- Email: [email protected]
Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia
Journal of the Saudi Society of Agricultural Sciences xxx (2018) xxx–xxx
Contents lists available at ScienceDirect
Journal of the Saudi Society of Agricultural Sciences journal homepage: www.sciencedirect.com
Full length article
Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia Riyadh Saleh Aljumaah Department of Animal Production, Food and Agriculture College, King Saud University, PO Box 2460, Riyadh 11451, Saudi Arabia
a r t i c l e
i n f o
Article history: Received 6 November 2017 Revised 29 January 2018 Accepted 11 February 2018 Available online xxxx Keywords: Goat Breeding simulation Subtropics Close scheme
a b s t r a c t Lack of a sustainable long-term breeding program in developing countries enhanced finding alternatives such as simulation solutions. The aim of this research work was to find solutions in order to simulate an optimized national Ardi goat breeding program under sub-tropical conditions of the Kingdom of Saudi Arabia (KSA). The simulated program was considered a close scheme of three tiers. They were nucleus, multiplier and commercial herds representing 1, 10, and 89%, respectively, of the total does populations (303,000 heads). The breeding nucleus structure was comprised of 10 selection groups (SG). The simulated scenarios resulted in predicting increment in meat and milk yield per doe by 0.153 kg and 0.145 l, respectively, under assumed genetic, production and physiological parameters. Furthermore, the bioinformatics solution provided simulated models of economically profitable. It is worthy to recommend a research work deal with providing reliable genetic estimates of Saudi national livestock in order to establish sustainable national breeding program. Ó 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Despite the fact that the kingdom of Saudi Arabia (KSA) is still far ahead from self-sufficiency of red meat and milk from local goat (MOA, 2013). In particular, goat farming is of primary importance to the national agricultural economy considering inputs needed for sheep or cattle farming. Most goat breeds in KSA are reared in the desert. The main breeds of goats in Saudi Arabia are Ardi (Aardi or Ardhi), Jabili, Bishi, Habsi and Harri (AlAmer, 2006; Aljumaah et al., 2012; Sabir et al., 2012; Al-Atiyat et al., 2015). Ardi is the largest breed and reared under harsh desert conditions. Their superior tolerance to the harsh environment, less demanding status of nutritional requirements and reasonable profit return from meat and milk production, make them the frontline in animal farming at KSA. It is well known that improving goat productivity depend mainly on genetic selection within each breed considering a well optimized breeding program design. Goat selection, in general, has mostly performed by farmers, and rarely by breeders (Galal, 2005). However, the farmers of goat are lacking skills for breeding
and genetically improving their herds beforehand as result of lacking knowledge of defining breeding objectives and selection criteria (Tabbaa and Al-Atiyat, 2009). In the absence of national goat breeding program in KSA, the most important step is sustainable breeding program design. AlSaef (2013a) recommended to develop meat and milk goat lines based on a wide range of breeding values found within goat breeds. Nevertheless, the sustainable breeding programs at national scale have been reported for goats in tropics and subtropics utilizing simulation and bioinformatics tools (Rewe et al., 2006; Roessler et al., 2009). In particular, bioinformatics tools such as related computer softwares facilitate optimizing breeding programs of different livestock using simulation approaches and predict best breeding program ahead. Most common bioinformatics software is ZPLAN+ software that provide genetic gain and economic return on comprehensive breeding program (Nitter and Graser, 1994). Therefore, the aim of this study was to show the results of simulated scenarios that optimize breeding program of Ardi goat under the sub-tropical conditions of KSA using ZPLAN+ software.
Peer review under responsibility of King Saud University.
2. Materials and methods
1. Introduction
2.1. Selection groups
Production and hosting by Elsevier E-mail address: [email protected]
The simulated breeding program mimicked a close scheme comprised of nucleus, multiplier and commercial tiers. The first
https://doi.org/10.1016/j.jssas.2018.02.001 1658-077X/Ó 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Aljumaah, R.S. Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia. Journal of the Saudi Society of Agricultural Sciences (2018), https://doi.org/10.1016/j.jssas.2018.02.001
2
R.S. Aljumaah / Journal of the Saudi Society of Agricultural Sciences xxx (2018) xxx–xxx
was the tier in which genetic gain estimated for bucks to be sires in the second tier or sires in the third. The nucleus was closed only to replacements from its herd based on selection index. The multiplier tier spread the gain of chosen animals for utilizing into the
Table 1 Transmission matrix of the breeding plan with 10 selection groups. Scheme
Selection Group
Size
Nucleus unit
BN > BN BN > DN DN > BN DN > DN BN > BM BN > DM BM > BP BM > DP DM > BP DM > DP
100 250 100 250 1000 1000 10,000 10,000 5000 5000
Multiplier unit
Table 2 Description of breeding objectives and their selection criteria. Breeding objectives
Economic Value
Description
Total Meat yield
40.4
Amount of meat produced from 180 days kid(s) per doe Amount of milk produced per doe
Total Milk yield 8.0 Selection Criteria Birth Weight (BWt) (kg) Weaning Weight (WWt) (kg) Daily gain Weight (DGWt) (kg) Total Milk production (TMP) (kg) Partial milk production (PMP) (kg) Prolificacy (PRFC) (kg) Dam Weight (DWt) (kg) Market live weight (MBwt) (kg)
