Breeding for profit: synergism between genetic improvement and livestock production (a review) D. L. Harris and S. Newman J Anim Sci 1994. 72:2178-2200. The online version of this article, along with updated information and services, is located on the World Wide Web at: http://jas.fass.org www.asas.org Downloaded from jas.fass.org by on May 6, 2011. Breeding for Profit: Synergism Between Genetic Improvement and Livestock Production (A Review)' Dewey L. Harris"92 and Scott Newman+ "ARS, USDA, Roman L. Hruska U S . Meat Animal Research Center, Clay Center, NE 68933-0166 and I A R S , USDA, Fort Keogh Livestock and Range Research Laboratory, Miles City MT 59301 ABSTRACT: Fifty years of research in animal objectives are reviewed with attention to common breeding and genetics are examined from four perspec- approaches. Where consensus is reached about a tives: 1) genetic prediction, 2 ) animal testing and breeding objective ( i n economic form) for a class of selection schemes, 3 ) dissemination of genetic im- livestock, this objective can be used in conjunction provement, and 4 ) definition of breeding objectives in with genetic predictions to rank animals within a economic form. Breeding in all classes of livestock has breeding population. Ranking without undue attention moved from a purebred appearance orientation to a to herd of origin facilitates a pyramid-shaped hierar- performance (either purebred or crossbred) orienta- chy of animals that can be fundamental to the tion. Unfortunately, the evolution from a performance functioning of breeding enterprises contributing im- orientation to an economic orientation is incomplete, provements to operations concerned with production. especially for some livestock classes. Placing breeding Genetic improvements should flow from proven geneti- objectives into a mathematical form on a sound cally superior animals to improved production sys- economic basis is key to integrating modern develop- tems. The tiers of the pyramid need to be organized ments in animal breeding into more purposeful relative to animals with differing levels of economic industry programs. Procedures used to develop such evaluation. Key Words: Animal Breeding, Production Economics, Selection Index, Integrated Systems, Breeding Programs J. h i m . Sci. 1994. 72:2178-2200 Introduction developing his original ideas. His contributions to scientific knowledge and breeding methodology con- This paper is dedicated to the memory of Dr. Lanoy tinue to be pertinent and vital. With real insight, he N. Hazel, one of those who, early in his professional stated the following in 1943: career, initiated the sequences of research reviewed here. Dr. Hazel died on October, 14, 1992, 50 yr after The idea of a yardstick or selection index for measuring the net merit of breeding animals is probably almost as old as the art of animal breeding itself. In practice several or many traits influence an animal's practical value, although they do so in varying degrees. The information regarding differ- ent traits may vary widely, some coming from an animal's relatives and some from the animal's own 'Acknowledgment and appreciation are expressed to John E. performance for traits which are expressed once or Temple and Sharon Stark for manuscript preparation. Helpful repeatedly during its life. . . . These factors make suggestions concerning a draft manuscript from Gary Bennett. Jim Brinks, Gordon Dickerson, Bill Hohenboken, Kreg Leymaster, Mike wise selection a complicated and uncertain proce- MacNeil, Chris Morris, Geoff Nicoll, Ra61 Ponzoni, Charles Smith, dure; in addition fluctuating, vague, and sometimes Terry Stewart, and Jim Wilton are acknowledged and appreciated. erroneous ideals often cause the improvement Also, stimulation from lively exchanges on the AGDG (Animal resulting from selection to be much less than could Geneticists Discussion Group) electronic bulletin board is ac- be achieved if these obstacles were overcome. knowledged. 2To whom reprint requests should be addressed: P.O. Box 166. Received June 18, 1993. Despite considerable progress in the ensuing 50 yr, Accepted April 7, 1994. these obstacles have not been fully overcome. 2178 Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2179 In the 1940s, before electronic computers, young Lanoy Hazel performed calculations for his quantitative studies on a desk calculator. Foundation Papers Hazel and Lush (1942) established the desirability of a linear function of recorded traits as a basis for The primary purpose of this paper is to review selection with greater expected response for an several key publications, integrate ideas, and extend objective than expected for other alternatives. Hazel thought on industry application of economic breeding (1943) developed such a linear function (selection objectives following from Hazel’s (1943) concept of index) of observations for specific animals, including aggregate genotype. The aim is not only to compare traits of these animals and of relatives to improve an the approaches used across several livestock classes “aggregate genotype.” The aggregate genotype was and functions, but to elucidate common principles, described t o “weight the gain made for each trait by especially from the more integrated poultry and dairy the relative economic value of that trait.” This classes, so as to suggest future emphases in less aggregate genotype represents a fundamental concept, mature breeding industries. However, to provide the breeding objective, that is seldom fully im- supporting perspectives, we first briefly examine the plemented in some livestock breeding industries. development of statistical procedures for prediction of Unknown to Hazel, Smith (1936) had presented a genetic differences as follows from Lush (1947a,b). similar approach for selection among plant varieties. The design of livestock testing and selection following Hazel’s paper was of landmark significance; it moti- from Dickerson and Hazel (1944) is then considered. vated and provided direction to many researchers Following from Lush‘s ( 1946) “functional stratifica- working with different classes of livestock for estimat- tion,” we discuss how corporate breeders, artificial ing genetic parameters and incorporating them into insemination ( AI) studs, and organizations of breed- ers have organized the production of improved genetic indexes that could be used to effect genetic changes products (frozen semen, chicks, crossbred gilts, bulls, toward economic breeding goals. In spite of this, boars, rams, etc.) to transfer and multiply genetic industry implementation has been slow for some wool- improvements in selected populations to improve and meat-producing classes of livestock in the United profitability of commercial livestock activities. The States. leverage of these functional arrangements is fun- Dickerson and Hazel (1944) studied the relative damental t o implementation in industry of scientific merits of performance testing and progeny testing as a principles from the other lines of study being re- basis for selection. These alternatives had evoked viewed. Conversely, the implications of necessary many subjective opinions about their relative merits. industry structures should be recognized to orient In addition to clarifying when each alternative is most further development of scientific breeding principles. effective and when they should be used sequentially, Downloaded from jas.fass.org by on May 6, 2011. 2180 HARRIS AND NEWMAN this paper was the first inquiry on the design of Genetic Prediction genetic improvement systems to achieve greater responses. Because of imperfect genetic control of the pheno- While reviewing the impact of the structure of type and because related individuals tend to have purebred livestock industries on genetic change, Lush similar genotypes, observations on members of the ( 1946) noted a ‘‘functional stratification” of breed same family can be used t o increase accuracy of organizations with “multipliers’ herds” intermediate predicted genetic differences as shown by Lush between a few “breeders’ herds” and the many “grade (1947a,b). The limitation of Lush’s (1947a,b) ap- herds.’’ A pyramid-shaped functional structure of proach was that it presumed equal family sizes and breed societies is implied and the flow of genetic did not consider other effects, such as herds or years. improvement to producers has been an area of concern An early method for adjusting for non-genetic to some breeding organizations. Practical implementa- effects on performance data was the herd-mate tions have occurred in commercial breeding with little deviation procedure of Henderson et al. (1954). This scientific study. With limited scientific documentation, was used in dairy evaluations prior to more accurate our review will involve considerable subjective obser- procedures accounting for differing amounts of data vation. contributing to deviations. Robertson ( 1955) sug- Lush (1947a,b) developed formulae extending the gested a procedure for combining information from concept of using observations on relatives, as in Hazel predictions of genetic differences from different (19431, to increase the accuracy of predicting genetic sources. In a major step, applied to poultry breeding, differences, but with a single trait and a one-way Osborne (1957a,b) extended Lush‘s (1947a,b) formu- classification structure of families of equal size. This lae to family structures with both full-sibs and half- initiated considerable activity in statistical prediction sibs. But Osborne’s formulae also presumed equal-size of genetic differences. Acknowledged for having families and some applications of his formulae were provided advice on several of these papers, Cochran not strictly correct. Henderson et al. (1959) first ( 1951) reviewed the statistical theory behind them, presented mixed-model equations for simultaneous thus placing later studies on a firm statistical estimation of fixed effects and prediction of random foundation. genetic effects but elaborated the principles through This review will include four subject areas: 1) several subsequent papers. Searle ( 1964) reviewed genetic prediction, 2 ) animal testing and selection similarities and differences of herd-mate deviation schemes, 3 dissemination of genetic improvement, procedures at use in New Zealand, Great Britain, and and 4 ) definition of breeding objectives in economic New York. form. Although largely studied separately, in applica- Henderson (1963) noted that, in Hazel’s (1943) tion, interrelationships need to be recognized, and approach, optimum selection toward a breeding objec- thus all four areas are reviewed in this paper. We will tive of a simple linear form intentionally not review several pertinent aspects of breeding research, specifically, experimental and field data analyses contributing to genetic and phenotypic H = : t a; PD; i parameter estimates and evaluation of selection re- sponse, development of statistical procedures other requires selection on an index or criterion than those involved in genetic prediction, and cross- breeding systems and use of breed resources except for their implications for breeding objectives for compo- I = a; EPDi i nent breeds. An earlier review by Dickerson and Willham (1983) focused on basic knowledge accrued from in which H is the breeding objective, the sum of the research to facilitate “quantitative genetic engineer- products of the progeny difference ( PD) for each trait ing” of livestock species. One of their projected future and its linear economic value (sometimes termed advances was the clarification of breeding objectives, economic weight, ai), and I is the optimum index, the especially in beef cattle and sheep. Our review will sum of expected progeny differences ( E P D ) for each of focus on breeding objectives and their utilization, the same economic traits weighted by the same which has received renewed attention in the past economic values and summed over the same traits. decade. This focus requires attention to the other Two problems exist in applying the selection index of three areas as well, back to the 1940s, as well as Hazel (1943). First, profit functions often are more integration into industry programs mutually benefi- complex than this additive and linear breeding cial to both breeders and producers. objective, and second, complete profit functions often The pragmatic question of our review concerns how include some economic traits for which EPD are not genetic improvement programs of breeders can syner- readily available. gistically affect economic concerns of livestock Wilton et al. (1968) and Wilton and Van Vleck producers. (1968, 1969) extended this substitution principle to Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2181 non-linear (quadratic) functions (including squares predict sire, dam, and progeny genetic differences, or products). Thus, for a breeding objective of the form even for animals without recorded data and for several traits (Schaeffer and Kennedy, 1986; Misztal and Gianola, 1987; Westell and Van Vleck, 1987; Wiggans and Misztal, 1987; Wiggans et al., 1988; Berger et al., 1989; Groeneveld and Kovac, 1990). This group of programs all calculate EPD (sometimes called ETA for estimated transmitting ability) or estimated the appropriate index is breeding values ( EBV). We will discuss EPD al- though all principles also apply to EBV, which are I = ai ( p i + EPDi) twice the corresponding EPD. i Harris (1964), Sales and Hill (1976a,b), and + Aij ( p i + EPDi) ( p j + EPDj) Foulley and Ollivier (19861, among others, stressed 'J the importance of accurate estimates of genetic parameters t o maximize response to index selection. with Aij being the relative economic value of squared Increased accuracy of genetic evaluation procedures is or product terms. ( p i + PDi) is the mean plus PD for preceded by estimation of variance and covariance each trait and ( p j + EPDj) is the mean plus EPD for components. Henderson ( 19531 described three each trait. However, Ronningen ( 197 1) demonstrated methods of variance component estimation that are that simple substitution did not extend to cubic still in use today. Methodologies using all genetic functions. Difficulties arise due to the occurrence of relationships and advanced numerical procedures higher-order moments of the multivariate distribu- have enhanced the accuracy of variance component tions in the expected covariance between indexes and objectives. estimation procedures (Henderson, 1987; Graser et Henderson ( 1966) proposed statistical methodology al., 1987; Meyer, 1989; Boldman and Van Vleck, for evaluating dairy sires from performance data 1991). Estimation of variance and covariance compo- collected under DHIA programs. Harris ( 196 6 ) devel- nents for a wide array of economically important traits oped formulae for simultaneously evaluating sires, is fundamental to the development of genetic improve- dams, and progeny within each contemporary group ment programs and is well reported in the literature allowing for unequal family sizes, overcoming the but will not be further discussed in this paper. limitation of Osborne's (1957a,b) procedure for poul- Modern statistical and computer capabilities allow try. Later, Henderson (1973) presented a thorough simultaneous prediction of EPD for several important statistical basis for mixed-model equations to simul- performance traits involving many flocks or herds over taneously provide best linear unbiased estimates years and generations. These EPD predict differences (BLUE) of nongenetic fixed effects and best linear between animals, but, for practical utility, must be unbiased predictions (BLUP) of genetic effects. As used in conjunction with a testing and selection computer capabilities increased, procedures were ex- scheme that accomplishes improvement (directed panded to facilitate more accurate predictions for more change) and transmits that improvement to the herds complex models and data sets. Henderson (1976) and flocks of commercial producers of livestock proposed an indirect procedure for generating the products in a mutually beneficial manner. inverse of the numerator relationship matrix, which is Efforts to collect and process performance data and needed to fully use the information from relatives. publish results incur expense. In a free enterprise Quaas (1976) developed a modified algorithm for situation, such effort will be undertaken only if there calculating this inverse. is an expectation for profit to the breeders or Henderson and Quaas (1976) extended BLUP organizations that are incurring those costs. methodology to include simultaneous prediction of genetic differences for several correlated traits. Hayes and Hill (1980) described the use of canonical Testing and Selection transformations for multivariate genetic data, and Arnason (1982) described the use of this transforma- Progeny tests, especially with large numbers of tion for developing multiple-trait selection indexes. offspring, should be considerably more accurate than This transformation reduced computing requirements performance tests of individual animals. Dickerson but required some restrictive assumptions about and Hazel ( 1944) developed formulae for predicting correlations. Another procedure for reducing comput- the rate of expected response to selection and com- ing requirements is the reduced animal model ( RAM) pared alternative schemes. In addition to selection of Quaas and Pollak (19801, which evaluates sires, accuracy, these formulae indicated that the influence dams, and progeny deviations as separate model of selection intensity and generation interval on the elements. Several researchers have developed al- effectiveness of selection often more than offset the gorithms for using animal models to simultaneously influence of accuracy. Many studies followed that Downloaded from jas.fass.org by on May 6, 2011. 