Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-26T05:28:33.628Z Has data issue: false hasContentIssue false
  • English
  • Français

The Farm Level Effects of Better Access to Information: The Case of Dart

Published online by Cambridge University Press:  28 April 2015

Darrell J. Bosch  and
Katherine L. Lee
Darrell J. Bosch
Affiliation:
Department of Agricultural Economics, Virginia Polytechnic Institute and State University
Katherine L. Lee
Affiliation:
Animal Breeding Services, Holstein Association
Get access Rights & Permissions [Opens in a new window]

Abstract

In this study, two methods of entering and accessing dairy herd records are compared: the traditional mail-in Dairy Herd Improvement (DHI) system and the Direct Access to Records by Telephone (DART) system, which provides more timely and convenient access to records. An evaluation of DART was carried out using mail survey responses from 117 DART users and telephone surveys of 40 randomly selected users. Results indicate that DART users are generally satisfied with the system and feel that it improves their herd management. Variations in use of the DART system by DART users are explained by herd, cost, and management variables. DART users and comparable non-DART, DHI users are compared with respect to gains in herd production efficiency. Results indicate that DART users made somewhat better gains in most efficiency measures but that the differences were generally not statistically significant.

Keywords

Dairy Herd Improvement Associationdairy recordsmicrocomputerssurveyvalue of information
Type
Submitted Articles
Information
Journal of Agricultural and Applied Economics , Volume 20 , Issue 2 , December 1988 , pp. 109 - 118
Copyright
Copyright © Southern Agricultural Economics Association 1988

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Antonovitz, F., and Roe, T.. “The Value of a Rational Expectations Forecast in a Risky Market: A Theoretical and Empirical Approach.” Amer. J. Agr. Econ., 66(1984):717–723.CrossRefGoogle Scholar
Baquet, A.E., Halter, A. N., and Conklin, F. S.. “The Value of Frost Forecasting: a Bayesian Appraisal.” Amer. J. Agr. Econ., 58(1976):511–520.CrossRefGoogle Scholar
Bosch, D.J., and Eidman, V. R.. “Valuing Information When Risk Attitudes are Nonneutral: An Application to Irrigation Scheduling.” Amer. J. Agr. Econ., 69(1987):658–668.CrossRefGoogle Scholar
Bradford, D., and Kelejian, H. “The Value of Information for Crop Forecasting in a Market System: Some Theoretical Issues.” Rev. Econ. Studies, 44(1977):519–531.CrossRefGoogle Scholar
Brown, C.A., and White, J. M.. “Immediate Effects of Changing Herd Size and Other Dairy Herd Improvement Measures.” J. Dairy. Sci., 56(1973):799–804.CrossRefGoogle Scholar
Clay, J.S.. Personal communication. Dairy Records Processing Center, Raleigh, NC, June 1987.Google Scholar
Erickson, R.W., and Meadows, C.E.. “An Analysis of High and Average Milk Production Farms.” J. Dairy. Sci., 56(1973):654.Google Scholar
Hayami, Y., and Peterson, W.. “Social Returns to Public Information Services: Statistical Reporting of U.S. Farm.Commodities.” Amer. Econ. Rev., 62(1972):119–130.Google Scholar
Hollander, M., and Wolfe, D. A.. Nonparametric Statistical Methods. New York: John Wiley and Sons, 1973.Google Scholar
King, R.P.. “Technical and Institutional Innovation in North American Grain Production: The New Information Technology.” Discussion Paper #16, Strategic Management Research Center, University of Minnesota, Minneapolis, 1984.Google Scholar
Krejcie, R.V., and Morgan, D. W.. “Determining Sample Size for Research Activities.” Educa tional and Psychological Measurement, 30(1970):607–610.CrossRefGoogle Scholar
Lave, L.B.. “The Value of Better Weather Information to the Raisin Industry.” Econometrica, 31(1963):151–164.CrossRefGoogle Scholar
Leuthold, R.M. “On Combining Information Theory and Bayesian Analysis.” Can. J. Agr. Econ., 19(1971):26–34.CrossRefGoogle Scholar
Muller, J. “On Sources of Measured Technical Efficiency: The Impact of Information.” Amer. J. Agr. Econ., 56(1974):730–738.CrossRefGoogle Scholar
Pirie, W.R.. “Nonparametric Methods.” Unpublished user's guide, Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, 1984.Google Scholar
Sonka, S.T.. “Computer-Aided Farm Management Systems: Will the Promise be Fulfilled?” in Agriculture in a Turbulent World Economy. Brookfield, VT: Gower Publishing, 1986.Google Scholar
Sonka, S.T., Mjelde, J. W., Dixon, B.L, and Lamb, P. J.. “Information as a Risk Management Tool: An Illustration for Climate Forecasts.” In Risk Analysis for Agricultural Production Firms: Implications for Managers, Policymakers, and Researchers. Proceedings of a Seminar Sponsored by Southern Regional Project S-180. Department of Agricultural Economics, Washington State University, Pullman, WA, 1986.Google Scholar
Swaney, D.P., Mishoe, J. W., Jones, J.W., and Boggess, W. G.. “Using Crop Models for Management: Impact of Weather Characteristics on Irrigation Decisions in Soybeans.” Trans. Amer. Soc. Agr. Eng., 26(1983):1808–14.CrossRefGoogle Scholar
Thompson, J.C., and Brier, G. W.. “The Economic Utility of Weather Forecasts.” Monthly Weather Review, 83(1955):249–254.2.0.CO;2>CrossRefGoogle Scholar
Tice, T.F., and Clouser, R. L.. “Stochastic Effect of Weather on Crop Production: The Value of Weather Information to Individual Corn Producers.” Amer. J. Agr. Econ., 62(1980):1109.Google Scholar
Webb, D.W, and Butcher, K.R.. DART Manual. Dairy Records Processing Center, Raleigh, 1986.Google Scholar
Zavaleta, L.R., Lacewell, R. D., and Taylor, C.R. “Open Loop Stochastic Control of Grain Sorghum Irrigation Levels and Timing.” Amer. J. Agr. Econ., 62(1980):785–791.CrossRefGoogle Scholar