|Livestock Research for Rural Development 22 (4) 2010||Notes to Authors||LRRD Newsletter||
Citation of this paper
There are distinct breeds suitable for diverse purposes in the different production environments or ecological zones. Farmers in different production systems have different trait preferences and the strategies followed by them are also as diverse as the agro-environments within which they operate. In order to design a viable breeding plan, farmers’ preferences for the different traits need to be investigated.
In this paper available tools and methods for defining livestock breeding objective traits are described, discussed and comparisons among them are made. The reviewed tools were: participatory rural appraisal (PRA), choice experiments, ranking of animals from own flock/herd and ranking of others animals. Each methodology may be appropriate to specific situation; however, it is recommended that a combination of approaches be used to precisely capture the breeding objective traits of livestock producers. Elucidation of objective traits using the tools with active involvement of producers can result in appropriate livestock genetic improvement that is well grounded in practical reality and truly reflects owners’ preferences.
Keywords: Choice experiments; phenotypic ranking; PRA; production systems
In developing countries livestock production is still mostly subsistence oriented and fulfils multiple functions (Wurzinger et al 2006; Roessler et al 2008). In these countries, a considerable number of livestock breeding programs have failed (Roessler et al 2008). Among others, reasons for the failures include limited involvement of farmers who are the final beneficiaries, in both planning and implementation, leading to ineffective breeding programs. Any development endeavor needs to be aligned to the specific goals of the target communities and production environments. Nevertheless, more often government policies still encourage and promote a small range of specialized ‘improved’ breeds (e.g. distribution of Holstein-Friesian heifers or their crosses to smallholders in Eastern Africa) where potential milk production or very limited production attributes are emphasized instead of due consideration of the broader livestock system functions (tangible and intangible products) and environmental constraints (Drucker et al 2001; Moll 2005; Moll et al 2007). These emanate from the perception that crossbreeding and/or replacement with exotic animals is the best option for improving productivity of indigenous livestock under smallholder conditions. Nevertheless, success has only been reported in the Kenyan highlands where the country’s successive governments instituted a number of changes in the provision of livestock production and marketing services that encouraged dairy production (Thorpe et al 2000). The breeding goals of livestock keepers are often comprehensive (Köhler-Rollefson 2000; Bebe et al 2003; Moll 2005; Moll et al 2007; Kosgey et al 2006; Roessler et al 2008) and are mainly driven by the underlying production systems (Wollny 2003; Ouma et al 2007). Smallholders also value the non-marketable by-products such as manure and appreciate the intangible benefits of livestock in insurance and display of status (Moll et al 2007).
The ‘improved’ breeds often do not have the adaptive attributes required to fulfill the multiple roles (Drucker et al 2001). Tibbo et al (2008) compared indigenous Menz and their crosses with Awassi and found the indigenous Menz sheep to be as profitable as their crosses to the Awassi breed. The comparative study conducted by Ayalew et al (2003) also indicated that within their local environments and resource-constrained production systems and broader market demands, local goat breeds can perform as good as exotic breeds. The authors further indicated that conventional productivity evaluation criteria are inadequate because they fail to capture non-marketable benefits of the livestock. Thus, as animal breeding programs which focus only on short-term market demands lead to unwanted side effects (Olesen et al 2000), efforts to genetically improve local breeds should always take into consideration the multiple breeding goals of the communities and respect their cultural preferences (Ayantunde et al 2007; Kosgey and Okeyo 2007).
In conventional livestock breeding, where recording systems are in place, the following major steps are considered necessary in defining breeding objectives: a) determination of breeding objective traits, b) derivation of economic values for each objective trait, c) choice of selection criteria, d) estimation of phenotypic and genetic parameters for the breed under consideration, and e) simulation of the breeding program. Under smallholder production systems, however, conventional breeding methods are constrained by absence of records, low level of literacy, small flock sizes per household and uncontrolled breeding (Kosgey 2004; Gizaw et al 2009a). To design viable genetic improvement schemes under smallholder production conditions, the prevailing production conditions and/or systems and production goals must be fully understood and views of the targeted communities duly taken into account. In this paper, available methodologies and tools applicable to characterization of production systems are reviewed within the context of livestock breeding objectives under smallholder situations in developing countries.
