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P. Hoeksma; Lr - Veehouderij En Omgeving; H. Schmitt; S. de Rijk; F. de Buisonjé; P. Sefeedpari. Effluenten van mestverwerkingsinstallaties. Effluenten van mestverwerkingsinstallaties 2021, 1 .
AMA StyleP. Hoeksma, Lr - Veehouderij En Omgeving, H. Schmitt, S. de Rijk, F. de Buisonjé, P. Sefeedpari. Effluenten van mestverwerkingsinstallaties. Effluenten van mestverwerkingsinstallaties. 2021; ():1.
Chicago/Turabian StyleP. Hoeksma; Lr - Veehouderij En Omgeving; H. Schmitt; S. de Rijk; F. de Buisonjé; P. Sefeedpari. 2021. "Effluenten van mestverwerkingsinstallaties." Effluenten van mestverwerkingsinstallaties , no. : 1.
Livestock production systems, such as dairy farming, are one of the most important contributors to resource use and if not managed well, it can be environmentally detrimental. Iranian livestock sector faces a variety of the challenges such as high costs of energy and environmental legislations as well as an increasing demand for dairy products to respond the growing population. This paper aims to contribute to the discussion on technical efficiency as a key indicator of energy use within dairy farming systems. A Window Data Envelopment Analysis (W-DEA) with energy use as inputs and milk production as output was modelled with data from 25 provinces during the last 22 years (1994–2016) in Iran. In addition, the Slack-Based Model (SBM) was used to compare the radial DEA model with non-radial SBM, both in a dynamic environment (window analysis). The average efficiency score of Iranian dairy farming production system was estimated at approximately 0.85. Through the years, three provinces including Zanjan, Ardabil and Hormozgan had the highest technical efficiencies. Window analysis represented that provinces are distinctive in terms of their technical efficiencies and energy consumption over the years. Applying the SBM model improved the accuracy of the estimated efficiency scores compared to the radial (DEA) model. Further analysis represented a significant difference between the technical efficiency of different milk production levels. Provinces that produced higher volumes of milk had lower technical efficiencies. Based on the results it can be concluded that there is a substantial space for upgrading the technical efficiency of dairy farming in Iran by improving resource use efficiency which leads to an optimized energy consumption. It is recommended to reform Iranian livestock farming policies by applying mechanized systems, optimal strategies for water, electricity and fossil fuel consumption, use of renewable energy and better feed management while enhancing milk productivity and technical efficiency. In this respect, it is suggested that policy makers consider different indicators such as energy use efficiency and environmental impacts when allocating subsidies and resources to different provinces and farms.
Paria Sefeedpari; Zeinab Shokoohi; Seyyed Hassan Pishgar-Komleh. Dynamic energy efficiency assessment of dairy farming system in Iran: Application of window data envelopment analysis. Journal of Cleaner Production 2020, 275, 124178 .
AMA StyleParia Sefeedpari, Zeinab Shokoohi, Seyyed Hassan Pishgar-Komleh. Dynamic energy efficiency assessment of dairy farming system in Iran: Application of window data envelopment analysis. Journal of Cleaner Production. 2020; 275 ():124178.
Chicago/Turabian StyleParia Sefeedpari; Zeinab Shokoohi; Seyyed Hassan Pishgar-Komleh. 2020. "Dynamic energy efficiency assessment of dairy farming system in Iran: Application of window data envelopment analysis." Journal of Cleaner Production 275, no. : 124178.
The global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practices.
Philip Shine; John Upton; Paria Sefeedpari; Michael D. Murphy. Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies 2020, 13, 1288 .
AMA StylePhilip Shine, John Upton, Paria Sefeedpari, Michael D. Murphy. Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies. 2020; 13 (5):1288.
Chicago/Turabian StylePhilip Shine; John Upton; Paria Sefeedpari; Michael D. Murphy. 2020. "Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses." Energies 13, no. 5: 1288.
