Journal of Agricultural Extension & Community Development (농촌지도와개발)

Korean Society of Agricultural Extension (한국농촌지도학회)
권호 표지

Information System Impact on Swine Productivety

양돈농가의 정보시스템 사용 효과 분석

  • Received : 2010.10.22
  • Accepted : 2010.12.10
  • Published : 2010.12.30
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Abstract

Researchers have different views on impact of information system use on productivity. The differences are fueled by 'the productivity paradox' insisted by Brynjofsson(1993). This paper intend to quantitate impacts of information system and to test the productivity paradox of using the information system. Restricted Maximumlikelihood Estimation(RMLE) method is applied on data from 81 farms adopting Pigplan system. The results find positive productivity improvement with information systems in swine farm. Adopting Pigplan system increases 0.52 in PSY(pigs per sow per year) and 0.087 in sow turnover. When it comes to region and farm size, region has impact on both PSY and sow turnover, while farm size does not. This result infers that local cooperatives, regardless of farm size, differentiate the impacts of the information system, implying that the ability to utilize information systems should be improved in organizational level.

Keywords

References

  1. 박덕병, & 이민수. (2002). 농업지식체계 접근에 의한 농업연구, 지도 연계를 위한 당면과제. 한국농촌지도학회지, 9, 199-214.
  2. 이성우, 임형백, & 조중구. (2004). 농가 정보화의 결정요인과 지역간 차이. 농업경제연구, 45, 47-81.
  3. 최영찬, & 문정훈. (2003). 정보요구분석 전략과 효과: 양돈업을 위한 농업경영정보 시스템 개발의 경우. 한국농업교육학회지, 35(4), 143-161.
  4. 한국농촌경제연구원. (2003). 농업인 의식구조 변화에 관한 여론조사 결과.
  5. 한국정보문화진흥원. (2003). 정보격차 해소 백서.
  6. Albrecht, T. L., & Bryant, C. (1996). Advanced in Segmentation Modeling for Health Communication and Social Marketing Campaigns. Journal of Health Communication, 1, 65-80. https://doi.org/10.1080/108107396128248
  7. Amponsah, W. A. (1995). Computer Adoption and Use of Information Services by North Carolina Commercial Farmers. Journal of Agricultural and Applied Economics, 27, 565-576.
  8. Baker, G. A. (1992). Computer Adoption and Use by New Mexico Nonfarm Agribusinesses. American Journal of Agricultural Economics, 74, 737-744. https://doi.org/10.2307/1242587
  9. Batte, M. T. (2004). Changing Computer use in Agriculture: Evidence from Ohio. Computers and Electronics in Agriculture: In Press.
  10. Batte, M. T., Jones, E., & Schnitkey, G. D. (1990). Computer Use by Ohio Commercial Farmers. American Journal of Agricultural Economics, 72, 935-945. https://doi.org/10.2307/1242625
  11. Bentz, Y., & Merunkay, D. (2000). Neural Networks and the Multinomial Logit for Branch Choice Modeling: a Hybrid Approach. Journal of Forecasting, 19(3), 177-200. https://doi.org/10.1002/(SICI)1099-131X(200004)19:3<177::AID-FOR738>3.0.CO;2-6
  12. Berry, M. J. A. & Linoff, G. S. (2000). Mastering Data Mining. New York: John Wiley & Sons.
  13. Bhattacharyya, A., Harris, T. R., Kvasnicka, W. G., & Veserat, G. M. (1997). Factors Influencing Rates of Adoption of Trichomoniasis Vaccine by Nevada Range Cattle Producers. Journal of Agricultural and Resource Economics, 22, 174-190.
  14. Bitchler, M., & Kiss, C. (2004). A Comparison of Logistic Regression, ${\kappa}$-Nearest Neighbor, and Decision Tree Induction for Campaign Management. Proceedings of the Tenth Americas Conference on Information Systems, New York, August, 1918-1925.
  15. Bound, D., & Ross, D. (1997). Forecasting Customer Response with Neural Network. Handbook of Neural Computation, G6.2, 1-7.
  16. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984), Classification and Regression and Regression Trees. Belmont, CA: Wadsworth.
  17. Brynjolfsson, E. (1993). The Productivity Paradox of Information Technology. Communications of ACM, 35, 66-77.
  18. Hamilton, S. & Chervany, N. (1981). Evaluating Information System Effectiveness- Part I: Comparing Evaluation Approaches. MIS Quarterly, 5, 55-69. https://doi.org/10.2307/249291
  19. Hayes, D. P., Pfeiffer, D. U., & Morris, R. S. (1998). Production and Reproductive Responses to Use of Dairy MAN: A Management Information System for New Zealand Dairy Herds. Journal of Dairy Science, 81(9), 2362-2368. https://doi.org/10.3168/jds.S0022-0302(98)70127-4
