Precision Soil Health.

Quantification and assessment of soil health involves determining how well a soil is performing its biological, chemical, and physical functions relative to its inherent potential. Due to high cost, labor requirements, and soil disturbance, traditional laboratory analyses cannot provide high resolution soil health information. Therefore, sensor-based approaches are important to facilitate cost-effective, site-specific management for soil health. In the Central Claypan Region, visible, near-infrared (VNIR) diffuse reflectance spectroscopy has successfully been used to estimate organic C, β-glucosidase, total N, and the biological score of the Soil Management Assessment Framework (SMAF). In contrast, VNIR spectroscopy was unable to accurately estimate important chemical and physical aspects of soil health, including bulk density, soil texture fractions, and extractable macronutrients (i.e., P and K). In this study, a sensor fusion approach was investigated that incorporated VNIR spectroscopy, soil apparent electrical conductivity (EC_a), and penetration resistance measured by cone penetrometer (i.e., cone index, CI). Soil samples were collected from two depths (0-5 and 5-15 cm) at 108 locations within a 10-ha research site encompassing different cropping systems and landscape positions. Soil health measurements and VNIR spectral data were obtained in the laboratory, while CI and EC_a data were obtained in situ. Calibration models for soil health measurements and SMAF scores were developed with partial least squares (PLS) regression. Models using sensor fusion of VNIR, EC_a, and CI data were compared to models obtained with VNIR data alone. The largest improvements were found for the physical and overall SMAF scores. Specifically, sensor fusion improved estimates of the overall SMAF score (R² = 0.78, RPD = 2.13) relative to VNIR alone (R² = 0.69, RPD = 1.82), reducing RMSE by 14%. The results of this study illustrate the potential for rapid, in-field quantification of soil health by fusing VNIR sensors with auxiliary data obtained from complementary sensors.