Agricultural systems engineering (greenhouse, fish farming, mushroom production)
R. Fathi; M. Ghasemi-Nejad Raeini; R. Hesampour
Abstract
Introduction: Environmental crises and resource depletion have adversely affected environmental resources and food security in the world. Therefore, with the global population growth in the coming years and the rising need to produce more food, attention must be given to environmental issues, energy ...
Read More
Introduction: Environmental crises and resource depletion have adversely affected environmental resources and food security in the world. Therefore, with the global population growth in the coming years and the rising need to produce more food, attention must be given to environmental issues, energy consumption, and sustainable production. The purpose of this study is to evaluate the pattern of energy consumption, environmental impacts, and optimization of the studied energy indicators in dairy cattle breeding industrial units in Khuzestan province, Iran.Materials and Methods: This research was conducted in Khuzestan province, located in the southwest of Iran. Energy indicators including energy ratio, energy efficiency, specific energy, and net energy were used to determine and analyze the relationships between the output and input energy. Additionally, the life cycle assessment methodology was used to assess the environmental impact. Life cycle assessment includes a goal statement, identification of inputs and outputs, and a system for assessing and interpreting environmental impacts, and can be a good indicator for assessing environmental issues related to production. The life cycle assessment method used in this study was CML-IA baseline V3.05, which includes the four steps of (1) selecting and classifying impact categories, (2) characterizing effects, (3) normalizing, and (4) weighting. Overall, 11 impact groups were studied. The Data Envelopment Analysis (DEA) method with the Anderson-Peterson model was used for optimization. This method identifies the most efficient production unit and makes it possible to rank all of the farms in the region. In this study, each production unit (farm) was considered a decision-making unit (DMU), and its production efficiency was determined based on two models. Namely, the Charnes, Cooper, and Rhodes (CCR) model also known as Constant Return to Scale (CRS), and the Banker, Charnes, and Cooper (BCC) model also known as Variable Return to Scale (VRS).Results and Discussion: The results showed that the input and output energies per cow per day were 173.34 and 166 MJ, respectively. Livestock feed and electricity accounted for 65.47% and 27.2% of the input energy, respectively, while the oil used for tiller-scraper lubrication of fertilizer collection accounted for only 0.01%, making it the lowest input energy. Energy efficiency, specific energy, and net energy were calculated as 0.95, 0.13 kg MJ-1, 7.51 MJ kg-1, and -7.20 MJ per cow, respectively. In the abiotic depletion impact group, animal feed, machinery, and livestock equipment had the highest environmental impacts. The results showed that animal feed had the highest environmental emissions in all impact groups except for abiotic depletion of fossil fuels where electricity had the greatest effect. CRS model determined that 7 units were efficient; with an average efficiency of 0.78. In the BCC model, 20 production units were calculated as highly efficient, and the average efficiency was computed to be 0.78.Conclusion: In dairy farms in Khuzestan province, animal feed and electricity were found to have the highest energy consumption. In most impact groups, animal feed had the highest environmental effects. Specifically, in the abiotic depletion impact group, animal feed, livestock machinery, and equipment had the highest environmental effects. Considering the length of the heat period and the intensity of the solar flux, the installation of solar panels on the farm's roof to generate electricity can help reduce the consumption of non-renewable energy and mitigate radiation intensity under the roof.
M. Taki; Y. Ajabshirchi; R. Abdi; M. Akbarpour
Abstract
In this research energy efficiency for greenhouse cucumber production in Shahreza township located in Esfahan province using data envelopment analysis (DEA) technique was studied. In this study, data were obtained from 25 randomize active vegetable greenhouses from 60 greenhouses in Shahreza township ...
Read More
In this research energy efficiency for greenhouse cucumber production in Shahreza township located in Esfahan province using data envelopment analysis (DEA) technique was studied. In this study, data were obtained from 25 randomize active vegetable greenhouses from 60 greenhouses in Shahreza township and villages environs. The results showed that the highest and lowest consumed energy were related to fuel and water inputs with 47% and 1.2% respectively. The results of data envelopment analysis showed in CCR and BCC models 24% and 36% of farmers were efficient and the others were inefficient. Mean technical efficiency, net technical efficiency and scale efficiency were calculated as 90.37, 95.09 94.6 respectively. Also technical efficiency of inefficiency units in CCR model was 87% that shows13% of total energy input could be saved with upgrade efficiency in these units. In this research, total saved and unsaved energy related to fuel consumption.
Y. Ajabshirchi; M. Taki; R. Abdi; A. Ghobadifar; I. Ranjbar
Abstract
In this research energy efficiency for dry wheat production in three levels including 0.1 up to2, 2.1 up to 5 and over 5.1 hectares for the farming year 2008-2009 in Silakhor plain located in Borujerd and Dorud divisions of Lorestan province was studied using data envelopment analysis (DEA) technique. ...
Read More
In this research energy efficiency for dry wheat production in three levels including 0.1 up to2, 2.1 up to 5 and over 5.1 hectares for the farming year 2008-2009 in Silakhor plain located in Borujerd and Dorud divisions of Lorestan province was studied using data envelopment analysis (DEA) technique. The results showed that the input energy for seed, fertilizer and pesticides had the highest levels of energy consumption and the share of that in each studied level were 63.63, 56 and 54.07 percent respectively. The results of data envelopment analysis showed that the average of energy efficiency levels were 82, 78 and 68 percent, respectively. First level, that consumes more input energy than other two studied levels, had highest energy efficiency, because in this level output yield were more than other levels. Technical efficiency of inefficiency units in CRS model in three levels is 79%, 77% and 66% respectively. This issue indicates that 21, 23 and 34 of total energy input could be saved with upgrade efficiency in these units. All wrong using and also all share of total saved energy in three levels related to grain, fertilizer and pesticides and then fuel consumption.
