Modeling energy and greenhouse gas emissions of rainfed barley production using machine learning in Nazarabad city, Alborz province

Document Type : Research Paper

Authors

Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

Abstract

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.

Keywords

Main Subjects

EXTENDED ABSTRACT

Background

Today, due to the limitation of fuel reserves, the problem of fuel and energy has become especially acute. The agriculture sector is one of the main consumers of energy, especially oil products. Therefore, the criteria for the productivity of agricultural production and the rational use of resources is to reduce energy consumption Optimum use of energy is one of the main requirements of sustainable agriculture. The increase in the demand for food production due to population growth will lead to excessive use of chemical fertilizers, agricultural machinery, insecticides and other production inputs, which will ultimately cause environmental problems and public health. The goal of the present study was investigating and modeling the energy and greenhouse gas emissions of barley production in Nazarabad city, Alborz, Iran.

Research Method

 In this research, modeling of energy and greenhouse gas emissions in barley cultivation in Nazarabad city was done using machine learning algorithms. The pattern of energy consumption and greenhouse gas production in barley production was investigated. Information was collected through a specialized questionnaire and simple random sampling was used to determine the sample size. Three algorithms of gradient boosting regression, decision tree, and random forest were used in modeling.

Findings

The obtained results showed that the average total energy consumption was 14443.16 MJ/h. The total global warming potential due to different activities in the farm was 10.641kg eq. CO2/ha. 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 obtained as 5.03, 0.34 kg/MJ, 2.91 MJ/kg and 58348 MJ respectively. Energy modeling was done with the decision tree, random forest and gradient boosting regression with the correlation vlaues 0.76, 0.79, and 0.76, respectively and  relative root mean square of 0.04, 0.05, and 0.06, respectively.

Conclusions

The results showed that the high use of chemical fertilizers not only increases energy consumption; rather, they cause environmental pollution and produce greenhouse gases. Non-renewable energies have the largest share with 83.16% of the total energy consumption. The results of this research showed that by reducing fuel consumption and reducing the consumption of chemical fertilizers, the amount of energy consumption and greenhouse gas emissions can be reduced.

Author Contributions

The idea and design of this research was done by Shahin Rafiei and Ali Jafari. The identification method, software and validation were done by Shahin Rafiei, and the research and collection of information and sources and the writing of the original draft were done by Seyed Omid Davdalmousavi. Project management was done by Shahin Rafiei and Seyyed Omid Davdal Mousavi did the review and editing. All authors have read and agreed to the published version of the manuscript.

Data availability

All data generated or analyzed during this study are available from the corresponding author on request.

Acknowledgments

This research was done by the Department of Biosystem Mechanics and Mechanization of the Agriculture and Natural Resources Campus of Tehran University, for which we are grateful. The authors would like to thank all participants of the present study.

Ethical considerations

The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.

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Iranian Journal of Biosystems Engineering
Volume 55, Issue 2
July 2025
Pages 129-147

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History

  • Receive Date: 13 August 2024
  • Revise Date: 19 November 2024
  • Accept Date: 15 December 2024

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