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A multi-agent model to help managing rainfall variability in the rainfed lowland rice ecosystem of Northeast Thailand

Lacombe G., Bousquet F., Naivinit W., Trébuil G.. 2004. In : IRRI. Mekong Rice Conference, 15-17 october 2004, Ho Chi Minh City, Viet-Nam. Metro Manila : IRRI, p. 1-13. Mekong Rice Conference, 2004-10-15/2004-10-17, Ho Chi Minh (Viet Nam).

Rainfed lowland rice (RLR) production is the main activity in northeast Thailand. Unpredictable droughts and coarse-textured soil are the main constraints usually cited to explain the low yields and economic poverty of this region. Past studies tried to improve the drought tolerance of rice varieties and hydrological functioning at the field level. How water is used at the farm level remains largely unknown. Consequently, it is relevant to understand the dynamic interactions between water availability and water-use in the RLR ecosystem. This article describes the development of an agent-based simulation tool based on multi-agent systems to explore adaptations of RLR cropping systems to rainfall variability. An environment representing the main biophysical entities involved in decision-making regarding water use is modeled and its hydrological functioning is verified. Preliminary simulations are presented to illustrate the model capacities. These preliminary simulations aim at evaluating the efficiency of numerous on-farm reservoirs to alleviate early drought at the vegetative stage. Simulations comparing scenarios with and without ponds show that ponds are less efficient at the beginning of the RLR cycle, when rains are still light. Pond efficiency is stable when the duration of the period separating the two peaks of RLR nursery sowings is more than 2 months. Below this threshold, ponds could not be completely refilled. The next step in the model development will consist in adding autonomous agents to simulate scenarios in which farmer agents cooperate to use water and learn collectively about its dynamics.

Mots-clés : oryza; précipitation; utilisation de l'eau; prise de décision; modèle de simulation; thaïlande; système multiagents

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