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Impact evaluation of a crop residue management project in Bahraich, Uttar Pradesh, India

Student name: Ms Shiva Bajpai
Guide: Dr Seema Sangita
Year of completion: 2022

Abstract:

To maintain food security which is threatened by rising population, crop production is needed to be increased worldwide. However, the rise in crop output is proportionate to the increase in crop residue generation. Farmers usually do not optimize the residue generated and they burn it on their fields in order to get rid of it, quickly. This creates the problem of stubble burning. Crop residue can be used for a variety of things, including mulching, generating organic fertilizer, and mixing with cow feed. One of the aforementioned uses is for energy generation; consequently, biomass from farm activities could be used to generate electricity. Bioenergy is a term for this type of energy. Crop leftovers have a higher energy potential due to their higher GCV, making them ideal raw materials for biofuel production. The government has launched various policies for promoting the utilisation of crop residue in manufacturing the Bio Coals.

This study focused on one such bio coal plant which uses crop residue for manufacturing bio coal briquettes and pellets and supplies it to NTPC Unchahar. The research focused on the detailed working mechanism of the project, how it helped in increasing the income of the farmers who supplied the crop residue to the plant and based upon the above gained information, policy suggestions was provided. Thus, in this study Three different methodological approaches were used to gauge the effect of the project on the outcome variables of interest (Increase in net income year or not). First, the mean values of the outcome and other related variables were compared between the treatment and control groups. Second, to measure the difference in the mean of the outcome variable (net income) between the treatment and the control group individuals, average treatment effect (ATE) was assessed using the T Test. Third, regression analysis was used to estimate the equation, with the outcome variable of interest as the dependent variable.