Determining the economic selection index for growth traits in the semi-intensive rearing system of Merkhoz goats Economic selection indices in the traditional goat breeding system

Document Type : Research Paper

Authors

1 Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran.

Abstract

The aim of this research was to study different economic selection indices to increase body weight in Markhoz goat breed. Body weight (BW) at different ages (birth, weaning, 6-month and 9-month) were categorized as several two- and three-traits selection indices. Genetic parameters were estimated with MTGSAM using the Bayesian statistical method. Selection index analyzes were done using SelAction software. The results of comparing three-trait indices showed that highest total economic gain resuled from I9 which was US$4.86. The total economic response for two-trait index belonged to I4 which was US$3.94 and higher than 5 others. The highest direct genetic gain from three-trait indices was predicted for 9-month weight in I8 and I9 indices to be about 0.63 kg. In addition, the highest direct genetic improvement resulting from two-trait indices was also predicted for the 9-month weight in the I3 to be 0.66 kg. Moreover, the selection and performance criteria revealed decrease in phenotypic variance, heritability, and genetic correlation of traits. These changes differed in alternative selection schemes, which influenced by the initial population parameters, selection intensity, direct or indirect selection, and the number of traits included in the selection index. In conclusion, to maximize the total economic gain, two selection indices I9 and I4 can be suggested for the current condition of the Markhoz goat population. However, to preserve the phenotypic/genetic variance of traits, it is necessary to focus on strategies such as selection intensity, economic coefficients, indirect selection, and increasing the number of traits in selection indices. 

Keywords

Main Subjects


Extended Abstract

Introduction

     Markhoz goat is one of important goat breeds in Iran. This research aimed to investigate different economic selection indices for improving growth and body weight in this endangered goat breed in a traditional breeding system.

 

Materials and methods

    Birth weight (BW), weaning weight (WW), 6-month weight (6 MW) and 9-month weight (9 MW) were included in several selection indices of two and three-trait indices to increase body weight. The responses to the selection for these traits using economic coefficients of 0.1, 3.00, 5.66, and 1.50 respectively were predicted. Furthermore, the magnitude of the Bulmer effect resulting from different selection indices were investigated. An equal number of male and female animals in three age classes were used in all studied selection indices. Therefore, the average generation interval and proportion of male to female in all selection indices were 2.224 and 1 to 6, respectively.

 

Results and Discussion

    As a result of selection, genetic gain, the total economic gain, and the changes in population parameters such as phenotypic variance, genetic correlations, and traits heritability were different in various selection indices. The results of the comparison of three-trait indices showed that the highest total economic gain resuled from I9 which was US$4.86. Also, the total economic response for two-trait I4 was predicted as 3.94 dollars which were more than 5 other two-trait indices. The highest predicted direct genetic gain resulted from three-trait indices was belonged to 9-month weight in I8 and I9 indices which was about 0.63 kg. Moreover, the economic gain resulting from this genetic improvement in the mentioned indices was measured as 43 and 55% of the total economic gains from I8 and I9 respectively. In addition, the highest direct genetic improvement resulting from two-trait indices was also predicted for the 9-month weight in the I3 to be 0.66 kg, which accounted for 99% of the total economic gain in that selection index.   Moreover, the selection and performance criteria revealed decrease in phenotypic variance, heritability, and genetic correlation of traits. These changes were different in alternative selection schemes because they can be influenced by the initial population parameters, the selection intensity for each trait, direct or indirect selections, and the number of included traits in each selection index.

 

Conclusion

    In conclusion, to maximize the total economic gain, two selection indices I9 and I4 can be suggested in the current condition of the Markhoz goat population. But in order to preserve the phenotypic/genetic variance of traits, in this endangered breed, it is necessary to pay more attention to strategies such as selection intensity, economic coefficients of traits, indirect selection, and maybe increasing the number of traits in selection indices.

