DOI QR코드

DOI QR Code

Effect of Experience, Education, Record Keeping, Labor and Decision Making on Monthly Milk Yield and Revenue of Dairy Farms Supported by a Private Organization in Central Thailand

  • Yeamkong, S. (Department of Animal Science, Kasetsart University) ;
  • Koonawootrittriron, S. (Department of Animal Science, Kasetsart University) ;
  • Elzo, M.A. (Department of Animal Sciences, University of Florida) ;
  • Suwanasopee, T. (Department of Animal Science, Kasetsart University)
  • Received : 2009.09.11
  • Accepted : 2009.11.05
  • Published : 2010.06.01

Abstract

The objective of this research was to assess the effect of experience, education, record keeping, labor, and decision making on monthly milk yield per farm (MYF), monthly milk yield per cow (MYC), monthly milk revenue per farm (MRF), and monthly revenue per cow (MRC) of dairy farms supported by a private organization in Central Thailand. The dataset contained 34,082 monthly milk yield and revenue records collected from January 2004 to December 2008 on 497 farms, and information on individual farmer experience and education, record keeping, and decision making obtained with a questionnaire. Farmer experience categories were i) no experience, ii) one year, iii) two to five years, iv) six to ten years, v) eleven to fifteen years, vi) sixteen to twenty years, and vii) more than twenty years. Farmer education categories were i) no education or primary school, ii) high school, and iii) bachelor or higher degree. Record keeping categories were: i) no records and ii) kept records. Labor categories were: i) family, ii) hired people, and iii) family and hired people. Decision making categories were: i) decisions made by farmers themselves, ii) decisions made with help from government officials, and iii) decisions made with help from organization staff. The mixed linear model contained the fixed effects of year-season, farm location-farm size subclass, experience, education, record keeping, labor, and decision making on sire selection, and the random effects of farm and residual. Results showed that longer experience increased (p<0.05) monthly milk yield (MYF and MYC) and revenue (MRF and MRC). Farms that hired people produced the highest (p<0.05) monthly milk yield (MYF and MYC) and revenue (MRF and MRC), followed by farms that used family, and the lowest values were for farms that used both family and hired people. Better educated farmers produced more MYC and MRC (p<0.05) than lower educated farmers. Farms that kept records had higher MYF and MRF (p<0.05) than those without records. Although differences among farms were non-significant, farms that received help from the organization staff had higher monthly milk yield (MYF and MYC) and revenue (MRF and MRC) than those that decided by themselves or with help from government officials. These findings suggested that dairy farmers needed systematic training and continuous support to improve farm milk production and revenues in a sustainable manner.