2182 HARRIS AND NEWMAN detailed pertinent performance traits for each class of pedigree sires to the remainder of the breed. livestock and explored alternative schemes. Hagedoorn was primarily concerned with poultry, but Rendel and Robertson ( 1950) extended the con- he advocated the scheme for all classes. The scheme cepts of Dickerson and Hazel (1944) by separating involved sire progeny tests and half-sib matings in genetic improvement into four parts due to selection of selected sire groups. The deleterious effects of inbreed- sires to produce sons, dams to produce sons, sires to ing from half-sib matings made Hagedoorn’s scheme produce daughters, and dams to produce daughters. Of impractical but the terminology of “nucleus” survives. course, matings produce both sons and daughters, but Analyses of pedigree structure of several breeds of in some classes of livestock especially dairy and layer U.S. livestock led Lush (1946) to conclude that there poultry, only sons of certain matings are kept at birth is usually a “functional stratification” of pure breeds to become candidates for selection. Separation of these into what he termed “breeders’ herds” and “rnul- four pathways had an impact on numerous studies. tipliers’ herds.” The multiplier herds were larger than Robertson (1957) studied optimum group size for breeders’ herds in combined size and provided sires testing. Smith (1959, 1960) evaluated testing into “grade herds” even larger in total size. These schemes for pigs and other animals. The design of “grade herds” would be production herds, not necessar- selection programs for various classes of livestock is ily purebred, but using purebred sires. Lush ques- greatly influenced by mating ratios and reproductive tioned whether much real genetic difference existed rates for each sex as well as what traits are recorded, between these strata. He judged that the differences when traits can be measured, and when selection can were largely in promotional effort. occur. Van Vleck (1964) provided a key analysis of Robertson and Asker (1951a,b), Robertson (19531, alternative dairy sire sampling procedures for evaluat- and Wiener ( 1953 ) noted similar multiplier struc- ing and selecting sires for AI use. This was followed by tures in British breeds. Wiener (1953) noted a Henderson’s ( 1966) procedure for genetic prediction of “conicoidal” shape to the number of sires registered by sire breeding values from such d a h . With the use of a herd graphed in relation to a herds position in the crossbreeding becoming more common, Smith (19641, breed structure. The “conicoidal” shape describes Moav (1966a,b,c), and Moav and Hill (1966) consid- concave sides. However, the metaphor of a pyramid ered how evaluation for specialized sire and dam lines came to be used as an aid in conceptualizing differed for each and how selection of parental expansion of genetic improvements from elite herds or combinations for use in producing crossbreds should flocks t o commercial herds or flocks through multipli- be made. In large animals, selection is usually on cation. Bichard ( 197 1) used the term “dissemination” purebred performance even if commercial production for this expansion. The term “ nucleus” continued to be is from crossbreds. However, a procedure for testing used t o describe the apex of the pyramid (James, both males and females from information recorded on 1977). The literal definition of nucleus would place it crossbred progeny (reciprocal recurrent selection) was at the center of the pyramid. Harris et al. (1984) proposed by Comstock et al. ( 1949) and has been used conceptualized a six-tier pyramid involving selection, by some corporate breeders of egg-laying chickens multiplication, crossing and reproduction, production, (Hunton, 1990). Other corporations merchandizing processing, and consumption. Improvement from layers have sequentially used individual and sib (both genetic selection passes not only from the top tier or purebred) performance tests followed by crossbred stratum to the reproduction and growth phases of progeny tests. production herds or flocks but also influences concerns Attention to the design of data collection and of processing and consumption. How this flow is selection schemes to optimize accuracy of evaluation, motivated is the topic of later paragraphs. intensity of selection, and generation interval to Smith ( 1981) reviewed the relation between improve the rate of genetic improvement can enhance benefits and costs for testing and selection, noting the value of statistical predictions. Even then, genetic diminished returns associated with a linear increase improvement efforts will likely not be justifiable of costs in relation to number of animals tested. He without procedures for expanding and disseminating noted that reproductive rate sets limits on selection that improvement into commercial production of intensity and thus response. Smith did not directly livestock for monetary reimbursement. address the implication that greater returns through multiplication and dissemination can support greater testing and selection efforts, but such a conclusion is Dissemination of Genetic Improvement consistent with his developments. Prior t o the 1940s, animal breeding was primarily Two metaphors have been used to symbolize the purebreeding with occasional crossbreeding to initiate transition between the system of genetic improvement development of new purebred populations. Poultry, (testing and selection) and the production system. both layers and broilers, were the first livestock to Hagedoorn ( 193 9 ) proposed a nucleus scheme move to crossbreeding. Before this, hybrid corn was a whereby the nucleus for a breed provided purebred definite success with improved performance due to Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2183 heterosis from crossing highly inbred lines making producing eggs. Broiler stock evaluation was not seed corn varieties proprietary products and attracting influenced as much as layer evaluation by public corporations t o develop and market inbred-hybrids on testing, but similar trends for production to be a large commercial scale. Some companies previously controlled by larger integrators supplied by large successful with hybrid corn began to inbreed and breeders directed the focus toward economy of produc- hybridize poultry in the 1940s. Other breeders, tion. Some large broiler producers are integrated, from previously successful with pure strains of various their own research flocks for genetic selection all the breeds of chickens, began to cross these strains and way through hatchery and production facilities to breed without intentional inbreeding of the strains. slaughter plants processing packaged brand-name To reap the commercial benefits of heterosis, three products. Such corporations are competitive and changes occurred in the poultry industry. First, elite profit-oriented and genetic improvement is directed breeding flocks expanded to accommodate more toward the economics of production. Reduced costs, strains and breeds. Second, multiplier flocks became new products, and increased consumption of broiler allied organizationally t o elite flocks (usually under meat attest to the efficiency of breeding and produc- the same ownership) and stocks were expanded tion coordinated through highly functional pyramid considerably to increase the volume available for sale. structures. Although such levels of integration This increase in sales supported larger and more through corporate ownership do not seem likely in complete testing programs in elite research flocks. large animal industries, the progress of the poultry Third, local hatcheries became franchises of breeding industries suggests the need for a more economic corporations with associated supply flocks producing orientation with organization that is closer to the the hatching eggs from crossbred matings. If the industry structure implied by a pyramid. crossbreeding system involved three- or four-way Dairy breeding activities evolved to an organized crosses, the mated grandparents to produce crossbred industry structure that can be symbolized by a parents could be controlled in corporate-owned facili- pyramid. But the motivation and vehicle for this ties so that the crossbred parents supplied to the structure were quite different from that of poultry. franchise hatcheries would be proprietary products as The U.S. dairy industry was the first livestock well as the crossbred commercial chicks, both for egg industry to record performance in an objective, quan- and meat production. For recent reviews of the titative, and logical manner through milk recording development of poultry breeding into multinational and butterfat testing for management purposes by the corporate businesses, see Arthur (19861, Hartmann Dairy Herd Improvement Associations ( DHIA) , as (1988), and Hunton (1990). described by Voelker ( 1981). In mid-century, when AI Commercial exploitation of heterosis led to the top became widely practiced, the DHIA data base was tiers of the conceptual pyramid becoming well-coordi- available to evaluate the genetic merit of numerous nated with the systematic flow of genetic improvement sires being used in many herds (Miller, 1981). moving one-way down the pyramid, in conjunction Statistical complexities motivated theoretical develop- with production of crossbred parent and commercial ments for prediction of genetic differences, which were stock. This was more systematic than the “functional reviewed earlier. As superior sires were identified and stratification” noted by Lush (1946) for large animal produced semen for sale, and as programs for produc- industries. ing and testing young sires were implemented, AI Competition became intense among corporate poul- studs became the apex of the pyramid with dissemina- try breeders, motivating considerable testing and tion occurring through the production and merchan- selection in their respective research flocks to achieve dizing of large numbers of ampules of frozen semen. more genetic improvement for their potential cus- Expansion occurs through the dilution and division of tomers than was being made by competitors. To aid semen ejaculates rather than through additional producers, random-sample performance tests were generatiods) of reproduction, as in poultry. Another started by several state organizations in the 1950s difference relative to poultry is that elite dairy females and provided comparisons on several traits; see the ( t o be dams of sons) are not located in a single or a recent historical review by Hartmann (1985). Start- few elite herds. They can occur in any performance- ing in 1963, combined summaries of results published recorded herd in the system. Thus, for cows, the apex by the USDA shifted the focus from performance to is not defined by the herd reputation but by the economics by providing an income over feed cost genetic evaluation of specific cows. Contract matings evaluation. In the 1970s entries in tests declined, and between proven sires and elite cows produce young this loss of entry fees led to the demise of several tests male candidates for testing to replace the proven sires and the combined summary ceased to be published in studs. Modern animal model genetic evaluation after 1978. Changes in the layer poultry industry to procedures make the identification of elite cows large, profit-oriented, integrated corporations changed accurate and feasible. Embryo transfer has been used the focus to economic objectives with breeding stock in recent years as a tool t o increase dissemination of sales and purchases oriented to the economics of the genetic superiority from elite cows. The procedures Downloaded from jas.fass.org by on May 6, 2011. 2184 HARRIS AND NEWMAN for producing, freezing, and distributing semen dictate closed and no genetic flow occurs up the pyramid that proven and young candidate sires be restricted t o because of disease risks. Many swine breeding organi- a few locations. A pyramid-shaped industry structure zations function similarly. Some disease problems based on proven genetic merit of individual animals have occurred in open nucleus schemes of sheep in reproducing through AI provides an indication of Australia (Ponzoni, 1993, personal communication). possibilities for other large animal classes. In their perceptive book on the development and Van Vleck (1977, 1988) reviewed the evidence for implementation of animal breeding theory, published genetic progress in U S . dairy cattle. Analyses indi- midway through the 50-yr period covered by this cated that progress had been considerable but much review, Lerner and Donald ( 196 6 ) were favorably less than theoretically possible. Much of the progress impressed with the development of large-scale breed- was from progeny testing and selection of AI sires. ing operations in the poultry industries and with the Cow evaluation is expected to become more effective improvements through AI on selection and use of with application of BLUP procedures (Henderson, dairy sires. On the other hand, these authors were 1973, 19761, especially for animal model procedures. concerned that purebred breeders of swine, sheep, and Hinks (1978) investigated the potential for breed- beef cattle, and their breed societies, were unreceptive ing schemes in dairy cattle with centralization of to the science of genetics. To quote those authors, intensive testing and selection as an alternative to the large-scale testing and selection through AI. Nicholas Their associations have a history of resistance to and Smith (1983) extended this work to include the scientific advances dating from their establish- use of multiple ovulation and embryo transfer for ment.. . . No other behavior should be expected. enhanced reproduction from desirable genotypes. Members of breed associations are individualists, These studies demonstrated potential for such ap- combining together for mutual advantage and proaches t o exceed improvement already realized for working collectively only for bare necessities. . . . simpler approaches. They are responsive to economic pressures but not Quantitative consideration of the dissemination of to advice or moralizing. genetic improvement from elite or nucleus herds or flocks was clarified through the paper of Bichard There has been a demise of breed societies in some ( 197 1). His concern was for a one-way flow through countries since then. herds from elite to multiplier to producer, possibly in Lerner and Donald did not fully anticipate the conjunction with crossbreeding. Parker ( 1970) growth in statistical procedures and the potential described the organization of Romney breeders in New these would have for synthesizing information from Zealand around development groups with a central numerous small breeding enterprises. The publication flock containing highest-producing ewes from of their book coincided with early steps in the producers’ flocks and providing rams back to members evolution of statistical prediction and widespread of the group. Daughter flocks, organized around the availability of computing equipment. Henderson’s central flock, disseminate and produce rams for (1976) procedure for developing the inverse of the producer use. Numerous other group schemes have numerator relationship matrix increased the value of developed in New Zealand and Australia for several pedigree data recorded by breed societies. In the breeds o r groups of breeders within a breed. Jackson United States, societies of beef and swine breeds now and Turner (1972) expanded the theory for such provide data processing services to their members to schemes, followed by James ( 1977 ), who considered open nucleus breeding systems in which not only was facilitate more effective use of both performance and there flow down the pyramid through the distribution pedigree records. of sires, but superior performing dams could move up Because of limits to multiplication and dissemina- the pyramid to the nucleus. For discussion of tradi- tion when reproductive rates are low, progressive tional stud breeding in Australia in relation to the corporate involvement with livestock classes with low development of group breeding schemes and their reproductive rates has been largely limited t o mer- implementation, see Peart ( 197 6 1. chandizing of semen. For effective dissemination to Industry structures for poultry predominantly in- occur, the genetic changes at the apex of the pyramid volve the production of the crossbred production have to be identified to be in directions to reap animals and usually crossbred parents. On the other economic rewards further down the pyramid, and a hand, dairy production and Australian and New portion of these rewards should flow up the pyramid to Zealand wool-sheep production still predominately reward and stimulate the developers. Two phrases to involve purebred production animals. The metaphor of describe this process are payment-for-value and value- the pyramid is versatile enough to aid conceptualiza- based marketing. With this two-way influence, the tion of the necessary organization of poultry, dairy, breeding and production segments of the industry will and sheep. be synergistic in their advances. However, for this to When nucleus flocks in poultry take extraordinary fully occur, precise identification of value in monetary measures to eliminate pathogens, the pyramid is terms is necessary. Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2185 Economic Breeding Objectives Barlow, 1987; Groen, 1989a,b; McClintock and Nicho- las, 1991). Willham (19791, in reviewing U.S. beef Before the foundation papers of the 1940s, breeding sire evaluation programs, stated that objectives for all livestock classes were predominantly visual. The objective was toward an “ideal type” for these programs place the responsibility of defining the breed. The expectation was that “form determines direction on the many breeders of the particular function,” with function meaning performance. Capa- breed in question. This should be their decision; bilities for genetic prediction have now encouraged however efforts must be made to make available to recording of some performance traits in most classes. them the basic facts on which reasonable direction Most breeding industries have evolved from objectives decisions can be made. of appearance to objectives involving performance, but these objectives are often not of a precise economic Willham recognized that breeders should decide their form. Thus the evolution seems t o be incomplete. own direction and, thereby, commit to it. It is noted Certainly, traits in most performance recording that, except for efforts of corporate geneticists, it is schemes are related t o the economics of production in rare for breeders of any class of livestock to define some way. However, combining genetic predictions, breeding objectives in mathematical and economic such as EPD, into a mathematical function to serve as terms. Better leadership from professional scientists, a criterion of selection is not often achieved, even but allowing breeders and their breeding stock cus- though Hazel ( 1943) specified the fundamental prin- tomers to have final choice of objectives, is occurring ciples 50 yr ago. in some cases (e.g., Fenwick et al., 1991). Ponzoni ( 1982) proposed integrating several U.S. swine breed associations, through STAGES aspects of selection through five stages, whereas (Swine Testing and Genetic Evaluation System) Harris et al. (19841, incorporating the potential of (Stewart et al., 1991), offer alternative breeding crossbreeding systems, listed the following nine steps objectives related to potential use of each breed in for systematically designing a breeding program: parentage of commercial breeding stock to direct construction of alternative indexes. 1. Describe the production system(s), Disturbed by the vagaries of production economics, 2. Formulate the objective of the system, many scientists have desired biological objectives free 3. Choose breeding system and breeds, from these difficulties. Attempting to define biological 4. Estimate selection parameters and economic indexes for swine, Fowler et al. (1976) proposed lean weights, tissue growth rate (LTGR) and lean tissue feed 5. Design animal evaluation system, conversion ( LTFC). The former is the ratio of lean 6. Develop selection criteria, tissue growth relative to the time during which the 7. Design matings for selected animals, growth occurred. The latter is the ratio of feed 8. Design system for expansion, and consumed to lean tissue growth. Lean tissue growth 9. Compare alternative combined programs. involves both total tissue growth and tissue composi- Both papers emphasize the importance of definition tion of that growth. Although these two biological of breeding objectives and developing selection criteria functions describe fundamental aspects of biological based on them. Wilton ( 1982) pointed out that the efficiency, they do not readily combine into one index substitution principle for index construction may be to provide a selection criterion. The three component used even when unequal amounts of information go traits, lean tissue growth, feed consumed, and time to into the calculation of the EPD, and substitution can slaughter, provide three fundamental elements of a be done for either linear or quadratic objectives. This profit function if it is assumed that costs are for feed, suggests an amalgamation of the selection index labor, and facilities, only lean tissue has value, labor approach of combining traits and economic weights as and facility costs are a function of time, and reproduc- in Hazel (1943), and the genetic prediction approach, tion and lactation costs are ignored. Simm et al. which combines information from relatives for each (1987a) concluded that these biological indexes im- trait (Henderson, 1973, 1976). Because of the com- plied economic weightings that are not necessarily mon origin of these two statistical approaches, it correct. Ponzoni and Davies (1989) concluded that makes sense to integrate them into a procedure with conventional indexes have advantages over biological the strengths of both. indexes because they enable monitoring of changes in In addition to Ponzoni (1982) and Harris et al. components and updating of objectives. Thus, it seems (19841, numerous others have stressed a formal that biological objectives, without economic values, are definition of the breeding objective as a necessary not complete descriptions of pertinent objectives of the foundation for designing genetic evaluation proce- producer. dures, selection practices, and integrated animal The fundamental necessity for an economic perspec- breeding programs (Harris, 1970; Dickerson, 1970, tive and payment-for-value was noted by Lerner and 1982; Danell, 1980; Carter, 1982; James, 1982; Donald (19661: Downloaded from jas.fass.org by on May 6, 2011. 2186 HARRIS AND NEWMAN Unless there is a change for the better in the increase payments. Genetic evaluation across herds or genetic and economic merit of each breed, it is flocks for several performance traits has taken the difficult to see how any outlays on breeding for industries toward such an arrangement. The remain- improvement can bring returns to the ultimate ing task is to combine these EPD into a single-valued financial source. Constructive breeding therefore measure of an animal’s worth to the customer’s profit must have positive aims, anticipate the future objective. correctly, and temper the rigours of genetic theory with a proper regard for costs and returns. Economic Values and Profit Equations Motivation for Genetic Improvement The basic economic values involved in the develop- ment of profit equations for any class of livestock, at Seedstock producers are in business primarily to the minimum, include 1 ) cost of feed per unit of feed make a profit, as are their breeding stock customers, weight, 2 ) cost of labor and facilities per unit of time who produce food (and fiber) products. Producer’s (may differ for different stages of the life cycle), 3 ) profits are influenced by consumers’ demand for their value per unit weight for product(s), and 4 ) cost of products. initial animals (breeding stock, young animals, etc.). Purchase of breeding stock involves a cost but can Costs of feed may reflect differences in cost related to provide a positive influence on the functioning of the nutritive content. Value of product may reflect quality, system by reducing other expenses or increasing such as body composition as a function of some income from output, or both. The producer will be measure of fatness. motivated to pay more for breeding stock if given Hazel (1943), defining an economic value, ai, assurance that profit will increase as a consequence of stated that “the relative economic value depends upon these increased costs. Products sold to earn income for the amount by which profit may be expected t o a breeder are primarily breeding stock (or alterna- increase for each unit of improvement in that trait.” tively semen). Efforts to improve the value of this The ai may be obtained as the partial derivatives of a product (and thus the income earned) are likely t o more complex profit equation with respect to each add expenses due to the extra labor of recording data, trait in the objective. The derivatives are evaluated a t registration and computer charges, marketing of the mean value of all other traits. Kendall and Stuart intact animals for slaughter, and so on. The breeder ( 1963) presented the formula for a Taylor’s expansion will be motivated if given assurance that greater for an arbitrary complex function and Harris ( 1970) income will adequately cover these increased ex- suggested taking first-order terms as a linear approxi- penses. mation. Later, Melton et al. (1979) and Ladd and In recent years, there has been considerable en- Melton (1979) expanded the theoretical basis for a couragement in both the scientific literature and the trait’s economic value as used in selection index. livestock press for value-based marketing of meat Alternatively, Pasternak and Weller ( 1993) developed animals. This means selling market animals on a an interactive computer procedure to calculate linear carcass-basis with more accurate measurement and weights for predicted changes when profit functions reimbursement for quality, primarily freedom from are nonlinear. For quadratic approximations, second extra muscular fatness. Such a marketing arrange- partial derivatives are necessary, as they are coeffi- ment better motivates livestock producers to provide cients of the quadratic terms as used by Akbar et al. improved (less f a t ) carcasses for sale to processors. (1989b) and Bandy et al. (1991). When g(x1, x2,. . . , This approach seems equally pertinent for the inter- Xk), have means, pi, and finite variances, and the face between breeders and producers. Breeders would function g is differentiable at xi = pi for all i, a Taylor’s be better motivated to develop breeding stock that expansion of g is improves the performance of the customers’ production systems if income received from breeding stock sales were in proportion to the expected added profitability for the customer. The principle of value-based marketing of breeding stock may seem obvious, but breeding stock have historically been merchandized based on characteris- tics and information trivially related to the expected ag where - indicates the first partial derivative of g contribution of the breeding stock to profitability of au;_ . the customer’s production system. More progressive with respect to xi evaluated at xi = pi for all values of i breeding industries resulted in poultry as marketing and ___ indicates the second partial derivative of g, a2g of breeding stock became oriented to economic con- adpj cerns. Breeders have to accept responsibility for first with respect to xi, then with respect to xj, developing information their customers need to assess evaluated at xi = pi for all i, Axi is xi - pi, and 0 (Ax) the added value of improved breeding stock and indicates that the remaining terms are of the third Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2187 order o r higher. This second partial derivative also has to give lower costs of producing progeny for the growth to be evaluated at the point of the mean of all traits. phase of the production system. Of course, after the Means may differ from herd to herd and are expected longevity trait is expressed by death or failure to to change over time. rebreed, reproduce, or lactate, it is too late to make Profit equations describing the breeding objective any direct selection decisions for that animal. Ancestor often include economic traits that are not routinely information is potentially available for descendants, recorded. Genetic evaluation of performance has but longevity data are subject to many management largely focused on weights at specified ages and, decisions, many of which are influenced by other maybe, some measure of female reproduction. These performance traits. traits are relatively easy and inexpensive to measure. The period between weaning of offspring and Too often genetic evaluation has focused only on parturition represents a major cost of the breeding output traits and has neglected costs of inputs. For herd that may be reduced through parent stock example, weight is the output of the growth process, genetically able to rebreed sooner. Thus, rebreeding number born is the output of the reproduction process, interval as well as age at first breeding are potential and number and weight weaned describe the outputs economic traits. However, expression of these traits is of the lactation process. subject to management decisions to such a large There are many cost-incurring inputs into livestock extent that they are seldom used as performance production systems that may be costly to measure. traits in genetic evaluation programs. Feed, whether prepared feed or range forage, is the Some U.S. corporate breeders have undoubtedly major cost of production for all classes of livestock. used unpublished indexes. Only one series of indexes, Any realistic attempt to develop a profit equation as a and the sets of economic values forming the basis for breeding objective must include feed consumption, them, have been presented in the scientific literature. even though it is dificult to measure. Feed may not A series of four sets of economic values and the directly be in the index, because it is usually only resulting indexes for Kimber Poultry Farms are recorded individually for experimental purposes or on presented in Kashyap et al. ( 198 1) and Bennett et al. a pen-basis in bull, ram, or boar test stations. In those (1981). These indexes included 16 traits. Based on a reanalysis of the Kimber data, fully published by cases, indirect prediction of the costs is necessary. Emsley et al. ( 19771, Emsley and Dickerson ( 1974) Indirectly through the process of developing selec- had earlier compared alternative indexes with up to tion indexes as prescribed by Hazel ( 1943), numerous 12 traits. All these sets of economic weights and the sets of economic weights were developed in earlier resulting indexes presume a linear and additive literature, although we mention only a few. Suther- aggregate genotype as specified by Hazel (1943). The land (1958) calculated indexes for selecting swine, in basis for the economic values used in these studies or addition to Hazel (1943) and one other. For sheep, the changes in them are not fully explained but are selection indexes for economic breeding objectives seemingly due to both shifts and reanalysis of the were developed by Hazel and Terrill (1946) and economics of production and marketing over the Givens et al. (19601, among eight others. Following several years the indexes were used. Feed consump- two earlier indexes, Swiger et al. (1965) and Dicker- tion was not one of the traits used, but cost of feed son et al. (1974) developed indexes for selection of seems to have been considered in the relative eco- beef cattle, and Soller et al. (1966) developed an nomic values for the production traits. Nicoll et al. index for dairy cattle following five earlier ones. (1979, 1990) and Nicoll and Johnson (1986) indi- Indexes for poultry selection have been developed by cated that progressive use of breeding objectives and Panse (19461, followed by 11 more, and recently by selection indexes has occurred in the state-owned Akbar et al. (1986a). Nagai et al. (1955) and Eisen Landcorp Farming organization in New Zealand. (1992) produced indexes for mice. Probably few of During the late 1960s, new attention was given to these indexes were ever used in breeding industries. defining breeding objectives. In an important study, In many of these developments, the attempt was to Moav and Moav (1966) considered the quantitative provide relative economic values only for those traits definition of objectives for broiler breeders. Dickerson for which there were data available to estimate the (1970) and Harris (1970) reviewed the principles of parameters needed (e.g., heritabilities and genetic merging biological and economic aspects of livestock and phenotypic correlations). This was inadequate production into bioeconomic objectives. These three because pertinent traits, including feed consumption, works shifted attention to nonlinear and nonadditive reproduction, and longevity, were often neglected. interrelationships between objectives and component Longevity of life or longevity of efficient reproduction traits. Nonlinearity allowed a more complete economic and lactation is of strong economic importance in most description but led to questions of whether the indexes livestock production systems. If the costs of producing should be linear and additive. ready-to-breed young animals exceeds the salvage Further discussion of principles has followed among value of a culled (for age, poor reproduction, or poor the livestock geneticists, for example see Gjedrem lactation) breeding adult, costs of producing the ( 1972 1, McClintock and Cunningham ( 1974), Dicker- breeding animals may be prorated over more parities son (1976, 19821, Brascamp (1978), Miller and Downloaded from jas.fass.org by on May 6, 2011. 