Energy use pattern and benchmarking of selected greenhouses in Iran using data envelopment analysis

2011 ◽  
Vol 52 (1) ◽  
pp. 153-162 ◽  
Author(s):  
M. Omid ◽  
F. Ghojabeige ◽  
M. Delshad ◽  
H. Ahmadi
Keyword(s):  
Data Envelopment Analysis ◽  
Energy Use ◽  
Data Envelopment ◽  
Use Pattern ◽  
Using Data

scholarly journals Energy use pattern and optimization of energy required for broiler production using data envelopment analysis

2016 ◽  
Vol 3 (2) ◽  
pp. 83-91 ◽  
Author(s):  
Sama Amid ◽  
Tarahom Mesri Gundoshmian ◽  
Gholamhossein Shahgoli ◽  
Shahin Rafiee
Keyword(s):  
Data Envelopment Analysis ◽  
Energy Use ◽  
Data Envelopment ◽  
Broiler Production ◽  
Use Pattern ◽  
Using Data

Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach

Energy ◽  
2011 ◽  
Vol 36 (5) ◽  
pp. 2765-2772 ◽  
Author(s):  
Seyed Hashem Mousavi-Avval ◽  
Shahin Rafiee ◽  
Ali Jafari ◽  
Ali Mohammadi
Keyword(s):  
Data Envelopment Analysis ◽  
Energy Use ◽  
Data Envelopment ◽  
Energy Use Efficiency ◽  
Use Efficiency ◽  
Dea Approach ◽  
Using Data

Study on energy use efficiency for paddy crop using data envelopment analysis (DEA) technique

2009 ◽  
Vol 86 (7-8) ◽  
pp. 1320-1325 ◽  
Author(s):  
Seyed Mehdi Nassiri ◽  
Surendra Singh
Keyword(s):  
Data Envelopment Analysis ◽  
Energy Use ◽  
Data Envelopment ◽  
Energy Use Efficiency ◽  
Use Efficiency ◽  
Using Data ◽  
Paddy Crop

scholarly journals Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis

2019 ◽  
Vol 11 (12) ◽  
pp. 3409 ◽  
Author(s):  
Ilahi ◽  
Wu ◽  
Raza ◽  
Wei ◽  
Imran ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Greenhouse Gas ◽  
Energy Use ◽  
Wheat Crop ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Wheat Production ◽  
Energy Inputs ◽  
Using Data

Energy is a major component in enhancing agricultural productivity for the rapidly growing world population. From that fact, a comprehensive analysis of energy inputs and outputs is required to conserve energy for future generations without threatening the food supply. Therefore, this study was performed in wheat production across important cropping zones of Punjab, Pakistan. In this study, the energy use pattern of wheat production was analyzed, and the degrees of technical efficiency of Decision Making Units (DMUs) were examined using Data Envelopment Analysis (DEA). Based on the results of the DEA analysis, the inefficient energy inputs were identified and further explored with the core objective of a significant reduction of excess valuable resources. Data were collected from conducting a face-to-face questionnaire of 200 farmers. The farms for sample were chosen randomly by a stratified normal approach. The results disclosed that the input energy of 34,430.98 MJ ha−1 was used up for wheat production with an output energy of 48,267.05 MJ ha−1. Energy use efficiency, specific energy, energy productivity, and net energy gain in wheat production were calculated as 1.4 MJ kg−1, 9.27 MJ kg−1, 0.10 MJ kg−1 and 13,836.07 MJ kg−1, respectively. The average technical, pure technical, and scale efficiency of DMUs were 0.668, 0.776, and 0.828, respectively, and 0.74% of consulted DMUs were functioning at decreasing returns to scale. Additionally, the significant energy consumption belongs to fertilizer, and diesel fuel, which contribute 65% of the total energy input. If these inputs are applied and managed in line with ours optimize value (29,388.5 MJ ha−1) could save 14.65% resources, which will eventually add the equal quantity in wheat-yield. The total Greenhouse Gas (GHG) emissions were calculated to be 866.43 kg CO2-eq ha−1. In conclusion, the results of the present study suggest that there is sensible capacity for enhancing the energy efficiency of wheat production in Pakistan by accompanying the recommendations for economical energy management, sustainable and efficient use of energy is extremely encouraged.

Data Envelopment Analysis (DEA) in the Psychological Sciences

2019 ◽  
Author(s):  
Jeffrey A. Shero ◽  
Sara Ann Hart
Keyword(s):  
Data Envelopment Analysis ◽  
Aggressive Behavior ◽  
Personality Traits ◽  
Educational Administration ◽  
Latent Variable ◽  
Organizational Management ◽  
Data Envelopment ◽  
Targeted Interventions ◽  
Component Skills ◽  
Using Data

Using methods like linear regression or latent variable models, researchers are often interested in maximizing explained variance and identifying the importance of specific variables within their models. These models are useful for understanding general ideas and trends, but often give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method with roots in organizational management that make such insights possible. Unlike models mentioned above, DEA does not explain variance. Instead, it explains how efficiently an individual utilizes their inputs to produce outputs, and identifies which input is not being utilized optimally. This paper provides readers with a brief history and past usages of DEA from organizational management, public health, and educational administration fields, while also describing the underlying math and processes behind said model. This paper then extends the usage of this method into the psychology field using two separate studies. First, using data from the Project KIDS dataset, DEA is demonstrated using a simple view of reading framework identifying individual efficiency levels in using reading-based skills to achieve reading comprehension, determining which skills are being underutilized, and classifying and comparing new subsets of readers. Three new subsets of readers were identified using this method, with direct implications leading to more targeted interventions. Second, DEA was used to measure individuals’ efficiency in regulating aggressive behavior given specific personality traits or related skills. This study found that despite comparable levels of component skills and personality traits, significant differences were found in efficiency to regulate aggressive behavior on the basis of gender and feelings of provocation.

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