Increased demand for food production, influenced by the constant growth of population, resulted in the agricultural production systems that are more energy and economy intensive. The aim of this study was to evaluate the energetic and economic efficiency of sugar beet and wheat production. Attention was given to the fertilizer usage and its share in energy consumption since it can amount to 50%. Data show that energy input in wheat production was 5.84 MJ·kg⁻¹ and in sugar beet it was 0.93 MJ·kg⁻¹. The highest share of energy input both in wheat and sugar beet was observed for fertilizers, 52.45% and 46.70%, respectively. Economic analysis has shown that wheat production is a low profitable production with a net return of only 20.69 USD·ha⁻¹, in comparison with sugar beet production with a net return of 513.53 USD·ha⁻¹. Costs related to the fertilizer use prevailed in total variable and total production costs. Economic analysis has also shown that the benefit-to-cost ratio was higher in sugar beet production (1.33) compared to wheat production (1.03). Furthermore, it was determined that these economic indicators were less sensitive in sugar beet production than in wheat production regarding the variation of fertilizer.

The increased use of plastic material in many different agricultural applications causes high volumes of waste. The mechanical recycling of postconsumption plastic is a suitable system for the recovery of these materials. With the aim of evaluating the mechanical and physical characteristics of plastic bars’ profiles, obtained by recycling different polymeric mixtures of agricultural plastic film. Experimental tests were performed on different blends of heterogeneous material. Bars, made from recycled agricultural plastic film were produced through mechanical recycling, mixed with calcium carbonate, high density polyethylene, ‘densified’ polyethylene from scrap material, wood powder, and glass fibers. GRAPHICAL ABSTRACT

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

The aim of this study is to develop an African Neutral Network (ANN) model for predicting temperature and relative humidity in the greenhouse during the calendar year. Input data are ambient temperature and relative humidity outside of the greenhouse chosen for a typical meteorological year for the location of the object and the values of temperature and relative humidity of the air in the greenhouse, obtained as a result of a TRNSYS dynamic simulation of the thermal load of the facility. The goal was to show that our ANN model is capable of making predictions over extended time period. The TRNSYS model was created on a real object, taking into account all relevant parameters, in order to describe the dynamic behavior of the object as good as possible. The ANN model is formed of 3 layers, where the number of neurons in the hidden layer is obtained by training different types in order to reach the optimal number of neurons, and the values of the learning rate and epochs are also varied. The activation functions were respectively the hyperbolic tangent in the hidden layer and the linear function in the output layer. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient were chosen as the statistical criteria for measuring of the network performance. MAE and RMSE showed that the model with 6 neurons in the hidden layer gave the best results of data prediction in relation to the input data. Statistical analysis of output shows that, the RMSE and Mean Absolute Error (MAE) between the measured and predicted temperature was 0.3166 °C and 0.1002°, and the relative humidity RMSE and MAE was 5.9% and 3.4%, respectively, which can satisfy the demand of greenhouse climate control. The results of the study showed that the model can be used with satisfactory accuracy to predict the parameters of the internal environment, in relation to the complex simulation of the thermal load of the object.