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SOIL BIOLOGY & BIOCHEMISTRY

A Model to Predict Soil Aggregate Stability Dynamics following Organic Residue Incorporation under Field Conditions

^a Univ. of Zurich, Winterthurerstr. 190, Zurich 8057, Switzerland
^b INRA, Agrocampus Rennes, UMR Sol, Agronomie Spatialisation, 65 Rue de Saint-Brieuc, CS 84215, 35042 Rennex Cedex, France
^c Agriculture and Agri-Food Canada, 2560 Hochelaga Blvd., Ste. Foy, QC G1V 2J3, Canada

^* Corresponding author (samuel.abiven{at}geo.uzh.ch).

Our ability to predict the effects of various organic amendments on soil aggregate stability is limited due to the complexity of the biological, chemical, and physical mechanisms involved. Based on previous experimental results, this study developed a model (Pouloud) to predict the dynamics of aggregate stability following the incorporation of various organic residues under field conditions. Following Monnier's conceptual model and previously published data, a lognormal function is first used to describe changes in aggregate stability after organic inputs under laboratory conditions. Using principal component analysis, the parameters of the lognormal function are associated with the biochemical characteristics of the organic products such as water-extractable polysaccharide, cellulose and hemicellulose, and lignin contents. To simulate aggregate stability dynamics under field conditions, the effects of soil moisture, soil temperature, and N availability are taken into account by specific functions obtained from the literature. When model simulations were compared with experimental results under field conditions, variations in aggregate stability were generally well reproduced. The sensitivity of the model to climate variations and organic residue characteristics was tested. Soil N availability and the substrate lignin content are major factors that influence the prediction of aggregate stability dynamics. Our results suggest that prediction of aggregate stability dynamics under field conditions using organic substrate characteristics and simple climatic data is possible. More work is required to test the model and broaden its applicability to other soil and climatic conditions.