Research Article
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Year 2020, Volume: 4 Issue: 3, 340 - 347, 15.09.2020

Damla Özsayın

https://doi.org/10.31015/jaefs.2020.3.13

Abstract

References

  • Aksoy, A., Külekçi, M. and Yavuz, F. (2011). Analysis of the factors affecting the of adoption of innovations a in dairy farms in Erzurum province. Turkey. African Journal of Agricultural Research, 6, 2966-2970. Doi: https://doi.org/10.5897/AJAR11.162
  • Asfaw, S., Shiferaw, B., Simtowe, F. and Haile, M.G. (2011). Agricultural technology adoption, seed access constraints and commercialization in Ethiopia. Journal of Development and Agricultural Economics, 3, 436-447. Retrieved from https://www.academicjournals.org/JDAE
  • Bamire A.S., Fabiyi, Y.L. and Manyong, B. (2002). Adoption pattern of fertilizer technology among farmers in the ecological zones of Southwestern Nigeria: a tobit analysis. Australian Journal of Agricultural Research, 53, 901- 910. Doi: https://doi.org/10.1071/AR01095
  • Beshir, H., Emana, B., Kassa, B. and Jema, H. (2012). Determinants of chemical fertilizer technology adoption in North Eastern Highlands of Ethiopia: the double hurdle approach. Journal of Research in Economics and International Finance, 1, 39-49. Retrieved from https://www.interesjournals.org/JREIF
  • Demir, N. and Yavuz, F. (2010). A regional comparative analysis of farmers’ approaches to livestock support policies. Atatürk University Journal of Agricultural Faculty, 41, 113-121. Retrieved from https://dergipark.org.tr/tr/download/article-file/35357
  • Gençdal, F., Terin, M. and Yıldırım, İ. (2015). A research on determination of factors affecting the artificial insemination of dairy farms: a case study of Van province of Gevaş district. Anadolu Journal Agricultural Sciences, 30, 254-259. Doi: https://doi.org/10.7161/anajas.2015.30.3.254-259
  • Howard, W.H. and Cranfield, J. (1995). Ontario beef producers' attitudes about artificial insemination. Canadian Journal of Agricultural Economics, 43, 305–314. Doi: https://doi.org/10.22004/ag.econ.171158
  • Howley, P., Donoghue, C.O. and Heanue, K. (2012). Factors affecting farmers’ adoption of agricultural innovations: a panel data analysis of the use of artificial insemination among dairy farmers in Ireland. Journal of Agricultural Science, 4, 171-179. Doi: https://doi.org/10.5539/jas.v4n6p171
  • Kaaya, H., Bashaasha, B. and Mutetikka, D. (2005). Determinants of utilization of artificial insemination (AI) services among Ugandan dairy farmers. Eastern Africa Journal of Rural Development, 21, 34-43. Doi: https://doi.org/10.4314/eajrd.v21i1.28370
  • Karahan, H., Özsayın, D., Karaman, S. (2015). Examination the socio-economic aspects of the business engaged in the cultivation of organic strawberries. Harran Journal of Agricultural Food Science, 19, 9-15. Retrieved from https://dergipark.org.tr/en/download/article-file/448727
  • Mal, P., Anik, A.R., Bauer, S. and Schmitz, P.M. (2012). Bt cotton adoption: a double-hurdle approach for North Indian farmers. Journal of Agrobiotechnology Management & Economics, 15, 294-302. Retrieved from https://www.agbioforum.org/v15n3/v15n3a05-mal.htm
  • Oladele, O.I., Antwi, M.A. and Kolawole, A.E. (2013). Factors influencing demand for animal health services and knowledge of biosecurity among livestock farmers along border villages of South Africa and Namibia. International Journal of Applied Research in Veterinary Medicine, 11, 123-129. Retrieved from https://www.jarvm.com/articles/Vol11Iss2/Vol11%20Iss2VETOladele.pdf
  • Özyürek, S., Koçyiğit, R. and Tüzemen, N. (2014). Structural features of dairy farmers in the Erzincan: the example of Çayırlı district. Journal of Tekirdağ Agriculture Faculty, 11, 19-26. Retrieved from http://jotaf.nku.edu.tr/T.Z.FDergisiY%C4%B1l2014-11say%C4%B1%283%29/duyuruayrinti/m/8254/11056/635
  • Paulos, A., Belay, K. and Hamito, D. (2004). Determinants of farmers’ willingness to pay for soil conservation practices in the Southeastern Highlands of Ethiopia. Land Degradation & Development, 15, 423-438. Doi: https://doi.org/10.1002/ldr.623
  • Sezgin, A., Kaya, E.T., Külekçi, M. and Kumbasaroğlu, H. (2010). Impact analysis on the adoption of the innovations generated for agricultural production: the case of Erzurum province. 9th National Congress of Agricultural Economics, Tokat, 22-24 Sept.
  • StataCorp (2005). Stata Longitudinal/Panel Data Reference Manual. College Station, TX: StataCorp LP.
  • Tambi, N.E., Mukhebi, W.A., Maina, W.O. and Solomon H.M. (1999). Probit analysis of livestock producers' demand for private veterinary services in the high potential agricultural areas of Kenya. Agricultural Systems, 59, 163-176. Doi: https://doi.org/10.1016/S0308-521X(98)00088-2
  • Tefera, S.S., Lagat, J.K. and Bett, H.K. (2014). Determinants of artificial insemination use by smallholder dairy farmers in Lemu-Bilbilo district, Ethiopia. International Journal of African and Asian Studies, 4, 91-98. Retrieved from https://iiste.org/Journals/index.php/JAAS/article/view/10019/10229
  • TURKSTAT (2018). Statistical indicators. Turkish Statistical Institute. Livestock Statistics Annual Report, Ankara. Available at http://www.turkstat.gov.tr/ (Accessed April 28, 2018).
  • Yamane, T. (1967). Elementary Sampling Theory. Englewood Cliffs. Prentice-Hall Inc, New Jersey.
  • Yohannes, R. (2014). Impact of artificial insemination of dairy cows on the livehoods of smallholder households: the case of Haramaya district. Master's Thesis. University of Haramaya, Ethiopia.

