Introduction So far, many studies have been conducted to evaluate the impact of input consumption patterns on energy, economic, and environmental indicators on horticultural and greenhouse crops in Iran. A review of these studies shows that the causes of the current situation in the systems have not been investigated. These studies are mostly reporting the current situation and the interventions and their effect on improving the input consumption pattern in the sustainability of the system have not been considered by researchers. Also, studies showed that the study location and products do not fit well with the volume of production in the horticultural and greenhouse sector of Iran. Therefore, in order to increase the effectiveness and future direction of studies in this field, this review study was conducted. In this article, Iranian horticultural and greenhouse production systems were reviewed and analyzed by reviewing the published articles between 2008 and 2018, using the PRISMA method. The PRISMA method is a well-known method for conducting systematic review studies. The PRISMA method includes the following sections: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions, and implications of key findings. In this article, 16 types of garden products and 6 types of greenhouse products were studied. Material and Methods In this study, the methods used to determine the status of energy consumption, economic and environmental patterns for horticultural and greenhouse crops were analyzed. For this purpose, the indicators of total energy consumption (TEI), energy efficiency (EUE), net energy (NE), and energy efficiency (EP) were examined in the section of energy. The issue of sensitivity analysis of energy inputs was also examined and the highest values of t-statistic and MPP were reported for products. In some articles, the data envelopment analysis method was used in systems performance analysis. The indicators used included technical efficiency (TE), pure technical efficiency (PTE), scale efficiency (SE), and energy-saving target ratio (ESTR). The results of them were summarized and reported. In some studies, the method of artificial neural networks and the Adaptive Neuro-Fuzzy Inference System were used. In general, in the present article, the challenges and risks in the methods used in previous studies were considered. The issue of sampling in the analysis of agricultural systems was discussed in detail and a new sampling procedure was proposed. To draw a general picture of energy and environmental indicators of orchard and greenhouse systems in Iran, the results published in the articles were reviewed. Not all researchers use the same equivalents in calculating the indices, and this makes the results of the studies slightly different from each other. The existence of such differences causes some deviations in comparing the results of similar articles in the same products. However, to adjust for these differences, averaging was used in the index report. Results and Discussion The study of the share of inputs in the total energy consumption shows that for horticultural products, the share of fertilizer and electricity inputs is very significant. In the case of greenhouse products, fuel input, which is mainly diesel, has the largest share of energy consumption. Walnuts have the lowest energy consumption and strawberries have the highest energy consumption among orchard products. Grapes, apples, and walnuts also have positive net energy, so they have the highest energy efficiency compared to other products. The most important inputs that have the greatest potential for energy savings in most products are diesel fuel and electricity. Among greenhouse crops in cucumber production, diesel fuel has great potential for energy savings that need to be reduced in future research. In the case of strawberry and rose products, electricity input has the greatest potential for energy savings. Knowing the potential of inputs that can be saved can be effective in changing the behavior of producers. Conclusion To increase the effectiveness of research in this area, such studies should be done dynamically and for at least two or more years. In the first year, the input consumption pattern should be extracted and after performing the consumption pattern modifying interventions, the effect of these actions should be evaluated in the following years. Data envelopment analysis methods and multi-objective genetic algorithm can be well used to develop solutions to improve input consumption patterns. The review of articles showed that the study of the effect of social factors on the behavior of various production systems has been neglected. Since the pattern of energy consumption in the agricultural sector is significantly dependent on the behavior of users and the characteristics of systems and methods of production, it seems necessary to pay attention to this factor to prepare and design any process improvement strategy in the system. In this study, a new procedure including three stages of analysis, redesign, and evaluation was proposed to complete the studies related to the analysis of agricultural systems. |
- Anonymous. 2017. The results of the survey of the country's horticulture. Tehran: Statistics Center of Iran. Report no.