Kids birth weight Weaning weight of kids Daily gain of kids Milk produced per lactation Partial milk produced in a lactation Number of kids per kidding Does weight at kidding Body weight of kids at marketing time (180 days)
commercial unit. The population of nucleus breeding was 2700 head, representing 09% of estimated total does in the country (303,000 heads). The breeding structure of the nucleus and multiplier units are described as 10 selection groups (SG). The SGs in the nucleus were the first and third to produce selected bucks and does, respectively, whereas the SGs of the second and fourth breed does of bucks and does for the nucleus, respectively. The SGs of the fifth and seventh was used for selecting bucks in the nucleus to produce does for the multiplier tier and bucks for the commercial tier, respectively. The SGs of the sixth and eighth produced does in the multiplier for breeding does of the multiplier and the bucks of the commercial, respectively. The SG of the ninth group produced bucks into the commercial tier to breed does and slaughtering animals. Lastly, the SG of the tenth group produced does into the commercial tier for breeding does into the commercial tier and as a slaughtering animal. The gene flow of sire or dam from one tier to another was not performed. Therefore a close breeding scheme of three tiers was applied. The following assumptions were considereating; all SGs had information records on each individual, mating was nature, selection criteria were measured, and selection was done by breeder (see Table 1). 2.2. ZPLAN+ modelling software The computer ZPLAN+ software was used (Täubert et al., 2010) for simulating a close breeding program of Ardi goat. The breeding objectives were to maximize the genetic gain of both milk and meat yields. As a consequence, selection criteria were total (TMY) and partial milk yield (PMY) of doe, total meat yield of the doe (TMY), weight of the doe at kidding (DWt), birth weight of kids (BWt), weaning weight of kids (WWt), daily gain of kids (DGK) and market weight of 6-month old kids (MWt) (Table 2). Furthermore, economic values of the breeding objectives were calculated from cost of inputs (Table 3). A socio-economic survey was
Table 3 Input parameters for modelling the breeding programs of Ardi goat and traits cost used in estimation breeding values of breeding objectives. Biological parameters
Value
Economic parameters
Cost (SAR)
Productive lifetime bucks in Nucleus unit Productive lifetime for does in breeding unit Productive lifetime for sires in Multiplier unit Productive lifetime for does in Multiplier unit Buck survival rate in Nucleus Doe survival rate in Nucleus Buck survival rate in Multiplier Doe survival rate in Multiplier Buck survival rate in commercial unit Doe survival rate in commercial unit Age at first calving for sires in Nucleus unit Age at first calving for dams in Nucleus unit Age at first calving for sires in Multiplier unit Age at first calving for dams in Multiplier unit
3.5 years 3.0 years 3.0 years 4.0 years 85% 85% 95% 95% 80% 85% 2.0 years 1.5 years 2.5 years 2.0 years
Price of feed Doe recording milk total Doe recording milk partial Recording birth weight Recording weaning weight Recording dam weight Recording prolificacy Recording daily gain Fixed costs Labour cost per animal Veterinary services cost/animal Reproduction costs per doe Price of milk per kilogramme Price of meat per kilogramme
50.0 10.0 10.0 15.0 5.0 2.5 2.5 50 20 20 10 7.5 8.0 40.0
Parameter Investment parameters Kidding interval Kidding rate Pre-weaning survival rate Post weaning survival rate Doe survival rate Doe weight Milk yield Lactation period Daily milk yield Replacement rate per doe per year Weaning age in days Sale age in days Economic Value for meat = 20.56 Economic Value for milk = 1.10 Date of calculation, March, 2017
Value
Parameter
Value
365,00 days 1.6 Kids 0.90% 0.94% 0.96% 60 kg 130 kg 150 days 0.70 l 0.20% 75 day 180 day
Constant of maintenance energy requirement Constant of energy for production Constant of energy for production Birth weight Weaning weight Daily gain Sale weight for males Sale weight for females Energy content in feed mix/kg Energy content in pasture/kg Energy content in concentrates/kg Supplements
0.35 0.40 0.35 3.50 kg 16.00 kg 0.15gram 40.00 kg 35.00 kg 2500 kcal 2000 kcal 3100 kcal 50.00 kcal
Please cite this article in press as: Aljumaah, R.S. Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia. Journal of the Saudi Society of Agricultural Sciences (2018), https://doi.org/10.1016/j.jssas.2018.02.001
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R.S. Aljumaah / Journal of the Saudi Society of Agricultural Sciences xxx (2018) xxx–xxx
Table 4 Phenotypic standard deviations (rP), Heritabilities and (Repetabilities) on the diagonal, Phenotypic (above the diagonal) and genotypic (lower the diagonal) correlations among selection criteria applied to the simulations scenarios. Traits
(rP)
Birth Wt
Daily gain
Dam Wt
Kid survival
Market Wt
Neonatal Mortality
Partial milk
Weaning Wt
Milk yield
Prolificacy
Birth Wt
0.82
0.20
0.16
0.38
0.18
0.12
0.3
0.34
0.13
0.3
Daily gain
0.01
10.41 (0.7) 0.3
0.4
0.54
0.80
0.05
0.4
0.09
0.4
0.15
Dam Wt
11.5
0.05
0.37 (0.63) 0.0
0.67
0.54
0.20
0.3
0.25
0.3
0.0
Kid survival
0.05
0.63
0.43
0.41 (0.90) 0.73
0.75
0.40
0.30
0.70
0.66
0.63
Market Wt
4.9
0.54
0.35
0.54
0.50 (0.83) 0.72
0.0
0.1
0.39
0.41
0.20
Neonatal Mortality
0.01
0.9
0.03
0.10
0.15
0.36 (0.78) 0.0
0.3
0.0
0.40
0.30
Partial milk
0.56
0.1
0.0
0.15
0.3
0.1
0.05 (0.38) 0.3
0.0
0.52
0.3
Weaning Wt
3.0
0.45
