The research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovative soil monitoring system utilizing Internet of Things (IoT) technology. This system, equipped with intelligent sensors, will operate autonomously, collecting real-time data to identify key trends in soil conditions. Our system employs smart soil sensors to measure macronutrient values up to a depth of 80 cm. These sensors will transmit data wirelessly. Laboratory research involved a two-month evaluation of the system’s performance across three distinct soil types collected from diverse geographical locations. Analysis of the three soil types yielded a model accuracy estimate of 0.01. A strong positive linear correlation (0.92) between moisture and macronutrients has been observed in two out of the three soil types. The results, particularly related to soil moisture, were averaged over the testing period. While precipitation values were not directly integrated into the modeling framework, they were calculated in l/m² to ensure accurate real-time estimates. The need for such advanced monitoring systems is critical for optimizing key soil macronutrients and enabling spatiotemporal mapping. This information is essential for developing effective strategies to mitigate soil aridification and prevent desertification.

Context: This research investigates the advantages of real-time monitoring of soil quality for various land management practices. It also highlights the significance of spatio-temporal soil modeling and mapping in providing a clear and visual understanding of how aridity changes over time and across different locations. Aims: This paper aims to provide a comprehensive guide to the key processes required for the development of a laboratory-based soil quality monitoring system. Methods: The applied methodologies involved the processes of sensor deployment, data acquisition infrastructure establishment, and sensor calibration. These procedures culminated in the development of a soil quality assessment model that was subsequently subjected to two months of laboratory testing using three distinct soil types. The analysis yielded a strong positive linear correlation between the measured and predicted soil quality values. Key Results: As expected, the assimilation of prior soil quality estimates within the modeling framework demonstrated a significant enhancement in the accuracy of real-time soil quality estimations. Conclusions: This research promotes the importance of iterative improvements of the soil quality monitoring system. The need for a long-term perspective and a plan for maintenance and continuous improvement of such systems in the ecosystem is important to improve the ease of making predictions to avoid soil aridization. The results of this research will be useful for researchers and practitioners involved in the design and implementation of soil monitoring systems.

The automotive industry is at a critical juncture, facing increasing pressure from stringent environmental regulations, resource scarcity, and global supply chain disruptions. This study aims to identify, model, and prioritize the key factors influencing the adoption of circular economy principles in the automotive sector. The Decision-Making Trial and Evaluation Laboratory method was applied to analyze the interdependencies among ten critical factors. Based on a contribution from a panel of six experts from a European automotive distributor with its main operating points in France and Romania, the study reveals a clear cause-and-effect relationship among the factors. Causal factors such as organizational culture, internal integration, regulatory policies, circular business models, and R&D capacity were identified as key drivers. Conversely, factors like top management commitment, stakeholder engagement, technological capability, sustainable materials management, and ecodesign were classified as effect factors, meaning they are influenced by other variables. The key practical contribution of this research is a strategic prioritization framework for decision-makers, offering guidance on how to effectively boost key factors, either directly or indirectly, to achieve a more efficient alignment with circular economy principles. By strategically leveraging these interdependencies, organizations can trigger a positive chain reaction, leading to improved performance in the dependent effect factors and ultimately accelerating the transition to a circular economy.