Trend in collection of tendu leaves in Kondagaon Chhattisgarh

Introduction

The leaves of Tendu tree (Diospyros melanoxylon) are an important non-timber forest product for Indian state of Chhattisgarh. Popularly known as "green gold of forest" which provides the seasonal employment and income of 13 lakhs families in Chhattisgarh. Chhattisgarh is the 9thlargest state in India with an area of 135,192 km 2 (52,198 sq m). Chhattisgarh is a forested state in central India. In Chhattisgarh, forest covers 5.6 million hectares area which occupies 41.33% of the total area (as per the latest report by the Indian Forest Service) and the rich forest resources include wood, Tendu leaves, honey and Lac. Approximately 3% is under very dense forest, 25.97% is moderately dense, 12.28% is open forest and 0.09% is scrub. Tendu (Diospyros melanoxylon) leave collection, one of the non-timber forest produce (NTFPs) largely distributed in central peninsular India such as Madhya Pradesh, Orissa, Chhattisgarh, Jharkhand, part of Maharashtra and Andhra Pradesh. The Tendu leaves are used as Bidi (Rural form of cigarette) wrappers. About 550 billion pieces of Bidi (rolled by 10 million people) are sold every year in India, according to the All-India Bidi Industry Federation. Moreover, gathering Tendu is labour-intensive and employs millions of tribals during the lean month of May, when they have very little else to earn from. As we know that Chhattisgarh is a tribal-dominated state, where most of the people are dependent on forest products. According to Census 2011, tribal's population covers 32.5% of total population who live in dense forest area of the state (C.G.) Like Surguja, Bastar, Deatewada, Sukma, Kanker, Bijapur, Bilaspur, Narayanpur. Gaurella-Pendra-Marwahi districts. In the 1960s, the governments realised the commercial value of Tendu. Most of the land from which Tendu was plucked belonged to the state governments. Tendu leaves were sold unplucked to contractors, who under-reported the collections. This resulted in revenue loss. The tribal people who collected these leaves also got precious little. Several states decided to change this and passed laws to 'Nationalise' the Tendu trade in the 1960-1970. The purpose: to ensure maximum revenue for the governments and better returns to the tribal collectors.

Materials and Methods

Location of study

The study will be conducted in Kondagaon district of the Chhattisgarh Baster Plateau Agro-climatic Zone. The area under study which situated at 19.5954° N latitude and 81.6646° E longitude.

Estimate of the trend in collections of Tendu leaves in Kondagaon Analytical method

The time series data on the yearly collections and sales of Tendu leaves will be analyzed by using Least Square Analysis, the various models will be as follows:

Model

Where, Y = Estimated Tendu leaves collection/sales for different years

The following formula will be used to perform a "t" test to determine the significance of the regression coefficient: To find out the contribution of Kondagaon, we will be showing it graphically and in percentages as well.

Results

Curve fitting of Tendu leaves collection in Kondagaon Chhattisgarh

Polynomial regression up to fourth order and exponential regression are presented in table 1 to 6 respectively. The table 1 revealed that the fitted regression line is not a good fit since F calculated was not found significant. Second order, third order and exponential regression models were not well fitted since F calculated values were not found significant as shown in Table no. 3, 4 and 5 Table 6 represent the ANOVA for fourth order regression equation. It was observed from the table that the fitted model is highly significant. Thus, the equation and R 2 value of the best fitted model for Tendu leaves collection are given in Table 2 the observed and fitted curve for the model was shown in Fig. 2. The critical observation of Table 3 indicated that fourth-order regression model was the best fitted model in explaining the Tendu leaves collection. The regression coefficient was found highly significant. The ANOVA for straight-line regression gives an F-value of 5.1218 with a p-value of 0.05346. Since the p-value is greater than 0.05 and 0.01, the regression is not significant at both 5% and 1% levels. Hence, there is no significant linear relationship between the variables. The ANOVA for the second order polynomial regression model shows an F-value of 2.4366 with a p-value (Significance F) of 0.1573. Since this p-value is greater than 0.05 and 0.01, the regression model is not significant at both 5% and 1% levels. The p-values of both regression coefficients (Linear and quadratic terms) are also greater than 0.05. Hence, the second order polynomial model does not provide a significant fit, and there is no significant relationship between the variables. The third order polynomial regression model has an F-value of 2.0705 with a p-value of 0.2056. Since the p-value is greater than 0.05, the model is not statistically significant.

Hence, the third order polynomial regression does not provide a significant fit to the data. The fourth order polynomial regression model has an F-value of 1.7747 with a p-value of 0.2708. As the p-value is greater than 0.05, the model is not significant, indicating that the fourth order polynomial does not fit the data significantly. The exponential regression model has an F-value of 4.7601 with a p-value of 0.0655. As the p-value exceeds 0.05, the model is not significant, indicating that the exponential function does not fit the data significantly.

Discussion

Curve fitting techniques were applied to analyze the trend in Tendu leaves collection in Kondagaon district of Chhattisgarh using straight line, polynomial regression models up to fourth order, and exponential regression. The adequacy of each model was assessed using ANOVA, F-test, p-values, and the coefficient of determination (R²). The straight-line regression model was found to be statistically non-significant, as the calculated F-value (5.1218) did not attain significance at either the 5% or 1% level. This indicates the absence of a significant linear relationship between time and Tendu leaves collection. Similarly, the second order polynomial regression model showed a non-significant F-value (2.4366) with a high pvalue (0.1573), indicating that inclusion of the quadratic term did not significantly improve the model fit. The regression coefficients were also not statistically significant.

The third order polynomial regression model further failed to provide a significant fit, as reflected by the non-significant Fvalue (2.0705). None of the regression coefficients corresponding to linear, quadratic, or cubic terms were statistically significant, suggesting that higher-order polynomial terms did not adequately explain the variation in Tendu leaves collection.

The fourth order polynomial regression model also resulted in a non-significant F-value (1.7747) at the 5% level. Although the regression coefficients were statistically non-significant, this model yielded the highest coefficient of determination (R²=0.437) among all models considered, indicating a comparatively better explanatory power. The exponential regression model was also found to be statistically non-significant, with an F-value of 4.7601 and a p-value of 0.0655, implying that the exponential trend does not adequately describe the pattern of Tendu leaves collection. Despite the lack of statistical significance in all tested models, the fourth order polynomial regression explained a relatively higher proportion of variability in the data, as indicated by its R² value.

Conclusion

References

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