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* Department of Animal Science, University of Sydney,
Bovine Research Australasia, Camden, NSW 2570, Australia
Corresponding author:
C.T.Westwood: e-mail;
charlottewestwood{at}xtra.co.nz
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Abbreviation key: ABV = Australian breeding value, , HD = highly degradable, , LD = low degradability, , MJME = megajoules of metabolizable energy, , NEB = negative energy balance, , 3-OH = 3-hydroxybutyrate, , UDP = undegradable protein
Key Words: fertility • genetic merit • multivariate • protein degradability
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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In a previous paper, Westwood et al. (2000) investigated the univariate associations between dietary protein and the genetic merit of cows, and the concentrations of metabolites, body condition, and the reproductive performance of lactating dairy cows. Farm management factors including sensitivity and specificity of estrous detection, voluntary waiting period, semen storage, and semen placement in the uterine tract will also modify reproductive performance. Given the potential for these management factors to confound univariate associations, the second paper of this series uses multivariate analytical methods to further investigate the reproductive response to dietary protein degradability and the genetic merit of cows. The objective of this study was to evaluate the factors that influenced the fertility of cows maintained under tightly controlled experimental conditions. We considered that by investigating several different measures of reproductive outcome, mechanisms that lower reproductive performance may be better understood, and the hierarchical level at which each factor may affect reproductive performance may be identified.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Cows of high genetic merit were defined as those with an Australian breeding value (ABV) fat plus protein greater than or equal to 31. Cows of low genetic merit had an ABV less than or equal to 15.
The study was conducted over a 2-yr period with cows calving year round. A maximum of 44 cows were present in the herd at any time, and herds were balanced for dietary and genetic merit group. All work was carried out with the approval of the Animal Care and Ethics Committee of the University of Sydney, Australia.
Data and Sample Collection
Daily DMI were calculated for each cow. Cows were milked twice daily and individual yields recorded. Cows were weighed and scored for body condition every 7 d. Body condition was scored using a five-point scale of 0.25 increments, where 1 = thin and 5 = obese (Edmondson et al., 1989).
Whole-milk samples for milk progesterone concentration were collected from each cow at 3-d intervals, beginning 7 d after calving. Weekly milk samples were collected until wk 10 of lactation for milk fat, protein, and lactose percentage analysis. Blood samples were taken weekly from wk –3 to wk 10 of lactation and analyzed for plasma urea, glucose, cholesterol, 3-hydroxybutyrate (3-OH), and serum NEFA. Plasma progesterone concentrations were determined with heparinized blood collected from each cow twice weekly for 24 d after each breeding.
Calculation of Energy Balance
Weekly energy balance was calculated for each cow using mean daily DMI/wk, mean daily milk production per week, weekly change in BW, and weekly percentage of milk fat, protein, and lactose. Daily metabolizable energy intake was calculated using formulated values for metabolizable energy for each ration.
Analytical Procedures
Plasma urea, glucose, and cholesterol concentrations were determined with commercial kits using a Cobas Mira autoanalyzer. Concentrations of plasma 3-OH were determined by auto analyzer according to the method of Zivin and Snarr (1973). NEFA concentrations were determined with Wako NEFA C kits (Wako Pure Chemical Industries, Osaka, Japan), with modifications described by Rabiee (1995).
Milk progesterone concentration was determined with commercial liquid- and solid-phase 125I-labeled progesterone kits (Orion Diagnostica, Espoo, Finland); a solid-phase 125I-labeled solid-phase kit (Orion Diagnostica) was used to assay plasma progesterone concentrations.
Reproductive Measures
A detailed description of the reproductive performance evaluation was given by Westwood et al. (2000). The reproductive health of all cows that had not been inseminated during the previous 42 d was monitored every 3 wk by palpation of the reproductive tract per rectum, commencing 21 ± 3.5 d after calving. Cattle were observed for estrus for at least 14 h/d. Sensitivity of estrous detection was increased by placing KaMaR heatmount detectors (Steamboat Springs, CO) on the tailhead of each cow. Cows were bred on the observation of primary and/or secondary heat signs. Cows were not bred before d 45 of lactation.
