- Volume 28 Issue 4
Yield tables are a frequently used data base for regional timber resource forecasting. A normal yield table is based on two independent variables, age and site (species constant), and applies to fully stocked (or normal) stands while empirical yield tables are based on average rather than fully stocked stands. Normal and empirical yield tables essentially have many limitations. The limitations of normal and empirical yield tables led to the development of variable density yield tables. Mathematical models for estimating timber yields are usually developed by fitting a suitable equation to observed data. The model is then used to predict yields for conditions resembling those of the original data set. It may be accurate for the specific conditions, but of unproven accuracy or even entirely useless in other circumstances. Thus, these models tend to be specific rather than general and require validation before applying to other areas. Dalbergia sissoo forms a major portion of irrigated plantations in the hot desert of India and is an important timber tree species where stem wood is primarily used as timber. Variable density yield model is not available for this species which is very crucial in long-term planning for managing the plantations on a sustained basis. Thus, the objective of this study was to develop variable density yield model based on the data collected from 30 sample plots of D. sissoo laid out in IGNP area of Rajasthan State (India) and measured annually for 5 years. The best approximating model was selected based on the fit statistics among the models tested in the study. The model develop was evaluated based on quantitative and qualitative statistical criteria which showed that the model is statistically sound in prediction. The model can be safely applied on D. sissooo plantations in the study area or areas having similar conditions.
yield;variable density;D. sissoo;arid region;India;fit statistics
- Prakash R. 1986. Forest management. International book distributors, Dehradun, 1986.
- Shvidenko A, Venevsky S, Raille G, Nilsson S. 1995. A system for evaluation of growth and mortality in Russian forests. Water, Air and Soil Pollution 82: 333-348. https://doi.org/10.1007/BF01182845
- Smaltschinski T. 1997 Grossregionale Holzaufkommensprognosen und nationale Forstinventuren. Unpublished manuscript, University of Gottingen, Germany, pp 199.
- Davis KP. 1966. Forest management: Regulation and valuation. 2nd ed. New York, McGraw-Hill, pp 519.
- Gadow Kv, Hui GY. 1999. Modelling forest development. Kluwer Academic Publisher, Dordrecht, pp 213.
- http://fennerschoolassociated.anu.edu.au/mensuration/Brackand Wood1998/S_GROWTH.HTM (downloaded on 31.03.2012).
- http://waterresources.rajasthan.gov.in/1climate.htm (downloaded on 15.05.2012).
- http://waterresources.rajasthan.gov.in/1soil.htm (downloaded on 15.05.2012).
- Husch B, Miller CI, Beers TW. 1982. Forest Mensuration. 3rd ed. John Wiley and Sons, New York, NY, pp 402.
- Kramer H. 1988. Waldwachstumslehre. Paul Parey, Hamburg, Berlin, pp 374.
- Kramer H, Akca A. 1987. Leitfaden zur Waldmeßlehre. J.D. Saunder's Verlag, Frankfurt a.M., 287 p.
- Longchang Lee, Wenkang Hao, Guoqing Weng. 1991. Study on the method of constructing variable-density yield table. Journal of Forestry Research 2: 95-99.
- Nagel J, Kehr IC. 1997. Instant Yield Programme (IYP). Paper presented at the XI World Forestry Congress organized at Antalya. Turkey during 13-22.
- Payandeh B, Wang Y. 1994. Relative accuracy of a new base-age invariant site index model. Forest Science 40: 341-348.
- Tewari VP, Kumar VSK. 2005. Growth and yield functions for Dalbergia sissoo plantations in the hot desert of india grown under irrigated conditions. Journal of Tropical Forest Science 17: 87-103.