Leveraging Greenhouse Gas Emissions Traceability in the Groundnut Supply Chain - Blockchain-Enabled Off-Chain Machine Learning as a Driver of Sustainability

Aug 5, 2026

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Sustainable agriculture, central to UN Sustainable Development Goals, requires targeted innovations to reduce environmental impacts across food systems. This study investigates greenhouse gas (GHG) emissions along the groundnut supply chain from Diourbel & Niakhar, Senegal, through cultivation, harvesting, and processing/shipping to the Port of Dakar. Predictive modeling based on machine learning leverages FAOSTAT and EDGAR datasets, following UN guidelines for SDG-aligned research. It further introduces blockchain-enabled off-chain machine learning using smart contracts on Hyperledger Fabric to safeguard emissions storage, strengthen predictive analytics against fraud, and improve transparency and data security. A decisionmaking dashboard supports actionable strategies to reduce GHG emissions throughout the chain.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Introduction

Sustainable development and GHG reduction context

Digital technologies, data sources, and operational challenges

Machine learning and blockchain for security, privacy, and transparency

Leveraging Greenhouse Gas Emissions Traceability in the Groundnut Supply Chain: Blockchain-Enabled Off-Chain Machine Learning as a Driver of Sustainability Zakaria El Hathat1 · V. G. Venkatesh2,3 · V. Raja Sreedharan4,8 · Tarik Zouadi1 · Arunmozhi Manimuthu5 · Yangyan Shi6 · S. Srivatsa Srinivas7 Accepted: 29 June 2024 / Published online: 30 July 2024 © The Author(s) 2024 Abstract As emphasized in multiple United Nations (UN) reports, sustainable agriculture, a key goal in the UN Sustainable Development Goals (SDGs), calls for dedicated efforts and innovative solutions. In this study, greenhouse gas (GHG) emissions in the groundnut supply chain from the region of Diourbel & Niakhar, Senegal, to the port of Dakar are investigated. The groundnut supply chain is divided into three steps: cultivation, harvesting, and processing/shipping. This work adheres to UN guidelines, addressing the imperative for sustainable agriculture by applying machine learning-based predictive modeling (MLPMs) utilizing the FAOSTAT and EDGAR databases. Additionally, it provides a novel approach using blockchain-enabled offchain machine learning through smart contracts built on Hyperledger Fabric to secure GHG emissions storage and machine learning’s predictive analytics from fraud and enhance transparency and data security. This study also develops a decisionmaking dashboard to provide actionable insights for GHG emissions reduction strategies across the groundnut supply chain. Keywords Groundnut supply chain · Sustainable agriculture · Greenhouse gas (GHG) emissions · Blockchain smart contracts · Machine learning · Senegal 1 Introduction The United Nations has delineated Sustainable Development Goal (SDG) 2 to “End hunger, achieve food security and improved nutrition, and promote sustainable agriculture.”1 1 [https://www.un.org/sustainabledevelopment/hunger/](https://www.un.org/sustainabledevelopment/hunger/) . * V. Raja Sreedharan [rajasreedharanv@cardiffmet.ac.uk](rajasreedharanv@cardiffmet.ac.uk) Zakaria El Hathat [zakaria.elhathat@uir.ac.ma](zakaria.elhathat@uir.ac.ma) [V](V). G. Venkatesh [vgv1976@gmail.com](vgv1976@gmail.com) Tarik Zouadi [tarik.zouadi@uir.ac.ma](tarik.zouadi@uir.ac.ma) Arunmozhi Manimuthu [maniasaldhinesh@gmail.com](maniasaldhinesh@gmail.com) Yangyan Shi [ys102@hotmail.com](ys102@hotmail.com) S. Srivatsa Srinivas [srivats.sss@gmail.com](srivats.sss@gmail.com); [srivatsa@iitj.ac.in](srivatsa@iitj.ac.in) These trends are emerging alongside reduced available land, intensified degradation of soil and biodiversity, and increased frequency and severity of weather events. The impacts of climate change on farm practices further complicate this situation. Consequently, the agribusiness sector 1 Rabat Business School, BEAR Lab, International University of Rabat, Rabat, Morocco 2 EM Normandie Business School, Metis Lab, Le Havre, France 3 Corvinus Institute of Advanced Studies (CIAS), Corvinus University of Budapest, Budapest, Hungary 4 Cardiff School of Management, Cardiff Metropolitan University, Cardiff, UK 5 Aston Business School, Aston University, Birmingham, UK 6 Macquarie Business School, Macquarie University, Sydney, Australia 7 Centre for Mathematical and Computational Economics, School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, Jodhpur, India 8 School of Business, Woxsen University, Sangareddy, Sangareddy, Telangana, India is anticipated to face challenges in the coming decades (Gupta et al., 2023). Modern farming—characterized by a large number of workers and the excessive use of machinery and fertilizers—is facing a transition period. Although the Green Revolution successfully fed the world’s fast-expanding demographics, it degraded the Earth’s soil and diversity and led to global warming. These extraction techniques are not manageable in the long-term. The COP262 set the agenda for addressing climate change with a focus on

FAQs

What supply-chain stages are analyzed for groundnuts in this study?

The groundnut supply chain is divided into three stages: cultivation, harvesting, and processing/shipping.

Which data sources support the machine-learning predictive modeling?

The study uses FAOSTAT and EDGAR databases to build machine-learning predictive modeling for GHG emissions.

How does blockchain enhance the proposed off-chain machine learning approach?

Blockchain-enabled off-chain machine learning via smart contracts on Hyperledger Fabric secures GHG emissions storage, protects predictive analytics from fraud, and improves transparency and data security.

Aug 5, 2026

Upload by Asher