Soil thermal conductivity (λ) plays a critical role in regulating heat dynamics at the land surface. In agricultural soils, it directly affects crop performance and can also influence soil management strategies. Nowadays, understanding the significance of soil λ has become vital for promoting sustainable agricultural practices. This review explores a wide range of measurement techniques for soil λ, including both traditional and advanced methods, and focuses on their applications, benefits, and limitations. Traditional steady-state methods-such as the guarded hot plate, thermal cell, steady-state needle probe, and box probe, are compared with transient techniques, including time-domain reflectometry, laser flash, transient hot wire, transient plane source, heat pulse, and transient line heat source. Although steady-state methods provide high accuracy in controlled laboratory conditions, they usually require extended testing times and are influenced by variations in soil water content and texture. Transient approaches, while more rapid and suitable for field conditions, find difficulties due to soil heterogeneity and environmental variability. Emerging technologies, such as fiber optic sensors and wireless sensor networks, give non-invasive, real-time monitoring with minimal soil disturbances, however, they are limited by higher prices and technological complexity. We evaluated them for their accuracy, efficiency, and adaptability to diverse agricultural conditions. This review underscores the necessity of precise soil λ measurements to enhance soil management strategies, improve resilience to environmental changes, and support sustainable agriculture. Important challenges in methods include calibration, sensitivity to soil characteristics, and practical field usability. It provides a valuable resource for researchers and practitioners, offering insights into integrating soil λ measurements into modern agricultural systems. Finally, future research directions are proposed, focusing on leveraging wireless sensor network technologies and machine learning to deepen our understanding of soil heat dynamics and their implications for sustainable farming practices.
Al-Shammary et al. (Fri,) studied this question.