Determinants of cattle deaths in Ethiopia

What is it about?

What are the key household- and region-level factors influencing cattle death in Ethiopia, and which count regression model best fits the analysis of cattle death data?" This study used a method called multilevel analysis, which showed that 14.6% of the difference in cattle deaths was due to the region people lived in. The best model to understand these deaths was the multilevel Zero-Inflated Negative Binomial (ZINB) regression model. It found that several factors affect cattle deaths, such as: Type of farming, Where cattle feed, How cattle are treated when sick, Whether they’re vaccinated, Size of land owned, Age and education level of the household head, Number of people in the household To help reduce cattle deaths, the Ministry of Agriculture should improve farmer education about vaccinations and strengthen veterinary services. It’s also recommended that farmers use mixed farming (raising both crops and animals) rather than just focusing on livestock. Managing household size and caring more for cattle welfare can also make a big difference.

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Photo by Monika Kubala on Unsplash

Photo by Monika Kubala on Unsplash

Why is it important?

The study of cattle mortality using multilevel logistic regression and count regression models holds significant importance for Ethiopia’s agricultural and economic sectors. Specifically, this research is important for the following reasons; Understanding Key Risk Factors, Regional Variation Analysis, Improving Statistical Modeling for Better Decision-Making, Supporting Livelihoods and Food Security, For Policy & Program Development.

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