M. Tabaraee; Kh. Parsapoor; S. Abed
Abstract
AbstractThe purpose of this study is investigating factors affect on probability of Willingness to pay sugar beet farmers for taking of agricultural extension services. logit model was used for cross sectional data in 2009 between 150 sugar beet farmers that were selected by random sampling in the city ...
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AbstractThe purpose of this study is investigating factors affect on probability of Willingness to pay sugar beet farmers for taking of agricultural extension services. logit model was used for cross sectional data in 2009 between 150 sugar beet farmers that were selected by random sampling in the city of Mashhad. In addition, tobit model was used to analyse factors affecting on Willingness to pay beet farmers for taking of agricultural extension services. Results of both model showed that age, experience in sugar beet cultivation and household size have negative and significant effect and education, cultivated area, yield, farm ownership, net revenue of sugar beet, need to extension services and concurrence of extension services have positive and significant effect on probability of Willingness to pay for agricultural extension services. comparing aggregate elasticity among different variables in tobit model showed that need to extension services has the maximum effect in increasing and farmer, s age has the maximum effect in decreasing Willingness to pay for taking of agricultural extention services respectively. According to results, implementation of semiprivate extension between great farmers, Forming associations and cooperatives for yeomen to pay extension costs as a group, Making diversity in the type and method of offering extension service based on age, experience and education suggested.
M. Rafati; Y. Azarinfar; R. Mohammadzadeh
Abstract
AbstractThe aim of this study was to selecting the suitable model for forecast land, production and Price of sugar beet in Iran. For this purpose, Models applied to forecast are ARIMA, Single and Double Exponential Smoothing, Harmonic, Artificial Neural Network and ARCH for period 1993-2008. Results ...
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AbstractThe aim of this study was to selecting the suitable model for forecast land, production and Price of sugar beet in Iran. For this purpose, Models applied to forecast are ARIMA, Single and Double Exponential Smoothing, Harmonic, Artificial Neural Network and ARCH for period 1993-2008. Results of Durbin-Watson tests, land, production and price of sugar beet series were found non stochastic and predictable. Based on the lowest forecasting error criterion, ARIMA is the best model for forecast production and price of sugar beet series. But in orther to forecast land of sugar beet, Neural Network model is the best. Hence, using the forecast method can affect on different policy about production via forecasting the fluctuation variables.Jel Classification: Q11 – D12 – C32 – C22