Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of Iran
This study aimed to evaluate the efficiency of energy consumption and economic analysis of different watermelon cultivation systems in Fars Province of Iran. Watermelon production systems were classified into five systems, namely, custom tillage (group 1), conservation tillage (group 2), tradit...
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2018-03-01
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doaj-b29ea8d576bc4efc8a0959b10bb342002020-11-25T00:27:03ZengIslamic Azad UniversityInternational Journal of Agricultural Management and Development2159-58522159-58602018-03-01816579Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of IranMaryam Lotfalian0Bahram Hosseinzadeh Samani1Mahdi Ghasemi-Varnamkhasti2M.Sc. Student, Department of Mechanical Engineering of Biosystems, Shahrekord University, IranAssistant Professor, Department of Mechanical Engineering of Biosystems, Shahrekord University, IranAssistant Professor, Department of Mechanical Engineering of Biosystems, Shahrekord University, IranThis study aimed to evaluate the efficiency of energy consumption and economic analysis of different watermelon cultivation systems in Fars Province of Iran. Watermelon production systems were classified into five systems, namely, custom tillage (group 1), conservation tillage (group 2), traditional planting (group3), semi mechanized planting (group 4), and mechanized planting (group 5). Data were collected from 317 watermelon producers from different parts of the province through face to face interviews. Multi-Layer Perceptron artificial neural networks were used to model the energy flows of watermelon production. The results showed that the greatest energy consumption belonged to mechanized planting system with the value of 81317.72 MJha-1 and with the productivity of 0.61 kgha-1 and energy use efficiency of 1.17. Clustering function with three inputs (human resources, machines and diesel fuel) showed that the difference between groups 2 and 4 is more than the other groups. The least energy consumption belonged to the conservative agriculture as78163.86 MJha-1 and the energy productivity and energy use efficiency about 0.64 kgha-1 and 1.22, respectively. The results of energy modeling showed that an ANN model with 9-10-1 structure was determined to be optimal for energy flow modeling of this system. Generally, it was concluded that the artificial neural network models can be applicable to prognosticate the energy flows of watermelon production. From an economic point of view, the least net profit belonged to traditional planting with the value of 2618.14$, and the most net return belonged to mechanized planting with the value of 2752.88$/ha.http://ijamad.iaurasht.ac.ir/article_538536_b7fc737f96f414d51047469f80330d57.pdfArtificial neural networksConservationEnergy use efficiencyMechanized |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Maryam Lotfalian Bahram Hosseinzadeh Samani Mahdi Ghasemi-Varnamkhasti |
spellingShingle |
Maryam Lotfalian Bahram Hosseinzadeh Samani Mahdi Ghasemi-Varnamkhasti Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of Iran International Journal of Agricultural Management and Development Artificial neural networks Conservation Energy use efficiency Mechanized |
author_facet |
Maryam Lotfalian Bahram Hosseinzadeh Samani Mahdi Ghasemi-Varnamkhasti |
author_sort |
Maryam Lotfalian |
title |
Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of Iran |
title_short |
Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of Iran |
title_full |
Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of Iran |
title_fullStr |
Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of Iran |
title_full_unstemmed |
Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of Iran |
title_sort |
energy flows modeling and economic evaluation of watermelon production in fars province of iran |
publisher |
Islamic Azad University |
series |
International Journal of Agricultural Management and Development |
issn |
2159-5852 2159-5860 |
publishDate |
2018-03-01 |
description |
This study aimed to evaluate the efficiency of energy consumption
and economic analysis of different watermelon
cultivation systems in Fars Province of Iran. Watermelon production
systems were classified into five systems, namely,
custom tillage (group 1), conservation tillage (group 2),
traditional planting (group3), semi mechanized planting (group
4), and mechanized planting (group 5). Data were collected
from 317 watermelon producers from different parts of the
province through face to face interviews. Multi-Layer Perceptron
artificial neural networks were used to model the energy flows
of watermelon production. The results showed that the greatest
energy consumption belonged to mechanized planting system
with the value of 81317.72 MJha-1 and with the productivity of
0.61 kgha-1 and energy use efficiency of 1.17. Clustering
function with three inputs (human resources, machines and
diesel fuel) showed that the difference between groups 2 and 4
is more than the other groups. The least energy consumption
belonged to the conservative agriculture as78163.86 MJha-1
and the energy productivity and energy use efficiency about
0.64 kgha-1 and 1.22, respectively. The results of energy
modeling showed that an ANN model with 9-10-1 structure
was determined to be optimal for energy flow modeling of this
system. Generally, it was concluded that the artificial neural
network models can be applicable to prognosticate the energy
flows of watermelon production. From an economic point of
view, the least net profit belonged to traditional planting with
the value of 2618.14$, and the most net return belonged to
mechanized planting with the value of 2752.88$/ha. |
topic |
Artificial neural networks Conservation Energy use efficiency Mechanized |
url |
http://ijamad.iaurasht.ac.ir/article_538536_b7fc737f96f414d51047469f80330d57.pdf |
work_keys_str_mv |
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1725341203846660096 |