- Asakereh, A., M. J. Shiekhdavoodi, M. Almassi, and M. Sami. 2010. Effects of mechanization on energy requirements for apple production in Esfahan province, Iran. African Journal of Agricultural Research 5: 1424-1429.
- Banaeian, N., and M. Zangeneh. 2011a. Estimating production function of walnut production in iran using cobb-douglas method. Agricultura Tropica Et Subtropica 44: 177-189.
- Banaeian, N., and M. Zangeneh. 2011b. Modeling energy flow and economic analysis for walnut production in Iran. Research Journal of Applied Sciences, Engineering and Technology 3: 194-201.
- Banaeian, N., M. Zangeneh, and M. Omid. 2010. Energy use efficiency for walnut producers using Data Envelopment Analysis (DEA). Australian Journal of Crop Science 4: 359-362.
- Banaeian, N., M. Omid, and H. Ahmadi. 2011. Energy and economic analysis of greenhouse strawberry production in Tehran province of Iran. Energy Conversion and Management 52: 1020-1025. https://doi.org/10.1016/j.enconman.2010.08.030
- Banaeian, N., M. Zangeneh, and S. Clark. 2020. Trends and Future Directions in Crop Energy Analyses: A Focus on Iran. Sustainability 12: 10002. https://doi.org/10.3390/su122310002
- Beza , E., J. V. Silva, L. Kooistra, and P. Reidsma. 2017. Review of yield gap explaining factors and opportunities for alternative data collection approaches. European Journal of Agronomy 82: 206-222. https://doi.org/10.1016/j.eja.2016.06.016
- Bolandnazar, E., A. Keyhani, and M. Omid. 2014. Determination of efficient and inefficient greenhouse cucumber producers using Data Envelopment Analysis approach, a case study: Jiroft city in Iran. Journal of Cleaner Production 79: 108-115. https://doi.org/10.1016/j.jclepro.2014.05.027
- Cochrane. 2021. Cochrane Handbook for Systematic Reviews of Interventions version 6.2 (updated February 2021).
- Farashah, H. R., S. A. Tabatabaeifar, A. Rajabipour, and P. 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 2: 65-74. DOI: 4236/ojee.2013.22010
- Ghatrehsamani, S., R. Ebrahimi, S. N. Kazi, A. Badarudin Badry, and E. Sadeghinezhad. 2016. Optimization model of peach production relevant to input energies- Yield function in Chaharmahal va Bakhtiari province, Iran. Energy 99: 315-321. https://doi.org/10.1016/j.energy.2015.07.078
- Houshyar, E., M. Mahmoodi-Eshkaftaki, and H. Azadi. 2017. Impacts of technological change on energy use efficiency and GHG mitigation of pomegranate: Application of dynamic data envelopment analysis models. Journal of Cleaner Production 162: 1180-1191. https://doi.org/10.1016/j.jclepro.2017.06.152
- Karimi, M., and H. Moghaddam. 2018. On-farm energy flow in grape orchards. Journal of the Saudi Society of Agricultural Sciences 17 (2): 191-194. https://doi.org/10.1016/j.jssas.2016.04.002
- Khoshnevisan, B., S. Rafiee, M. Omid, and H. Mousazadeh. 2013a. Reduction of CO2 emission by improving energy use efficiency of greenhouse cucumber production using DEA approach. Energy 55: 676-682. https://doi.org/10.1016/j.energy.2013.04.021
- Khoshnevisan, B., S. Rafiee, M. Omid, M. Yousefi, and M. Movahedi. 2013b. Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy 52: 333-338. https://doi.org/10.1016/j.energy.2013.01.028
- Khoshnevisan, B., S. Rafiee, and H. Mousazadeh. 2014a. Application of multi-layer adaptive neuro-fuzzy inference system for estimation of greenhouse strawberry yield. Measurement 47: 903-910. https://doi.org/10.1016/j.measurement.2013.10.018
- Khoshnevisan, B., S. Rafiee, and H. Mousazadeh. 2014b. Environmental impact assessment of open field and greenhouse strawberry production. European Journal of Agronomy 50: 29-37. https://doi.org/10.1016/j.eja.2013.05.003