0.4
0.2
0.45
0.37
0.0
0.27 (0.59) 0.13
0.0
0.25
Milk yield
25.5
0.2
015
0.45
0.75
0.2
0.3
0.89
0.33 (0.60) 0.1
0.35
Prolificacy
0.3
0.3
0.25
0.0
0.34
0.25
0.23
0.25
0.42
0.38 (0.71) 0.26
performed in order to provide the economic data and value of the costs. The estimating economic breeding value (EBV) was estimated based on the model developed by Rewe et al. (2006). The economic optimization for simulated scenario was then performed utilizing the generated EBVs for both breeding objectives of meat and milk yield. 2.3. Input parameters The record parameters of phenotype, performance and pedigree were the records of Ardi goat nucleus farm in a Dhrumaa region near Riyadh city, KSA. The input simulated data were presented in Table 3. The genetic parameters were based on collecting and from literature for data Ardi goat and tropical goat breeds (AlSaef, 2013a, 2013b; Kebedeet al., 2011; Rashidi et al., 2011; Khalil et al., 2010; Al-Atiyat et al., 2010; Tabbaa and Al-Atiyat, 2009 (Tables 3 and 4). They were phenotypic standard deviation, heritability, repeatability and genetic and phenotypic correlation of selection criterion (Table 4).
3. Results and discussion The genetic gain for each selection group was illustrated in Fig. 1. The genetic gain was simulated for each group and thus a resulted breeding duration was 3.6 years. During this duration, the predicted annual genetic gains (Delta gain; DG) of the breeding objectives for meat and milk yields were noticed. The highest DG was achieved in the selection groups of BN > DN and DN > DN. In general, higher genetic gain was reported in nucleus scheme in a comparison with other schemes. However, genetic gain for both objectives reduced in multiplier scheme. There were no results of genetic gain in the commercial scheme because of difficulty in performing recording in this scheme. The noticed results that the increase in proportions of selected animals did not reflect an increase in genetic gain. This result is expected because the genetic gain is more related to how genetically superior the selected animals rather than the selected animals. Therefore, the selected animals of higher proportion for the multiplier and the commercial schemes had less genetic superiority than those selected for nucleus scheme. Consequently, in agreement with Archer et al. (2004), the increase in the genetic gain would be achieved when more selected bucks used. Furthermore, selection intensity
(0.12) 0.25
increases for breeding males resulting in higher genetic gains compared to does (Rewe et al., 2010). The bucks of the nucleus had the highest impact on the genetic gain. This is the usual case of goats breeding in favor of producing more meat (Shrestha and Fahmy, 2007). This impact was because of their genes solely flow into the other two tiers. Indeed, the achieved genetic gain in the studied breeding objectives would come from an appropriate and suitable studied selection criteria (Table 2). It seems, as a selection criterion, that body weight at marketing was the contributor to the increase in the genetic gain of meat production, whereas milk yield per doe was the main contributors to milk production. The resulted improvement in genetic gain was resulted from increasing in selection accuracy. However, some difficulties were assumed for measuring milk yield criterion in the multiplier tier on the selected does because the recording was not performed. This assumption was based on the survey outcomes that Ardi goat farmers are not in favor of selection for milk production. Generally, there are difficulty to access the local market, where goat milk can be sold for preferable and worthy prices. In most livestock species reared in the desert, the ability of owners to access well established markets are limited (Al-Atiyat, 2014a). The resulted simulated genetic gain per doe with cost and profit values were optimistic (Table 5). The average genetic gain of meat and milk yield per doe were 0.153 kg and 0.145 l, respectively, in a 3.61 years breeding program. As a consequence of the increase of the genetic gain per doe, profitability increased. A study with Hejaz goat, Al-Atiyat and Aljumaah (2013) reported an increase of 0.199 and 0.107 kg in milk and meat yield, respectively. Similarly, the average genetic gain of 100 grams of meat and 120 milliliter of milk were reported for simulated breeding scheme of Jordanian Black Bedouin goat (Al-Atiyat, 2014b). A simulation study showed that there was little difference in annual genetic gain ranged of 0.8702–0.8724 and 0.365–0.367 kg of six-month old kids for Western Lowland and Abergelle goat, respectively (Abegaz et al., 2014). In the same study, relatively lower genetic gains of 0.066–0.0697 kg of milk yield for Abergelle goats, whereas higher range of 0.114–11.37 kg of milk yield was predicted from alternatives. More higher genetic gain (0.261–0.809) kg milk yield were predicted in different alternatives of Kenyan dairy goat (Bettet et al., 2012). The economic return per doe and selected trait of interest; milk and meat production are presented in Table 5. The total return per doe as a result of genetic gain was 36.48 SAR. The major contribu-
Please cite this article in press as: Aljumaah, R.S. Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia. Journal of the Saudi Society of Agricultural Sciences (2018), https://doi.org/10.1016/j.jssas.2018.02.001
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R.S. Aljumaah / Journal of the Saudi Society of Agricultural Sciences xxx (2018) xxx–xxx
Fig. 1. Percentage of annual genetic gain change of milk and meat production, selected animal and their selection intensity for Ardi goat in the breeding program scheme.
