Food Production Index Forecasting for Sustainable Food Systems in Türkiye: A Machine Learning-Based Approach.

Ferhan Balci Torun, Mehmet Kayakuş, Onder Kabas, Georgiana Moiceanu, Mariana-Gabriela Munteanu

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ferhan Balci TorunDepartment of Gastronomy and Culinary Arts, Faculty of Tourism, Akdeniz University, 07058 Antalya, Türkiye.ORCID 0000-0002-8972-1087

Mehmet KayakuşDepartment of Biomedical Engineering, Faculty of Engineering, Akdeniz University, 07058 Antalya, Türkiye.ORCID 0000-0003-0394-5862

Onder KabasDepartment of Machine, Technical Science Vocational School, Akdeniz University, 07070 Antalya, Türkiye.ORCID 0000-0003-0703-4804

Georgiana MoiceanuDepartment of Entrepreneurship and Management, Faculty of Entrepreneurship, Business Engineering and Management, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania.ORCID 0000-0002-9483-0894

Mariana-Gabriela MunteanuBiotechnical Systems Department, Faculty of Biotechnical Systems Engineering, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania.

Funding

National University of Science and Technology POLITEHNICA Bucharest PubArt Project

6 · The paper itself

Abstract

Sustainable food systems are increasingly challenged by climate change, resource constraints, market volatility, and growing food demand, making accurate forecasting of food production essential for food security and long-term sustainability. Despite the growing use of machine learning in agricultural forecasting, studies directly modeling the Food Production Index (FPI) within a sustainable food systems framework remain limited, particularly in emerging economies. This study addresses this gap by forecasting Türkiye's Food Production Index using agricultural, macroeconomic, and trade-related indicators covering the period 1962-2023. Seven predictive approaches, including Multiple Linear Regression (MLR), Bayesian Ridge Regression, Support Vector Regression (SVR), Random Forest, Gradient Boosting, Artificial Neural Networks (ANNs), and K-Nearest Neighbors (KNN), were comparatively evaluated using R

Indexed as

data-driven policymakingfood production indexfood securityfood system resiliencemachine learningresource efficiencysustainable agriculturesustainable food systems

Identifiers

PMID42650507

DOI10.3390/foods15162814

PMCPMC13512847

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.