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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 was 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. The 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.

Location:

Start Date:

End Date:

Program Area: Socio-economic and Policy

Non-commodity: Diffusion

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.

Methodology:

Theoretical framework Economic analysis of farmers’ technology adoption decision is deeply rooted in 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 modern technology also has a 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 analyze 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. 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 specifically 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 the level of soil fertility2 determined from soil samples collected from representative locations and information on the 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. The dependent variable: farmers’ environmental awareness index4 Figure 1 summarizes the construction procedure of the farmers’ environmental awareness index. Farmers’ perception of the environmental impacts of technological change is elicited in two steps. First, a set of 12 specific environmental impacts was read to the respondents who were asked to reveal their opinion on each of these impacts (Ej). A value of 1 is assigned for each of the impact indicators where the farmer recognizes the impact, and 0 otherwise. The selection of the list of indicators was based on the Focus Group Discussions (FGD) with the farmers during a pre-testing stage prior to the administration of the structured questionnaire. In the next step, farmers were then asked to reveal the relative importance of each impact indicator on a five-point scale (Rm). A score of 1 is assigned for least importance and 5 for very high importance. These ranks are then converted into weighted scores (Wq). A weight of 0.2 is assigned for the lowest rank of 1 and a weight of 1 is assigned for the highest rank of 5. A zero weight is assigned for indicators where the farmer does not recognize the impact. Then the overall environmental awareness index (EAI) for each farmer is computed by summing up the weighted scores of each impact indicator and then dividing by the total number of impacts.

Source of Information: University of Plymouth PEARL https://pearl.plymouth.ac.uk

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Achievement/Findings:

Among the socio-economic factors, education and extension contacts play an important role in raising awareness. This clearly provides an opportunity to design and strategize the information dissemination process through existing educational institutions and agricultural extension systems. Several studies highlighted the use of extension education to promote conservation (e.g., Neupane et al., 2002; Mbaga-Semgalawe and Folmer, 2000; and BaiduForson, 1999). Regional characteristics (state of infrastructure and soil fertility status) also influence environmental awareness. This may very well justify improvement in rural infrastructure, as it seems to facilitate access to resources vis-à-vis improved information. Poor rural infrastructure has been identified as one of the major impediments to agricultural development in Bangladesh (Ahmed and Hossain, 1990). The promotion of soil fertility status, however, would require considerable effort in disseminating important conservation information as well as crop production practices and crop mixes to suit specific agro-ecological niches. In this context, it may be mentioned that the Soil Resources Development Institute (SRDI) in collaboration with five other institutes8 launched a project to prepare a ‘Land and Soil Resource Use Guide’ (in Bangla) for each of the 460 sub-districts of the country in the early 1980s. The manual consists of the physical and chemical test results of soil for each soil series, a soil map drawn on a 1:50,000 scale for each sub-district, plus a fertilizer recommendation guide for major and minor crops. These manuals are then distributed to Block Supervisors, the lowest unit of agricultural extension officials. However, considerable delay (about 15 years since the project started to collect soil samples) in publishing the complete set of manuals reduced its current effectiveness in planning. Nevertheless, these manuals can still serve as a basis to identify suitable crops for each soil series complemented with an updated fertilizer recommendation guide as well as extension services. In fact, areas that are fertile are also home to relatively higher levels of modern wheat acreage as well as legume crops (that fix soil nitrogen), particularly, the survey villages in the Jessore region (Rahman, 1998).

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