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Measuring productive efficiency of horticultural greenhouses in Iran: A data envelopment analysis approach

Authors :
Heidari, Mohammad Davoud
Omid, Mahmoud
Mohammadi, Ali
Source :
Expert Systems with Applications. Jan2012, Vol. 39 Issue 1, p1040-1045. 6p.
Publication Year :
2012

Abstract

Abstract: The objective of this study was to determine energy use efficiency in greenhouse cucumber production using a non-parametric production function. The method used to quantify energy use efficiency or, more specifically, excess input energy used, is data envelopment analysis (DEA). DEA creates a ‘‘best practice’’ production frontier based on the growers that produce their level of cucumber yield with the least amount of input energy. These greenhouses then serve as benchmarks against which the energy use inefficiency of all other growers can be measured. The data used in this study was obtained through a face-to-face questionnaire method in the surveyed region – Yazd province of Iran. The total energy input and energy output were calculated to be 1168023 and 124663MJha−1, respectively. According to this information, the energy productivity was 0.14kgMJ−1, and net energy value was estimated to be –1043360MJha−1 indicating energy has been lost in greenhouse cucumber production. Basic DEA models (CCR and BCC) were used to measure the technical efficiency of the growers based on seven energy inputs human labor, diesel fuel, FYM, fertilizers, chemicals, water for irrigation and electricity, and a single output included yield of cucumber (kgha−1). The CCR and BCC models indicated 10 and 25growers were efficient, respectively. The average values of overall technical efficiency, pure technical efficiency and scale efficiency of growers were found to be 0.8235, 0.9273 and 0.8880, respectively. The results also revealed that about 21% of the total input resources could be saved if the growers follow the input package recommended by the DEA. The maximum contribution to the total energy savings is 53% from diesel fuel, followed by electricity (27%). [Copyright &y& Elsevier]

Details

Language :
English
ISSN :
09574174
Volume :
39
Issue :
1
Database :
Academic Search Index
Journal :
Expert Systems with Applications
Publication Type :
Academic Journal
Accession number :
65941084
Full Text :
https://doi.org/10.1016/j.eswa.2011.07.104