Machine Learning Predictive Modeling of Agricultural Sustainability Indicators - Sudarsono, Raden Roro Shafira Meisy; Wandebori, Harimukti (Indonesian Journal of Applied Statistics, 2023)
Modern-day researchers are provided with data abundance that has its drawback: increased analysis complexity. Approaching this issue through traditional data analysis techniques provides only partial solutions to the complex situation. This research offers analytical and predictive models based on machine‐learning algorithms (linear regression, random forest, and generalized additive model) that can be used to assess and improve the Common Agricultural Policy (CAP) impact over agricultural sustainability in European Union (EU) countries, providing the identification of proper instruments that can be adopted by EU policymakers and CAP Council in financial management of the policy. The chosen methodology elaborates custom‐developed models based on a dataset containing 22 relevant indicators, considering three main dimensions contributing to the EU sustainable agriculture development goals in the CAP context: social, environment, and economic. The results showed that sustainable agriculture parameters influenced by the relevant indicators could be modeled with both linear and non-linear regression approaches by utilization of real-time data using machine learning. The predictive analytic models provide satisfactory performance and could be adopted by researchers and practitioners as policy impact monitoring and controlling tools, not only the EU but also for other countries that have or plan to adopt similar agricultural policies.Keywords: Agricultural policies, common agricultural policy, machine learning, rural development, sustainable agriculture