow over the first 35 d postpartum were determined using CENSOR procedure. The log10 PPAI was used in testing significance of these associations. The GH, IGF-I, insulin, NEFA, and BHBA data were log10 transformed before analyses. Untransformed means are presented in results.
Variables that had a consistent association with variance in PPAI were defined as those with 3 or more sampling days being significant (P < 0.05). Variables with a consistent and relevant association were defined as those with at least 1 of the 3 significant sampling times accounting for 10% (R2 > 0.1) or more of the variance in PPAI. Variables described as having an inconsistent association with PPAI were those with 1 or 2 sampling times having significance (P < 0.05), whereas variables with no significant sampling times (P > 0.05) were defined as unrelated.
Data of 2 cows were excluded from general analyses because the animals were removed from the trial during the first 35 d postpartum.
| RESULTS |
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| DISCUSSION |
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Underfeeding during the final few weeks before calving appeared to create a degree of energetic stress, as indicated by elevated NEFA and BHBA. This effect was associated with a tendency for a delayed time to first ovulation. Average intakes of 4.8 kg of DM/d per cow equivalent to about 1% BW and 51% of energy requirements (Roche et al., 2005, 2007) reduced insulin and IGF-I within several days, compared with cows on higher average DMI representing about 2.5% BW and 131% of energy requirements. The elevation in GH observed just before calving among all cows was initiated earlier and was greater in magnitude in those with low feed intakes prepartum. Insulin and IGF-I enhance estrogenic function in the early postpartum dominant follicle (Spicer et al., 1993; Diskin et al., 2003). Uncoupling of the GH–IGF-I axis is a homeorhetic condition that occurs around calving in dairy cattle (Rhoads et al., 2005), and is considered to repress follicular function. Interestingly, the difference in blood concentration of IGF-I converges as the animal approaches calving, with the PreH cows declining to a greater degree than PreL cows as parturition approached. The reason for this convergence is not clear, but it may be a result of differential timing in the uncoupling of the somatotropic axis. A sustained period of negative energy balance reduced the expression of GH receptor-1A in liver tissue (Breier et al., 1988). The prepartum feed restriction may have caused the "uncoupling" of the somatotropic axis to occur earlier than would otherwise have happened; this premise is consistent with the lower IGF-I concentrations evident in the PreL treatment cows. Still, IGF-I concentration would naturally be expected to decline as parturition approached in the PreH treatment cows, causing the plasma profiles to converge in the week before calving. The evident convergence of precalving treatments in plasma NEFA and BHBA concentration in the week before calving is perplexing, because it is mainly a result of a lowering of the concentration of these metabolites in the PreL cows. It is not clear why indicators of energy balance should become more positive as calving approaches. It is interesting however, because plasma concentrations of metabolites reflecting the energy status of the cow are not greatly affected by energy consumption in the week before calving, and whatever compensatory mechanism facilitates this may be the reason for the lack of association between these metabolites and PPAI.
The relevance of PreL on PPAI could be accounted for by the half-unit difference in BCS at calving in the current study. Previous studies show a reduction in PPAI of 3 to 4 d with each 0.5-unit increase in BCS (1 to 10 scale; Grainger et al., 1982; McDougall et al., 1995a). This result reinforces the practical utility of using BCS as an indicator of long-term energetic status. The current study found a negative association between plasma albumin and PPAI at various time points through the transition period. The consistency and relevance of this association was similar to that between BCS and PPAI. Yet the mechanism by which restricted intake prepartum increased PPAI is unlikely to involve albumin specifically, because there was no dietary treatment effect on plasma albumin through the transition period. Albumin may have an important long-term regulatory influence on the reproductive axis, although the relationship between albumin and PPAI is perhaps more a consequence of altered energy balance with scavenging of circulating albumin as an amino acid source for gluconeogenesis (Bell et al., 2000).
Postpartum pasture intake did not affect PPAI, BCS, GH, IGF-I, or insulin in the current study. This pattern of response is similar to that previously reported in first-calving Holstein-Friesian heifers managed on pasture (Burke et al., 1995; Chagas et al., 2006). These studies demonstrated that the negative effect of restricted feeding prepartum in heifers is not overcome by generous pasture allowances after calving. It would appear from the present study that cows adjusted to high or low pasture intakes by producing more or less milk to best achieve a genetically determined metabolic state. Even so, metabolic state was not fully restored to that of cows on high pasture intakes as demonstrated with reduced plasma glucose and elevated NEFA and BHBA (HL treatment group only). Nevertheless, concentrations of these metabolites during the postpartum period were not associated with variance in PPAI, irrespective of treatment groups. Furthermore, the magnitude of the effect of low pasture intakes postpartum on these metabolites was insufficient to measurably affect BCS, which declined similarly whether postpartum intakes were low or high. Low postpartum intakes did reduce BW. The difference can probably be accounted for by gut content differences at the time of weighing, and potentially, reduced tissue masses at sites (e.g., muscle, internal fat, skin, udder) that would not be accounted for in BCS (Berry et al., 2007). Nevertheless, BW was less well associated with PPAI compared with BCS.
The apparent ability of these animals to easily reduce milk energy output when faced with low energy intake may be an acquired genotypic feature of cows selected for seasonal pasture-based dairy systems. Regardless, the current study shows that for a pasture-only diet, nutritional and metabolic state during the prepartum stage is the most critical period for influencing PPAI. This interpretation is consistent with the findings of others within pasture-based dairy systems (Burke et al., 1995; McDougall et al., 1995b). Chagas et al. (2006) described the prepartum nutritional effect on PPAI as one having an "endocrine memory" that recursively influences PPAI irrespective of postpartum nutrition when diet consists solely of pasture. Body condition score and GH were at least 2 variables having such carryover effects, whereas most other variables were ameliorated by postpartum nutritional conditions.
| CONCLUSIONS |
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Prepartum dietary energy intakes that maintain elevated insulin and IGF-I and support a desirable BCS at calving are an important consideration when pasture is the sole dietary source through the calving transition of mature dairy cows. Because much of the variance in PPAI was unaccounted for, despite considerable variation in PPAI among the cows studied, longer-term genetic selection for avoidance of cows with prolonged PPAI could be a worthwhile consideration. The results also suggest that selection against long PPAI could proceed without compromising genetic merit for milk yield, because there was no relationship among these variables.
| ACKNOWLEDGEMENTS |
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| FOOTNOTES |
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3 Present address: University of Tasmania, School of Agricultural Science, PO Box 3523, Burnie, Tasmania 7320, Australia. ![]()
Received for publication November 26, 2006. Accepted for publication May 30, 2007.
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