Table 5 Genetic Gain per Time Unit, Discounted Return, Discounted breeding costs per animal unit. Genetic Gain per Time Unit Mean breeding Interval Meat yield (kg) Milk yield (l)
3.61 0.153 0.145
Discounted Return Total per animal unit (SAR) Meat production (SAR) Milk production (SAR)
36.48 29.68 6.66
worthy to consider as an alternative to select out better goat individuals. 4. Conclusion
Discounted breeding costs per animal unit Fix Costs (SAR) 0.284 Variable costs (SAR) 11.56 Costs sum (SAR) 11.84 Discounted profit (SAR) 24.50
tor to this return came from meat production (29.68 SAR), whereas milk production had a limited share (6.66 SAR). Furthermore, discounted breeding profit was 24.50 SAR per doe. These results declare that simulating close breeding scheme for Ardi goat in order to increase their milk and meat production would result in a notable increase in genetic gain and thus a profit per doe. Generalizing this outcome per herd or national population would have resulted in huge increment in genetic gain as well as economic profits. For modeling feasibility of improving meat production of Black Bedouin goats enterprises in Jordan, the overall annual profit was economically profitable per doe (0.264 EUR or around 1.5 SAR) (Al-Atiyat et al., 2010). Our result indicated that the genetic gain of breeding objectives in the present breeding program was estimated under subtropical conditions of KSA. Thus, the simulated breeding program had reliable genetic gain of the traits and thus
The present study shows good potential for genetic improvement of the Ardi goat breed for more milk and meat production. The successful breeding program scheme is the close three tier scheme breeding program that mimics the real condition of for improving productivity in subtropical conditions rearing Ardi goat in KSA. Therefore, such breeding program is a sustainable program of the short term period in which genetic gain and profitability increased. Further work on the application of the long term breeding program is needed, and estimation of required genetic and economic values of Ardi goat breed is also required. It is worthy to recommend a research work deal with providing reliable genetic estimates of Saudi national livestock in order to establish a reliable and sustainable national breeding program. Acknowledgement This work was supported by King Abdulaziz City for Science and Technology [Project number M-P-34-21], which is gratefully acknowledged. The author would like to extend his sincere appreciation to professor Raed Al-Atiyat for his cooperation and great help in the data analysis.
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R.S. Aljumaah / Journal of the Saudi Society of Agricultural Sciences xxx (2018) xxx–xxx breeding programs for two Ethiopian goat breeds. Acta Agraria Kaposváriensis 18 (1), 47–55. Alamer, M., 2006. Physiological responses of Saudi Arabia indigenous goats to water deprivation. Small Ruminant Res. 63 (1–2), 100–109. Aljumaah, R.S., Musthafa, M., Al-Shaikh, M., Badri, O., Hussein, M., 2012. Genetic diversity of Ardi goat based on microsatellite analysis. African J. Biotech. 11 (100), 16539–16545. Al-Atiyat, R.M., 2014a. Role of small-scale dairy sector in food security and poverty alleviation. J. Food Agric. Environ. 12 (2), 427–433. Al-Atiyat, R.M., 2014b. Sustainable breeding program of black bedouin goat for conserving genetic diversity: simulated scenarios for InSitu conservation. Jordan J. Agric. Sci. 10 (1), 83–95. Al-Atiyat, R.M., Rewe, T., Herold, P., Zárate, A., 2010. A Simulation study to compare different breeding scenarios for Black Bedouin Goat in Jordan. Eg. J. Sheep Goats Sci. 5 (1), 83–92. Al-Atiyat, R.M., Alobre, M., Aljumaah, R.S., Alshaikh, M.A., 2015. Microsatellite based genetic diversity and population structure of three Saudi goat breeds. Small Ruminant Res. 130, 90–94. Al-Atiyat, R.M., Aljumaah, R.S., 2013. Simulated breeding scenarios for improving Hejaz goat performance in subtropics. J. Food Agric. Environ. 11 (2), 440–444. Al-Saef, A.M., 2013a. Genetic parameters for milk yield and components traits of Saudi Arabian Aradi, Damascus goats and their crosses. Asia Life Sci. 22 (2), 479–488. Al-Saef, A.M., 2013b. Genetic and phenotypic parameters of body weights in Saudi Aradi goat and their crosses with Syrian Damascus goat. Small Ruminant Res. 112 (1–3), 35–38. Bettet, R.C., Okeyo, A.M., Kosgey, I.S., Kahi, A.K., Peters, K.J., 2012. Evaluation of alternative selection objectives and schemes for optimisation of village goat improvement programs. Livest. Res. Rural Dev., 24–31 Galal, S., 2005. Biodiversity in goats. Small Ruminant Res. 60, 75–81. Kebede, T., Haile, A., Dadi, H., Alemu, T., 2011. Genetic and phenotypic parameter estimates for reproduction traits in indigenous Arsi-Bale goats. Trop. Anim. Health Prod. 11, 34–38.