- Khoshnevisan, B., S. Rafiee, and H. Mousazadeh. 2014c. Application of multi-layer adaptive neuro-fuzzy inference system for estimation of greenhouse strawberry yield. Measurement 47: 903-910. https://doi.org/10.1016/j.measurement.2013.10.018
- Khoshnevisan, B., H. M. Shariati, S. Rafiee, and H. Mousazadeh. 2014d. Comparison of energy consumption and GHG emissions of open field and greenhouse strawberry production. Renewable and Sustainable Energy Reviews 29: 316-324. https://doi.org/10.1016/j.rser.2013.08.098
- Khoshnevisan, B., S. Rafiee, M. Omid, H. Mousazadeh, and S. Clark. 2014e. Environmental impact assessment of tomato and cucumber cultivation in greenhouses using life cycle assessment and adaptive neuro-fuzzy inference system. Journal of Cleaner Production 73: 183-192. https://doi.org/10.1016/j.jclepro.2013.09.057
- Khoshnevisan, B., S. Rafiee, J. Iqbal, S. Shamshirband, M. Omid, N. B. Anuar, and A. W. Abdul Wahab. 2015. A comparative study between artificial neural networks and adaptive neuro-fuzzy inference systems for modeling energy consumption in greenhouse tomato production: A case study in isfahan province. Journal of Agricultural Science and Technology 17 (1): 49-62.
- Khoshroo, A., R. Mulwa, A. Emrouznejad, and B. Arabi. 2013. A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production. Energy 63: 189-194. https://doi.org/10.1016/j.energy.2013.09.021
- Loghmanpour Zarini, R., H. Yaghoubi, and A. Akram. 2013. Energy use in citrus production of mazandaran province in iran. African Crop Science Journal 21: 61-65.
- M. Yousefi, M. O., Sh. Rafiee, and B. Khoshnevisan. 2013. Modeling GHG emission and energy consumption in selected greenhouses in Iran. Energy and environment 4: 511-518.
- Mahmoudi, N., M. Almassi, A. M. Borghei, M. Ghahderijani, and M. R. Asadi Asad Abad. 2012. Estimation of energy consumption indicators in pistachio production of Khatam cityYazd state. Advances in Environmental Biology 6: 1740-1744.
- Mardani, A., and H. Taghavifar. 2016. An overview on energy inputs and environmental emissions of grape production in West Azerbayjan of Iran. Renewable and Sustainable Energy Reviews 54: 918-924. https://doi.org/10.1016/j.rser.2015.10.073
- Mohammadi, A., and M. Omid. 2010. Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Applied Energy 87: 191-196. https://doi.org/10.1016/j.apenergy.2009.07.021
- Mohammadi, A., S. Rafiee, S. S. Mohtasebi, and H. Rafiee. 2010. Energy inputs – yield relationship and cost analysis of kiwifruit production in Iran. Renewable Energy 35: 1071-1075. https://doi.org/10.1016/j.renene.2009.09.004
- Mohammadshirazi, A., A. Akram, S. Rafiee, S. H. Mousavi Avval and E. Bagheri Kalhor. 2012. An analysis of energy use and relation between energy inputs and yield in tangerine production. Renewable and Sustainable Energy Reviews 16: 4515-4521. https://doi.org/10.1016/j.rser.2012.04.047
- Moher, D., A. Liberati, J. Tetzlaff, and D. G. Altman. 2009. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Annals of Internal Medicine 151: 264-269. doi: https://doi.org/10.1136/bmj.b2535
- Mohseni, P., A. M. Borgheei, and M. Khanali. 2019. Energy Consumption Analysis and Environmental Impact Assessment of Grape Production in Hazavah Region of Arak City. Journal of Agricultural Machinery 9: 177-193. (In Persian). http://doi.org/10.22067/jam.v9i1.67645
- Mousavi-Avval, S. H., A. Mohammadi, S. Rafiee, and A. Tabatabaeefar. 2012. Assessing the technical efficiency of energy use in different barberry production systems. Journal of Cleaner Production 27: 126-132. https://doi.org/10.1016/j.jclepro.2012.01.014
- Munn, Z., M. D. J. Peters, C. Stern, C. Tufanaru, A. McArthur, and E. Aromataris. 2018. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology 18: 143.
