391-1 Soil Salinity Assessment Via Portable X-Ray Fluorescence Spectrometry.

Poster Number 1620

See more from this Division: SSSA Division: Pedology
See more from this Session: General Pedology: II

Wednesday, November 6, 2013
Tampa Convention Center, East Exhibit Hall

Samantha Swanhart1, David C. Weindorf2, Autumn Acree3, Noura Bakr1, Yuanda Zhu1, Courtney Nelson1 and Kayla Shook1, (1)School of Plant, Environmental, and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA
(2)Plant and Soil Science, Texas Tech University, Lubbock, TX
(3)School of Plant, Environmental & Soil Sciences, Louisiana State University AgCenter, Baton Rouge, LA
Saline soil has historically been defined as a soil containing sufficient salts more soluble than gypsum (e.g., various combinations of Na+, Mg2+, Ca2+, K+, Cl-, SO42-, HCO3- and CO3-) to the extent that soil fertility is severely reduced across a wide array of climates and geological settings. Thus, it is not germane to specific soil characteristics, such as texture or parent material. As technology has advanced, so has soil testing and evaluation for optimal soil health characterization. Traditional methods of measuring soil salinity have proven accurate; however, most are labor intensive and require laboratory analysis. Given the success of previous studies using PXRF as a tool for measuring soil characteristics, the evaluation of soil salinity with PXRF spectrometry seems timely. Not only does this newer technology offer more accurate, quantifiable data to investigators, it produces results in-situ, in seconds. Samples were collected from the soil surface (0-15 cm), sealed in plastic bags, and returned to Louisiana State University for laboratory analysis where they were air-dried and passed through a 2 mm sieve prior to additional analysis. Standard soil characterization was conducted to include loss on ignition (LOI) organic matter, particle size analysis, electrical conductivity, and elemental quantification. Regression models were developed to correlate determined elemental concentration to EC results using statistical analysis software (SAS 9.3). Both simple and multiple linear regressions were employed in this study. In order to meet the assumptions for simple and multiple linear regressions, logarithmic transformation was used to normalize the variables to obtain a normal distribution for the error term (residual, ei). While both models resulted in similar acceptable R2 (0.86, and 0.87, respectively), simple linear regression is recommended given its simplicity.

See more from this Division: SSSA Division: Pedology
See more from this Session: General Pedology: II

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