In agri-food supply chains (ASCs), consumers pay for agri-food products produced by farmers. During this process, consumers emphasize the importance of agri-food safety while farmers expect to increase their profits. Due to the complexity and dynamics of ASCs, the effective traceability and management for agri-food products face huge challenges. However, most of the existing solutions cannot well meet the requirements of traceability and management in ASCs. To address these challenges, we first design a blockchain-based ASC framework to provide product traceability, which guarantees decentralized security for the agri-food tracing data in ASCs. Next, a Deep Reinforcement learning based Supply Chain Management (DR-SCM) method is proposed to make effective decisions on the production and storage of agri-food products for profit optimization. The extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed blockchain-based framework and the DR-SCM method under different ASC environments. The results show that reliable product traceability is well guaranteed by using the proposed blockchain-based ASC framework. Moreover, the DR-SCM can achieve higher product profits than heuristic and Q-learning methods. INDEX TERMS Agri-food supply chains, agri-food safety, product traceability, profit optimization, blockchain, deep reinforcement learning.

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Agricultural trade significantly promotes the economic boom in developing countries. Extensive traditional agricultural production methods have increased the pressure on the agricultural environment by expanding agricultural trade, which has attracted the attention of many scholars. This study aims to empirically examine the impacts of agricultural trade on economic growth and agricultural environmental pollution in Bangladesh from 1972 to 2019, using an Auto Regressive Distributed Lag (ARDL) model with a structural break to examine the long-run and short-run determinants of agricultural environmental pollution in Bangladesh. The ARDL bounds analysis methodology showed that it does not support the hypothesis that agricultural trade led to environmental pollution in the long-run. The results suggest a relationship between economic growth, energy, and FDI towards agricultural environmental pollution, indicating a positive long-run relationship. Furthermore, in the short run, agricultural trade indicates positive drivers towards agricultural environmental pollution. Therefore, it is recommended that the enhancement of trade liberalization policies should ensure cleaner technologies and products that could help reduce environmental pollution.

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