Calving to first ovulation interval was defined by the first increase in milk progesterone >6 nmol/L after calving; ovulation was assumed to have occurred 5 d before elevation of progesterone concentration. Intervals from calving to first estrus and first service were defined as intervals to estrus or service events accompanied by ovulation (progesterone concentration <6 nmol/L at the time of event). Conception and pregnancy rate to first service were calculated only for services accompanied by ovulation.
Early establishment of pregnancy (initial conception) was defined by an elevated progesterone concentration that persisted beyond d 24 after insemination. Successful pregnancy was defined by pregnancy palpable per rectum at d 42 after mating. Interval from calving to initial conception was calculated with progesterone concentrations; time to successful pregnancy was calculated with palpation records. Early embryonic death was defined as loss of conceptus between initial conception diagnosis and confirmation of pregnancy by rectal palpation.
Statistical Analyses
Univariate analysis of the relationships between the genetic merit of cows, the degradability of dietary protein, and reproductive performance indicated a number of significant associations (Westwood et al., 2000). Reproductive failure is unlikely to be the result of a single dominant factor and is more likely the result of a combination of factors. Multivariate analyses permit the simultaneous evaluation of a large number of explanatory variables to examine their effect on the outcome variable and allow for the examination of interactions between these variables and control confounding. Therefore the data were further explored using multivariate analyses.
Categorical and continuous variables that were investigated as potential predictors are listed in Table 1
and Table 2. Univariate analyses were performed using survival analysis (BMDP 1L; BMDP Statistical Software Inc., Los Angeles, CA) to assess the association between a prognostic variable and time to a reproductive event. Continuous variables were categorized into top, middle, and lower quartiles to examine the effect of each stratum of prognostic variable on time to an event. Associations between categorical prognostic variables and expression of estrus at ovulation, conception to first service, and probability of conception or pregnancy by d 150 of lactation were assessed using 
2 analysis (BMDP 4F). Effects of continuous variables on estrous expression and probability of conception or pregnancy were assessed using one-way analysis of covariance (BMDP 1V).
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Only the most significant univariate relationship among a group of highly correlated variables was retained for multivariate analysis. Variables that had a univariate P-value less than or equal to 0.25 were included for evaluation using Cox’s regression model (BMDP 2L) (time to an event) or backwards stepwise logistic regression (BMDP LR; probability of an event occurring). For logistic regression models, change in maximal log likelihood values were used to indicate the RL2 for each model. Because models for time to a reproductive event were partial maximal log likelihood models, RL2 were not calculated.
Linearity between dependent and independent variables was assessed graphically by examining quartile cutpoints for continuous independent variables plotted against the proportion of individuals with the outcome of interest. Interactions between variables were assessed by examining correlation matrices, by examination of coefficients, and by testing the significance of interactions among variables. Colinearity between variables was tested by evaluation of the underlying biological relationships between variables, by examination of correlation matrices, and by examination of changes in coefficients as new variables entered or were removed from stepwise models. Confounding was assessed by evaluating changes in coefficients as new terms were entered into multivariate models. Methods used in model development are described by Hosmer and Lemeshow (1989).
All multivariate models included diet and genetic merit as significant effects or as potential confounders. Neither variable was a significant confounder for any final regression model.
One cow was culled at d 77 of lactation as a result of chronic vagal indigestion. Milk progesterone concentrations were not determined, in error, for that cow. Reproductive data for that cow was, therefore, excluded from analysis, and the results for 81 cows presented.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Interval from Calving to First Estrus
Cows that had a first ovulation later than d 53 of lactation were 1.6 times more likely to have an increased interval from calving to first estrus compared to cows that ovulated before d 21 of lactation (P = 0.001).
