In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution.

This paper presents the application of a reinforcement learning (RL) algorithm, concretely Q-Learning, as the core of a decision support system (DSS) for a steelmaking subprocess, the Composition Adjustment by Sealed Argon-bubbling with Oxygen Blowing (CAS-OB) from the SSAB Raahe steel plant. Since many CAS-OB actions are selected based on operator experience, this research aims to develop a DSS to assist the operator in taking the proper decisions during the process, especially less experienced operators. The DSS is intended to supports the operators in real-time during the process to facilitate their work and optimise the process, improving material and energy efficiency, thus increasing the operation’s sustainability. The objective is that the algorithm learns the process based only on raw data from the CAS-OB historical database, and on rewards set according to the objectives. Finally, the DSS was tested and validated by a developer engineer from the CAS-OB steelmaking plant. The results show that the algorithm successfully learns the process, recommending the same actions as those taken by the operator 69.23% of the time. The algorithm also suggests a better option in 30.76% of the remaining cases. Thanks to the DSS, the heat rejection due to wrong composition is reduced by 4%, and temperature accuracy is increased to 83.33%. These improvements resulted in an estimated reduction of 2% in CO₂ emissions, 0.5% in energy consumption and 1.5% in costs. Additionally, actions taken based on the operator’s experience are incorporated into the DSS knowledge, facilitating the integration of operators with lower experience in the process.

The present paper attempts to investigate whether the Sub-Measure 6.1 “Start-Up Aid for Young Farmers” payments of the 2014–2020 Rural Development Programme have a role to play in explaining Greek farms’ export orientation and export performance through a statistical analysis of farm-level data. The results demonstrate considerable differences in farm structures between beneficiaries and non-beneficiaries and provide support for the contention that beneficiaries are more likely to operate more productive and economically efficient farms. However, the results also provide a sobering assessment of the role of the young-farmer-related subsidies in stimulating export orientation and export performance.

The conservation of environmental resources is aimed at ensuring the continuity of ecosystem services for future generations and maintaining ecosystem integrity. Given the extensive reliance of agriculture on the environment, it is crucial to identify factors that impact the quality of ecosystem services (ESs), which can be regulated at large and heterogeneous national or European scales. This research, conducted within the Polish use case of the AGRICORE project, aims to demonstrate the feasibility of establishing indicators depicted in three ES categories, which can be shaped under the actions of the Common Agricultural Policy (CAP). The study was conducted based on national sources, mostly the database of the Central Statistical Office. The analyses of regression showed a significant impact of selected agricultural productivity factors on the key performance indicators (KPIs) assessing the level of selected ESs. The yield of cereal grains, which quantitatively expresses the potential of current crop production, depended to the greatest extent (r = 0.81) on a comprehensive indicator of the agricultural production space suitability, as well as on the rise of the level of nitrogen fertilization (r = 0.68), and also on the reduced share of permanent grassland in the agricultural area (r = −0.53). It was proved that in territorial units, in which the level of nitrogen fertilization per 1 ha was greater, the share of soils with favorable pH > 5.5 was also greater. The gross nitrogen balance had a positive and significant correlation with the level of investment subsidies (r = 0.86), the share of agricultural land in the total area (r = 0.67), and the level of nitrogen fertilization (r = 0.66). Notably, there were positive correlations between the level of environmental subsidies and the increase in forestation (r = 0.68) and also between air quality and the share of cereals in the sowing structure (r = 0.86). Additionally, the impact of agricultural productivity factors on cultural eco-services was found, e.g., the share of ecological land had a positive impact on the number of natural monuments, the area of nature reserves, the number of agritourists, and agritourism nights, while the share of cereals in the sowing structure negatively correlated with the most of analyzed cultural indicators. These results are useful for the development of a module for the ABM model that employs the desired environmental parameters to provide different assessments of the impact of selected agricultural productivity factors and ecosystem services on the economic farm status.

This paper presents the results of a survey conducted electronically in the years 2020-2022 within the framework of the AGRICORE Horizon project. It concerned the Agri-Environment-Climate Measure M10 within the Rural Development Programme 2014-2020 and aimed to quantify the impact of its effects...