Evaluating the Impact of Agricultural Technology on Greenhouse Gas Emissions Using Machine Learning
Abstract
Agriculture is a significant contributor to global warming, primarily due to the release of greenhouse gases like methane (CH4) and nitrous oxide (N2O). These gases have a much higher global warming potential than carbon dioxide (CO2), necessitating targeted strategies for their reporting and reduction. This study applies machine learning models, specifically XGBoost and Support Vector Machine (SVM), to evaluate how technological advancements in agriculture influence greenhouse gas emissions. The dataset used includes emission data from various crops and farming technologies. Findings reveal that certain crops considerably elevate emissions, and in some cases, new technologies exacerbate the issue. XGBoost achieved 99.6% accuracy in predicting emission mitigation, proving its effectiveness in developing climate change mitigation plans for agriculture. Support Vector Machine also performed well, with an accuracy of 99.5%. This research underscores the need for precise approaches in managing greenhouse gas emissions through technology-driven policies.
Downloads
References
L. Dézma, S. Bigazzi, M. Sarrica, A. Siegler, S. Serdült, and V. Rizzoli, “Social Representation of Global Climate Change: An Exploratory Study Focusing on Emotions,” J. Constr. Psychol., vol. 0, no. 0, pp. 1–22, 2024, doi: 10.1080/10720537.2024.2310828.
Dr. Deniz EKINCI, “Sensitive Approaches for Global Climate Change,” EPRA Int. J. Clim. Resour. Econ. Rev., no. June, pp. 26–36, 2024, doi: 10.36713/epra17525.
Ifeanyi Onyedika Ekemezie and Wags Numoipiri Digitemie, “Climate Change Mitigation Strategies in the Oil & Gas Sector: a Review of Practices and Impact,” Eng. Sci. Technol. J., vol. 5, no. 3, pp. 935–948, 2024, doi: 10.51594/estj.v5i3.948.
C. Kreft, R. Huber, D. Schäfer, and R. Finger, “Quantifying the impact of farmers’ social networks on the effectiveness of climate change mitigation policies in agriculture,” J. Agric. Econ., vol. 75, no. 1, pp. 298–322, 2024, doi: 10.1111/1477-9552.12557.
A. Nurramadhani, R. Riandi, A. Permanasari, and I. R. Suwarma, “Low-Carbon Food Consumption for Solving Climate Change Mitigation: Literature Review with Bibliometric and Simple Calculation Application for Cultivating Sustainability Consciousness in facing Sustainable Development Goals (SDGs),” Indones. J. Sci. Technol., vol. 9, no. 2, pp. 261–268, 2024, doi: 10.17509/ijost.v9i1.67302.
I. B. P. Swardanasuta, N. R. K. Sandy, N. A. Rohmah, Y. Arindah, and F. Kartiasih, “The Effect of Industrial Value Added, Energy Consumption, Food Crop Production, and Air Temperature on Greenhouse Gas Emissions in Indonesia: A Time Series Analysis Approach,” J. Pertan. Agros, vol. 26, no. 1, pp. 4848–4865, 2024, [Online]. Available: http://dx.doi.org/10.37159/j. p agros.v26i1.3876
A. de J. Vargas-Soplín, A. Meyer-Aurich, A. Prochnow, and U. Kreidenweis, “Alternative uses for urban autumn tree leaves: A case study in profitability and greenhouse gas emissions for the city of Berlin,” J. Clean. Prod., vol. 470, no. March, 2024, doi: 10.1016/j.jclepro.2024.143290.
P. F. González, M. J. Presno, and M. Landajo, “Tracking the change in Spanish greenhouse gas emissions through an LMDI decomposition model: A global and sectoral approach,” J. Environ. Sci. (China), vol. 139, pp. 114–122, 2024, doi: 10.1016/j.jes.2022.08.027.
S. Song, J. Lian, K. Skowronski, and T. Yan, “Customer base environmental disclosure and supplier greenhouse gas emissions: A signaling theory perspective,” J. Oper. Manag., vol. 70, no. 3, pp. 355–380, 2024, doi: 10.1002/joom.1272.
L. Lambiasi, D. Ddiba, K. Andersson, M. Parvage, and S. Dickin, “Greenhouse gas emissions from sanitation and wastewater management systems: a review,” J. Water Clim. Chang., vol. 15, no. 4, pp. 1797–1819, 2024, doi: 10.2166/wcc.2024.603.
S. Chowhan, M. M. Rahman, R. Sultana, M. A. Rouf, M. Islam, and S. A. Jannat, “Agriculture Policy and Major Areas for Research and Development in Bangladesh,” Sarhad J. Agric., vol. 40, no. 3, pp. 819–831, 2024, doi: 10.17582/journal.sja/2024/40.3.819.831.
S. D. Keesstra et al., “European agricultural soil management: Towards climate-smart and sustainability, knowledge needs and research approaches,” Eur. J. Soil Sci., vol. 75, no. 1, pp. 1–24, 2024, doi: 10.1111/ejss.13437.
Chidiogo Uzoamaka Akpuokwe, Adekunle Oyeyemi Adeniyi, Seun Solomon Bakare, and Nkechi Emmanuella Eneh, “Legislative Responses To Climate Change: a Global Review of Policies and Their Effectiveness,” Int. J. Appl. Res. Soc. Sci., vol. 6, no. 3, pp. 225–239, 2024, doi: 10.51594/ijarss.v6i3.852.
G. Cascone, A. Scuderi, P. Guarnaccia, and G. Timpanaro, “Promoting innovations in agriculture: Living labs in the development of rural areas,” J. Clean. Prod., vol. 443, no. October 2023, p. 141247, 2024, doi: 10.1016/j.jclepro.2024.141247.
C. Gaudreau, L. Guillaumie, É. Jobin, and T. A. Diallo, “Nurses and Climate Change: A Narrative Review of Nursing Associations’ Recommendations for Integrating Climate Change Mitigation Strategies,” Can. J. Nurs. Res., 2024, doi: 10.1177/08445621241229932.
S. Sai and R. Parimi, “Optimizing Financial Reporting and Compliance in SAP with Machine Learning Techniques,” vol. 5, no. 8, pp. 13–22, 2018.
E. Priyono, T. Al Fatah, S. Ma’mun, and F. Aziz, “Tubercolusis Segmentation Based on X-ray Images,” J. Med. Informatics Technol., pp. 101–104, 2023, doi: 10.37034/medinftech.v1i4.22.
D. Zhu, B. Yu, D. Wang, and Y. Zhang, “Fusion of finite element and machine learning methods to predict rock shear strength parameters,” J. Geophys. Eng., vol. 21, no. June, pp. 1183–1193, 2024, doi: 10.1093/jge/gxae064.
H. A. Javaid, “Revolutionizing AML : How AI is leading the Charge in Detection and Prevention,” vol. 7, pp. 1–9, 2024.
Md Rasheduzzaman Labu and Md Fahim Ahammed, “Next-Generation Cyber Threat Detection and Mitigation Strategies: A Focus on Artificial Intelligence and Machine Learning,” J. Comput. Sci. Technol. Stud., vol. 6, no. 1, pp. 179–188, 2024, doi: 10.32996/jcsts.2024.6.1.19.
J. Lu, “Gas Emission Characterization and Monitoring Algorithm in the Process of Agricultural Waste Resource Treatment,” pp. 3158–3173, 2024.
M. Homaira and R. Hassan, “Prediction of Agricultural Emissions in Malaysia Using Machine Learning Algorithms,” Int. J. Perceptive Cogn. Comput., vol. 7, no. 1, p. 33, 2021.
D. Saha, B. Basso, and G. P. Robertson, “Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems,” Environ. Res. Lett., vol. 16, no. 2, 2021, doi: 10.1088/1748-9326/abd2f3.
J. O. Asibor, P. T. Clough, S. A. Nabavi, and V. Manovic, “A machine learning approach for country-level deployment of greenhouse gas removal technologies,” Int. J. Greenh. Gas Control, vol. 130, no. October 2022, p. 103995, 2023, doi: 10.1016/j.ijggc.2023.103995.
N. Nishat, M. M. Rahman, M. A. Mim, and A. S. M. Shoaib, “Enhancing Air Pollution Control With Machine Learning in the Automation Field,” Acad. J. Bus. Adm. Innov. Sustain., vol. 4, no. 2, pp. 40–53, 2024, doi: 10.69593/ajbais.v4i2.68.
P. C. Lopez, “chemotools: A Python Package that Integrates Chemometrics and scikit-learn,” J. Open Source Softw., vol. 9, no. 100, p. 6802, 2024, doi: 10.21105/joss.06802.
P. Ullagaddi, “Safeguarding Data Integrity in Pharmaceutical Manufacturing,” J. Adv. Med. Pharm. Sci., vol. 26, no. 8, pp. 64–75, 2024, doi: 10.9734/jamps/2024/v26i8708.
S. A. Eftekhar Afzali, M. A. Shayanfar, M. Ghanooni-Bagha, E. Golafshani, and T. Ngo, “The use of machine learning techniques to investigate the properties of metakaolin-based geopolymer concrete,” J. Clean. Prod., vol. 446, no. February, p. 141305, 2024, doi 10.1016/j.jclepro.2024.141305.
D. R. I. M. Setiadi, K. Nugroho, A. R. Muslikh, S. W. Iriananda, and A. A. Ojugo, “Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition,” J. Futur. Artif. Intell. Technol., vol. 1, no. 1, pp. 23–38, 2024, doi: 10.62411/faith.2024-11.
S. Talib, S. Sudin, and M. Dzikrullah Suratin, “Penerapan Metode Support Vector Machine (Svm) Pada Klasifikasi Jenis Cengkeh Berdasarkan Fitur Tekstur Daun,” PROSISKO J. Pengemb. Ris. dan Obs. Sist. Komput., vol. 11, no. 1, pp. 26–34, 2024, doi: 10.30656/prosisko.v11i1.7911.
D. Ç. Boğa, M. Boğa, and C. Tırınk, “Turkish Journal of Agriculture - Food Science and Technology Comparison of Nonlinear Functions to Define the Growth in Intensive Feedlot System with XGBoost Algorithm,” vol. 12, no. 8, pp. 1408–1416, 2024.


Copyright (c) 2024 Journal of Information Systems and Informatics

This work is licensed under a Creative Commons Attribution 4.0 International License.
- I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
- I certify that all authors have approved the publication of this and there is no conflict of interest.
- I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
- I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
- I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
- If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
- I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)