Microfarms are commercial soil-based market gardens that cultivate less than 1.5 ha of organic vegetables per farmer in rural France. They seek to make a living on small acreage by using innovative strategies that combine high land-use intensity with low input and few mechanized practices, and directly sell a wide range of vegetables. Few academic studies have focused on microfarms. Our research objective was to build a simulation model of microfarms income and agricultural area based on farmers’ expertise. The originality of our approach that we coined as “grounded modeling” (Glaser and Strauss, 2009) was to implement an interactive development pipeline based on inductive qualitative analysis and farmers’ participation to collect data, build and validate a model adapted to the specificity of microfarms, rather than using pre-existing models. Based on extensive data collection and interactions with 20 microfarms, we built a stochastic simulation model (MERLIN) at the farm level, which combined (i) two mixed models to predict yields and workload according to farming practices for 50 crops, and (ii) a crop-planning model. One major innovation of the MERLIN model is to generate cropping plans that match the complex and temporal commercial requirements for direct selling of vegetable boxes through community-supported agricultural schemes. The model was validated based on a case-study designed with microfarmers which involved different sets of strategic choices (3 technical systems, 2 marketing strategies, 3 investment hypotheses), climate (mild or cold) and chosen annual workload (1,800h; 2,500h or 3,000h). Our model was judged relevant and legitimate by agricultural practitioners because it was not prescriptive and it simulations combined different types of strategies in accordance with a global approach favored by organic farmers. Grounded modeling is a promising method to create generic knowledge specifically adapted to radical organic farming systems. However, the epistemological implications of grounded modeling require further investigation, which may benefit from the transdisciplinary framework developed in agroecological studies.