0709-B1

Farm Forestry for Socio-Economic Development

R.K. Dixit^[1], Ashwani Kumar and K. Prasad

Abstract

A tree-planting programme called farm forestry was launched in India in the late 1970s with the objective of persuading farmers to grow fuelwood and fodder for their own use and to reduce pressure on natural forests. In the present study attempts have been made to evaluate the performance of the farm forestry programme and correlate it with socio-economic variables. The information regarding problems faced by farmers during the adoption has been highlighted with a view to making the policy-makers and executives aware of the facts and issues. Data were collected with the help of a structured and pre-tested questionnaire from the 137 farmers of the study area. Six main socio-economic variables, i.e. Adoption Quotient (A.Q.), Land, Education, Social Participation and Family Income were analysed using statistical tools like correlation, regression and Principal Component Analysis. A smaller subset of the variables or new variables was traced, which explained maximum variation in the farm forestry programme. It was found that Land, Education, Income (LEI) was the first variable, and Social Participation was the second one. These two variables were capable of explaining 80% variation in the whole farm forestry programme. It was indicated that proper technical guidance and incentives for improvement in income of the farmers would obtain the desired results. It was also established that proper use of wastelands and resolving problems like biotic pressure, lack of technical knowledge, lack of funds and availability of proper markets for the products of respondents are necessary to make the programme a success.

Introduction

Tree planting programme called farm forestry was launched in India in the late 1970 with the objective of persuading farmers to grow fuelwood and fodder for their own use and to reduce pressure on natural forests. It was initially thought that farmers would plant a few trees on homesteads or uncultivated lands (GOI, 1976). However, the programme started in a large scale in the state of Uttar Pradesh when World Bank aided social forestry project was launched in the year 1979-80 (Tiwari, 1983). In this way all possible help and encouragement was offered to individual land owners to plant trees (Gupta, 1986). The presumption was that the farm forestry would complement the efforts at enhanced supply of fuelwood, small timber and fodder. The FFP (Farm Forestry Programme) was intended to remove as many of the socio-economic constraints as possible so that the maximum number of farmers can begin to take part in tree growing. In the past very little research has been done to study socio-economic impact of FFP in a forest division, which is a unit for forest management and administration. The scarcity of reliable information has lead to controversies and debates created more by impressions, isolated field visits and pre-determined opinions than by objective empirical information. It is hoped that present study would explore new thrust areas of research for the future researchers and academicians that would be useful for planners, implementers and those concerned with such type of programmes to understand better not only in India but all over the world. Evaluation of performance, short comings, strength, weakness and expectations of local people could evolve better management of the projects launched in rural areas in the broader context of poverty alleviation, employment generation and allied activities.

Materials and Methods

A social forestry division, Kanpur situated in the state of Uttar Pradesh implementing FFP was selected as study area. The rural area of this division was further divided in six forest ranges. One village was randomly selected from each range. Thus total six villages were selected. In each village farmers practicising farm forestry were selected for interviews to make a total 137. Socio-economic profile of respondents was obtained with the help of "Socio-economic status scale (Rural)" developed by Pareek and Trivedi (1964). The socio-economic variables Land, Education, Social participation and Income were mainly used in this study. Farmer's participation was computed by their A.Q. (Adoption Quotient) and added as sixth variable. Data were collected with the help of a structured and pre-tested questionnaire. Data were tabulated, analysed and interpreted applying various sophisticated statistical methods like Correlation, Regression and Principal component Analysis and Factor analysis with special reference to varimax rotation. Details of socio-economic variables selected for qualitative analysis are as follows:

(i)A.Q.(Y)-The level of adoption was measured by the Adoption Quotient formula developed by Chattopadhyay (1963) and used by Ray et. al. (1996) and Dixit (2001). A.Q. is a ratio scale designed to quantify the adoption behaviour of an individual. To measure the adoption quotient potentiality, extent, time and consistency were considered. The numerical value as calculated from the formula was used for individual respondent.

(ii)Land(X₁) - It is quantum of land possessed by each respondent in hectares.

(iii)Education(X₂)- The six categories were given score as per increase in degree of education i.e. Illiterate - 0, can read only - 1, can read and write - 2, Primary - 3, Middle - 4, High School - 5, Graduate and above - 6.