Paria Sefeedpari; Lr - Veehouderij En Omgeving; Marion De Vries; Fridtjof De Buisonjé; Deni Suharyono; Bram Wouters; Windi Al Zahra; Lr - Animal Breeding & Genomics. Composting dairy cattle feces at Indonesian small-scale dairy farmsa : results of a composting trial in Lembang Sub-District, West Java. Composting dairy cattle feces at Indonesian small-scale dairy farmsa : results of a composting trial in Lembang Sub-District, West Java 2020, 1 .
AMA StyleParia Sefeedpari, Lr - Veehouderij En Omgeving, Marion De Vries, Fridtjof De Buisonjé, Deni Suharyono, Bram Wouters, Windi Al Zahra, Lr - Animal Breeding & Genomics. Composting dairy cattle feces at Indonesian small-scale dairy farmsa : results of a composting trial in Lembang Sub-District, West Java. Composting dairy cattle feces at Indonesian small-scale dairy farmsa : results of a composting trial in Lembang Sub-District, West Java. 2020; ():1.
Chicago/Turabian StyleParia Sefeedpari; Lr - Veehouderij En Omgeving; Marion De Vries; Fridtjof De Buisonjé; Deni Suharyono; Bram Wouters; Windi Al Zahra; Lr - Animal Breeding & Genomics. 2020. "Composting dairy cattle feces at Indonesian small-scale dairy farmsa : results of a composting trial in Lembang Sub-District, West Java." Composting dairy cattle feces at Indonesian small-scale dairy farmsa : results of a composting trial in Lembang Sub-District, West Java , no. : 1.
Adaptive neural fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modeling and control of uncertain systems. In this paper, we proposed an ANFIS based modeling approach (called MLANFIS) where the number of data pairs employed for training was adjusted by application of clustering method. By employing this method, the number of data required for learning step and thereby its complexity were significantly reduced. The results obtained were compared with those obtained by using artificial neural networks (ANNs). Inputs to the first group were feed supply, fuel and machinery and the ones to second cluster were pullet, electricity and labor energies. Finally, the outputs of aforementioned networks were considered as inputs to ANFIS 3 network and predicted values of egg yield were derived. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE) parameters of ANFIS 3 network were calculated as 0.92, 448.126, 0.014, respectively showing that ANFIS compared with ANNs with statistical parameters as 0.81, 751.96 and 0.019 respectively, can properly predict the egg yield of poultry farms. As a recommendation for future studies, ANFIS models with multi-layered structures can be developed to find the optimum number of layers.
Paria Sefeedpari; Shahin Rafiee; Asadollah Akram; Kwok Wing Chau; Seyyed Hassan Pishgar-Komleh. Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach. Computers and Electronics in Agriculture 2016, 131, 10 -19.
AMA StyleParia Sefeedpari, Shahin Rafiee, Asadollah Akram, Kwok Wing Chau, Seyyed Hassan Pishgar-Komleh. Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach. Computers and Electronics in Agriculture. 2016; 131 ():10-19.
Chicago/Turabian StyleParia Sefeedpari; Shahin Rafiee; Asadollah Akram; Kwok Wing Chau; Seyyed Hassan Pishgar-Komleh. 2016. "Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach." Computers and Electronics in Agriculture 131, no. : 10-19.
In this case study we document the effects of a transition to organic and low-input production methods on energy efficiency and greenhouse gas (GHG) emissions for a student educational farm in Kentucky, USA. This diverse farm system of 167 ha included crop and livestock enterprises: beef cattle, pigs, sheep, goats, chickens (broilers and layers), grains, hay and vegetables/fruit. The analysis compares the farm's material inputs, outputs and emissions across three periods: pre-transition (2007–2008), transition (2009–2011) and post-transition (2012–213). The changes implemented during the transition period included: increasing the amount of organically-managed cropland from 3% to 25% of the farm's land area; replacing an indoor-confinement pig enterprise with an outdoor rotational system; and eliminating grain from cattle rations to finish animals on pasture and hay. The implementation of these management practices resulted in relatively little change in total energy inputs and outputs, though somewhat lower levels of both were observed during the transition period. There was a steady decline in the use of non-renewable energy inputs during the study period and a subsequent decline in GHG emissions per unit area and per unit of energy output. The trends in energy and GHG emissions were explained largely by the smaller livestock herd sizes that were required to accommodate the new management practices and a corresponding increase in organic crop production proportional to the decrease in livestock production. The expansion of organic crop production resulted in a marked reduction in N fertilizer use and concomitant reduction in GHG emissions. A decrease in the use of fossil energy sources, including natural gas, electricity and gasoline, also contributed to the trends, though diesel consumption increased during the study period. This case study demonstrates the capacity to reduce the use of non-renewable resources and generation of GHG emissions with organic and low-input practices and highlights the interdependencies and interactions among enterprises within diversified farm systems.
