Abstract
The ag land soil tables display data used to make baseline agricultural land assessments at the soil map unit level in South Dakota. The purpose is to assess ag land for property taxes. The following doucument is a description of the method used to determine ag land assessments and the values used in the determination. The values described in the below methods and descriptions can be found in the “Table 1”, “Table 2”, “Table 3”, and “Plants” tabs. For more information on the background of the research and methods please see “A Change in Highest and Best Use Policy in South Dakota Has a Sizable Impact on Agricultural Land Assessments” in Choices Magazine (Elliott et al., 2019).Many U.S. states employ a use-value formula to assess agricultural lands for property tax purposes. This formula is 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. 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. In addition, the intended agriculture gross income must be at least 10% of the assessed value, or the parcel exceeds a specified size.
The impetus for agricultural use-value assessments in South Dakota was that the market values of agricultural land deviate from their perceived economic value; further motivation was that the qualified “arm’s length” transactions were too infrequent in local areas to effectively use a sales comparison approach. Most of the use-value formulas used for valuing agricultural land in the United States are similar in that they largely utilize annual USDA National Agricultural Statistics Service (NASS) production, price, and cash rental data and Natural Resources Conservation Service (NRCS) soil survey data to determine agricultural land assessments.
The procedure described here is a revision and update of 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 Ag land for property tax purposes. The seven steps used to determine Ag land assessments in South Dakota using the use-value formula are:
Develop representative crop yields and determine a comparative crop rating for every soil mapping unit utilizing NRCS Soil Survey data and USDA-NASS data. The crop rating is comparable within the individual county.
Determine a comparative range rating based on representative usable forage amounts for each soil map unit.
Determine a balance point factor (Malo and Westin, 1978) to equate range ratings with crop ratings.
Determine the percentage the soil was cropped according to multiple years from the USDA-NASS cropscape.
Develop a most probable use rating that reflects the most likely use for each soil map unit based on the soil attributes, topography, crop yield attributes, and use patterns in the region.
Develop an opinion of the highest and best use for each soil map unit. In determining the highest and best use, analyze the relevant legal, physical, and economic factors to the extent necessary to support the highest and best use conclusion(s) (Appraisal Foundation. 2019, Standards Rule 1-3b).
When an income capitalization approach is necessary for credible assignment results, 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).
Values used to make assessments for each soil map unit in the selected county are displayed in the “Table 1”, “Table 2”, and “Table 3” tabs.
Soil map unit - A soil map unit is a collection of areas defined and named the same in terms of their soil components (e.g., series) or various areas or both. Soil map units are the basic unit of a soil map. Each soil map unit differs in some respect from all others in a survey area. Each map unit contains major component(s) and minor components. Each area of soil on a map is a delineation. Soil delineation boundaries are drawn wherever there is a significant change to the type of soil. Soil delineations typically relate to the shift in underlying land-forms(e.g., floodplain, back-slope, terrace).
Income capitalization approach -Specific appraisal techniques applied to develop a value indication for a property based on its earning capability and calculated by the capitalization of property income.
Highest and Best Use - The reasonably probable use of a property that results in the highest value. The four criteria that the highest and best use must meet are legal permissibly, physical possibility, financial feasibility, and maximum productivity. 2. The use of an asset that maximizes its potential and that is possible, legally permissible, and financially feasible. The highest and best use may be for the continuation of an asset’s existing use or some alternative use. It would be the use that a market participant would have in mind for the asset when formulating the price that it would be willing to bid.
Use value assessment - An assessment based on the value of property as it is currently used, not on its market value considering its highest and best use. This sort of assessed value is sometimes used where legislation has been enacted to preserve farmland, timberland, or other open space land on urban fringes
Most Probable Use - 1. The use to which a property will most likely be put based on market analysis and the highest and best use conclusion. The most probable use is the basis for the most probable selling price of the property. See also the most probable selling price. 2. Highest and best use in the context of market value.
Mass appraisal - The process of valuing a universe of properties as of a given date using standard methodology, employing common data, and allowing statistical testing.
Mass appraisal model - A mathematical expression of how supply and demand factors interact in a market.
County – The county boundaries were determined by the South Dakota Department of Revenue. Several counties did not provide county boundaries. In those cases, alternative sources for political boundaries were used.