(iv)Social participation (X₃)- The five categories were given score as per increase in social participation i.e. No social participation - 0, Member of one organisation - 1, Member of more than one organisation - 2, Officer bearer - 3, Wider public leader - 6.

(v)Income (X₄)- Family income for each respondent per year was given score in increasing order i.e. less than 10000 - 1, 10000 to 20000 - 2, greater than 20000 - 3.

(vi)Problems(X₅)- The respondents faced problems like biotic pressure, lack of technical knowledge, lack of funds and unavailability of proper market for their products. A three point rating scale was excerised with the following weights for calculating scores of individual respondents.

Agree 3, Indifferent - 2 and Disagree- 1

Results and Discussion

The degree of association among the socio-economic variables for the respondents has been presented in Table-1. From this table it is evident that degree of association between socio-economic variables is highly significant. This implies that these socio-economic variables were directly associated with each other. The inverse degrees of association were found between A.Q., Land, Education, Social participation and Income all with the Problems. The correlation between A.Q. x Problems indicated the fact that maximum problems were faced due to participation in FFP, followed by more utilization of land. It was also concluded that higher education does not facilitate for participation in FFP.

Table-1: Degree of association between the socio-economic variables for sample farmers

NS:Non- Significant
* Significant at 5 per cent level of significance
*** Significant at 0.1 per cent level of significance

The correlation between Income x Problems (- 0.102) remained at forth place which implies that with a little increase in the income the problems decrease. The least r between Social participation x Problems (0.017) showed devotion of time in FFP affected a little to its existing agrarian practices. Conclusively, in this programme problems were existing during the adoption and availability of land. As far as problems are concerned most of the respondents are faced with lack of technical knowledge (34%) and lack of funds (30%). To resolve these two problems related technical staff should be sound enough to impart knowledge and to solve the problem of funds, some incentives should also be involved. Once the respondent is motivated properly biotic damages (15%) could be well under control. The respondents also faced with acute marketing problems (21%) of their products. To overcome, it opening of marketing channels and discouragement of intermediaters should be taken into consideration.

Individual effect of socio-economic variables Land, Education, Social participation, Income and Problems on adoption of farm forestry:

A univariate analysis for individual regressors for N=137 respondents is given in Table-2. In addition to it variances were tested for the Analysis of variance (ANOVA), which are given in Table-3.

Table-2: Individual effect of Land (X₁), Education (X₂), Social participation (X₃), Income (X₄) and Problems (X₅) on A.Q.(Y)

*Significant at 5 per cent level of significance
***Significant at 0.1 per cent level of significance

Table-3: ANOVA

** Significant at 1 per cent level of significance

The analysis revealed that for unit change in variable Land, the rate of change of A.Q. with respect to Land is +2.16 with correlation coefficient (r) =0.64 and coefficient of determination (r²⁾ =0.42. Education, if changed by 1 unit the A.Q. increased more than 2 fold (2.32), r = 0.48 and r² =0.23. Unit change in Social participation brings about 1.72 times change in A.Q., r = 0.29, r² = 0.08. For unit change in Income the A.Q. increased by four fold (4.05) with r=0.40, and r² =0.16. Lastly, there is an inverse relationship between variables A.Q. and Problems since unit change in Problems decreased A.Q. by - 2.68. The value of r was observed to be 2.63 and r²=0.39.

Now it became imperative to apply the multiple regression technique to come at the conclusive inference of the all five independent variables clubbed together. The regression equation along with ANOVA is given as under:

In order to evaluate the combined effect of Land (X₁), Education (X₂), Social participation (X₃), Income (X₄) and Problems (X₅) on A.Q. of FFP analysis of variance (ANOVA) technique was applied. The calculated value of F (45.99) was observed to be significant at 1 per cent level of probability. This indicated the fact that there is high degree of heterogeneity among the respondents. As it is evident from multiple regression equation that multiple regression coefficient for land (+1.2) is positive and significant at 5 per cent level of probability. The regression coefficient for the Problems (- 2.05) is negative and significant at 5 per cent level of significance. Keeping X₂, X₃, X₄ and X₅ constant for unit change in Land the A.Q. changes by 1.22. Similarly, for education (X₂), keeping all other variables constant the effect on adoption is 0.35. In case of Social participation (X₃) a unit change in Social participation increases the A.Q. by 0.28. For the forth variable X₄ i.e. income, a unit change in income, keeping rest of variables constant increases the income by 1.10. It is relevant to state here that the there is meager increase in income due to adoption of FFP. Lastly for the Problems (X₅) a unit change in Problems adversely affects the A.Q. more than 100%. This is a clear indicator of the fact that Land, Education, Social participation and Income have direct bearing on FFP. In FFP Land and Income played significant role whereas Education and dearth of Social participation failed to bring a desired level of effect. As it has been stated in our previous discussion that Problems faced by farmers took a chronic situation due to lack of technical knowledge, scarcity of funds, abnormal marketing situation and biotic damage. The analysis further revealed that coefficient of determination (R²) is equal to 0.64 whereas adjusted R² (R_adj²)is 0.62. Thus the whole criteria of independent variables shoulders 62% of the variation in A.Q. due to these five variables. Therefore, there is ample scope of inclusion of some more independent variables for further investigations in evaluation of FFP.

