Bananas, the world’s most popular fruit, face significant damage from the burrowing nematode Radopholus similis. Fallow deployment effectively controls nematode populations by creating unfavourable soil conditions for the parasite. Despite the success of fallowing, it is essential to uproot and replant banana plants with clean seeds. Previous research focused on optimizing banana crop yield through systematic fallowing and clean seed replanting. However, a broader issue arises regarding the trade-off between allowing natural vegetative reproduction, which is cost-free but supports pest proliferation, and planting clean seeds which induce costs but reduce pest populations. This paper optimizes the number of cropping seasons with vegetative reproduction separated by fallow periods and clean seed replanting. The study evaluates two approaches: the first explores the spaces of all possibilities and compares the profit of each deployment strategy while the second utilizes a genetic algorithm which performs better on long time horizons.

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Combinatorial Optimization of Banana Clean Seed Reliance

  • Israël Tankam Chedjou

摘要

Bananas, the world’s most popular fruit, face significant damage from the burrowing nematode Radopholus similis. Fallow deployment effectively controls nematode populations by creating unfavourable soil conditions for the parasite. Despite the success of fallowing, it is essential to uproot and replant banana plants with clean seeds. Previous research focused on optimizing banana crop yield through systematic fallowing and clean seed replanting. However, a broader issue arises regarding the trade-off between allowing natural vegetative reproduction, which is cost-free but supports pest proliferation, and planting clean seeds which induce costs but reduce pest populations. This paper optimizes the number of cropping seasons with vegetative reproduction separated by fallow periods and clean seed replanting. The study evaluates two approaches: the first explores the spaces of all possibilities and compares the profit of each deployment strategy while the second utilizes a genetic algorithm which performs better on long time horizons.