NRCS Area – The name given to the specified geographic area in which NRCS locates the soil.
Soil Map Unit Name - Name of the soil mapping unit.
Soil Map Unit Symbol - Symbol for each soil map unit that is unique within a NRCS area.
Component Info - is a list of the soil components that makeup the soil map unit. Each component is separated by a semicolon. The soil component name is listed, followed by the percentage the component is expected to be on the soil map unit and the component land capability class (I-VIII) and land capability subclass (e, w, s, c).
Land Capability Class – The broadest category in the land capability classification system for a soil map unit. This column displays the dominant capability class, under non-irrigated conditions, for the soil map unit based on the composition percentage of all components in the map unit. Each soil component that makes up the soil map unit has its own land capability class and subclass. However, the soil map unit land capability class represents the dominant class of the soil map unit and is reported by NRCS.
Land Capability Subclass - is the second category in the land capability classification system. Class codes e, w, s, and c are used for land capability sub-classes. Subclass e is made up of soils for which the susceptibility to erosion is the dominant problem or hazard affecting their use. Erosion susceptibility and past erosion damage are the major soil factors that affect soils in this subclass. Subclass w is made up of soils for which excess water is the dominant hazard or limitation affecting their use. Poor soil drainage, wetness, a high water table, and overflow are the factors that affect soils in this subclass. Subclass s is made up of soils that have soil limitations within the rooting zone, such as shallowness of the rooting zone, stones, low moisture-holding capacity, low fertility that is difficult to correct, and salinity or sodium content. Subclass c is made up of soils for which the climate (the temperature or lack of moisture) is the major hazard or limitation affecting their use. The subclass represents the dominant limitation that determines the capability class. Within a capability class, where the kinds of limitations are essentially equal, the sub-classes have the following priority: e, w, s, and c. Sub-classes are not assigned to soils or miscellaneous areas in capability classes I and VIII.
HBU - Is the highest and best use of the soil as determined by the soil map unit land capability class. The historical practice by South Dakota Department of Revenue has been to base the initial highest and best use of the soil map unit as cropland if the land capability class is I-III and non-cropland if the land capability class is V-VIII. The historical practice for determining the highest and best use of class IV soils is to compare the crop rating and the range rating. If the crop rating is greater than the range rating, then the class IV soil map unit highest and best use is determined to be cropland.
Acres – is the number of acres of each soil mapping unit within the county. The acres reported in this table may differ from the total soil acres reported by NRCS (muacres) because some soil map unit acres can be outside of the county boundaries.
Crop Rating - the crop rating was derived from the NRCS representative crop yields for each soil map unit component using a three-step process. The crops used to determine a crop rating were corn, soybeans, spring wheat, winter wheat, and bromegrass-alfalfa in the eastern and central crop reporting districts, and corn, spring wheat, winter wheat, and bromegrass-alfalfa in the western crop reporting districts. The first step is to derive a representative crop yield for each soil component. Crop yields by soil component that were reported by NRCS were used. If the soil component crop yield was missing in the NRCS database then a imputation model was used to impute the missing crop yield. The soil map unit representative crop yield is the weighted mean of the soil component representative crop yields where the weight is the composition percentage of all components in the soil map unit according to NRCS. The second step is to derive a crop yield rating for each crop for each soil map unit. For example, the corn yield rating is derived by dividing the soil map unit’s corn representative yield by the max representative corn yield for all the soil map units in the county. This same procedure is done for each of the crops. The third step is to derive the average of all the crop yield ratings for each soil map unit to determine the crop rating. For example, if the soil map unit had a corn yield rating of .5, a soybean yield rating of .6, a spring wheat yield rating of .6, a winter wheat yield rating of .65, and a bromegrass-alfalfa yield rating of .55, then the crop rating for the soil map unit would be .58= (.5+.6+.6+.65+.55)/5. The minimum value a crop rating can be is .1, and the maximum is 1.
