期刊
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
卷 86, 期 5, 页码 1354-1367出版社
WILEY
DOI: 10.1002/saj2.20466
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资金
- Oregon State University Extension Service
- Oregon State University Agricultural Research Foundation
This study compared nonhazardous lime requirement estimate (LRE) methods to the SMP method in predicting incubation lime requirement (LRi) for Oregon soils. The results showed that the Sikora, modified Mehlich, and Sikora-2 buffer methods had the potential to replace the SMP method in terms of accuracy. A multilinear model also effectively predicted LRi. The Moore-Sikora method showed precision issues, and the single addition of Ca(OH)2 method was less accurate than other evaluated methods.
The Shoemaker, McLean, and Pratt (SMP) buffer pH method has been used historically in Oregon for estimating lime requirement, however concerns regarding hazardous waste disposal have caused increased interest in nonhazardous methods. The objective was to compare nonhazardous lime requirement estimate (LRE) methods to the SMP method for predicting incubation lime requirement (LRi) for Oregon soils. Twenty-four soils (pH <= 5.5) were incubated with seven rates of CaCO3 ranging from 0 to 22.4 Mg ha(-1) and incubated for 90 d at 19 degrees C. Seven different LRE methods were evaluated and regressed against LRi for pH targets of 5.6, 6.0, and 6.4 using linear regression. The Sikora (r(2) = .91-.93), modified Mehlich (r(2) = .87-.90), and Sikora-2 (r(2) = .82-.93) buffer methods showed potential as alternatives to SMP (r(2) = .91-.93) based on accuracy. A multilinear model combining clay content, KCl-extractable Al, soil organic matter (SOM), extractable Mg, and pH(1:2) also effectively predicted LRi (R-2 = .97). The Moore-Sikora method (r(2) = .89-.93) showed precision issues due to high replication variability. The single addition of Ca(OH)(2) method (r(2) = .67-.74) was less accurate than other evaluated methods. In conclusion, there were several viable non-hazardous LRE method alternatives to the SMP buffer method for Oregon soils, based on accuracy and precision.
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