Speaker
Description
This study evaluates daily profit variability in Morocco’s sardine fisheries by combining a Markov-chain bioeconomic framework with a differential-equation logistic growth model for sardine biomass. Focusing on the Atlantic waters off Agadir and projecting to 2025, the model links population dynamics to economic performance while explicitly capturing weather-driven operational uncertainty. Historical weather records are distilled into three discrete states—Good, Medium, and Bad—whose transition probabilities feed a daily profit simulator. Results show that spring and summer deliver the highest expected returns with the lowest downside risk, whereas winter is marked by greater volatility and reduced profitability. The analysis identifies an optimal fishing effort of roughly 583 trips (or effort units), yielding a maximum expected annual profit of about 9.9 million MAD at an equilibrium biomass near 1.03 million kg—parameters consistent with sustainable harvesting. Seasonal transition matrices, daily weather-forecast algorithms, and state-contingent profit estimates are provided to support adaptive decision-making. By embedding environmental uncertainty into economic projections, the framework offers actionable guidance for fishery managers and skippers seeking to balance profitability with long-term stock sustainability, ultimately supporting the resilience of Morocco’s coastal communities.
