Crop yields are an essential factor in appraising farmland values, establishing rental arrangements, determining farm sale and loan values, and making assessments for taxation.
However, because they are dependent on weather, soil and management conditions, crop yields are highly variable. While some soils achieve high productivity, others have texture, depth, and drainage limitations that lower their yields. These diverse variations make it challenging to compare two soils directly.
Soil productivity indexes (PIs) are a way to simplify that process.
By taking into account many factors that determine yield potential, a soil Productivity Index (PI) provides a single number to rate soil quality. This value can also be used to determine the farmland value, the cost to rent acres of farmland, and any other variable linked to crop yield.
Several publicly available soil PIs cover a range of geographies, while others focus on specific crops. Most PI system soil data ratings are maintained by USDA-NRCS, which can be used to map soil types within a field.
Initially developed at Iowa State University in the 1970s, the Iowa Corn Suitability Rating Index, or CSR, began as a way to classify soils in the state. When the methodology was updated in 2014, the new index was called the CSR2.
The CSR2 is Iowa's most widely used soil PI, and it is only available for soils in that state. By combining historical corn yield records and soil characteristics, the CSR2 produces a relative rating of corn productivity in the state.
The CSR2 brings transparency to the Iowa agricultural sector, allowing comparisons among different soils and fair price calculations adjusted by soil productivity.
In this blog post we'll answer the following questions:
What do CSR2 ratings mean?
What is a good CSR2 rating in Iowa?
How is CSR2 calculated?
How are crop yields related to CSR2?
How are farmland values and rental prices related to CSR2?
Soil PIs, including CSR2, are unitless; they are relative and not absolute productivity measures.
The range of CSR2 is 5-100, with 5 being the minimum (if the equation gives a value lower than 5, it is set to 5). Higher numbers indicate more productive soils.
The CSR2 assumes a soil map unit is adequately managed, artificially drained where required, there is no land leveling or terracing and it is not irrigated.
Soil qualities in Iowa follow the guidelines below:
High - CSR2 higher than 83: Very productive soils. The average yield for this group in the state is 193 bu/ac.
Medium - CSR2 65-82: Productive soils with some properties that limit yield to remain below the excellent ones. The average yield for this group in the state is 188 bu/ac.
Low - CSR2 below 65: Productive soils with some properties that limit yield to remain below the excellent ones. The average yield for this group in the state is 176 bu/ac.
The average yields are for all the parcels in the group using FBN yield estimates from 2018 to 2021.
The CSR2 is calculated using an equation with six unique components to assess soil productivity. According to Iowa State University, the CSR2 equation is:
This CSR2 equation, developed using average corn yield data, soil benchmarks, and university trial data points, represents the following variables:
S - the taxonomic subgroup class of the series of the soil map unit (MU). (For example, Typic Argiudolls.)
M - the family particle size class. (For example, fine silty.)
W - relates to the available water holding capacity (AWC) of the series. (For example, 9 inches of water.)
F - the field condition of a particular MU. (For example, slope, flooding, ponding, erosion class, and topsoil thickness.)
D - the soil depth and tolerable rate of soil erosion using the T factor, soil loss tolerance (in tons per acre), provided by SSURGO.
EJ - an expert judgment correction factor. EJ is normally used with parent materials that have very high bulk density and/or are unusually clayey or sandy.
The values referenced above are accessible through the Natural Resources Conservation Service’s (NRCS) online program called “Web Soil Survey.”
Even though yields change over time, relative differences between soils tend to be more stable — in most cases, a high-quality soil in the 1970s is still a high-quality soil in 2023. Thus, indexes are good at capturing those differences and do not need to be updated often.
That said, crop management and genetics do improve over time. This means that the relationship between indexes and yields should be updated periodically to estimate the expected yield for a given soil with current management conditions.
CSR2 captures yield differences between different counties.
County Yield vs. CSR2
SOURCE: Average USDA county yields (2016-2021). Average CSR2 for soils that had corn or soybeans at least one year in the last 5 years.
The figure above shows the association between CSR2 and yield. The relationship is linear and consistent across crops.
The CSR2 for corn is 0.32 while the CSR2 for soybeans is 0.29, which indicates that the prediction is not exact and there is a margin for error in this calculation. Note that the median error for corn is +/- 6.9 bu/ac, and for soybeans is +/- 1.9 bu/ac.
The CSR2 was created to classify soils based on production capacity. Since yield is one of the main variables determining a farm's economic outcomes, CSR2 is also associated with economic variables like cropland values and rents.
The figure below illustrates two examples of that linear relationship.
2022 County Cropland Value and Rent vs. CSR2
SOURCE: FBN estimates for 2022 cropland values and rent. Average CSR2 for soils that had corn or soybeans at least one year in the last 5 years.
Differences in CSR2 translate into differences in farmland value and rental prices. In the case of cropland value, the R2 is 0.49 and the median error is +/-575 $/ac. For cropland rent, the R2 is 0.48 and the median error is +/- 16 $ac.
The FBN® Iowa Farmland Values Report (Winter 2022) provides timely, data-driven insights about local land markets and valuations to empower you in future farmland transactions.
Unlike any other land valuation publication, FBN's latest farmland valuation series delivers deep insights into current market conditions based on multiple data points, including soil quality, land productivity, historical ag yield data and real estate transaction records.
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