Comparison of Simulation Techniques Based on Milk Yield Data a Linear Mixed Model Approach
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Date
2010-06
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Addis Abeba university
Abstract
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.
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Keywords
Monte Carlo, Bootstrap, Simulation, Model, Linear Mixed Model, Milk Yield