Multi-Objective Swarm-Based Optimization for Climate-Adaptive Crop Rotation Planning

Authors

  • Moussa Traoré Department of Computer Science, Faculty of Sciences and Techniques, University of Bamako, Bamako, Mali.
  • Fatima Zahra Benali * Department of Computer Science, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

https://doi.org/10.48313/maa.v1i3.102

Abstract

Crop rotation is a cornerstone of sustainable agriculture, yet planning optimal multi-year rotation sequences under escalating climate uncertainty remains a formidable challenge, particularly in climate-vulnerable regions of Africa. Existing approaches including rule-based heuristics, Linear Programming (LP) formulations, and single-objective evolutionary algorithms fail to simultaneously address the competing demands of economic viability, environmental stewardship, and climate resilience across extended planning horizons. This paper presents Multi-Objective Swarm-based optimization for Climate-adaptive Crop Rotation Planning (MOSWARM-CRP), a novel Multi-Objective Particle Swarm Optimization (MOPSO) variant that integrates ensemble climate projections from CMIP6, an embedded soil nutrient dynamics simulator, and multi-criteria economic–environmental–resilience objective functions to generate Pareto-optimal crop rotation plans. Key algorithmic innovations include a climate-scenario-aware fitness evaluation mechanism operating across ten downscaled climate scenarios (SSP2-4.5 and SSP5-8.5), a discrete–continuous hybrid particle encoding for crop assignments and input parameters, Pareto-based archive management with adaptive grid and crowding distance, and a seasonally constrained adaptive inertia weight mechanism. MOSWARM-CRP is evaluated on two case studies representing distinct agro-climatic zones: the Ségou Region of Mali (semi-arid Sahel, rainfed systems) and the Meknès-Fès Region of Morocco (semi-arid Mediterranean, mixed rainfed–irrigated systems). Comparative experiments against NSGA-II, MOEA/D, standard MOPSO, SPEA2, single-objective Genetic Algorithms (GAs), and expert-designed Rule-Based Rotations (RBRs) demonstrate that MOSWARM-CRP achieves 15–23% higher Expected Annual Profit (EAP), 18–31% lower cumulative soil nitrogen depletion, and 12–20% superior Climate Resilience Scores (CRSs) relative to the best-performing baselines. Ablation studies confirm the independent contributions of the climate integration, soil dynamics, and adaptive inertia components. The proposed framework offers actionable decision support for smallholder and semi-commercial farmers seeking climate-adaptive rotation strategies in Sub-Saharan and North Africa.

Keywords:

Particle swarm optimization, Climate adaptation, Sustainable agriculture, Food security, Climate change, CMIP6

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Published

2024-09-19

Issue

Vol. 1 No. 3 (1): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Traoré, M. ., & Benali, F. Z. . (2024). Multi-Objective Swarm-Based Optimization for Climate-Adaptive Crop Rotation Planning. Metaheuristic Algorithms With Applications, 1(3), 293-315. https://doi.org/10.48313/maa.v1i3.102

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