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Variables Contributing to Farmers; Attitude Towards Ipm Practices in Rice Cultivation in Godagari, Rajshahi

M. Mostafizur Rahman
Department of Agronomy and Agricultural Extension, University of Rajshahi, Rajshahi 6205, Bangladesh.

Abstract/ Executive Summary:

To identify the factors contributing to the farmers’ attitude towards Integrated Pest Management (IPM) practices in rice cultivation, 158 family heads of the Farmers Field Schools (FFSs) at Godagari Upzila under Rajshahi District were chosen at random to constitute the subjects of the study. Data were collected from 5 September, 2006 to 28 January, 2007. Twenty four selected characteristics of the farmers were considered as the independent variables while the attitude towards IPM practices in rice cultivation was the only dependent variable. Chi-square tests revealed that all variables except age, family size, farm size, supervision of crop production, annual income and peer relationship differed significantly with the attitude of the farmers. Stepwise multiple regression analysis indicated that aspiration alone contributed more than three-fifths (65.10%) of the total variation, whereas knowledge about pests, predators and risk orientation had also considerable contribution to the attitude towards IPM practices. Results of the path co-efficient analysis showed that aspiration, knowledge about pests and predators, risk orientation, mass extension contact and communication exposure had strong direct and indirect effects on the attitude of the farmers towards IPM practices in rice cultivation in the study area.

Keywords: Contributing variables, Farmers’ attitude, IPM, Rice cultivation, Barind.

Location: Godagari Upzila of Rajshahi Distric

Start Date: 05-09-2006

End Date: 28-01-2007

Program Area: Knowledge Management

Non-commodity: Constraints

Objectives:

To examine the characteristics contributed to farmers’ attitude towards IPM practices in rice cultivation.

Methodology:

The study was conducted in Godagari Upzila of Rajshahi District which is considered as Barind tract. Populations of the study area were the Farmers Field Schools (FFSs) farmers for rice cultivation. The total IPM trained up farmers through FFSs in this Upzila was 1050 from 42 FFSs, of which 15% were randomly selected as the representative sample of the study by using a Table of Random numbers (Kerlinger 1973). Data were collected from a sample of 158 farmers by using an interview schedule from 5 September, 2006 to 28 January, 2007. Measurement of independent variables: The independent variables such as age (X1), education (X2), family size (X3) and farm size (X12) were measured by using the measuring units of year, year of schooling, number of members and hectare, respectively. Family education (X4) score was determined by adding all individuals’ education score of a family and divided by the effective family size (family members under 6 years were not considered). Training experience (X5) was computed by the number of days of training that a farmer had received during last five years under different agricultural training programmes except the IPM training. Four point Likert type scale was used on 15 statements to measure supervision of crop production (X6). The agricultural knowledge (X7), knowledge about environmental pollution (X8), knowledge about pests and predators in rice (X9) and knowledge in IPM (X10) of the respondents were calculated by asking relevant questions. The family labour force (X11) was computed by assigning scores for the members of the family able to perform agricultural work. The annual income in Taka (X13) was measured on the basis of total earnings by all the members of the family. Ownership of the agricultural implements (X15) was calculated by the number of different agricultural implements and their corresponding scores. For measuring socio-economic status of the farmers (X14), scores on self education, family education, farm size, annual income, formal group affiliation, types of house owned, availability of latrine, source of drinking water, ownership of agricultural implements and material possessions were summed up. The scores on the above ten items were added together to compute the socio-economic status of an individual farmer. Cosmopoliteness (X17) of a farmer was measured by computing a cosmopoliteness score on the basis of his frequency of visits to six different places outside his own social system. Statistical analyses: Chi-square (χ 2 ) was computed to explore the relationship between the selected characteristics of the farmers and their attitude towards IPM practices in rice cultivation. In order to find out the contribution of independent variables to farmers’ attitude towards IPM practices, step-wise multiple regression analysis was computed (Dropper and Smith 1981), which avoided the problems of multi-colinearity.

Source of Information: J. Life Earth Sci., Vol. 5: 11-15, 2010 ISSN 1990-4827

Article Link (Online Journal):

Funding Source:

Total Budget (Tk.) :

Achievement/Findings:

All characteristics of the respondent farmers except age (X1), family size (X3), supervision of crop production (X6), farm size (X12), annual income (X13) and peer relationship (X19) correlated significantly with their attitude towards IPM practices in rice cultivation in Godagari, Rajshahi. Five such variables as aspiration (X21), knowledge about pests and predators (X9), risk orientation (X20), mass extension contact (X18(iii) and communication exposure (X18) were important as they explained 72.50% of the total variations in predicting the farmers’ attitude. Of them, aspiration alone explained 65.10% and it had the highest positive direct effect (0.541) and the lowest indirect effect (0.266) on the farmers’ attitude towards IPM practices, suggesting its vital role in the whole process. In addition, mass extension contact (0.262), risk orientation (0.186), and knowledge about pests and predators in rice (0.178) had direct effects, while the indirect effect was mostly channeled through other variables such as communication exposure (-0.176), which had the highest indirect effect (0.671).

Knowledge Management: Journal