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Title: PREDICTION OF BORON ADSORPTION BY FIELD SAMPLES OF DIVERSE TEXTURES

Author
item Goldberg, Sabine
item Corwin, Dennis
item Shouse, Peter
item Suarez, Donald

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/18/2005
Publication Date: 8/4/2005
Citation: Goldberg, S.R., Corwin, D.L., Shouse, P.J., Suarez, D.L. 2005. Prediction of boron adsorption by field samples of diverse textures. Soil Science Society of America Journal. 69(5):1379-1388.

Interpretive Summary: Boron is a specifically adsorbing anion that can be detrimental to plants at elevated levels. Detrimental levels can occur because of high levels of boron in the soil solution or from additions of boron via the irrigation water. Adsorption of boron by 15 soil samples constituting five depths of three sites from the Broadview Water District in the Western San Joaquin Valley of California was evaluated and predicted using a chemical model and easily measured soil chemical characteristics. Our results will benefit scientists who are developing models of boron movement in arid zone soils. The results can be used to improve predictions of boron behavior in soils and thus aid action and regulatory agencies in the management of soils and waters which contain elevated concentrations of boron.

Technical Abstract: Soil texture often varies dramatically in both vertical and horizontal directions in field situations and affects the amount of B adsorbed and B movement. Boron adsorption on 15 soil samples (Lillis soil series: very-fine, smectitic thermic Halic Haploxerert) constituting 5 depths of each of three sites from the Broadview Water District in the western San Joaquin Valley of California was investigated as a function of solution pH (5-11). Boron adsorption increased with increasing solution pH, reached an adsorption maximum around pH 9, and decreased with further increases in solution pH. The constant capacitance model was able to describe B adsorption on the soil samples as a function of solution pH by simultaneously optimizing three surface complexation constants. The model was able to predict B adsorption using surface complexation constants calculated from easily measured chemical parameters using a regression prediction equation approach. 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. Both modeling approaches were well able to predict the B adsorption behavior with the greatest deviation being about 40% in a couple of cases. 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 both spatially and vertically under diverse environmental and agricultural conditions.