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Research Detail

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Muhammad Humayun Kabir*
Department of Agricultural Extension and Information System, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh

Ruslan Rainis
Department of Geography, School of Humanities, Universiti Sains Malaysia, Penang, Malaysia

Md. Javed Azad
Department of Agricultural Extension and Information System, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh

Past studies on the adoption of integrated pest management (IPM), analyzed the significance of non-spatial factors (social, economic, institutional and management factors etc.) in influencing farmers’ decision to adopt IPM while the present study analyzed spatial factors in addition to these non-spatial factors to address the questions-i. Do the spatial factors significantly influence the farmers to adopt IPM? If yes, then to what extent they do affect IPM adoption? The data were collected from 331 vegetable farmers of Narsingdi district, Bangladesh, by conducting a household survey. Farmers’ nineteen characteristics under five broad groups, namely social, economic, institutional, management and spatial factors were analyzed. The result of the binary logistic regression model revealed that two spatial factors namely the distance of farmers’ house from the nearest market and the distance from agriculture office, along with some specific social, economic, institutional and management factors, significantly influenced the farmers’ to use IPM. It is also observed from the model that the role of spatial factors was important in influencing IPM adoption. However, with regard to the level of importance, their contribution was less than those of economic and institutional factors but more than those of social and management factors. The influences of these factors in practicing IPM are discussed individually as well as group based. The findings show significance in domestic policy making.

  Spatial Factors, Adoption, IPM, Logistic Regression Model
  Narsingdi district of Bangladesh
  
  
  Pest Management
  IPM

Since spatial factors were considered as a new dimension, thus this study examined not only they are important or not but also make a comparison between their importance with others. Therefore, the objectives of the study are to identify the factors that significantly influence vegetable farmers’ IPM adoption and to assess the spatial factors that influence in the adoption of IPM.

Structured questionnaire and Global Positioning System (GPS) were used to collect data from the farmers of Narsingdi district, an intensive vegetable growing region of Bangladesh. By following a multistage sampling technique, twelve villages in Belabo upazila (sub-district) of Narsingdi district were selected for the survey. These villages were selected based on vegetable cultivation intensity, frequency of pesticide application and the existence of IPM program. A total number of 1926 vegetable farmers of the selected villages were obtained from the respective upazila agriculture office considered as population of the study. By considering time and other resources, a total of 331 vegetable farmers were selected as sample, comprising 17% from the total population. These farmers were selected randomly for the interview.

The vegetable farmers nineteen characteristics under five broad categories namely; social, economic, institutional, management and spatial were considered in the study. An in-depth interview based on structured questionnaire was held with the sample farmers to collect the data related to non-spatial factors or characteristics. The questionnaire was divided into four parts. The first part was related to farmers’ social characteristics measured by the variables of age, education, household size and perception towards IPM. The second part highlighted farmers’ economic characteristics in terms of the variables such as farm size, area under vegetable cultivation, annual income and land ownership status. The third part evaluated farmers’ participation towards institutional support like farmer field school training, contact with extension agent, field day demonstration and membership of IPM club. The fourth and last part measured some of the farmers managerial activities such as the use of improved variety, time spent on the farm per day and the number or type of vegetable grown in a year. On the other hand, spatial data were collected by using a handheld GPS. GPS coordinates were recorded from each of the survey respondent’s place of residence and four key locations such as upazila agriculture office, nearest market, pesticide store and national highway. Later by using Arc GIS (version 9.3) software, the distance between the farmers’ house and these four focal points were measured. 

The farmers’ nineteen characteristics were treated as independent variables or predictors in the study. Some of these are binary while others are continuous. The dependent variable adoption of IPM is binary in nature. The type and Map of Belabo upazila, Narsingdi district, Bangladesh showing vegetable growers’ house and four key locations (agriculture office, market, pesticide store and highway). measuring techniques of both independent and dependent variable. As the dependent variable may have any one option of the two; either the adoption or rejection, then to justify the impact of independent variables on IPM adoption, binary logistic regression model is considered the most appropriate. The model was applied in the study twice. In the first stage, as independent variables, social, economic, institutional and management factors were considered. In the second stage, spatial factors in addition to these were entered. That means the difference between two stages of the logistic regression model is spatial factors where in the first stage these factors are absent and present in the second stage. This was done for better understanding about the effect of spatial factors in the adoption of IPM.

Prior to entering the independent variables into the model, they were checked to identify the colinearity problem. In the first stage, there was no colinearity among the independent variables. As a result all (15) factors were entered in the model. In the second stage, the independent variable (distance of the farmers’ house to national highway) was excluded from the model as it shows a high degree of correlation with another independent variable (distance to agriculture office) and a low degree of correlation with dependent variable. Therefore, eighteen independent variables were finally considered to be included into the second stage of the model.

  Journal of Geographic Information System, 2017, 9, 98-113
  https://doi.org/10.4236/jgis.2017.92007
Funding Source:
1.   Budget:  
  

Studies on determinants of adoption of IPM during the last three decades explored various social, economic, institutional and management factors as important determinants. This study analyzed spatial factors, besides others, to identify the important factors for vegetable farmers adoption of IPM by taking the issue of adoption behavior is complex and related with assorted factors. In addition, the study discussed the importance of the factors according to their categories besides individually. Regarding the first issue, it was observed through statistical analysis that farmers’ IPM adoption decision was influenced by various spatial, social, economic, institutional and management factors such as FFS training, extension contact, land ownership, perception towards IPM, distance to nearest market, use of improved variety and distance to agriculture office. So, in order to increase the adopters of IPM, DAE should take the initiative to facilitate more FFS training and extension contact for the farmers. Moreover, during the selection session of farmer for training program and for extension contact, emphasize should be given to those who are tenant farmers, who have unfavorable perception towards IPM and use local variety, who live close to the market but far away from agriculture office as these farmers show less interest to adopt IPM. Regarding the second issue, it was discovered that the importance of spatial factors in the adoption of IPM was higher than those of social and management factors but lower than those of economic and institutional factors. Though there were variations among the significant factors regarding their level of contribution, for policy making, all significant factors should be considered in order to improve the adoption rate of IPM. However, in case of fund, time and other limitations focus can be concentrated on the few top most important factors. Many factors are relevant with the technology adoption but only twenty characteristics were selected for investigation in this study. Moreover, the study is conducted in a specific location of Bangladesh. Therefore, study should conduct with considering various factors giving emphasize on spatial items and a different location. 

  Journal
  


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