Soil texture affects the conversion factor of electrical conductivity from 1:5 soil-water to saturated paste extracts
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Bo-Seong SEO
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Young-Jae JEONG
[1
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Nu-Ri BAEK
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Department of Rural and Bio-systems Engineering (BK21), Chonnam National UniversityDepartment of Rural and Bio-systems Engineering (BK21), Chonnam National University
Nu-Ri BAEK
[1
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Hyun-Jin PARK
[1
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Hye In YANG
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Max Planck Institute for BiogeochemistryDepartment of Rural and Bio-systems Engineering (BK21), Chonnam National University
Hye In YANG
[3
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Se-In PARK
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National Institute of Environmental ResearchDepartment of Rural and Bio-systems Engineering (BK21), Chonnam National University
Se-In PARK
[4
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Woo-Jung CHOI
[1
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[1] Department of Rural and Bio-systems Engineering (BK21), Chonnam National University
[2] Department of Renewable Resources, University of Alberta
[3] Max Planck Institute for Biogeochemistry
[4] National Institute of Environmental Research
[5] AgriBio Institute of Climate Change Management, Chonnam National University
Electrical conductivity(EC) of soil-water extracts is commonly used to assess soil salinity. However, its conversion to the EC of saturated soil paste extracts(ECe), the standard measure of soil salinity, is currently required for practical applications. Although many regression models can be used to obtain ECe from the EC of soil-water extracts, the application of a site-specific model to different sites is not straightforward due to confounding soil factors such as soil texture. This study was conducted to develop a universal regression model to estimate a conversion factor(CF) for predicting ECefrom EC of soil-water extracts at a 1:5 ratio(EC1:5), by employing a site-specific soil texture(i.e., sand content). A regression model, CF = 8.910 5e0.010 6sand/1.298 4(r~2= 0.97,P < 0.001), was developed based on the results of coastal saline soil surveys(n = 173) and laboratory experiments using artificial saline soils with different textures(n = 6, sand content = 10%–65%) and salinity levels(n = 7, salinity = 1–24 dS m-1). Model performance was validated using an independent dataset and demonstrated that ECeprediction using the developed model is more suitable for highly saline soils than for low saline soils. The feasibility of the regression model should be tested at other sites. Other soil factors affecting EC conversion factor also need to be explored to revise and improve the model through further studies.
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Univ Foggia, STAR AgroEnergy Res Unit, Dept Agr Food & Environm, Massimo Monteleone, Foggia, ItalyUniv Foggia, STAR AgroEnergy Res Unit, Dept Agr Food & Environm, Massimo Monteleone, Foggia, Italy
Monteleone, Massimo
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Lacolla, Giovanni
Caranfa, Gianraffaele
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Univ Bari Aldo Moro, Dept Agr & Environm Sci Di SAAT, Via Amendola 165-A, I-70126 Bari, ItalyUniv Foggia, STAR AgroEnergy Res Unit, Dept Agr Food & Environm, Massimo Monteleone, Foggia, Italy
Caranfa, Gianraffaele
Cucci, Giovanna
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Univ Bari Aldo Moro, Dept Agr & Environm Sci Di SAAT, Via Amendola 165-A, I-70126 Bari, ItalyUniv Foggia, STAR AgroEnergy Res Unit, Dept Agr Food & Environm, Massimo Monteleone, Foggia, Italy