BREEDING FOR EFFICIENCY IN LIVESTOCK PRODUCTION: DEFINING THE ECONOMIC OBJECTIVES 1

Transcription

1 BREEDING FOR EFFICIENCY IN LIVESTOCK PRODUCTION: DEFINING THE ECONOMIC OBJECTIVES 1 OST of the research work in animal M breeding to date has been effectively concerned with methods of genetic evaluation and the nature of responses to selection. However, research leading to the complete definition of realistic selection goals does not seem to have been adequate. In this paper, the economic aspects of efficiency of production in meat animals will be considered. Suggestions will be made concerning information needed for the complete definition of selection goals and concerning utilization of these definitions to arrive at a selection criterion. Efficiency of production is an often mentioned objective of the meat animal industries as well as other agricultural industries. Efficiency is defined (Webster, 1956) as "effective operation as measured by a comparison of production with cost in energy, time, money, etc." Three main aspects of efficiency in livestock production are: DEWEY L. HARRIS DEKALB AgResearch, Inc., DeKalb, Illinois 1. Efficiency of animal industries relative to other food industries 2. Efficiency of animal industries relative to each other 3. Efficiency of one producer relative to others in the same industry. The primary manifestation of the first of these will be in the magnitude of money spent by the consuming public for animal products relative to other food products. The second aspect of efficiency leads to the relative levels of consumption of beef, pork, poultry meat, et cetera. The primary goal of most livestock producers is, very simply, to make money. Their objective, like that of most people, is to carry out a profitable operation so as to secure an adequate income for realizing desires as to living conditions, education for children, etc. The efficiencies of the animal industries relative to each other and to other food industries and the relative trends in these industries may have short term influences upon the economic 1 Invited paper presented before the Breeding and Genetics Section of the 61st Annual Meeting of American Society of Animal Science at Purdue University, August returns for the participants in that industry. However, greater profit or return on investment for all the participants in an industry will usually lead to either expansion within that industry or to new participants in the industry. So the long term effects of industrywide trends towards greater efficiency will not be greater profit for the participants in that industry but will be increased production, lower costs to the consumer and greater consumption of the products. These results are not undesirable, but they do not satisfy the primary objectives of the livestock producer. Hence, the main source of long-term profitability for a livestock producer seems to lie in his efficiency relative to other livestock producers. Because of the extremely large number of livestock producers, the competitive nature of their individual operations is not as obvious as is the competition which goes on in an industry with a small number of competing organizations, such as the manufacture of automobiles. However, even this profitability may not be completely long-term. In a dynamic industry, what was relatively efficient in the past may become relatively inefficient now or at some point in the future. The foregoing simplified description of the economic aspects of industries in a free enterprise system seems necessary to draw attention to the fact that if the livestock producer's primary objective is the relative efficiency of his operation, the animal breeder should also be concerned with. this objective of his potential customers. This viewpoint is necessary because the producer's primary reason for buying certain breeding stock and the price he will pay will be based upon his assessment of how the progeny or descendants of this stock will contribute to the profit or return on investment of his operation. He will have similar criteria for deciding on management practices, expenditures on facilities, purchases of feed, et cetera. How accurately these criteria are developed and used may be open to question in many cases, but the objectives seem clear. 860