PCA (Principal Component Analysis) of FFP:

From the complex set of variables it became imperative to deduct a smaller sub set of the variables or new variables which explain the maximum variation in the FFP. In the present investigation it was found that Land, Education, Income (LEI) was the first variable and Social participation, the second one. Now for the sake of convenience the aforesaid two variables are capable of explaining 80 per cent variation in whole farm forestry programme. At the same time the remaining variables will be under control automatically. In the present investigation also previous six variables namely A.Q., Land, Education, Social participation, Income and Problems were taken for the PCA analysis. Its roots variation and correlations were calculated as shown in Table-4 below:

Table- 4: Principal components analysis of Farm Forestry Programme

The first factor in PCA explained 51.10 per cent variation in the FFP. The factor with highest loading was Land (0.8699), followed by A.Q. (0.8195) Education (0.7714) Social participation (0.5804) and Problems (-0.5057). The interclass correlation was 0.4398. The second factor could exhibit 19.89 per cent variation in this programme. The highest loading in this factor was for A.Q. (0.4013). As far as variable Problems is concerned there was an increment while compared to first factor (-0.7790) in the negative direction. The correlation in this case was 0.3562. In third factor the highest loading was Social participation (0.5986) and this factor could explain 9.44 per cent variation. For third factor correlation was found to be 0.0031. From PCA it is clear that A.Q., Land, Social participation, Education and Income went in the positive direction whereas the problems stretched the FFP opposite to these variables.

Varimax rotation:

These loaded factors were rotated by Varimax method of rotation and the extracted factors with communalities are given in following Table-5:

Table-5: Rotated matrix using Varimax method of rotation

The new variable traced are as given below:

First variable: Land-Education-Income (LEI)

The factors loading values are 0.790, 0.774. This may however be noted that we have taken loading more than 0.75 because this LEI variable is capable of explaining 50.37 per cent variation in the FFP.

Second variable: Social participation

In second factor since no value of loading is more than 0.75, it was decided to switch over to third factor wherein a single variable Social participation (SP) was observed. Its loading value was 0.792 and communality was 0.967.

Conclusion

The study indicated the fact that farm forestry programme needs attention on land, education and income of the farmers. It is relevant to state here that technical guidance with incentives for improving income and interest in social participation would not only fetch the desired results in farm forestry programme but also mitigate the mounting problems of deforestation. It would also play an important role in India's strategy to deal with ecological balance and socio-economic crisis.

Bibliography

Chattopadhyay, S.N., 1963. A study of some Psychlogical correlates of Adoption of Innovations in farming, Unpublished Ph.D Thesis, Division of Agricultural Extension, Indian Agricultural Research Institute, New Delhi.

Dixit, R.K., 2001. Studies on Socio-Economic Impacts of Farm Forestry on Local Communities in a Social Forestry Division, Ph.D. Thesis, Forest Research Institute, Dehradun.

GOI, 1976. Report on the National Commission on Agriculture, New Delhi, Ministry of Agriculture and cooperation, Government of India.

Gupta, Tirath, 1986. Farm Forestry. Indian Institute of Management, Ahmedabad.

Pareek, U. and G. Trivedi, 1964. Manual of the Socio-Economic status scale (Rural). Manasayan, Delhi.

Ray, G.L., K.B. Bhattacharya and S.K. Maity, 1996. A study in Forestry Extension, Naya Prokash, Calcutta.

Tiwari, K.M., 1983. Social Forestry for Rural Development. International Book Distributors, Dehradun.