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* Dexcel Ltd., Hamilton, New Zealand
Teagasc, Moorepark Dairy Production Research Centre, Fermoy, Co. Cork, Ireland.
1 Corresponding author: john.roche{at}utas.edu.au
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Key Words: pasture • seasonal calving • reproduction • body condition score
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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The postpartum delay in hyperphagia results in a mobilization of body tissue reserves to support milk production (Bauman and Currie, 1980). Both the duration and severity of this negative energy balance (NEBAL) have been reported to influence reproduction (Beam and Butler, 1999), but the effects are not consistent. For example, Ruegg and Milton (1995) reported no effect of BCS on reproduction indices, whereas others have reported significant effects (Waltner et al., 1993; Gillund et al., 2001; Buckley et al., 2003).
Furthermore, in studies in which BCS has been reported to affect reproduction, there have been inconsistencies in the reported effect. For example, Buckley et al. (2003), Gillund et al. (2001), and Waltner et al. (1993) reported a lack of effect of BCS at calving on reproductive performance, whereas others (Markusfeld et al., 1997; Titterton and Weaver, 1999) reported a significant effect. Possible reasons contributing to discrepancies among studies include the system of milk production, the sample population analyzed, the frequency of BCS measurement, the model of analysis, the definitions of both the BCS and reproductive parameters investigated, and variation in the parameters within the sample population. In addition, most of the aforementioned studies were undertaken either in one season or on one farm, and BCS is influenced by year (Gallo et al., 1996), feeding level (Mao et al., 2004; Roche et al., 2006), system of milk production (Washburn et al., 2002), parity (Gallo et al., 1996; Mao et al., 2004), and the genetic makeup of the animal (Berry et al., 2002; Roche et al., 2006), possibly adding to the inconsistency in reported results. Furthermore, results from nonseasonal production systems may not be directly applicable to seasonal systems.
Although relationships between BCS and reproductive performance in dairy cattle have received attention in the international literature, fewer studies have attempted to quantify any associations between the more objective measure of BW and reproductive success in dairy cattle. Buckley et al. (2003) reported a significant effect of BW at the start of the herd breeding season, DIM at nadir BW, and BW change from the start of breeding to 90 d thereafter on pregnancy rate at first service, suggesting that BW is potentially an important determinant of the likelihood of reproductive success. Nonetheless, there is a paucity of information relating BW to reproductive performance, especially under seasonal-calving dairy production systems.
The objective of this study was to quantify the direction and strength of the associations among BCS, BW, and indicators of reproductive performance under a compact seasonally calving pasture-based system of milk production.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Body condition score and BW were assessed within 1 wk of calving and at every 2 wk during the intercalving period following the morning milking. Body condition score was assessed by palpating individual body parts, and an average score was recorded on a 10-point scale, where 1 was emaciated and 10 was obese (Roche et al. 2004). These scores can be converted to the 5-point scale using the regression equation generated by Roche et al. (2004; 5-point = 1.5 + 0.32 x 10-point). Body weight was measured using a calibrated electronic scale (Gallaghers, Hamilton, New Zealand). In total, 68,986 BCS records and 68,980 BW records were available for inclusion in the analysis. The mean number of BCS and BW records per lactation was 23. Parity number varied from 1 to 12.
Research Farm
The Ruakura No. 2 dairy farm in Hamilton has been used for farm systems-based research since the 1940s. The period in question incorporated 64 research treatment farmlets, with comparisons of different pasture species and cultivars, different grazing rotation lengths, different systems that optimized the use of nitrogen fertilizer and supplementary feeds, the most profitable stocking rate for grazing dairy systems, and the profitability of Holstein-Friesian and Jersey heifers under grazing systems undertaken during multiple lactations (141 different herd x year farmlets).
Soils were fertile silt loams (Aquic Dystandepts, Haplic Andaquepts, Umbric Vitrandepts) and peaty silt loams (Humic Haptorthod). The farm received annual "maintenance" dressings of 50 kg of K/ha as muriate of potash in November and 54 kg of P/ha and 55 kg of S/ha as single superphosphate in March. Across the years being studied, the average nitrogen application rate varied from 172 to 286 kg of N/ha.
