Objectives were to derive equations and obtain estimates per cow of days to conception, milk production, semen purchases, calvings, and reproductive failures based on the probability of estrus detection and AI conception. The net benefits of changing rates of estrus detection, including changes in milk production, semen purchases, and replacement inventories (culled cows and calves) were converted to annual values and multiplied by fixed prices to obtain estimates of the annual financial benefit of a change in rates of estrus detection. Improvement in milk production because of a 1-d decrease in DIM at conception was dependent on DIM, peak milk production, and monthly rate of decline in daily milk production. Variation was considerable in expected benefits of improved estrus detection. Under the assumption that replacement was not planned prior to breeding (in which case improved estrus detection would have no value), estimated financial benefits for increasing the probability of estrus detection from 60 to 70% with a 70% AI conception rate were $6/yr; increasing from 20 to 30% the rate of estrus detection with a 50% AI conception rate increased estimated annual benefits to $83. This wide range of values occurred with fixed costs and prices so that fluctuating prices would introduce further variation in financial benefits. Derived equations allow point estimates of expected benefits with input values estimated.
The objectives of this research were to estimate 1) the annual increase in profitability and 2) the value of a unit of semen because of an increase of 1 in percentage of AI conception rate. Factors contributing to increased annual profit per cow included more milk production, less semen purchased, more calvings, and fewer reproductive cullings. With a 7% average monthly rate of decline in milk production between peak and end of lactation, $ .24/kg of milk net income over feed cost, $ 20/unit of semen, $ 150 per calving, and $ 400 per reproductive culling, values for increased annual profitability per cow ranged from $ .88 to $ 7.36, and increased value per unit of semen ranged from $ .60 to $ 5.01, when estrus detection and AI conception were each constrained to minima of 30% and maxima of 70%. Additional probability of concept was worth less at high rates of estrus detection and of AI conception. Within-herd variation can be attributed to differences between cows in milk production, asset value of the cow based on future expectations of milk production, and seasonal differences in milk price and rates of estrus detection. Further differences between herds can be expected as producers vary in discounting values for uncertainty of information. Therefore, generalizations from a single set of assumptions may be limited, and application of methods to specific cows and producers may be desirable.
Objectives were 1) to develop DMI and milk prediction equations, 2) to use these equations to simulate group and individual feeding of dairy herds, and 3) to estimate effects of group and individual feeding on FCM production. University of New Hampshire data were used to predict DMI from previous DMI and cow and ration characteristics. The same data were used to predict milk production from DMI and previous milk production. Feeding was simulated for 100 cows over 50 4-wk periods in a number of trials. Effects of individual feeding, additional groups, herd calving intervals, and within-herd variation of annual milk production per cow on daily FCM per cow were isolated in average and high producing herds. Changing from one group to individual feeding can increase daily FCM per cow by .5 to 1.1 kg and two groups to individual feeding by 0 to .8 kg without changing total herd nutrient intake. Reallocation of the same amount of nutrients to two groups instead of one can increase daily milk production by .15 to .8 kg of FCM per cow, reallocation to three groups instead of two by 0 to .6 kg of FCM per cow, and reallocation to four groups instead of three by 0 to .35 kg of FCM per cow.
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