MODELLING THE RELATIONSHIP BETWEEN FARMER’S ATTITUDE TOWARDS FARMING AND FARM PRACTICES: A CASE STUDY OF SMALLHOLDER FARMERS IN TANZANIA

Abstract

Different models have been used in analyzing agricultural data to establish the level of agricultural productivity. This is traceable to various factors including land size, use of inputs, use of extension and modern technology, labour, capital etc. However, few researchers have tried to understand the attitudes of farmers towards farming and how this affects their on-farm practice. A TNS Global farmer’s study in Tanzania funded by Bill and Melinda Gates 2011 has focused on farmer’s agricultural productivity. This was accomplished using a mix of Simple Regression and descriptive analysis based on various factors of production. Findings showed that the more the farmers spent resources appropriately on factors that affect productivity: correct use of inputs, timeliness in land preparation, planting and input application etc, the better the land productivity. But those who actually improved on-farm practice were less than 50% of the target population. However, the entire population was exposed to the same treatment by the project. This is definitely an interesting result. Also, one would wish to understand why the success rate is that low. In this study, we have used the TNS data to try and understand if farmer’s attitude towards farming has a relation with their positive change in practice which would likely increase production. We attempted the extraction of attitudinal constructs using factor analysis. Factor analysis on 43 likert-scale questions about farmer’s attitudes was performed in order to obtain farmers’ attitudinal segments. Six factors corresponding to different themes of farmer attitudes were obtained. These are Information focus- “always seeking information to improveâ€, Negative – “don’t tell me to change, status quo is saferâ€, Change orientation – “very keen to see what new farming technologies are out thereâ€, Passive dependence – “quite dependent on other people, who cannot start something until they have seen success with other farmers; these are laggardsâ€, Heritage - “Farming is my destinyâ€, and Resigned unhappiness - “No hope to improve; so would prefer to be something elseâ€. Furthermore, we used regression analysis to assess the impact of various other observable variables on the attitudinal segmentation. Thus, this revealed a positive relationship between farmer’s attitudes and their level of agricultural productivity. This is with the more positive information focused farmers showing energies to perform well, while the negative ones who have not very good attitude are not performing very well. On average, an increase in the covariates studied here reinforced positive attitudes and lowered scores for the negative attitudes. The analysis presented in this thesis forms a basis for further research into the impact of different attitudes of farmer’s productivity.