Associations between dry period length and time to culling and pregnancy in the subsequent lactation

Determining the Association of the Dry Period Duration with Dystocia and Stillbirths in Dairy Cows by Considering Parity, Season, and Gestation Length

This study aimed to assess the relationship between the length of the dry period (DL) and the risks of dystocia and stillbirths in Holstein cows. A total of 1072 healthy cows in lactations 1 and ≥2 were categorized based on the DL (mean: 58.07 ± 0.33 days) into three groups: short DL (<40 days; 13.0% of cows), medium DL (40-70 days; 60.1%), and long DL (>70 days; 26.9%). Dystocia occurred in 12.4% of cows, while stillbirths accounted for 4.1% of calves. The medium DL group had the highest number of unassisted calvings. The dystocia rates were 11.4% for cows with gestation (GA) ≤ 274 days; 6.0% for cows with GA = 275-282 days; and 19.9% for cows with GA ≥ 283 days (p < 0.001). In season 1 (November-April), dystocia occurred in 15.0% of cases compared to 8.8% in season 2 (May-October) (p < 0.001). The stillbirth rates ranged from 3.6% to 4.0% for cows with medium and long DLs; while those with a short DL had 5.8% (p < 0.05). The stillbirth rates were 5.2% for cows with GA ≤ 274 days; 3.2% for cows with GA = 275-282 days; and 5.1% for cows with GA ≥ 283 days (p < 0.001). Season 1 had a stillbirth rate of 4.8%, while season 2 had a stillbirth rate of 3.3% (p < 0.001). This research provides insights that could assist the dairy industry in making informed decisions to reduce the incidence rates of dystocia and stillbirths in cows.

Association of pathogen-specific clinical mastitis in the first 100 days of first lactation with productive lifetime: An observational study comparing competing risks models for death and sale with the Cox model

The objective of this observational study was to study the association between clinical mastitis (CM) (Streptococcus spp., Staphylococcus aureus, Staphylococcus spp., Escherichia coli, Klebsiella spp., cases with other treated or other not treated organisms, CM without growth) occurring in a dairy cow's first 100 days (d) of her first lactation and her total productive lifetime, ending in death or sale (for slaughter). Data were collected from 24,831 cows in 5 New York Holstein herds from 2004 to 2014. Two analytical approaches were compared. First, removals (death, sale) were treated as competing events in separate survival analyses, in proportional subdistribution hazards models. In one, death was coded as the event of interest and sale as the competing event; in another, sale was the event of interest and death the competing event. Second, traditional survival analysis (Cox proportional hazards) was conducted. In all models, the time variable was number of days from date of first calving until event (death or sale) date; if the cow was alive at study end, she was censored. Models were stratified by herd. Ten percent of cows died; 48.4 % were sold. In the competing risks analysis, E. coli and CM without growth were associated with death; the former with an increased hazard rate of death, the latter with a lower one. Streptococcus spp., Staph. aureus, Klebsiella spp., cases with other treated or untreated organisms, and CM without growth were associated with higher hazard rates of sale. The Cox proportional hazards model's hazard rates were higher than those in the competing risks model in which death was the event of interest, and resembled those in the model in which sale was the event of interest. Four additional Cox models, omitting dead or sold cows, or censoring each, were also fitted; hazard ratios were similar to the above models. Proportional subdistribution hazards models were appropriate due to competing risks (death, sale); they produce less-biased estimates. A study limitation is that while proportional subdistribution hazards models were appropriate, they have the illogical feature of keeping subjects at risk for the event of interest even after experiencing the competing event. This is, however, necessary in estimating cumulative incidence functions. Another limitation concerns pathogen variability among study farms, implying that CM decisions are farm-specific. Misclassification of 'dead' vs. 'sold' cows was also possible. Nevertheless, the findings may help in optimizing management of cows contracting specific types of CM early in productive lifetime.

A stochastic animal life cycle simulation model for a whole dairy farm system model: Assessing the value of combined heifer and lactating dairy cow reproductive management programs

This analysis introduces a stochastic herd simulation model and evaluates the estimated reproductive and economic performance of combinations of reproductive management programs for both heifers and lactating cows. The model simulates the growth, reproductive performance, production, and culling for individual animals and integrates individual animal outcomes to represent herd dynamics daily. The model has an extensible structure, allowing for future modification and expansion, and has been integrated into the Ruminant Farm Systems model, a holistic dairy farm simulation model. The herd simulation model was used to compare outcomes of 10 reproductive management scenarios based on common practices on US farms with combinations of estrous detection (ED) and artificial insemination (AI), synchronized estrous detection (synch-ED) and AI, timed AI (TAI, 5-d CIDR-Synch) programs for heifers; and ED, a combination of ED and TAI (ED-TAI, Presynch-Ovsynch), and TAI (Double-Ovsynch) with or without ED during the reinsemination period for lactating cows. The simulation was run for a 1,000-cow (milking and dry) herd for 7 yr, and we used the outcomes from the final year to evaluate results. The model accounted for incomes from milk, sold calves, and culled heifers and cows, as well as costs from breeding, AI, semen, pregnancy diagnosis, and calf, heifer, and cow feed. We found that the interaction between heifer and lactating dairy cow reproductive management programs influences herd economic performance primarily due to heifer rearing costs and replacement heifer supply. The greatest net return (NR) was achieved when combining heifer TAI and cow TAI without ED during the reinsemination period, whereas the lowest NR was obtained when combining heifer synch-ED with cow ED.

Effects of limits in milking capacity, housing capacity, or fat quota on economic optimization of dry period lengths

The economically optimal dry period length (DPL) of dairy cows remains a topic of interest. Increasing daily milk production and improved management of the transition period require frequent evaluation of the optimal DPL. The economically optimal DPL also depends on the most limiting farm resource such as milking capacity, housing capacity, or fat quota. Therefore, the objective of this study was to determine economically optimal DPL under farm constraints on milking capacity, housing capacity, and fat quota given variations in 12 input factor levels. We developed a deterministic whole herd simulation model, including a nonlinear optimizer of the DPL in the first 3 parities. The model included estimates of milk, fat, and protein yield deviations in the subsequent parity and hazard ratios of culling risk and pregnancy rates as functions of the DPL in the current parity. The DPL could vary between 20 and 90 d with step size of 1 d. In addition to a one-factor-at-a-time analysis, we used a definitive screening design and a space-filling design with Latin hypercube sampling to determine important linear and curvature effects of input factors and their interactions. Results indicated that the economically optimal DPL were typically between 35 and 50 d under a large variation in input factor levels. The opportunity costs of equal DPL in all parities were small compared with optimal policies where the DPL were allowed to vary between parities. The DPL under the parlor constraint were generally less than 5 d longer than the optimal DPL under the housing constraint. The optimal DPL under the quota constraint were between those under the parlor and housing constraints. Opportunity costs compared with 50 d dry were often small, but in some cases large. A formal global sensitivity analysis revealed important interactions of input factors that were not discovered with one-factor-at-a-time analyses. In conclusion, economically optimal DPL were often shorter than typically are recommended. Adding uncertainty about the date of calving at the date of dry-off might extend these optimal DPL by some days depending on the risk attitude of the decision maker.