Sean Clark; Benyamin Khoshnevisan; Paria Sefeedpari. Energy efficiency and greenhouse gas emissions during transition to organic and reduced-input practices: Student farm case study. Ecological Engineering 2016, 88, 186 -194.
AMA StyleSean Clark, Benyamin Khoshnevisan, Paria Sefeedpari. Energy efficiency and greenhouse gas emissions during transition to organic and reduced-input practices: Student farm case study. Ecological Engineering. 2016; 88 ():186-194.
Chicago/Turabian StyleSean Clark; Benyamin Khoshnevisan; Paria Sefeedpari. 2016. "Energy efficiency and greenhouse gas emissions during transition to organic and reduced-input practices: Student farm case study." Ecological Engineering 88, no. : 186-194.
This study focused on the capability of two artificial intelligent approaches, including Artificial Neural Networks (ANNs) and Multi-Layered Adaptive Neural Fuzzy Inference System (MLANFIS), as a prediction tool to model and forecast milk yield on the basis of energy consumption in dairy cattle farms of Iran. For this purpose, data was collected from 50 farms in Tehran province, Iran. For the purpose of gaining the best accurate ANFIS model, five energy inputs were clustered into two groups based on their energy share in total energy consumption and an ANFIS network was trained for each cluster. The results of statistical parameter evaluation showed that ANFIS 1 and ANFIS 2 from layer one were not as accurate as ANFIS 3 network (layer two) whereas, coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values were 0.75, 1256.72 and 0.129 for ANFIS 1 and 0.65, 1409.43 and 0.144 for ANFIS 2 and 0.93, 681.85 and 0.063 for ANFIS 3 network, respectively. These results were considerably better than ANNs model with R2, RMSE and MAPE calculated as 0.85, 1052.413 and 0.0702, respectively. Eventually, the outcomes revealed that multi-layered ANFIS contrasted to ANNs modeling could successfully predict the milk yield level accurately. Hence, it is recommended that the multi-layered ANFIS can potentially be applied as an alternative approach. ? 2015 Academic Journals Inc.Department of Civil and Environmental Engineerin
Paria Sefeedpari; Shahin Rafiee; Asadollah Akram; Kwok Wing Chau; Seyyed Hassan Pishga Komleh. Modeling Energy Use in Dairy Cattle Farms by Applying Multi-Layered Adaptive Neuro-Fuzzy Inference System (MLANFIS). International Journal of Dairy Science 2015, 10, 173 -185.
AMA StyleParia Sefeedpari, Shahin Rafiee, Asadollah Akram, Kwok Wing Chau, Seyyed Hassan Pishga Komleh. Modeling Energy Use in Dairy Cattle Farms by Applying Multi-Layered Adaptive Neuro-Fuzzy Inference System (MLANFIS). International Journal of Dairy Science. 2015; 10 (4):173-185.
Chicago/Turabian StyleParia Sefeedpari; Shahin Rafiee; Asadollah Akram; Kwok Wing Chau; Seyyed Hassan Pishga Komleh. 2015. "Modeling Energy Use in Dairy Cattle Farms by Applying Multi-Layered Adaptive Neuro-Fuzzy Inference System (MLANFIS)." International Journal of Dairy Science 10, no. 4: 173-185.
Seyyed Hassan Pishgar Komleh; A. Keyhani; Paria Sefeedpari. Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and Sustainable Energy Reviews 2015, 42, 313 -322.
AMA StyleSeyyed Hassan Pishgar Komleh, A. Keyhani, Paria Sefeedpari. Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and Sustainable Energy Reviews. 2015; 42 ():313-322.