In order to set up a breeding program, the target production system has to be well understood and characterized in the context of other farming or off-farm activities (Wollny 2003; Mbuku et al 2006; Kosgey and Okeyo 2007). The description of the production environment should be detailed and distinction made of the target groups within the area for which the breeding program is derived, as different target groups may have various perceptions and priorities (Sölkner et al 1998). Farmers usually have intimate knowledge of their respective local environments, conditions, problems, priorities and trait preferences and their relative rankings. However, “outsiders” who are sometimes investigating local communities with only partial system context knowledge, are often not familiar with this knowledge (Sumberg et al 2003). Consequently inappropriate “improvement” schemes are introduced or adopted. For instance, the Horro Sheep Breeding and Improvement Ranch (1982-1999) located in Horro Guduru (Western Ethiopia) and established by the Ethiopian Ministry of Agriculture, failed causing huge financial and material loses (OADB 2001) due to unsuitability of the area for sheep production. According to this report, the surrounding farmers were neither consulted initially regarding suitability of the area for sheep production nor their opinion heard until most animals died.
Production systems and production objectives are determined by agro-ecology and commonly differ in terms of stress factors, such as water shortages, disease and parasites as well as temperature extremes (Ouma et al 2004; 2007). These conditions largely determine the breeding or production purposes, suitability of breeds/genotypes, and breeding methods particularly in small and large ruminants that depend strongly on their production environments. The strategies followed by resource-poor farmers and their trait preferences are as diverse as the highly variable agro-environments within which they practice (Reece and Sumberg 2003; Roessler et al 2008). Alternative tools/methods used for the description of the prevailing production system are outlined in the following section.
Conroy (2005) emphasizes the importance of full use of any existing sources of information before starting participatory development activities. Secondary information on what breeds/genotypes have been tried out before, major crops being produced, varieties, availability and quality of feeds, the endemic diseases and levels of challenges, meteorological variables, etc. should be collected and synthesized to identify knowledge gap and have better insight.
Diagnostic studies involve understanding of farming systems and planning for experiments to address farmers’ problems (Franzel and Crawford 1987; Doré et al 1997). The objectives of such studies are to develop a basic understanding of how the farming system operates and use this information to identify problem areas or areas where potentials are not being fully realized and thus could benefit from intervention. These may differ for different farmers, therefore, initial steps focus on identification and clustering of the target groups on gender, occupation, wealth status, age, literacy level basis, etc (Besley and Case 1993; Roberts 1996; Sinn et al 1999; Sumberg et al 2003; Teratanavat 2005). Diagnostic studies can be carried out with simple check-lists to facilitate discussion with key informants. In order to develop effective interventions, it is necessary to first understand what farmers are doing, why they are doing it in a given or particular way. Thus, information gathered during the diagnostic study is used to identify areas within the farming system where improvements can be made.
Formal surveys are based on administering structured questionnaires using the information collected from secondary sources and the diagnostic studies. Pre-testing of the questionnaires before final administration is crucial to ensure that the questions being asked are socially appropriate, and that the expected responses are within the expected bounds. Amendments are then made based on pre-test results. Selection of respondents is done by employing the correct sampling procedures and techniques to avoid biasness. Should enumerators/translators be used for interviewing, they must be properly trained about the subjects under consideration. It is also important to prepare a version of the questionnaire in the local language.
In livestock production systems study, questionnaire survey enables to investigate flock/herd size and structure, off-take rates, purpose of keeping different species and importance and use of livestock products. Breeding practices such as source and breed(s) of males used in the herd/flock, traits perceived important by owner, prevalent diseases that occur in the farm, treatment methods (both modern and traditional) and vaccination calendar etc., can be identified. The survey should also capture indigenous knowledge on the management of livestock and breeding practices. For socio-economic aspects, marketing channels and opportunities for animals and animal products, economic valuation of production (production costs, returns from sales), institutional settings that affect breeding and animal management including marketing and decision mechanisms at household level can be documented.