Expression of Estrus at First Ovulation
The model contained six variables (Table 3) that explained almost 50% of the variance for estrous display at first ovulation. The model was a good predictor of expression of estrus at first ovulation. With a cutpoint to optimize allocation to groups (silent estrus, visual estrus, and total ovulations) from the model, approximately 86% of cows were correctly classified.
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) explained 36% of the variation in the probability of a cow showing estrus at second ovulation. With a cutpoint of 0.53, each group is approximately 76% correctly classified.
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Initial Conception to First Service
We defined initial conception to first service as a mating to first true service (that is, service accompanied by ovulation) that resulted in an elevated milk or plasma progesterone concentration of greater than 6 nmol/L that persisted beyond d 24 after mating. Cows that lost less than 51 kg of BW between wk 1 before calving and wk 6 of lactation were 3.7 times more likely to conceive at first service compared with herdmates that lost more than 109 kg (Table 5). Cows in the LD dietary group were 3.2 times more likely to conceive at first service than were cows fed the HD diet. The model explained 13.1% of the variance in probability of conception to first service. Sixty-seven percent of cows were correctly allocated to groups when a cutpoint of 0.41 was used.
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). An interaction between change in BW and dietary protein degradability significantly predicted time to initial conception. Cows fed the HD diet and losing more than 76 kg of BW between 1 wk before calving and wk 6 of lactation were almost twice as likely to have a longer interval from calving to conception than cows fed the LD diet and that lost less than 76 kg during the same period (Figure 1). The model did not fit the criteria for the Cox’s proportional hazards model because plots overlapped, therefore, data were reassessed using the Weibull accelerated failure time model.
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). Cows that lost more than 76 kg of BW between precalving and wk 6 of lactation, and that were slow to initiate luteal activity after calving were 4.4 times more likely to have an extended calving-to-successful-pregnancy interval than were cows that lost less than 76 kg during the same period, and ovulated before d 31 of lactation.
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). Cows with a higher plasma cholesterol nadir were 2.3 times more likely to conceive by d 150 of lactation than cows with a low nadir of plasma cholesterol. Cows fed the LD ration were three times more likely to conceive by d 150, than were those fed the HD diet. The final model explained 29.1% of the variance. With a cutpoint of 0.19, approximately 74% of each group (conceived, failed to conceive, and total) were correctly classified.
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). Approximately 73% of each group were correctly classified using a cutpoint of 0.31. A significant interaction term entered the model. Cows that produced more than 1393 L of cumulative FCM by d 42 of lactation and that were slow to resume ovulation after calving were 10 times less likely to be pregnant by d 150 of lactation than were cows that also produced more than 1393 L of FCM during early lactation, but had a short interval from calving to first ovulation.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Dietary Protein Degradability
Diet was not accepted in the model evaluating expression of estrus at first ovulation; however, a proxy for diet, plasma urea, did significantly predict expression of estrus at first ovulation because cows with higher concentrations of urea were less likely to show estrus. Feeding the HD diet was associated with higher concentrations of plasma urea than for the LD group during the first 10 wk of lactation (Westwood et al., 2000). In contrast, at second ovulation, cows fed the HD diet were more likely to show estrus, despite the fact that these cows tended to mobilize more body condition after calving (Westwood et al., 2000). While the interval from calving to NEB nadir did not differ significantly between dietary groups (Westwood et al., 2000), the HD group regained positive energy balance more rapidly than the LD group. A more favorable energy balance may have favored estrus expression at second and subsequent ovulations for the HD group.
