Logit analysis of factors affecting adoption of improved bread wheat (Triticum aestivum L.) variety: The case of Yilmana Densa District, West Gojam, Ethiopia

Abstract

Yilmana Densa is one of the potential wheat growing District in West Gojam Zone, but the level of adoption is not studied for the last 15 years and a number of farmers are still using local varieties which are known for their low yield and disease susceptibility. Therefore, the purpose of this study was to identify potential factors affecting adoption of improved bread wheat variety, using the logistic regression (binary logit) analysis. A three stage (purposive for wheat growing kebeles, simple random sampling for sample kebeles and systematic random sampling for sample households) sampling procedure was employed to select the sample households. Finally, 120 sample respondents were selected from the sampling frame based on probability proportional to size (PPS) of wheat growers using systematic random sampling procedure. Secondary data (sampling frame, population, productivity etc) were collected from different sources. Quantitative data (farm income, farming experience, farm size, family size, etc), qualitative data (access to; credit, extension contact, input, field day etc) were also gathered. The result indicated that out of 21 identified explanatory variables, 11 of them had affected adoption significantly. Over all, of 120 sample respondents, only 35.83% (N=43) were found to be adopters of improved bread wheat varieties whereas 64.17% (N=77) of wheat growing farmers are being used local variety named Kubsa which was released before two decades and became susceptible to yellow rust and other foliar diseases. Institutional factors have been found overweighed than individual, economic and kebele (the lowest administration hierarchy in Ethiopia) level factors. The study revealed that giving due consideration for the significant variables would promote the adoption of improved bread wheat varieties. Furthermore, policy and development interventions should also be consolidated. The model result indicated that (the model chi-square value) the parameters indicated in the model taken together were significantly different from zero at less than 1 percent level of significance. The value of chi-square (×2 = 105.24) also indicated the goodness of fitted model. The chi- square goodness-of-fit test statistics of the model shows that the model fits the data with significance at 1% level. This shows that the independent variables are relevant in explaining the farmers’ decision to to adopt improved bread wheat varieties.