Kristina Gesevičienė

Vytautas Magnus University,

Kaunas, Lithuania

E-mail: kristina.geseviciene@vdu.lt

ORCID 0000-0002-1547-858X 

Investigating eco-efficiency of EU field crop farms – a neural network approach for assessing the importance of agri-environmental subsidies

Astrida Miceikienė

Vytautas Magnus University,

Kaunas, Lithuania 

E-mail: astrida.miceikiene@vdu.lt

ORCID 0000-0003-1432-7971

Vesa Antti Niskanen

Vytautas Magnus University,

Kaunas, Lithuania 

E-mail: vesa.niskanen@vdu.lt

ORCID 0000-0003-3320-1338

Abstract. Agri-environmental subsidies (AES) are described as a key agricultural policy tool used to promote environmentally friendly farming. The European Green Deal has set ambitious targets for the neutralization of greenhouse gas emissions, which also set higher targets for agriculture's contribution to this goal. Improving agricultural eco-efficiency is seen as one of the most cost-effective ways to achieve sustainable agricultural development. The empirical evidence on the combined environmental and economic significance of AES schemes on the eco-efficiency of EU agriculture is limited. This research aims to assess the eco-efficiency of field crop farms in EU countries and to investigate the importance of AES and other selected economic and policy factors on their eco-efficiency. Data Envelopment Analysis (DEA) was used to assess eco-efficiency for EU field crop farms. Multiple regression analysis and multilayer perceptron (MLP) neural networks were then applied to evaluate the importance of AES and other factors. Analysis of the determinants of eco-efficiency and comparative analysis using t-tests were then used to identify significant differences between different eco-efficiency groups. The study indicated the decline in eco-efficiency of EU field crop farms overall during the time period analyzed. The AES was identified as the most important factor at the level of EU countries, particularly for the countries in the lower eco-efficiency group. This highlights the importance of targeted environmental support measures, AES in particular, especially for newer Member States, to improve agricultural sustainability across the EU. 

Received: May, 2024

1st Revision: March, 2025

Accepted: June, 2025

DOI: 10.14254/2071-789X.2025/18-2/9

JEL Classification: Q18, Q57, C45, Q56, C61

Keywords: agri-environmental subsidy, artificial neural network, European Green Deal, sustainable agriculture, agricultural eco-efficiency