South Dakota Agricultural Land Soil Productivity Tool

Written collaboratively by Matthew Elliott, Lisa Elliott, Tong Wang, and Douglas Malo.

Background

Many U.S. states employ a use-value formula to assess agricultural lands for property tax purposes. These formulas are based on an income capitalization approach to appraisal, which values agricultural land by dividing the estimated annual net income from agricultural production (or cash rent) by a capitalization rate. Most of the use-value formulas used for valuing agricultural land in the United States are similar in that they mostly utilize annual production, price, and cash rental data from USDA-NASS and soil survey data from USDA-NRCS.

South Dakota has used a use-value formula to assess all agriculture property since 2010. To qualify as agriculture property in South Dakota, the principal use of the property must be the raising and harvesting of crops, timber, or fruit trees; the rearing, feeding, and management of farm livestock, poultry, fish, or nursery stock; the production of bees and apiary products; or for horticulture. Additionally, the intended agriculture gross income must be at least 10% of the assessed value, or the parcel must exceed a specified size.

Updated Soil Rating System

We have updated an earlier soil productivity rating system (Malo et al., 1990; Malo and Westin, 1978) used by the South Dakota Department of Revenue (DOR) to assess agricultural land for property tax purposes. The soil rating system is used in conjunction with the USDA-NASS annual crop data and pastureland rental data to determine agricultural land assessments in South Dakota. The steps taken in updating the soil rating system are briefly described as follows:

1. Develop adjusted representative crop yields for every soil mapping unit by calibrating NRCS representative yields to match USDA-NASS county reported yields. The adjustment is based on the weighted average of soil attributes (CPI, crop ratings), topography (slope), and climate factors (precipitation) for cropland areas within each county, ensuring yield estimates reflect current productivity levels and local growing conditions.
2. Determine a comparative crop rating for every soil mapping unit by normalizing adjusted crop yields relative to the county maximum. Crop ratings range from 0.1 to 1.0 and are comparable within the individual county.
3. Determine a comparative range rating based on representative usable forage amounts for each soil map unit. Usable forage is calculated by adjusting NRCS representative forage yields based on ecological plant composition and forage value ratings.
4. Determine a balance point factor (Malo and Westin, 1978) to equate range ratings with crop ratings on a comparable scale.
5. Determine the cropping percentage of each soil over multiple years using the USDA-NASS Cropland Data Layer (CDL) from 2010–2019.
6. Develop a most probable use rating that reflects the most likely use for each soil map unit based on the soil, topography, crop yield attributes, and use patterns in the region. A machine learning model estimates the probability that each soil will mostly be used as cropland based on soil attributes, adjusted yields, climate factors, and location.
7. Develop an opinion of the highest and best use for each soil map unit. In determining the highest and best use, an appraiser must analyze the relevant legal, physical, and economic factors to the extent necessary to support the appraiser’s highest and best use conclusion(s) (Appraisal Foundation, 2019, Standards Rule 1-3b).
8. When an income capitalization approach is necessary for credible assignment results, an appraiser must: analyze comparable rental data as are available and the potential earnings capacity of the property to estimate the gross income potential of the property (Appraisal Foundation, 2019, Standards Rule 1-4c).

More details on the background of the research, methods, and findings are available in Elliott et al. (2019).

About the Tool

We have developed a web-based tool that provides a scientifically-based system for agricultural land assessment using USDA-NRCS soil data and USDA-NASS market data. The tool is organized into the following sections:

Overview Tab

The landing page provides a summary of the tool’s purpose and a guide to each section. Users can navigate from the Overview to any of the tabs described below.

County Soil Tables

The County Soil Tables tab allows users to browse soil productivity data for any South Dakota county. Users select a county from a dropdown to view detailed tables of soil ratings, adjusted crop yields, forage production, and physical soil properties for every soil map unit in the county.

The data is organized across three downloadable tables:

• Table 1 (Ratings & Classifications): Crop rating, range rating, highest and best use (HBU), land capability class, NRCS area, component composition, and percentage cropped for each soil map unit.
• Table 2 (Yields & Production): Adjusted crop yields (corn, soybeans, spring wheat, winter wheat, bromegrass-alfalfa hay), usable forage, range production, and animal unit months.
• Table 3 (Soil Properties): Average slope, available water capacity, sand/silt/clay percentages, K-factor (erodibility), crop productivity index, historical cropping percentage, and predicted cropland probability.

County-level summary cards display weighted average crop and range ratings, average crop productivity index, and average cropping percentage, which serve as benchmarks for comparing individual parcels. Each table can be exported as an Excel file.

