This paper presents a study on the establishment and the capitalization of a remontant red raspberry crop, the Polka variety, on a privately agricultural land area of 0.2 ha in a crop with a support system in V using a geotextile membrane for soil mulching and the method of micro-irrigation by drip. It has been shown that the annual gross profit is advantageous for diversifying the population incomes of rural areas, and the red raspberry is economically profitable regarding cultivation because the recovery of the invested sum is achieved in a maximum of 5 years after the establishment of the culture. The aim of this paper is to explore the growth and commercialization of red raspberry cultivation on privately owned arable land in rural Romania, emphasizing its potential for productivity and sustainability in the context of the circular economy. This initiative not only delivers substantial profits for investors but also fosters rural development and boosts local income levels. The study demonstrates that this cultivation method of red raspberry, aligned with the principles of the circular economy, enhances sustainability by reducing waste, optimizing resource use, and involving local communities in production cycles.

The global transition toward sustainable energy systems and the growing complexity of energy data require advanced analytical approaches that capture nonlinear, multidimensional, and temporally dependent relationships. This study proposes a widespread machine learning (ML) framework for electricity generation from renewable sources. The dataset includes 3649 records from 176 countries between 2000 and 2020, with 21 economic, demographic, and environmental indicators. To evaluate the impact of input dimensionality, two experimental scenarios were developed: one using all available features and another using a reduced subset derived through ten feature selection techniques (filter, wrapper, and hybrid). Four ML algorithms—Artificial Neural Network (ANN), Gradient Boosting Regression (GBR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF)—were implemented and assessed using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). To reduce the risk of data leakage and provide a more realistic evaluation for panel data, PanelSplit cross-validation was applied while preserving the temporal structure of the observations. In addition, Friedman and Wilcoxon signed-rank tests with Bonferroni correction were used to assess the statistical significance of performance differences among models. The results show that all models achieved strong predictive accuracy, with ensemble methods outperforming the neural network. RF had the best overall performance (MSE = 39.6791, MAE = 1.5859, RMSE = 6.2991, R² = 0.9955), followed by GBR and XGBoost. Correlation analysis confirmed the presence of strong relationships among several energy indicators, supporting the need for dimensionality reduction. SHAP analysis identified Land Area, Electricity from Fossil Fuels, and Renewables as the dominant predictors of renewable electricity generation. These outcomes illustrate that combining feature selection, panel-aware validation, statistical testing, and explainable machine learning supplies a robust and interpretable framework for understanding global renewable electricity generation and supporting data-driven decision-making in sustainable energy planning.

Intercropping systems can enhance land-use efficiency and generate early income in young orchards during the non-productive phase of perennial trees. This paper evaluates the agroeconomic performance of an organic blackcurrant–walnut intercropping system implemented under two planting configurations: (i) planting blackcurrant on walnut rows (782 plants/ha) and (ii) planting blackcurrant between walnut rows (1250 plants/ha). The study was conducted as a comparative case study in young walnut orchards in South-West Oltenia, Romania. Results indicate that, although the between-row configuration involves higher establishment and maintenance costs, it achieves higher yields and revenue, leading to improved economic efficiency and return on investment. These findings support intercropping as a viable strategy for enhancing the economic performance of young walnut plantations.