Al-Khaza’leh, J, Reiber, C, Al-Baqain, R & Valle, ZA. (2015). A comparative economic analysis of goat production systems in Jordan with an emphasis on water use Livestock Research for Rural Development, 27(5). http://www.lrrd.org/lrrd27/5/khaz27081.html.
Allier, A, Lehermeier, C, Charcosset, A, Moreau, L & Teyssèdre, S. (2019). Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection. Frontiers in Genetics, 10(X), 1006. https://doi.org/10.3389/fgene.2019.01006.
Asroush, F, Mirhoseini, SZ, Badbarin, N, Seidavi, A, Tufarelli, V, Laudadio, V, Dario, C & Selvaggi, M. (2018). Genetic characterization of Markhoz goat breed using microsatellite markers. Archive of Animal Breeding, 61(4), 469-73. https://doi.org/10.5194/aab-61-469-2018.
Bett, RC, Kosgey, IS, Bebe, BO & Kahi, AK. (2007). Breeding goals for the Kenya dual purpose goat. II. Estimation of economic values for production and functional traits. Trop Anim Health Prod, 39(7), 467-75. http;//doi.org/10.1007/s11250-007-9013-5.
Biscarini, F, Nicolazzi, EL, Stella, A, Boettcher, PJ & Gandini, G. (2015). Challenges and opportunities in genetic improvement of local livestock breeds. Frontiers in Genetics, 6 http;//doi.org/10.3389/fgene.2015.00033.
Burns, JG, Eory, V, Butler, A, Simm, G & Wall, E. (2022). Review: Preference elicitation methods for appropriate breeding objectives. Animal, 16(6), 100535. https://doi.org/10.1016/j.animal.2022.100535.
Cavanaugh J.E. & Neath A.A. (2019) The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. WIREs Computational Statistics 11, e1460. https://doi.org/10.1002/wics.1460
Conington, J, Bishop, SC, Grundy, B, Waterhouse, A & Simm, G. (2001). Multi-trait selection indexes for sustainable UK hill sheep production. Animal Science, 73(3), 413-23. https://doi.org/10.1017/S1357729800058380.
Dostál, Z & Pospíšil, L. (2018). Conjugate gradients for symmetric positive semidefinite least-squares problems. International Journal of Computer Mathematics, 95(11), 2229-39. https://doi.org/10.1080/00207160.2017.1371701.
Du, M, Bernstein, R, Hoppe, A & Bienefeld, K. (2021). Short-term effects of controlled mating and selection on the genetic variance of honeybee populations. Heredity, 126(5), 733-47. https://doi.org/10.1038/s41437-021-00411-2.
Dubeuf, JP & Boyazoglu, J. (2009). An international panorama of goat selection and breeds. Livestock Science, 120(3), 225-31. https://doi.org/10.1016/j.livsci.2008.07.005
Falconer, D & Mackay, TF (1996) Introduction to quantitative genetics. . Harlow, UK: Longmans.
Gunia, M, Mandonnet, N, Arquet, R, Alexandre, G, Gourdine, JL, Naves, M, Angeon, V & Phocas, F. (2013). Economic values of body weight, reproduction and parasite resistance traits for a Creole goat breeding goal. Animal, 7(1), 22-33. https://doi.org/10.1017/s1751731112001413.
Hazel, LN, Dickerson, GE & Freeman, AE. (1994). The Selection Index—Then, Now, and for the Future. Journal of Dairy Science, 77(10), 3236-51. https://doi.org/10.3168/jds.S0022-0302(94)77265-9.
Hill, WG & Mackay, TFC. (2004). D. S. Falconer and Introduction to Quantitative Genetics. Genetics, 167(4), 1529-36. https://doi.org/10.1093/genetics/167.4.1529.
Hubert de Rochambeau., Florence Fournet-Hanocq. & Khang, JVT. (2000). Measuring and managing genetic variability in small populations. Annual Zoo Technology, 49(2), 77-93 https://doi.org/10.1051/animres:2000109