2188 HARRIS AND NEWMAN Pearson (19791, Dane11 (19801, Thompson (19801, This prompts the question of what to do about Hill (1981a,b), Pearson and Miller (19811, Carter unrecorded traits of economic importance. Without (1982), Pearson (1982), Wilton (1982), Harris et al. EPD, there cannot be substitution for the PD in the (19841, Roux and Scholtz (19841, Groen (1989a,b), breeding objective function. Whenever feasible, ex- Bright (1991), and h e r and Fox (1992). Especially panding the performance recording scheme to include active in recent years in elucidating and applying all economically important traits is ideal. An alterna- breeding objectives have been the livestock geneticists tive is to include another trait ( o r set of traits) that of New Zealand and Australia, including Clarke and provides a reliable indicator of the unobserved eco- Rae (1977), Nicoll et al. !1979), Ponzoni (1979, 1982, nomic trait. A reliable indicator has a high genetic 1985, 1986, 1988a,b), Morris (1980, 19811, Ponzoni correlation with the economic trait and has sufficient and Walkley (19811, James (1982, 19871, Goddard variability to be indirectly selected. A reliable indica- (19831, Nicoll and Johnson (19861, Barlow (1987, tor is also relatively easy to measure, heritable, and 1989), McArthur (19871, Upton et al. (19881, John- measured early in life. An economic trait, such as feed son et al. (19891, Ponzoni and Newman (19891, consumption, can perhaps be predicted either pheno- Barwick and Hammond (19901, Brash et al. (19901, typically or genetically from the observed traits of McArthur and del Bosque Gonzalez (19901, Ponzoni weight, growth rate, milk production, and so forth. and Gifford (19901, Fenwick et al. (19911, and With equal amounts of information, it is a straight- Newman et al. (1992). McClintock and Cunningham forward application of Hazel’s ( 1943) procedure to (1974) pointed out that when some traits are include in the index a predictor for an unobserved expressed later in life than others, economic discount- economic trait because of the genetic and phenotypic ing of weights becomes necessary. correlations between the economic trait and the Goddard (1983) concluded that, in many situa- observed indicator traits. With unequal information, tions, a linear approximation is adequate and achieves the task is somewhat more difficult. This indirect at least as much change in population means as any prediction approach has been used for the economic other function. The operational difficulty is that for trait of feed consumption by Stewart et al. (1990), linear approximations (Harris, 19701, economic Ponzoni and Newman (19891, and Newman et al. weights are the first partial derivatives of a more (1992). For clarification in further discussion, we will complex function relative to each trait evaluated at the refer to direct EPD and indirect EPD for observed population means of all other traits. Goddard recog- performance traits and other economic traits, respec- nized the difficulties in linear indexes due to changing tively. The accuracy of indirect EPD depends on the means for traits with intermediate optimums, which magnitude of the genetic correlations with observed had prompted Kempthorne and Nordskog (1959) to traits on those individuals. The choice between the propose restricted indexes that avoid change in some need t o directly record feed consumption and the ability to indirectly predict it has been studied by trait or function of traits. Stewart et al. (1990) Arboleda et al. (1976a,b), Wing and Nordskog decided not to use the linear approximation approach (1982a,b), Wing et al. (19831, and Katle (1992) for for the STAGES program because genetic differences egg-production poultry and needs t o be further inves- between herds would lead to several differing sets of ai tigated for other classes. values for relative economic weights for several herds When substitution indexes involve unequal and these weights should be changed as the genetic amounts of information in the EPD, indirect EPD are mean of each herd changes. If linear approximations needed for the unobserved but economically important of complex functions are accurate in the short term, traits. This is a feasible extension of the work of then one complex function can also approximate Henderson and Quaas (1976) if all the appropriate several linear functions for differing sets of sub- variances and covariances are known. To reduce population means. The appropriate nonlinear function calculations, Stewart et al. (1990) determined in- allows the use of a single objective function and a direct EPD for feed while retransforming the EPD for single index function of EPD for a whole breed with transformed traits relating t o growth rate and fatness. numerous herds participating, but this requires a This is the eigenvector canonical transformation common objective. proposed by Hayes and Hill (1980) and Arnason Ponzoni (1982) and Harris et al. (1984) emphasize (1982). Other transformations for achieving inde- that the breeding objective function should be devel- pendence among traits have been reviewed by Jensen oped initially focusing on what traits are economically and Mao (1988) and Lin and Smith (1990). Both important and how they contribute to the economics of selection index (Hazel, 1943) and BLUP (Henderson, production. This should be done whether or not those 1973, 1976) methodologies should take into account traits can readily be components of a testing, evalua- genetic and phenotypic covariances between traits. tion, and selection scheme. In other words, the set of Substitution indexes could be developed from univari- economic traits is not necessarily the same (and ate BLUP predictors, but there would be some loss of usually is not the same) set of traits that are recorded accuracy and indirect EPD would not be feasible. in a computerized genetic evaluation scheme. Schneeberger et al. (1992) presented formulae for Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2189 predicting economic traits from performance traits in Alternative Forms of Breeding Objectives the form of genetic multiple regression equations with direct EPD substituted for PD genetic values of Hazel’s ( 1943) definition of aggregate genotype performance traits. The results would include what we oriented the objective toward improving profit, and have termed indirect EPD. With their equations, the most developments have been in that direction. calculations could be done later and separate from the Dickerson (1970, 1976, 19821, Harris (19701, and direct EPD calculations. James (1982) suggested that ratios of expenses and outputs might sometimes be more appropriate as Restricted and Desired Gains Indexes objective functions. However, profit functions have remained attractive because their simpler form more Brief mention of constraints on optimization of the readily leads to use of the substitution principle. selection index procedure is warranted. Kempthorne Brascamp et a1 (1985) and Smith et al. (1986) and Nordskog ( 1959 ) were concerned about the showed that the consequences of profit functions or inadequacy of linear aggregate genotype functions economic ratios were much the same for objectives when there is a nonlinear relationship between a trait pertinent to meat animals with some simple restric- and net value, such as egg weight in chickens. This is tions on the development of profit functions. Ponzoni a curvilinear relationship with an intermediate opti- (1988b) reached a similar conclusion for sheep. Amer mum for the population mean. The relative economic and Fox (1992) had an alternative viewpoint as they weight could be positive, zero, or negative for hfferent presented a “neoclassical” model based on theory of populations and for different points in time. With the competitive firm. strong negative associations with other economically important traits, the economic weight would likely Dairy geneticists have been particularly interested oscillate from zero to positive and back to zero as the in ratios as objective functions. Most current programs population changed from the optimum and back. A focus on repeated records for single lactations. positive weight results from the need for selection Researchers, recognizing the economic importance of pressure to correct the inadequacy of the index giving longevity (length of productive life), noted that profit no selection pressure to the trait based on an on a lifetime basis is not greatly related to longevity if erroneous economic weight of zero for egg weight. The the profit per lactation is small. Because a constraint solution recommended by Kempthorne and Nordskog in most dairy production facilities is on the number of (1959) was to impose a Lagrangian multiplier to cows in production a t one time, Pearson and Miller restrict population change t o zero. More complex ( 198 1 and Pearson ( 1982 ) have proposed that ) restrictions have been proposed (Tallis, 1962; Har- lifetime net income per year reflect longevity, by ville, 1975; Brascamp, 1978). A comment from Mal- making it the denominator of the ratio; others have lard ( 1972) is translated as “the restricted selection preferred the ratio of income t o costs, which may be index procedure is a dangerous and arbitrary tool you considered an economic efficiency ratio. Dickerson can only utilize when there are strong reasons to be (1970, 1982) and Newman et al. (1985) preferred the doubtful about economic weights.’’ Many have consid- ratio of costs per unit product. Meat animal geneticists ered a rigid restriction too extreme when economic have not addressed the importance of productive life to concerns include curvilinearity. the degree t o which dairy geneticists have. When Most index developers have also evaluated these others do, some of the same concerns may predicted changes in component traits and examined appear. them for reasonable responses. After Henderson’s A major dilemma of defining breeding objectives is ( 1963) substitution index facilitated the application of that sometimes there are compelling logical reasons economic weights last rather than first, Pesek and for expressing breeding objectives as a ratio or some Baker (1969) suggested a desired gains approach other nonlinear function. However, optimization of whereby the desired gains values were chosen by the selection indexes seems only to be exact for linear and, breeder as a first step to obtaining index coefficients. sometimes, quadratic objective functions. The authors cite the advantage of not needing Some profit functions for meat animals involve economic values. Many do not agree this is an cubic terms (e.g., a product of number of progeny, advantage. Some argue strongly that restricted or desired gains approaches will likely compromise the weight, and value as a function of leanness) to economic effectiveness of the index approach (e.g., determine income for a parent animal. An economic Gibson and Kennedy, 1990) with high risk of reducing trait, such as days to market, involves the reciprocal of progress below maximum. Others (e.g., Carrick and a performance trait, such as rate of gain. How should Ponzoni, 1991) believe that such apprb-ches may be such functions be handled? Three alternatives are 1) the best alternative when economic values are difficult develop a linear approximation to the more complex to specify or are in risk of being erroneous, disease function and use the substitution index for that resistance being one trait of concern. Piper and Barger approximation; 2 ) develop a quadratic approximation ( 1988) reached a similar conclusion. Reliable eco- for the more complex function and use the appropriate nomic-based objective functions, likely nonlinear, are substitution index; and 3 ) use a direct substitution ideal, but they are not always attainable. approach by replacing PD with EPD in the more Downloaded from jas.fass.org by on May 6, 2011. 2190 HARRIS AND NEWMAN complex function. The last approach is also an stricted indexes. Despite the nonlinearity, EPD and approximation due to neglecting higher-order distribu- means are directly substituted in this function. tional parameters. Gianola and Fernando ( 1986) Economic traits and performance traits are the same point out that, following Cochran’s (1951) conditional except for the possible lack of observations on feed expectation argument, substitution in a quadratic consumption residual. objective function ignores terms involving prediction For broiler chickens, Akbar et al. (1986b) devel- error variances and covariances that vary when there oped a complex income over feed cost function for is differing amounts of information; but these values production of three-way-cross broilers relative to a are often not calculated in practice. Gianola and single grandparent female. This function is basically a Fernando (1986) advocated the potential of Bayesian linear function of several higher-order products of approaches for overcoming difficulties of nonlinearity terms (cubic and higher). Thus, linear and quadratic in objective functions. However, this theoretical ap- approximations were developed to facilitate use of proach has yet to achieve practical application. EPD substitutions. Feed consumption is included for Ponzoni and Newman (1989) and Newman et al. broilers and might be recorded as a performance trait (1992), for beef cattle, preferred the linear approxi- but likely would be predicted as an indirect EPD with mation approach, in spite of a possible increase in high correlations to weight ( a t a fured age) and accuracy from a quadratic approximation. Akbar et al. composition of gain. The limitations are that no (198613) developed a quadratic approximation to a measure of fatness and no measure of parent and more complex profit equation for broilers. Stewart et grandparent feed costs are included. Economic weights al. (1990), for swine, used the third approach, but the differ among the three strains going into the three- departures from a quadratic function seemed minor. way cross, with most emphasis on female reproduction To summarize this section, the efforts of both traits in the maternal strains relative t o none in the breeders and producers are directed by their profit paternal strain. motives. The breeders should query the economic Swine. Stewart et al. (1990) developed a profit concerns of their producer-customers to arrive at a set function for swine considering the expenses and of economic traits and a breeding objective function. In income for a single breeding female and her total life- response, genetic evaluation must expand to include time progeny. Routine performance data for number of indirect EPD for economic traits not