Definition of breeding objective should be the activity, after defining the production system in designing genetic improvement strategies. The concept and structure of conventional livestock breeding objective was initially formalized by Hazel (1943) and it defines the traits of importance and the direction of genetic improvement (Borg 2004). Breeding objective is defined as the traits to be improved, the cost of production and the revenue from product sales related to a genetic change in each trait. Economic values are the relative importance of traits in a given system and can be derived only if breeding objectives are defined in economic terms (Kahi and Nitter 2004; Rewe et al 2006) that may vary from breed/genotype to breed /genotype or from region or production systems to region/production system for the same breed (Hazel 1943).
Complete economic assessments of costs and revenues for low input systems in developing countries are difficult and are rarely available mainly due to illiteracy, lack of formal performance and pedigree record, small flock sizes, leading to too much noise and lack of precision. In addition, the many roles animals play in smallholder systems, makes it difficult to apportion the overall attributes against the many factors involved (Kosgey et al 2003). In developing countries many important functions of livestock are embedded in traits that are not traded in the market, although valuable to the keepers (Scarpa et al 2003b). When most of the environmental and public goods are not traded in the market, the value of the good shall be inferred through the application of the stated preference methods, which derive values from responses to hypothetical questions (Alpizar et al 2001; Freeman 2003; Birol et al 2006). Prior to that, however, most important traits should be determined based on results from production system and diagnostic studies. Only few priority attributes that would optimize the overall gain should be considered as objective traits in order to design simple but effective breeding plans for easy implementation under farmers’ conditions. Involving livestock producers in objective traits identification and incorporating the identified traits in the design of breeding plans encourage them to actively participate in implementation activities. In the next section, methodologies and tools applicable to elicit objective traits under smallholder circumstances in developing countries are discussed.
Participatory rural appraisal (PRA) is an approach that involves local communities as active analysts of their own situations whereby they estimate, quantify, compare, rank/score and list priorities of resources, constraints and opportunities based on their circumstances (Chambers 1994; Bhandari 2003). Various alternative approaches can be used to rank/score the subject under investigation with PRA techniques such as asking the respondents to make drawings on the ground, using sticks of different sizes or known number of grains/pebbles, etc. For instance, Gizaw et al (2009b) working with two indigenous sheep breeds of Ethiopia provided producers with pebbles to rate trait categories. The process involves listing of pre-identified traits which is normally done with knowledgeable local villagers. Then producers are asked to rank/assign a score for each of the traits or trait categories.
Methods for valuing non-market, public goods are categorized as revealed and stated preference methods (Alpizar et al 2001; Birol et al 2006). Revealed preference methods use actual choices made by consumers in related or surrogate markets, in which the non-market good is implicitly traded, to estimate the value of the non-market good. It has the advantage of being based on actual choices made by individuals. However, there are also a number of drawbacks; most notably that the valuation is conditioned on current and previous levels of the non-market good and the impossibility of measuring non-use values, i.e. the value of the non-market good not related to usage such as existence value, altruistic value and bequest value (Alpizar et al 2001). As a result, stated preference methods have been developed to solve the problem of valuing those non-market goods that have no related or surrogate markets. In these approaches, consumer preferences are elicited directly based on hypothetical, rather than actual, scenarios (Alpizar et al 2001; Birol et al 2006). Stated preference methods can be used to cover a wider range of attribute levels in cases where revealed data do not encompass the range of proposed quality or quantity changes in the attributes of a public good (Birol et al 2006).
