The research utilizes primary data for analytical tools which have been collected from field surveys through using a structured questionnaires. The samples were selected through a multi-stage purposive sampling technique. Bangladesh is divided into eight administrative divisions. Among them, two districts namely, Dinajpur and Mymensingh two divisions were selected for the necessary data collection on the basis of rice farming concentration. From each district, four sub-districts and four villages were selected after consultation with key informants from the Department of Agricultural Extension (DAE) and Bangladesh Rice Research Institute (BRRI). A total of 200 farm households from the selected sub-districts were interviewed purposively along with some focus group discussions. The primary criterion for selecting these regions is the concentration of rice farming activities through mechanization. After data collection, the farms were classified into two groups to study the technical efficiency: a) farms with more than 50% mechanization (those farms which use farm machinery for operating equal to or more than 50% of agricultural operations); and b) farms with less than 50% mechanization (those farms which practice mechanization for operating less than 50% farming operations. Land preparation, planting, weeding, fertilizer application, pesticides application, irrigation, harvesting and threshing are the main eight farming operations in rice production that are considered for possible mechanization in the study areas. Descriptive statistical tools such as; average, percentages, ratios, etc. were used to explore the extent of farm mechanization. The technical efficiency of rice-growing farmers was measured by using non-parametric analysis (data envelopment analysis). After that, the Tobit regression model gave estimations for the impact of farm mechanization on-farm technical efficiencies.
Measurement of farmers’ perception about farm mechanization: Farmers’ perception regarding farm mechanization is investigated in this research. For this perception index, a Likert scale questionnaire is followed. A Likert scale questionnaire is the one in which the subjects are asked to mark how much they agree with the point of view in the item (statement) (Elia et al. 2015; Jannat and Uddin 2016). In this study, this scale is used to assess the perception regarding the use of mechanization in agriculture. The research includes 7 positive statements related to the use of farm mechanization following a 5-point Likert scale score. The scoring is as follows: Strongly agree—( 2); Agree—( 1); Neither agree nor disagree—(0); Disagree—(-1) Strongly disagree—(-2). The perception index for each statement was calculated by using the perception index (PI). The mean score for each statement was also calculated. The perception index for each statement has been arranged in rank order according to the extent of agreement. The perception index is found to vary from 80 to - 2 for sampled farmers.
Profitability analysis of rice production Per hectare profitability of enterprise production, from the viewpoint of individual farmers, was measured in terms of gross return, gross margin, the net return, and benefit-cost–ratio. The formula needed for the calculation of profitability is discussed as follows (Dillon and Hardaker 1993):
Gross return (GR) The following equation was used to estimate GR: GR = P 9 Q; where GR is the gross return; P is the sale price of the product; and Q is the yield per hectare. Gross margin was calculated by:
GM = GR - TVC; where GM is the gross margin; GR is the gross return; and TVC is the total variable cost. The following algebraic form of net return was used for estimation: NR = GR - (TFC + TVC); where NR is the net return; GR is the gross return; TFC is the total fixed cost, and TVC is the total variable cost. Benefit-cost–ratio (BCR); The formula of calculating BCR (undiscounted) was as follows: BCR = GR - (TFC + TVC); where BCR is the benefit-cost–ratio; TFC is the total fixed cost; and TVC is the total variable cost.
An empirical model for evaluating the impact of farm mechanization on technical efficiency To assess the effectiveness of farm mechanization in raising farmers’ technical efficiency, an empirical approach consisting of two parts has been employed in this research following Nasrin et al. (2018). At first, a non-parametric approach is employed to compute technical efficiency scores for individual farms. Technical efficiency (TE) is related to the farm’s ability to achieve the highest possible output from a given level of input or obtaining a given level of output using minimum feasible amounts of inputs (Varian 1992). Efficient utilization of resources is more important than maximizing the number of resources for both the economic and social welfare of the country. For that purpose, technical efficiency in rice production is considered in this research. Efficiency can be estimated by employing either parametric or non-parametric methods. However, Nasrin et al. (2018) argued that the parametric approach may not be appropriate when farmers face different factor endowments following explanations of (Ali and Flinn 1989). This situation is also observed in this research and therefore, a non-parametric approach has been employed. Data envelopment analysis (DEA) is one of the most important non-parametric approaches for estimating efficiency which does not impose any prior parametric restrictions on the production technology as compared to the parametric approach and hence is less sensitive to model misspecification (Cooper et al. 2007). It avoids the requirement of any distributional assumption for the inefficiency terms (Coelli 1995). There is no specific criterion regarding which method is superior to another, so the choice of a particular method depends on the researcher. Therefore, DEA, a nonparametric mathematical programming approach to efficiency estimation, has been applied in this research. This approach allows the researcher to estimate efficiency scores for any sample size. Moreover, it has the advantage of evaluating technical as well as allocative and economic efficiencies.