The system of milk production was seasonal, with approximately 50% of cows calving in 2 wk, 40% calving in the next 4 wk, and the remaining cows calving during wk 7 and 8. Cows with a calving due date later than wk 8 into the seasonal calving period were hormonally induced to calve during wk 7 and 8 using a 2-step combination of dexamethasone (Opticortenol S; Novartis Animal Health, Basel, Switzerland; Voren; Boehringer-Ingelheim, Alkmaar, the Netherlands) and prostaglandin (Estrumate; Schering-Plough Coopers, Wellington, New Zealand). Inductions were performed only if SCC at dry off were <200,000, BCS of cows were 
5.0, and blood Mg and 
-glutamyl transferase measured the week preceding planned induction did not indicate health concerns.
Grazing regimens varied very little among treatment farmlets. In general, herbage was grazed when between 2 and 3 leaves had regrown on the majority of perennial ryegrass tillers (approximately 2,500 kg of DM/ha in spring, 4,000 kg of DM/ha in summer, and 3,000 kg of DM/ha in autumn and winter—all measurements were to ground level). Postgrazing residuals approximated 40 mm during the winter or spring, and 60 mm during the summer or autumn. Detailed descriptions of management decision rules for No. 2 dairy are provided by Macdonald and Penno (1998).
Data Editing and Generation of Variables of Interest
Reproduction.
A total of 2,594 service records were available for inclusion in the analysis. The routine mating management policy at No. 2 dairy was to record any cows exhibiting signs of estrus prior to PSM. Estrous detection was performed by twice-daily visual observation of estrous behavior with the aid of the tail-painting technique (Macmillan et al., 1988). Cows not detected in estrus by PSM were presented for veterinary examination. Those without a palpable corpus luteum were treated with an intravaginal controlled internal drug-releasing insert (InterAg, Hamilton, New Zealand) according to the Genermate program (Cliff et al., 1995). Artificial insemination was performed for the first 6 wk from PSM, followed by a further 6 wk of natural breeding. Pregnancy diagnosis was performed by manual palpation of uterine contents at least 5 wk after the end of the 12-wk mating period.
From the raw data set, PSM each year was determined as the first service date of a lactating animal within year; no outlier service dates existed and no service dates within cow-lactation were within 5 d of each other. If estrus was detected (CYCLE) in a cow prior to the start of the breeding season, the cow received a value of 1 for CYCLE or was otherwise zero. Premating estrous records were available from 1996 to 2000 and were included in the analysis of CYCLE (928 lactation records).
The 21-d submission rate (SR21) was constructed by coding cows with an insemination date within the first 21 d from PSM as 1, whereas those with no insemination date within the first 21 d were coded as zero. Pregnant to first service (PFS) was coded as 1 if an animal received only one service and was diagnosed as pregnant at the end of the season. Service dates resulting in a successful pregnancy were validated with subsequent calving dates where available. Lactations with more than one service, or where the animal was diagnosed as nonpregnant, were allocated a PFS of zero.
Pregnant within 21 d of the onset of breeding (P21) was coded as 1 if a lactation record with at least one service did not receive a service following 21 d of breeding and was subsequently confirmed as pregnant. A lactation record received a P21 record of zero if a service was obtained sometime after 21 d of breeding, or if the animal was diagnosed as nonpregnant. Similar descriptors were used for pregnant within either 42 (P42) or 84 d (P84) after PSM.
BCS and BW.
The BCS and BW variables generated were those believed to have the greatest potential influence on reproduction, and are consistent with previous international studies (Ruegg and Milton, 1995; Domecq et al., 1997; Buckley et al., 2003). Variables of interest were the BCS and BW 8 wk prior to calving, at calving, at the nadir, at PSM, and at first service, as well as the level and daily rate of BCS and BW change between key time periods. Days postcalving to both the BCS and BW nadir were also of interest.
