This manuscript reviews the effect of progesterone (P4) during timed AI protocols in lactating dairy cows. Circulating P4 is determined by a balance between P4 production, primarily by the corpus luteum (CL), and P4 metabolism, primarily by the liver. In dairy cattle, the volume of luteal tissue is a primary determinant of P4 production; however, inadequate circulating P4 is generally due to high P4 metabolism resulting from extremely elevated liver blood flow. Three sections in this manuscript summarise the role of P4 concentrations before breeding, near the time of breeding and after breeding. During timed AI protocols, elevations in P4 are generally achieved by ovulation, resulting in an accessory CL, or by supplementation with exogenous P4. Elevating P4 before timed AI has been found to decrease double ovulation and increase fertility to the timed AI. Slight elevations in circulating P4 can dramatically reduce fertility, with inadequate luteolysis to the prostaglandin F2α treatment before timed AI being the underlying cause of this problem. After AI, circulating P4 is critical for embryo growth, and for establishment and maintenance of pregnancy. Many studies have attempted to improve fertility by elevating P4 after timed AI with marginal elevations in fertility. Thus, previous research has provided substantial insights into mechanisms regulating circulating P4 concentrations and actions. Understanding this prior research can focus future research on P4 manipulation to improve timed AI protocols.

The main objective of this study was to evaluate the relationship between circulating anti-Müllerian hormone (AMH) and superovulatory response of dairy cows. Holstein cows (n = 72) were milked twice daily and housed and fed individually in tiestalls. All animals were synchronized and flushed at 70 ± 3 d in milk (DIM), near peak production (39.6 kg/d). Blood samples for AMH analysis were collected at 3 different stages of a synchronized estrous cycle [at a random stage (40 ± 3 DIM), proestrus (50 ± 3 DIM), and diestrus (57 ± 3 DIM)]. Body weights were measured weekly from calving until embryo collection. Statistical analyses were performed with Proc CORR and Proc GLIMMIX of SAS (SAS Institute Inc., Cary, NC). The 3 AMH samples from individual cows were correlated and not influenced by day of cycle. Surprisingly, AMH tended to be negatively correlated with body weight loss from calving to embryo collection (r = −0.22). More importantly, average AMH was highly associated (r = 0.65) with superovulation response (number of corpora lutea on the day of the flush, CLN), total structures collected (r = 0.48), and total transferable embryos (r = 0.37), but not percentage of fertilized embryos (r = −0.20) or degenerate embryos (r = 0.02). When cows were classified into quartiles (Q) of circulating AMH (Q1 = 0.01 to 82.6 pg/mL; Q2 = 91.1 to 132.5 pg/mL; Q3 = 135.3 to 183.8 pg/mL; Q4 = 184.4 to 374.3 pg/mL), we observed a >2-fold difference between first and fourth AMH quartiles in superovulation response (CLN: Q1 = 12.0 ± 1.5; Q2 = 14.7 ± 2.0; Q3 = 17.2 ± 1.2; Q4 = 25.6 ± 1.5) and embryo production. In conclusion, circulating AMH concentration was strongly associated with superovulation response, and evaluation of AMH could be used to identify cows with greater responses to superstimulation and thus improve efficiency of super-ovulation programs in dairy cows.

Reproductive management programs that synchronize ovulation can ovulate a smaller than normal follicle, potentially resulting in inadequate progesterone (P4) concentrations after artificial insemination (AI). Ovulation of the dominant follicle of the first follicular wave with human chorionic gonadotropin (hCG) treatment can produce an accessory corpus luteum and increase circulating P4 concentrations. This manuscript reports the results of 2 separate analyses that evaluated the effect of hCG treatment post-AI on fertility in lactating dairy cows. The first study used meta-analysis to combine the results from 10 different published studies that used hCG treatment on d 4 to 9 post-AI in lactating dairy cows. Overall, pregnancies per artificial insemination (P/AI) were increased 3.0% by hCG treatment post-AI [34% (752/2,213) vs. 37% (808/2,184); Control vs. hCG-treated, respectively]. The second study was a field research trial in which lactating Holstein cows (n=2,979) from 6 commercial dairy herds were stratified by parity and breeding number and then randomly assigned to one of 2 groups: control (no further treatment, n=1,519) or hCG [Chorulon i.m.: 2,000 IU (in 3 of the herds) or 3,300 IU (in 3 herds); n=1,460] on d 5 after a timed AI (ovulation synchronized with Ovsynch, Presynch-Ovsynch, or Double-Ovsynch). In a subset of cows, the hCG profile and P4 changes were determined. Treatment with hCG increased P4 (4.3 vs. 5.3 ng/mL on d 12). Pregnancies per AI were greater in cows treated with hCG (40.8%; 596/1,460) than control (37.3%; 566/1,519) cows. Interestingly, an interaction among treatment and parity was observed; primiparous cows had greater P/AI after hCG (49.7%; 266/535) than controls (39.5%; 215/544). In contrast, older cows receiving hCG (35.7%; 330/925) had similar P/AI to controls (36.0%; 351/975).Thus, targeted use of hCG on d 5 after TAI enhances fertility about 3.0% (based on meta-analysis) to 3.5% (based on our field trial). Surprisingly, this fertility-enhancing effect of hCG was very large in first-lactation cows but not observed in older cows in the field study. Future research is needed to confirm these intriguing results and to determine why older cows did not have improved fertility after hCG treatment.