Kargar Borzi, N, Ayatollahi Mehrgardi, A, Asadi Fozi, M & Vatankhah, M. (2017). Determining the appropriate selection index for Rayeni Cashmere goat under pasture-based production system. Animal Production Science, 58(9). http://dx.doi.org/10.1071/AN16570.
Kargar Borzi, N, Mehrgardi, A & Abassi, MA. (2017). Breeding Objectives and Desired-Gain Selection Index for Rayeni Cashmere Goat in Pasture System. Iranian Journal of Applied Animal Science, 7(4), 631-6
Kargar Borzi, N & Mokhtari, MS. (2020). The Comparison of Four Economical Selection Indices for Improving the Performance of Kermani Sheep under Rural Production System. Iranian Journal of Applied Animal Science, 10(4), 631-7
Kheirabadi, K & Rashidi, A. (2016). Genetic description of growth traits in Markhoz goat using random regression models. Small Ruminant Research, 144(X), 305-12. https://doi.org/10.1016/j.smallrumres.2016.10.003.
LE CORRE, V & KREMER, A. (2012). The genetic differentiation at quantitative trait loci under local adaptation. Molecular Ecology, 21(7), 1548-66. https://doi.org/10.1111/j.1365-294X.2012.05479.x.
Lobo, R, Facó, O, Lobo, A & Villela, L. (2010). Brazilian goat breeding programs. Small Ruminant Research 89(149-54. 10.1016/j.smallrumres.2009.12.038. https://doi.org/10.1016/j.smallrumres.2009.12.038
Lopes, F, Borjas, A, Corrêa da Silva, M, Facó, O, Lôbo, R, Fioravanti, MC & McManus, C. (2012). Breeding goals and selection criteria for intensive and semi-intensive dairy goat system in Brazil. Small Ruminant Research, 106(2-3), 110–7. https://doi.org/10.1016/j.smallrumres.2012.03.011.
Lopes, F, Corrêa da Silva, M, Miyagi, Es, Facó, O & McManus, C. (2013). Comparison of selection indexes for dairy goats in the tropics. Acta Scientiarum. Animal Sciences, 35(321-8. 10.4025/actascianimsci.v35i3.16049.
Manaf Hosseini, A., 2004. Sheep breeding, Kimia gostar.
Macedo, FL, Christensen, OF & Legarra, A. (2021). Selection and drift reduce genetic variation for milk yield in Manech Tête Rousse dairy sheep. JDS Communications, 2(1), 31-4. https://doi.org/10.3168/jdsc.2020-0010.
27.Meyer K. (1992) Variance components due to direct and maternal effects for growth traits of Australian beef cattle. Livestock Production Science 31, 179-204. https://doi.org/10.1016/0301-6226(92)90017-X
Madsen, P & Jensen, J. (2008). A user’s guide to DMU. A package for analysing multivariate mixed models version, 6(X), 1-33
Madsen, P, Milkevych, V, Gao, H, Christensen, OF & Jensen, J (2014) DMU - A Package for Analyzing Multivariate Mixed Models in Quantitative Genetics and Genomics.
Mrode, R (2014) Linear Models For The Prediction Of Animal Breeding Values.
Mueller, JP, Getachew, T, Rekik, M, Rischkowsky, B, Abate, Z, Wondim, B & Haile, A. (2021). Converting multi-trait breeding objectives into operative selection indexes to ensure genetic gains in low-input sheep and goat breeding programmes. Animal, 15(5), 100198. https://doi.org/10.1016/j.animal.2021.100198.
Nazari-Ghadikolaei, A, Mehrabani-Yeganeh, H, Miarei-Aashtiani, SR, Staiger, EA, Rashidi, A & Huson, HJ. (2018). Genome-Wide Association Studies Identify Candidate Genes for Coat Color and Mohair Traits in the Iranian Markhoz Goat. Frontiers in Genetics, 9(105), 1-15. https://doi.org/10.3389/fgene.2018.00105.