included in the matings, failed matings and conceptions, time to set of performance traits. From these, a selection successful rebreeding, and so on, were not included. index to guide breeders’ selections should be devel- The objective only describes the economics of the oped. Objectives and index functions might differ interval between mating and weaning for a single between sets of customers of different breeders, but parity. Economic and performance traits for two each breeder should have only one function t o direct portions of the life cycle, those involved in sow-herd change in his or her herd or flock. reproduction and lactation and those involved in postweaning growth, are considered separately be- Example Objectives cause sire and dam breeds influence them differently. The mathematical form, development, and traits The postweaning function (PWI) represents a involved in breeding objective functions published single growing animal from weaning at 6.8 kg to since 1980 vary considerably. None is perfect; all have marketing at 105 kg. The sow-herd function been developed with some omissions, usually for represents the period from conception to weaning for practical expediency, due to lack of parameter esti- one sow but incorporates the PWI of each weaned mates or exclusion from current data recording animal in the litter. Alternative indexes are developed systems. Poultry objectives tend to be more inclusive for different objectives pertaining to a breed’s use in and complex. Comparison between classes of livestock commercial crosses. The terminal sire index ( T S I ) is suggest future enhancements in some classes. simply a numerical coding of the postweaning function Poultry. Fairfull et al. (1991) presented an income (PWI) to deviate from a mean of 100. An indirect over feed cost function for layer chickens with seven EPD is used for feed per kilogram of gain, which is traits. This function is decidedly nonlinear and has predicted from the genetic variances and covariances curvilinear relationships between egg value and with backfat and days to 105 kg. Because number weight, between egg value and Haugh units ( a weaned is multiplied by the other traits, the sow-herd measure of albumen quality), and between probability subobjective is close to being a quadratic function. The of breakage and specific gravity ( a measure of shell departure from the quadratic form comes from the thickness). Feed expenses are predicted from body nature of days to weaning as a ratio fimction of weight, egg weight, and egg number, but the function number weaned and 21-d litter weight. Because this allows for residuals of the predictions if feed consump- was approximately a quadratic function, it seemed tion is observed. This function incorporates an inter- expedient to still use the substitution principle. mediate optimum for egg weight, which motivated However, a crossbred mean needs to be added to each Kempthorne and Nordskog (1959) to develop re- EPD prior to substitution. With such a substitution, Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2191 the sow-herd subobjective with coding to a mean of Numerous genetic correlations, heritabilities, and 100 gives a Maternal Line Index (MLI). A general phenotypic correlations need to be accurately esti- purpose index ( G P I ) is obtained as an average of MLI mated to predict the necessary indirect EPD for the and TSI, which is appropriate for breeds used in economic traits from observations on the performance rotational crossbreeding systems. These alternative traits. The proposed evaluation system allows con- indexes are practical implementations of Moav’s siderable flexibility for recording or not recording (1966a,b,c) concept of specialized sire and dam lines. individual performance traits. Of course, the more Sheep. The breeding objective developed by Ponzoni traits that are recorded for candidates and relatives, (1986) is designed for the Australian wool industry. It the greater the accuracy will be. is a linear approximation for a 1,000-ewe flock profit ar D i y Cattle. Breeding objectives for dairy animals function. The objective function can be used for are of special interest because longevity is sometimes substitution of within-flock EBV coming from a quite incorporated into the economic concerns. Effort has flexible data recording program in association with also gone into including feed costs, though they are wool testing (Brien and Kent, 1992). Economic traits not yet incorporated into indexes published for dairy for feed intake are indirectly predicted from geneti- sires and cows. Balaine et al. (1981) presented a cally correlated traits. Full pedigree records are often lifetime phenotypic profit function including 12 traits. not available in Australian sheep breeding operations Several of the terms involve the product of number of and across-flock evaluations are not attempted, al- lactations in the productive life multiplied by the though some sire referencing schemes are in place. average value for a lactation. Thus, this equation is WOOLPLAN, the Australian performance recording quadratic and substitution of EPD can be directly service for the Merino and allied breeds (Ponzoni, applied. Several traits, including number of lactations, 19871, uses a simplified version of Ponzoni’s (1986) herd life, number of mastitis treatments, and number objective. of breeding services have not been included in past Simm and Dingwall (1989) developed indexes for data recording services but may be in the future. meat-type sheep. Simm et al. (1987b) developed Noting that feed energy costs differ for fat and protein indexes for lean meat production in New Zealand components of milk, Allaire and Thraen (1985) sheep. LAMBPLAN is another Australian evaluation developed a linear function for output adjusted for program (Banks, 1990) designed for breeds used as feed cost including prediction of energy costs as a terminal sires for lamb production. Their breeding hnction of fat and protein yield. This function is for objective for lean growth is 3.3.weight ( k g ) - 10.0-fat just one lactation. Allaire and Gibson (1992) recently depth ( m m ) with a high growth option for selection on developed a linear breeding objective to improve an weight alone and a high lean option with selection economic efficiency ratio. A difficulty is that longevity against fatness. This form of presentation facilitates in a dairy herd is likely to be related to level of use with EPD. The basis for this weighted function is performance (Allaire and Cunningham, 1980). Allaire not presented in available publications but is a desired and Gibson (1992) offer an approach to this concern gains approach (Banks, 1992, personal communica- that should be considered by meat animal geneticists. tion). This is the adjustment of cow herd-life for lactation Beef Cattle. In the United Kingdom, Simm et al. milk yield to give a genetic trait in the objective. (1986) developed selection indexes to increase the Gibson et al. (1992) developed several alternative efficiency of lean meat production in beef cattle. Their indexes for varied current and proposed component breeding objective and selection indexes included food pricing arrangements for Canada. Even though opti- conversion efficiency. This was followed by work from mum indexes differ for differing situations, these Australia (Ponzoni and Newman, 1989), New authors note that industry efficiency justifies using a Zealand (Newman et al., 19921, and Canada (Enns et single index. al., 1992; MacNeil and Newman, 1992; MacNeil et al., Another approach for dairy is that of Strandberg 1992). Newman and MacNeil (1992, unpublished ( 1992a,b), who calculated two lifetime functions, net data) have developed a breeding objective function for income per year of productive life and lifetime income a typical beef production system of the northern Great divided by costs. These are used as alternative single- Plains of the United States. This function is presented trait objectives. Linear selection indexes were devel- in Harris and Newman (1992). The target production oped that include early-in-life measurements. Al- system is spring calving, weaning at 6 mo, grazing for though this study was informative about the relation- approximately 200 d to gain .57 kg gain per day, then ships between alternative indexes, these indexes do feedlot growth to gain 1.36 kg per day for 135 d to not allow incorporation of unequal amounts of infor- yield slaughter animals around 545 kg at approxi- mation from relatives through substitution of EPD mately 18 mo of age. A linear approximation of the into an economic function. Such an economic function profit function for a single annual reproduction cycle is can probably be developed from the data described by used. The set of economic traits is quite different from Strandberg (1992a). The calculations are similar t o the set of performance traits that might be evaluated. those of Balaine et al. (1981) but the data set is Downloaded from jas.fass.org by on May 6, 2011. 2192 HARRIS AND NEWMAN larger so that genetic rather than just phenotypic for predicting feed from body weight, egg weight, and relationships might be estimated. rate of lay. Stewart et al. (1991) included extra feed for number weaned and days to weaning in swine. Common Structure Ponzoni (1986) included ewe feed intake and off- spring feed intake as economic traits to be predicted The preceding discussion emphasized several differ- indirectly from other traits. For dairy, Allaire and ences among the several objectives and indexes for Thraen (1985) deducted energy costs for fat and different classes of livestock. However, definite protein content of milk. similarities in structure are also noted. All classes Stewart et al. (1990) included feed per unit of gain have one or more products; all have inputs of breeding and days per unit of gain to a fixed market weight at stock and feed. Facilities, labor, and scheduling are 105 kg to account for offspring feed costs and offspring involved in each. Weather and pathogens might have labor and facilities costs, respectively. For most other disturbing influences in all. The major economic objectives, age a t marketing and at weaning are categories of concern are 1) product income, 2 1 female considered fixed, so labor and facilities costs are fixed. parent expenses, and 3 ) progeny growth expenses. Of Akbar et al. (1986b) provide the only example with course, when eggs or milk are the primary products, livability included in the objective function with progeny growth is absent or secondary. Subdividing broiler, grower, and layer livability included. Although these further, product income usually involves a usually considered lowly heritable, such traits are number of progeny per parent, b ) product per progeny, economically important in other classes but might be and c) quality of product. Examples of component evaluated as herd-life as in Allaire and Gibson ( 1992) traits for progeny per parent are rate of lay, settabil- for dairy. ity, fertility, and hatchability for broilers (Akbar et al., 1986b), number born alive and number weaned in Single vs Multiple Objectives litter for swine (Stewart et al., 1990), weaning rate for beef (Newman et al., 19921, and number of Poultry and swine production, and t o a lesser lactations and age at first calving for dairy (Balaine et degree dairy production, have evolved t o be more al., 1981). Example economic traits for the product intensive with confinement housing, artificial ventila- per progeny category are age at first egg and rate of tion, and prepared rations. These systems facilitate lay for layers (Fairfull et al., 1991), body weight and more control over the nutritional and environmental dressing percentage for broilers (Akbar et al., 1986b3, conditions under which the animals function. Under fleece weight at certain stages of life for wool sheep such systems, the objective when purchasing breeding (Ponzoni, 19861, and weaning weight (Newman et stock is to obtain a high potential for reproductive, al., 1992) for beef and annual milk production for growth, and(or) lactation performance. This is fol- dairy. The quality of product category includes traits lowed by the producer attempting to provide the for specific gravity, egg weight, and Haugh units for nutritional and environmental conditions to support layers (Fairfull et al., 19911, dressing percentage and the greater performance of resulting progeny. Thus, percentage of breast-thigh-leg for broilers (Akbar et breeding improvement, to a large degree, can be al., 1986b), backfat for swine (Stewart et al., 19901, accomplished by a single objective for increased fiber diameter for wool (Ponzoni, 1986), and percent- efficiency of production. Of course, when terminal age of fat and protein for dairy (Allaire and Thraen, crossing systems are used, breeds contributing to the 1985). Traits reflecting products in addition to the production of parent females merit greater economic primary one are body weight for spent layers (Fairfull emphasis upon traits relating to the efficiency of et al., 19911, body weights for wool sheep (Ponzoni, female reproduction. By contrast, breeds producing 19861, and calf weight and final cow weight for dairy terminal sires justify more emphasis on male (Balaine et al., 1981). reproduction and the economic importance of traits of For the two categories of parent female costs and efficient growth is greater than for maternal breeds. progeny costs, each has often been subdivided into Beef cattle and sheep, especially parent stock, are feed costs as a function of feed weight and into labor usually produced under more extensive conditions that and facilities costs as a function of time. Labor and use range grasses. Thus they are subject to extremes facilities costs for parent females are often considered of temperature and availability of forages. Interac- constant for a fixed period of time (such as an annual tions of breed differences with environment are cycle), but Stewart et al. (1990) included days to important with Bos indicus breeds adapted to tropical weaning for swine and Balaine et al. (1981) included or subtropical conditions. With numerous variations of charges for number of mastitis treatments, number of production systems, there might be several breeding breeding services, and herd-life for dairy. Ponzoni and objectives, one for each production and marketing Newman (1989) and Newman et al. (1992) itemized system, with a regional basis or other bases for some specific marketing and husbandry costs in their differentiating production systems. Unfortunately, re- objectives for beef. Fairfull et al. (1991) directly search has not specified these production systems or considered feed for layers as a cost but with an option described how breeding objectives differ between Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2193 them. All producers should have the specific objective Barwick and Hammond, 1990; Brash et al., 1990; when purchasing breeding stock of improving the Fenwick et al., 1991) to a computerized procedure for profitability of their operations. The several producer- assisting breeders to develop beef objectives in Austra- customers of a breeder may have somewhat different lia, termed B-OBJECT. This is used in conjunction objectives, but the breeder needs a single objective with the national beef genetic evaluation scheme, (direction) to guide selection. Normally this objective BREEDPLAN. Features of the B-OBJECT procedure would be the average of the objectives of the cus- include partial budgeting, cost calculations, sales tomers. Of course, breeders strive to obtain more pricing from recent history, discounting costs and customers and are likely to prefer a broad-purpose incomes for differences in time, and several alterna- objective so as t o include a wide mix of potential tives to accommodate ways of managing forage customers. They can satisfy differences in needs of availability. To quote one of the developers (Barlow, customers from the genetic variability that occurs in 19891, “Customization should ensure the greatest the progeny of selected parents. adoption and commitment to use of breeding objec- There are genotype-environment interactions for tives.,, numerous traits as expressed in various production systems. More study is needed to clarify whether the Role of Simulation Modeling differences in objectives justify segmentation of the U.S. beef or sheep industries and use of different Simulation modeling has received considerable objective functions within breeds for specialized attention in the last 20 yr. Many studies have focused production systems (different pyramids). Objectives on the impact of genetic improvement programs on differing between breeds according to how the breeds livestock production systems. Simulation studies of are used in crossbreeding systems seem clearly this nature in beef are by Wilton et al. (1974), Morris justified. The pertinent question for a breeder is et al. (19761, Sanders and Cartwright (1979a,b), whether greater genetic improvement can be made for Notter et al. (1979a,b,c), Congleton and Goodwill a specific objective ( t o achieve more breeding stock (1980a,b,c), Bourdon and Brinks (1987a,b,c), and sales or sales at a higher price from a narrower base of Lamb et al. (1992a,b,c). Similar studies in swine potential customers) than for a more general objective include that of Tess et al. (1983a,b,c),De Roo (19871, for a broader base of potential customers. The answer De Vries (1989a,b,c), De Vries et al. (1989, 1990a,b), requires a thorough analysis of variability in profit De Vries and van der Steen (19901, Pomar et al. objectives of producer-customers across the livestock (1991a,b,c), and Faust et al. (1992a,b). Two series of industry that are served by a group of breeders. This studies in sheep are by Blackburn and Cartwright requires the definitions of breeding objectives for (1987a,b,c) and Wang and Dickerson (1991a,b,c). several segments of the industry to assess differences. Specific questions addressed by these simulation studies vary considerably, but all involve the interface Customized Indexes between genetic improvement and production systems for livestock. The profit functions discussed as breed- Breeding objectives, especially for ruminants, might ing objectives in this paper involve the same interface be specific for an individual breeder and be oriented to and might be described as single-equation models. A the needs of the group of producers that are the single equation is necessary for index construction. customers. Then, the breeders and producers should With the single-equation form, there is risk of develop the objectives to direct their efforts (Willham, oversimplification. 