Create Parcel Soil Report

The Create Parcel Soil Report tab enables users to generate a detailed soil analysis for a specific parcel of land. This is the primary tool for parcel-level assessment work. The process is as follows:

1. Navigate to the area of interest using a county dropdown, a legal description input (e.g., 1-108N-62W), or by panning and zooming on the satellite basemap.
2. Draw a polygon or rectangle on the map to outline the parcel boundary using the drawing tools on the left side of the map.
3. Click “Create Report” to generate the analysis. The tool automatically:
   • Queries the USDA SSURGO database to identify all soil map units within the drawn area and their acreages.
   • Retrieves BLM PLSS legal descriptions for the sections covered by the parcel.
   • Generates a satellite map image with the parcel boundary and color-coded soil unit overlays.
   • Joins the SSURGO results with the SDSU soil productivity database to produce detailed per-soil data.
4. Review the report, which includes:
   • Legal Descriptions: all PLSS sections intersecting the drawn parcel.
   • Satellite Soil Map: a satellite image showing the parcel boundary and soil map unit boundaries with a legend.
   • Soil Area Summary: each soil map unit in the parcel with pixel count, percentage of total area, and acreage.
   • Table 1 (Ratings & Classifications): crop rating, range rating, HBU, land capability class, NRCS area name, component composition, and percent cropped for each soil, with a weighted-average subtotal row.
   • Table 2 (Adjusted Yields & Production): adjusted yields for corn, soybeans, spring wheat, winter wheat, and hay; usable forage; and range production, with a weighted-average subtotal row.
   • Table 3 (Soil Properties): slope, available water capacity, sand/silt/clay, K-factor, and crop productivity index, with a weighted-average subtotal row.
5. Export the results as a PDF report or download individual tables as Excel files.

Comparing Parcel Data to County Averages

The parcel soil report is designed to be used alongside the County Soil Tables to determine whether an upward or downward adjustment from the county average value is warranted for a specific property:

6. Identify the parcel’s weighted average crop rating and/or range rating from the TOTAL / WTD AVG row in the parcel report.
7. Find the county average crop and range ratings from the County Soil Tables tab.
8. Calculate the productivity ratio by dividing the parcel rating by the county average rating. A ratio above 1.0 indicates the parcel is more productive than average; below 1.0 indicates it is less productive.
9. Apply the ratio to the county’s average market value, capitalized rental rate, or capitalized net income to estimate the parcel’s adjusted value per acre.

Example: A parcel has a weighted average crop rating of 0.85. The county average crop rating is 0.72 and the county average market value for cropland is $5,000/acre.

• Productivity Ratio: 0.85 ÷ 0.72 = 1.18
• Adjusted Parcel Value: $5,000 × 1.18 = $5,900 per acre
• This parcel’s soils are 18% more productive than the county average, supporting an upward adjustment.

Methodology

The Methodology tab provides a comprehensive description of data sources, calculation methods, valuation formulas, definitions, data limitations, and field verification requirements. It includes:

• Detailed explanations of how adjusted crop yields, usable forage, crop and range ratings, historical cropland use, highest and best use classifications, and predicted cropland probability are calculated.
• Step-by-step valuation instructions using market values, rental values, or net income with the income capitalization approach.
• Worked examples for cropland and non-cropland valuation adjustments.
• Definitions of key terms (soil map unit, income capitalization approach, highest and best use, use value assessment, most probable use, mass appraisal).
• Descriptions of all table columns and their data sources.
• Guidance on data limitations and the professional field verification required before applying soil data to specific properties.

Acknowledgements / Contact

The Acknowledgements / Contact tab lists the project team, contact information, and funding acknowledgements:

• Project Director: Matthew S. Elliott, Associate Professor, Ness School of Management and Economics.
• Co-Project Directors: Lisa Elliott, Douglas Malo, and Tong Wang.
• The project is supported by the South Dakota Agricultural Experiment Station at South Dakota State University and by Hatch project accession No. 1006890 and No. 1017800 from the USDA National Institute of Food and Agriculture.

For questions or more information about the tool, contact Dr. Matthew Elliott.

South Dakota Agricultural Soil Productivity Tool

References

• Appraisal Foundation. Appraisal Standards Board. Uniform Standards of Professional Appraisal Practice. Appraisal Foundation, 2018.
• Appraisal Standards Board. 2018. Uniform Standards of Professional Appraisal Practice, 2018–2019 edition. Washington, DC: Appraisal Foundation.
• Appraisal Institute. 2015. The Dictionary of Real Estate Appraisal, 6th edition. Chicago, IL: Appraisal Institute.
• Dotzour, M.G., T.V. Grissom, C.H. Liu, and T. Pearson. 1990. “Highest and Best Use: The Evolving Paradigm.” Journal of Real Estate Research 5: 17–32.
• Justin, B.P. 2019. Valuing Rural America: Foundations of Data Analysis. Glendale, CO: American Society of Farm Managers and Rural Appraisers.
• Klingebiel, A.A. 1958. “Soil Survey Interpretation—Capability Groupings.” Soil Science Society of America Journal 22: 160–163.
• Malo, D.D. and F.C. Westin. 1978. Soils of South Dakota. Bull. 656. SD Agr. Exp. Sta., South Dakota State University, Brookings, SD.
• Natural Resources Conservation Service. 2000. Soil Surveys Can Help You…Appraising Farmland.
• South Dakota Department of Revenue. 2019. Ag Land Pilot Study.
• U.S. Department of Agriculture. 2008–2017. USDA National Agricultural Statistics Service Cropland Data Layer. Published Crop-Specific Data Layer [Online]. Washington, DC.
• U.S. Department of Agriculture. Soil Survey Geographic (SSURGO) Database. Washington, DC: Natural Resources Conservation Service.
• Westin, F.C. and D.D. Malo. 1978. Soils of South Dakota. Bull. 656. SD Agr. Exp. Sta., South Dakota State University, Brookings, SD.
• Wilson, D.C. 1995. “Highest and Best Use Analysis: Appraisal Heuristics versus Economic Theory.” Appraisal Journal 63: 11.