Business Predictive Analytics of Smallholder Indonesian Maize using Vector Error Correction

Abstract

Maize stands as a cornerstone of Indonesia's agricultural landscape, serving both as a vital food source and an essential fortifier. However, the marketing process of smallholder maize in Indonesia has yet to reach an optimal level of efficiency. This research endeavors to delve into the vertical integration of smallholder maize in the Indonesian agricultural sector.To conduct this analysis, we employ a forward-looking predictive model, applying the Vector Error Correction Model (VECM) to analyze time series data related to smallholder maize in Indonesia. Our findings yield critical insights that shed light on the intricate dynamics of smallholder maize markets in the archipelago.Notably, our research underscores the long-term integration between producer-level smallholder maize markets and consumer-level smallholder maize markets in Indonesia. This integration implies that changes in producer-level smallholder maize prices are intrinsically linked to shifts in consumer-level smallholder maize prices in the country. These findings provide a valuable foundation for collaborative efforts within the agricultural sector, guiding stakeholders toward more effective strategies for optimizing smallholder maize markets in Indonesia.