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United States Department of Agriculture

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Soil Drainage and Productivity Indexes

This webpage provides information on the Drainage Index (DI) and Productivity Index (PI) of all soils that are classified within the US system of Soil Taxonomy. These data aid in the identification of areas at risk to various forest insects and diseases because of their ability to identify regions of potential tree stress (see the 2013-2027 National Insect and Disease Forest Risk Assessment).

For help with soil taxonomy terminology, please visit NRCS Soil Taxonomy. This work was performed under the supervision of Dr. Randall Schaetzl, Department of Geography, Environment, and Spatial Sciences of Michigan State University, under contract with (and supported by) the US Forest Service.

Please note that the tabular and GIS data downloads from this site have been updated using the 2016 SSURGO database.

FHAAST theme art and Michigan State University logo

In cooperation with the Department of Geography, Environment, and Spatial Sciences of Michigan State University and the US Forest Service, Forest Health Protection's Forest Health Assessment and Applied Sciences Team.

Photograph of a well-drained Mollisol (a Lithic Hapludoll), with a Drainage Index of 33. The shallowness of the bedrock lowers the water-holding capacity of the soil, hence the low DI value. Photo by Randall Schaetzl, Department of Geography, Michigan State University. Photograph of a well-drained Mollisol (a Lithic Hapludoll), with a Drainage Index of 33. The shallowness of the bedrock lowers the water-holding capacity of the soil, hence the low DI value. Photo by Randall Schaetzl, Department of Geography, Environment, and Spatial Sciences of Michigan State University.

Photograph of a poorly-drained Inceptisol (a Typic Endoaquept), with a Drainage Index of 80. Photo by Randall Schaetzl, Department of Geography, Michigan State University. Photograph of a poorly-drained Inceptisol (a Typic Endoaquept), with a Drainage Index of 80. Photo by Randall Schaetzl, Department of Geography, Environment, and Spatial Sciences of Michigan State University.

Photograph of a somewhat excessively-drained Spodosol (a Typic Haplorthod), with a Drainage Index of 40. Note the prominent Photograph of a somewhat excessively-drained Spodosol (a Typic Haplorthod), with a Drainage Index of 40. Note the prominent "tongues" in this soil - typical of many Spodosols. Photo by Randall Schaetzl, Department of Geography, Environment, and Spatial Sciences of Michigan State University.


Soil Drainage Index

The Drainage Index (DI), originally named the "natural soil wetness index" (Hole and Campbell 1986, Schaetzl 1986), is a measure of long-term soil wetness. It is designed to represent, as an ordinal number, the amount of water that a soil contains and makes available to plants under normal climatic conditions. It is not meant to mimic the concept of "plant available water", which is mostly dependent upon soil texture. The DI only loosely/secondarily takes soil texture into consideration. The main factor affecting the DI is the depth to the water table and the soil volume available for rooting, for a plant can get at this form of water readily. The DI concept was first initiated by Hole (1978) and Hole and Campbell (1985), and expanded upon by Schaetzl (1986).

The DI ranges from 0 to 99. The higher the DI, the more water the soil can and does, theoretically, supply to plants. Sites with DI values of 99 are essentially open water. A soil with a DI of 1 is as thin and dry as bare bedrock. The DI is derived from the soil's taxonomic subgroup classification in the US system of Soil Taxonomy, and (optionally) its soil map slope class. Because a soil's taxonomic classification is not (initially) affected by such factors as irrigation or artificial drainage, the DI does not change as soils become irrigated or drained (unless the long-term effects of this involve a change in the soil's taxonomic classification). Instead, the DI reflects the soil's NATURAL wetness condition. Each soil SERIES has, in theory, its own unique DI. Some soil series span two or more drainage classes; in this case the DI that is used is the one that would normally be used for a soil with that subgroup classification.

Schaetzl, R.J., Krist, F.J. Jr., Stanley, K.E., and C.M. Hupy. 2009. The Natural Soil Drainage Index: An Ordinal Estimate of Long-Term, Soil Wetness. Physical Geography 30:383-409. (3.5 MB PDF)

Soil Productivity Index

Like the DI, the Productivity Index (PI) is an ordinal measure, but of the productivity of a soil. The PI uses family-level Soil Taxonomy information, i.e., interpretations of taxonomic features or properties that tend to be associated with low or high soil productivity, to rank soils from 0 (least productive) to 19 (most productive). The index has wide application, because, unlike competing indexes, it does not require copious amounts of soil data, e.g., pH, organic matter, or CEC, in its derivation. GIS applications of the PI, in particular, have great potential. For regionally extensive applications, the PI may be as useful and robust as other productivity indexes that have much more exacting data requirements.