Factors affecting the use of artificial insemination of farmers in dairy farming

Year 2020, Volume: 4 Issue: 3, 340 - 347, 15.09.2020

Damla Özsayın

https://doi.org/10.31015/jaefs.2020.3.13

Abstract

The study was carried out in the districts of Harmacık, Keles, Orhaneli and Büyükorhan known as the mountain region of Bursa province, Turkey with the purpose of determining factors affecting farmers' use of artificial insemination in dairy farming. The number of the farms was determined using a stratified random sampling method. The data were collected from 252 dairy cattle farms between April and December 2018. The study employed the logit model to evaluate the data. The results revealed that the educational level of the farmers, dairy farming experience, the number of dairy cattle, livestock diseases, off-farm income, artificial insemination support and access to veterinary services variables had a positive effect on farmers' use of artificial insemination; whereas age and household size variables had a negative impact. As a result, the use of artificial insemination in dairy farm can be increased with farmers who are open to innovations regarding livestock activity and knowledgeable about artificial insemination. 

Keywords

Artificial insemination Dairy cattle Income Logit model

References

  • Aksoy, A., Külekçi, M. and Yavuz, F. (2011). Analysis of the factors affecting the of adoption of innovations a in dairy farms in Erzurum province. Turkey. African Journal of Agricultural Research, 6, 2966-2970. Doi: https://doi.org/10.5897/AJAR11.162
  • Asfaw, S., Shiferaw, B., Simtowe, F. and Haile, M.G. (2011). Agricultural technology adoption, seed access constraints and commercialization in Ethiopia. Journal of Development and Agricultural Economics, 3, 436-447. Retrieved from https://www.academicjournals.org/JDAE
  • Bamire A.S., Fabiyi, Y.L. and Manyong, B. (2002). Adoption pattern of fertilizer technology among farmers in the ecological zones of Southwestern Nigeria: a tobit analysis. Australian Journal of Agricultural Research, 53, 901- 910. Doi: https://doi.org/10.1071/AR01095
  • Beshir, H., Emana, B., Kassa, B. and Jema, H. (2012). Determinants of chemical fertilizer technology adoption in North Eastern Highlands of Ethiopia: the double hurdle approach. Journal of Research in Economics and International Finance, 1, 39-49. Retrieved from https://www.interesjournals.org/JREIF
  • Demir, N. and Yavuz, F. (2010). A regional comparative analysis of farmers’ approaches to livestock support policies. Atatürk University Journal of Agricultural Faculty, 41, 113-121. Retrieved from https://dergipark.org.tr/tr/download/article-file/35357
  • Gençdal, F., Terin, M. and Yıldırım, İ. (2015). A research on determination of factors affecting the artificial insemination of dairy farms: a case study of Van province of Gevaş district. Anadolu Journal Agricultural Sciences, 30, 254-259. Doi: https://doi.org/10.7161/anajas.2015.30.3.254-259
  • Howard, W.H. and Cranfield, J. (1995). Ontario beef producers' attitudes about artificial insemination. Canadian Journal of Agricultural Economics, 43, 305–314. Doi: https://doi.org/10.22004/ag.econ.171158
  • Howley, P., Donoghue, C.O. and Heanue, K. (2012). Factors affecting farmers’ adoption of agricultural innovations: a panel data analysis of the use of artificial insemination among dairy farmers in Ireland. Journal of Agricultural Science, 4, 171-179. Doi: https://doi.org/10.5539/jas.v4n6p171
