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Articles by S.W. Roan
Total Records ( 3 ) for S.W. Roan
  B.Y. Wang , L.H. Chien and S.W. Roan
  This study presents the POMA-BROILER Model, a computer simulation developed to evaluate the optimal market age of broilers. This model was written in the Visual BASIC programming language and uses the windows operating system. The model was developed from a Sensitivity Analysis Method and is based on the concept that marginal cost must not exceed marginal return. It uses various input data including feed information (Crude Protein (CP), Metabolisable Energy (ME) and feeding stages) an equation for the feed conversion ratio, cost conditions (chicks, feed, labour, water and power, medical treatment, depreciation and miscellaneous costs) and a growth regression equation. The model then compares the calculated results with the range for acceptable market weight. The marginal cost and marginal return are calculated every day to determine the decision-making point for maximum profit. These results could represent a valuable reference for use in adjusting the strategy for broiler production and management.
  B.Y. Wang, , S.A. Chen and S.W. Roan
  This study compared the relationship between egg production and the number of pullets, laying hens, culling birds and molting birds in Taiwan through Traditional Regression Methods and ANN (Artificial Neural Network) Models. Egg production data and the number of laying hens associated with each data set were gathered from the National Animal Industry Foundation for dates between January 2001 and March 2011, totalling 123 data sets. The final regression equations were: Traditional Regression Model: case = 2.77 + 0.696 Pmonth – 0.00621 Pmonth2 – 0.00163 pullet + 0.0025 laying, R2 = 0.699; ANN Model: case = 2.82+0.113 Pmonth – 0.00871 Pmonth2 – 0.00157 pullet + 0.0024 laying, R2 = 0.965. These results show that the ANN Method is more accurate than traditional Regression Models for predicting egg production in Taiwan.
  S. Saengwong , C. Jatuporn and S.W. Roan
  In Taiwan, the livestock production sector is the primary provider for agricultural and domestic meat consumption which is shared among pork, poultry and beef. Livestock prices fluctuate based on demand, supply and other factors such as the outbreak of diseases, increased production costs, consumer behaviour, foreign competition and natural disasters. This study attempts to model the possible cointegration of price elasticity, demonstrate the causality for a directional relationship and forecast the future prices of broiler, cattle, duck and hog in Taiwan by using time series analyses such as the unit root, Johansen cointegration, Granger causality and variance decomposition tests. The Johansen cointegration test indicated significant price elasticity among the variables. The long-run Granger causality test showed that a bidirectional relationship exists between hog and broiler prices and that a unidirectional relationship exists from the duck price to the hog price. The Autoregressive Integrated Moving Average (ARIMA) and variance decomposition methods were used to predict the future livestock price and the riskiness of shocks in a future 12 months period.
 
 
 
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