Range Rating - The range rating is derived from the NRCS representative range production and ecological data on plant species associated with the soil components using a 3 step process. The first step is to derive a forage value rating (FVR) from the ecological data for each soil component. The forage value rating of the soil is the average forage value of all the plant species associated with the soil component using the plant ratings reported in the “Plants” tab. The forage value rating is then multiplied by the NRCS representative range production for the soil component and multiplied by .5 to derive a usable forage for each soil component that accounts for trampling and best management practices for maintaining forage. The soil map unit usable forage is based on the composition percentage of all components in the soil map unit and is calculated as the weighted mean usable forage for all the soil components associated with the soil map unit. The second step to calculate the range rating was to derive a usable forage rating by dividing each soil map unit’s usable forage by the maximum usable forage for all the soil map units in the county. The third step to derive the range rating was to multiply the usable forage rating by the balance point factor of 1.06 (Westin and Malo, 1978). The balance point factor is used to equate crop rating with usable forage rating. The minimum value a range rating can have is .1, and the maximum is 1. Calculated range rating values below .1 or above 1 were replaced with .1 and 1 respectively.
Balance Point Factor - The range rating for each soil map unit was equated to the crop rating using the average ratio of usable forage ratings to crop ratings for soils with a land capability class of IV. In practice, most soils in land capability class I, II, and III are most productive as cropland, while soils in class V, VI, and VII are most productive as native grass and/or timber. Soils in land capability class IV have very severe limitations for cropland but are used for both cropland and non-cropland. Therefore, it is useful to estimate the point where the non-cropland productivity is expected to be equal to cropland productivity for class IV soils. This point was estimated by taking the average range rating for class IV soils and dividing by the average crop rating for class IV soils in the state. The usable forage rating is then multiplied by the balance point factor. This procedure re-scales the usable forage rating to the same productivity scale as the crop rating. The rating was renamed to range rating after multiplying the usable forage rating by the balance point factor. Using the range rating, a comparison of non-cropland and cropland productivity of the soil is made by examining whether the crop rating is greater than the range rating. The state wide average was necessary for the calculation because some counties do not have adequate numbers of land capability class IV soils with limitations for erosion, wetness, shallowness, or climate.
Adjusted Crop Rating - The adjusted crop rating is derived by dividing the crop rating of the soil by the weighted average crop rating for all cropland soils in the county. The weighted average crop rating only includes crop ratings for soils where cropland is the highest and best use designation. The weight used in the average is the soil map unit acres in the county. Therefore, soils with small acres in the county have a minimal influence on the weighted average crop rating in the county.
Adjusted Range Rating - The adjusted range rating is derived by dividing the range rating of the soil by the weighted average range rating for all non-cropland soils in the county. The weighted average range rating only includes range ratings for soil map units where non-cropland is the highest and best use designation. The weight used in the average is the soil map unit acres in the county. Therefore, soils with small acres in the county have a minimal influence on the weighted average range rating in the county.
Assessed Value - For cropland soils, the assessed value is determined by multiplying the adjusted crop rating by the ratio of the cropland productivity value for the county multiplied by 35% based on the landlord share and dividing by the capitalization rate of 6.6%. For non-cropland highest and best use soils, the assessed value is determined by multiplying the adjusted range rating by the non-cropland productivity value for the county divided by 6.6%. An explanation of the method and values used to determine the cropland and non-cropland productivity for each county can be found on the South Dakota Department of Revenue website.
Representative Yields (Corn, Soybeans, Spring Wheat, Winter Wheat, Bromegrass-Alfalfa)- is the expected yield per acre of the crop without supplemental irrigation. NRCS reports the representative yields for each soil component. For soil components where the representative crop yields are missing in the SSURGO database, a model was used to impute the missing values based on soil attributes in the SSURGO database. The soil attributes used to impute missing values include the land capability class, the land capability subclass, the representative slope, the geographic location, the k factor, and the crop productivity index.
Usable Forage – is the estimated pounds per acre of usable forage for the soil mapping unit. This value was calculated by SDSU by multiplying the representative range production in the NRCS database by the average forage value rating (FVR) of the soil and then multiplying by .5 to account for loss from trampling and best management practices for maintaining forage. The forage value rating was derived by rating the plant species associated with the soil map unit soil component using the FVR rating reported in the “Plants” tab and the plant species and percentages in the plant info column.
