Modelling the relationship between farmer attitude towards farming, and on farm practice a case study of smallholder farmers in Tanzania

Abstract

Different models have been used in analysing agricultural data to establish level of agricultural productivity given various factors including land size, use of inputs, use of extension and modern technology, labour, capital etc. A few researchers have tried to understand farmers‟ attitudes towards farming and how this affects their on-farm practice A TNS Global farmers‟ study in Tanzania funded by Bill and Melinda Gates 2011- focused on farmer agricultural productivity using a mix of Simple Regression and descriptive analysis based on the 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 there land productivity. But those who actually improved on-farm practice were less than 50% of the target population, yet the entire population was exposed to the same treatment by the project. This is definitely an interesting result. One would wish to understand why the success rate is that low In this study, I have used the TNS data to try and understand if farmers‟ attitude towards farming has a relation with their positive change in practice which would likely increase production. I attempted 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, Negative-don‟t tell me to change, status quo is safer‟, Change orientation, Passive dependence, Heritage-„Farming is my destiny‟, Resigned unhappiness- „No hope to improve so would prefer to be something else‟. Then used regression analysis to assess the impact of various other observable variables on the attitudinal segmentation, which revealed a positive relationship between farmer attitudes and their level of agricultural productivity with the more positive, information focused farmers showing energies to perform well while the negative ones who have somewhat not very good attitude 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 different attitudes have on farmers‟ productivity.