Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/5/2016
Publication Date: 8/1/2016
Citation: Veum, K.S., Sudduth, K.A., Kitchen, N.R. 2016. Sensor based soil health assessment. International Conference on Precision Agriculture Abstracts & Proceedings. July 31-August 3, 2016, St. Louis, Missouri. Available: https://www.ispag.org/proceedings/?action=abstract&id=2096.
Interpretive Summary: Soil health is linked to enhanced agricultural profitability and environmental protection. Assessment and monitoring of soil health is critical, especially on marginal and degraded soils in areas such as the Central Claypan Region of Missouri. However, soil health is traditionally evaluated using a range of costly and time-consuming chemical, physical, and biological laboratory measurements. Estimating soil health using on-the-go in-field soil sensors such as visible and near-infrared (VNIR) reflectance spectroscopy, soil apparent electrical conductivity (ECa), and penetration resistance measured by cone penetrometer (i.e., cone index, CI) would save time and money for scientists and producers. In this study, we evaluated the benefit of combining ECa, and CI information with VNIR data to estimate soil health for a range of perennial grassland and annual cropping systems. Soil health was quantified by using the Soil Management Assessment Framework (SMAF), which translates laboratory measurements into comprehensive scores related to crop productivity, environmental protection, and other important soil functions. The model using VNIR in conjunction with ECa and CI successfully estimated 78% of the variability in the overall SMAF scores and reduced the root mean squared error by 14% over VNIR alone. The results of this study support the use of sensor fusion for in-field soil health assessment to drive management decisions and increase profitability. Overall, this study will benefit scientists and producers by demonstrating the potential for rapid, inexpensive, and high resolution soil health assessment in the field.
Technical Abstract: 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 data. 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 biological components of soil health as well as Soil Management Assessment Framework (SMAF) scores. In contrast, estimation models for important chemical and physical aspects of soil health have been less successful with VNIR spectroscopy. In this study, a sensor fusion approach was investigated that incorporated VNIR spectroscopy, soil apparent electrical conductivity (ECa), 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 ECa data were obtained in situ. Calibration models were developed with partial least squares (PLS) regression and model performance was evaluated using coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD). Models integrating ECa and CI with VNIR reflectance data improved estimates of the overall SMAF score (R2 = 0.78, RPD = 2.13) relative to VNIR alone (R2 = 0.70, RPD = 1.82), reducing RMSE by 14%. Improved models were also achieved for estimates of several biological, chemical, and physical indicators of soil health. 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.