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Khalil, M.H., Mohamed, K.M., Al-Saef, A.M., Zeitoun, M.M., El-Zarei, M.F., 2010. Crossbreeding components for growth, carcass and meat composition traits in crossing Saudi Aradi with Damascus goats. Small Ruminant Res. 94, 10–16. MOA (Minsitry of Agriculture), 2013. Annual statistical book-Numeration of Livestocks. Nitter, G., Graser, H.U., 1994. ZPLAN – a PC program to optimize livestock selection programs. In: Proceedings 5th World Congress on Genetics Applied to Livestock Production 22, pp. 77–78. Rashidi, A., Bishop, S.C., Matika, O., 2011. Genetic parameter estimates for preweaning performance and reproduction traits in Markhoz goats. Small Ruminant Res. 1, 100–106. Rewe, T.O., Indetie, D., Ojango, J., Kahi, A., 2006. Breeding objectives for the Boran breed in Kenya: model development and application to pasture-based production systems. Anim. Sci. J. 77, 163–177. Rewe, T.O., Herold, P., Piepho, H.-P., Kahi, A.K., ValleZárate, A., 2010. Genetic and economic evaluation of a basic breeding programmer for Kenya Boran cattle. Trop. Anim. Health Prod. 42, 327–340. Roessler, R., Herold, P., Willam, A., Piepho, H.-P., Thuy, L.T., Valle Zárate, A., 2009. Modelling of a breed group recording scheme for market-oriented smallholder pig producers in Northwest Vietnam. Livestock Sci. 123, 241–248. Sabir, J., Mohammed, S.M., Mutawakil, H.Z., El-Hanafy, A.A., Mohamed, M.A., 2012. Genetics similarity among four breeds of goat in Saudi Arabia detected by random amplified polymorphic DNA marker. Afr. J. Biotechnol. 11 (17), 3958– 3963. Shrestha, J.N.B., Fahmy, M.H., 2007. Breeding goats for meat production: a review (3) selection and breeding strategies. Small Ruminant Res. 67, 113–125. Tabbaa, M., Al-Atiyat, R., 2009. Breeding objectives, selection criteria and factors influencing them for goat breeding in Jordan. Small Ruminant Res. 84, 8–15. Täubert, H., Reinhardt, F., Simianer, H., 2010. ZPLAN+: A new software to evaluate and optimize animal breeding programs. In: Proceedings of the 9th World Congress on Genetics Applied to Livestock Production. 1–6 August 2010, Leipzig, Germany.
Please cite this article in press as: Aljumaah, R.S. Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia. Journal of the Saudi Society of Agricultural Sciences (2018), https://doi.org/10.1016/j.jssas.2018.02.001
Contents lists available at ScienceDirect
Journal of the Saudi Society of Agricultural Sciences journal homepage: www.sciencedirect.com
Full length article
Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia Riyadh Saleh Aljumaah Department of Animal Production, Food and Agriculture College, King Saud University, PO Box 2460, Riyadh 11451, Saudi Arabia
a r t i c l e
i n f o
Article history: Received 6 November 2017 Revised 29 January 2018 Accepted 11 February 2018 Available online xxxx Keywords: Goat Breeding simulation Subtropics Close scheme
a b s t r a c t Lack of a sustainable long-term breeding program in developing countries enhanced finding alternatives such as simulation solutions. The aim of this research work was to find solutions in order to simulate an optimized national Ardi goat breeding program under sub-tropical conditions of the Kingdom of Saudi Arabia (KSA). The simulated program was considered a close scheme of three tiers. They were nucleus, multiplier and commercial herds representing 1, 10, and 89%, respectively, of the total does populations (303,000 heads). The breeding nucleus structure was comprised of 10 selection groups (SG). The simulated scenarios resulted in predicting increment in meat and milk yield per doe by 0.153 kg and 0.145 l, respectively, under assumed genetic, production and physiological parameters. Furthermore, the bioinformatics solution provided simulated models of economically profitable. It is worthy to recommend a research work deal with providing reliable genetic estimates of Saudi national livestock in order to establish sustainable national breeding program. Ó 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Despite the fact that the kingdom of Saudi Arabia (KSA) is still far ahead from self-sufficiency of red meat and milk from local goat (MOA, 2013). In particular, goat farming is of primary importance to the national agricultural economy considering inputs needed for sheep or cattle farming. Most goat breeds in KSA are reared in the desert. The main breeds of goats in Saudi Arabia are Ardi (Aardi or Ardhi), Jabili, Bishi, Habsi and Harri (AlAmer, 2006; Aljumaah et al., 2012; Sabir et al., 2012; Al-Atiyat et al., 2015). Ardi is the largest breed and reared under harsh desert conditions. Their superior tolerance to the harsh environment, less demanding status of nutritional requirements and reasonable profit return from meat and milk production, make them the frontline in animal farming at KSA. It is well known that improving goat productivity depend mainly on genetic selection within each breed considering a well optimized breeding program design. Goat selection, in general, has mostly performed by farmers, and rarely by breeders (Galal, 2005). However, the farmers of goat are lacking skills for breeding
and genetically improving their herds beforehand as result of lacking knowledge of defining breeding objectives and selection criteria (Tabbaa and Al-Atiyat, 2009). In the absence of national goat breeding program in KSA, the most important step is sustainable breeding program design. AlSaef (2013a) recommended to develop meat and milk goat lines based on a wide range of breeding values found within goat breeds. Nevertheless, the sustainable breeding programs at national scale have been reported for goats in tropics and subtropics utilizing simulation and bioinformatics tools (Rewe et al., 2006; Roessler et al., 2009). In particular, bioinformatics tools such as related computer softwares facilitate optimizing breeding programs of different livestock using simulation approaches and predict best breeding program ahead. Most common bioinformatics software is ZPLAN+ software that provide genetic gain and economic return on comprehensive breeding program (Nitter and Graser, 1994). Therefore, the aim of this study was to show the results of simulated scenarios that optimize breeding program of Ardi goat under the sub-tropical conditions of KSA using ZPLAN+ software.