Reproductive measures of conception success were negatively influenced by the HD diet, a finding consistent with previous reports (Folman et al., 1981; Bruckental et al., 1989; Son et al., 1996). The negative effects of increased protein degradability on conception were, however, accentuated by concurrent loss of BW during early lactation. Relatively high conception rates (> 50% to first service) have been reported for Australian and New Zealand dairy cows fed large amounts of rapidly RDP (Williamson and Fernandez-Baca, 1992); however, lower conception efficiency has been reported for American and European herds fed diets high in RDP. Despite a similar genetic base, Australasian dairy cows generally calve in low body condition, lose less BW during early lactation (Abe et al., 1994; Mackle et al., 1996), and produce less milk than cows in North America. These observations suggest that the depth and duration of nutrient deficit is less severe in Australasian than in North American cattle. Blood concentrations of NEFA and 3-OH are lower and appetite is less depressed during early lactation than in cows that calve with greater reserves of body fat, and that mobilize more body tissue during early lactation. Australasian dairy cows may, therefore, better tolerate ingestion of rapidly degradable dietary proteins than North American cows, because negative effects of feeding more RDP on reproduction are not exacerbated by marked body tissue loss.
Effect of Genetic Merit
This study had limited power to detect subtle effects of genetic merit on fertility, and negative results should not be assumed to be no effect. Genetic merit of cows was not a significant predictor for interval from calving to first ovulation or for calving to first estrus. This finding contrasted with our hypothesis that, because improved genetic merit is associated with greater mobilization of body tissue in cows (Veerkamp et al., 1995; Westwood et al., 2000), and more mobilization of body tissue delayed resumption of ovarian activity (Butler and Smith, 1989), cows of higher genetic merit would be at greater risk of prolonged interval from calving to first ovulation. While the energy balance for cows of superior genetic merit was significantly lower than for those of lower merit (Westwood et al., 2000), the interval from calving to NEB nadir did not differ significantly between genetic groups. The interval from calving to NEB nadir, rather than magnitude of NEB, has been significantly associated with the onset of ovarian activity (Canfield and Butler, 1991), a finding that may reflect the absence of association in this study.
Cows of superior genetic merit were less likely to display estrus at first ovulation and may reflect the lower nutrient balance of those cows in early lactation (Westwood et al., 2000). Spicer et al. (1990) found that expression of estrus was reduced for cows with a more negative energy balance.
Relationship with Blood Metabolites
Relationships between blood metabolites and the reproductive performance of dairy cows reported previously (Eldon et al., 1988; Huszenicza et al., 1988) have been substantiated by our findings. Ratios of glucose:3-OH ratio and concentrations of plasma cholesterol were positively associated with expression of estrus at first ovulation. A lower glucose:3-OH ratio reflects a relative shortage of gluconeogenic substrates, and blood glucose concentrations are lower and 3-OH concentrations are higher during a period of NEB (Herdt et al., 1981; Canfield and Butler, 1990; Harrison, 1990). Time series analysis (Lean et al., 1992) found that plasma cholesterol concentrations were positively correlated with energy balance for dairy cows in early lactation.
Higher concentrations of plasma cholesterol were associated with a shorter interval from calving to conception, and with greater probabilities of conception and successful pregnancy by d 150 of lactation, a finding consistent with those of Kappel et al. (1984) and Ruegg et al. (1992). The mechanisms by which cholesterol may influence the fertility of dairy cattle are unclear. Improved fertility for cows with higher concentrations of plasma cholesterol may reflect other aspects of a more positive energy balance, rather than a causal relationship between higher cholesterol and fertility per se. However, Rabiee and Lean (2000) found that uptake of glucose and cholesterol by the ovary are strongly correlated in sheep and cattle. Both of these metabolites are vital to ovarian function and may provide evidence of a mechanism whereby a negative nutrient balance can influence ovarian metabolism. In vitro studies also showed a regulatory role for blood cholesterol concentrations in steroid production by ovarian tissue (Gwynne and Strauss, 1982).
Higher concentrations of plasma cholesterol may also reflect less severe hepatic lipidosis and improved hepatic function. Concentrations of plasma cholesterol and lipoproteins lipids are highly correlated (Van den Top et al., 1995), and higher concentrations of lipoprotein lipids were associated with less lipid infiltration of liver (Rayssiguer et al., 1988). Hepatic lipidosis during early lactation has been associated with reduced reproductive efficiency and an increased incidence of metabolic and health disorders (Reid and Roberts, 1983). Higher serum concentrations of NEFA lowered the probability of conception by d 150, a finding that may reflect excessive mobilization of body fat reserves and consequent perturbation of hepatic function and hormonal metabolism.