Rashidi, A, Bishop, SC & Matika, O. (2011). Genetic parameter estimates for pre-weaning performance and reproduction traits in Markhoz goats. Small Ruminant Research, 100(2), 100-6. https://doi.org/10.1016/j.smallrumres.2011.05.013.
Rashidi, A, Moradi Shahr Babak, M, Mirai Ashtiani, SR & Zandi, MB (1398) Estimation of the economic coefficients of important production traits in Markhz goat using the bio-economic model method. In: The Third Iranian Congress of Animal Sciences. (In Persian), Iran.
Rutten, MJM, Bijma, P, Woolliams, JA & van Arendonk, JAM. (2002). SelAction: Software to Predict Selection Response and Rate of Inbreeding in Livestock Breeding Programs. Journal of Heredity, 93(6), 456-8. https://doi.org/10.1093/jhered/93.6.456.
Sajjad, T (2012) Quantitative Genetic Application in the Selection Process for Livestock Production. In: Livestock Production (ed. by J. Khalid), p. Ch. 1. IntechOpen, Rijeka.
Sadeghi-Sefidmazgi, A., Shahrbabak, M.M., Javaremi, A.N., Ashtiyani, S.R.M. and Eymer, P.R., 2012. Estimation of economic values and financial losses associated with dystocia for Holstein dairy cattle of Iran. Iranian Journal of Animal Science (IJAS), 42(4), pp.345-353. https://doi.org/10.1017/S1751731110001655
Sepulveda, B.J., Muir, S.K., Bolormaa, S., Knight, M.I., Behrendt, R., MacLeod, I.M., Pryce, J.E. and Daetwyler, H.D., 2022. Eating Time as a Genetic Indicator of Methane Emissions and Feed Efficiency in Australian Maternal Composite Sheep. Frontiers in Genetics, 13, p.883520. https://doi.org/10.3389/fgene.2022.883520
Scholtens, M, Lopez-Villalobos, N, Lehnert, K, Snell, R, Garrick, D & Blair, HT. (2020). Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd. Animals 11(1). https://doi.org/10.3390/ani11010024.
Sölkner, J, Grausgruber, H, Okeyo, AM, Ruckenbauer, P & Wurzinger, M. (2008). Breeding objectives and the relative importance of traits in plant and animal breeding: a comparative review. Euphytica, 161(1), 273-82. https://doi.org/10.1007/s10681-007-9507-2.
Thompson, R & Meyer, K. (1986). A review of theoretical aspects in the estimation of breeding values for multi-trait selection. Livestock Production Science, 15(4), 299-313. https://doi.org/10.1016/0301-6226(86)90071-0.
Tseveenjav, B, Garrick, DJ, Batjargal, E & Yondon, Z. (2020). Economic selection index to improve fiber quality in Mongolian Cashmere goats. Livestock Science, 232(103898. https://doi.org/10.1016/j.livsci.2019.103898.
Van Grevenhof, EM, Van Arendonk, JA & Bijma, P. (2012). Response to genomic selection: the Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting. Genetic Selection Evolution, 44(1), 26. https://doi.org/10.1186/1297-9686-44-26.
Vatankhah, M, Talebi, M & Bagheri, M. (2010). A Comparison of Breeding Objectives of Native Black Goat in Different Rearing Systems: 2. A Determination of the Economic Values. Iranian Journal of animal Science, 41(3), 193-201
Villanueva, B., Wray, N. R., & Thompson, R. (1993). Prediction of asymptotic rates of response from selection on multiple traits using univariate and multivariate best linear unbiased predictors. Animal Science, 57(1), 1-13. https://doi.org/10.1017/S0003356100006541
Ziadi, C, Muñoz-Mejías, E, Sánchez, M, López, MD, González-Casquet, O & Molina, A. (2021). Selection Criteria for Improving Fertility in Spanish Goat Breeds: Estimation of Genetic Parameters and Designing Selection Indices for Optimal Genetic Responses. Animals 11(2). https://doi.org/10.3390/ani11020409.