Profitability or cost-benefit analysis Profitability or cost-benefit analysis includes calculation of detailed costs of production and return from maize on a per hectare basis. The total cost (TC) is composed of total variable costs (TVC) and total fixed costs (TFC). TVC includes costs of human labour (both family supplied and hired labour, wherein the cost of family supplied labour is estimated by imputing market wage rate), mechanical power; seed, manure, chemical fertilizers; pesticides; and irrigation. TFC includes land rent (if owned land is used then the imputed value of market rate of land rent is applied) and interest on operating capital. The gross return (GR) is computed as total maize output multiplied by the market price of maize. Profits or gross margin (GM) is defined as GR– TVC, whereas the Net return (NR) is defined as GR– TC. Finally, the Benefit-Cost Ratio (BCR) is computed as GR/TC.
Analytical framework: the stochastic cost frontier model A limitation of profitability analysis presented above is that it does not tell us whether farmers are achieving the maximum potential yield and profit from their production process. However, an analysis of economic efficiency allows such information to be generated at the individual producer level which is important for farmers, policymakers and other stakeholders alike.
A cost function, which is a dual of the underlying production function, is defined as a function of input prices and output level. Specifying a cost function avoids the problem of endogeniety of variables used in modelling. This is because input prices are considered exogenous in nature and is not determined within the model. A conventional cost function assumes perfect efficiency in production which is not a valid assumption given widespread evidence of inefficiency in the agricultural production processes worldwide. However, the specification of a stochastic cost frontier function allows us to identify the level of inefficiency (specifically economic inefficiency) in the production process at the individual producer level. Economic efficiency, also known as cost efficiency, results from both technical efficiency and allocative efficiency. Technical efficiency refers to a producer’s ability to obtain the highest possible output from a given quantity of inputs (Rahman, 2003). Allocative efficiency refers to a producer’s ability to maximise profit given technical efficiency. A producer may be technically efficient but allocatively inefficient (Hazarika and Alwang, 2003). Therefore, economic/cost efficiency refers to a producer’s ability to produce the maximum possible output from a given quantity of inputs at the lowest possible cost.
Study areas and the sample farmers Maize 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 maize area index for each greater district.
A multistage sampling procedure was adopted to select the sample farmers. First, three areas were selected according to the rank of MAI as well as percent of total winter maize area. The selected regions are Kushtia, Bogra and Dinajpur which covered 59% of total maize area of the country. In the second stage, one new district was chosen from each aforesaid selected greater district according to higher percent of maize area and ease of communication. Then, one upazila (sub-district) from each new district and one union from each upazila were selected purposively. Finally, three villages (one from each union) were selected randomly for collection of primary data. In the third stage, a number of steps were followed to select the households to ensure a high level of representation. At first, a list of all maize growing farmers was collected from the Department of Agricultural Extension (DAE). Then, these farm holdings were stratified into three standard farm-size categories commonly adopted in Bangladesh (e.g., Rahman and Hasan, 2008). Then, a total of 300 maize producing households were selected following a standard stratified random sampling procedure. A structured questionnaire was administered for data collection which was pre-tested prior to finalization. Data on production technologies of maize, inputs, outputs and prices were recorded seasonally by three visits covering the crop season. First visit was done just after sowing of seeds, second visit following completion of all intercultural operations and the last one after harvesting and threshing of the crop. Data also includes socio-economic profile of the sampled farmers. The survey covered winter maize growing period from November 2006 to April 2007.