
تعداد نشریات | 162 |
تعداد شمارهها | 6,623 |
تعداد مقالات | 71,544 |
تعداد مشاهده مقاله | 126,892,326 |
تعداد دریافت فایل اصل مقاله | 99,937,647 |
مدلسازی انرژی و انتشارات گازهای گلخانهای تولید محصول جو دیم با بهرهگیری از یادگیری ماشین در شهرستان نظرآباد، استان البرز | ||
مهندسی بیوسیستم ایران | ||
دوره 55، شماره 2، تیر 1403، صفحه 1-19 اصل مقاله (2.14 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijbse.2024.377733.665562 | ||
نویسندگان | ||
سیدامید داودالموسوی؛ شاهین رفیعی* ؛ علی جعفری | ||
گروه مهندسی مکانیک ماشینهای کشاورزی، دانشکده فنی و مهندسی کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
چکیده | ||
انتخاب روشهای صحیح و مناسب عملیاتهای زراعی باعث کاهش مصرف انرژی و کاهش تولید گازهـای گلخانهای در تولیـدات محصولات کشاورزی میشود. در این مطالعه مقادیر انرژی ورودی، خروجی و انتشار گازهای گلخانهای تولید جو در شهرستان نظرآبادِ استان البرز مورد بررسی قرار گرفت. مقادیر مختلف کاربرد نهادهها و اطلاعات جامع در هر مرحله از کاشـت تـا برداشت از طریق مصاحبه و پر کردن پرسشنامههای تخصصی جمعآوری شد. مقادیر انرژی مصرفی و انتشارات با استفاده از ضرایب تبدیل انرژی و انتشار گازهای گلخانهای استخراجشده از منابع محاسبه شد. باتوجهبه نتایج بهدستآمده میانگین انرژی کل مصرفی MJ/ha 16/14443 به دست آمد. مقدار پتانسیل گرمایش جهانی کل ناشی از فعالیتهای مختلف در مزرعه 77/650 کیلوگرم معادل کربندیاکسید در هکتار بوده است. بیشترین انتشار گازهای گلخانهای مربوط به کود شیمیایی نیتروژن و سوخت دیزل بوده است. شاخصهای نسبت انرژی، بهرهوری انرژی، شدت انرژی و انرژی خالص به ترتیب 03/5، kg/MJ 34/0،MJ/kg 91/2 و MJ58348 به دست آمد. مدلسازی انرژی با سه روش رگرسیونی درخت تصمیم، رگرسیون جنگل تصادفی و رگرسیون گرادیانی تقویتشده انجام شد و ضریب همبستگی آنها به ترتیب برابر 76/0، 79/0 و 76/0 و جذر میانگین مربعات خطای نسبی به ترتیب برابر 04/0، 05/0 و 06/0 محاسبه شد. نتایج نشان داد که روش رگرسیونی درخت تصمیم قادر است بادقت بیشتری مقادیر انرژی را پیشبینی کند. تحلیل حساسیت با SHAP انجام شد و تأثیرگذارترین نهاده روی پیشبینی انرژی کود شیمیایی نیتروژن بود. | ||
کلیدواژهها | ||
تحلیل حساسیت؛ کارایی انرژی؛ جو؛ یادگیری ماشین | ||
عنوان مقاله [English] | ||
Modeling energy and greenhouse gas emissions of rainfed barley production using machine learning in Nazarabad city, Alborz province | ||
نویسندگان [English] | ||
seyed omid davodalmosavi؛ shahin rafiee؛ ali Jafari | ||
Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
چکیده [English] | ||
Choosing the correct and appropriate methods of agricultural operations reduces energy consumption and greenhouse gas production in the emissions of agricultural crops. In this study, the amount of energy input, energy output, and greenhouse gas emissions of barley production in Nazarabad city of Alborz province were investigated. Various amounts of inputs and comprehensive information were collected at each stage from planting to harvesting through interviews and filling specialized questionnaires. Energy consumption and emissions were calculated using energy conversion coefficients and greenhouse gas emissions extracted from the sources. According to the obtained results, the average total energy consumption was 14443.16 MJ ha-1. The total global warming potential due to different activities in the farm was 650.77 kg equivalent of carbon dioxide per hectare. The highest emission of greenhouse gases was related to nitrogen fertilizer and diesel fuel. The indices of energy ratio, energy efficiency, energy intensity, and net energy gain were 5.03, 0.34 kg/MJ, 2.91 MJ/Kg, and 58348 MJ, respectively. Energy modeling was done with three methods: decision tree regression, random forest regression, and enhanced gradient regression that, their correlation coefficients were 0.76, 0.79 and 0.76 respectively, and the root mean square errors were calculatd 0.04, 0.05 and 0.06 respectively The results showed that the decision tree regression method is able to predict energy values more accurately. Sensitivity analysis was performed with SHAP and the most influential input on energy prediction was nitrogen fertilizer. | ||
کلیدواژهها [English] | ||
: Sensitivity analysis, barley, energy efficiency, Machine Learning | ||
مراجع | ||