The BCS and BW precalving were determined as the BCS or BW record nearest to 8 wk precalving but between 6 and 10 wk precalving. Where 2 BCS or BW records were available equidistant from wk 8, then the earlier record precalving was retained. Additionally, to determine the precalving change in BCS and BW, all BCS and BW records in the 9 wk prior to calving were retained. A linear regression in PROC REG (SAS Institute, 2006) was fitted through these records for each lactation separately and the linear coefficient was determined; the linear regression was fitted only through lactations with at least 2 precalving records. The regression coefficient was recoded as 1, 2, or 3 if the regression coefficient was negative, zero, or positive, respectively. The BCS and BW record at calving were considered to be the first records postcalving but within 7 d of calving. Nadir BCS and BW were the first postcalving records immediately followed by 2 higher consecutive values. Days postcalving corresponding to nadir BCS or nadir BW were also retained.
The BCS and BW at first service were the records nearest (either prior to or following) the first service date, but within 7 d of the service date. Where 2 BCS or BW records were available equidistant from the first service date, then the later record postcalving was retained. Similar methodology and criteria were adopted to obtain the BCS and BW records at the start of the breeding season.
Body condition score and BW change from calving to nadir, nadir to PSM and first service, and calving to PSM and first service were calculated as the BCS or BW at calving or nadir less the BCS or BW at the time period under investigation; hence, a positive value is indicative of a loss in BCS or BW. The rate of loss was determined as the difference divided by the DIM to the respective time point. The number of records for each variable is summarized in Table 1
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Additional Explanatory Variables.
Parity was recoded as 1, 2, 3, 4, and 5+. Week of the year at calving was determined for all lactations. Cows calving prior to wk 27 (i.e., early July) were grouped together, and cows calving later than wk 35 (i.e., early September) were grouped together. Year was determined as the year of calving. The interval from calving to first service was calculated per lactation as the number of days from calving to when the animal had its first recorded service. Also, because of the seasonal calving (and mating) season operated in New Zealand, the submission rate and pregnancy rate at the start of the breeding season may be a function of the number of days from calving to the start of the breeding season. Hence, the interval from calving to the start of the breeding season was calculated for each lactation record.
Variables considered as class variables were parity, breed, week of the year at calving, year of calving, and treatment farmlet operating on the research farm since 1986. The results are presented based on the average solutions across all years and treatments.
Statistical Analysis
BCS and BW.
The partial correlations between some of the BCS and BW variables were estimated using PROC CORR (SAS Institute, 2006). Additionally, the effect of parity, breed, year of calving, and week of the year at calving on some of the BCS and BW variables was determined using mixed-model methodology in PROC MIXED (SAS Institute, 2006). Within these analyses, cow was treated as a random effect and parity, breed, year at calving, and week at calving were included as fixed effects in the model. The significance of the fixed effects in the model was determined using the F-test. The ratio of the cow variance to the sum of the cow and residual variance was used to calculate the repeatability of the alternative BCS and BW definitions.
Reproduction Variables.
For the purpose of the present analyses, 6 reproduction variables were identified as important for a pasture-based seasonally calving dairy production system, those variables being CYCLE, SR21, PFS, P21, P42, and P84.
The binary nature of the reproduction traits, coupled with the repeated records per cow across years necessitated the use of generalized estimating equations in PROC GENMOD (SAS Institute, 2006) to model the logit of the probability of a positive estrus, submission, or pregnancy outcome. Cow was included as a repeated effect, with a compound symmetry correlation structure assumed among records within cow. The empirical solutions are reported in the present study. The level of significance associated with each explanatory variable was based on the generalized estimating equation score statistic.
A separate data set was created in which lactation records missing information on any of the possible explanatory variables investigated were removed. A multivariate model was developed for each dependent variable separately using a 2-stage approach incorporating a stepwise forward–backward algorithm. First, adjustment variables such as parity, breed, treatment, year at calving, week of the year at calving, and the interval from calving to the start of the breeding season (for SR21, P21, P42, and P84) or the interval from calving to first service (for PFS) were tested in the model. The levels of significance for entry and staying in the model were P < 0.20 and P < 0.05, respectively. Higher order polynomials on the continuous interval traits were also tested in the model.