Chicago/Turabian StyleSeyyed Hassan Pishgar Komleh; A. Keyhani; Paria Sefeedpari. 2015. "Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)." Renewable and Sustainable Energy Reviews 42, no. : 313-322.
Paria Sefeedpari; Shahin Rafiee; Asadollah Akram; Seyyed Hassan Pishgar Komleh. Modeling output energy based on fossil fuels and electricity energy consumption on dairy farms of Iran: Application of adaptive neural-fuzzy inference system technique. Computers and Electronics in Agriculture 2014, 109, 80 -85.
AMA StyleParia Sefeedpari, Shahin Rafiee, Asadollah Akram, Seyyed Hassan Pishgar Komleh. Modeling output energy based on fossil fuels and electricity energy consumption on dairy farms of Iran: Application of adaptive neural-fuzzy inference system technique. Computers and Electronics in Agriculture. 2014; 109 ():80-85.
Chicago/Turabian StyleParia Sefeedpari; Shahin Rafiee; Asadollah Akram; Seyyed Hassan Pishgar Komleh. 2014. "Modeling output energy based on fossil fuels and electricity energy consumption on dairy farms of Iran: Application of adaptive neural-fuzzy inference system technique." Computers and Electronics in Agriculture 109, no. : 80-85.
Paria Sefeedpari; Zeinab Shokoohi; Yassaman Behzadifar. Energy use and carbon dioxide emission analysis in sugarcane farms: a survey on Haft-Tappeh Sugarcane Agro-Industrial Company in Iran. Journal of Cleaner Production 2014, 83, 212 -219.
AMA StyleParia Sefeedpari, Zeinab Shokoohi, Yassaman Behzadifar. Energy use and carbon dioxide emission analysis in sugarcane farms: a survey on Haft-Tappeh Sugarcane Agro-Industrial Company in Iran. Journal of Cleaner Production. 2014; 83 ():212-219.
Chicago/Turabian StyleParia Sefeedpari; Zeinab Shokoohi; Yassaman Behzadifar. 2014. "Energy use and carbon dioxide emission analysis in sugarcane farms: a survey on Haft-Tappeh Sugarcane Agro-Industrial Company in Iran." Journal of Cleaner Production 83, no. : 212-219.
Artificial Neural Network (ANN) is an appropriate tool for forecasting the non-linear relationships across many scientific studies. In this study a back-propagation (BP) learning algorithm was chosen to predict the environmental indices of potato production in Iran. Data were collected randomly from 260 farms in Fereydonshahr city, located in Esfahan province by face to face questionnaire method. Initially, Life cycle assessment (LCA) methodology was developed to assess all the environmental impacts associated with potato cultivation in the studied region. The six LCA indices including global warming potential (GWP), eutrophication potential (EP), human toxicity potential (HTP), terrestrial ecotoxicity potential (TEP), oxidant formation potential (OFP) and acidification potential (AP) were selected as target outputs. Farm gate and one tone of potato produced were chosen as system boundary and functional unit. To find the best topology, several ANN models with different number of hidden layers and neurons in each layer were developed. To assess the best performance, a topology with highest coefficient of determination (R2), lowest root mean square error (RMSE) and mean absolute error (MAE) was selected as optimum architecture. Accordingly, ANN model with 11–10–6 structure showed the best performance. RMSE for GWP, HTP, EP, OFP, AP and TEP was computed as 0.037, 0.005, 0.057, 0.032, 0.048 and 0.037, respectively. Also, MAEs for this model were calculated as 0.028, 0.001, 0.039, 0.022, 0.035 and 0.027 for GWP, HTP, EP, OFP, AP and TEP, respectively. Evaluation of the results revealed that the developed ANN model (11–10–6 architecture) appears to be appropriate tool in predicting environmental indices of potato production.
Benyamin Khoshnevisan; Shahin Rafiee; Mahmoud Omid; Hossein Mousazadeh; Paria Sefeedpari. Prognostication of environmental indices in potato production using artificial neural networks. Journal of Cleaner Production 2013, 52, 402 -409.
