Accurate estimation of photovoltaic (PV) panels’ temperature is crucial for an accurate assessment for both the electrical and thermal aspects and performances. In this study we propose an advanced simulation approach linking a double-diode (DD) electrical model using the Artificial hummingbird algorithm; for parameter extraction; and a two-dimensional finite-difference-based thermal model. The electrical-sub model is firstly validated in comparison to experimental data figuring in literature using three types of PV technologies, with a relative error of about 2%. Then, the coupled model is validated using in-situ experimental setup consisting of the usage of thin-film PV technology, temperature sensors, weather station and an infrared camera. The results from both simulations and experiments exhibit strong alignment with a relative error of not higher than 2%; mainly due to the used material calibration uncertainties and external perturbations. This holistic model can be indeed further optimized, still, it has a potential to advance the development in the research area of PV systems.Future efforts could involve additional experimentation to validate the model for different seasons of the year.

Maize is a versatile crop that serves as a staple food for millions of people and provides various raw materials. Its adaptability to different climates and potential makes it economically valuable. However, the ongoing emissions of greenhouse gases pose significant challenges to sustain maize production. Sustainable agricultural practices are crucial to mitigate greenhouse gas emissions and reduce carbon footprints. Conservation tillage practices based on no-till promote carbon sequestration, and reduce carbon footprints compared to conventional tillage. These practices potentially improve soil health and water productivity. This chapter explores various aspects to sustain maize production, with a focus on conventional and conservation tillage systems, engineering technologies, carbon footprint reduction. It discusses also the challenges and perspectives in achieving sustainable maize production. It begins with an overview of conventional maize farming, highlighting its practices and challenges. The second section explores the advantages of conservation tillage in maize production. The third part focuses on engineering technologies and precision agriculture tools, as well as remote sensing. In the fourth section, strategies for reducing carbon emissions and adopting clean energy in maize farming are considered. The final part addresses the challenges and perspectives for sustaining maize production, discussing barriers, opportunities, and potential solutions.

Cereal–legume forage mixtures provide sustainable agronomic and environmental benefits for arid Mediterranean agricultural systems, but their productivity is often limited by within-field soil spatial variability. Site specific management practices are required to address the yield gaps. This study aimed to identify management zones in a no-till Triticale–Avena–Pea mixture by integrating apparent electrical conductivity (ECa) and NDVI measurements. ECa was mapped using an EM38-MK2 sensor, while NDVI was derived from UAV and Sentinel-2 imagery at four key growth stages. Plant biomass data and soil samples were collected for evaluating and explaining the yield gaps. Using K-means clustering, three management zones were delineated using ECa and NDVI data. Results showed a yield gap of 46% in fresh biomass between high- and low-productivity zones, associated with strong gradients in soil organic matter and total nitrogen availability. The delineation of management zones enabled targeted agronomic interventions, such as adjusting seeding rates in the high-productivity area and prioritizing soil improvement strategies in the low-productivity zone, thereby enhancing input-use efficiency.