Following the completion of the first stage, a stepwise algorithm was again invoked for BCS and BW variables separately with the previously identified significant adjustment variables forced into the model; the levels of significance for entry and staying in the model were again set to P < 0.20 and P < 0.05, respectively. The existence of multicollinearity was continuously investigated with the introduction of a new explanatory variable into the model. The presence of multicollinearity was investigated using the variance inflation factor and condition index produced by PROC REG (SAS Institute, 2006) as well as the change in model solutions with the introduction of the new independent variable in the model. Biologically plausible interactions between significant main effects were also tested in the multivariate analysis. When the stepwise algorithm was complete, the final multivariate model was run on the complete data set.
Odds ratios were derived by acquiring the exponent of the partial regression coefficients. Odds ratios compare opposing probabilities to determine which is the more likely result for a given outcome; in this instance, the outcome is the probability of an animal cycling prior to PSM being submitted for insemination in the first 21 d of the breeding season, or becoming pregnant at various time points relative to the PSM. An odds ratio greater than 1 implies an increased likelihood of a positive outcome, whereas the contrary is true of an odds ratio less than 1. For example, an odds ratio of 2 reflects double the likelihood of a positive outcome. When the independent variable is continuous, then the odds ratio relates to a one-unit incremental change. However, where a nonlinear association exists between a continuous independent variable and the binary dependent variable, an odds ratio is not presented because the odds is a function of the reference value used. Furthermore, the probability of a successful outcome was estimated using the results from the analyses as
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where 
is the predicted intercept of the model, and 
i is the predicted regression coefficient for independent variable Xi.
In some cases, the units of measurement (e.g., kilograms) were small, thus leading to small, yet sometimes significant, odds ratios. To avoid a loss of information by restricting the number of decimal places presented, some odds ratios and associated standard errors were transformed to a per-unit standard deviation using the standard deviation of the trait in question across the sample population.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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. Within the data set, 5% of animals were considered fat (i.e., BCS 6.0) at calving, whereas 23% were considered thin (i.e., BCS 
4.0). On average, cows lost 0.73 BCS units and 53 kg between calving to the respective nadir. The mean (±SD) DIM taken to represent BCS and BW at calving was 4 d (±2 d), whereas DIM at PSM and at first service were 65 (±17.7 d) and 74 (±19.4 d). Mean cycle, SR21, PFS, P21, P42, and P84 were 71, 95, 58, 55, 74, and 91%, respectively. Mean 60- and 305-d milk yields in the sample population were 1,152 and 4,697 kg, respectively. There was a linear decline in the odds of an animal cycling prior to PSM as the week of the year at calving increased, but this factor did not affect any of the other reproductive variables. Year of calving was significant for all reproductive measures other than P84, but the effect was inconsistent and there was no evident trend.
Correlations Between BCS and BW
The partial correlation coefficients between the various BCS and BW parameters are summarized in Table 2. Because nadir BCS and BW occurred at different DIM and were therefore defined separately, only the correlation between calving BCS and BW reflects the same DIM. This is reflected in the weak association (r = 0.15) between DIM at which the nadir BCS and nadir BW are reached. The correlation between BCS and BW at calving (r = 0.32) suggests that BCS explains 10% of the variation in BW at calving. The correlation between the amount of BCS and BW lost from calving to their respective nadirs was strongest. Nevertheless, only 15% of the variation in BW loss was explained by BCS loss. Animals calving at a higher BCS had a higher nadir BCS, but lost more BCS at a greater rate and for a longer period of time. The same was true of BW.
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. The BCS at calving was greater in first-calving animals, decreased in animals calving for the second time, and progressively increased with parity. Second-parity animals had significantly lower BCS at calving and nadir than those in all other parities, although the amount and rate of BCS loss to nadir in second-parity animals was not significantly different from other parities with the exception of first parity, which was less than any of the other parity means. Although statistically significant, the parity differences were biologically small. Body weight at calving and nadir increased with parity, and only second-parity cows differed in their DIM to nadir BW. Postpartum BW loss increased with parity. Breed significantly affected BCS and BW at calving and nadir, with Holstein-Friesian cows heavier but lower in BCS. Holstein-Friesian cows also had fewer days to BCS and BW nadir.
