The objective of the present study was to identify and quantify relationships between body condition score (BCS) and body weight (BW) in dairy cows with reproduction variables in pasture-based, seasonal-calving dairy herds. Over 2,500 lactation records from 897 spring-calving Holstein-Friesian dairy cows were used in the analyses. Eleven BCS- and 11 BW-related variables were generated, including observations at calving, nadir, planned start of mating (PSM), and first service, as well as days to nadir and the amount and rate of change between periods. The binary reproductive variables were cycling by PSM, mated in the first 21 d from PSM, pregnant to first service, and pregnant in the first 21, 42, and 84 d of the seasonal mating period. Generalized estimating equations were used to identify BCS and BW variables that significantly affected the probability of a successful reproductive outcome. After adjusting for the fixed effect of year of calving, parity (for cycling by PSM only), and the interval from calving to either first service or PSM, reproductive performance was found to be significantly affected by BW or BCS at key points, and by BCS and BW change during lactation. All reproductive response measures were negatively affected when BCS and BW measures indicated an increased severity and duration of the postpartum negative energy balance. In particular, cycling by PSM was positively associated with calving BCS, whereas pregnancy at 21, 42, and 84 d post-PSM were positively associated with nadir BCS and BW gain post-PSM, and negatively associated with BCS loss between calving and nadir. The results highlight the important role that BCS and BW loss has on reproductive performance, especially in seasonal-calving dairy systems because of the short period between calving and PSM.

Management, nutrition, production, and genetics are the main reasons for the decline in fertility in the modern dairy cow. Selection for the single trait of milk production with little consideration for traits associated with reproduction in the modern dairy cow has produced an antagonistic relationship between milk yield and reproductive performance. The outcome is a multi-factorial syndrome of subfertility during lactation; thus, to achieve a better understanding and derive a solution, it is necessary to integrate a range of disciplines, including genetics, nutrition, immunology, molecular biology, endocrinology, metabolic and reproductive physiology, and animal welfare. The common theme underlying the process is a link between nutritional and metabolic inputs that support complex interactions between the gonadotropic and somatotropic axes. Multiple hormonal and metabolic signals from the liver, pancreas, muscle, and adipose tissues act on brain centers regulating feed intake, energy balance, and metabolism. Among these signals, glucose, fatty acids, insulin-like growth factor-I, insulin, growth hormone, ghrelin, leptin, and perhaps myostatin appear to play key roles. Many of these factors are affected by changes in the somatotropic axis that are a consequence of, or are needed to support, high milk production. Ovarian tissues also respond directly to metabolic inputs, with consequences for folliculogenesis, steroidogenesis, and the development of the oocyte and embryo. Little doubt exists that appropriate nutritional management before and after calving is essential for successful reproduction. Changes in body composition are related to the processes that lead to ovulation, estrus, and conception. However, better indicators of body composition and measures of critical metabolites are required to form precise nutritional management guidelines to optimize reproductive outcomes. The eventual solution to the reduction in fertility will be a new strategic direction for genetic selection that includes fertility-related traits. However, this will take time to be effective, so, in the short term, we need to gain a greater understanding of the interactions between nutrition and fertility to better manage the issue. A greater understanding of the phenomenon will also provide markers for more targeted genetic selection. This review highlights many fruitful directions for research, aimed at the development of strategies for nutritional management of reproduction in the high-producing subfertile dairy cow.

The primary objective of this study was to identify relationships between endometritis and metabolic state during the calving transition and early lactation periods. A subset of mixed age and breed dairy cows (n=78) from a seasonal, pasture-grazed herd of 389 cows was examined. The selected cows were grouped as having endometritis at d 42 postpartum or being unaffected by endometritis. Endometritis was defined as >6% (upper quartile) of uterine nucleated cells being polymorphonuclear cells (H-PMN; n=38); unaffected by endometritis was defined as ≤1% of nucleated cells being polymorphonuclear (L-PMN; n=40). Milk yield was determined at each milking, and milk composition (fat and protein) was determined at 2-wk intervals. Blood samples collected on d -14, 0 (d of calving), 4, 7, 14, 28, and 42 were analyzed for indicators of energy status (nonesterified fatty acids, glucose, and urea), liver function (albumin, globulin, glutamate dehydrogenase, and aspartate aminotransferase), inflammation (haptoglobin), and mineral status (Ca and Mg). Samples collected weekly from d 21 to 63 or 70 were analyzed for progesterone content. The postpartum anovulatory interval was defined to end on the first day postpartum that plasma progesterone concentration was ≥1 ng/mL. A greater percentage of H-PMN cows failed to ovulate before d 63 or 70 (34%) compared with L-PMN cows (10%), although the proportions of cows ovulating within either polymorphonuclear group was similar through d 56 postpartum. Plasma concentrations of albumin and the albumin:globulin ratio were consistently lower in H-PMN cows. Plasma Mg was lower, whereas glutamate dehydrogenase and aspartate aminotransferase were higher, in H-PMN cows during early lactation compared with L-PMN cows. Circulating metabolites indicative of energy status (nonesterified fatty acids, glucose, and urea) were not different between polymorphonuclear groups. Among 3- to 5-yr-old cows, daily milk yield for the first 42 d after calving was lower for H-PMN cows than for L-PMN cows. Among cows >5 yr old, protein percentage was lower in H-PMN cows compared with L-PMN cows. In summary, endometritis at 42 d postpartum in the herd studied was associated with an increased likelihood of remaining anovulatory. These cows had lower albumin concentrations throughout the calving transition period, perhaps indicating impaired liver function, with lower plasma Mg and evidence of hepatocellular damage in early lactation. Similar profiles of nonesterified fatty acids and glucose indicated that energy status was not a risk factor for endometritis.

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This study tested the hypothesis that a commercially available system for detecting estrus based on cow activity would perform similarly to that of typical, visual assessment of mounting indicators placed on the tail head of the cow. The hypothesis was applied to a large, pasture-grazed, seasonal-calving dairy herd, and the technology was tested as a stand-alone system. One of 2 types of commercially available collar-mounted activity meters was fitted to 635 cows, and the activity data collected during the 37-d artificial breeding period were analyzed. The first collar-mounted activity meter monitored activity only (AO collars), whereas the second meter measured activity and rumination characteristics (AR collars). Only activity data were used in the current study. Activity-based estrus alerts were initially identified using the default activity threshold value recommended by the manufacturer, but a range of activity threshold values was then analyzed to determine their effect on estrus detection performance. Milk progesterone data and insemination records were used to identify gold standard positive (n = 835) and negative (n = 22,660) estrus dates, to which activity alerts were compared. Visual assessment of mounting indicators resulted in a manual detection performance of 91.3% sensitivity (SN), 99.8% specificity (SP), and 94.5% positive predictive value (PPV). The AR collars achieved 76.9, 99.4, and 82.4% for SN, SP, and PPV, whereas the AO collars achieved 62.4, 99.3, and 76.6% for SN, SP, and PPV, respectively. The observed performance of the activity systems may be underestimated due to test design and applied assumptions, including determining the date of estrus. Lowering the activity threshold from the default value improved sensitivity but the number of false positive alerts was considered to become unmanageable from a practical perspective as sensitivity reached peak values. Time window analysis, receiver operating characteristic curves, and curves of SN and PPV were found to be useful in the analysis and interpretation of results. They generate relevant performance data that allow for meaningful comparisons between similar studies. Although the 2 activity systems tested did not perform to the high level of manual estrus detection found in this study, the potential exists for these systems to be a valuable tool on farms with lower estrus detection performance or for farmers managing larger herds.