Contract Farming in the Mekong Delta's Rice Supply Chain: Insights from an Agent-Based Modeling Study

Abstract

In this paper, we use agent-based modeling (ABM) to study different obstacles to the expansion of contract rice farming in the context of Mekong Delta (MKD)'s rice supply chain. ABM is a bottom-up approach for modeling the dynamics of interactions among individuals and complex combinations of various factors (e.g., economic, social or environmental). Our agent-based contract farming model focuses on two critical components of contractual relationship, namely financial incentives and trust. We incorporate the actual recurrent fluctuations of spot market prices, which induce both contractor and farmer agents to renege on the agreement. The agent-based model is then used to predict emergent system-wide behaviors and compare counterfactual scenarios of different policies and initiatives on maintaining the contract rice farming scheme. Simulation results firstly show that a fully-equipped contractor who opportunistically exploits a relatively small proportion (less than 10%) of the contracted farmers in most instances can outperform spot market-based contractors in terms of average profit achieved for each crop. Secondly, a committed contractor who offers lower purchasing prices than the most typical rate can obtain better earnings per ton of rice as well as higher profit per crop. However, those contractors in both cases could not enlarge their contract farming scheme, since either farmers' trust toward them decreases gradually or their offers are unable to compete with the benefits from a competitor or the spot market. Thirdly, the results are also in agreement with the existing literature that the contract farming scheme is not a cost-effective method for buyers with limited rice processing capacity, which is a common situation among the contractors in the MKD region. These results yield significant insights into the difficulty in expanding the agricultural contracting program in the MKD's rice supply chain.