Organic products have grown ever more popular in recent years due to increased concerns regarding one’s health, the environment, and sustainability. The COVID-19 pandemic has only accelerated this growth, prompting producers to adapt to a rapidly growing market while maintaining the same standard of quality. We have conducted a questionnaire-based pilot study on 190 organic food consumers from Bihor county, Romania, in order to analyze the main factors influencing customers’ beliefs regarding organic food consumption, as well as assess the extent to which their consumption frequency changed after the pandemic. A principal component analysis was performed, dividing the factors into two categories referring to intrinsic and extrinsic characteristics of the products, respectively. It was noticed that the most important cues were ranked the same by our 190 respondents, regardless of their socio-demographical background. At the same time, people who consumed organic products more frequently before the pandemic have either maintained or increased their consumption, while more indifferent consumers maintained or decreased the proportion of organic foods in their diet.

Reducing food waste is an important objective in order to raise awareness of the negative effects it produces. The lack of information regarding the efficient use of food will affect the environment and the health of each of us. The objective of the paper is to present the behaviour of Romanian consumers regarding food waste. By means of a semi-structured questionnaire administered to 267 consumers, the following aspects were investigated: the place where they serve meals, the preparation of meals at home, purchase preferences, the motivation for food waste, and selective collection. Consumption typologies identify a higher share of food waste in urban areas and a better use of food in rural areas. The people from rural areas ate more than 50% of their meals at home. The preferred location for procuring food was, by far, the supermarket or hypermarket in both urban and rural environments. Young people throw away more waste than older food consumers. Awareness about food waste is more accentuated in young adults, without a significant correlation to the area of origin.

We evaluated patients who presented with thyroid dysfunction correlated symptoms that started when the Government took important measures to reduce the spread of COVID-19. These measures have influenced the safety of many people’s jobs. Data were collected from 378 patients that were clinically evaluated at the Endocrinology Department, between September 2020 and January 2021. Their health status modifications were statistically analyzed in correlation with their life and work changes. These changes were induced by measures associated with the COVID-19 pandemic. The lifestyle changes correlated with the COVID-19 pandemic have been present in both categories of patients: euthyroid and dysthyroid patients; 87.50% of euthyroid patients physically felt the pandemic-induced changes in their lives. It resulted in changes in lifestyle and job insecurity has a statistically significant influence (p < 0.01) on their state of health. The presence of life/work changes in men is strongly reflected in their state of health (p = 0.0004). Work instability that occurred as a side effect of the COVID-19 pandemic induced symptoms that made many people believe they have an endocrinology disorder.

The current paper presents the monitoring of soil and nutrient losses through runoff as the result of the climate change. The experimental results were recorded on the standard plots for runoff and erosion control. The results of the studies carried out highlight that total nitrogen losses range from 0.05 kg/ha to 12.60 kg/ha, potassium displaced from the plots has values between 0.02 and 1.35 kg/ha, the humus associated with the eroded soil includes values between 0.51 and 176.5 kg/ha, phosphorus is recorded with maximum values of 0.39 kg/ha, the total iron losses have values between 3.14 and 431.16 g/ha, manganese has losses from 1.69 to 290.82 g/ha, copper losses range from 0.15 to 7.62 g/ha, and, regarding zinc, the values are within the limits of 0.25–57.22 g/ha. The maximum values of the displacement of fertilizing elements from the sloping agricultural lands, by means of liquid and solid leaks, are registered at the plots left as permanent black fields, the plots practically unprotected against the rain erosion. The experiments were performed at the Soil Erosion Research and Development Station “Mircea Moțoc”, Perieni/Staţiunea de Cercetare Dezvoltare pentru Combaterea Eroziunii Solului “Mircea Moţoc”, Perieni (SCDCES—MM, Perieni), Vaslui County, an institution strategically located in an area subject to erosion soil. Additionally, the paper envisages how the National Rural Development Programme (NRDP) and the Common Agricultural Policy (CAP) can be of use in this area by analysing past lessons and providing an overview of what was done.

The establishment of a young farmer in the rural economy is a key stage in the process of farm succession in the rural development environment. In this matter, Pillar II of the Common Agricultural Policy (CAP) has a distinct approach related to financing the initiatives of this establishment. A young farmer can obtain funds for their agricultural activity by submitting a funding project proposal to the national agency. The success of a funding project proposal depends on various factors. In this paper, a model of prediction and classification using supervised learning algorithms, primarily Random Forest (RF) and Logistic Regression (LR), was developed to predict project selection outcomes and identify the key determinants of success. This was developed in relation to proposals submitted in the period 2014–2021 through Sub-Measure 6.1 and through the intervention for the young farmer installation intervention under the 2023–2027 CAP Strategic Plan (DR-30 (2023–2027)—Young Farmer Installation, indicated in this paper as DR 30) for the period of 2023–2027. Using the historical data related to this proposal, several models that use automated learning were developed in order to predict the success of a proposal based on specific determinants. In addition, a classification model was used to determine patterns in the proposal data, obtaining several project proposal clusters with common characteristics. The variables and selection criteria with the greatest impact on the final score and probability of acceptance were identified, highlighting the differences between sub-measures and the implications for generational renewal policies in rural areas. The novelty of this study lies in the integration of predictive modeling, classification, and clustering within a unified, policy-oriented analytical framework applied to real administrative data. The results reveal that project selection outcomes are driven primarily by formal scoring components, while structural characteristics such as farm economic size and planned investment play a secondary but consistent role across programming periods. These findings provide actionable insights for refining selection criteria and advisory mechanisms under the Common Agricultural Policy.