Investigating energy consumption and economic indicators of rose production using machine learning: A case study of Damghan County

Document Type : Research Paper

Authors

• seyed omid davodalmosavi ¹
• Farzaneh Bahadori ²
• shahin rafiee ¹

¹ Department of Agricultural Machinery Mechanical Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

² Associate Professor, Agricultural Research and Education Center, Semnan Province, Agricultural Research, Education and Extension Organization, Semnan, Iran

Abstract

Keywords

• Medicinal plants
• rose
• energy management
• economic

Main Subjects

• energy management

EXTENDED ABSTRACT

Introduction

Medicinal plants, especially rose (Rosa damascena), play an important role in the agricultural economy of Iran due to their extensive applications in the perfumery, food, and pharmaceutical industries. With the increasing global demand for plant products, it is essential to optimize energy consumption and analyze the economic aspects of the production of these plants. Previous studies have shown that agricultural production systems are often faced with high energy waste and costs, but few studies have addressed the modeling of these parameters with modern tools such as machine learning. This study aimed to analyze the energy and economic aspects of rose production in Damghan County and provide predictive models based on machine learning algorithms.

Research Method

 In this study, energy modeling and economic analysis of rose production in Damghan County was conducted using machine learning algorithms. The energy consumption pattern and economic and cost analysis of rose production were examined. Information was collected through a specialized questionnaire and interview, and simple random sampling was used to determine the sample size. Three algorithms were used for modeling: gradient boosting regression (GBR), enhanced gradient boosting regression (XGBR), and random forest regression (RFR).

Results

The total energy consumption in production was 43438.83 megajoules per hectare. Electrical energy and nitrogen fertilizer accounted for the largest share of energy consumption, accounting for about 79.60 and 9.92 percent of the total energy consumption, respectively. The energy ratio, energy efficiency, energy intensity, and net energy added were 0.08, 0.021 kg/MJ, 47.20 MJ/kg, and net energy was -39574 megajoule, respectively. The results of economic analysis showed that the total production costs of rose are 44.395 million tomans per hectare. Animal fertilizer, with a share of 33.15 percent, and human labor, with a share of 29.47 percent of the total costs, accounted for the largest share of the production costs. For energy, GBR, XGBR, and RFR were the best models with R2 values of 0.99, 0.97, and 0.92, respectively, and for the economic production model, GBR, XGBR, and RFR were the best models with R2 values of 0.99, 0.96, and 0.88.

Conclusions

This study shows that rose production in the study area is economically profitable and justifiable, although energy consumption pattern analysis indicates significant energy waste in rose production. The application of machine learning methods in this study demonstrated the high ability of this technology to predict and optimize resource consumption. The results showed that intelligent management of key inputs such as water resources, irrigation systems, and fertilizers can help reduce energy consumption, reduce costs, and increase profits. These findings emphasize the need to apply advanced technical solutions and adopt modern and mechanized management methods to achieve sustainable and efficient rose production, in a way that maintains economic justification while optimizing resource consumption.

Author Contributions

The idea and design of this study were carried out by Farzane Bahadori and Seyed Omid Davodalmousavi. The identification method, software and validation were carried out by Shahin Rafiee and the research and data and resource collection and writing of the first draft were carried out by Seyed Omid Davodalmousavi. The project management was carried out by Farzane Bahadori and Seyed Omid Davodalmousavi. All authors have read and agreed to the published version of the manuscript.

Data Access

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

Acknowledgements

This study was carried out by the Forest and Rangeland Research Department, Agricultural and Natural Resources Research and Education Center of Semnan Province, Agricultural Research, Education and Extension Organization, Semnan, Iran, to whom we are grateful. The authors would like to thank all the participants in the present study.

Ethical Considerations

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

Conflict of Interest

The author declares no conflicts of interest.