Data envelopment analysis (DEA) is a non-parametric research technique based on a mathematical optimization method. Since was first developed in ‘78, the method is used in various sectors of economy and at different levels (companies, counties, regions, etc.). Our purpose is to apply DEA at regional level by using various inputs and outputs to analyse the performance of agriculture practiced in plain, hill and mountain areas. Thirty-six counties were classified into three categories based on their geographical main characteristics, respectively: group I – with 50-100% plain areas (20 counties); group II - with 50-80% hill areas (8 counties); group III - with 50-80% mountain areas (8 counties). For these groups were computed, under input-oriented option, CRS and VRS technical scores from which we calculated scale efficiencies. This empirical research shows that exists clear differences of performance between areas with similar geographical characteristics in terms of production factors (work, land and mechanization) allocation and outputs. Our results show that there are only 14 counties (5 in plain areas, 5 in hill areas and 4 in mountain areas) completely achieving DEA efficiency and operate at their optimal scale. In conclusion, in majority of areas the overall efficiency of agriculture is not reached, these regions needing to decrease the input levels (especially work hours that are too high compared with productivity) or to increase the output levels (production value) through a better use of fix capital and higher yields

Since the enlargement of the European Union, the CEE (Central and Eastern European) countries have brought a larger share of agricultural and forest land with high potential for biomass and bioenergy. The progress of bioeconomy is intercorrelated with the dimension of agriculture, which is the major provider of biomass for food and feed and for other bio-based industries. This research aims to assess the dimension of agriculture-based and food-based bioeconomy, with focus on the production and use of biomass. Conducted over the 2008–2019 period with data from the EU and OECD, the study pointed out the role of CEE countries in the European bioeconomy. Thus, we estimated that the bioeconomy market reached, in 2019, a turnover of almost EUR 324 billion (around 14% of the EU level), respectively, EUR 79 billion from agriculture-based sectors and EUR 116.8 billion from food-based sectors. The number of employees has decreased; in 2019, in bioeconomy sectors were employed 6.9 million people (almost 40% of the EU bioeconomy employment). Regarding the production of biomass, the study has revealed an increase of the share in EU production from 25.7% in 2008 to 27.8% in 2019, due to a growth of biomass by 7.1%. With this study, we emphasize the need to support more sustainable demand of biomass and to make the bioeconomy market more competitive. In addition, we point out several problems of the bioeconomy sector, such as insufficient data, low productivity, and the unused or underused sources of biomass.

The main objective of this article is the analysis of crop rotation applied to three crop farms of different sizes in Romania. The period under study is before the implementation of the “Environmentally beneficial practices applicable to arable land” eco-scheme, included in the National Strategic Plan of Romania, 2023–2027, in the section on direct payments related to subsidies. The analysed crop farms have different areas, as follows: Farm A—64.97 ha, Farm B—615.50 ha and Farm C—2881.33 ha. This research aims to highlight the agro-environmental conditions specific to each farm that are required to be met by 2023. The data analysis highlights the fact that farms under 100 ha, such as Farm A, must apply the conditions of the eco-scheme as follows: 3.25 ha with non-productive elements, including uncultivated land; 6.50 ha cultivated with crops from the Leguminosae family, 16.25 ha cultivated with cereals, 19.49 ha cultivated with corn, 12.99 ha cultivated with sunflowers, 3.25 ha cultivated with watermelon and 3.25 ha cultivated with vegetables. These farms do not apply modern technologies and have fragmented surfaces, and must have both uncultivated areas and areas planted with nitrogen-fixing crops, but must also practice crop diversification. Farms of over 100 ha, such as Farm B, must have 30.78 ha of non-productive elements and 246.20 ha cultivated with crops from the Leguminosae family, of which 30.78 ha are perennial crops and 215.42 ha are annual crops, such as soy. Crop diversification is also recommended, with 92.33 ha cultivated with cereals, 184.65 ha cultivated with corn and 61.55 ha cultivated with oil crops, sunflower and/or rapeseed. Farms the size of Farm C must have 144.07 ha of non-productive elements, 720.33 ha must be cultivated with crops from the Leguminosae family, such as soy, and diversify crops as follows: 720.33 ha—cereals, 720.33 ha—corn, 518.64 ha—sunflower and/or rapeseed and 57.63 ha—potatoes.

This study aims to investigate the behavior of consumers in rural mountain tourism, with a particular focus on sustainability and durability. Questionnaire surveys were conducted in 2022 in Bucharest during the national tourism fair, collecting responses from 212 visitors based on their availability to participate. Our purpose was to gather their opinions on visiting the rural areas of the Carpathian Mountains in Romania and to reveal distinct patterns in tourist preferences. The responses were analyzed using the Statistical Package for the Social Sciences (SPSS) 20.0, where descriptive analyses and the Mann–Whitney U test were performed. The study results indicated that perceptions and service satisfaction vary based on demographic and economic characteristics. Women slightly favored higher-quality accommodations and dining services, while lower-income and secondary-educated respondents expressed higher contentment with local hospitality. This research emphasizes the need for a nuanced approach to tourism services that promotes local community engagement and sustainable practices to ensure long-term environmental and economic viability. There is a clear need to develop tourism strategies that align with sustainability principles, ensuring that the Carpathian Mountains region remains attractive and resilient for future generations.

The fruit sector in Romania and, particularly, the apple production sector, is considered to have been in decline in recent decades. Changes in the behavior and consumption habits of the population, together with climate change and the increase in fruit imports, have caused shortages in this sector. Apples are regarded as a national fruit in Romania, but recently, there have been significant transformations in apple production and consumption. In order to practically observe the recorded information published in a database for this sector, we developed a questionnaire, which was completed by people in Romania. The aim of this paper was to identify the types of apple consumer profiles in Romania. The reason for choosing this specific fruit was linked to the investigated objective of the projects to which this article is related. Regarding the approached methodology, the paper focused on analyzing descriptive statistics, frequencies and clustering, by which means we designed a pattern of the apple consumer profiles in Romania. Furthermore, the profiles of respondents from two regions of Romania (South-Muntenia and South-East Regions) were explored in more detail. The findings of this research indicate a young, educated consumer profile and a distinct pattern revealed by the three clusters from the specific regions.

The purpose of the present paper is to identify viable solutions for organising organic producers groups and supply chains at county level. With this purpose in mind, our research aim was to evaluate which is the best networking solution for 40 organic farmers in Calarasi County. This statistical approach was based on multidimensional scaling and hierarchical clustering methods that permitted us to identify three possible clusters in which to group the organic producers. The evaluation results of the agricultural patterns of each cluster demonstrated that only one cluster is viable for establishing a producers’ group. The main characteristics of this cluster are: farmers are in a proximity range of maximum 40 km; at least 75% of farmers have similar farming system types; over 65% of farmers own big farms; the best place for a joint pooling, storing or selling point is the cluster centre (Calarasi). In this way, we consider that a statistical viable solution for clustering and the promotion of networking between farms with similar agricultural patterns can contribute to the creation and establishment of cost-effective supply chains and strong producers’ groups.