Animal Units Monthly – The Animal Unit Months (AUMs) was calculated by dividing the usable forage by 900 lbs. 900 lbs. of forage is the estimated forage needed to support one animal unit (e.g., 1000 lb. cow) for 30 days.
Percentage the soil map unit has been cropped - is the percent of cropland pixels on the soil map unit between 2010-2019. The percentage of cropland pixels was calculated by summing the number of cropland pixels and non-cropland pixels on the soil map unit. Cropland pixels include band values of 1–61 and 196–254, and non-cropland pixels include band values of 63–80 and 131–195 in the USDA-NASS cropland data layers (2010-2019). The historically cropped percentage on the soil map unit is the ratio of cropland pixels and the sum of cropland and non-cropland pixels. Pixels that are associated with development areas and surface water are excluded from the calculation.
Probability, the soil map unit, will mostly be used as cropland - is the estimated probability that the soil map unit will primarily be used as cropland. This estimate was derived by using a random forest regression model. The indicators of possible use in the model include the soil map unit’s average slope, the soil map unit’s standard deviation of slope, the available water holding capacity in the top 25 and 50 centimeters, the percentage of sand, silt, and clay in the topsoil, the representative crop yields, the adjusted representative crop yields, the average precipitation on the soil in July, June, and August, the longitude and latitude of the soil, the crop productivity index, and the crop rating. To train the model, we used the percentage the soil map unit has been cropped above 50% as the target variable. For soil map units that have a low probability of being mostly used as cropland (e.g. 0-10%), we have high confidence the soil map unit as a whole will not be used intensely as cropland. Conversely, for soil map units with a high probability of being mostly used as cropland (e.g. 90-100%), we he have high confidence the soil map unit as a whole will be used intensely as cropland. For soil map units where the probability is estimated to be between 30-70% probability of mostly being used as cropland, we are generally uncertain to how the soil map unit as a whole will be used (See Limitations Section).
Average Slope- The average difference in elevation expressed as a percent on the soil map unit in the county. The average slope was calculated using USGS National Elevation Dataset. The resolution of the digital elevation product used for the calculations was 10 square meters.
Standard Deviation of Slope - a measure of the variation in slope on the soil map unit in the county. 68% of the soil map unit is expected to have slopes within one standard deviation of the average slope.
Crop Productivity Index- An index of the capacity of a soil map unit to produce a specific plant under a defined management system.
K Factor- An erodibility factor which quantifies the susceptibility of soil particles to detachment by water. The K factor reported in the table is the weighted average k factor for all the soil components in the top layer of the soil.
Average Precipitation - the average precipitation on the soil map unit between 2010-2019 according to PRISM gridded weather maps.
Sand - Mineral particles 0.05mm to 2.0mm in equivalent diameter as a weight percentage of the less than 2 mm fraction in the top layer.
Silt- Mineral particles 0.002 to 0.05mm in equivalent diameter as a weight percentage of the less than 2.0mm fraction in the top layer.
Clay- Mineral particles less than 0.002mm in equivalent diameter as a weight percentage of the less than 2.0mm fraction in the top layer.
Available water holding capacity in top 25 cm and 50 cm- The volume of water that the soil map unit, to a depth of 25 centimeters and 50 centimeters, can store that is available to plants. It is reported as the weighted average of all components in the soil map unit, and is expressed as centimeters of water. AWS is calculated from AWC (available water capacity), which is commonly estimated as the difference between the water contents at 1/10 or 1/3 bar (field capacity) and 15 bars (permanent wilting point) tension, and adjusted for salinity and fragments.
Percentage of ponding- The percentage of the soil map unit that is subject to ponding according to the NRCS.
Adjusted Crop Yields- is an adjustment to NRCS representative yields to reflect expected yield levels for 2020 given USDA-NASS county reported yields. The model used to adjust the NRCS representative yields includes the weighted average soil, topography, and climate attributes of cropland areas in the respective counties.
Ecological Plant Info- is the truncated list of plant species and percentage of the plant species that makes up the representative range production by soil component. The list consists of the soil component name in the soil map unit followed by the the percent the component makes up the map unit. Inside the curly brackets following the component name is the plant species, the percent the species makes up the range production for that component, and the forage value rating. Each species name, percent and fvr rating is separated by a semi colon. Different component plant lists that make up the soil map unit are separated using a verticle bar. Some lists are truncated due to many soil components and plant species.
