Diffusion of biogas plants in rural Gujarat

Abstract

Biogas plants offer many benefits for regions facing cooking fuel scarcity. It utilizes cattle dung to provide clean burning gas. The refuse of the plant(slurry) retains the nutrient value of dung and hence is useful as manure. In India, 18 lakh plants were installed till March 1993. The National Programme of Biogas Development (NPBD), started in 1981 could accelerate the installation of biogas plants in most states. During 1992-93, 1.9 lakh plants were installed in the country. This was 11 percent more than previous year’s installation. Although NPBD was started as far back as 1981 in Gujarat, It could achieve only 18 per cent of potential by 1993. Hence the objective of the study is to understand the factors influencing adoption and to suggest a marketing strategy so that adoption can be accelerated. The data collection involved interview with 150 adopters and 119 non-adopters from 13 villages belonging to Jamnagar, Mahesana and Valsad districts of Gujarat. A structured questionnaire was used for the purpose. Secondary data was used for modeling diffusion process at village and district levels. Biogas plants of Deenbanthu model were found to be reliable. The expenditure on repairs of common problems was quite low. Hence annual expenditure on maintenance was less. Seventeen per cent of the plants were laying abandoned. The proportion of plants that were abandoned did not differ much for different models. Time between failures was found to follow Lognormal distribution. It was found that actual feed rate varied widely among users. The mean feed rate was lower than specified rate and did not change with capacity of plant. We have examined the possible association between feed rate and certain factors like size of family, cattle holding etc. No clear pattern emerged. This might be due to indifferent usage of biogas plants. Feed rate was found to follow Beta distribution. Analysis of survey suggested that the process of diffusion is imitative in nature. By modeling this process, the rate of diffusion was estimated for all districts. The rate was found to be determined by population density and per capita value of agriculture. Factors influencing adoption were identified using multivariate analysis. It was found that adopter characteristics like age of head of household, level of schooling of household and value of durable owned (VDO) influence adoption. Agency related factors like, price, promotion, and distance to service facility also influenced adoption. Dummy variables indicating small farmers was positive and scheduled cast/tribe was negative. The analysis was repeated at Jamnagar, Mahesana and Valsad. The significant factors were different. Elasticity of proportion of adopters was computed for each of the significant factors at each district as well as when they are pooled. When all districts are pooled, elasticities of promotion, age of head of household and distance to service facility were found to be high. At Jamnagar, level of schooling, VDO, promotion and service elasticities were found to be high, while at Mahaesana, fuel expenditure and price elasticity were high. At Valsad, elasticities of age and service were high. Analysis was conducted to distinguish early and late adopters. Early adopters owned more land, cattle, durable goods and purchased more fuel before installing biogas plants. Age and schooling did not distinguish between these two groups. As diffusion process was found to be imitative in nature, the method of promotion should stimulate interaction of adopters and non-adopters. This can be achieved by installing plants for demonstration (seeding) or taking potential beneficiaries for plant visits. Determinants of rate of diffusion suggest that diffusion will be higher at places of higher income and facing fuel scarcity. Using the estimated coefficients, GAIC can identify new areas where diffusion is likely to be high. Promotion and service facility development may be directed to these areas to increase diffusion. The recommendations for program managers are formulated based on the elasticities at each of the districts. At Mahesana, the agency should introduce different methods of promotion than what is being practiced now. It should target families who spend more money on purchase of fuel. At Jamnagar, the agency should strengthen promotion and target families with high income and schooling. It should accelerate service facility development by training more persons to conduct repairs. At Valsad, they should target young families and develop service facilities. They also should introduce more features to the product.