There are three types of questionnaire that can be used in stated preference studies (Hensher 1994). These are ranking experiment, choice experiment and rating experiment. In a ranking experiment, respondents must order the hypothetical situations in order of preference. The task becomes more complicated in a rating experiment, as respondents must be able to order their responses in order of preference and are asked to indicate how much they prefer one alternative over others. As a result a rating experiment is considered to be too demanding for respondents. According to Hensher (1994), limited relevant information could be provided below the 4th ranking of the respondent as at that stage it is too difficult for respondents to distinguish between choices.
A popular stated preference method used to elicit preferences for attributes of different goods based on utility theory is a choice experiment (McFaden 1974). Though the method has been widely used in other fields like transport (McFaden 2001; Train 2009), its application for the valuation of livestock attributes is more recent and only few published studies are available in the literature (Scarpa et al 2003a, b; Wurzinger et al 2006; Ouma et al 2007; Roessler et al 2008; Omondi et al 2008a, b; Kassie et al 2009). These authors indicated that choice experiments are important tools to value both tangible and intangible traits. Wurzinger et al (2006) reported that choice experiments are important for identifying selection criteria in traditional production systems where literacy level is low and recording practices are not in place. It provides robust information about what attributes and attribute levels producers want most and how much value they place on the different attributes. However, choice experiments have to be pre-tested thoroughly and number of attributes in the profile and number of levels for each attribute should be determined (Alpizar et al 2001; Nganje et al 2004).
Ranking of animals from own herd/flock implies grading of own animals based on reproduction and production performances and other attributes under own management and farming system. No literature report is found on ranking of animals from own flock/herd. The idea is that owners may have particular preferences for animals they raise based on some of the traits that can be observed on them. The procedure is, for example, to ask the owner to choose the 1st best, 2nd best, etc. dairy cows from her/his herd and to ask reasons for so ranking and record the reasons. A farmer may have her/his own ‘ideal cow’ in mind that fitted her/his farming system and management in terms of fertility, calf survival and growth, etc. Particular focus need be given how s/he makes decisions about ranking of the animals and what does s/he frequently referring to when making the decisions. According to Warui and Kaufmann (2005) the method helps to pin-point interrelationships between production aims, characteristics of livestock resources, environment and the respective prevailing management.
The most interesting aspect of ranking animals from own herd/flock is that most family members are taking part in the ranking activities as it is done at the owner’s homestead or nearby. Family members involving in the ranking activities remind each other about reproduction history of their animals and other events as there are no written records kept by the smallholder farmers. They depend on recalled memory regarding the performance and pedigree of their animals. The other advantage is that the animals ranked are under similar management, though in some cases certain classes of animals get preferential treatments (eg. milking cows, working oxen, etc). In addition, ranking is based on a complex of traits not based only on the superiority/inferiority of a single trait. For instance, if ewe is a twin bearer but hardly rear her lambs or if their growth performance is poor or stunted, owners would not rank the ewe as good. Instead, they may go for a single bearer ewe which lambs’ grow faster and viable at least to weaning (personal observation). Harvey and Baker (1989) also reported similar situation that farmers selected ewes that had superior subsequent performance (number of lambs weaned) to their culled group of ewes. After having detailed information from the owner and animals got ranked (eg. 1st best, 2nd best, etc.), measurements can be taken on each ranked animal focusing only on attributes frequently mentioned. In case of female animals, additional information on reproductive performances can also be collected as recalled by owners. The approach may put some light on the association between smallholders’ indigenous selection criteria and the modern knowledge of animal breeding.
Based on results obtained using the methods/tools described, alternative breeding plans involving different levels of recording can be simulated. After having the most preferred breeding traits identified, the basic principles of simulation of alternative breeding plans are the same with that of the conventional breeding program. Nevertheless, only few and easily recordable traits have to be considered in the breeding plans simulation for smallholders as most of them are illiterate. Finally, the simulated alternatives must be presented to the targeted groups to discuss on each alternative and decide which alternative they will implement.
