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Protocol for indicator scoring in the soil management assessment framework (SMAF)

Published online by Cambridge University Press:  18 September 2009

B.J. Wienhold*
Affiliation:
USDA-ARS, AgroEcosystem Management Research Unit, 279 Plant Sciences, East Campus, University of Nebraska, Lincoln, NE 68583, USA.
D.L. Karlen
Affiliation:
USDA-ARS, National Soil Tilth Laboratory, 2150 Pammel Drive, Ames, IA 50011, USA.
S.S. Andrews
Affiliation:
USDA-NRCS, National Soil Quality Technology Development Team, 200 E. Northwood Street, 410, Greensboro, NC 27401, USA.
D.E. Stott
Affiliation:
USDA-ARS, National Soil Erosion Research Laboratory, 275 S. Russell Street, West Lafayette, IN 47907, USA.
*
*Corresponding author: Brian.Wienhold@ars.usda.gov

Abstract

Assessment tools are needed to evaluate agronomic management effects on critical soil functions such as carbon sequestration, nutrient cycling and water partitioning. These tools need to be flexible in terms of selection of soil functions to be assessed and indicators to be measured to ensure that assessments are appropriate for the management goals. The soil management assessment framework (SMAF) is being developed to meet this need. The SMAF uses soil physical, chemical and biological indicator data to assess management effects on soil function using a three-step process for (1) indicator selection, (2) indicator interpretation and (3) integration into an index. While SMAF is functional in its present format, it is intended to be malleable so that user needs can be met. Development of additional indicator interpretation scoring curves is one way that this framework can be expanded. Scoring curve development is a multi-step process of identifying an indicator, determining the nature of the relationship of the indicator to a soil function, programming an algorithm and/or logic statements describing that relationship and validating the resulting scoring curve. This paper describes the steps involved in developing an SMAF scoring curve. Scoring curves for interpreting water-filled pore space (WFPS) and Mehlich extractable potassium (K) were developed using the described protocol. This protocol will assist users of the SMAF in understanding how the existing scoring curves were developed and others interested in developing scoring curves for indicators that are not in the current version.

Type
Research Papers
Copyright
Copyright © Cambridge University Press 2009

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References

1.Doran, J.W. and Parkin, T.B. 1994. Defining and assessing soil quality. In Doran, J.W., Coleman, D.C., Bazdicek, D.F., and Stewart, B.A. (eds). Defining Soil Quality for a Sustainable Environment. Soil Science Society of America Special Publication No. 35, Soil Science Society of America, Madison, WI. p. 3–21.CrossRefGoogle Scholar
2.Doran, J.W. and Parkin, T.B. 1996. Quantitative indicators of soil quality: a minimum data set. In Doran, J.W. and Jones, A.J. (eds). Methods for Assessing Soil Quality. Soil Science Society of America Special Publication No. 49, Soil Science Society of America, Madison, WI. p. 2537.Google Scholar
3.Andrews, S.S., Karlen, D.L., and Cambardella, C.A. 2004. The soil management assessment framework: a quantitative soil quality evaluation method. Soil Science Society of America Journal 68:19451962.CrossRefGoogle Scholar
4.Wienhold, B.J., Andrews, S.S., and Karlen, D.L. 2004. Soil quality: a review of the science and experiences in the USA. Environmental Geochemistry and Health 26:8995.Google Scholar
5.Wienhold, B.J., Pikul, J.L. Jr, Liebig, M.A., Mikha, M.M., Varvel, G.E., Doran, J.W., and Andrews, S.S. 2006. Cropping system effects on soil quality in the Great Plains: synthesis from a regional project. Renewable Agriculture and Food Systems 21:4959.Google Scholar
6.SWCS. 2008. Beyond T: Informing Sustainable Soil Management. Soil and Water Conservation Society, Ankeny, IA. Available at Web site http://www.swcs.org/documents/filelibrary/beyondtreport.pdf (verified February 18, 2009).Google Scholar
7.Karlen, D.L. and Stott, D.E. 1994. A framework for evaluating physical and chemical indicators of soil quality. In Doran, J.W., Coleman, D.C., Bezdicek, D.F., and Stewart, B.A. (eds). Defining Soil Quality for a Sustainable Environment. Soil Science Society of America Special Publication No. 35, Soil Science Society of America, Madison, WI. p. 5372.Google Scholar
8.Doran, J.W., Mielke, L.N., and Power, J.F. 1990. Microbial activity as regulated by soil water-filled pore space. In Transactions of the 14th International Congress of Soil Science, Kyoto, Japan. International Soil Science Society, Wageningen, The Netherlands. p. 94100.Google Scholar
9.Halliday, D.J. and Trenkel, M.E. (eds). 1992. International Fertilizer Association World Fertilizer Use Manual. International Fertilizer Industry Association, Paris, France.Google Scholar
10.Ding, W., Cai, Y., Cai, Z., Yagi, K., and Zheng, X. 2007. Soil respiration under maize crops: effects of water, temperature, and nitrogen fertilization. Soil Science Society of America Journal 71:944951.CrossRefGoogle Scholar
11.Bundy, L.G. and Andraski, T.W. 1999. Site-specific factors affecting corn response to starter fertilizer. Journal of Production Agriculture 12:664670.Google Scholar
12.Tisdale, S.L., Nelson, W.L., and Beaton, J.D. 1985. Soil Fertility and Fertilizers. 4th ed. MacMillian Publishing Company, New York, NY.Google Scholar