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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #310728
Title: Estimating a soil quality index with VNIR reflectance spectroscopy

Author
item Veum, Kristen
item Sudduth, Kenneth - Ken
item KREMER, ROBERT - Retired ARS Employee
item Kitchen, Newell

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/17/2014
Publication Date: 2/3/2015
Citation: Veum, K.S., Sudduth, K.A., Kremer, R.J., Kitchen, N.R. 2015. Estimating a soil quality index with VNIR reflectance spectroscopy. Soil Science Society of America Journal. 79:637-649.

Interpretive Summary: Increased soil quality is linked to improved agricultural profitability and environmental protection. Assessment and monitoring of soil quality is critical in the Central Claypan Region of Missouri, where claypan soils have a high potential for runoff, soil erosion, and soil degradation. Soil quality is traditionally evaluated using a range of chemical, physical, and biological laboratory measurements, causing soil quality assessments to be costly and time-consuming. Estimating soil quality using on-the-go in-field soil sensors (such as visible and near-infrared (VNIR) reflectance spectroscopy) would save time and money for scientists and producers, and provide valuable information to drive management decisions and increase profitability. In this study we evaluated the ability of using VNIR reflectance from oven-dry and field-moist soil samples to estimate soil quality for a range of perennial grassland and annual cropping systems. Soil quality was quantified using the Soil Management Assessment Framework (SMAF), which translates laboratory measurements into comprehensive soil quality scores related to crop productivity, environmental protection, and other important soil functions. VNIR models successfully estimated > 80% of the variability in organic C, and reliably estimated other biological soil quality indicators using oven-dry or field-moist soil. The addition of auxiliary variables, such as bulk density or extractable P, improved estimation of the overall SMAF soil quality score. The results of this study support the use of VNIR sensors for in-field soil quality assessment, even under conditions of variable soil moisture content. For a more robust estimation of a comprehensive soil quality index, VNIR sensors could be combined with other complementary sensors or with simple auxiliary field or lab analyses. Overall, this study will benefit scientists and producers by demonstrating the potential for rapid, inexpensive, and high resolution soil quality assessment in the field.

Technical Abstract: Sensor-based approaches to assessment and quantification of soil quality are important to facilitate cost-effective, site-specific soil management. The objective of this research was to evaluate the ability of visible, near-infrared (VNIR) diffuse reflectance spectroscopy to estimate multiple soil quality indicators (SQIs) and Soil Management Assessment Framework (SMAF) scores. A total of 234 soil samples from two depths (0-5 and 5-15 cm) were obtained in 2008 from 15 agricultural management systems located in the Central Claypan Region of Missouri. The VNIR spectra were obtained on oven-dried and field–moist soil, and calibration models were developed with partial least squares (PLS) regression. Models were evaluated using the coefficient of determination (R2) and residual prediction deviation (RPD). Excellent estimation results were achieved using oven-dry soil for organic C, ß-glucosidase, total N, the biological SMAF score, the organic C score, and the ß-glucosidase score (RPD = 2.0, R2 = 0.76). Using field-moist soils, excellent estimation results were achieved for organic C and the organic C score (RPD = 2.1, R2 = 0.80). Incorporating the bulk density score and P score as auxiliary variables with the VNIR spectra improved estimation of the overall SMAF soil quality score for oven-dry soil (RPD = 2.0, R2 = 0.76) and field-moist soil (RPD = 1.9, R2 = 0.75). These results demonstrate the robustness of VNIR estimation of biological SQIs, and illustrate the potential for rapid, in-field quantification of soil quality by fusing VNIR sensors with auxiliary data obtained from complementary sensors or supplemental analyses.