Browsing by Author "Rutto, Erick K."

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An ensemble learning model for prediction of artificial insemination outcomes in Kenyan dairy cows: a case study.
(KCA University, 2025) Rutto, Erick K.
Artificial insemination is a vital reproductive technology for smallholder dairy systems, yet its adoption in Kenya and East Africa remains low majorly due to poor success rates influenced by complex management, animal-related, and farmer-related factors. The ability to predict the outcome of each insemination based on the available influencing factors using a decision support tool will boost the adoption of this breeding technology. The background of such a tool is machine learning models. This case study sought to develop an ensemble machine learning model to predict the artificial insemination outcome of dairy cows in smallholder systems. This was addressed through four objectives: assessing influencing factors for artificial insemination outcome, evaluating existing machine learning models using smallholder farmer data, developing a tailored ensemble model for predicting insemination outcome in dairy cows in smallholder systems, and testing performance on heterogeneous data from small scale farmers. The study utilized data on pregnancy diagnosis outcome obtained from smallholder dairy farmers in Kakamega and Kisumu counties of Kenya. Additional data from regions with the same ecological zones in Tanzania and Ethiopia were incorporated to corroborate the findings in the east African context. A total of 1347 pregnancy diagnosis outcome records were used to test the predictive ability of five models such as Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF) and Decision Trees (DT). The outcomes of these models were then fitted to Logistic Regression (LR) stacked ensemble model and its performance compared with XGBoost stacked ensemble. Results from feature importance analysis identified estrus synchronization (0.23), cow age (0.16), body condition score (0.13), membership to cooperative (0.09), and fodder growing (8.81%) as key predictors, while conventional health factors like vaccination and deworming showed minimal impact. Assessing existing base models for this type of data it was found that Random Forest (RF) outperformed all others (accuracy: 0.88). Logistic Regression-based ensemble achieved robust results (accuracy: 0.86) and exceeded XGBoost ensembled model but was outperformed by RF. The study highlights the importance of management decisions over traditional health interventions and the strengths of tree-based models in handling such type of data providing a framework for data-driven reproductive management.