Household Food Security Predictive Purposes and Automated Model Systems for Data from Lake Victoria Watershed

Verdiana Grace Masanja, Philbert Mugabo


Mathematical techniques have been used to predict household food expenditure using data collected from 216 randomly sampled households in Kenya, Tanzania and Uganda in the Lake Victoria watershed. Findings reveal that household size and annual household income if taken together have an influence on annual household food expenditures.

We have built a model that combines predictive and decision theories and have used the model to design a decision support system to classify the researched households into categories of food security levels. The Decision Support System shows that 13.3% of respondent households are food secure, 35.5% are average food secure and 51.2% are food insecure for all the three countries. We believe the data from Kenya, Tanzania and Uganda are similar due to the fact that the living style of population in Lake Victoria watershed is almost the same regardless of the effort of each country.

Since Rwanda is in Lake Victoria watershed we assume population lives the same style as studied countries. We applied the decision support system on the Rwanda governance structure and proposed a data collection system and information management system which are incorporated in the decision support system to be used by the local government leaders. This should aid decision making in monitoring household food security by local governments and can be extended to districts in the Lake Victoria watershed in Burundi, Kenya, Rwanda, Tanzania and Uganda. Data collection and model use will not need extra inputs in terms of funds, software and expertise.


food security; predicting food expenditures in East Africa; decision support systems, regression models

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