2 EFFICIENCY OF PRODUCTION 861 Thus, the goal of genetic improvement in livestock should be one of the following: 1. PROFIT 2. RETURN ON INVESTMENT 3. COST PER UNIT PRODUCTION Since the animal breeder's primary unit of selection is usually the individual animal, these goals need to be expressed on a peranimal basis. Some difficulty occurs here in that these terms are, to a degree, functions of the enterprise--its size, arrangement, et cetera. However, this difficulty is not insurmountable, and it is feasible to develop mathematical functions for these factors for the individual animal. Of course, PROFIT=INCOME--EXPENSES Also, RETURN ON INVESTMENT INCOME --EXPENSES COST PER UNIT PRODUCTION EXPENSES -- PRODUCT However, to be complete, we need to adjust the amount of product in this last function for the quality of that product. In the present context, the only realistic definition is that quality is equal to or at least propgrtional to value per unit of production. Thus COST PER UNIT PRODUCTION EXPENSES --PRODUCT*QUALITY -- EXPENSES INCOME Therefore, in all three forms, we are concerned with the magnitude of EXPENSES or COSTS OF PRODUCTION relative to IN- COME or, equivalently, quantity of PRO- DUCT adjusted for QUALITY. Hence, we need to study an individual animal's contribution to INCOME and EXPEN- SES to obtain an indication of the traits of economic importance and the relative economic importance of each. We will be using swine terminology in this development, but similar functions could be developed for beef cattle or broiler chickens with some changes in terminology. For sheep the additional income for the sale of wool needs to be incorporated into the formulae. Swanson (1965) has developed formulae for the economic as- pects of sheep production, but these are for sheep enterprises rather than the individual animals. INCOME for meat animals (on a peranimal basis), when the products are the wholesale cuts, can be expressed as INCOME /' CARCASS '/[ CARCASS =l WEIGHT ]\QUALITY ] [ SLAUGHTER ~[ DRESSING =k WEIGHT ]I, PERCENTAGE) cuts PCT. each \[ VALUE per '~ WHOLESALE ] [ UNIT WEIGHT ] CUT ]\ for each CUT ] VALUE per UNIT WEIGHT for each CUT might be further expanded in terms of marbling score, loin eye area, etc., if these contribute to the value of the cut. EXPENSES for meat animals on an individual basis can be expanded as EXPENSES=( SLAUGHTER COSTS )+[ ~,COSTS] FEED '~. + FEEDING LABOR "~. /' SOW and and FACILITIES COSTS ] t klitter COSTS ] [ SLAUGHTER'~ ~--- k COSTS ] [ COST per "~[ FEED '~ \UNIT FEED jk CONSUMPTION ] and FACILITIES REACH 4- + COSTS per UNIT SLAUGHTER ~-- TIME WEIGHT / IF/SOW HERD\ [ COSTOF "~ /'No. of'~ /LABOR and / PRODUC- ) +1 LIT- ] FACILITIES \ ING GILT \ TERS ] ~ COSTS per / L.\ LITTER / SOW and\ /" BOAR "~'l fsalvage'~ LITTER[+[ COSTS VALUE FEED I ~ per ] --~ forold l COST / \LITTER/J \ SOW / No. of LITTERS)(AVG LITTER SIZE)(PIG SURVIVAL) with SALVAGE VALUE for OLD SOW -- VALUE per '~[OLD SOW'~ UNIT WEIGHT/k WEIGHT,] and SOW and LITTER FEED COST -- COST per "~/FEED CONSUMPTION UNIT FEED]k of SOW and LITTER ] If the feeding period consists of a growing period and a finishing period with different types of rations and/or facilities and labor

3 862 HARRIS requirements for each, there can be two FEED COSTS and two FEEDING LABOR and FACILITIES COSTS. FEED CON- SUMPTION may be considered as a trait of interest or it may be expanded as FEED [ [SLAUGHTER'/ CONSUMPTIONZ[~ WEIGHT /- WEANING'i] FEED per WEIGHT.] 1 ~ UNIT GAIN I Similarly, TIME TO REACH SLAUGHTER WEIGHT can be considered as a trait or it can be expressed as TIME TO REACH _ SLAUGHTER WEIGHT-- SLAUGHTER'~ [ WEANING'I ( WEIGHT l--i, WEIGHT ] (RATE OF GAIN) Also, FEED CONSUMPTION of SOW and LITTER, if not measured directly, might be approximated fairly accurately by FEED CONSUMPTION a' b [ SOW ~ ' c of SOW and LITTER ~ t tweight)-i- AVG "~/' AVG 'i /" AVG BIRTH / / LITTER I q-d[ WEANING-- WEIGHT]\ SIZE ] \ WEIGHT AVG "~/" AVG "~1 BIRTH I/LITTERIt PIG "~ WEIGHT]\ SIZE ] SURVIVAL] with appropriate values of a, b, c and d. Study of these formulae indicate several economic constants that are necessary to reflect the economic importance of the indicated traits. These are: 1. VALUE per UNIT WEIGHT for each CUT 2. SLAUGHTER COSTS 3. FEEDING LABOR and FACILITIES COSTS per UNIT TIME 4. COST per UNIT FEED (Growing and Finishing) 5. COST OF PRODUCING GILT 6. SOW HERD LABOR and FACILI- TIES COSTS per LITTER 7. COST per UNIT FEED (Sow and Starting) 8. BOAR COSTS per LITTER. As mentioned earlier, VALUE per UNIT WEIGHT for each CUT, rather than being considered a constant, might be considered a function of other characteristics of the cut. Similarly, COST of PRODUCING GILT might be a function of the body weight of the gilt since the body weight would influence the feed requirements for producing that gilt. Since the wholesale cuts are considered as the final product, the by-products of slaughter are considered to be constant and to have been deducted from slaughter costs. Of course, what are being described as being economic "constants" are really quite variable as they change considerably from time to time and place to place. However, since the breeder will not usually find it feasible to breed for specific marketing and cost situations, he is concerned with breeding for all conditions or, equivalently, average conditions at some time in the future, say, five to ten generations later. So, a strong research need in animal breeding is a series of economic studies that will yield average values for use as the economic constants with whatever adjustment is possible for the anticipated changes in these averages in the foreseeable future. There are some other terms in these formulae that may be either economic constants or genetic traits of interest depending upon the constraints placed on them by the management or economic alternatives being considered. These are 1. SLAUGHTER WEIGHT 2. SLAUGHTER AGE 3. TIME TO SLAUGHTER 4. CARCASS QUALITY as evaluated in the live animal 5. NO. of LITTERS. These alternatives do not influence the validity of these formulae; they just determine whether these quantities are constants or variables or variables with some constraints on them. If a constant slaughter weight and/or age is considered, it should probably be the optimum weight and/or age from economic considerations for the genetic group under consideration. However, if some genetic groups have one optimum slaughter weight and/or age, while other groups have another, we have a form of genotype-environment interaction, and the procedure (as for all situations involving genotype-environment interactions) needs to be either 1.breed separately for each of the alternative plans, 2. test under one scheme and achieve direct response for that scheme and correlated response for other schemes, 3. test under several schemes, i.e., a sample of possibilities or