Peer review under responsibility of King Saud University.
2. Materials and methods
1. Introduction
2.1. Selection groups
Production and hosting by Elsevier E-mail address: [email protected]
The simulated breeding program mimicked a close scheme comprised of nucleus, multiplier and commercial tiers. The first
https://doi.org/10.1016/j.jssas.2018.02.001 1658-077X/Ó 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Aljumaah, R.S. Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia. Journal of the Saudi Society of Agricultural Sciences (2018), https://doi.org/10.1016/j.jssas.2018.02.001
2
R.S. Aljumaah / Journal of the Saudi Society of Agricultural Sciences xxx (2018) xxx–xxx
was the tier in which genetic gain estimated for bucks to be sires in the second tier or sires in the third. The nucleus was closed only to replacements from its herd based on selection index. The multiplier tier spread the gain of chosen animals for utilizing into the
Table 1 Transmission matrix of the breeding plan with 10 selection groups. Scheme
Selection Group
Size
Nucleus unit
BN > BN BN > DN DN > BN DN > DN BN > BM BN > DM BM > BP BM > DP DM > BP DM > DP
100 250 100 250 1000 1000 10,000 10,000 5000 5000
Multiplier unit
Table 2 Description of breeding objectives and their selection criteria. Breeding objectives
Economic Value
Description
Total Meat yield
40.4
Amount of meat produced from 180 days kid(s) per doe Amount of milk produced per doe
Total Milk yield 8.0 Selection Criteria Birth Weight (BWt) (kg) Weaning Weight (WWt) (kg) Daily gain Weight (DGWt) (kg) Total Milk production (TMP) (kg) Partial milk production (PMP) (kg) Prolificacy (PRFC) (kg) Dam Weight (DWt) (kg) Market live weight (MBwt) (kg)
Kids birth weight Weaning weight of kids Daily gain of kids Milk produced per lactation Partial milk produced in a lactation Number of kids per kidding Does weight at kidding Body weight of kids at marketing time (180 days)
commercial unit. The population of nucleus breeding was 2700 head, representing 09% of estimated total does in the country (303,000 heads). The breeding structure of the nucleus and multiplier units are described as 10 selection groups (SG). The SGs in the nucleus were the first and third to produce selected bucks and does, respectively, whereas the SGs of the second and fourth breed does of bucks and does for the nucleus, respectively. The SGs of the fifth and seventh was used for selecting bucks in the nucleus to produce does for the multiplier tier and bucks for the commercial tier, respectively. The SGs of the sixth and eighth produced does in the multiplier for breeding does of the multiplier and the bucks of the commercial, respectively. The SG of the ninth group produced bucks into the commercial tier to breed does and slaughtering animals. Lastly, the SG of the tenth group produced does into the commercial tier for breeding does into the commercial tier and as a slaughtering animal. The gene flow of sire or dam from one tier to another was not performed. Therefore a close breeding scheme of three tiers was applied. The following assumptions were considereating; all SGs had information records on each individual, mating was nature, selection criteria were measured, and selection was done by breeder (see Table 1). 2.2. ZPLAN+ modelling software The computer ZPLAN+ software was used (Täubert et al., 2010) for simulating a close breeding program of Ardi goat. The breeding objectives were to maximize the genetic gain of both milk and meat yields. As a consequence, selection criteria were total (TMY) and partial milk yield (PMY) of doe, total meat yield of the doe (TMY), weight of the doe at kidding (DWt), birth weight of kids (BWt), weaning weight of kids (WWt), daily gain of kids (DGK) and market weight of 6-month old kids (MWt) (Table 2). Furthermore, economic values of the breeding objectives were calculated from cost of inputs (Table 3). A socio-economic survey was
Table 3 Input parameters for modelling the breeding programs of Ardi goat and traits cost used in estimation breeding values of breeding objectives. Biological parameters
Value
Economic parameters
Cost (SAR)