Effect of Milk Production, DMI, BW, and BCS
Higher production of FCM during early lactation was associated with a longer interval from calving to first ovulation and with a reduced likelihood of expression of estrus and successful pregnancy by d 150. Cows with higher DMI had a greater probability of expression of estrus at first ovulation, and improved likelihood of pregnancy by d 150 of lactation. DMI and milk yield are major determinants of energy balance, but also reflect the ingestion and loss of other nutrients. It is probable that protein and mineral balances also influence fertility; hence, the term nutrient balance should be preferred to energy balance in general discussion of the influence of nutrition on fertility.
Mechanisms by which more severe nutrient deficits may affect reproductive performance require further definition. Follicular development begins during early lactation when nutrient balance is lowest. It has been hypothesized that metabolic conditions associated with NEB may perturb ovarian follicular development and reduce oocyte quality (Britt, 1992) and lower concentrations of plasma progesterone (Villa-Godoy et al., 1988; Spicer et al., 1990; Britt, 1992; Ljøkjel et al., 1995), possibly reflecting reduced luteal viability, impaired synthesis of progesterone, or altered clearance of progesterone by the liver. Quantitative studies of nutrient balance, ovarian and hepatic flux of progesterone, and fertility in dairy cows are required to elucidate these relationships.
Effect of Season of Calving
Winter-calved cows had a significant delay in time to first ovulation compared with those that calved during other seasons. These findings are consistent with previous studies (Bulman and Lamming, 1978; Montgomery et al., 1980; Peters and Riley, 1982; Eldon and Olafsson, 1986). Inconsistent management practices, particularly with regard to nutrition, have confounded some studies investigating the relationship between season and time to first ovulation. In this study, rations did not change significantly with season. The mechanisms by which season influenced the resumption of ovarian activity after calving remain unclear, but may reflect photoperiodic effects that have been identified in cattle (Peters and Riley, 1982).
Effect of Interval from Calving to First Ovulation
The negative effects of anestrus on reproductive performance have been reported previously (Thatcher and Wilcox, 1973; Lucy et al., 1992; Senatore et al., 1996). Early resumption of normal ovarian function can modify reproductive outcome because luteal function is improved and concentrations of plasma progesterone increase from first to subsequent estrous cycles after calving (Stevenson and Britt, 1979; Staples et al., 1990). Conception success is positively associated with progesterone concentration in the preceding luteal phase.
The findings in all cases indicated that cows with a shorter interval to ovulation and less metabolic stress, as indicated by lower milk production, lower serum NEFA, or higher DMI, had better reproductive performance. Cows that have successfully adapted to lactation by eating enough, by controlling tissue mobilization or with lower milk yields are more fertile if they also have an early resumption of cyclicity. Univariate studies will not have detected this pattern of response and this response may explain, in part, differences between studies.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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FCM production and DMI, factors that reflect energy, protein, and mineral balance, influenced reproductive outcomes. Availability of specific substrates for metabolism and those that are associated with mobilization of body tissue may mediate associations between NEB and fertility previously identified in other studies, because concentrations of serum NEFA and plasma cholesterol significantly predicted reproductive outcomes in this study.
Cows of higher genetic merit in this study had lower estimated energy balances. The inclusion of variables associated with body tissue mobilization in predictive models suggests that continued genetic selection for higher milk yield and greater partitioning of nutrients towards production, at the expense of body tissue reserves, will place dairy cows at greater risk of reproductive failure. The identification and implementation of practical strategies to address these concerns are essential objectives for future management practices.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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FOOTNOTES |
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Received for publication January 22, 2002. Accepted for publication June 17, 2002.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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