4 EFFICIENCY OF PRODUCTION find optimum environment for the genetic group in question, if environment is predictable or controllable9 For traits like slaughter weight and/or age which are controllable and the optimum is predictable and for genetic differences such as between breeds or between crosses, the fourth alternative seems preferable9 For genetic differences within breeds or crosses when the interaction is not extremely large, the preferred alternative is probably the second. However, if the genotype-environment interaction is extreme, the third or the first alternative may be necessary. Besides the choices of time of slaughter, these considerations are pertinent for age of weaning and number of litters. The formulae described earlier indicate several traits that are of economic importance9 The breeder has to be concerned with decisions concerning which of these traits should be included for evaluation in his breeding program and the emphasis to be placed on these in selection9 The relative emphasis to be placed on the traits in a selection program depends on the combination of 1. economic importance of each of the traits 2. potential for genetic improvement for each of the traits 3. genetic interrelationship between traits9 The potential for genetic improvement involves 1. genetic variability 2. accuracy of measuring these differences (both directly and indirectly through correlated traits). The decision as to whether or not to include a trait (e.g., feed consumption) in a testing program depends on 1. economic importance 2. potential for genetic improvement (from direct selection relative to correlated response to other traits) 3. cost of measurement in labor, facilities, and time (generation interval). In other words, this decision depends on the additional amount of economic improvement that can be made relative to the "cost" of making that improvement. Hazel (1943) developed index selection theory for the situation where the objective of selection was a linear function of the genotypic or additive genetic values for each trait9 However, the formulae developed earlier in the present paper describing the selection goals of meat animal breeding are definitely not linear functions of the genetic traits of interest. There are seemingly at least two obvious approaches to reconcile the difference between the complex functions which describe our selection objective and the known index selection procedure. The first is to use a linear approximation to the complex function, and the second is to use an analogous procedure with the complex function. Turning to calculus, we find that for a complex function, symbolized as H(G1, G2, 9. 9, Gn), an increment of change in H can be expanded as where n3~ i---1 G~ A Gi + o(ag0' ~i G=,~ represents the first par- tim derivative of the function H with respect to the variable Gi evaluated at the point where all the G variables are equal to their mean values, and O(AGi) 2 represents terms involving second or higher order powers of the increments of deviation of the Gi from their means. For most functions the higher order terms will ]be of negligible magnitude relative to the first terms, and, thus, the complex function H can be approximated n fairly well by H==/x-[- I~ al (Gi--~i) i~l where This relationship was seemingly implied by Hazel (1943) when he said "the relative economic value for each trait depends upon the amount by which profit may be expected to increase for each unit of improvement in that trait." In fact, much of this paper to this point was probably implied in that sentence. Note that in this approximation the ai values depend on the population means. Thus, a different set of ai values are probably needed

