Stand density and basal area prediction of unthinned irrigated plantations of Eucalyptus camaldulensis in the hot desert of India.
Growth modelling is an essential prerequisite for evaluating the consequences of a particular management action on the future development of a forest ecosystem. Mathematical growth models are not available for many tree species in India. The objectives of this study were to estimate potential stand density and model the actual tree density and basal area development in pure even-aged stands of Eucalyptus camaldulensis. Relationships between quadratic mean diameter and stems ha(-1) were developed, and parameter values of this relationship were used to establish the limiting density line. Two different models were compared to describe the natural decrease of stem number. The model including site index as one of the variables performed slightly better than the model without site index. Seven different stand level models also were compared for predicting basal area in the stands. The models tested in this paper belong to the path invariant algebraic difference form of a nonlinear model. They can be used to predict future basal area as a function of stand variables like initial basal area, age or dominant height, and stems ha(-1) and are crucial for evaluating different silvicultural treatment options. The performance of the models for basal area was evaluated using different quantitative criteria. Among the seven models tested, the two models proposed by Pienaar and Shiver and Forss et al. had the best performance. The equations proposed to predict future basal area and stem number are related and, therefore, simultaneous regression technique has also been used to investigate the differences between parameter coefficients obtained by fitting the equations separately and jointly.
Tewari
V P
VP
Arid Forest Research Institute, P.O. Krishi Mandi, New Pali Road, Jodhpur 342 005, India. vptewari@yahoo.com
D003889:Desert Climate; D005052:Eucalyptus / Q000254:growth & development* / Q000378:metabolism; D005544:Forecasting / Q000379:methods; D016468:Forestry; D007194:India; D017711:Nonlinear Dynamics; D018547:Plant Stems; D012044:Regression Analysis; D014197:Trees; D014867:Water / Q000378:metabolism
D014867:Water