Schaetzl, R.J. Krist, F.J. Jr., and B.A. Miller. 2012. A Taxonomically Based, Ordinal Estimate of Soil Productivity for Landscape-Scale Analyses. Soil Science 177:288-299. (7.1 MB PDF)

References

Hole, F.D. 1978. An approach to landscape analysis with emphasis on soils. Geoderma 21:1-13.
Hole, F.D. and J.B. Campbell. 1985. Soil Landscape Analysis. Rowman and Allanheld, Totowa, NJ 196 pp.
Schaetzl, R.J. 1986. A soilscape analysis of contrasting glacial terrains in Wisconsin. Annals Assoc. Am. Geogs. 76:414-425.

Contacts

For more details about the DI and/or PI workings and theory, to request more detailed copies of posters or have questions answered, contact Dr. Randall Schaetzl at the Department of Geography, Environment, and Spatial Sciences of Michigan State University.


Data Downloads

The simplest way to view Drainage Index (DI) and Productivity Index (PI) in a GIS is to use the 240 meter raster renditions.

If interested in viewing SSURGO soil polygons with DI and PI attribution join the csv text file table available from the “Master DI - PI join table by 2016 SSURGO Mapping Unit (MUKEY)” link below to the MUPOLYGON feature class in the SSURGO file geodatabase. SSURGO soils data are GIS equivalents of soil survey maps and can be downloaded directly from NRCS.

  • NOTE: there can be changes to post-2016 versions of SSURGO that prevent a clean join to all MUKEY records in the Master DI_PI by MUKEY join table. If requiring a 2016 version of SSURGO soil polygons please contact Mark.Zweifler@usda.gov.

The “Master DI - PI join table by 2016 SSURGO Mapping Unit (MUKEY)” table uses soil taxonomy from the predominant component on each of the over 300,000 SSURGO mapping units in the contiguous 48 states as the basis for calculating DI and PI. Use the MUKEY field to join this table to the SSURGO ‘components’ table.

The “Master DI-PI join table by 2016 SSURGO component” provides DI and PI values for the over 1 million components in the SSURGO database components table for the contiguous 48 states. Use the COKEY field to join this table to the SSURGO ‘components’ table.

Downloadable DI and PI GIS data and Tables
Description File SIZE/TYPE
DI 2016 rasters (240 meter cell resolution) Zipped file geodatabase 74 MB ZIP
PI 2016 rasters (240 meter cell resolution) Zipped file geodatabase 91 MB ZIP
Master DI-PI join table by 2016 SSURGO Mapping Unit (MUKEY) 1.8 MB ZIP
Master DI_PI join table by 2016 SSURGO component 4 MB ZIP
DI modifiers table 87 KB PDF
PI modifiers table 122 KB PDF
DI and PI SubGroup Values in 2016 SSURGO 403 KB XLSX
DI and PI By Soil Series 3 MB XLSX

Research Links

Combining National Forest Inventory Data with Soil Drainage Index to Assess Forest Health Vulnerability Poster Combining National Forest Inventory Data with Soil Drainage Index to Assess Forest Health Vulnerability Poster.

Downloadable Research Files
Description File SIZE/TYPE
2019 Poster: "Combining National Forest Inventory Data with Soil Drainage Index to Assess Forest Health Vulnerability" 2 MB PDF
PI manuscript (2012): "A Taxonomically Based Ordinal Estimate of Soil Productivity for Landscape-Scale Analyses" 7 MB PDF
DI manuscript (2009): "The Natural Soil Drainage Index: An Ordinal Estimate of Long-Term Soil Wetness" 3.5 MB PDF
2012 Poster: "The Soil Productivity Index: Taxonomically Based, Ordinal Estimates of Soil Productivity" 3.5 MB PDF
2011 Poster: "Landscape (Soil) Wetness Map of the Conterminous United States" Presented at the 2011 Esri International Users Conference, San Diego, CA, in 2012 4 MB PDF
ESRI's Interactive Flash Player Map Book Volume 27: Landscape (Soil) Wetness Map of the Conterminous United States (see pages 70-71) Web link
2011 Poster: "The Soil Fertility and Drainage Indexes: Taxonomically Based, Ordinal Estimates of Relative Soil Properties" 3.5 MB PDF
2008 Poster: "Introducing and Applying a Soil Wetness Index Designed for Modeling, GIS and Mapping Applications"  Presented at the 11th North American Forest Soils Conference, Blacksburg, VA, in 2008 1.6 MB PDF
2007 Poster: "Using the New Natural Soil Drainage Index to Highlight and Explain Soil Wetness Patterns in Michigan"  Presented at the annual meeting of the Michigan Academy of Science, Arts and Letters, Big Rapids, MI, in 2007 1.4 MB PDF
2007 Poster: "Introducing a New Index of Soil Wetness, and its Potential as a Landscape/Landform Visualization Tool" Presented at the north-central regional meeting of the Geological Society of America, in Lawrence, KS, in 2007 1.3 MB PDF
2006 Poster: "The National Soil Drainage Index Map, a Factor in Forest Health Risk Assessment" 14 MB PDF