5 864 HARRIS for each genetic group. And if the final product is to be a crossbred population, the ai values should involve the crossbred means rather than the purebred means, even for selection within a purebred population. We have also made another approximation in this approach. Since breeding values, genotypic values, or additive genetic values are average phenotypic values, when we use a phenotypic economic function as the H(G1, G2,..., Gn), we are violating the inequality that the average of a complex function is not equal to the complex function of the averages of the basic variables. But, in many situations, the latter may be a fairly good approximation for the former. In the approximation for H, the ~ and /~ values can be dropped out to give simply H ~ ~ aigi, the ai can be "relative" economic i~l values, and the coefficients in the derived index can be recoded without changing the expected progress from selection based on the resulting index. However, I would like to raise the question--should THESE THINGS BE DONE? When these changes are not made, the index values can directly predict the response to selection in terms of the basic economic units such as dollars and cents. This form of the index should give a considerably more informative viewpoint than that achieved with abstract index values. Of course, a prediction of genetic improvement in terms of these economic units will be strictly valid only for those conditions where the basic economic constants are still the same as used in the index construction. The approximation involving partial derivatives has seldom, if ever, been used in deriving the economic values to be used in index construction. What is usually done is to calculate the change in profit (or other economic function) for an increment of change in the specific trait. This method can potentially be better than the partial derivative approach. The partial derivative gives the slope (or rate of change) of the profit function at the point of the population means. What is really relevant is the rate of change in the profit function from the point of the population means to a point to which the population will be moved by selection. Of course, the rate of change in the profit function will differ with differing magnitudes of the change in the primary trait when there is curvilinearity. The most sophisticated ap- proach to index construction would be an iterative procedure. Using estimated ai values, an index would be constructed and the increment of improvement of each trait predicted. Then these increments would be used to recalculate the ai values. Then the increments of improvement would again be developed, and so on, until a stable solution is obtained. The value of using this approach will depend upon the degree of non-linearity of the function. This approach needs further investigation for the type of function developed in this paper. An alternative to using the linear approximations would be to simply substitute index values for each trait into the complex func- tion. Recall that when H z ~ aigi, the re- suiting index function is of the form I z t=1 aiii, where Ii = Gi. Hence, when we have a complex function H(G1, G2,..., Gn), it seems reasonable to use I z H(I1, I2, 9 I~) as the selection criterion. The main argument for this approach is its operational simplicity. This simplicity comes from the fact that the complex function H will involve the same economic constants for all sets of population means. The linear approximation requires different ai values for each set of population means. In summary, there seem to be the following alternatives for constructing selection criteria when a complex function describes the selection objective: 1. develop a linear approximation to the complex function for each population a. by calculus b. by direct calculation for realistic increments of change from the population means c. by an iterative procedure for the predicted increments of improvement 2. by direct use of index values for each trait in the complex function. There is need for further study of the relative advantages and disadvantages of these alternatives. In conclusion, it should be pointed out that the difference between INCOME and EX- PENSES as developed in the foregoing formulae may involve the PROFIT of two or three participants in the meat animal industries and too often, the PROFIT of the indi-

6 EFFICIENCY OF PRODUCTION 865 vidual participants does not accurately reflect their individual contributions to the overall PROFIT. This segmentation of the industries (e.g., feeder calf producer, feed lot operator, and packer) with marketing arrangements that do not sufficiently lead to accurate payment for value is seen to be a serious detriment to effective animal breeding. To illustrate this difficulty we pose the question-why should a feeder calf producer or a feeder pig producer use breeding stock with superior genetic merit for feed efficiency in the feedlot or for carcass quality when he sells his calves or pigs through an auction with no identification of the breeding except a few visual indications? This detriment to effective breeding might be overcome by either one of or a combination of the following: 1. integration of participants in the different segments 2. better marketing communication between segments 3. brand names of breeding stock. Even though there have been some movements in these directions in recent years, it is not clear how far the meat animal industries will go. However, it should be pointed out that all three of these trends have become factors in the poultry industries, and a major manifestation is more effective poultry breeding. Literature Cited Hazel, L. N The genetic basis for constructing selection indexes. Genetics 28: Swanson, V. B Genetic and economic factors in sheep production. Ph.D. Thesis. Iowa State University, Ames, Iowa. Webster Webster's New Collegiate Dictionary. G. & C. Merriam Co., Springfield, Mass.