Abrishambaf, O., Faria, P., Vale, Z., & Corchado, J. M. (2019). Energy Scheduling Using Decision Trees and Emulation: Agriculture Irrigation with Run-of-the-River Hydroelectricity and a PV Case Study. Energies, 12(20), 3987. Aghighi, H., Azadbakht, M., Ashourloo, D., Shahrabi, H. S., & Radiom, S. (2018). Machine learning regression techniques for the silage maize yield prediction using time-series images of Landsat 8 OLI. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 4563-4577. Amirahmadi, E., Moudrý, J., Konvalina, P., Hörtenhuber, S. J., Ghorbani, M., Neugschw&tner, R. W.,... & Kopecký, M. (2022). Environmental Life Cycle Assessment in Organic & Conventional Rice Farming Systems: Using a Cradle to Farm Gate Approach. Sustainability, 14(23), 15870. Apazhev, A. K., Fiapshev, A. G., Shekikhachev, I. A., Khazhmetov, L. M., Khazhmetova, A. L., & Ashabokov, K. K. (2019). Energy efficiency of improvement of agriculture optimization technology and machine complex optimization. In E3S Web of Conferences (Vol. 124, p. 05054). EDP Sciences. Canakci, M., Topakci, M., Akinci, I. & Ozmerzi, A. (2005). Energy use pattern of some field crops & vegetable production: Case study for Antalya Region, Turkey. Energy Conversion & Management, 46(4), 655-666. Davodalmosavi, S. O., rafiee, S., Jafari, A., & rafiee, A. (2024). Analysis and modeling of energy and the amount of greenhouse gas production in apple production using machines laerning in Nazarabad city. Agricultural Mechanization, 8(4), 81-96. doi: 10.22034/jam.2024.58882.1259 Davodalmousavi,Sid omid, Rafiee, Shahin, and Jafari. (2023). Analysis and modeling of peach energy using machine in Nazarabad city. Biosystem Engineering of Iran. (In Persian) Dekamin, M., Kheiralipour, K., & Afshar, R. K. (2022). Energy, economic, and environmental assessment of coriander seed production using material flow cost accounting and life cycle assessment. Environmental Science and Pollution Research, 29(55), 83469-83482. Dewi, C., & Chen, R. C. (2020). Decision making based on IoT data collection for precision agriculture. Intelligent Information & Database Systems: Recent Developments 11, 31-42. Elhami, B., Raini, M. G. N., Taki, M., Marzban, A., & Heidarisoltanabadi, M. (2021). Analysis & comparison of energy-economic-environmental cycle in two cultivation methods (seeding & transplanting) for onion production (case study: central parts of Iran). Renewable Energy, 178, 875-890 Fabiani, S., Vanino, S., Napoli, R., & Nino, P. (2020). Water energy food nexus approach for sustainability assessment at farm level: An experience from an intensive agricultural area in central Italy. Environmental Science & Policy, 104, 1-12. Fan, X., Zhang, W., Chen, W., and Chen, B., (2020). Land–water–energy nexus in agricultural management for greenhouse gas mitigation. Applied Energy 265: 114796. Farajian, L., Moghaddasi, R., and Hosseini, S., (2018). Agricultural energy demand modeling in Iran: Approaching to a more sustainable situation. Energy Reports 4: 260–265 Ghasemi Varnamkhasadi, Mehdi, Hashemi Garmdara, Seyed Mahmoud, and Hashemi Garmdara, Seyed Ali. (2014). Investigating energy indicators and optimizing its consumption in peach production, a case study: Saman region in Chaharmahal and Bakhtiari province. Agricultural Machinery, 5(1), 206-216. . (In Persian) Gholamrezaee, H. , Kheiralipour, K. , & Rafiee, S. (2021). Investigation of energy and environmental indicators in sugar production from sugar beet. Journal of Environmental Science Studies, 6(2), 3540-3548. (In Persian) Heremans, S., Dong, Q., Zhang, B., Bydekerke, L., & Van Orshoven, J. (2015). Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data. Journal of Applied Remote Sensing, 9(1), 097095-097095. Heydari Sultanabadi. (2023). Determination of energy production function in water wheat of Isfahan province. Energy Engineering and Management, 11(1), 116-127. IPCC. (1995). Climate Change, the Science of Climate Change. In: Houghton, J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A., and Maskell, K. (Eds). Intergovernmental panel on climate change. Cambridge: Cambridge University Press. Jagtap, S. T., Phasinam, K., Kassanuk, T., Jha, S. S., Ghosh, T., & Thakar, C. M. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51, 793-797. Jat, H. S., Jat, R. D., Nanwal, R. K., Lohan, S. K., Yadav, A. K., Poonia, T.,... & Jat, M. L. (2020). Energy use efficiency of crop residue management for sustainable energy and agriculture conservation in NW India. Renewable Energy, 155, 1372-1382. Kaab, A., Sharifi, M., Mobli, H., Nabavi-Pelesaraei, A., & Chau, K. W. (2019). Combined life cycle assessment & artificial intelligence for prediction of output energy & environmental impacts of sugarcane production. Science of the Total Environment, 664, 1005-1019. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers & electronics in agriculture, 147, 70-90. Kamir, E., Waldner, F., & Hochman, Z. (2020). Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 124-135. Kheiralipour, K. (2022). Sustainable Production: Definitions, Aspects, and Elements. Nova Science Publishers. Lal, R. 2004. Carbon emission from farm operations. Environment International 30: 981-990 Kheiralipour, K., & Sheikhi, N. (2021). Material and energy flow in different bread baking types. Environment, development and sustainability, 23, 10512-10527. Kramer, K.J., Moll, H.C., and Nonhebel, S. (1999). Total greenhouse gas emissions related to the Dutch crop production system. Agriculture, Ecosystems and Environment 72: 9-16 Kumar, T., Jyoti, K., & Singla, S. K. (2021). Design and Development of Machine Learning Model for Crop Yield Prediction. Looney, & Sharifzadeh. (2022). A review of water, energy and food correlation studies in Iran: necessity, challenges and proposed solutions. Sustainability, Development and Environment, 3(3), 29-49. (in farsi) Maarefi, T., Ebrahimian, H., Dehghanisanij, H., Sharifi, M., & Delbaz, R. (2022). Life cycle assessment for major agricultural crops and different irrigation systems around Lake Urmia. Iranian Journal of Irrigation & Drainage, 16(3), 624-638. MAJ. Department of Jihad-e-Agriculture ofIran.(2022). Annual agricultural statistics, Fromhttp://www.maj.ir/ MAJ. Statistical Center of Iran. (2022). The estimated population of each city, From http://www.amar.org.ir. Manafi Dastjardi, Mohammad, & Lari, Amir. (2015). Evaluation and comparison of energy indicators in wheat fields in the cities of Alborz province. Biosystem Engineering of Iran, 47(4), 779-771. doi: 10.22059/ijbse.2017.60274 Mobtaker, H. G., Keyhani, A., Mohammadi, A., Rafiee, S., & Akram, A. (2010). Sensitivity analysis of energy inputs for barley production in Hamedan Province of Iran. Agriculture, Ecosystems & Environment, 137(3-4), 367-372. Molaei, K. , Keyhani, A. , Karimi, M. , Kheiralipour, K. , & Ghasemi V, M. (2009). Energy Ratio in Dryland Wheat - Case Study: Eghlid Township. Iranian Journal of Biosystems Engineering, 39(1), - (In Persian) Moradi, R., and Pourghasemian, N., (2017). Greenhouse gases emission and global warming potential as affected by chemicals inputs for main cultivated crops in Kerman province: I- Cereal. Journal of Agroecology 9(2): 389-405. DOI: 10.22067/JAG.V9I2.42033 (In Persian) Morellos, A., Pantazi, X. E., Moshou, D., Alex&ridis, T., Whetton, R., Tziotzios, G., & Mouazen, A. M. (2016). Machine learning based prediction of soil total nitrogen, organic carbon & moisture content by using VIS-NIR spectroscopy. Biosystems Engineering, 152, 104-116. Mostafaeipour, A., Fakhrzad, M. B., Gharaat, S., Jahangiri, M., Dhanraj, J. A., B&, S.& Mosavi, A. (2020). Machine learning for prediction of energy in wheat production. Agriculture, 10(11), 517 Nadernejad, F., Imani, D. M., & Rasouli, M. R. (2022). A Data-driven Model for Predicting the Yield of Recoverable Sugar from Sugarcane. Journal of Agricultural Machinery, 12(4), 543-558. Nie.p, M. Roccotelli, M.P. Fanti, Z. Ming, Z. Li, (2021)Prediction of home energy consumption based on Gradient boosting regression tree, Energy Rep. Payandeh, Z., Jahanbakhshi, A., Mesri-Gundoshmian, T., & Clark, S. (2021). Improving energy efficiency of barley production using joint data envelopment analysis (DEA) and life cycle assessment (LCA): Evaluation of greenhouse gas emissions and optimization approach. Sustainability, 13(11), 6082. . (In Persian) Pourhasan N, Shah-Hosseini R, Seydi S T. (2021)Deep Learning-based Classification Method for Crop Mapping Using Time Series Satellite Images. 