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Soil Chemistry

Prediction of Boron Adsorption by Field Samples of Diverse Textures

Contribution from USDA-ARS, George E. Brown Jr., Salinity Lab., 450 W. Big Springs Road, Riverside, CA 92507

^* Corresponding author (sgoldberg{at}ussl.ars.usda.gov)

Soil texture often varies dramatically in both vertical and horizontal directions in field situations and affects the amount of B adsorbed and B movement. This study was conducted to evaluate the effect of clay content on B adsorption and to test the predictive ability of the constant capacitance model to describe B adsorption as related to changes in clay content. Boron adsorption on 15 soil samples constituting five depths of each of three sites in the western San Joaquin Valley of California was investigated. Boron adsorption increased with increasing pH, reached an adsorption maximum around pH 9, and decreased with further increases in pH. The model was able to describe B adsorption on the soils by simultaneously optimizing three surface complexation constants. The model was able to predict B adsorption by using surface complexation constants calculated from easily measured chemical parameters. The model was also able to predict B adsorption at all of the depths using the surface complexation constants predicted with the chemical properties of one of the surface depths and a surface area value calculated from clay content. These results are very encouraging, suggesting that for a particular soil series, B adsorption for various sites and depths in a field can be predicted using only clay content and the chemical information from a different site in the same field. Incorporation of the prediction equations into chemical speciation-transport models will allow simulation of soil solution B concentrations in horizontal and vertical space under diverse environmental and agricultural conditions.

Abbreviations: CRM, coefficient of residual mass • IOC, inorganic carbon • M, mean difference • OC, organic carbon • OF, optimized fit • PE, depth-specific prediction • PEs9, prediction with parameters for Site 9 0- to 30-cm depth • PEs33, prediction with parameters for Site 33 0- to 30-cm depth • PEs49, prediction with parameters for Site 49 0- to 30-cm depth • RMSE, root mean square error • SA, surface area

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