2.1. Data Sources and Sample Size This study used the data of a sub-project of IPM IL, which was initiated in Jessore and Barisal region of Bangladesh during 2013 to disseminate the above-mentioned IPM practices through a three-year training programme. A total of 838 farmers were selected by IPM IL from 104 randomly selected villages. Face-to-face interviews were conducted in the selected villages during 2015. Out of 838 farmers, 168 farmers cultivated bitter gourd during 2015. All of those bitter gourd growers were included in the analysis to achieve the objectives. Among the recommended practices, bitter gourd growers’ adopted only three practices: sex pheromone trap, yellow stick trap and poultry refuse for soil amendment. Thus, in this study bitter gourd growers were considered to be an IPM adopter if he or she adopted any one of the three IPM practices. Out of 168 farmers, 81 farmers were considered as IPM adopters, of which 66 farmers adopted one practice and 15 adopted two practices.
2.2. Analytical Technique Descriptive statistics and econometric modeling were used to achieve the objectives. Major analytical techniques that were used are as follows.
2.2.1. Adoption Analysis Both binary and multinomial probit regression were used to identify the factors affecting the adoption of bitter gourd IPM practices. For the binary probit model, a farmer is considered to be an adopter if they adopted any one of the recommended IPM practices, in which case they are given a score of one, and if otherwise are given 0.
Yi* = Yia − Yina > 0 = a + zXi + ui , where ui~N(0, 1), i = 1 . . . n
Y = 1 if Y* > 0, Otherwise 0
where, Yi* is the latent variable representing the probability of farmers deciding to adopt IPM. Yia and Yina represents IPM adopters and non-adopters, respectively. Xi represents the socio-economic and technological factors affecting the adoption decision, and z is the vector of parameters to be estimated.
Since the dependent variable “adoption of IPM” takes more than one value, a multinomial probit regression was used, which is an extension of binary model in the case where the dependent variable can take more than two values.
Pr(Y = j| Xi ) = a + zXi + ui
where, Y taking on the values (0,1,2).
2.2.2. Impact Evaluation To measure the productivity and efficiency impacts of IPM, both a conventional stochastic frontier production function (SFPF) and a multi stage sample selection stochastic frontier production function approach were used. First, all available data were used to estimate a pooled as well as separate SFPF models for adopters and non-adopters, ignoring any biases [24,27–31]. Second, to control for biases from observed characteristics, propensity score matching (PSM) was implemented using the whole sample. Finally, two separate SFPFs, one for adopters and one for non-adopters, were estimated using the matched sample obtained from the PSM to correct for selectivity bias from unobserved characteristics. Thus, the models incorporate corrections for both sources of bias. The study also employed inverse probability weighted regression adjustment (IPWRA) to measure the impacts of IPM adoption on productivity and pesticide applications. The stochastic frontier production function (SFPF) is appropriate for assessing technical efficiency (TE), when the data are repeatedly inclined by measurement errors and other stochastic factors [30,36]. To measure the technical efficiency (ability of a farm producing maximum output from the minimum quantity of inputs) of a farm producing bitter gourd, a production function was specified as follows;
Yi = f (Xi , βi ) + εi
where Yi is the crop output of the ith farm, Xi represents explanatory variables, and εi represents the error terms. The model postulates that the error term εi is made of the following two independent components.