Productive lifetime bucks in Nucleus unit Productive lifetime for does in breeding unit Productive lifetime for sires in Multiplier unit Productive lifetime for does in Multiplier unit Buck survival rate in Nucleus Doe survival rate in Nucleus Buck survival rate in Multiplier Doe survival rate in Multiplier Buck survival rate in commercial unit Doe survival rate in commercial unit Age at first calving for sires in Nucleus unit Age at first calving for dams in Nucleus unit Age at first calving for sires in Multiplier unit Age at first calving for dams in Multiplier unit
3.5 years 3.0 years 3.0 years 4.0 years 85% 85% 95% 95% 80% 85% 2.0 years 1.5 years 2.5 years 2.0 years
Price of feed Doe recording milk total Doe recording milk partial Recording birth weight Recording weaning weight Recording dam weight Recording prolificacy Recording daily gain Fixed costs Labour cost per animal Veterinary services cost/animal Reproduction costs per doe Price of milk per kilogramme Price of meat per kilogramme
50.0 10.0 10.0 15.0 5.0 2.5 2.5 50 20 20 10 7.5 8.0 40.0
Parameter Investment parameters Kidding interval Kidding rate Pre-weaning survival rate Post weaning survival rate Doe survival rate Doe weight Milk yield Lactation period Daily milk yield Replacement rate per doe per year Weaning age in days Sale age in days Economic Value for meat = 20.56 Economic Value for milk = 1.10 Date of calculation, March, 2017
Value
Parameter
Value
365,00 days 1.6 Kids 0.90% 0.94% 0.96% 60 kg 130 kg 150 days 0.70 l 0.20% 75 day 180 day
Constant of maintenance energy requirement Constant of energy for production Constant of energy for production Birth weight Weaning weight Daily gain Sale weight for males Sale weight for females Energy content in feed mix/kg Energy content in pasture/kg Energy content in concentrates/kg Supplements
0.35 0.40 0.35 3.50 kg 16.00 kg 0.15gram 40.00 kg 35.00 kg 2500 kcal 2000 kcal 3100 kcal 50.00 kcal
Please cite this article in press as: Aljumaah, R.S. Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia. Journal of the Saudi Society of Agricultural Sciences (2018), https://doi.org/10.1016/j.jssas.2018.02.001
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R.S. Aljumaah / Journal of the Saudi Society of Agricultural Sciences xxx (2018) xxx–xxx
Table 4 Phenotypic standard deviations (rP), Heritabilities and (Repetabilities) on the diagonal, Phenotypic (above the diagonal) and genotypic (lower the diagonal) correlations among selection criteria applied to the simulations scenarios. Traits
(rP)
Birth Wt
Daily gain
Dam Wt
Kid survival
Market Wt
Neonatal Mortality
Partial milk
Weaning Wt
Milk yield
Prolificacy
Birth Wt
0.82
0.20
0.16
0.38
0.18
0.12
0.3
0.34
0.13
0.3
Daily gain
0.01
10.41 (0.7) 0.3
0.4
0.54
0.80
0.05
0.4
0.09
0.4
0.15
Dam Wt
11.5
0.05
0.37 (0.63) 0.0
0.67
0.54
0.20
0.3
0.25
0.3
0.0
Kid survival
0.05
0.63
0.43
0.41 (0.90) 0.73
0.75
0.40
0.30
0.70
0.66
0.63
Market Wt
4.9
0.54
0.35
0.54
0.50 (0.83) 0.72
0.0
0.1
0.39
0.41
0.20
Neonatal Mortality
0.01
0.9
0.03
0.10
0.15
0.36 (0.78) 0.0
0.3
0.0
0.40
0.30
Partial milk
0.56
0.1
0.0
0.15
0.3
0.1
0.05 (0.38) 0.3
0.0
0.52
0.3
Weaning Wt
3.0
0.45
0.4
0.2
0.45
0.37
0.0
0.27 (0.59) 0.13
0.0
0.25
Milk yield
25.5
0.2
015
0.45
0.75
0.2
0.3
0.89
0.33 (0.60) 0.1
0.35
Prolificacy
0.3
0.3
0.25
0.0
0.34
0.25
0.23
0.25
0.42
0.38 (0.71) 0.26
performed in order to provide the economic data and value of the costs. The estimating economic breeding value (EBV) was estimated based on the model developed by Rewe et al. (2006). The economic optimization for simulated scenario was then performed utilizing the generated EBVs for both breeding objectives of meat and milk yield. 2.3. Input parameters The record parameters of phenotype, performance and pedigree were the records of Ardi goat nucleus farm in a Dhrumaa region near Riyadh city, KSA. The input simulated data were presented in Table 3. The genetic parameters were based on collecting and from literature for data Ardi goat and tropical goat breeds (AlSaef, 2013a, 2013b; Kebedeet al., 2011; Rashidi et al., 2011; Khalil et al., 2010; Al-Atiyat et al., 2010; Tabbaa and Al-Atiyat, 2009 (Tables 3 and 4). They were phenotypic standard deviation, heritability, repeatability and genetic and phenotypic correlation of selection criterion (Table 4).
