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Impact of Environmental Production Conditions on Productivity and Efficiency: a Case Study of Wheat Farmers in Bangladesh.

Sanzidur Rahman
School of Geography, Faculty of Social Science and Business, University of Plymouth Drake Circus, Plymouth, PL4 8AA, United Kingdom

M. Kamrul Hasan
On-Farm Research Division (OFRD), Bangladesh Agricultural Research Institute (BARI), Gazipur - 1701, Bangladesh

Abstract/ Executive Summary:

Environmental conditions significantly affect production but are often ignored in studies analyzing productivity and efficiency leading to biased results. In this study, we examine the influence of selected environmental factors on productivity and efficiency in wheat farming in Bangladesh. Results reveal that environmental production conditions significantly affect the parameters of the production function and technical efficiency, as well as correlates of inefficiency. Controlling for environmental production conditions improves technical efficiency by 4 points (p<0.01) from 86% to 90%. Large farms are more efficient relative to small and medium-sized farms (p<0.01 and 0.05), with no variation among regions. Policy implications include, soil fertility improvement through soil conservation and crop rotation, improvement in managerial practices through extension services and adoption of modern technologies, promotion of education, strengthening the research-extension link, and development of new varieties that have higher yield potential and are also suitable for marginal areas.

Keywords: Stochastic production frontier, Environmental conditions, Wheat, Bangladesh

Location: Dinajpur and Rajshahi, and one from medium intensity areas Jamalpur district, Bangladesh

Start Date: 00-11-2003

End Date: 00-04-2004

Program Area: Crop-Soil-Water Management

Commodity: Wheat

Objectives:

To examine the influence of selected environmental factors on productivity and efficiency in wheat farming in Bangladesh.

Methodology:

The stochastic production frontier approach, developed by Aigner et al., (1977), is utilized in this study. We extend the framework and include variables representing environmental production conditions in addition to physical inputs to explain productivity performance as described by Sherlund et al, (2002). The stochastic production frontier for the ith farmer is written as: Y_i = F(X_i,W_i)-u_i+v_i, (1). where Y_iis the output, X_i is the vector of physical inputs, Wi is the vector of relevant environmental variables that control production conditions, v_i is assumed to be independently and identically distributed N(0,σ ² _v) two-sided random error, independent of the u_i; and the u_i is a non-negative random variable), (u_i ≥ 0) associated with the inefficiency in production which is assumed to be independently distributed as truncation at zero of the normal distribution with mean –Z_iδ, and variance σu ² (|N(–Ziδ,σ ² u|), where Z_i are the correlates of inefficiencies on the farm i. In this formulation, the output is assumed to be strictly monotonically increasing in both physical inputs as well as environmental conditions. Most studies in the literature typically estimate: Y_i= g(x_iW_i^*)-u_i^*+v_i^*, (2). Where W_i^* ⊆W_i omits some or all of the elements of W_i, and, therefore, results in biased estimates of the parameters of the production function, overstatement of technical inefficiency, as well as biased correlates of inefficiency (Sherlund et al., 2002). In determining the predictors of production efficiency, we use the single-stage approach proposed by Battese and Coelli (1995) wherein the technical inefficiency parameter is related to a vector of farm-specific managerial and household characteristics subject to statistical error. Selection of the study area and sample farmers: Wheat is cultivated almost all over the country, though the intensity of planted area and land suitability are not equal in all regions. Therefore, we computed a wheat area index for each greater district. The wheat area index for the jth district is expressed as:WI = (Area_j /GCA_j)*100, (5). where WI is the wheat area index, Area is the wheat area and GCA is the gross cropped area. Based on this index, wheat-growing regions were classified into three levels of intensity: high intensity (WI>8.0), medium intensity.

Source of Information: Journal of environmental management Bangladesh PEARL https://pearl.plymouth.ac.uk

Article Link (Online Journal): http://hdl.handle.net/10026.1/4026

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

The present study examined the impact of environmental production conditions on the production performance of wheat producers in Bangladesh. The environmental production conditions, within which the farmer operates, are considered vital but are often arbitrarily omitted in productivity and efficiency studies, resulting in biased estimates of the production parameters, efficiency scores and correlates of inefficiency. Our results demonstrate the validity of this claim for Bangladeshi wheat farmers. Poor land type, poor soil fertility and delay in sowing results in significant production loss. Controlling for environmental production conditions improves technical efficiency by 4 points (p<0.01) from 86% to 90%. Farmers’ managerial practices, particularly, timely sowing and fertilizer application, use of mechanical power, higher education and diverse sources of agricultural information, all significantly improve efficiency. Nevertheless, the scope to raise wheat production remains limited with the existing set of varieties and technologies because farmers have already adopted 100% percent of popular modern varieties and are also producing at a high level of efficiency (90%). The results of our study have significant policy implications as it demonstrates the need to evaluate farmers’ production performance using an extended framework of analysis, that takes into account the environmental production conditions within which farmers have to operate. Otherwise, the upward bias of inefficiency measures, widely reported in the literature, would lead to a redirection of scarce resources to less than optimal uses. Also, in modelling predictors of inefficiency, it is important to use an elaborate set of variables, particularly managerial variables unique to each crop studied, so that management factors that directly affect production performance can be addressed through policy redressing.

Knowledge Management: Journal