An Optimization Strategy for Reducing CO₂ in Livestock Farming with IoT Integration and Decision Support System Approach Using Linear Programming
Abstract
Introduction: Livestock waste mismanagement contributes significantly to CO₂ emissions, adversely affecting animal health and environmental sustainability. This study aims to develop an optimization strategy for reducing CO₂ levels in livestock environments through the integration of Internet of Things (IoT) technology and a Decision Support System (DSS) using Linear Programming. Methods: IoT sensors were deployed to monitor environmental parameters such as CO₂ levels, temperature, and humidity in real time. A Linear Programming (LP) model was formulated to determine the optimal frequency of two CO₂-reducing actions: spraying Effective Microorganisms (EM4) and performing waste dredging. The objective was to maximize CO₂ reduction under cost and time constraints. The model iteratively updated its parameters based on sensor data feedback, ensuring dynamic and adaptive optimization. Results: Simulation results indicated that the LP model successfully identified optimal actions within predefined constraints. The optimal strategy was spraying EM4 eight times over eight days, achieving a CO₂ reduction of 800 ppm with a total cost of Rp 400,000—within the Rp 500,000 budget limit and 8-hour duration constraint. Validation through simulation confirmed the model’s accuracy, with prediction deviations consistently falling within an acceptable threshold (±20 ppm). Conclusions: The integration of IoT with an LP-based DSS offers a practical and efficient solution for CO₂ reduction in livestock farming. This system enhances decision-making for environmental management, demonstrating potential for scalable application in sustainable agriculture. Future work should incorporate more environmental variables and broader validation to improve model generalizability and precision.
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