Pi-Di Citations

A selection of papers and theses that have cited/used the Productivity and Drainage indices.

Soil Productivity Index and Soil Drainage Index

Costanza, J.K., Faber-Langendoen, D., Coulston, J.W. and D.N. Wear. 2018. Classifying Forest Inventory Data into Species-based Forest Community Types at Broad Extents: Exploring Tradeoffs among Supervised and Unsupervised Approaches. Forest Ecosystems 5:1-17.

Connallon, C.B. and R.J. Schaetzl. 2017. Geomorphology of the Chippewa River Delta of Glacial Lake Saginaw, Central Lower Michigan, USA. Geomorphology 290:128-141.


Gadoth-Goodman, D. 2017. Can Short-Rotation Harvests Increase Management Options for the Endangered Kirtland's Warbler? Master’s Thesis, Michigan State University, East Lansing.


Ingwell, L.L., Lacroix, C., Rhoades, P.R., Karasev, A.V. and N.A. Bosque-Pérez. 2017. Agroecological and Environmental Factors Influence Barley Yellow Dwarf Viruses in Grasslands in the US Pacific Northwest. Virus Res. 241:185-195.


Wilson, D.C. and A.R. Ek. 2017. Imputing Plant Community Classifications for Forest Inventory Plots. Ecol. Indicators, 80:327-336.


Wilson, D.C. and A.R. Ek. 2017. Imputing Plant Community Classification from Associated Forest Inventory and Physiographic Data in Minnesota, USA. Ecol. indicators, 79:73-82.

Wilson, D. 2016. Imputation of Ecological Detail using Associated Forest Inventory, Plant Community and Physiographic Data. PhD Dissertation, University of Minnesota, Minneapolis.

Deo, R.K. 2014. Application of an Imputation Method for Geospatial Inventory of Forest Structural Attributes across Multiple Spatial Scales in the Lake States, USA. PhD Dissertation, Michigan Technological University, Houghton.

Phillips, J.D. 2013. Evaluating Taxonomic Adjacency as a Source of Soil Map Uncertainty. Eur. J. Soil Sci. 64:391-400.

Pitel, N.E. 2010. An Assessment of Sugar Maple Condition following Defoliation by Forest Tent Caterpillar: Investigating Soil Chemistry. Master’s Thesis, New York State University, New York.

Soil Productivity Index

Bush, E., 2019. Development of a Dryland Corn Productivity Index for Kansas. Master’s Thesis, Kansas State University, Manhattan.


Darijani, F., Veisi, H., Liaghati, H., Nazari, M.R. and K. Khoshbakht. 2019. Assessment of Resilience of Pistachio Agroecosystems in Rafsanjan Plain in Iran. Sustainability 11:1-14.


Kim, T.J., Wear, D.N., Coulston, J., and R.H. Li. 2019. Forest Land Use Responses to Wood Product Markets. For. Policy and Econ. 93:45-52.


Iverson, L.R., Peters, M.P., Prasad, A.M. and S.N. Matthews. 2019. Analysis of Climate Change Impacts on Tree Species of the Eastern US: Results of DISTRIB-II Modeling. Forests 10:302.

Ge, M., Edwards, E.C. and S. Akhundjanov. 2018. Land Ownership and Irrigation on American Indian Reservations. CEnREP Working Paper No.18-017:1-56.


Kim, T.J., Wear, D.N., Coulston, J. and R. Li. 2018. Forest Land Use Responses to Wood Product Markets. Forest Policy and Econ. 93:45-52.