  20. Huffman, W. E., & Mercier, S. (1991). Joint Adoption of Microcomputer Technologies: An Analysis of Farmers' Decisions. Review of Economics and Statistics, 73, 541-546. https://doi.org/10.2307/2109583
  21. Jarvis, A. M. (1990). Computer Adoption Decisions-Implication for Research and Extension: The Case of Texas Rice Producers. American Journal of Agricultural Economics, 72, 1388-1394. https://doi.org/10.2307/1242560
  22. Lazarus, W. F., Rutz, D. A., Miller, R. W., & Brow, D. A. (1989). Costs of Existing and Recommended Manure Management Practices for House Fly and Stable Fly (Diptera: Muscidae) Control on Dairy Farms. Journal of Economic Entomology, 82(4), 1145-1151.
  23. Littell, R. C., Henry, P. R., & Ammerman, C. B. (1996). Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science, 76, 1216-1231.
  24. Losinger, W. C., & Heinrichs, A. J. (1996). Dairy Operation Management Practices and Herd Milk Production. Journal of Dairy Science, 79(3), 506-514. https://doi.org/10.3168/jds.S0022-0302(96)76393-2
  25. Morwitz, G. V., & Schmittlein, D. (1992). Using Segmentation to Improve Sales Forecasts Based on Purchase Intent: Which 'Indenders' Actually Buy?, Journal of Marketing Research, 29, 391-405. https://doi.org/10.2307/3172706
  26. Moutinho, L., Curry, B., Davies, F., & Rita, P. (1994). Neural Network in Marketing. New York: Routledge.
  27. Mukherjee, C., White, H., and Wuyts, M. (1998). Econometrics and Data Analysis for Developing Countries, New York: Routledge.
  28. Murthy, K. S. (1998). Automatic Construction of Decision Trees from Data: A Multi-disciplinary Survey. Data Mining and Knowledge Discovery, 2, 345-389. https://doi.org/10.1023/A:1009744630224
  29. Perlich, C., Provost, F., & Simonoff, J. S. (2003), Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. Journal of Machine Learning Research, 4, 211-255.
  30. Putler, D. S., & Zilberman, D., (1988), Computer Use in Agriculture: Evidence from Tulare County, California. American Journal of Agricultural Economics, 70, 790-802. https://doi.org/10.2307/1241920
  31. Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 181-106.
  32. Quinlan, J. R. (1993). C4.5: Program of Machine Learning. CA., Morgan Kaufman Publishing.
  33. Rogers, E. M. (2003). Diffusion of Innovation. NY: Free Press.
  34. Rogers, E. M., & Storey, J. D. (1987). Communication Campaigns. In C.R. Berger, & S. H. Chaffee (Eds.), Handbook of Communication Science. Newbury Park, CA: Sage, 817-846.
  35. Shrestha, R. B., & Gopalakrishnan, C. (1993). Adoption and Diffustion of Drip Irrigation Technology: An Econometric Analysis. Economic Development and Cultural Change, 41, 407-418. https://doi.org/10.1086/452018
  36. Tomaszewski, M. A., Sudweeks, E. M., Dijkhuizen, A. A., & Huirne, R. B. M. (1997). Determining the effect of a management information system on herd production. Journal of Dairy Science, 80, 148.
  37. Tomaszewski, M. A., Van Asseldonk, M. A. P. M., Dijkhuizen, A. A., & Huirne, R. B. M. (2000), Determining farm effects attributable to the introduction and use of a dairy management information system. The Netherlands Agricultural Economics, 23(1), 79-86. https://doi.org/10.1111/j.1574-0862.2000.tb00085.x
  38. Van Asseldonk, M. A. P. M., Huirne, R. B. M., Dijkhuizen, A. A., Tomaszewski, M. A., & Harbers, A. G. F. (1998). Effects of Information Technology on Dairy Farms in The Netherlands: An Empirical Analysis of Milk Production Records. Journal of Dairy Science, 81(10), 2752-2759. https://doi.org/10.3168/jds.S0022-0302(98)75832-1
  39. Verstegen, J. A. A. M., Huirne, R. B. M., Dijkhuizen, A. A. & King, R. P. (1995). Quantifying Economic Benefits of Sow-Herd Management Information Systems Using Panel Data. American Journal of Agricultural Economics, 77(2), 387-396. https://doi.org/10.2307/1243548
  40. Verstegen, J. A. A. M., & Huirne, R. B. M. (2001). The Impact of Farm Management on Value of Management Information Systems. Computers and Electronics in Agriculture, 30, 51-69. https://doi.org/10.1016/S0168-1699(00)00156-3
  41. Woodburn, M. R., Ortmann, G. F., & Levin, J. B. (1994). Computer Use and Factors Influencing Computer Adoption among commercial Farmers in Natal Province, South Afirca. Computers and Electronics in Agriculture, 11, 183-194. https://doi.org/10.1016/0168-1699(94)90007-8