Comparison of Simulation Techniques Based on Milk Yield Data a Linear Mixed Model Approach

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Addis Abeba university


The term simulation has been used in a number of scientific disciplines. Agriculture is one of scientific disciplines in which simulation technique has been used to investigate theoretical as well as practical problems. Simulation has a capability of producing relevant answers based on incomplete and small datasets. In Ethiopia, researchers have often been challenged by such type of data. As a result, this study is aimed to compare which computer based simulation techniques to approximate the results of the previously accomplished researches. We have obtained 15 years of data from Debre Zeit Research Station of the International Livestock Research Institute and Holetta Agricultural Research Centre of the Ethiopian Institute of Agricultural Research for this study. We compared the two most familiar simulation techniques namely Monte Carlo and bootstrap simulations by using the results of linear mixed model fitted for each dataset. We found that both Monte Carlo and bootstrap simulations can approximate the farm and genetic group effects equally. Lactation length and daily milk yield are found to be significant (P<0.0001) in both simulation techniques. Unlike for bootstrap simulation, season and period of calving are found to be significant for Monte Carlo simulation. On the basis of the findings, this study reached a conclusion that Monte Carlo simulation has a better approximation. Key words: Monte Carlo, bootstrap, simulation, model, linear mixed model, milk yield.



Monte Carlo, Bootstrap, Simulation, Model, Linear Mixed Model, Milk Yield