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Environmental Impacts of Modern Agricultural Technology Diffusion in Bangladesh: an Analysis of Farmers’ Perceptions and Their Determinants

Sanzidur Rahman
Hallsworth Research Fellow School of Economic Studies University of Manchester Manchester, UK

Abstract/ Executive Summary:

Farmers’ perception of the environmental impacts of modern agricultural technology diffusion and factors determining such awareness were examined using survey data from 21 villages in three agro-ecological regions of Bangladesh. Results reveal that farmers are well aware of the adverse environmental impacts of modern agricultural technology, although their awareness remains confined within visible impacts such as soil fertility, fish catches, and health effects. Their perception of intangible impacts such as, toxicity in water and soils is weak. Level and duration of modern agricultural technology adoption directly influence awareness of its adverse effects. Education and extension contacts also play an important role in raising awareness. Awareness is higher among farmers in developed regions, fertile locations and those with access to off-farm income sources. Promotion of education and strengthening extension services will boost farmers’ environmental awareness. Infrastructure development and measures to replenish depleting soil fertility will also play a positive role in raising awareness.

Keywords: Bangladesh, Environmental impacts, ‘Green Revolution’, Multivariate analysis.

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Start Date:

End Date:

Program Area: Socio-economic and Policy

Non-commodity: Adoption and mitigation technology, Impact

Objectives:

The hypothesis is that the ‘level’ and ‘duration’ of adoption of this modern agricultural technology would positively influence farmers’ awareness of its environmental consequences in addition to other farm and farmer specific socio-economic factors. The next section describes the methodology and data. Section three provides the results and the final section concludes.

Methodology:

Theoretical framework Economic analysis of farmers’ technology adoption decision is deeply rooted on the assumption of utility maximization (e.g., Baidu-Forson, 1999; Adesina and Baidu-Forson, 1995; and Adesina and Zinnah, 1993). The underlying utility function, which ranks the preference of individual farmers of a given technology, is not observable. What is observed is a set of farm and farmer specific socio-economic characteristics that influence farmers’ decision to adopt a given technology, which is assumed to provide him/her with a certain level of perceived utility. In addition to socio-economic factors determining adoption, farmers’ perception of the modern technology also has significant influence on adoption decisions (Negatu and Parikh, 1999; and Adesina and Zinnah, 1993). Following this adoption – perception paradigm, we postulate that, at the post adoption stage, an observable set of technology attributes and farm specific socioeconomic characteristics will similarly influence farmers’ awareness of the adverse environmental impacts associated with the adopted technology. This is because a farmer’s perception (in this case environmental awareness) may be determined by his/her experience of growing the new variety, extension visits, his/her knowledge about the modern variety and other conditions (Negatu and Parikh, 1999). The econometric model Among the limited dependent variable models widely used to analyse farmers’ decision making processes, Tobit analysis has gained importance since it uses all observations, both those are at the limit, usually zero (e.g., non-adopters), and those above the limit (e.g., adopters), to estimate a regression line, as opposed to other techniques that uses observations which are only above the limit value (McDonald and Moffit, 1980). In our case, farmers could be unaware of any environmental impacts of modern agricultural technology even after adoption. Therefore, there are a number of farmers with zero environmental awareness at the limit. In such case, the application of Tobit analysis is most suited because of the censored nature of the data. The stochastic model underlying Tobit may be expressed as follows (McDonald and Moffit, 1980). Study regions and the data The study is based on farm-level cross section data for crop year 1996 collected from three agro-ecological regions of Bangladesh. The survey was conducted from February to April 1997. The specific selected regions were Jamalpur (representing wet agroecology), Jessore (representing dry agroecology), and Comilla (representing both wet agroecology and an agriculturally developed area). A multistage random sampling technique was employed to locate the districts, then the thana (subdistricts), and then the villages in each of the three subdistricts and finally the sample households. A total of 406 households from 21 villages (175 households from eight villages of Jamalpur Sadar thana, 105 households from six villages of Manirampur thana and 126 households from seven villages of Matlab thana) form the sample for the study. Detailed crop input-output data were collected for 10 groups of crops1 . The dataset also includes information on level of soil fertility 2 determined from soil samples collected from representative locations and information on level of infrastructure 3 development in the study villages. The empirical model The estimated empirical model uses a set of technological attributes, farm-specific socioeconomic characteristics and regional characteristics as explanatory variables that are assumed to influence farmers’ environmental awareness. Choice of the explanatory variables is based on the adoption – perception literature with similar justification thereof.

Source of Information: University of Plymouth PEARL https://pearl.plymouth.ac.uk Faculty of Science and Engineering School of Geography, Earth and Environmental Sciences

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Achievement/Findings:

Education and extension contacts also play an important role in raising awareness. Awareness is higher among farmers in developed regions, fertile locations and those with access to off-farm income sources. Promotion of education and strengthening extension services will boost farmers’ environmental awareness. Infrastructure development and measures to replenish depleting soil fertility will also play a positive role in raising awareness.

Knowledge Management: Report/Proceedings