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The social discount rate is derived from an inter-temporal social utility model or land-use projects in India. The social discount rate is a function o f two parameters: the elasticity of social marginal utility of consumption and the growth rate of per capita real consum tion. The final results for the social discount rate and the elasticity o{ocial marginal utility of consum tion are 2 per cent and -1.4 respectively. found in the literature. These values are plausib f e and comparable with other estimatesSocial forestry programmes, which include forestry and a roforestry as Rural Development and orest Department. At least 25 er cent of the total outlay for rural development schemes in India is presently eing earmarked for social forestry. The prime aim of these programmes is to rovide the basic providing employment for the poor and thereby generating some income with which to raise their living standards. Social forestry programmes, which are subsistence-oriented and labour-intensive, have existed in India for a long time, and similar programmes are currently being implemented in more than 50 developing countnes (Foley and Barnard, 1984). as a consequence of the failure of the trickle-down strategy resulting in the need to develo measures for attacking the chronic poverty directly. This strong shift in avour of a redistribution-with-growth strategy is reflected in Indian planning mainly from the mid-l970s, when many anti-poverty and rural development programmes were initiated. This pa er describes the estimation of the social discount rate, based on the trade-off g etween present and future consumption, for India.Social forestry systems are inherently long-term in nature. This means that discounting remotely accrued benefits at a high discount rate critically influences decisions regarding social forestry development (compared with other, short-term uroiects). At high discount rates. not only do social forestry components, are being im lemented in India using investment B unds from the consumption needs, 1.e. sta le food, fuelwood, fodder, an B small timber for construction and agricultura P implements. Equally important is the objective of

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A study of 13 sample plots (0.01 ha) in 6 forests showed that the average number of topsoil samples required per plot to secure 95 per cent confidence limits for a range about the mean of + l o per cent was 6 for total nitrogen, 9 for total phosphorus and 29 for 0.5 M acetic acid extractable nutrients (calcium, magnesium, phosphorus and potassium). In some plots 95 per cent confidence intervals, based on 5 composite samples. for extractable nutrients were as large as the range of mean values for different plots within a forest. High variability within plots causes large variation in correlation coefficients between tree growth and soil properties so that the intensity of sampling should be of the order indicated above to identify factors affecting growth. For predicting timber yield the accuracy of regression equations containing predictors of high within-plot Variability is too low to be of practical value. The sampling effort required to achieve a given level of precision does not increase greatly when plot size is increased from 0.01 to 0.1 ha. Since Yield Class, the most useful measure of growth, is not designed for areas less than 0.5 ha, the use of the larger plot size is recommended.