11 (1) :129-142. (In Persian) Pourmehdi, K., & Kheiralipour, K. (2023). Compression of input to total output index and environmental impacts of dryland and irrigated wheat production systems. Ecological Indicators, 148, 110048. Pourmehdi, K., & Kheiralipour, K. (2024). Net energy gain efficiency, a new indicator to analyze energy systems, case study: Comparing wheat production systems. Results in Engineering, 22, 102211. Rafiee, S., Avval, S. H. M., & Mohammadi, A. (2010). Modeling & sensitivity analysis of energy inputs for apple production in Iran. Energy, 35(8), 3301-3306. Ramedani, Z., Alimohammadian, L., Kheialipour, K., Delpisheh, P., & Abbasi, Z. (2019). Comparing energy state and environmental impacts in ostrich and chicken production systems. Environmental science and pollution research, 26, 28284-28293. Sadr, & Islami. (2021). Climatic adjustments on pistachio yield using C&R decision tree algorithm 20.1001. 1.23453419. 1400.9. 1.6. 3. Agricultural Meteorology, 9(1), 53-62. (In Persian) Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling lime & SHAP: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, & Society (pp. 180-186). Snyder, C., Bruulsema, T., Jensen, T., and Fixen, P. (2009). Review of greenhouse gas emissions from crop production systems and fertilizer management effects. Agriculture, Ecosystems and Environment 133: 247-266. Stas, M., Van Orshoven, J., Dong, Q., Heremans, S., & Zhang, B. (2016, July). A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT. In 2016 fifth international conference on agro-geoinformatics (agro-geoinformatics) (pp. 1-5). IEEE. Su, Y. X., Xu, H., & Yan, L. J. (2017). Support vector machine-based open crop model (SBOCM): Case of rice production in China. Saudi journal of biological sciences, 24(3), 537-547. Taleghani, A., Almassi, M., & Ghahderijani, M. (2020). Environmental evaluation and optimization of energy use and greenhouse gases mitigation for farm production systems in Mashhad, Iran. Environmental Science and Pollution Research, 27, 35272-35283. Vahedi,k A., & Zarifneshat, S. (2021). Evaluation Energy Flow and Analysis of Energy Economy for Irrigated Wheat Production in Different Geographical Regions of Iran. Journal of Agricultural Machinery, 11(2), 505-523. doi: 10.22067/jam.v11i2.81747 (in persian) Van den Broeck, G., Lykov, A., Schleich, M., & Suciu, D. (2022). On the tractability of SHAP explanations. Journal of Artificial Intelligence Research, 74, 851-886. Wei, Yixuan, Xingxing Zhang, Yong Shi, Liang Xia, Song Pan, Jinshun Wu, Mengjie Han, & Xiaoyun Zhao. (2018). “A Review of Data-Driven Approaches for Prediction & Classification of Building Energy Yang, Y., Shahbeik, H., Shafizadeh, A., Masoudnia, N., Rafiee, S., Zhang, Y.,... & Aghbashlo, M. (2022). Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries. Renewable Energy, 201, 70-86 Yousefi, M., Mahdavi Damghani, A., and Khoramivafa, M., 2016. Comparison greenhouse gas (GHG) emissions and global warming potential (GWP) effect of energy use in different wheat agroecosystems in Iran. Environmental Science and Pollution Research 23(8): 7390–7397 Zhang X.D., (2020)Machine Learning, in a matrix Algebra Approach to Artificial Intelligence, Springer ,USA,223-440 Zhang, L., Traore, S., Ge, J., Li, Y., Wang, S., Zhu, G.,... & Fipps, G. (2019). Using boosted tree regression & artificial neural networks to forecast upl& rice yield under climate change in Sahel. Computers & Electronics in Agriculture, 166, 105031. Ziyai . M, Hossein Panahi .F, Walizadeh .J, & Barabadi .(2013), Comparing the efficiency of wheat and barley production in terms of energy consumption and productivity in Sistan and Baluchistan province. (in pershan) | ||
آمار تعداد مشاهده مقاله: 74 تعداد دریافت فایل اصل مقاله: 83 |