Designing price-contingent vegetable rotation schedules using agent-based simulation

Abstract

Chinese vegetable production cooperatives supply their members, mostly smallholder farmers, with a rotation schedule for the year. Since vegetable prices are not stable throughout the year, designing a rotation schedule that maximizes expected profits, distributes farmers' profits more equitably, maintains the diversity of produce in the market, and reduces the risk of pests and diseases, requires adaptive, price-contingent rotation schedules (here, called "self-adaptive adjustment"). This study uses an agent-based simulation (ABS) to design self-adaptive rotation schedules that deliver these aims. The selfadaptive adjustment strategy was more profitable for farmers when faced with price volatility, and more equitable as well. This work provides a decision-support tool for managers of Chinese vegetable production cooperatives to provide farmers with more profitable and equitable rotation schedules.

Parallel crop planning based on price forecast

“…Furthermore, the harvest time of crops can be adjusted according to the predicted weekly average price. This kind of detailed adjustment is not involved in the other related works 25 …”

“…When a large number of crops come into the market, the price will drop seriously. Although the frequent implementation of scheduling adjustment can better complete the price fitting, they may cause further “catch‐up” phenomenon and affect the total profit 25 . Such phenomenon can be modeled through game theory 53 where conflict may exist, and the price become an emergent property of the system.…”

“…Often a fixed-price information is used. 18,24,25 However, dynamic price information based on price prediction is rarely used.…”

“…This kind of detailed adjustment is not involved in the other related works. 25 6.3 | Price is the environment or the result of agent behavior?…”

“…The idea of agent‐based modeling has been widely used in economics, 35 traffic, 36 smart grid, 37 and social evolution 38 . Li et al 25 applied agent‐based method to the crop planning problem.…”