1979). Because of the mathematical nature and Simulation modeling offers potential for more economic content, few livestock breeders, except for detailed and mechanistic understanding of the inter- some corporate poultry and swine geneticists, have face between breeding and production. Tess et al. themselves developed breeding objectives as precise as (1983a,b,c) and Wang and Dickerson (1991a,b,c) those discussed. thoroughly studied the economic impact of fundamen- The Australians have responded to the dilemma tal biological traits on production systems of swine that breeders should develop objectives but geneticists and sheep, respectively. The tangent slopes of curves have the combination of technical abilities and moti- describing the relationships studied could be used as vation to develop objective functions. Stewart et al. relative economic values in a linear approximation to ( 1988) adapted the STAGES objective functions and the models and thus become a linear objective indexes to Australian economics but allowed a weight- function. In addition, with simulation, the question of ing of the sow-herd subobjective and the postweaning the economic value of genetic changes can be inter- subobjective according to anticipated future market- related to questions concerning the overall design of ings. Also, they developed a computer procedure breeding program. Multi-stage selection, integration of allowing modification of economic values for specific selection and crossbreeding, and the system for conditions of the breeder and the anticipated cus- disseminating genetic improvement all seem appropri- tomers. This approach is termed “customized indexes.” ate for further study by simulation modeling. In The customized index concept has been extended particular, the papers referenced above by Faust, (Upton et al., 1988; Barlow, 1989; Upton, 1989; Lamb, De Roo, and De Vries and their co-authors Downloaded from jas.fass.org by on May 6, 2011. 2194 HARRIS AND NEWMAN focused on optimum industry structure and dissemina- 4 are all needed to achieve the full potential for tion. Design of production systems that efficiently use genetic improvement in a relevant direction. For this genetically improved livestock performance is also a improvement to reach production herds or flocks, a pertinent use of simulation. For example, in intensive dissemination system (aspect 3 ) is necessary. Defi- production systems for poultry and swine, changes in ciencies in any one of the four reviewed aspects of the diets are often required as performance potentials breeding limit the impact of the other three. change. If the design of a breeding program is complete, synergistic combinations of the four will be chosen (Harris et al., 1984). Bichard (1987) and Webb System Integration (1989) discussed those four components from the perspective of large corporate swine breeders mer- It seems clear that all four avenues of inquiry chandizing seedstock to producers. A large corporation reviewed here are relevant and pertinent components with breeding, production, and marketing integrated of effective animal breeding programs. The four basic together has the view presented by Nicoll (1989, components of an effective breeding industry, what- 1990). Difficulties occur for implementation in the ever the class of livestock or location, are breeding industries of some countries, such as the United States, where loosely organized small breeders 1. computerized statistical procedures to use the are sources of breeding stock for cattle, sheep, and recorded performance and pedigree information to sometimes swine. When there are many small herds accurately predict genetic differences in the eco- or flocks participating in the industry, the appropriate nomic traits dissemination structure will likely be decided by 2. a systematic testing and selection scheme industry participants in the marketplace. This struc- designed to optimize selection intensity and gener- ture is more naturally achieved when the breeding ation interval and to record relevant performance objective is mutually agreed on and the conceptual traits closely associated with the components of pyramid is closely related to the economic evaluation economic concern to livestock producers of individual animals. A good example of many 3. a dissemination structure to disseminate and breeders and producers organizing together and im- expand the genetic improvements resulting from plementing these four aspects is the group breeding selection at the apex of the pyramid down through schemes of Australian Merino breeders supported by the intermediate tiers. The expansion and pay- the testing and data services of WOOLPLAN directed ment for value of genetic products must provide toward breeding objectives such as those defined by adequate leverage to profitably reimburse the Ponzoni (1991/92). However, there are reports (Pon- breeder(s) for their efforts. The last component is zoni, 1992) of difficulties sometimes occurring due to needed to properly orient the other three. differing objectives for different environments, chang- 4. an economic breeding objective function that ing objectives over time, and differing perceptions of reflects the impact on the economy of production of objectives by breeders in a group. Nicoll (1993, their respective products by livestock producers, personal communication) reports similar difficulties including cases in which the production animals for group breeding schemes in New Zealand. are crossbred. The free enterprise business viewpoint is that, in a coordinated industry, selection by the breeder on the Dickerson and Hazel (1944) pointed out that the appropriate index should genetically improve the rate of genetic improvement involves four components: breeder’s herd in traits of economic benefit to his intensity of selection, accuracy of selection, generation producer-customers. When producer-customers pur- interval, and genetic variability. The first three will chase breeding stock (or other genetic products), they likely differ for each of the four selection paths also practice selection, both among breeders and described by Rendel and Robertson (1950). The among breeding animals for sale within a herd. If the economic breeding objective specifies which traits combination of genetic improvement in breeders’ herds contribute in what degree to the pertinent genetic and the selection by producer-customers is in a n variability that is the real object of selection. The appropriate economic direction, economic improve- testing and selection scheme determines the intensity ment in the functioning of production will result. But of selection and the generation interval for each of the the producer should be sharing the resulting economic four paths. Genetic prediction procedures determine improvement in his herd by paying for his breeding the accuracy of selection for each of the four paths. stock purchases in relation to the increased value of This involves not only the accuracy of the EPD for the improved genetic product. With this increased each performance trait but also the accuracy of income, the successful breeder is rewarded and predicting non-recorded economic traits and the ac- motivated to continue his efforts at genetic improve- curacy of combining them. Because the rate of ment. Such synergistic partnerships between breeders improvement involves products of accuracy, variabil- and producers seems likely to increase in the future as ity, and intensity divided by interval, aspects 1,2, and the industries become more business-oriented. Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2195 To facilitate techniques such as semen or embryo Further studies are warranted but the approach may collection, elite animals may need t o be brought need to be in response to organizational and opera- together at one or more facilities. When breeding is tional concerns as well as statistical concerns. For carried out in conjunction with a pathogen control now, any differences in effectiveness are judged to be program, as is frequent in poultry and swine, one-way minor relative to the advantages accruing from an flow of genetic material down the pyramid may be economically sound breeding objective. advantageous. On the other hand, an open nucleus scheme is genetically advantageous when the animal’s position in the hierarchy is related more to genetic Literature Cited prediction for the breeding objective function than to herd location. For example, elite dairy cows may occur Akbar, M. K., N. R. Gyles, and C. J. Brown. 1986a. Theory and in any milk recording herd. After they are recognized application of selection indices for the improvement of modern poultry stocks. Arkansas Agric. Exp. Sta. Bull. 888. as having this status, the possibility of moving them Akbar, M. K., D. L. Harris, and C. R. Arboleda. 1986b. Development to a nucleus herd depends on the technologies to be of the relative economic weights for linear and quadratic bi- used for further reproduction and on the risks to oeconomic objectives in commercial broilers. Poult. Sci. 65:1834. pathogen control programs used in producing breeding Allaire, F. R., and E. P. Cunningham. 1980. Culling on low milk stock. Disease problems are also noted (Ponzoni, yield and its economic consequences for the dairy herd. Livest. Prod. Sci. 7:349. 1993, personal communication) for the open nucleus Allaire, F. R., and J . P. Gibson. 1992. Genetic value of herd life schemes of Australian Merinos. With open schemes, adjusted for milk production. J. Dairy Sci. 75:1349. introduction into the elite herd or flock might require Allaire, F. R., and C. S. Thraen. 1985. Prospectives for genetic extraordinary procedures for pathogen control. improvement in the economic efficiency of dairy cattle. J . Dairy The four aspects of animal breeding will be Sci. 68:3110. h e r , P. R., and G. C. Fox. 1992. Estimation of economic weights in modified in form, but all will remain pertinent, if and genetic improvement using neoclassical production theory: An when new techniques become practical. Enhanced alternative to rescaling. Anim. Prod. 54:341. reproduction techniques (e.g., multiple ovulation, Arboleda, C. R., D. L. Harris, and A. W. Nordskog. 1976a. Efficiency embryo transfer, cloning) will increase the intensity of of selection in layer-type chickens by using supplementary selection and possibly increase the rate of dissemina- information on feed consumption. I. Selection index theory. Theor. Appl. Genet. 48:67. tion down the tiers of the pyramid. Improvement in Arboleda, C. R., D. L. Harris, and A. W. Nordskog. 1976b. Efficiency the accuracy of selection (e.g., through marker- of selection in layer-type chickens by using supplementary assisted selection from gene mapping) may increase information on feed consumption. 11. Application to net income. the rate of genetic improvement at the apex but Theor. Appl. Genet. 48:75. recording, evaluation, expansion, and dissemination Arnason, T. 1982. Prediction of breeding values for multiple traits in small non-random mating (horse) populations. Acta Agric. will still be necessary. Gene insertion (e.g., transgen- Scand. 32:171. ics) might someday give a different form of genetic Arthur, J . A. 1986. A evaluation of industry breeding program for improvement, but recording, evaluation, expansion, egg type chickens. In: G . E. Dickerson and R. K. Johnson ( E d . ) and dissemination will still be needed. For these Proc. Third World Cong. Genet. Appl. to Livest. Prod. p 325. Univ. of Nebraska, Lincoln. future possibilities, economic breeding objectives will Balaine, D. S., R. E. Pearson, and R. H. Miller. 1981. Profit func- still be relevant to facilitate decisions concerning tions in dairy cattle and effect of measures of efficiency and modification of genetic improvement and production prices. J. Dairy Sci. 64:87. systems and for performing benefit-cost assessment. Bandy, T. R., D. L. Harris, and T. S. Stewart. 1991. Empirical index development for bioeconomic objectives in a three-way cross in mice. J. Anim. Breed. Genet. 108:123. Banks, R. 1990. LAMBPLAN: An integrated approach t o genetic Implications improvement for the Australian lamb industry. Proc. Aust. Assoc. h i m . Breed & Genet. 8:237. Genetic evaluation for an economic breeding objec- Barlow, R. 1987. An introduction to breeding objectives for livestock. tive will facilitate the sorting of breeding animals into Proc. Aust. Assoc. Anim. Breed & Genet. 6:162. Barlow, R. 1989. Breeding objectives-current state of knowledge. In: the proper tiers of the pyramid-shaped dissemination S. Newman ( E d . ) Proc. A.P.C. Committee Workshop on Beef structure. Striving for payment-for-value as described Cattle breeding, Mt. Gambier, South Australia. by this genetic evaluation will motivate the industry Banvick, S. A,, and K. Hammond. 1990. Apportioning emphasis to function to its potential for providing improved between the seed-stock producer and user in establishing the genetic products to the production portion of the breeding objective. Proc. Aust. Assoc. Anim. Breed & Genet. 8: 79. industry. Economic orientation of genetic evaluation Bennett, G. L., G. E. Dickerson, T. S. Kashyap, and J.A.B. Emsley. with consensus by many industry participants as to its 1981. Effectiveness of multiple trait index progeny-test selec- validity is an alternative to corporate ownership for a tion for field performance of strain-cross layers. 11. Predicted progressive breeding industry. However, consensus and realized responses. Poult. Sci. 60:22. does not come easily. Uncertainties and differences of Berger, P. J., G. R. Luecke, and J . A. Hoekstra. 1989. Iterative algorithms for solving mixed model equations. J . Dairy Sci 72: opinion remain about the accuracy and the operational 514. expediencies of linear, quadratic, or other approxima- Bichard, M. 1971. Dissemination of genetic improvement through a tions to nonlinear and nonadditive objective functions. livestock industry. Anim. Prod. 13:401. Downloaded from jas.fass.org by on May 6, 2011. 2196 HARRIS AND NEWMAN Bichard, M. 1987. Problems of across-population genetic evaluation De Vries, A. G. 1989a. A method to incorporate competitive position within improvement schemes. Proc. Aust. Assoc. h i m . Breed.. in the breeding goal. Anim. Prod. 48:221. & Genet. 6:103. De Vries, A. G. 198913. A model to estimate economic values of traits Blackburn, H. D., and T. C. Cartwright. 1987a. Description and in pig breeding. Livest. Prod. Sci. 21:49. validation of the Texas A & 11 sheep simulation model. J . 1. De Vries, A. G. 1989c. Selection for production and reproduction Anim. Sci. 65:373. traits in pigs. Thesis. Wageningen, The Netherlands. Blackburn, H. D., and T. C. Cartwright. 