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BCS, BW, and CYCLE
The likelihood of an animal being detected in estrus before PSM was affected by year of calving, week of the year at calving, and parity. Because of the reduced data set available for this analysis, only Holstein-Friesian cows were included in the analysis. The effect of the BCS and BW traits that significantly (P < 0.05) influenced CYCLE are summarized in Table 4. Higher BCS precalving, at calving, or during lactation were associated with a greater probability of a cow having been detected in estrus before PSM. A similar result was found for BW, with heavier cows having a greater likelihood of having been detected in estrus prior to PSM. However, a curvilinear relationship between BCS at the start of the breeding season and the logit of the probability of CYCLE was apparent, with the highest predicted probability (84%) in animals with a BCS of 5.5 at PSM and a lower probability with either a higher or lower BCS. A similar trend was observed for BW at PSM, with the optimum being 570 kg. Both effects were significant in the multivariate analysis. Cows that lost more BCS and BW from calving to nadir, or to PSM, had a significantly reduced likelihood of being detected in estrus prior to PSM. Additionally, higher odds of an animal cycling prior to the PSM were observed in cows that reached nadir BCS and BW prior to the PSM and first service, respectively, a result that was substantiated by the effect of nadir DIM on CYCLE. Both effects remained significant in the multivariate analysis. The intercepts of the multivariate model were –9.35 (SE = 2.22) and –10.47 (SE = 4.52) for the BCS and BW models, respectively.
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). Holstein-Friesian cows had a reduced likelihood of a positive SR21 compared with Jersey cows.
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. Level of BCS at calving or at nadir had no significant effect on SR21. However, both the daily rate of change in BCS postpartum and the amount of change between calving and nadir were significant predictors of SR21. A greater likelihood of SR21 was evident for cows that lost BCS at a faster rate between calving and first service; the standardized odds ratio was 2.09, implying that cows losing BCS at a daily rate of one standard deviation greater than the sample mean (0.0144 BCS units/d) were more than twice as likely to be inseminated in the first 21 d of the breeding season compared with those with an average rate of BCS change. In comparison, cows losing more BCS between calving and nadir were less likely to be inseminated in the first 21 d from PSM. A greater probability of a positive outcome for SR21 was found in the univariate analysis when nadir BCS occurred prior to first service, but this parameter lost its significance when the nadir BCS and rate of change of BCS prior to nadir were accounted for in the multivariate analysis. Following adjustment for the effects of BW change, animals attaining nadir BW earlier and those that had lost BW most quickly between calving and first service were more likely to be inseminated in the first 21 d from PSM. The intercepts of the BCS and BW multivariate models were 3.73 (SE = 0.71) and 6.44 (SE = 0.89), respectively.
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Higher BCS at key periods of lactation were associated with greater odds (1.17 to 1.26) of a positive PFS (Table 6). By using the average year solutions and the average calving to first service interval of the sample population, the probability of PFS declined from 59 to 54% as BCS at first service declined by one BCS unit from the sample mean BCS of 4.3 at first service. The increased amount and rate of BCS loss postcalving was associated with reduced odds of a successful pregnancy to first service. Following the inclusion of BCS change to first service in the multivariate model, both BCS at calving and BCS at first service significantly (P < 0.05) affected PFS to an equivalent degree; the P-value and solutions were identical for both traits because both traits were used in the calculation of BCS to first service, which was already included in the model. Despite the part–whole relationship between either BCS at calving or BCS at first service and BCS loss from calving to nadir, collinearity was not a problem if either BCS at calving or BCS at first service was included in the multivariate model along with BCS loss to first service.
