Our objective was to compare the economic and reproductive performance of programs combining timed artificial insemination (TAI) and different levels of AI after estrus detection (ED) using a daily Markov-chain model. A dairy herd was modeled with every cow following daily probabilistic events of aging, replacement, mortality, pregnancy, pregnancy loss, and calving. The probability of pregnancy depended on the combination of probability of insemination and conception rate (CR). All nonpregnant cows had a probability of pregnancy between the end of the voluntary waiting period and days in milk cutoff for AI. After the cutoff, cows were labeled as do not breed and replaced when milk production was below a minimum milk threshold. A similar model was created to represent a replacement heifer herd to simulate and adjust the supply and demand of replacements. The net value (NV) of a program was the sum of milk income over feed cost, replacement and mortality cost, income from newborns, and reproductive costs. The model was used to compare the NV of 19 programs. One program used 100% TAI (42% CR for first TAI and 30% for second-and-later services), whereas the other programs combined TAI with ED. The proportion of cows receiving AI after ED for the combined programs ranged from 30 to 80%, with levels of CR of 25, 30, and 35%. As the proportion of cows receiving AI after ED increased, the CR of cows receiving TAI decreased. The combined programs with CR of 35% for cows receiving AI after ED had the greatest NV and reproductive performance at all levels of ED. The program using 100% TAI had greater NV and better reproductive performance than all programs with 25% CR after ED inseminations, whereas it had very similar performance to combined programs with up to 60% of cows receiving AI after ED and 30% CR. The factor with the greatest relative contribution to the differences among programs was income over feed cost, followed by replacement and reproductive costs. Adjusting the days in milk cutoff for AI to match the supply and demand of heifer replacements improved the NV of all programs except for those with 25% CR after ED, which had either no change or a decrease in NV. In summary, the economic value of reproductive management programs combining TAI and ED depended on the proportion of cows receiving AI after ED and the resulting CR. Adjusting the heifer supply and demand increased the NV of programs with heifer surplus and decreased the NV of programs with heifer deficit.

This article evaluates the estimated economic impact of nutritional grouping in commercial dairy herds using a stochastic Monte Carlo simulation model. The model was initialized by separate data sets obtained from 5 commercial dairy herds. These herds were selected to explore the effect of herd size, structure, and characteristics on the economics and efficiency of nutrient usage according to nutritional grouping strategies. Simulated status of each cow was updated daily together with the nutrient requirements of net energy for lactation (NEL) and metabolizable protein (MP). The amount of energy consumed directly affected body weight (BW) and body condition score (BCS) changes. Moreover, to control the range of observed BCS in the model, constraints on lower (2.0) and upper (4.5) bounds of BCS were set. Each month, the clustering method was used to homogeneously regroup the cows according to their nutrient concentration requirements. The average NEL concentration of the group and a level of MP (average MP, average MP+0.5SD, or average MP+1SD) were considered to formulate the group diet. The calculated income over feed costs gain (IOFC, $/cow per yr) of having >1 nutritional group among the herds ranged from $33 to $58, with an average of $39 for 2 groups and $46 for 3 groups, when group was fed at average NEL concentration and average MP+1SD concentration. The improved IOFC was explained by increased milk sales and lower feed costs. Higher milk sales were a result of fewer cows having a milk loss associated with low BCS in multi-group scenarios. Lower feed costs in multi-group scenarios were mainly due to less rumen-undegradable protein consumption. The percentage of total NEL consumed captured in milk for >1 nutritional group was slightly lower than that for 1 nutritional group due to better distribution of energy throughout the lactation and higher energy retained in body tissue, which resulted in better herd BCS distribution. The percentage of fed N captured in milk increased with >1 group and was the most important factor for improved economic efficiency of grouping strategies.

The objective of this study was to determine the effect of reproductive performance on dairy cattle herd value. Herd value was defined as the herd's average retention payoff (RPO). Individual cow RPO is the expected profit from keeping the cow compared with immediate replacement. First, a daily dynamic programming model was developed to calculate the RPO of all cow states in a herd. Second, a daily Markov chain model was applied to estimate the herd demographics. Finally, the herd value was calculated by aggregating the RPO of all cows in the herd. Cow states were described by 5 milk yield classes (76, 88, 100, 112, and 124% with respect to the average), 9 lactations, 750 d in milk, and 282 d in pregnancy. Five different reproductive programs were studied (RP1 to RP5). Reproductive program 1 used 100% timed artificial insemination (TAI; 42% conception rate for first TAI and 30% for second and later services) and the other programs combined TAI with estrus detection. The proportion of cows receiving artificial insemination after estrus detection ranged from 30 to 80%, and conception rate ranged from 25 to 35%. These 5 reproductive programs were categorized according to their 21-d pregnancy rate (21-d PR), which is an indication of the rate that eligible cows become pregnant every 21 d. The 21-d PR was 17% for RP1, 14% for RP2, 16% for RP3, 18% for RP4, and 20% for RP5. Results showed a positive relationship between 21-d PR and herd value. The most extreme herd value difference between 2 reproductive programs was $77/cow per yr for average milk yield (RP5 - RP2), $13/cow per yr for lowest milk yield (RP5 - RP1), and $160/cow per yr for highest milk yield (RP5 - RP2). Reproductive programs were ranked based on their calculated herd value. With the exception of the best reproductive program (RP5), all other programs showed some level of ranking change according to milk yield. The most dramatic ranking change was observed in RP1, which moved from being the worst ranked for lowest milk yield to the second ranked for highest milk yield. Within a reproductive program, RPO changed based on the stage of lactation at pregnancy. Cows getting pregnant in the early stage of a lactation had higher RPO compared with getting pregnant later in the lactation. However, the RPO at calving was similar for early and late lactation pregnancies.

The objective of this study was to determine the optimum replacement policy for Holstein dairy herds in Iran using a dynamic programming model. Cows were described in terms of state variables that included milk production class, parity, pregnancy status, and month in milk with a 1-mo stage length. The objective function maximized the net present value of cows over a 15-yr planning horizon. Markov simulation was used to estimate expected herd dynamics under the optimal decision plan determined by dynamic programming. Stochastic elements included probabilities of pregnancy and abortion, production level, and involuntary culling. The optimum annual culling rate was estimated to be 31.4%, and cows had an expected herd life (time from first calving until culling) of 3.18 yr. High replacement cost and low carcass value resulted in only 2.87% voluntary culling (i.e., optimal model-based replacement). Assuming a heat detection rate of 0.4, cows averaged 2.8 services per lactation under the optimal policy. Sensitivity analyses were carried out to evaluate the effect of milk price, herd-average production, feed cost, heifer price, and carcass value on optimum replacement decisions. Herd-average production, replacement cost, and risk of involuntary culling were important factors affecting the optimal culling policy. Changes in the price of feed, calves, and milk and the probability of pregnancy had no considerable effect on the optimal policy considering the market situation in Iran during 2008.

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