Simple and robust model to estimate liveweight of Ethiopian Menz sheep

Heart girth (HG) bands have been predominantly used in Ethiopia by smallholder farmers, traders and extension workers to estimate liveweight (LW) of livestock. They are produced using recommended and published predictive models from Ethiopia. More recently, some farmers and traders have abandoned the bands due to perceived inaccuracy of LW estimation and reverted to eye-ball estimations. The present study generated a novel algorithm using multiple criteria to develop a robust predictive model for LW estimation of Ethiopian Menz sheep by using HG. Subsequently, recommended models currently in use in Ethiopia were evaluated for accuracy in predicting LW, using data of the present study. Liveweight and HG of 420 Menz sheep were measured. Simple linear model (SLM), Box–Cox (SLM with LW0.75), quadratic and allometric models were used to describe the relationship between LW and HG. Algorithms used to validate the models included data exploration, model construction and model redeployment. Results showed that all models had similar R2 (≈0.82). All models fitted the criteria of residual analysis and robustness against extreme values. However, only Box–Cox was robust against data redeployment, with 95th percentile of prediction error (PE) less than 10%. Accordingly, a Box–Cox model (LW0.75 = –9.71 + 0.289 (HG)) is robust and can be used to accurately predict LW of Menz sheep. The 95th percentile of PE of existing, recommended models was higher than 10; thus, they cannot be recommended to accurately predict LW of Menz sheep. The present study concludes that an approach based on regressing LW on HG, and then selecting models with the highest R2, is inadequate to generate accurate and robust prediction models. This highlights the importance of model redeployment to generate accurate prediction models. Calibrated HG bands are suitable alternatives to weighing scales in rural areas of Ethiopia because they are cheaper and not subject to maintenance. Thus, their accuracy and robustness in estimation of LW is vital for sustainable use.