Agri-food modelling: socio-economic data and data-gaps

Socio-economic models in the agricultural sector serve as essential tools for policymakers, land managers, and researchers, enabling the analysis and improvement of agricultural markets as well as the socio-economic conditions of farmers and rural communities. However, these models often face limitations due to data gaps, which restrict their accuracy and applicability. This paper identifies these gaps in data availability across a selection of agricultural models and introduces an initial framework for the Nostradamus module, designed to enhance analytical capabilities for both policy formulation and farm-level decision-making. A snowball literature review was conducted to compile a dataset of 108 economic models relevant to agriculture, focusing on their data coverage, consumption patterns, and inherent limitations. Our analysis categorizes the primary data requirements into three groups that are needed for model processing: (1) data explicitly noted as missing in model limitation sections, (2) data collected through surveys due to absence in existing databases, and (3) the minimum data requirements for complex, data-intensive models. This study provides a comprehensive overview of data utilization in modern agricultural modelling tools, offering valuable insights for developers of future databases. It highlights the specific data needs of policymakers, land managers, and researchers worldwide, facilitating the creation of more robust and data-driven socio-economic models for agricultural applications.