3. Results and discussion The genetic gain for each selection group was illustrated in Fig. 1. The genetic gain was simulated for each group and thus a resulted breeding duration was 3.6 years. During this duration, the predicted annual genetic gains (Delta gain; DG) of the breeding objectives for meat and milk yields were noticed. The highest DG was achieved in the selection groups of BN > DN and DN > DN. In general, higher genetic gain was reported in nucleus scheme in a comparison with other schemes. However, genetic gain for both objectives reduced in multiplier scheme. There were no results of genetic gain in the commercial scheme because of difficulty in performing recording in this scheme. The noticed results that the increase in proportions of selected animals did not reflect an increase in genetic gain. This result is expected because the genetic gain is more related to how genetically superior the selected animals rather than the selected animals. Therefore, the selected animals of higher proportion for the multiplier and the commercial schemes had less genetic superiority than those selected for nucleus scheme. Consequently, in agreement with Archer et al. (2004), the increase in the genetic gain would be achieved when more selected bucks used. Furthermore, selection intensity
(0.12) 0.25
increases for breeding males resulting in higher genetic gains compared to does (Rewe et al., 2010). The bucks of the nucleus had the highest impact on the genetic gain. This is the usual case of goats breeding in favor of producing more meat (Shrestha and Fahmy, 2007). This impact was because of their genes solely flow into the other two tiers. Indeed, the achieved genetic gain in the studied breeding objectives would come from an appropriate and suitable studied selection criteria (Table 2). It seems, as a selection criterion, that body weight at marketing was the contributor to the increase in the genetic gain of meat production, whereas milk yield per doe was the main contributors to milk production. The resulted improvement in genetic gain was resulted from increasing in selection accuracy. However, some difficulties were assumed for measuring milk yield criterion in the multiplier tier on the selected does because the recording was not performed. This assumption was based on the survey outcomes that Ardi goat farmers are not in favor of selection for milk production. Generally, there are difficulty to access the local market, where goat milk can be sold for preferable and worthy prices. In most livestock species reared in the desert, the ability of owners to access well established markets are limited (Al-Atiyat, 2014a). The resulted simulated genetic gain per doe with cost and profit values were optimistic (Table 5). The average genetic gain of meat and milk yield per doe were 0.153 kg and 0.145 l, respectively, in a 3.61 years breeding program. As a consequence of the increase of the genetic gain per doe, profitability increased. A study with Hejaz goat, Al-Atiyat and Aljumaah (2013) reported an increase of 0.199 and 0.107 kg in milk and meat yield, respectively. Similarly, the average genetic gain of 100 grams of meat and 120 milliliter of milk were reported for simulated breeding scheme of Jordanian Black Bedouin goat (Al-Atiyat, 2014b). A simulation study showed that there was little difference in annual genetic gain ranged of 0.8702–0.8724 and 0.365–0.367 kg of six-month old kids for Western Lowland and Abergelle goat, respectively (Abegaz et al., 2014). In the same study, relatively lower genetic gains of 0.066–0.0697 kg of milk yield for Abergelle goats, whereas higher range of 0.114–11.37 kg of milk yield was predicted from alternatives. More higher genetic gain (0.261–0.809) kg milk yield were predicted in different alternatives of Kenyan dairy goat (Bettet et al., 2012). The economic return per doe and selected trait of interest; milk and meat production are presented in Table 5. The total return per doe as a result of genetic gain was 36.48 SAR. The major contribu-
Please cite this article in press as: Aljumaah, R.S. Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia. Journal of the Saudi Society of Agricultural Sciences (2018), https://doi.org/10.1016/j.jssas.2018.02.001
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R.S. Aljumaah / Journal of the Saudi Society of Agricultural Sciences xxx (2018) xxx–xxx
Fig. 1. Percentage of annual genetic gain change of milk and meat production, selected animal and their selection intensity for Ardi goat in the breeding program scheme.
Table 5 Genetic Gain per Time Unit, Discounted Return, Discounted breeding costs per animal unit. Genetic Gain per Time Unit Mean breeding Interval Meat yield (kg) Milk yield (l)
3.61 0.153 0.145
Discounted Return Total per animal unit (SAR) Meat production (SAR) Milk production (SAR)
36.48 29.68 6.66
worthy to consider as an alternative to select out better goat individuals. 4. Conclusion
Discounted breeding costs per animal unit Fix Costs (SAR) 0.284 Variable costs (SAR) 11.56 Costs sum (SAR) 11.84 Discounted profit (SAR) 24.50
tor to this return came from meat production (29.68 SAR), whereas milk production had a limited share (6.66 SAR). Furthermore, discounted breeding profit was 24.50 SAR per doe. These results declare that simulating close breeding scheme for Ardi goat in order to increase their milk and meat production would result in a notable increase in genetic gain and thus a profit per doe. Generalizing this outcome per herd or national population would have resulted in huge increment in genetic gain as well as economic profits. For modeling feasibility of improving meat production of Black Bedouin goats enterprises in Jordan, the overall annual profit was economically profitable per doe (0.264 EUR or around 1.5 SAR) (Al-Atiyat et al., 2010). Our result indicated that the genetic gain of breeding objectives in the present breeding program was estimated under subtropical conditions of KSA. Thus, the simulated breeding program had reliable genetic gain of the traits and thus
The present study shows good potential for genetic improvement of the Ardi goat breed for more milk and meat production. The successful breeding program scheme is the close three tier scheme breeding program that mimics the real condition of for improving productivity in subtropical conditions rearing Ardi goat in KSA. Therefore, such breeding program is a sustainable program of the short term period in which genetic gain and profitability increased. Further work on the application of the long term breeding program is needed, and estimation of required genetic and economic values of Ardi goat breed is also required. It is worthy to recommend a research work deal with providing reliable genetic estimates of Saudi national livestock in order to establish a reliable and sustainable national breeding program. Acknowledgement This work was supported by King Abdulaziz City for Science and Technology [Project number M-P-34-21], which is gratefully acknowledged. The author would like to extend his sincere appreciation to professor Raed Al-Atiyat for his cooperation and great help in the data analysis.
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Please cite this article in press as: Aljumaah, R.S. Simulated genetic gain of a close breeding program for Ardi goat in Saudi Arabia. Journal of the Saudi Society of Agricultural Sciences (2018), https://doi.org/10.1016/j.jssas.2018.02.001