Notes- Special notes have been included in the tables to flag possible issues with the baseline assessment. Users of the data should consider the notes and examine the context of the soil, the soil data, and the individual soil delineations. Adjustments from the baseline assessment may be necessary.
The data included in the tool for making Ag land assessments are for NRCS soil map units. Variations in soil attributes and topography occur across the soil map unit. When the soil attributes, topography, or surrounding context change, adjustments are necessary for smaller units of analysis than the soil map unit. For example, a soil map unit with a high probability of being mostly used as cropland may have a specific soil delineation that is surrounded by soil delineations that are used intensely as non-cropland. Thus the soil delineation would have a different probability of being utilized as cropland than what is reported for the soil map unit. Further, an individual parcel may have a particular soil delineation where the average percentage of the slope is much higher than the average percentage slope of the whole soil map unit. In that case, an adjustment may be necessary to the soil delineation. Finally, while NRCS reports the dominant land capability class for each soil map unit, each map unit is made up of soil components that have unique land capability classes and land capability sub classes. When the local unit of analysis (parcel or soil delineation) contains a different percentage of the soil components than the soil map unit, then an adjustment may be necessary. For example, a soil delineation may have 50% of the area with a component with poor cropland productivity while the soil map unit may consist of less than 25% of the poor cropland productivity soil component. In this particular case, an adjustment is necessary to the soil delineation. Adjustments to the highest and best use or the crop and range rating may be required. Users of this data should examine the soil map unit data and make adjustments to smaller units of analysis depending on the context and deviations in soil attributes or topography from the soil map unit as a whole.
For more information contact Dr. Matthew Elliott (matthew.elliott@sdstate.edu).
Financial Support for this study was provided by the State of South Dakota through a special appropriation authorized during the 2016 South Dakota legislative session. We gratefully acknowledge that this project was also 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. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.
We would like to extend a special thanks to Dr. Larry Janssen, Lesley Coyle, Wendy Semmler, Russ Hanson, Brenda Forman, and Angela Ehlers for their review of the content and methods used.
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.
Elliott, M.E., L.E. Elliott, T.W. Wang, and D.M. Malo. 2019. “A Change in Highest and Best Use Policy in South Dakota Has a Sizable Impact on Agricultural Land Assessments” Choices. Quarter 4. Available online: http://www.choicesmagazine.org/choices-magazine/submitted-articles/a-change-in-highest-and-best-use-policy-in-south-dakota-has-a-sizable-impact-on-agricultural-land-assessments
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 1.” Soil Science Society of America Journal 22: 160–163.
Malo, D.D., M.G. Holland, and R.J. Malo. 1990. Soil Productivity Ratings and Estimated Yields for Clay County, South Dakota. Plant Science Pamphlet #34. Plant Science Department. South Dakota Agricultural Experiment Station. ABS College. SDSU. Brookings 57007-2141.
Soil Surveys Can Help You…Appraising Farmland. Natural Resources Conservation District, 2000, www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs144p2_061995.pdf.
South Dakota Department of Revenue 2019. Ag Land Pilot Study. Available online: http://sdlegislature.gov/docs/interim/2019/documents/DALA10242019-A.pdf.
U.S. Department of Agriculture. 2008–2017. USDA National Agricultural Statistics Service Cropland Data Layer. 2008–2017. Published Crop-Specific Data Layer [Online]. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service. Available online: https://nassgeodata.gmu.edu/CropScape/.
U.S. Department of Agriculture. Soil Survey Geographic (SSURGO) Database. Washington, DC: U.S. Department of Agriculture, Natural Resources Conservation Service. Available online: https://sdmdataaccess.sc.egov.usda.gov [Accessed 08 01, 2018].
Westin, F.C. and D.D. Malo. 1978, Soils of South Dakota. Bull. 656. SD Agr. Exp. Sta., College of Agric. And Biol. Sci., South Dakota State University, Brookings, 57007.
Wilson, D.C. 1995. “Highest and Best Use Analysis: Appraisal Heuristics versus Economic Theory.” Appraisal Journal 63: 11