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BCS, BW, and P21, P42, and P84
The BCS and BW variables that significantly influenced P21, P42, and P84 as well as the associated solutions or odds ratios are summarized in Tables 7, 8, and 9, respectively. An established pregnancy in the first 21 d from PSM was influenced by year of calving as well as a quadratic regression on days from calving to PSM. The probability of P21 increased with the interval from calving to the start of the breeding season up to 77 d and declined thereafter (Figure 1). The probability of being pregnant 42 d into the breeding season was influenced by year, parity, and a quadratic regression on the interval from calving to PSM, whereas P84 was affected by a linear regression on the interval from calving to PSM.
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The effect of calving BW on P21 was also quadratic, with a lower P21 in light and heavy cows, although no effect of BW during lactation was observed. Greater BW loss in the period immediately postcalving was associated with reduced P21 and P42, but not P84. The BW variable that affected all 3 pregnancy traits in the multivariate analyses was the rate of BW change in the 4 wk immediately following first insemination, with the probability of a successful pregnancy increasing with the rate of BW gain. The data inferred an increase of 3 percentage units in the probability of a cow being pregnant at each of these time points when BW gain following first insemination increased from the mean of 0.47 to 1 kg/d. The intercepts of the multivariate model for BCS when the dependent variables were P21, P42, and P84 were 0.79 (SE = 0.58), –0.19 (SE = 0.66), and 3.18 (SE = 0.39), respectively; the corresponding values for the BW multivariate model were 0.19 (SE = 0.61), 1.96 (SE = 0.65), and 1.59 (SE = 0.47), respectively.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Solutions for both univariate and multivariate analyses are presented because although multivariate solutions account for associations between BCS and BW variables, the individual variables are important in their own right, as they may be managed more easily than variables reported to be significant in the multivariate analysis with which they are related. For example, BCS at calving is positively associated with the proportion of cows that cycled prior to PSM in the univariate analysis, but it is not significant after accounting for nadir BCS in the multivariate analysis. However, Roche et al. (2006) reported a failure of nutrition to significantly affect the rate of BCS loss between calving and nadir because of the uncoupled somatotropic axis, making it difficult to alter nadir BCS through postcalving cow management. Because nadir BCS is positively correlated with BCS at calving (r = 0.51), management of BCS at calving may be the most effective method of managing nadir BCS. Therefore, its relationship with reproductive variables, in this case CYCLE, in the univariate analysis is noteworthy.
Although only 5% of cows were considered fat (BCS 6) at calving, 23% were considered thin (BCS 4). This classification of data coupled with a coefficient of variation for calving BCS of 14% indicates ample variation in calving BCS. In general, the mean of the BCS variables reported herein are similar to those published elsewhere (Waltner et al., 1993; Pryce et al., 2001; Buckley et al., 2003; Roche et al., 2006) following adjustment for the measurement scale (Roche et al., 2004). The DIM at BCS nadir was earlier than the 60 to 80 DIM reported by Mao et al. (2004) and the approximately 120 DIM reported by Pryce et al. (2001), but was later than the median of 35 d reported in Irish Holstein-Friesians (Buckley et al., 2003). Mean BW at different stages of lactation were generally lower than reported elsewhere (Buckley et al., 2003), reflecting the smaller frame size and lighter type of dairy cow that is traditionally used in New Zealand (Roche et al., 2006). Repeatability estimates of BW within cow across DIM and lactations are slightly higher than previously reported estimates (0.53 and 0.35; Badinga et al., 1985; Abdallah and McDaniel, 2000), whereas repeatabilities for BCS are similar to earlier reports (Berry et al., 2003).
The most important variables associated with reproductive success in the present data set are nadir BCS, the amount of BCS lost between calving and nadir, and the rate of BW gain post-PSM. They suggest that reproduction is compromised by NEBAL; as the severity of NEBAL increases, the likelihood of a successful pregnancy becomes less. These findings are consistent with those of Buckley et al. (2003) in seasonal pasture-based systems and Gillund et al. (2001), Pryce et al. (2001), and Loeffler et al. (1999) in TMR-fed dairy cows. Although others have failed to report any significant relationships between BCS loss early postpartum and reproductive performance (Ruegg and Milton, 1995; Domecq et al., 1997), there were tendencies for impaired reproductive performance in cows that lost more body condition.