In addition to hypothetical choice experiments, phenotypic ranking of animals has recently been employed to capture information about selection criteria of stock owners (Ndumu et al 2008). For phenotypic ranking of live animals, identifying/marking and randomly assigning of animals of similar age, size and condition into different sub-groups for ranking are crucial. It would also be interesting if animals of different colors can be included to elicit their preferences for the different colors and attach value to each color type. Involving relevant stakeholders other than livestock owners like local traders can also help to attach values to animals of different age, color and size, etc. Randomly regrouping or reshuffling of animals at certain interval is necessary during the course of the ranking process to minimize biasness. Then each interviewee is asked by a well trained enumerator to rank animals of the different groups according to his/her own preferences and to provide reasons why s/he ranked the animals in that order. The ranking can be conducted first based on phenotype alone where after the interviewee is provided with additional information on each animal in the form of life history including production and/or reproduction performances; then to investigate decision changes to be made by the respondents.
If more decision change is made due to provision of life history, it may either indicate that attributes provided in the form life history are very important to the respondents or respondents need informed decision to select animals under consideration for breeding. If significant decision changes are observed, the interviewee should be asked as to which of the provided attributes in the form of life history have influenced her/his decision, then to take measurements on those specific attributes. Production and reproduction performances of these particular animals may be evaluated in relation to the measurements taken to investigate the association between the measurements and animal performances. Ndumu et al (2008) working with Ugandan Ankole cattle, reported that the methodology of preference ranking combining phenotype and a hypothetical life history provided better insight into stock owners’ selection criteria than ranking animals based on phenotype alone. Evidently, providing life history allowed capturing information on relative importance of phenotype versus production and health traits.
The different methods described for defining breeding objective traits have advantages and shortfalls. One has to analyze the practical situation on the ground before deciding on the method to be used. However, Ndumu et al (2008) who compared three different methods (survey, phenotypic ranking of live animals and a hypothetical choice experiment) suggested a combination of at least two methods to be used in order to avoid overlooking of any important question for selection. The authors also indicated that these methods are applicable for situations where farmers have low levels of literacy and/or only a few years of formal education.
The different tools mentioned can be used to define livestock breeding objectives traits with active participation of producers to design livestock breeding plans. All methods may not be suitable for every situation. One method may work better than the other under some situations and at some stages. For instance, survey questionnaire enables to undertake the overall situation analysis of an area and to capture lists of objective traits to be considered important under a given system at early stage. However, the information captured at survey stage may be broad and need to be narrowed to focus only on few but very important traits. In addition, breeding program to be designed for the smallholders should be simple and may include one or two priority traits that can be easily measured on the candidates meant for selection. Traits preferred at this stage can be used for designing of choice experiments.
Choice experiment is important to value both tangible and intangible traits, where the later traits are valued using pictorial representation that otherwise could not be assessed. Ranking of animals from own herd/flock is important to capture information on production and reproduction performances. More detailed information may also be captured as owner judges her/his animals based on different traits including animals’ behavior. Analysis of owner’s views and the measurements taken on ranked animals may enable to blend indigenous knowledge with the sciences of animal breeding. This method also provides information (in the form of life history) to the phenotypic ranking of group of live animals, which focuses only on observable traits indicating that breeders may need informed decision to select animals for breeding. Thus, unless for cost and labor effects using multiple methods may give better insight in eliciting smallholders’ livestock attributes preferences.
In this review, different tools and methods for defining production systems and livestock breeding objective traits are described, discussed and comparisons among them are made. Key issues identified were: a) understanding of production systems, b) identification and clustering of target groups on gender, occupation, wealth status, age, literacy level, etc. basis, c) elucidation of objective traits with active involvement of the target groups, and d) to use the most important identified traits for simulation of the genetic improvement program. Thus, an appropriate genetic improvement programs that truly reflect owners’ preferences and well grounded in practical reality may be simulated.