  • Kaaya, H., Bashaasha, B. and Mutetikka, D. (2005). Determinants of utilization of artificial insemination (AI) services among Ugandan dairy farmers. Eastern Africa Journal of Rural Development, 21, 34-43. Doi: https://doi.org/10.4314/eajrd.v21i1.28370
  • Karahan, H., Özsayın, D., Karaman, S. (2015). Examination the socio-economic aspects of the business engaged in the cultivation of organic strawberries. Harran Journal of Agricultural Food Science, 19, 9-15. Retrieved from https://dergipark.org.tr/en/download/article-file/448727
  • Mal, P., Anik, A.R., Bauer, S. and Schmitz, P.M. (2012). Bt cotton adoption: a double-hurdle approach for North Indian farmers. Journal of Agrobiotechnology Management & Economics, 15, 294-302. Retrieved from https://www.agbioforum.org/v15n3/v15n3a05-mal.htm
  • Oladele, O.I., Antwi, M.A. and Kolawole, A.E. (2013). Factors influencing demand for animal health services and knowledge of biosecurity among livestock farmers along border villages of South Africa and Namibia. International Journal of Applied Research in Veterinary Medicine, 11, 123-129. Retrieved from https://www.jarvm.com/articles/Vol11Iss2/Vol11%20Iss2VETOladele.pdf
  • Özyürek, S., Koçyiğit, R. and Tüzemen, N. (2014). Structural features of dairy farmers in the Erzincan: the example of Çayırlı district. Journal of Tekirdağ Agriculture Faculty, 11, 19-26. Retrieved from http://jotaf.nku.edu.tr/T.Z.FDergisiY%C4%B1l2014-11say%C4%B1%283%29/duyuruayrinti/m/8254/11056/635
  • Paulos, A., Belay, K. and Hamito, D. (2004). Determinants of farmers’ willingness to pay for soil conservation practices in the Southeastern Highlands of Ethiopia. Land Degradation & Development, 15, 423-438. Doi: https://doi.org/10.1002/ldr.623
  • Sezgin, A., Kaya, E.T., Külekçi, M. and Kumbasaroğlu, H. (2010). Impact analysis on the adoption of the innovations generated for agricultural production: the case of Erzurum province. 9th National Congress of Agricultural Economics, Tokat, 22-24 Sept.
  • StataCorp (2005). Stata Longitudinal/Panel Data Reference Manual. College Station, TX: StataCorp LP.
  • Tambi, N.E., Mukhebi, W.A., Maina, W.O. and Solomon H.M. (1999). Probit analysis of livestock producers' demand for private veterinary services in the high potential agricultural areas of Kenya. Agricultural Systems, 59, 163-176. Doi: https://doi.org/10.1016/S0308-521X(98)00088-2
  • Tefera, S.S., Lagat, J.K. and Bett, H.K. (2014). Determinants of artificial insemination use by smallholder dairy farmers in Lemu-Bilbilo district, Ethiopia. International Journal of African and Asian Studies, 4, 91-98. Retrieved from https://iiste.org/Journals/index.php/JAAS/article/view/10019/10229
  • TURKSTAT (2018). Statistical indicators. Turkish Statistical Institute. Livestock Statistics Annual Report, Ankara. Available at http://www.turkstat.gov.tr/ (Accessed April 28, 2018).
  • Yamane, T. (1967). Elementary Sampling Theory. Englewood Cliffs. Prentice-Hall Inc, New Jersey.
  • Yohannes, R. (2014). Impact of artificial insemination of dairy cows on the livehoods of smallholder households: the case of Haramaya district. Master's Thesis. University of Haramaya, Ethiopia.
There are 21 citations in total.

