During the cheese ripening, airflow pattern and climatic conditions inside cheese-ripening rooms are determinant for cheese weight losses. Due to the variation of air velocity inside ripening chambers, homogeneity in the distribution of climatic conditions is very hard to achieve at every single point of it. It is very difficult to characterize climatic distributions inside cheese-ripening rooms. Indeed, it is inconceivable to install sensors everywhere inside ripening chambers to pick up for instance temperature and relative humidity. Associated to the fact that little data have been published for the ripening cheese, we are hence faced with imprecise and incomplete knowledge. In practice, it is common that some model parameters may be represented by single probability distributions, justified by substantial data, while others are more faithfully represented by possibibility distributions due to the partial nature of available knowledge. This paper applies recent methods, designed for the joint propagation of variability and imprecision, to a cheese ripening mass loss model. Joint propagation methods provide lower \& upper probability bounds of exceeding a certain value of cheese mass losses.