Various methods were evaluated and compared in relation to efficiency in identifying breeding objective traits. Choice experiment is important to value both tangible and intangible traits, but attributes used to design the choice experiments have to be identified using other tools like production system studies. Ranking of animals from own herd/flock enables to capture different traits based on owner’s preferences including production and reproduction performances and behavior. In ranking of group of live animals by others (not the stock owner) producers need informed decision (as it values only observable traits) to select animals for breeding. Thus, the use of a combination of methods gives better insights to explore the preferences of the owners than using a single method.
Our sincere thank is to the Austrian Government for financial support.
Alpizar F, Carlsson F and Martinsson P 2001 Using choice experiments for non-market valuation. Working Papers in Economics no. 52. Department of Economics, Göteborg University. 37 pp.
Ayalew W, Rischkowsky B, King J M and Bruns E 2003 Crossbreds did not generate more net benefits than indigenous goats in Ethiopian Smallholdings. Agricultural Systems 76:1137-1156.
Ayantunde A A, Kango M, Hiernaux P, Udo H M and Tabo R 2007 Herders’ perceptions on ruminant livestock breeds and breeding management in South Western Niger. Human Ecology 35:139-149.
Bebe B O, Udo H M J, Rowlands G J and Thorpe W 2003 Smallholder dairy systems in the Kenya highlands: breed preferences and breeding practices. Livestock Production Science 82:117–127.
Besley T and Case A 1993 Modeling technology adoption in developing countries. The American Economic Review 83:396-402.
Bhandari B B 2003 Participatory Rural Appraisal, Module 4. Institute for global environmental strategies. http://enviroscope.iges.or.jp/contents/eLearning/waterdemo/bhandari_m4.pdf
Birol E, Kontoleon A and Smale M 2006 Combining revealed and stated preference methods to assess the private value of agro-biodiversity in Hungarian home gardens. International Food Policy Research Institute (IFPRI). Discussion Paper 156. http://www.ifpri.org/sites/default/files/publications/eptdp156.pdf
Borg C R 2004 Developing breeding objectives for Targhee sheep. MSc Thesis, Virginia Polytechnic Institute and State University. 71 pp.
Chambers R 1994 Participatory Rural Appraisal (PRA): Analysis of experience. World Development 22(9):1253-1268.
Conroy C 2005 Participatory livestock research: a guide. Bourton Hall, Bourton-on-Dunsmore, Warwickshire CV23 9QZ, UK. 304 pp.
Doré T, Sebillotte M and Meynard J M 1997 A diagnostic method for assessing regional variations in crop yield. Agricultural Systems 54:169-188.
Drucker A G, Gomez V and Anderson S 2001 The economic valuation of farm animal genetic resources: a survey of available methods. Ecological Economics 36:1-18. http://www.ilri.org/html/Valuation%20Methods%20Review.pdf
Franzel S and Crawford E W 1987 Comparing formal and informal survey techniques for farming systems research: A case study form. Agricultural Administration and Extension 27:13-33.
Freeman A M 2003 The Measurement of environmental and resource values: Theory and methods. 2nd edition. Washington, D.C., Resources for the Future.
Gizaw S, Komen H and van Arendonk J A M 2009a Optimal village breeding schemes under smallholder sheep farming systems. Livestock Science (In Press) doi:1016/j.livsci.2009.10.001.
Gizaw S, Komen H and van Arendonk J A M 2009b Participatory definition of breeding objectives and selection indexes for sheep breeding in traditional systems. Livestock Science (In Press) doi:1016/j.livsci.2009.10.0016.
Harvey T G and Baker R L 1989 Performance of visually or objectively culled ewe hoggets. Proceedings of the New Zealand Society of Animal Production. 49:215-220.
Hazel L N 1943 The genetic basis for construction of selection indices. Genetics 28:476-490.
Hensher D A 1994 Stated preference analysis of travel choices: the state of practice. Transportation 21:107-133.
Kahi A K and Nitter G 2004 Developing breeding schemes for pasture based dairy production systems in Kenya. I. Derivation of economic values using profit functions. Livestock Production Science 88:161-177.
Kassie G T, Abdulai A and Wollny C 2009 Valuing traits of indigenous cows in Central Ethiopia. Journal Agricultural Economics 60(2):386-401.