198713. Simulated genotype, De Vries, A. G., and H.A.M. van der Steen. 1990. Optimal use of environment and interaction effects on performance characters nucleus and testing capacity in a pig breeding system with sire of sheep. J. Anim. Sci. 65:387. and dam lines. Livest. Prod. Sci. 25:217. Blackburn, H. D., and T. C. Cartwright. 1987c. Simulated produc- De Vries, A. G . , H.A.M. van der Steen, and G. de Roo. 1989. Optimal tion and biological efficiency of sheep flocks in a shifting en- population size and sowhoar ratio in a closed dam line of pigs. vironment. J . Anim. Sci. 65:399. Livest. Prod. Sci. 22:305. Boldman, K. G., and L. D. Van Vleck. 1991. Derivative-free re- De Vries, A. G., H.A.M. van der Steen, and G. de Roo. 1990a. Effects stricted maximum likelihood estimation in animal models with of family size in selection and testing in a closed dam line of a sparse matrix solver. J . Dairy Sci. 74:4337. pigs. Livest. Prod. Sci. 24:47. Bourdon, R. M., and J . S. Brinks. 1987a. Simulated efficiency of De Vries, A. G., H.A.M. van der Steen, and G. de Roo. 199Ob. Multi- range beef production. I. Growth and milk production. J. Anim. stage selection in a closed dam line of pigs. Livest. Prod. Sci. 24: Sci. 65:943. 161. Bourdon, R. M., and J . S. Brinks. 198713. Simulated efficiency of Dickerson, G. E. 1970. Efficiency of animal production-modeling the range beef production. 11. Fertility traits. J. Anim. Sci. 65:956. biological components. J . Anim. Sci. 302349. Bourdon, R. M., and J . S. Brinks. 1987c. Simulated efficiency of Dickerson, G. E. 1976. The choice of selection objectives in meat range beef production. 111. Culling strategies and nontradi- producing animals. In: D. Lister, D. N. Rhodes, V. R. Fowler, tional management systems. J . h i m . Sci. 65:963. and M. F. Fuller ( E d . ) Meat Animal Growth and Productivity. Brascamp, E. W. 1978. Methods on economic optimization of animal p 449. NATO Advanced Study Institutes Series A Life breeding plans. Rapport B-134, Res. Inst. for Anim. Husb. Sciences. “Schoonoord,” Zeist, The Netherlands. Dickerson, G. E. 1982. Principles in establishing breeding objectives Brascamp, E. W., C. Smith, and D. R. Guy. 1985. Derivation of in livestock. In: R. A. Barton and W. C. Smith ( E d . ) Proc. economic weights from profit equations. Anim. Prod. 40:175. World Congr. Sheep and Beef Cattle Breed. 1:s. Brash, L. D., A. S. del Bosque Gonzalez, and J. R. Fenwick. 1990. Dickerson, G. E., and L. N. Hazel. 1944. Effectiveness of selection on Breeder-customised economic values for beef traits. Proc. Aust. progeny performance as a supplement to earlier culling in Assoc. Anim. Breed. & Genet. 8:111. livestock. J. Agric. Res. 69:459. Brien, F. D., and J. H. Kent. 1992. A software package for data Dickerson, G. E., N. Kunzi, L. V. Cundiff, R. M. Koch, V. H. processing by WOOLPLAN-accredited fleece testing laborato- Arthaud, and K. E. Gregory. 1974. Selection criteria for effi- ries. In: T. Rudder ( E d . ) Proc 10th Aust. Assoc. Anim. Breed. cient beef production. J . Anim. Sci. 39:659. Genet. 10:527. Dickerson, G. E., and R. L. Willham. 1983. Quantitative genetic Bright, G. 1991. Economic weights from profit equations: Appraising engineering of more efficient animal production. J . Anim. Sci. their accuracy. Anim. Prod. 53:395. 57(Suppl. 2):248. Carrick, M. J . , and R. W. Ponzoni. 1991. Including disease resis- Eisen, E. J. 1992. Restricted index selection in mice designed to tance in breeding objectives. In: G. D. Gray and R. R. change body fat without changing body weight: Direct Woolaston ( E d . ) Breeding for Disease Resistance in Sheep. p responses. Theor. Appl. Genet. 83:973. Emsley, A,, and G. E. Dickerson. 1974. Predicting a 12-trait ag- 147. Australian Wool Corp., Melbourne. gregate breeding value from indexes composed of fewer than 12 Carter, A. H. 1982. Efficiency of production in the pasture-animal traits in egg production chickens. Proc. XV World Poult. Cong. grazing complex. In: R. A. Barton and W. C. Smith ( E d . ) Proc. p 157. World Cong. Sheep and Beef Cattle Breed. 1:23. Emsley, A., G. E. Dickerson, and T. S. Kashyap. 1977. Genetic Clarke, J. N., and A. L. Rae. 1977. Technical aspects of the National parameters in progeny test selection for field performance of Sheep Recording Scheme (SHEEPLAN). Proc. N. Z. SOC. i m . h strain-cross layers. Poult. Sci. 56:121. Prod. 37:183. Enns, R. M., M. D. MacNeil, S. Newman, and J . Stewart-Smith. Cochran, W. G. 1 951. Improvement by means of selection. Proc. 1992. Relative economic values for specialized sire and dam Second Berkeley Symp. Math. Stat. and Prob. p 449. lines in Canada. Proc. West. Sect. Am. SOC. Anim. Sci. 43:124. Comstock, R. E., H. F. Robinson, and P. H. Harvey. 1949. A breeding Fairfull, R. W., A. J. McAllister, and R. S. Gowe. 1991. A profit procedure designed to make maximum use of both general and function for white leghorn layer selection. Proc. Natl. Breeders’ specific combining ability. Agron. J. 41:360. Roundtable 40:36. St. Louis, MO. Congleton, W. R., Jr., and R. E. Goodwill. 1980a. Simulated compari- Faust, M. A,, 0. W. Robison, and M. W. Tess. 1992a. Genetic and sons of breeding plans for beef production. I. A dynamic model economic analyses of female replacement rates in the dam- to evaluate the effect of mating plan on herd age structure and daughter pathway of a hierarchical swine breeding structure. J. productivity. Agric. Syst. 5:207. Anim. Sci. 70:2053. Congleton, W. R., Jr., and R. E. Goodwill. 1980b. Simulated compari- Faust, M. A,, M. W. Tess, and 0. W. Robison. 1992b. A bioeconomic sons of breeding plans for beef production. 11. Hereford, Angus simulation model for a hierarchical swine breeding structure. J. and Charolais sires bred to Hereford, Angus and Hereford- Anim. Sci. 70:1760. Angus dams to produce feeder calves. Agric. Syst. 5221. Fenwick, R., J. Allen, S. Barwick, J . Bertram, B. Freer, W. Fuchs, K. Congleton, W. R., Jr., and R. E. Goodwill. 1980~. Simulated compari- Hammond, A. McDonald, D. Nicol, and B. Wilton. 1991. Inter- sons of breeding plans for beef production. 111. Systems for national Breedplan School. 1991 Handbook. September, 1991, producing feeder calves involving intensive culling and addi- Univ. of New England, Armidale, NSW, Australia. tional breeds of sire. Agric. Syst. 5:309. Foulley, J. L., and L. Ollivier. 1987. A note on criteria of coherence Danell, 0. E. 1980. Studies concerning selection objectives in animal for the parameters used to construct a selection index. J. Anim. breeding. V. Consideration of long and short term effects in Breed. Genet. 103:81. defining selection objectives in animal breeding. Thesis. Up- Fowler, V. R., M. Bichard, and A. Pease. 1976. Objectives in pig psala, Sweden. V31. breeding. Anim. Prod. 23:365. De Roo, G. 1987. A stochastic model to study breeding schemes in a Gianola, D., and R. L. Fernando. 1986. Bayesian methods in animal small pig population. Agric. Syst. 25:l. breeding theory. J. Anim. Sci. 63:217. Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2197 Gibson, J. P., and B. W. Kennedy. 1990. The use of constrained Henderson, C. R. 1973. Sire evaluation and genetic trends. Proc. selection indexes in breeding for economic merit. Theor. Appl. h i m . Breed. Genet. Symp. p 10. Am. SOC. Anim. Sci., Cham- Genet. 80:801. paign, IL. Gibson, J . P., N. Graham, and E. B. Burnside. 1992. Selection Henderson, C. R. 1976. A simple method for computing the inverse indexes for production traits of Canadian dairy sires. Can. J. of a numerator relationship matrix used in prediction of breed- Anim. Sci. 72:477. ing values. Biometrics 3250. Givens, S. C., Jr., R. C. Carter, and J . A. Gaines. 1960. Selection Henderson, C. R. 1987. Statistical methods in animal improvement: indexes for weanling traits in spring lambs. J . Anim. Sci. 19: Historical overview. In: D. Gianola and K. Hammond ( E d . ) 134. Advances in Statistical Methods for Genetic Improvement of Gjedrem, T. 1972. A study on the definition of the aggregate geno- Livestock. p 1. Animal Genetics and Breeding Unit, Univ. of type in a selection index, Acta Agric. Scand. 2 2 : l l . New England, NSW, Australia. Goddard, M. E. 1983. Selection indices for non-linear profit func- Henderson, C. R., H. W. Carter, and J . T. Godfrey. 1954. Use of the tions. Theor. Appl. Genet. 64:339. contemporary herd average in appraising progeny tests of dairy Graser, H.-U., S. P. Smith, and B. Tier. 1987. A derivative-free bulls. J. Anim. Sci. 13:959 (Abstr.). approach for estimating variance components in animal models Henderson, C. R., 0. Kempthorne, S. R. Searle, and C. M. von by restricted maximum likelihood. J. Anim. Sci. 64:1362. Krosigk. 1959. The estimation of environmental and genetic Groen, A. F. 1989a. Economic values in cattle breeding. I. Influences trends from records subject to culling. Biometrics 15:192. of production circumstances in situations without output limi- Henderson, C. R., and R. L. Quaas. 1976. Multiple trait evaluation tations. Livest. Prod. Sci. 22:l. using relatives’ records. J . Anim. Sci. 43:1188. Groen, A. F. 1989b. Economic values in cattle breeding. 11. In- Hill, W. G. 1981a. Design and economics of animal breeding pro- fluences of production circumstances in situations with output grams. Proc. Aust. Assoc. Anim. Breed. & Genet. 2:3. limitations. Livest. Prod. Sci. 22:17. Hill, W. G. 1981b. Assessment of breeding values in selection pro- Groeneveld, E., and M. Kovac. 1990. A generalized computing proce- grams. Proc. Aust. Assoc. Anim. Breed. & Genet. 2:227. dure for setting up and solving mixed linear models. J. Dairy Hinks, C.J.M. 1978. The use of centralised breeding schemes in Sci. 73:513. dairy cattle improvement. Anim. Breed. Abstr. 46:291. Hagedoorn, A. L. 1939 and later editions. Animals Breeding. Crosby Hunton, P. 1990. Industrial breeding and selection. In: R. D. Craw- Lockwood and Son, London. ford ( E d . ) Poultry Breeding and Genetics. p 985. Silver, Am- Harris, D. L. 1964. Expected and predicted progress from index sterdam. selection involving estimates of population parameters. Biomet- Jackson, N., and H. N. Turner. 1972. Optimal structure for a rics 20:46. cooperative nucleus breeding system. Proc. Aust. SOC.Anim. Harris, D. L. 1966. Evaluation of genetic merit in a n hierarchical Prod. 9:55. mating system with unequal numbers. Poult. Sci. 45:1990 James, J. W. 1977. Open nucleus breeding systems. Anim. Prod. 24: (Abstr. j. 287. Harris, D. L. 1970. Breeding for efficiency in livestock production: James, J. W. 1982. Economic aspects of developing breeding objec- Defining the economic objectives. J. h i m . Sci. 30:860. tives: General considerations. In: J.S.F. Barker, K. Hammond, Harris, D. L., and S. Newman. 1992. How does genetic evaluation and A. E. McClintock ( E d . ) Future Developments in the become economic improvement? In: Proc. Symp. on Application Genetic Improvement of Animals. p 228. Academic Press, Aus- of Expected Progeny Differences ( E P Dj to Livestock Improve- tralia. ment. ASAS 84th Annu. Mtg., Aug. 10, 1992, Pittsburgh, PA. p James, J. W. 1987. Breeding objectives for the Merino industry: An 3-1. academic perspective. Proc. Merino Imp. Programs in Austra- Harris, D. L., T. S. Stewart, and C. R. Arboleda. 1984. Animal lia. Leura, NSW, Australia. breeding programs: A systematic approach to their design. Jensen, J., and I. L. Mao. 1988. Transformation algorithms in AAT-NC-8. ARS, USDA, Peoria, IL. analysis of single trait and multitrait models with equal design Hartmann, W. 1985. Random sample poultry tests-their develop- matrices and one random factor per trait: A review. J. Anim. ment and present status in European countries. World’s Poult. Sci. 662750. Sci. J . 41:153. Johnson, D. L., A. L. Rae, and J . N. Clarke. 1989. Technical aspects Hartmann, W. 1988. From Mendel t o multi-national in poultry of the animal plan system. Proc. N. Z . SOC. Anim. Prod. 49:197. breeding. Br. Poult. Sci. 29:3. Kashyap, T. S., G. E. Dickerson, and G. L. Bennett. 1981. Effective- Harville, D. A. 1975. Index selection with proportionality con- ness of progeny test multiple trait index selection for field straints. Biometrics 31:223. performance of strain-cross layers. I. Estimated responses. Hayes, J. F., and W. G. Hill. 1980. A reparameterization of a genetic Poult. Sci. 60:l. selection index to locate its sampling properties. Biometrics 36: Katle, J. 1992. Genetic and environmental consequences of including 237. residual food consumption in a multi-trait selection program for Hazel, L. N. 1943. The genetic basis for constructing selection laying hens. Acta Agric. Scand. 42:63. indexes. Genetics 28:476. Kempthorne, O., and A. W. Nordskog. 1959. Restricted selection Hazel, L. N., and J. L. Lush. 1942. The efficiency of three methods of indices. Biometrics 15:lO. selection. J. Hered. 33:393. Kendall, M. G., and A. Stuart. 1963. The advanced theory of statis- Hazel, L. N., and C. E. Terrill. 1946. The construction and use of a tics. Vol. I. Distribution theory. Charles Griffin and Co., Lon- selection index for range Rambouillet lambs. J. h i m . Sci. 5 4 1 2 don. (Abstr.). Ladd, G. W., and B. E. Melton. 1979. Economic value of genetic Henderson, C. R. 1953. Estimation of variance and covariance com- shifts in the production function. In: G. W. Ladd ( E d . ) Econom- ponents. Biometrics 9:226. ics Department Staff Paper 98: Applications of Economics in Henderson, C. R. 1963. Selection index and expected genetic ad- Plant and Animal Breeding. Iowa State University Press, vance. In: W. D. Hanson and H. F. Robinson ( E d . ) Statistical Ames. Genetics and Plant Breeding. NAS-NRC Publ. 982. p 1. Lamb, M. A,, M. W. Tess, and 0. W. Robison. 1992a. Evaluation of Henderson, C. R. 1966. A sire evaluation method which accounts for mating systems involving five breeds for integrated beef unknown genetic and environmental trends, herd differences, production systems: I. Cow-calf segment. J . Anim. Sci. 70:689. seasonal, age effects, and differential culling. Proc. Symp. on Lamb, M. A., M. W. Tess, and 0. W. Robison. 1992b. Evaluation of Estimating Breeding Values of Dairy Sires and Cows. Washing- mating systems involving five breeds for integrated beef ton, DC. production systems: 11. Feedlot segment. J . h i m . Sci. 70:700. Downloaded from jas.fass.org by on May 6, 2011. 2198 HARRIS AND NEWMAN Lamb, M. A., M. W. Tess, and 0. W. Robison. 1992c. Evaluation of Newman, S., D. L. Harris, and D. P. Doolittle. 1985. Economic mating systems involving five breeds for integrated beef efficiency of lean tissue production through crossbreeding: Sys- production systems: 111. Integrated system. J. Anim. Sci. 70: tems modeling with mice. I. Definition of the bioeconomic objec- 7 14. tive. J. Anim. Sci. 60:385. Lerner, I. M., and H. P. Donald. 1966. Modern Developments in Newman, S., C. A. Morris, R. L. Baker, and G. B. Nicoll. 1992. Animal Breeding. Academic Press, London. Genetic improvement of beef cattle in New Zealand: Breeding Lin, C. Y., and S. P. Smith. 1990. Transformation of multitrait to objectives. Livest. Prod. Sci. 32:111. unitrait mixed model analysis of data with multiple random Nicholas, F. W., and C. Smith. 1983. Increased rates of genetic effects. J. Dairy Sci. 73:2494. change in dairy cattle by embryo transfer and splitting. Anim. Lush, J. L. 1946. Chance as a cause of changes in gene frequency Prod. 36:341. within pure breeds of livestock. Am. Nat. 80:318. Nicoll, G. B. 1989. Performance and financial returns of two Romney Lush, J. L. 1947a. Family merit and individual merit as basis for flocks sired by Waihora or commercial rams. N. Z. J. Agric. Res. selection. Part I. Am. Nat. 81:241. 2:37. Lush, J. L. 1947b. Family merit and individual merit as basis for Nicoll, G. B. 1990. Application of nucleus breeding schemes in a selection. Part 11. Am. Nat. 81:362. corporate setting: Sheep, beef, cattle and deer. Proc. 4th World MacNeil, M. D., and S. Newman. 1992. Relative economic values for Cong. Genet. Appl. Livest. Prod. XV:357. traits affecting profitability of beef production in Canada. Proc. Nicoll, G. B., A. E. Gibson, and D. C. Dalton. 1979. The recording Beef Improv. Fed. 24:40. and data-handling procedures used in the Angus Cattle Breed- MacNeil, M. D., S. Newman, R. M. Enns, and J . Stewart-Smith. ing Programme of the Rotorua Land Development District of 1992. Temporal variation in relative economic values for the Department of Lands and Survey (Mimeo). specialized sire and dam lines in Canada. J. Anim. Sci. Nicoll, G. B., and D. L. Johnson. 1986. Selection applied in the 7O(Suppl. 1):140 (Abstr.). Angus Breeding Scheme of the New Zealand Land Develop- Mallard, J . 