AMA StyleBenyamin Khoshnevisan, Shahin Rafiee, Mahmoud Omid, Hossein Mousazadeh, Paria Sefeedpari. Prognostication of environmental indices in potato production using artificial neural networks. Journal of Cleaner Production. 2013; 52 ():402-409.
Chicago/Turabian StyleBenyamin Khoshnevisan; Shahin Rafiee; Mahmoud Omid; Hossein Mousazadeh; Paria Sefeedpari. 2013. "Prognostication of environmental indices in potato production using artificial neural networks." Journal of Cleaner Production 52, no. : 402-409.
Paria Sefeedpari; Shahin Rafiee; Asadollah Akram. Identifying sustainable and efficient poultry farms in the light of energy use efficiency: a Data Envelopment Analysis approach. Journal of Agricultural Engineering and Biotechnology 2013, 1 -8.
AMA StyleParia Sefeedpari, Shahin Rafiee, Asadollah Akram. Identifying sustainable and efficient poultry farms in the light of energy use efficiency: a Data Envelopment Analysis approach. Journal of Agricultural Engineering and Biotechnology. 2013; ():1-8.
Chicago/Turabian StyleParia Sefeedpari; Shahin Rafiee; Asadollah Akram. 2013. "Identifying sustainable and efficient poultry farms in the light of energy use efficiency: a Data Envelopment Analysis approach." Journal of Agricultural Engineering and Biotechnology , no. : 1-8.
This study was done to evaluate the energy balance between the inputs and output per unit area and to examine the effect of different farm sizes on total energy inputs and output of wheat production in Esfahan province of Iran. For this purpose data were collected by using a face-to-face questionnaire. The total energy input and output are calculated as 31.5 and 44.6 GJ ha-1, respectively. The highest energy consumer was chemical fertilizer and followed by diesel fuel and seed energy with share of 64, 14 and 8%, respectively. Total green house gas emission was 756.11 kgCO2eq ha-1 where chemical fertilizer and diesel fuel had the highest contribution. The energy ratio, energy productivity and net energy values are 1.49, 9.82 kg MJ-1 and 13.1 GJ ha-1, respectively. The forms of direct, indirect, renewable and non-renewable energies of wheat production are calculated as 6.5, 25, 5.3 and 26.2 GJ ha-1 at 21, 79, 17 and 83% of the total energy input, respectively. The results of regression analysis which is applied to find the relationship between energy inputs and wheat yield indicate the significant effect of water for irrigation, seed, chemical fertilizer and machinery energy input on wheat yield. It is concluded that use of 10 MJ in forms of direct, indirect, renewable and nonrenewable energy, leads to 3.0, 0.4, 2.8 and 0.6 kg ha-1 growth in wheat yield, respectively. The results of farm size analysis show very large farms have better energy use efficiency due to better energy management. Keywords: Energy balance, life cycle assessment, green house gases (GHG) emission, sensitivity analysis, farm size, wheat, Iran.
Ghahderijani Mohammad; Hassan Pishgar Komleh Seyyed; Keyhani Alireza; Sefeedpari Paria; Mohammad Ghahderijani; Seyyed Hassan Pishgar Komleh; Alireza Keyhani; Paria Sefeedpari. Energy analysis and life cycle assessment of wheat production in Iran. African Journal of Agricultural Research 2013, 8, 1929 -1939.
AMA StyleGhahderijani Mohammad, Hassan Pishgar Komleh Seyyed, Keyhani Alireza, Sefeedpari Paria, Mohammad Ghahderijani, Seyyed Hassan Pishgar Komleh, Alireza Keyhani, Paria Sefeedpari. Energy analysis and life cycle assessment of wheat production in Iran. African Journal of Agricultural Research. 2013; 8 (18):1929-1939.
Chicago/Turabian StyleGhahderijani Mohammad; Hassan Pishgar Komleh Seyyed; Keyhani Alireza; Sefeedpari Paria; Mohammad Ghahderijani; Seyyed Hassan Pishgar Komleh; Alireza Keyhani; Paria Sefeedpari. 2013. "Energy analysis and life cycle assessment of wheat production in Iran." African Journal of Agricultural Research 8, no. 18: 1929-1939.
