The Vietnamese Mekong Delta has undergone in recent years a considerable transformation in agricultural land-use, fueled by a boom of the exportation, an increase of population, a focus on intensive crops, but also environmental factors like sea level rise or the progression of soil salinity. These transformations have been, however, largely misestimated by the 10-year agricultural plans designed at the provincial levels, on the predictions of which, though, most of the large-scale investments (irrigation infrastructures, protection against flooding or salinity intrusion, and so on) are normally planned. This situation raises the question of how to explain the divergence between the predictions used as a basis for these plans and the actual situation. Answering it could, as a matter of fact, offer some insights on the dynamics at play and hopefully allow designing them more accurately. The dynamics of land-use change at a scale of a region results from the interactions between heterogeneous actors and factors at different scales, among them institutional policies, individual farming choices, land-cover and environmental changes, economic conditions, social dynamics, just to name a few. Understanding its evolution, for example, in this case, to better support agricultural planning, therefore requires the use of models that can represent the individual contributions of each actor or factor, and of course their interactions. We address this question through the design of an integrated hybrid model of land-use change in a specific and carefully chosen case study, which relies on the central hypothesis that the main force driving land-use change is actually the individual choices made by farmers at their local level. Farmers are the actors who decide (or not) to switch from one culture to another and the shifts observed at more global levels (village, district, province, region) are considered, in this model, as a consequence of the aggregation of these individual decisions. The central component of our hybrid model is then an agent-based model of farmers, provided with a sophisticated mechanism of decision-making that is influenced, at different degrees, by their perception of the contexts in which they act or interact with other actors. The economic context, accessible by them through the market prices of crops, plays a role, as well as the changes observed or forecasted in their physical context (land-cover changes, salinity rise) or the decisions made by others in their social Coupling Environmental and Socio-Economic Models context (neighbors, family members, opinion leaders). The model of farmers is coupled, through this decision-making mechanism, with other independent sub-models, each of them carrying out a realistic description of one of these contexts. Since the dynamics depicted in these sub-models obey to different logics, operate at different scales and rely on different data, they are represented using appropriate modeling techniques: the spatial model is based on GIS information on parcels, soils, and ...

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 17129The contribution was presented at MABS 2015: https://www.openabm.org/event/mabs-2015-sixteenth-internationalworkshop-multi-agent-based-simulation Abstract. Farmers are the key actors of land-use change processes. It is thus essential to choose a suitable architecture for farmer behavior to model such processes. In this paper, we compared three models with different architectures to model the farmer behavior in the coastal areas of the Ben Tre province: (i) The first one is a probabilistic model that allows farmer to select the land-use pattern based on land change probability; (ii) The second model is based on multi-criteria decision making and takes into account the land suitability of the parcel and the farmer benefit; (iii) The third model used a BDI (Beliefs -Desires -Intentions) architecture. For each of these models, we have compared the difference between simulated data and real data by using the Fuzzy Kappa coefficient. The results show the suitability of the BDI architecture to build land-use change model and to support decision-making on land-use planning.

Agricultural land-use changes pose challenges for land managers in terms of ensuring the implementation of local land-use plans. This paper aims to build a land-use change model named MEKOLUC (Mekong Delta land-use change) for simulating land-use changes under the impacts of socioeconomic factors (profitability of land-use types, societal impacts on neighborhoods) and environmental factors (soil, salinity, persistence of salinity). The salinity diffusion map was generated using GAMA software and employed Markov cellular automata to depict the spread of salinity under the influence of dike and sluice gate system operations. The land-use decision-making process was based on multi-criteria selection of the main factors, which were land suitability, land convertibility, density of land use in the neighborhood and profitability of land-use patterns. The input data for the case study were historical land-use maps from 2005, 2010 and 2015 of Soc Trang, a coastal province in the Mekong Delta. The model was calibrated using a land-use map from 2010 (with kappa = 0.86) and was verified with land-use maps from 2015 and 2020 with deviations from 0 to 19%. The simulated results showed that shrimp–rice farming areas have been shrinking, even though these are recommended as sustainable farming systems. Inversely, intensive rice crops tended to change to rice–vegetable crops, vegetable crops or perennial fruit trees, which are projected to be well adapted to climate and salinity intrusion by 2030. This case study shows that the developed model is an essential tool for helping land managers and farmers build land-use plans.