Natural luteolysis involves multiple pulses of prostaglandin F2alpha (PGF) released by the nonpregnant uterus. This study investigated expression of 18 genes from five distinct pathways, following multiple low-dose pulses of PGF. Cows on Day 9 of the estrous cycle received four intrauterine infusions of 0.25 ml of phosphate-buffered saline (PBS) or PGF (0.5 mg of PGF in 0.25 ml of PBS) at 6-h intervals. A luteal biopsy sample was collected 30 min after each PBS or PGF infusion. There were four treatment groups: Control (n = 5; 4 PBS infusions), 4XPGF (4 PGF infusions; n = 5), 2XPGF-non-regressed (2 PGF infusions; n = 5; PGF-PBS-PGF-PBS; no regression after treatments), and 2XPGF-regressed (PGF-PBS-PGF-PBS; regression after treatments; n = 5). As expected, the first PGF pulse increased mRNA for the immediate early genes JUN, FOS, NR4A1, and EGR1 but unexpectedly also increased mRNA for steroidogenic (STAR) and angiogenic (VEGFA) pathways. The second PGF pulse induced immediate early genes and genes related to immune system activation (IL1B, FAS, FASLG, IL8). However, mRNA for VEGFA and STAR were decreased by the second PGF infusion. After the third and fourth PGF pulses, a distinctly luteolytic pattern of gene expression was evident, with inhibition of steroidogenic and angiogenic pathways, whereas, there was induction of pathways for immune system activation and production of PGF. The pattern of PGF-induced gene expression was similar in corpus luteum not destined for luteolysis (2X-non-regressed) after the first PGF pulse but was very distinct after the second PGF pulse. Thus, although the initial PGF pulse induced mRNA for many pathways, the second and later pulses of PGF appear to have set the distinct pattern of gene expression that result in luteolysis.

Ruminal digestion of neutral detergent fiber (NDF) is affected in part by the proportion of NDF that is indigestible (iNDF), and the rate at which the potentially digestible NDF (pdNDF) is digested. Indigestible NDF in forages is commonly determined as the NDF residue remaining after long-term in situ or in vitro incubations. Rate of pdNDF digestion can be determined by measuring the degradation of NDF in ruminal in vitro or in situ incubations at multiple time points, and fitting the change in residual pdNDF by time with log-transformed linear first order or nonlinear mathematical treatments. The estimate of indigestible fiber is important because it sets the pool size of potentially digestible fiber, which in turn affects the estimate of the proportion of potentially digestible fiber remaining in the time series analysis. Our objective was to compare estimates of iNDF based on in vitro (IV) and in situ (IS) measurements at 2 fermentation end points (120 and 288h). Further objectives were to compare the subsequent rate, lag, and estimated total-tract NDF digestibility (TTNDFD) when iNDF from each method was used with a 7 time point in vitro incubation of NDF to model fiber digestion. Thirteen corn silage samples were dried and ground through a 1-mm screen in a Wiley mill. A 2×2 factorial trial was conducted to determine the effect of time of incubation and method of iNDF analysis on iNDF concentration; the 2 factors were method of iNDF analysis (IS vs. IV) and incubation time (120 vs. 288h). Four sample replicates were used, and approximately 0.5g/sample was weighed into each Ankom F 0285 bag (Ankom Technology, Macedon, NY; pore size=25 µm) for all techniques. The IV-120 had a higher estimate of iNDF (37.8% of NDF) than IS-120 (32.1% of NDF), IV-288 (31.2% of NDF), or IS-288 technique (25.7% of NDF). Each of the estimates of iNDF was then used to calculate the rate of degradation of pdNDF from a 7 time point in vitro incubation. When the IV-120 NDF residue was used, the subsequent rates of pdNDF digestion were fastest (2.8% h(-1)) but the estimate of lag was longest (10.3h), compared with when iNDF was based on the IS-120 or IV-288 NDF residues (rates of 2.3%h(-1) and 2.4%h(-1); lag times of 9.7 and 9.8 h, respectively). Rate of pdNDF degradation was slowest (2.1% h(-1)) when IS-288 NDF residue was used as the estimate of iNDF. The estimate of lag based on IS-288 (9.4h) was similar to lag estimates calculated when IS-120 or IV-288 were used as the estimate of iNDF. The TTNDFD estimates did not differ between treatments (35.5%), however, because differences in estimated pools of iNDF resulted in subsequent changes in rates and lag times of fiber digestion that tended to cancel out. Estimates of fiber digestion kinetic parameters and TTNDFD were similar when fit to either the linear or nonlinear fiber degradation models. All techniques also yielded estimates of iNDF that were higher than predicted iNDF based on the commonly used ratio of 2.4 × lignin.