P. Sefeedpari; M. Ghahderijani; S. H. Pishgar-Komleh. Assessment the effect of wheat farm sizes on energy consumption and CO2emission. Journal of Renewable and Sustainable Energy 2013, 5, 023131 .
AMA StyleP. Sefeedpari, M. Ghahderijani, S. H. Pishgar-Komleh. Assessment the effect of wheat farm sizes on energy consumption and CO2emission. Journal of Renewable and Sustainable Energy. 2013; 5 (2):023131.
Chicago/Turabian StyleP. Sefeedpari; M. Ghahderijani; S. H. Pishgar-Komleh. 2013. "Assessment the effect of wheat farm sizes on energy consumption and CO2emission." Journal of Renewable and Sustainable Energy 5, no. 2: 023131.
Habib Reyhani Farashah; Seyed Ahmad Tabatabaeifar; Ali Rajabipour; Paria Sefeedpari. Energy Efficiency Analysis of White Button Mushroom Producers in Alburz Province of Iran: A Data Envelopment Analysis Approach. Open Journal of Energy Efficiency 2013, 02, 65 -74.
AMA StyleHabib Reyhani Farashah, Seyed Ahmad Tabatabaeifar, Ali Rajabipour, Paria Sefeedpari. Energy Efficiency Analysis of White Button Mushroom Producers in Alburz Province of Iran: A Data Envelopment Analysis Approach. Open Journal of Energy Efficiency. 2013; 02 (02):65-74.
Chicago/Turabian StyleHabib Reyhani Farashah; Seyed Ahmad Tabatabaeifar; Ali Rajabipour; Paria Sefeedpari. 2013. "Energy Efficiency Analysis of White Button Mushroom Producers in Alburz Province of Iran: A Data Envelopment Analysis Approach." Open Journal of Energy Efficiency 02, no. 02: 65-74.
An artificial neural network (ANN) model was developed to assess the energy input-output prediction in dairy farms of Iran. Data used were culled from 50 randomly selected farms using face to face questionnaire approach. The energy input-output analysis was carried out for the parameters of ANN model. Based on performance measures, single hidden layers with 16 neurons in the hidden layer were finally selected as the best configuration for predicting energy output. In this study, we calculated total energy input and output to be 53,102 and 58,315 MJ cow
Paria Sefeedpari; Shahin Rafiee; Asadollah Akram. Application of artificial neural network to model the energy output of dairy farms in Iran. International Journal of Energy Technology and Policy 2013, 9, 82 .
AMA StyleParia Sefeedpari, Shahin Rafiee, Asadollah Akram. Application of artificial neural network to model the energy output of dairy farms in Iran. International Journal of Energy Technology and Policy. 2013; 9 (1):82.
Chicago/Turabian StyleParia Sefeedpari; Shahin Rafiee; Asadollah Akram. 2013. "Application of artificial neural network to model the energy output of dairy farms in Iran." International Journal of Energy Technology and Policy 9, no. 1: 82.
This study aims at finding the input–output energy use and the relationship between energy input levels on yield in southern part of Tehran province, Iran. Besides, the energy analysis was carried out based on different farm operations. Data were collected from 40 corn silage (as animal feed) farms, using face to face questionnaire method. The total energy input consumption was 36.5 GJ ha−1; in which chemical fertilizers with 11.8 GJ ha−1(with 32.3%), followed by diesel fuel and water for irrigation (with 26.5% and 24.9%, respectively) were highly contributed to the total energy use. Energy ratio, energy productivity, specific energy and net energy indices were 3.49, 1.45 kg MJ−1, 0.69 MJ kg−1 and 90563.3, respectively. The operation-wise analysis showed that land preparation and plant protection operations had significantly high energy consumption (4224.6 and 2446.0 MJ ha−1, respectively). The econometric results revealed that chemical fertilizers, fuel, water, human labor had a positive impact on output level. Moreover, as a result of this study, corn silage production has experienced a substantial increase in non-renewable energy use. Additionally, land preparation, planting and post-harvest operations were used in excess.