Details

Primary Language English
Subjects Agricultural, Veterinary and Food Sciences
Journal Section Research Articles
Authors

Damla Özsayın 0000-0003-1127-267X

Publication Date September 15, 2020
Submission Date February 14, 2020
Acceptance Date August 4, 2020
Published in Issue Year 2020 Volume: 4 Issue: 3

Cite

APA Özsayın, D. (2020). Factors affecting the use of artificial insemination of farmers in dairy farming. International Journal of Agriculture Environment and Food Sciences, 4(3), 340-347. https://doi.org/10.31015/jaefs.2020.3.13
AMA Özsayın D. Factors affecting the use of artificial insemination of farmers in dairy farming. int. j. agric. environ. food sci. September 2020;4(3):340-347. doi:10.31015/jaefs.2020.3.13
Chicago Özsayın, Damla. “Factors Affecting the Use of Artificial Insemination of Farmers in Dairy Farming”. International Journal of Agriculture Environment and Food Sciences 4, no. 3 (September 2020): 340-47. https://doi.org/10.31015/jaefs.2020.3.13.
EndNote Özsayın D (September 1, 2020) Factors affecting the use of artificial insemination of farmers in dairy farming. International Journal of Agriculture Environment and Food Sciences 4 3 340–347.
IEEE D. Özsayın, “Factors affecting the use of artificial insemination of farmers in dairy farming”, int. j. agric. environ. food sci., vol. 4, no. 3, pp. 340–347, 2020, doi: 10.31015/jaefs.2020.3.13.
ISNAD Özsayın, Damla. “Factors Affecting the Use of Artificial Insemination of Farmers in Dairy Farming”. International Journal of Agriculture Environment and Food Sciences 4/3 (September 2020), 340-347. https://doi.org/10.31015/jaefs.2020.3.13.
JAMA Özsayın D. Factors affecting the use of artificial insemination of farmers in dairy farming. int. j. agric. environ. food sci. 2020;4:340–347.
MLA Özsayın, Damla. “Factors Affecting the Use of Artificial Insemination of Farmers in Dairy Farming”. International Journal of Agriculture Environment and Food Sciences, vol. 4, no. 3, 2020, pp. 340-7, doi:10.31015/jaefs.2020.3.13.
Vancouver Özsayın D. Factors affecting the use of artificial insemination of farmers in dairy farming. int. j. agric. environ. food sci. 2020;4(3):340-7.
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