The effect on reproduction of a BCS change in early lactation is further supported by the effect of a BW change (either amount or rate) on P21 and P42. These results are in agreement with previous reports by Heinonen et al. (1988), who reported inferior reproductive performance in cows that lost more than 10% of BW postcalving compared with cows that lost less than 10% of BW postcalving. Youden and King (1977) also reported a significant effect of BW change around the time of service on conception rate. Further support for an effect of energy balance on reproduction is evident in the positive association between BW gain during the 4 wk following first service and P21, P42, and P84, which is indicative of a return to positive energy balance prior to PSM. These findings are consistent with those of Buckley et al. (2003), who found greater PFS in cows exhibiting greater BW gain during the 90 d following PSM.
Physiologically, NEBAL manifests itself in delayed ovarian activity by impinging on the pulsatile secretion of LH, the follicular responsiveness to LH, and ultimately through shutting down follicular estradiol production (Diskin et al., 2003). Beam and Butler (1997) reported that follicles emerging after the NEBAL nadir, rather than before, exhibited greater growth and diameter, enhanced estradiol production, and were more likely to ovulate. This is consistent with the positive relationship between PFS and P21 and rate of BW change during the 4 wk after PSM.
However, although a NEBAL-mediated suppression of LH pulsatility and the consequential delay in PPAI is important, and may be influencing PFS and P21, it is unlikely to be the BCS-mediated factor affecting P42 or P84, considering that any cow not cycling by PSM received progesterone treatment and that estrus was initiated artificially. Therefore, other factors associated with the extent and severity of the NEBAL were likely affecting the ovary and pregnancy directly.
It must be pointed out that, although statistically significant, the biological effect of NEBAL severity and the extent to which cows are exposed to a positive energy balance on reproductive success is small. A further required consideration is whether it is possible to influence this effect of energy balance through nutrition. Figure 2 represents the predicted probability of a successful PFS, P42, and P84 at various BCS nadirs, and amounts of BCS lost prenadir and BW gain post-PSM. Although a 3-unit difference in nadir BCS equated to 12-, 15-, and 9-percentage unit differences in PFS, P42, and P84, respectively, a more realistic 1-unit-lower nadir BCS was equivalent to only a 4-, 5-, and 3-percentage unit decline in PFS, P42, and P84, respectively. Similarly, the effect of BCS loss between calving and nadir on successful PFS, P42, and P84 was 4.4, 4.8, and 3.2 percentage units/BCS unit lost, respectively. In other words, a 0.25-unit increase in nadir BCS (or a 0.25-unit decline in BCS lost prenadir) would increase PFS, P42, and P84 by approximately 1%. Regression equations generated from Roche et al. (2006) suggest that grazing cows would require 150 to 175 kg of DM concentrates in the first 60 d of lactation to elicit such a response in BCS. Such a small predicted change in pregnancy rate may explain why research trials to date have failed to show an association between supplementation of grazing dairy cows and final pregnancy rate (Fulkerson et al., 2001; Kolver et al., 2005).
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The effect of calving BCS is also noteworthy because it is arguably the BCS variable most easy to alter through management. The positive effect of BCS and BW at calving on the probability of exhibiting estrus prior to PSM and pregnancy to first service is consistent with previous research (Markusfeld et al., 1997; Reksen et al., 2002). Reksen et al. (2002) reported the delayed resumption of luteal function in thinner cows, similar to that described by Beam and Butler (1997) in cows undergoing severe NEBAL, and Markusfeld et al. (1997) reported that thinner cows at calving were more likely to have inactive ovaries, although the measured effect was greater in younger cows. This interaction of calving BCS with parity is consistent with results from this data set and suggests that earlier parity cows (first and second parity) may benefit from greater BCS at calving, as recommended by Macdonald and Roche (2004). This interaction with parity may explain why Loeffler et al. (1999) identified first parity as a risk factor for conception failure to first AI.
