Ensuring sustainability in the agri-food sector requires comprehensive data analysis. This study examines missing data patterns in a large-scale survey of Italian agri-food companies within the Agritech National Center, focusing on sustainability variables. The basic idea is that failure to provide a value for these variables indicates little attention to the issue of sustainability. We employ graphical models to infer the conditional independence structure among missingness indicators, which are modeled as binary variables. We consider two classes of graphical models: graphical log-linear models and graphical Ising models. The graphical Ising models allow us to handle a larger set of variables, integrate fully observed variables. The models are applied to the Agritech data.