Estimating Soil Solution Nitrate Concentration from Dielectric Spectra Using Partial Least Squares Analysis
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Since 1905, the Department of Agricultural Engineering, now the Department of Agricultural and Biosystems Engineering (ABE), has been a leader in providing engineering solutions to agricultural problems in the United States and the world. The department’s original mission was to mechanize agriculture. That mission has evolved to encompass a global view of the entire food production system–the wise management of natural resources in the production, processing, storage, handling, and use of food fiber and other biological products.
History
In 1905 Agricultural Engineering was recognized as a subdivision of the Department of Agronomy, and in 1907 it was recognized as a unique department. It was renamed the Department of Agricultural and Biosystems Engineering in 1990. The department merged with the Department of Industrial Education and Technology in 2004.
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1905–present
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- Department of Agricultural Engineering (1907–1990)
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- College of Agriculture and Life Sciences (parent college)
- College of Engineering (parent college)
- Department of Industrial Education and Technology, (merged, 2004)
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Abstract
Fast and reliable methods for in situ monitoring of soil NO3–N concentration could help guide efforts to reduce NO3–N losses to ground and surface waters from agricultural systems. While several studies have been done to indirectly estimate NO3–N concentrations from time domain spectra, no research has been conducted using a frequency domain technique. Hence, the goal of this laboratory study was to estimate NO3–N concentrations from frequency-response data obtained in a frequency range of 5 Hz to 13 MHz. Dielectric spectra of soil samples wetted to five different volumetric water contents (VWC) with 24 solutions containing different concentrations of KNO3 and KCl were analyzed using a partial least squares (PLS) regression method. Global models could not estimate NO3–N concentrations with sufficient accuracy. Models based on the imaginary part of the permittivity were better than those based on the real part. The PLS model estimates were improved when low VWC data and high Cl− concentration were eliminated, reducing the RMSE for NO3–N from 57 to 28 mg L−1. The best results were obtained when the PLS models were constructed at fixed VWC levels using the data without high Cl− concentration. The performance of these models improved with increasing VWC level, reaching the lowest RMSE of 18 mg L−1 at VWC of 0.30 m3 m−3.
Comments
This article is from Soil Science Society of America Journal 76, no. 5 (2012): 1536–1547, doi:10.2136/sssaj2011.0391.