- Nabavi-Pelesaraei, A., R. Abdi, S. Rafiee, and H. G. Mobtaker. 2014. Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach. Journal of Cleaner Production 65: 311-317. https://doi.org/10.1016/j.jclepro.2013.08.019
- Nabavi-Pelesaraei, A., S. Rafiee, H. Hosseinzadeh-Bandbafha, and S. Shamshirband. 2016. Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks. Journal of Cleaner Production 133: 924-931. https://doi.org/10.1016/j.jclepro.2016.05.188
- Namdari, M., A. Asadi Kangarshahi, and N. Akhlaghi Amiri. 2011a. Econometric Model Estimation and Sensitivity Analysis of Input for mandarin Production in Mazandaran Province of Iran. Research Journal of Applied Sciences, Engineering and Technology 3: 464-470.
- Namdari, M., A. Asadi Kangarshahi, and N. Akhlaghi Amiri. 2011b. Input-output energy analysis of citrus production in Mazandaran province of Iran. African Journal of Agricultural Research 6: 2558-2564.
- Nikkhah, A., B. Emadi, and S. Firouzi. 2015. Greenhouse gas emissions footprint of agricultural production in Guilan province of Iran. Sustainable Energy Technologies and Assessments 12: 10-14.
- Nikkhah, A., M. Royan, M. Khojastehpour, and J. Bacenetti. 2017. Environmental impacts modeling of Iranian peach production. Renewable and Sustainable Energy Reviews 75: 677-682. https://doi.org/10.1016/j.rser.2016.11.041
- Nikkhah, A., B. Emadi, H. Soltanali, S. Firouzi, K. A. Rosentrater, and M. S. Allahyari. 2016. Integration of life cycle assessment and Cobb-Douglas modeling for the environmental assessment of kiwifruit in Iran. Journal of Cleaner Production 137: 843-849. https://doi.org/10.1016/j.jclepro.2016.07.151
- Pahlavan, R., M. Omid, and A. Akram. 2011. Energy use efficiency in greenhouse tomato production in Iran. Energy 36: 6714-6719. https://doi.org/10.1016/j.energy.2011.10.038
- Pahlavan, R., M. Omid, and A. Akram. 2012a. The Relationship between Energy Inputs and Crop Yield in Greenhouse Basil Production. Journal of Agricultural Science and Technology 14: 1243-1253.
- Pahlavan, R., M. Omid, and A. Akram. 2012b. Application of Data Envelopment Analysis for Performance Assessment and Energy Efficiency Improvement Opportunities in Greenhouses Cucumber Production. Journal of Agricultural Science and Technology 14: 1465-1475.