Keywords

References

  1. Bewley, J., R. W. Palmer and D. B. Jackson Smith. 2001. Modeling milk production and labor efficiency in modernized Wisconsin dairy herds. J. Dairy Sci. 84:705-716 https://doi.org/10.3168/jds.S0022-0302(01)74525-0
  2. Borisutsawat, K. 1996. Factors affecting adoption of technology by dairy producers at Nongpho Dairy Cooperative. M.S. Thesis, Kasatsert University, Bangkok, Thailand
  3. Boonyanuwat, K., O. Vetchabootsakorn and U. Intarachote. 1995. The optimum farm size for dairy farm in Saraburi Province. In: 1995 Annual Research Report in Dairy Cattle. Animal Husbandry Division, Department of Livestock Development. Thailand. pp. 332-349
  4. Cicek, H., M. Tandogan, Y. Terzi and M. Yardimci. 2007. Effects of some technical and socio-economic factors on milk production costs in dairy enterprises in Western Turkey. World J. Dairy Food Sci. 2:69-73
  5. Department of Livestock Development. 2009. Livestock infrastructure information in Thailand: 2008. Ministry of Agriculture and Cooperative. Bangkok, Thailand
  6. Garcia, O., T. Hemme, S. Rojanasthien and J. Younggad. 2005. The economics of milk production in Chiang Mai, Thailand, with particular emphasis on small-scale producers, FAOPPLPI working paper no. 20, pp. 50
  7. Hanna, D., I. A. Sneddon, V. E. Beattie and K. Breuer. 2006. Effects of the stockperson on dairy cow behavior and milk yield. Anim. Sci. 82:791-797 https://doi.org/10.1017/ASC2006092
  8. Jindatajak, Y., S. Namta and C. Vongnagnagorn. 2004. Milk yield and fertility of crossbred Holstein Friesian 75% (TMZ) at farmers management. In: 2004 Annual research report dairy cattle. Animal Husbandry Division, Department of Livestock Development. Thailand. pp. 1-15
  9. Kanchanasinith, P. 1999. The Comparative study of knowledge, attitude and practices affecting productivity of dairy farming in Chiang Mai Province. M.S. Thesis, Chiang Mai University, Chiang Mai, Thailand
  10. Kivaria, F. M., J. P. T. M. Noordhuizen and A. M. Kapaga. 2006. Evaluation of the hygiene quality and associated public hazards of raw milk marketed by smallholder dairy producers in the Dares Salaam region, Tanzania, Trop. Anim. Health Prod. 38:185-194 https://doi.org/10.1007/s11250-006-4339-y
  11. Koonawootrittriron, S., M. A. Elzo and T. Thongprapi. 2009. Genetic trends in a Holstein×Other breeds multibreed dairy population in Central Thailand. Livest. Sci. 122:186-192 https://doi.org/10.1016/j.livsci.2008.08.013
  12. Losinger, W. C. and A. J. Heinrichs. 1996. Dairy operation management practices and herd milk production. J. Dairy Sci. 79:506-514 https://doi.org/10.3168/jds.S0022-0302(96)76393-2
  13. Ngongoni, N. T., C. Mapiye, M. Mwale and B. Mupeta. 2006. Factors affecting milk production in the smallholder dairy sector of Zimbabwe. Livest. Res. Rural Dev. 18:5
  14. Office of Agricultural Economics. 2009. The situation and trend of important agriculture goods: 2009. Ministry of Agriculture and Cooperative. Bangkok, Thailand
  15. Rhone, J. A., S. Koonawootrittriron and M. A. Elzo. 2008a. A survey of decision making practices, educational experiences, and economic performance of two dairy farm populations in Central Thailand. Trop. Anim. Health Prod. 40:475-482 https://doi.org/10.1007/s11250-007-9123-0
  16. Rhone, J. A., S. Koonawootrittriron and M. A. Elzo. 2008b. Factors affecting milk yield, milk fat, bacterial score, and bulk tank somatic cell count of dairy farms in the central region of Thailand. Trop. Anim. Health Prod. 40:147-153 https://doi.org/10.1007/s11250-007-9074-5
  17. Rhone, J. A., S. Koonawootrittriron and M. A. Elzo. 2008c. Record keeping, genetic selection, educational experience and farm management effects on average milk yield per cow, milk fat percentage, bacterial score and bulk tank somatic cell count of dairy farms in the Central region of Thailand. Trop. Anim. Health Prod. 40:627-636 https://doi.org/10.1007/s11250-008-9141-6
  18. SAS. 2004. SAS 9.13 Help and documentation. SAS Institute Inc., Cary, NC, USA
  19. Seangjun, A. and S. Koonawootrittriron. 2007. Factors effecting on and association among purchasing price, fat content, bacterial contamination, and somatic cell count of raw milk yield producing by members of a dairy cooperative in Central Thailand. In: Proceedings of the 46th Kasetsart University Annual Conference, January 29-February 1, 2008. Bangkok, Thailand. pp. 146-154
  20. Srinoy, B., C. Chantalakhana, S. Saithanoo and K. Pattamarakha. 1999. The adoption of recommended practices by dairy farmes in Southern Thailand. Asian-Aust. J. Anim. Sci. 12:1116-1122
  21. Suphalux, S., 2001. Application of technology for increasing the capability of Muak Lek farmer’s cooperative in dairy production. M.S. Thesis, Kasetsart University, Bangkok, Thailand
  22. Thai Meteorological Department. 2009. The weather in Thailand. Available Source: http://www.tmd.go.th/info/info.php_m? FileID=22, May 1, 2009
  23. Thai Meteorological Department. 2007. The weather in Thailand. Available Source: http://www.tmd.go.th/info/info.php_m? FileID=22, May 1, 2009
  24. Thijae, K. 1999. Sustainable environmental management of small dairy farmers in Chiang Mai Province. M.S. Thesis, Chiang Mai University, Chiang Mai, Thailand
  25. Tomaszewski, M. A. 1993. Record-keeping systems and control of data flow and information retrieval to manage large high producing herds. J. Dairy Sci. 76:3188-3194 https://doi.org/10.3168/jds.S0022-0302(93)77657-2
  26. Uthaiwan, W. 1992. Factors affecting knowledge and practices of dairy farmers in Changwat Chiang Mai. M.S. Thesis, Chiang Mai University, Chiang Mai, Thailand
  27. Wittayagone, P. 1999. Dairy farmers’ satisfaction with dairy production extension, Sankamphaeng District, Chiang Mai province. M.S. Thesis, Chiang Mai University, Chiang Mai, Thailand
  28. Yeamkong, S., S. Koonawootrittriron, M. A. Elzo and T. Suwanasopee. 2009. Milk quantity, quality and revenue in dairy farms supported by a private organization in Central Thailand. Livest. Res. Rural Dev. (Submitted)

Cited by

  1. Room for manoeuvre in time of the workforce in dairy production systems vol.41, pp.12, 2012, https://doi.org/10.1590/S1516-35982012001200010
  2. Relationships between work organisation and size of dairy farms: a case study based in Vietnam vol.44, pp.7, 2012, https://doi.org/10.1007/s11250-012-0128-y
  3. A parametric test evaluating smallholder farmers’ training needs in Uganda vol.8, pp.3, 2018, https://doi.org/10.1108/JADEE-08-2016-0053
  4. Factors Influencing Genetic Change for Milk Yield within Farms in Central Thailand vol.24, pp.8, 2010, https://doi.org/10.5713/ajas.2011.10401
  5. Milk yield, fat yield and fat percentage associations in a Thai multibreed dairy population vol.51, pp.3, 2010, https://doi.org/10.1016/j.anres.2016.12.008
  6. Genetic parameters and trends for daughters of imported and Thai Holstein sires for age at first calving and milk yield vol.51, pp.5, 2017, https://doi.org/10.1016/j.anres.2017.12.003
  7. Accuracy of Genomic-Polygenic and Polygenic Breeding Values for Age at First Calving and Milk Yield in Thai Multibreed Dairy Cattle vol.19, pp.3, 2010, https://doi.org/10.2478/aoas-2019-0032
  8. How Information Communication Technology Can Enhance Evidence-Based Decisions and Farm-to-Fork Animal Traceability for Livestock Farmers vol.2020, pp.None, 2020, https://doi.org/10.1155/2020/1279569