Submission during the first 21 d of the breeding period is an important measure of reproductive success in seasonal dairy systems, predicating how compact the following calving period will be. Buckley et al. (2003) reported a reduced SR21 in cows in low BCS in early lactation, but no such effect of BCS was evident in the study reported here. However, any effect was probably masked by the use of progesterone treatment to initiate estrous cycles in noncycling subjects. The greater number of cows anestrous at PSM and the positive association between calving BCS and CYCLE are consistent with the lower SR21 reported by Buckley et al. (2003). Further support for this is the reduced PFS in cows calving in lower BCS because cows induced to ovulate typically have a reduced pregnancy rate to that service (McDougall and Compton, 2005). Although calving BCS was positively related to onset of estrus and PFS, it did not affect the likelihood of a successful pregnancy at either 21, 42, or 84 d after PSM. However, it is not possible to say from the present data set whether calving BCS would have had a negative effect on P42 and P84 if anestrous intervention had not been available, a more likely future prospect in the modern climate of consumer concern regarding food production and animal welfare. However, the results of Waltner et al. (1993) and Buckley et al. (2003) also indicated no discernible effect of calving BCS on P42 (and hence P84). The results of McDougall and Compton (2005) concur that the positive benefits of progesterone treatment manifest early in the breeding season and disappear as the season progresses. Therefore, it appears that calving BCS is important in the onset of estrus, but as DIM increase, its effect becomes less important on other reproduction variables. This is consistent with the results of Markusfeld et al. (1997), who reported that a low BCS at calving reduced fertility mainly by delaying the onset of ovarian activity, and that the effect of calving BCS on reproduction indices diminished with time postcalving.
The increase in PFS associated with a 1-unit increase in BCS at PSM in the current data set (3 percentage units) is equivalent to the reported increase of 9 percentage units in PFS per unit BCS at 10 wk in UK Holsteins (Pryce et al., 2001) when the difference between the 10-point and 5-point systems of scoring is accounted for (Roche et al., 2004). Unlike in the present data set, however, neither Buckley et al. (2003) nor Pryce et al. (2001) reported a significant interaction between parity and BCS in early lactation. Nonetheless, this interaction is consistent with the previously discussed interaction between calving BCS and parity, and is plausible because of the strong correlation between calving and nadir BCS (0.51).
Previously reported studies and the study presented here suggest a negative impact of calving BCS or BCS in early lactation on reproductive success early in the breeding program (SR21 or PFS), with no effect on P42 or P84. However, all of these studies (including our own) evaluated the effect of BCS variables in one year on the reproductive parameters within the same year. The lower PFS of cows that calved in poorer BCS in the current study, and the lack of effect on P42, implies that a greater proportion of cows became pregnant between wk 5 and 8 of the breeding period. This is consistent with the results presented by McDougall and Compton (2005), who reported no effect of treatment of anestrous cows on pregnancy at 56 d. Nevertheless, this pregnancy delay has implications for the calving spread in the subsequent year, and possible consequences for the timing of successful future pregnancies. Further studies are required to determine the multilactational effect of low calving BCS on the timing of pregnancy and ultimately on cow survivability in seasonal calving systems.
The increase in SR21 observed in cows that lost BCS and BW most rapidly between calving and first service was unexpected, considering that the rate of change of BCS was the most strongly correlated BCS variable with the amount of BCS lost (r = 0.75), a factor reported to reduce SR21 in this study and in the results reported by others (Beam and Butler, 1999; Buckley et al., 2003). This inconsistency may be a result of differences in the duration of NEBAL. Rates of BCS and BW loss were negatively correlated with the duration of NEBAL, with the days of NEBAL declining with a greater rate of loss in both measures. This suggests that the duration of NEBAL may be more important than the rate at which cows lose weight. Such a premise is also supported by the negative relationship between DIM to nadir BW and SR21.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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FOOTNOTES |
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Received for publication April 25, 2006. Accepted for publication July 7, 2006.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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-) and pregnancy rate (-
-) in the first 21 d of the breeding season, as well as the predicted pregnancy rate to first service (-
-) across the intervals from calving to the start of the breeding season and calving to first service, respectively represented in the data set.