- Pahlavan, R., M. Omid, and A. Akram. 2012c. Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy 37: 171-176. https://doi.org/10.1016/j.energy.2011.11.055
- Pahlavan, R., M. Omid, S. Rafiee, and S. H. Mousavi-Avval. 2012d. Optimization of energy consumption for rose production in Iran. Energy for Sustainable Development 16: 236-241. https://doi.org/10.1016/j.esd.2011.12.001
- Pishgar-Komleh, S. H., M. Omid, and M. D. Heidari. 2013. On the study of energy use and GHG (greenhouse gas) emissions in greenhouse cucumber production in Yazd province. Energy 59: 63-71. https://doi.org/10.1016/j.energy.2013.07.037
- Rafiee, S., S. H. Mousavi Avval, and A. Mohammadi. 2010. Modeling and sensitivity analysis of energy inputs for apple production in Iran. Energy 35: 3301-3306. https://doi.org/10.1016/j.energy.2010.04.015
- Rajabi Hamedani, S., A. Keyhani, and R. Alimardani. 2011. Energy use patterns and econometric models of grape production in Hamadan province of Iran. Energy 36: 6345-6351. https://doi.org/10.1016/j.energy.2011.09.041
- Royan, M., M. Khojastehpour, B. Emadi, and H. G. Mobtaker. 2012. Investigation of energy inputs for peach production using sensitivity analysis in Iran. Energy Conversion and Management 64: 441-446. https://doi.org/10.1016/j.enconman.2012.07.002
- Salami, P., H. Ahmadi, and A. Keyhani. 2010. Energy use and economic analysis of strawberry production in Sanandaj zone of Iran. Biotechnology, Agronomy and Society and Environment 14: 653-658.
- Salehi, M., R. Ebrahimi, A. Maleki, and H. Ghasemi Mobtaker. 2014. An assessment of energy modeling and input costs for greenhouse button mushroom production in Iran. Journal of Cleaner Production 64: 377-383. https://doi.org/10.1016/j.jclepro.2013.09.005
- Salehi, M., A. Maleki, H. G. Mobtaker, S. Rostami, and H. Shakeri. 2016. Investigation of energy inputs and CO2 emission for almond production using sensitivity analysis in Iran. Agricultural Engineering International: CIGR Journal 18: 158-166.
- Shabani, Z., S. Rafiee, H. Mobli, and E. Khanalipur. 2012. Optimization in energy consumption of carnation production using data envelopment analysis (DEA). Energy Systems 3: 325-339.
- Shabanzadeh, M., R. Esfanjari Kenari, and A. Rezaei. 2017. Investigating the energy pattern of tomato production in khorasan razavi province. Journal of Agricultural Machinery 6: 524-536. (In Persian). http://doi.org/10.22067/jam.v6i2.37724
- Soltanali, H., A. Nikkhah, and A. Rohani. 2017. Energy audit of Iranian kiwifruit production using intelligent systems. Energy 139: 646-654. https://doi.org/10.1016/j.energy.2017.08.010
- Tabatabaie, S. M. H., S. Rafiee, and A. Keyhani. 2012. Energy consumption flow and econometric models of two plum cultivars productions in Tehran province of Iran. Energy 44: 211-216. https://doi.org/10.1016/j.energy.2012.06.036
- Tabatabaie, S. M. H., S. Rafiee, A. Keyhani, and A. Ebrahimi. 2013a. Energy and economic assessment of prune production in Tehran province of Iran. Journal of Cleaner Production 39: 280-284. https://doi.org/10.1016/j.jclepro.2012.07.052
- Tabatabaie, S. M. H., S. Rafiee, A. Keyhani, and M. D. Heidari. 2013b. Energy use pattern and sensitivity analysis of energy inputs and input costs for pear production in Iran. Renewable Energy 51: 7-12. https://doi.org/10.1016/j.renene.2012.08.077
- Taghavifar, H., and A. Mardani. 2015. Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network. Journal of Cleaner Production 87: 159-167. https://doi.org/10.1016/j.jclepro.2014.10.054
- Taki, M., R. Abdi, M. Akbarpour, and H. G. Mobtaker. 2013. Energy inputs - Yield relationship and sensitivity analysis for tomato greenhouse production in Iran. Agricultural Engineering International: CIGR Journal 15: 59-67.
- Yousefi, M., F. Darijani, and A. Alipour Jahangiri. 2012. Comparing energy flow of greenhouse and open-field cucumber production systems in Iran. African Journal of Agricultural Research 7: 624-628.
|