The Professional Animal Scientist 20 (2004):429–436
Comparison of Breeding and Marketing Systems for Red Angus Cattle Using an Integrated Computer-Based Spreadsheet K. E. MILLER*, J. C. WHITTIER*,1, PAS, R. K. PEEL*, R. M. ENNS*, J. E. BRUEMMER*, and W. J. UMBERGER† *Department of Animal Sciences and †Department of Agricultural and Resource Economics, Colorado State University, Fort Collins 80523
Abstract An integrated computer-based spreadsheet was developed with data from 581 Red Angus-sired calves to compare synchronized AI and natural service breeding systems. This comparison was based on input costs, genetic merit of sires used for mating, and calf marketing system, using differences in net return. The spreadsheet integrated four elements into a decision summary: bull costs, AI costs, genetics merit, and marketing options. An economic sensitivity analysis was used to identify trends and key variables in the net return of each decision. Three prominent variables identified from economic analysis were bull purchase price, semen price, and percent genetic change. Bull purchase price was a primary factor in changes in net return; semen costs and genetic merit change explained rearrangements in ranking of net return. These two variables altered the ranking based on whether the estrous synchronization protocol used estrus
detection or timed AI. The spreadsheet identified AI to be more cost effective than natural service when calves are marketed as finished cattle. Net revenue from AI calves was greater in all retained ownership scenarios; the weaned marketing scenario caused net return to vary by synchronization system for the combinations of costs and changes in genetic merit. However, there was a wide variance in identifying which breeding system provided the greatest benefit when calves were marketed as feeder cattle. Retaining ownership through finish and marketing either on the cash market or on a grid proved to be advantageous to AI in all of the estrous synchronization protocols provided. The economic advantage ranged from $142.98 to $214.16 per head compared with marketing at weaning. The spreadsheet developed provides a useful tool for evaluating the economic impacts of breeding system decisions. (Key Words: Beef Cattle Breeding System, Artificial Insemination, Natural Service, Net Return)
Introduction 1
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Artificial insemination has been used for many years to improve or
preserve genetics of animals. Technologies have improved significantly over the past 70 yr, and AI can be used in most cattle production systems today. Artificial insemination has not been used extensively in beef cattle production systems in the U.S. Less than 10% of all beef cowherds use this technology (NAHMS, 1998), whereas 90% of the U.S. dairy herds use AI. This vast difference in adoption rates of AI can be mostly attributed to differences in management systems. Beef cow managers suggest, “AI requires too much time and labor to be implemented” as the primary reason for lack of adoption of AI in beef herds (NAHMS, 1998; Sprott, 1999; Sumpter et al., 1999). The lack of AI could be limiting net return in U.S. beef to the operations. Several decision tools that compare the costs of AI to natural service breeding systems have been developed (Loseke et al., 1990; Schafer et al., 1990; Werth et al., 1992; Tess and Kolstad, 2000a,b; Johnson, 2002); however, comparing the net return to an operation accounting for breeding costs, change in average age, change in genetic merit, and impact of vari-
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ous calf marketing programs into a single application for decision making has yet to be constructed. The objective of this investigation was to determine whether a dynamic model could be developed to accurately determine economic impacts when comparing natural service to AI in beef cattle.
Materials and Methods An integrated computer-based spreadsheet was developed using Microsoft威 Excel. The spreadsheet was developed in a stepwise fashion to provide a structured and directed movement through the decision process. Input of bull costs, AI costs, genetic evaluation, and marketing options were used to compare the breeding systems. Inputs were incorporated into a decision summary that provided information comparing natural service to 12 AI breeding systems, each with a different estrous synchronization method. Bull Costs. Inputs supplied by the user were used to develop a matrix for evaluation of cost per pregnancy in a natural service system and clean-up bull costs used in the AI cost section. Number of cows to be bred, average number of expected breeding seasons for a sire, cow-to-bull ratio, and expected final pregnancy rate were used to calculate the number of cows a sire will service over his life in the herd. Other inputs were used to compute the variable and fixed costs per bull and total number of bulls required. Purchase price of the natural service sire was multiplied by 10% to produce a risk value. Risk value is a non-cash cost that is associated with the bull in case of termination of service (failed breeding soundness exam, health concerns, etc.) prior to the expected number of breeding seasons. This value can be saved and accumulated over time to purchase an additional bull if circumstances occur. The cost per pregnancy ma-
Miller et al.
trix is given on a pregnancy rate from 60 to 100% in increments of 5%. Although it would not be realistic to consider 100% pregnancy rate, this range allows the user to evaluate the possible impact of a wide array of outcomes. This allows comparison of costs from a deviation from the expected final pregnancy rate. AI Costs. The integrated computer-based spreadsheet was designed to compare the costs across different estrous synchronization protocols, as well as to natural service programs. Artificial insemination costs were designed to allow the users to input costs and labor constraints that are specific to their operations. Costs specific to the estrous synchronization protocols, drug costs, and semen costs, were input by the user. Labor constraints that are important to estrous synchronization were also entered by the user. Number of people required for gathering and moving cattle, number of hours required for gathering and sorting cows, labor costs, number of days for estrus detection, and AI technician costs were all inputs that influenced the cost and success of the AI program. Twelve different estrus synchronization protocols, listed in Table 1, were evaluated at the expected AI pregnancy rates given the previously mentioned inputs. Calculations using the inputs, number of times an animal required processing, estrous response, and conception rates formed a matrix that provided information to compare costs at different success rates. This matrix also integrated the information from the “Bull Costs” sheet to supply a clean-up bull cost to achieve the final pregnancy rate. Users may modify the days of estrus detection, but the structure list provides the framework for the cost analysis and economic sensitivity analysis. Labor hours were separated into three different categories: moving
and sorting cattle, processing, and estrus detection. Separating labor into these categories provided information regarding the cost of labor per AI pregnancy. The total labor hours required for moving and sorting cattle were computed as a product of user inputs of number of people and number of required hours. The number of estrous synchronization products that are given and AI dictate the number of times an animal requires processing for estrous synchronization. These inputs provide information regarding the processing labor hours needed to complete the project. An average of 2 min per animal per each processing time (including AI) and four individuals to process cattle were assumed (personal experience). Labor hours for processing were calculated using these inputs and assumptions. Total labor hours required for estrus detection were computed using the user input for number of days for estrus checking and assumptions within the spreadsheet program. The spreadsheet assumes two people can effectively detect estrus in 150 head of cows in an average of 1.5 h/d. The average estrus detection time of 1.5 h/ d assumes a cumulative effect of labor hours. For example, estrus detection time 12 h post-prostaglandin injection in a melengestrol acetate (MGA). Select protocol is not as time consuming as the 48- to 60-h estrus detection time, because of the number of females that would be expressing estrus. Estrus detection time of 12 h may be