Köhler-Rollefson I 2000 Management of animal genetic diversity at community level. GTZ, Eschborn.
Kosgey I S 2004 Breeding objectives and breeding strategies for small ruminants in the tropics. PhD Thesis, Wageningen University. http://library.wur.nl/wda/dissertations/dis3546.pdf
Kosgey I S, Baker R L, Udo H M J and van Arendonk J A M 2006 Successes and failures of small ruminant breeding programs in the tropics: a review. Small Ruminant Research 61:13–28.
Kosgey I S, Okeyo A M 2007 Genetic improvement of small ruminants in low-input, smallholder production systems: Technical and infrastructural issues. Small Ruminant Research 70: 76 - 88.
Kosgey I S, van Arendonk J A M and Baker R L 2003 Economic values of traits of meat sheep in medium to high production potential areas of the tropics. Small Ruminant Research 50:187-202.
Mbuku S M, Kosgey I S and Kahi A K 2006 Identification systems and selection criteria of pastoral goat keepers in northern Kenya – Implications for a breeding program. Tropentag, October 11 - 13, 2006, University of Bonn, Bonn, Germany. http://www.tropentag.de/2006/abstracts/full/525.pdf
McFaden D 1974 Conditional logit analysis of quantitative choice behavior. Zarembka, P. (editor, Frontiers in Econometrics, New York: Academic Press. pp105-142.
McFaden D 2001 Economic choices. The American Economic Review 91(3):351-378.
Moll H A J 2005 Costs and benefits of livestock systems and the role of market and non-market relationships. Agricultural Economics 32(2):181-193.
Moll H A J, Staal S J and Ibrahim M N M 2007 Smallholder dairy production and markets: A comparison of production systems in Zambia, Kenya and Sri Lanka. Agricultural Systems 94:593-603.
Ndumu D B, Baumung R, Wurzinger M, Drucker A G, Okeyo A M, Semambo D and Sölkner J 2008 Performance and fitness traits versus phenotypic appearance in the African Ankole Longhorn cattle: A novel approach to identify selection criteria for indigenous breeds. Livestock Science 113:234-242.
Nganje W, Hearne R, Orth M and Gustafson C 2004 Using choice experiments to elicit farmers’ preferences for crop and health insurance. Selected Paper Presented at the AAEA Annual Meeting Denver, Colorado, August 2004. http://ageconsearch.umn.edu/bitstream/20357/1/sp04ng01.pdf
OADB 2001 OADB (Oromia Agricultural Development Bureau). Project proposal on establishment of Horro cattle breeding and improvement center. Livestock breeding centers co-ordination and pastoral development department. January 2001, Finfinne, Ethiopia.
Olesen I, Groen A F and Gjerde B 2000 Definition of animal breeding goals for sustainable production systems. Journal of Animal Science 78:570-582. http://jas.fass.org/cgi/reprint/78/3/570.pdf
Omondi I A, Baltenweck I, Drucker A G, Obare G A and Zander K K 2008a Valuing goat genetic resources: a pro-poor growth strategy in the Kenyan semi-arid tropics. Tropical Animal Health and Production 40:583-596.
Omondi I A, Baltenweck I, Drucker A G, Obare G A and Zander K K 2008b Economic valuation of sheep genetic resources: implications for sustainable utilization in the Kenyan semi-arid tropics. Tropical Animal Health and Production 40:615-626.
Ouma E, Abdulai A and Drucker A 2007 Measuring heterogeneous preferences for cattle traits among cattle-keeping households in East Africa. American Journal of Agricultural Economics 89(4):1005-1019.
