Research Article | Published: 01 March 2000

Application of Econometric Modelling in Forestry Planning and Evaluation: A case for Discriminant Analysis

Ajay Kumar Mahapatra

Indian Journal of Forestry | Volume: 23 | Issue: 1 | Page No. 46-56 | 2000
DOI: https://doi.org/10.54207/bsmps1000-2000-K0I151 | Cite this article

Abstract

People’s participation in forestry is enhanced if the project design is based on their needs, priorities economic opportunities and constraints. Accurate accounting of both monetary and non-monetary benefits is needed by resource managers in order to address problems and prospects in forest management. While simple economic analysis only weight cost and financial benefit, appropriate econometric models account for the rationality of landowners in the context of their resource domain and assumed risk. Appraisal of factors that impede or facilitate technology transfer at the household and regional level can provide clues to make adaptive strategies from a user perspective and to asses spatial and temporal dimension of innovation in forestry. Application of discriminant analysis in predicting farm forestry participation is made by use of household data to demonstrate the used of multivariate technique in assessing the land owner’s decision.

Keywords

Access Options

250/-

Buy Full Access in HTML Format

Instant access to the full article.

Get access to the full version of this article. Buy Full Access in HTML Format

References

1. Alviar, N.G. (1972). The use of hand tractors by lowland rice farmers in Laguna. Journal of Agrarian Economic Development, 2(January): 54-243.

Google Scholar

2. Belly, K.L.; Matney, T.G.; Hodges, J.D.; Deen, R.T. and Golez, J.C.G. (1993). Tree grade prediction for Missisipi bottomland hardwoods using discriminant analysis. South African Journal of Applied Forestry, 17(3):120-123.  https://doi.org/10.1093/sjaf/17.3.120

Google Scholar

3. Goldestein, M. and Dillon, W.R. (1978). Discrete discriminant analysis. Wiley series.

4. Green, W.H. (1990). Econometric analysis. New York, MacMillan.

5. Huberty, Carl J. (1994). Applied Discriminant analysis. John Wiley and Sons. Inc., New York. Jordan, Charles B.K. (1988). Forestry program fights rural poverty. Journal of Forestry, May 37-40.

6. Lachenbruch, P.A. (1975). Discriminant analysis. New York, Hafner Press.

Google Scholar

7. Le may, M.V. and Tait, D.E. (1994). Classification of cedar, aspen and true fir as decayed versus sound. Canadian Journal of Forestry Research, 24(10):2068-2077.  https://doi.org/10.1139/x94-265

Google Scholar

8. Niccolucci, Michael J. (1989). Predicting salability of timber sale offerings in forest service Northern region. USDA department of agriculture research paper. INT-418.  https://doi.org/10.5962/bhl.title.69038

Google Scholar

9. Norusis, Marija J. (1988). SPSS/PC+TM V2.0 Base Manual for the IBM PC/XT/AT and PS/2 Chicago, Illinois, SPSS Inc.

10. Okorie, A. (1992). Rural banking in Nigeria, empirical evidence of indicative policy variables for Anambra, State. Agricultural Economics, 7(1):13-23.  https://doi.org/10.1016/0169-5150(92)90018-T

Google Scholar

11. Pindyck, R.S. and Rubienfield, D.L. (1981). Econometric models and economic forecasts. New York., McGraw Hill.

Google Scholar

12. Reeve, I.J. and Black, A.W. (1994). Understanding farmers’ attitude to land degradation: some methodological considerations. Land degradation rehabilitation. Chichester, West Sussex, England. Wiley 5(3):179-189.  https://doi.org/10.1002/ldr.3400050302

Google Scholar

13. Rice, R.M., Pilsbury, N.H. and Schmidt, K.W. (1985). A risk analysis approach for using discriminant functions to manage logging-related landslides on granitic terrain. Forest Science, 31(3):772-784.

Google Scholar

14. Shaw, A.B. and Da Costa, R.C. (1985). Differential levels of technology adoption and returns to scale in guyanese rice industry. Canadian Jrl. Agric. Econ. Review, 33(1):18-26.  https://doi.org/10.1111/j.1744-7976.1985.tb02039.x

Google Scholar

15. Shim, S. and Drake, M.F. (1990). Consumer intention to purchase apparel by mail order: Belief, attitude and decision process variables. Clothing Textile Research Journal, 9(1):18-26.  https://doi.org/10.1177/0887302X9000900103

Google Scholar

16. Twight, B.W. and Lyden, F.J. (1989). Measuring forest service bias. Journal of Forestry, 87(5):35-41.  https://doi.org/10.1093/jof/87.5.35

Google Scholar

17. Welch, F. (1978). The role of investment in human capital in agriculture. In: Schultz, T.W. (eds.) Distortion of agricultural incentives, Indian University Press. Bloomington.

Google Scholar

18. Zube, E.H. and Simcox, D.E. (1987). Arid lands, riparian landscapes and measurement conflicts. Environmental Management, 11(4):529-535.  https://doi.org/10.1007/BF01867660

Google Scholar

About this article

How to cite

Mahapatra, A.K., 2000. Application of Econometric Modelling in Forestry Planning and Evaluation: A case for Discriminant Analysis. Indian Journal of Forestry, 23(1), pp.46-56. https://doi.org/10.54207/bsmps1000-2000-K0I151

Publication History

Manuscript Published on 01 March 2000

Share this article

Anyone you share the following link with will be able to read this content: