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 off-chain 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 decision-making dashboard to provide actionable insights for GHG emissions reduction strategies across the groundnut supply chain.

正如联合国 (UN) 多份报告所强调的那样，可持续农业是联合国可持续发展目标 (SDG) 中的一个关键目标，需要付出不懈的努力和创新的解决方案。在这项研究中，调查了从塞内加尔迪乌贝尔和尼亚哈尔地区到达喀尔港的花生供应链中的温室气体 (GHG) 排放。花生供应链分为三个步骤：种植、收获和加工/运输。这项工作遵循联合国指导方针，通过利用FAOSTAT和EDGAR数据库应用基于机器学习的预测模型（MLPM）来解决可持续农业的迫切需要。此外，它还提供了一种新颖的方法，通过基于 Hyperledger Fabric 构建的智能合约，使用支持区块链的链下机器学习，以保护温室气体排放存储和机器学习的预测分析免受欺诈，并增强透明度和数据安全性。这项研究还开发了一个决策仪表板，为整个花生供应链的温室气体减排战略提供可行的见解。