Ouma E, Obare G, Abdulai A and Drucker A 2004 Assessment of farmer preferences for cattle traits in smallholder cattle production systems of Kenya and Ethiopia. Proceedings Deutscher Tropentag, October 5-7, 2004, Berlin, Germany. http://www.ilri.org/Link/Publications/Files/Assessment%20of%20Farmer%20Preferences%20for%20Cattle%20Traits%20in%20Smallholder%20Cattle%20Production%20Systems%20of%20Kenya%20and%20Ethiopia.pdf
Reece J D and Sumberg J 2003 More clients, less resources: toward a new conceptual framework for agricultural research in marginal areas. Technovation. 23:409-421.
Rewe T O, Indetie D, Ojango J M K and Kahi A K 2006 Breeding objectives for the Boran breed in Kenya: Model development and application to pasture-based production systems. Animal Science Journal 77:63-177.
Roberts B O 1996 Livestock production, age and gender among the Keiyo of Kenya. Human Ecology 24:215-230.
Roessler R, Drucker A G, Scarpa R, Markemann A, Lemke U, Thuy LT and Valle Zárate A 2008 Using choice experiments to assess smallholder farmers' preferences for pig breeding traits in different production systems in North–West Vietnam. Ecological Economics 66(1):184-192.
Scarpa R, Drucker A G, Anderson S, Ferraes-Ehun N, Gomez V, Risopatron C R and Rubio-Leonel O 2003a Valuing genetic resources in peasant economies: the case of ‘hairless’ Creole pigs in Yucatan. Ecological Economics 45:409-426.
Scarpa R, Ruto E S K, Kristjanson P, Radeny M, Drucker A G and Rege J E O 2003b Valuing indigenous cattle breeds in Kenya: an empirical comparison of stated and revealed preference value estimates. Ecological Economics 45:427-443.
Sinn R, Ketzis J and Chen T 1999 The role of woman in the sheep and goat sector. Small Ruminant Research 34:259-269.
Sölkner J, Nakimbugwe H and Zarate A V 1998 Analysis of determinants for success and failure of village breeding programmes. In: Proceedings of the Sixth World Congress on Genetics Applied to Livestock Production, 11–16 January 1998, Armidale, NSW, Australia. 25:273-280.
Sumberg J, Okali C and Reece D 2003 Agricultural research in the face of diversity, local knowledge and the participation imperative: theoretical considerations. Agricultural Systems 76:739-753.
Teratanavat R 2005 Essays on consumer purchase decisions and health and nutrition information on functional foods. MA Dissertation. The Ohio State University. 188 pp.
Thorpe W, Muriuki H G, Omore A, Owango M O and Staal S 2000 Dairy development in Kenya: the past, the present and the future. Paper prepared for the Annual Symposium of the Animal Production Society of Kenya, March 22-23, 2000, Nairobi, Kenya. http://www.smallholderdairy.org/publications/Conference/Thorpe%20et%20al-2000-Dairy%20dev%20past%20present%20future-APSK.pdf
Tibbo M, Aragaw K, Philipsson J, Malmfors B, Näsholm A, Ayalew W and Rege J E O 2008 A field trial of production and financial consequences of helminthosis control in sheep production in Ethiopia. Preventive Veterinary Medicine 84:152–160.
Train K E 2009 Discrete choice methods with simulation, 2nd edition, Cambridge: Cambridge University Press.
Warui H and Kaufmann B 2005 Characterization of livestock resources in the systems context using a cybernetic approach. Training Report. Institute of Animal Production in the Tropics and Subtropics, Hohenheim University, http://www.ilri.org/html/Characterisation.pdf
Wollny C B A 2003 The need to conserve farm animal genetic resources in Africa: should policy makers be concerned? Ecological Economics 45:341-351.
Wurzinger M, Ndumu D, Baumung R, Drucker A G, Okeyo A M, Semambo D K and Sölkner J 2006 Assessing stated preferences through the use of choice experiments: valuing (re)production versus aesthetics in the breeding goals of Ugandan Ankole cattle breeders. 8th World Congress on Genetics Applied to Livestock Production, August 13-18, 2006, Belo Horizonte, MG, Brazil.
Received 27 January 2010; Accepted 20 February 2010; Published 1 April 2010
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