1972. La theorie et le calcul des index de selection avec ment and Management Corporation. Proc. 3rd World Cong. restrictions: Synthese critique. Biometrics 28:713. Genet. Appl. Livest. Prod. 1x413. McArthur, A.T.G. 1987. Weighting breeding objectives-an economic Notter, D. R., J. 0. Sanders, G. E. Dickerson, G. M. Smith, and T. C. approach. Proc. Aust. Assoc. Anim. Breed. & Genet. 6:179. Cartwright. 1979a. Simulated efficiency of beef production for a McArthur, A.T.G., and A. S. del Bosque Gonzalez. 1990. Adjustment midwestern cow-calf-feedlot management system. I. Milk of annual economic values for time. Proc. Aust. Assoc. Anim. production. J. Anim. Sci. 49:70. Breed. & Genet. 8:103. Notter, D. R., J. 0. Sanders, G. E. Dickerson, G. M. Smith, and T. C. McClintock, A. E., and E. P. Cunningham. 1974. Selection in dual Cartwright. 1979b. Simulated efficiency of beef production for a purpose cattle populations: Defining the breeding objective. midwestern cow-calf-feedlot management system. 11. Mature Anim. Prod. 18:237. body size. J. Anim. Sci. 49:83. McClintock, A. E., and F. W. Nicholas. 1991. The implications of Notter, D. R., J. 0. Sanders, G. E. Dickerson, G. M. Smith, and T. C. advanced breeding techniques. MRC Proj. US.016 Report. Cartwright. 1979c. Simulated efficiency of beef production for a Melton, B. E., E. 0. Heady, and R. L. Willham. 1979. Estimation of midwestern cow-calf-feedlot management system. 111. Cross- economic values for selection indices. Anim. Prod. 28:279. breeding systems. J. Anim. Sci. 49:92. Meyer, K. 1989. Estimation of genetic parameters. In: W. G. Hill and Osborne, R. 1957a. The use of sire and dam family averages in T.F.C. Mackay ( E d . ) Evolution and Animal Breeding. p 161. increasing the efficiency of selective breeding under a hierar- C.A.B. International, Wallingford, U. K. chal mating system. Heredity 11:93. Miller, P. D. 1981. Artificial insemination organizations. J . Dairy Osborne, R. 1957b. Family selection in poultry: The use of sire and Sci. 64:1283. dam family averages in choosing male parents. Proc. Royal SOC. Miller, R. H., and R. E. Pearson. 1979. Economic aspects of selection. Edinburgh, Sect. B 66:374. Anim. Breed. Abstr. 47:281. Panse, V. G. 1946. An application of the discriminant function for Misztal, I., and D. Gianola. 1987. Indirect solution of mixed model selection in poultry. J. Genet. 47:242. equations. J . Dairy Sci. 70:716. Parker, A.G.H. 1970. The New Zealand Romney development group. Moav, R. 1966a. Specialised sire and dam lines. I. Economic evalua- Wool Technol. Sheep Breed. p 19. tion of crossbreds. Anim. Prod. 8:193. Pasternak, H., and J . I. Weller. 1993. Optimum linear indices for Moav, R. 196613. Specialised sire and dam lines. 11. The choice of the nonlinear profit functions. Anim. Prod. 55:43. most profitable parental combination when component traits Pearson, R. E. 1982. Economic aspects of the choice of a breeding are genetically additive. Anim. Prod. 8:203. objective. Proc. 2nd World Congr. Genet. Appl. Livest. Prod. VI: Moav, R. 1966c. Specialised sire and dam lines. 111. Choice of the 50. most profitable parental combination when component traits Pearson, R. E., and R. M. Miller. 1981. Economic definition of total are genetically non-additive. Anim. Prod. 8:365. performance, breeding goals, and breeding values for dairy Moav, R., and W. G. Hill. 1966. Specialised sire and dam lines. IV. cattle. J. Dairy Sci. 65:857. Selection within lines. Anim. Prod. 8:375. Peart, G. R. 1976. Sociological, economic, business and genetic Moav, R., and J. Moav. 1966. Profit in a broiler enterprise as a aspects of sheep group breeding schemes. In: G. J . James, D. E. function of egg production of parent stocks and growth rate of Robertson, and R. J. Lightfoot ( E d . ) Proc. Int. Sheep Breeding their progeny. Br. Poult. Sci. 7:5. Congress, Muresk, WA. p 188. Western Aust. Inst. Technol. Morns, C. A. 1980. Some benefits and costs of genetic improvement Perth, WA. in New Zealand's sheep and beef cattle industry. N. Z. J. Exp. Pesek, J., and R. J. Baker. 1969. Desired improvement in relation to Agric. 8:331. selection indices. Can. J. Plant Sci. 49:803. Morns, C. A. 1981. Economic values of net reproduction and weight- Piper, L. R., and I. A. Barger. 1988. Resistance to gastro-intestinal for-age for use in genetic calculations applied to Australian beef strongyles: Feasibility of a breeding programme. Proc. 3rd cattle herds. Aust. J. Exp. Agric. Anim. Husb. 21:464. World Cong. Sheep and Beef Breed. p 593. Morris, C. A., J. J. Parkins, and J. W. Wilton. 1976. Effects of creep Pomar, C. P., D. L. Harris, and F. Minvielle. 1991a. Computer feeding, mature cow weight and milk yield on farm gross simulation model of swine production systems: I. Modeling the margins in an integrated beef production model. Can. J. Anim. growth of young pigs. J . Anim. Sci. 69:1468. Sci. 56:87. Pomar, C., D. L. Harris, and F. Minvielle. 1991b. Computer simula- Nagai, J . , M. Yoshida, and M. Naito. 1955. Individual selection of tion model of swine production systems: 11. Modeling body mice by the selection index method. Anim. Breed. Abstr. 26: composition and weight of female pigs, fetal development, milk 347. production, and growth of suckling pigs. J. Anim. Sci. 69:1489. Downloaded from jas.fass.org by on May 6, 2011. BREEDING FOR PROFIT 2199 Pomar, C., D. L. Harris, P. Savoie, and F. Minvielle. 1991c. Com- sociated traits for improvement of a single important trait. puter simulation model of swine production systems: 111. A h i m . Prod. 23:l. dynamic herd simulation model including reproduction. J. Sanders, J . O., and T. C. Cartwright. 1979a. A general cattle produc- h i m . Sci. 69:2822. tion systems model. I: Structure of the model. Agric. Syst. 4 ( 3 ) : Ponzoni, R. W. 1979. Objectives and selection criteria for Australian 217. Merino sheep. Proc. Aust. Assoc. h i m . Breed. & Genet. 1:320. Sanders, J. O., and T. C. Cartwright. 197913. A general cattle produc- Ponzoni, R. W. 1982. Breeding objectives in sheep improvement tion systems model. 11: Procedures used for simulating animal programmes. Proc. 2nd World Congr. Genet. Appl. Livest. Prod. performance. Agric. Syst. 4(3):289. 1:619. Schaeffer, L. R., and B. W. Kennedy. 1986. Computing strategies for Ponzoni, R. W. 1985. Linear approximation of non-linear selection solving mixed model equations. J. Dairy Sci. 69575. indices: An example with Australian Merino sheep. Z. Tierz. Schneeberger, M., S. A. Barwick, G. H. Crow, and K. Hammond. Zuechtungsbiol. 102:395. 1992. Economic indices using breeding values predicted by Ponzoni, R. W. 1986. A profit equation for the definition of the BLUP. J . Anim. Breed. Genet. 109:180. breeding objective of Australian Merino sheep. J. h i m . Breed. Searle, S. R. 1964. Review of sire-proving methods in New Zealand, Genet. 103:342. Great Britain, and New York State. J. Dairy Sci. 47:402. Ponzoni, R. W. 1987. WOOLPIAN-design and implications for the Simm, G., and W. S. Dingwall. 1989. Selection indices for lean meat Merino industry. In: B. J . McGuirk ( E d . ) Merino Improvement production in sheep. Livest. Prod. Sci. 21:223. Programs in Australia. pp 25-40, Australian wool carp., Mel- Simm, G., C. Smith, and J.H.D. Prescott. 1986. Selection indices to bourne. improve the efficiency of lean meat production in cattle. Anim. Ponzoni, R. W. 1988a. Accounting for both income and expense in Prod. 42:183. the development of breeding objectives. Proc. Aust. Assoc. Simm, G., c . Smith, and R. Thompson. 1987a. The use of Product h i m . Breed. & Genet. 755. traits such as lean growth rate as selection criteria in animal Ponzoni, R. W. 198813. The derivation of economic values combining breeding. Anim. Prod. 45:307. income and expense in different ways: ~n example with Aus- Sinim, G., M. J . Young, and P. R. Beatson. 1987b. An economic tralian Merino sheep. J. Anim. Breed. Genet. 105:143. selection index for lean meat production in New Zealand sheep. Ponzoni, R. W. 1991/92. Reducing fiber diameter with new WOOL- h i m . Prod. 45:465. PLAN options. Wool Techno]. Sheep Breed. 34:136. Smith, C. 1959. A comparison of testing schemes for pigs. h i m . Ponzoni, R. W. 1992. Genetic improvement of hair sheep. FA0 Prod. 1:113. Anim. Prod. and Health Paper 101. p 109. Smith, C. 1960. Efficiency of animal testing schemes. Biometrics. 16: Ponzoni, R. W., and R. L. Davies. 1989. An evaluation of biological 408. and conventional pig selection indices, Aust. J, kc. 29: Smith, C. 1964. The use of specialized sire and dam lines in selection 775. for meat production. Anim. Prod. 6:337. ponzoni, R, w,, and D, R. Gifford. 1990, ~ ~breeding ~ Smith, C. 1981. Levels of investment in testing the genetic improve-~ ~ l ~ ~ i ~ tives for Australian Cashmere Goats. J. h i m . Breed. Genet. ment of livestock. Livest. Prod. Sci. 8:193. 107:351. Smith, C., J. W. James, and E. W. Brascamp. 1986. On the deriva- Ponzoni, R. W., and S. Newman. 1989. Developing breeding objec- tion of economic weights in livestock improvement. Anim. Prod. tives for Australian beef cattle production. Anim. Prod. 49:35. 43545. Ponzoni, R. w,, and J , ~ , wa1kley, 1981, Objectives and selection ~ , Smith, H. F. 1936. A discriminant function for plant selection. Ann. criteria for Dorset sheep in Australia. Livest. Prod. Sci. 8:331. Eugen. 7:240. Soller, M., R. Bar-Anan, and H. Pasternak. 1966. Selection of dairy Quaas, R. L. 1976. Computing the diagonal elements and inverse of a large numerator relationship matrix. Biometrics 32:949. cattle for growth rate and milk production. Anim. Prod. 8:109. Stewart, T. S., D. H. Bache, D. L. Harris, M. E. Einstein, D. L. Quaas, R. L., and E. J . Pollak. 1980. Mixed model methodology for Lofgren, and A. P. Schinckel. 1990. A bioeconomic profit func- farm and ranch beef cattle testing programs. J. Anim. Sci. 51: tion for swine production: Application to developing optimal 1277. multitrait selection indexes. J . Anim. Breed. Genet. 107:340. Rendel, J. M., and A. Robertson. 1950. Estimation of genetic gain in Stewart, T. s,, D, massen, and K, Hammond, 1988. Developing a milk yield by selection in a closed herd of dairy cattle. J. Genet. custom selection index for your herd. Anim. Genet. Breed. Unit, 50:l. Univ. New England. p 12. Robertson, A. 1953. A numerical description of breed structure. J. Stewart, T, s,, L, Lofgren, D, L, Harris, M, E. Einstein, and A, p, D, Agric. Sci. 43334. Schinckel. 1991. Genetic improvement programs in livestock: Robertson, A. 1955. Prediction equations in quantitative genetics. Swine testing and genetic evaluation system (STAGES). J. Biometrics 11:95. Anim. Sci. 69:3882. Robertson, A. 1957. Optimum group Size in Progeny testing and Strandberg, E, 1992a. Lifetime performance in dairy cattle. Defini- family selection. Biometrics 13:442. tion of traits and influence of systematic enxironmental factors. Robertson, A., and A. A. Asker. 1951a. The genetic history and Acta Agric. Scand., Sect. A 42:71. breed-structure of British Friesian cattle. Emp. J. ExP. AfiC. Strandberg, E. 199213. Lifetime performance in dairy cattle. Genetic 19:113. parameters and expected improvement from selection. Acta Robertson, A., and A. A. Asker. 1951b. The expansion of a breed of Agric. Scand., Sect. A 42:127. dairy cattle. Emp. J . Exp. Agric. 19:191. Sutherland, T. M. 1958. An index for selecting hogs using data from Ronningen, K. 1971. Selection index for quadratic and cubic models a testing station. Ph.D. Dissertation. Iowa State Univ., Ames. of the aggregate genotype. Meld. No%. Landbmkshogsk. 50:1. Swiger, L. A,, K. E. Gregory, L. J. Sumption, B. C. Breidenstein, and Roux, C. Z., and M. M. Scholtz. 1984. Breeding goals for optimal V. H. Arthaud. 1965. Selection indexes for efficiency of beef total life cycle production systems. In: J . H. Hofmeyr and production. J . h i m . Sci. 24:418. E.H.H. Meyer ( E d . ) Proc. 2nd World Cong. Sheep and Beef Tallis, G. M. 1962. A selection index for optimum genotype. Biomet- Cattle Breed. p 444. South African St,ud Book and Livestock rics 18:120. Improvement Association. Pretoria, South Africa. Tess, M. W., G. L. Bennett, and G. E. Dickerson. 1983a. Simulation Sales, J . , and W. G. Hill. 1976a. Effect of sampling errors on of genetic changes in life cycle efficiency of pork production. I. A effciency of selection indices. I. Use of information from rela- bioeconomic model. J . h i m . Sci. 56:336. tives for single trait improvement. Anim. Prod. 22:l. Tess, M. W., G. L. Bennett, and G. E. Dickerson. 1983b. Simulation Sales, J., and W. G. Hill. 197613. Effect of sampling errors on of genetic changes in life cycle efficiency of pork production. 11. efficiency of selection indices. 11. Use of information on as- Effects of components on efficiency. J . Anim. Sci. 56:354. Downloaded from jas.fass.org by on May 6, 2011. 2200 HARRIS AND NEWMAN Tess, M. W., G. L. Bennett, and G. E. Dickerson. 1 9 8 3 ~Simulation . Webb, A. J . 1989. Animal breeding practice. In: W. G. Hill and of genetic changes in life cycle efficiency of pork production. 111. T.F.C. MacKay ( E d . ) Evolution and Animal Breeding. p 195. Effects of management systems and feed prices on importance C.A.B. International, Wallingford, U.K. of genetic components. J . h i m . Sci. 56:369. Westell, R. A,, and L. D. Van Vleck. 1987. Simultaneous genetic Thompson, R. 1980. A note on the estimation of economic values for evaluation of sires and cows for a large population of dairy selection indexes. Anim. Prod. 31:115. cattle. J. Dairy Sci. 70:1006. Upton, W. H. 1989. The application of selection index procedures to Wiener, G. 1953. Breed structure in the pedigree Ayrshire cattle BREEDPLAN herds. Anim. Genet. Breed. Unit, Dept. of Agric., population in Great Britain. J . Agric. Sci. 43:123. Armidale, NSW, Australia (Mimeo). Wiggans, G. R., and I. Misztal. 1987. Supercomputer for animal Upton, W. H., A.T.G. McArthur, and R. J. Farquharson. 1988. model evaluation of Ayrshire milk yield. J. Dairy Sci. 70:1906. Economic values applied to breeding objectives: A decentralised Wiggans, G. R., I. Misztal, and L. D. Van Vleck. 1988. Implementa- approach for BREEDPLAN. Proc. Aust. Assoc. Anim. Breed. & tion of animal model. J . Dairy Sci. 7l(Suppl. 2):54. Genet. 7:95. Willham, R. L. 1979. Evaluation and direction of beef sire evaluation Van Vleck, L. D. 1964. Sampling the young sire in artificial insemi- programs. J . Anim. Sci. 49592. nation. J. Dairy Sci. 47:441. Wilton, J . W. 1982. Choice of selection criteria in breeding for a Van Vleck, L. D. 1977. Theoretical and actual genetic progress in defined objective. Proc. 2nd World Congr. Genet. Appl. Livest. dairy cattle. In: E. Pollak, 0. Kempthorne, and T. B. Bailey, J r . Prod. VI:60. ( E d . 1 Proc. Intl. Conf. on Quantitative Genet. p 543. Iowa State Wilton, J . W., D. A. Evans, and L. D. Van Vleck. 1968. Selection Univ., Ames. indices for quadratic models of total merit. Biometrics 24:937. Van Vleck, L. D. 1988. Observations on selection advances in dairy Wilton, J . W., C. A. Morris, E. A. Jenson, A. 0. Leigh, and W. C. cattle. In: B. S. Weir, M. M. Goodman, E. J. Eisen, and G. Pfeiffer. 1974. A linear programming model for beef cattle Namkoong !Ed.) Proc. Second Int. Conf. on Quantitative Ge- production. Can. J . Anim. Sci. 54:693. Wilton, J . W., and L. D. Van Vleck. 1968. Selection of dairy cows for net. p 724. Sinauer Associates, Sunderland, MA. economic merit. J . Dairy Sci. 51:1680. Voelker, D. E. 1981. Dairy Herd Improvement Associations. J . Dairy Wilton, J . W., and L. D. Van Vleck. 1969. Sire evaluation for Sci. 64:1269. economic merit. J . Dairy Sci. 52:235. Wang, C. T., and G. E. Dickerson. 1991a. A deterministic computer Wing, T. L., and A. W. Nordskog. 1982a. Use of individual feed simulation model of life-cycle lamb and wool production. J . records in a selection program for egg production efficiency. I. Anim. Sci. 69:4312. Heritability of the residual component of feed efficiency. Poult. Wang, C. T., and G. E. Dickerson. 1991b. Simulation of life-cycle Sci. 61:226. efficiency of lamb and wool production for genetic levels of Wing, T. L., and A. W. Nordskog. 198213. Use of individual feed component traits and alternative management options. J . records in a selection program for egg production efficiency. 11. Anim. Sci. 69:4324. Effectiveness of different selection indexes. Poult. Sci. 61:231. Wang, C. T., and G. E. Dickerson. 1991c. Simulated effects of Wing, T. L., H. Singh, and A. W. Nordskog. 1983. Use of individual reproductive performance on life-cycle efficiency of lamb and feed records in a selection program for egg production eff- wool production a t three lambing intervals. J. Anim. Sci. 69: ciency. 111. Relative effectiveness of two-stage selection. Poult. 4338. Sci. 62:721. 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