Ayram, C.A.C., Mendoza, M.E., Etter, A. and D.R.P. Salicrup. 2017. Anthropogenic Impact on Habitat Connectivity: A Multidimensional Human Footprint Index Evaluated in a Highly Biodiverse Landscape of Mexico. Ecol. Indicators 72:895-909.


Bouza P.J., Saín C., Videla L., Dell’Arciprete P., Cortés E., and J. Rua. 2017 Soil–Geomorphology Relationships in the Pichiñán Uraniferous District, Central Region of Chubut Province, Argentina. In: Rabassa J. (ed.), Advances in Geomorphology and Quaternary Studies in Argentina. Springer Earth System Sciences. Springer. pp. 77-99.


Hengl, T., Leenaars, J.G., Shepherd, K.D., Walsh, M.G., Heuvelink, G.B., Mamo, T., Tilahun, H., Berkhout, E., Cooper, M., Fegraus, E. and I. Wheeler. 2017. Soil Nutrient Maps of Sub-Saharan Africa: Assessment of Soil Nutrient Content at 250 m Spatial Resolution Using Machine Learning. Nutrient Cycling in Agroecosystems 109:77-102.


Ibáñez, I., Katz, D.S. and B.R. Lee. 2017. The Contrasting Effects of Short-term Climate Change on the Early Recruitment of Tree Species. Oecologia 184:701-713.


Marko, O., Brdar, S., Panić, M., Šašić, I., Despotović, D., Knežević, M. and V. Crnojević. 2017. Portfolio Optimization for Seed Selection in Diverse Weather Scenarios. PloS ONE 12:1-27.


Shrestha, P. 2017. Importance of Concentrated Flow Paths in Agricultural Watersheds of Southern Illinois. Master’s Thesis, Southern Illinois University, Carbondale.


Schaetzl, R.J. 2017. Soils of the Northern Lake States Forest and Forage Region. In: The Soils of the USA. West, L.T., M.J. Singer, and A. Hartemink (eds.), Springer, New York. pp. 191-208.

National Academies of Sciences, Engineering, and Medicine, 2016. Pathways to Urban Sustainability: Challenges and Opportunities for the United States. National Academies Press.


Prasad, A.M., Iverson, L.R., Matthews, S.N. and M.P. Peters. 2016. A Multistage Decision Support Framework to Guide Tree Species Management under Climate Change via Habitat Suitability and Colonization Models, and a Knowledge-based Scoring System. Landscape Ecol. 31:2187-2204.

Albano, C.M. 2015. Identification of Geophysically Diverse Locations that may Facilitate Species’ Persistence and Adaptation to Climate Change in the Southwestern United States. Landscape Ecol. 30:1023-1037.


Bonfante, A. and J. Bouma. 2015. The Role of Soil Series in Quantitative Land Evaluation when Expressing Effects of Climate Change and Crop Breeding on Future Land Use. Geoderma 259:187-195.


Krohn, B., 2015. Switching to Switchgrass: Pathways and Consequences of Bioenergy Switchgrass entering the Midwestern Landscape. PhD Dissertation, University of Minnesota, Minneapolis.


Prasad, A.M. 2015. Macroscale Intraspecific Variation and Environmental Heterogeneity: Analysis of Cold and Warm Zone Abundance, Mortality, and Regeneration Distributions of Four Eastern US Tree Species. Ecol. and Evolution 5:5033-5048.

Coleman, T.W., Jones, M.I., Courtial, B., Graves, A.D., Woods, M., Roques, A. and S.J. Seybold. 2014. Impact of the First Recorded Outbreak of the Douglas Fir Tussock Moth, Orgyia pseudotsugata, in Southern California and the Extent of its Distribution in the Pacific Southwest Region. For. Ecol. Mgmt. 329:295-305.


Häring, T., Reger, B., Ewald, J., Hothorn, T. and B. Schröder. 2014. Regionalizing Indicator Values for Soil Reaction in the Bavarian Alps–from Averages to Multivariate Spectra. Folia Geobot. 49:385-405.


Jamroz, E., Weber, J. and M. Dębicka. 2014. Trophic Soil Index of the Rusty Soils Affected by Clear-Cutting in the Spała Forest District. Sylwan 158:669-674.


Kim, K.S., Kim, S.J., Do Park, K., Lee, C.W., Ryu, J.H., Choi, J.S., Jeon, W.T., Kang, H.W. and M.T. Kim. 2014. Assessment of Sustainable Production on Paddy Field Treated with Green Manure Crops Using Sustainability Index. 한국토양비료학회지 47:165-171.


