|DEISS, L - Universidade Federal Do Parana
|DE MORAES, A - Universidade Federal Do Parana
Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/7/2017
Publication Date: 10/12/2017
Citation: Deiss, L., Franzluebbers, A.J., De Moraes, A. 2017. Soil texture and organic carbon fractions predicted from near-infrared spectroscopy and geostatistics. Soil Science Society of America Journal. 81:1222-1234.
Interpretive Summary: Soil organic carbon and its fractions are important components for regulating environmental quality and yield potential of agricultural systems. Better understanding of soil organic carbon dynamics at field-to-watershed scales will help improve soil management and its influence toward improving food security, mitigating greenhouse gas emissions, enhancing agricultural resilience, and conserving natural biodiversity. A USDA-Agricultural Research Service scientist in Raleigh, North Carolina collaborated with scientists at the Federal University of Parana to determine lateral and vertical distributions of soil texture and soil organic carbon fractions in a young agroforestry system experiment on a Coastal Plain site in North Carolina. We found highly acceptable relationships to be able to predict soil texture and soil organic carbon fractions by scanning soil with near-infrared spectroscopy, a tool that is quick, inexpensive, and relatively easy to perform on a large number of samples in a short period of time. This approach could be used to predict spatial distribution of soil texture and soil organic carbon fractions in this agroforestry system to allow efficient assessment of management changes with time and better predict small-scale input requirements. These results will inform scientific understanding to be able to improve nutrient application recommendations for farmers throughout the region.
Technical Abstract: Field-specific management could help achieve agricultural sustainability by increasing production and decreasing environmental impacts. Near-infrared spectroscopy (NIRS) and geostatistics are relatively unexplored tools that could reduce time, labor, and costs of soil analysis. Our objective was to efficiently determine lateral and vertical distributions of soil texture and soil organic carbon (SOC) fractions in an emerging agroforestry system experiment (7-ha field) on a Coastal Plain site in North Carolina. To predict soil properties from a large number of samples collected from this field, NIRS was calibrated against laboratory-determined properties. Support vector machine was a multivariate model that performed better than partial least squares to obtain greater precision with NIRS for all soil properties. To predict soil properties with precision across the field, geostatistical modeling with maximum likelihood and ordinary kriging was used. Combining the two modeling processes, root mean square error (RMSE) and RMSE relative to data set mean (% RMSE) was 67 g kg-1 for sand (9.3% RMSE), 34 g kg-1 for clay (22.7% RMSE), 1.63 g kg-1 for total organic C (26.7% RMSE), 0.67 g kg-1 for particulate organic C (36.1% RMSE), and 24 mg CO2-C kg-1 3 d-1 for the flush of CO2 (29% RMSE). We conclude that the combination of NIRS and geostatistics produced acceptable errors, and therefore, could be used to predict spatial distribution of soil texture and SOC fractions in this agroforestry system to allow efficient assessment of management changes with time and better predict small-scale input requirements.