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Several factors contribute to poor irrigation management. A set of natural, social, environmental, institutional and technological variables can collectively result in complex and messy problems. Traditional engineering systems are not well suited for addressing coupled and interlinked types of problem. To this end, a hybrid network model is introduced to study the cumulative impact of these variables on the performance of irrigation projects. The model is based on the theory of complex adaptive systems and agent-based modelling. The Gezira scheme in Sudan is used as a case study in order to convert the vicious circle through adopting an improved and most viable irrigation management system. Six key variables are selected as dominant driving forces that influence the performance of the irrigation scheme. These are scheme governance, agricultural research, agricultural inputs and technologies, farmers, water user associations and the irrigation agency responsible for operation and maintenance. The scope of the analysis is to explore the dynamics of system enhancement under different scenarios of policy intervention. The results revealed the need for balanced and diversified investment in all entities to ensure sustainable growth. The findings emphasize the importance of having interventions with moderate growth patterns that seek to enhance cooperation and positive feedback among entities. K E Y W O R D S agent-based models, complex adaptive system, irrigation management, network model, system connectivity, system growth Résumé Plusieurs facteurs contribuent à une mauvaise gestion de l'irrigation. Un ensemble de variables naturelles, sociales, environnementales, institutionnelles et technologiques peuvent entraîner collectivement des problèmes complexes et désordonnés. Les systèmes d'ingénierie traditionnels ne sont pas bien adaptés * Approche de système adaptatif complexe pour une meilleure gestion de l'irrigation: le cas du programme Gezira au Soudan

An agricultural sector model is developed for Sudan to study the impact of different policy interventions on enhancing agricultural productivity and efficient water use for irrigation. The model considers three farming systems (irrigation, mechanized and traditional rainfed systems). The country is divided into five geographic regions (central, eastern, northern, Kordofan and Darfur regions). The input to the model consists of six key agricultural inputs: land, water, labour, machinery, seeds, fertilizers and pesticides. The model is designed to optimize the production activities for 10 major crops that include cotton, wheat, sorghum, millet, groundnut, sesame, sugar cane, banana, beans and dates. The General Algebraic Modelling System (GAMS) optimization engine is used to optimize the allocation of resources with the objective of maximizing the net economic returns from agricultural production activities. The model is calibrated using long-term average historical records for the period 1970-2018. The results showed good model prediction capability in meeting the domestic demand and in explaining the key variables that influence the farming activities across the regions.

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