Mohamed Nadhar Khan, Sahubar Ali and Ramli, Razamin and Baten, Md Azizul (2015) Enhanced DEA model with undesirable output and interval data for rice growing farmers performance assessment. In: 2nd Innovation and Analytics Conference & Exhibition (IACE 2015), 29 September –1 October 2015, TH Hotel, Alor Setar, Kedah, Malaysia.

Full text not available from this repository. (Request a copy)

Abstract

Agricultural production process typically produces two types of outputs which are economic desirable as well as environmentally undesirable outputs (such as greenhouse gas emission, nitrate leaching, effects to human and organisms and water pollution).In efficiency analysis, this undesirable outputs cannot be ignored and need to be included in order to obtain the actual estimation of firms efficiency.Additionally, climatic factors as well as data uncertainty can significantly affect the efficiency analysis. There are a number of approaches that has been proposed in DEA literature to account for undesirable outputs.Many researchers has pointed that directional distance function (DDF) approach is the best as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, it has been found that interval data approach is the most suitable to account for data uncertainty as it is much simpler to model and need less information regarding its distribution and membership function.In this paper, an enhanced DEA model based on DDF approach that considers undesirable outputs as well as climatic factors and interval data is proposed. This model will be used to determine the efficiency of rice farmers who produces undesirable outputs and operates under uncertainty.It is hoped that the proposed model will provide a better estimate of rice farmers’ efficiency.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Published in AIP Conference Proceedings, Volume 1691, Issue 1, ISBN: 978-0-7354-1338-2
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Quantitative Sciences
Depositing User:	Mr. Sahubar Ali Mohamed Nadhar Khan
Date Deposited:	18 Jan 2017 07:16
Last Modified:	18 Jan 2017 07:16
URI:	https://repo.uum.edu.my/id/eprint/20672

Actions (login required)

View Item