Paria Sefeedpari; Shahin Rafiee; Seyyed Hassan Pishgar Komleh; Mohammad Ghahderijani. A source-wise and operation-wise energy use analysis for corn silage production, a case study of Tehran province, Iran. International Journal of Sustainable Built Environment 2012, 1, 158 -166.
AMA StyleParia Sefeedpari, Shahin Rafiee, Seyyed Hassan Pishgar Komleh, Mohammad Ghahderijani. A source-wise and operation-wise energy use analysis for corn silage production, a case study of Tehran province, Iran. International Journal of Sustainable Built Environment. 2012; 1 (2):158-166.
Chicago/Turabian StyleParia Sefeedpari; Shahin Rafiee; Seyyed Hassan Pishgar Komleh; Mohammad Ghahderijani. 2012. "A source-wise and operation-wise energy use analysis for corn silage production, a case study of Tehran province, Iran." International Journal of Sustainable Built Environment 1, no. 2: 158-166.
Seyyed Hassan Pishgar Komleh; M. Ghahderijani; Paria Sefeedpari. Energy consumption and CO2 emissions analysis of potato production based on different farm size levels in Iran. Journal of Cleaner Production 2012, 33, 183 -191.
AMA StyleSeyyed Hassan Pishgar Komleh, M. Ghahderijani, Paria Sefeedpari. Energy consumption and CO2 emissions analysis of potato production based on different farm size levels in Iran. Journal of Cleaner Production. 2012; 33 ():183-191.
Chicago/Turabian StyleSeyyed Hassan Pishgar Komleh; M. Ghahderijani; Paria Sefeedpari. 2012. "Energy consumption and CO2 emissions analysis of potato production based on different farm size levels in Iran." Journal of Cleaner Production 33, no. : 183-191.
This study examined the energy use,greenhouse gas(GHG) emission and the relationship between energy inputs and yield of cotton production in Iran. Data were collected randomly from 57 cotton farms using a face to face questionnaire. The results revealed the total energy of 31 237 MJ ha−1 which fertilizer, diesel fuel, and machinery were the main energy consuming inputs. Total GHG emission was 1195 kg CO2eq ha−1, and machinery, diesel fuel, and irrigation had the highest emissions. Energy ratio and energy productivity were 1.85 and 0.11 kg MJ−1, respectively. In order to explore the relationship between inputs and outputs, the Cobb-Douglas production function was applied and it was deduced that machinery, fertilizer, diesel fuel, and biocide energies had significant effect on cotton yield. Also, the results of marginal physical productivity technique indicated that an additional use of 1 MJ ha−1 from each of the biocide, machinery, and diesel fuel would lead to an increase in production by 1.68, 0.45, and 0.38 kg ha−1, respectively. The share of direct, indirect, renewable, and non-renewable energies was 40%, 60%, 29%, and 71%, respectively.
S.H. Pishgar-Komleh; Paria Sefeedpari; M. Ghahderijani. Exploring energy consumption and CO2 emission of cotton production in Iran. Journal of Renewable and Sustainable Energy 2012, 4, 033115 .
AMA StyleS.H. Pishgar-Komleh, Paria Sefeedpari, M. Ghahderijani. Exploring energy consumption and CO2 emission of cotton production in Iran. Journal of Renewable and Sustainable Energy. 2012; 4 (3):033115.
Chicago/Turabian StyleS.H. Pishgar-Komleh; Paria Sefeedpari; M. Ghahderijani. 2012. "Exploring energy consumption and CO2 emission of cotton production in Iran." Journal of Renewable and Sustainable Energy 4, no. 3: 033115.
Paria Sefeedpari. Assessment and Optimization of Energy Consumption in Dairy Farm: Energy Efficiency. Iranian Journal of Energy and Environment 2012, 1 .
AMA StyleParia Sefeedpari. Assessment and Optimization of Energy Consumption in Dairy Farm: Energy Efficiency. Iranian Journal of Energy and Environment. 2012; ():1.
Chicago/Turabian StyleParia Sefeedpari. 2012. "Assessment and Optimization of Energy Consumption in Dairy Farm: Energy Efficiency." Iranian Journal of Energy and Environment , no. : 1.