Li, H., 2014. Farmers' Switchgrass Adoption Decision under Different Market Scenarios - An Agent Based Modeling Approach. Master’s Thesis, Michigan State University, East Lansing.


Ligmann-Zielinska, A., Kramer, D.B., Cheruvelil, K.S. and P.A. Soranno. 2014. Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve their Analytical Performance and Policy Relevance. PLOS ONE 9(10): e109779. https://doi.org/10.1371/journal.pone.0109779

Häring, T. 2013. Spatial Prediction Methods for the Assessment and Mapping of Forest Site Characteristics. PhD Dissertation, Technische Universität, München.

Soil Drainage Index

Hofmeister, K.L., Nave, L.E., Riha, S.J., Schneider, R.L. and M.T. Walter. 2019. A Test of Two Spatial Frameworks for Representing Spatial Patterns of Wetness in a Glacial Drift Watershed. Vadose Zone J. 18:1-17.

Blewett, W.L., Lusch, D.P., Schaetzl, R.J., and S.A. Drzyzga. 2018, A Century of Change in the Methods, Data, and Approaches to Mapping Glacial Deposits in Michigan. In: Kehew, A.E., and B.B Curry (eds.), Quaternary Glaciation of the Great Lakes Region: Process, Landforms, Sediments, and Chronology. Geol. Soc. Am. Spec. Paper 530:39–67.


Calabrese, S., Richter, D.D. and A. Porporato. 2018. The Formation of Clay-Enriched Horizons by Lessivage. Geophys. Res. Letters 45:7588-7595.


Malone, B.P., McBratney, A.B. and B. Minasny. 2018. Description and Spatial Inference of Soil Drainage using Matrix Soil Colours in the Lower Hunter Valley, New South Wales, Australia. PeerJ:1-20.


Salfer, J.T. 2018. Modeling Pre-Settlement Wetlands in Northern Minnesota. Master’s Thesis, Minnesota State University, Mankato.

Luehmann, M.D. and R.J. Schaetzl. 2017. Late Pleistocene Deltas in the Lower Peninsula of Michigan, USA. In: Kehew, A.E., and B.B. Curry (eds.), Quaternary Glaciation of the Great Lakes Region: Process, Landforms, Sediments, and Chronology. Geol. Soc. Am. Spec. Paper 530:163-177.

Hill, B.H., Kolka, R.K., McCormick, F.H. and M.A. Starry. 2014. A Synoptic Survey of Ecosystem Services from Headwater Catchments in the United States. Ecosystem Services 7:106-115.


Kowal, V.A., Schmolke, A., Kanagaraj, R. and D. Bruggeman. 2014. Resource Selection Probability Functions for Gopher Tortoise: Providing a Management Tool Applicable across the Species’ Range. Environ. Mgmt. 53:594-605.


Scherr, S.J., Buck, L., Willemen, L., and J.C. Milder. 2014. Ecoagriculture: Integrated Landscape Management for People, Food, and Nature. In: Van Alfen N., Encyclopedia of Agriculture and Food Systems 3:1-17.

Chase, K.D. 2013. Forest Stand Preference of Sirex nigricornis, and Sirex noctilio Hazard in the Southeastern United States. PhD Dissertation, Mississippi State University, Starkville.


Schaetzl, R.J., Enander, H., Luehmann, M.D., Lusch, D.P., Fish, C., Bigsby, M., Steigmeyer, M., Guasco, J., Forgacs, C. and A. Pollyea. 2013. Mapping the Physiography of Michigan with GIS. Phys. Geog. 34:2-39.


Shartell, L.M., Lilleskov, E.A. and A.J. Storer. 2013. Predicting Exotic Earthworm Distribution in the Northern Great Lakes region. Biol. Invasions 15:1665-1675.


Touchstone, R.B. 2013. Automated Template C: Created by James Nail 2011V2. 01. PhD Dissertation, Mississippi State University, Starkville.

Mikesell, L.R. 2012. Lithostratigraphic Correlation at Various Spatial Scales in the Livermore Basin at the Lawrence Livermore National Laboratory, California, USA. Michigan State University. PhD Dissertation, Michigan State University, East Lansing.


Olson, M.G. 2012. Remote Sensing of Forest Health Trends in the Northern Green Mountains of Vermont. Master’s Thesis, University of Vermont, Burlington.

Pitel, N.E. 2010. An Assessment of Sugar Maple Condition following Defoliation by Forest Tent Caterpillar: Investigating Soil Chemistry. Master’s Thesis, New York State University, New York.