United States 

Department of 

Agriculture

Natural 

Resources 

Conservation 

Service

User Guide for the 

National Commodity Crop 

Productivity Index (NCCPI) 

Version 3.0, 2022
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Cover Images

Clockwise from upper left: Corn, soybeans, and wheat fields and

harvesting of cotton. Photos courtesy of USDA-FPAC Business Center.
iii

Contents

Preface................................................................................................. v

User Guide for the National Commodity Crop Productivity 

Index (NCCPI), Version 3.0 ................................................................. 1

Overall Crop Submodel Design............................................................ 2

History and Differences Between the Models ...................................... 5

Fuzzy Logic Subrules........................................................................... 6

Crisp Attributes Subrules....................................................................17

Discussion .........................................................................................20

Appendices .......................................................................................23

Appendix 1.—What Is Different?........................................................24

Appendix 2.—Reports and Data.........................................................26

Appendix 3.—Evaluations ..................................................................31

Appendix 4.—Properties With a Possible Result of “Null Not Rated” . 92

Appendix 5.—Root-Limiting Layers in the NCCPI..............................93

Appendix 6.—Calculations Used to Manipulate Data .........................94

References ......................................................................................111

Glossary ..........................................................................................113

v 

Preface

The National Commodity Crop Productivity Index (NCCPI) model is a 

national soil interpretation that is not intended to replace other crop

production models developed by individual States. At present, NCCPI is 

generated in the National Soil Information System (NASIS) environment 

and is reported in the Soil Data Mart, which provides data to the public 

interfaces Web Soil Survey and Soil Data Access. It presently pertains

to only nonirrigated crops. Eventually, it will be expanded to include

irrigated crops, rangeland, and forestland productivity.

The NCCPI, version 3.0, arrays soils according to their inherent

capacity to produce dryland (nonirrigated) commodity crops. Most of the 

NCCPI criteria relate directly to the ability of soils, landscapes, and 

climates to foster crop productivity. A few criteria relate to factors that 

can limit use of the land (e.g., surface boulders). All criteria used in the 

index affect crop culture and production and are referred to as factors 

affecting inherent productivity. The rating indices are derived using the 

rule-based, fuzzy logic interpretations module of NASIS.

Inherent productivity is considered nearly invariant over time.

Temporary fluctuations in productivity caused by good or bad

management and year-to-year variations in weather are not addressed. 

More permanent changes in soil properties that cause significant

changes in productivity can affect the NCCPI when current NASIS 

information is used. For example, extreme erosion, compaction, land 

leveling, and salinization could cause such changes.

Traditional steps in creating soil survey interpretations are:

1. Study an array of soils having known, documented 

performance.

2. Construct conceptual and mathematical relationships that 

predict soil performance.

3. Use the National Cooperative Soil Survey (NCSS) soil 

database (NASIS) to generate predictions on a wider array of 

soils.

4. Test predictions against the soil survey knowledge base, that 

is, known local relationships that are accumulated from 

personal experience; field observations by soil scientists (soil 

surveyors) and NCSS cooperators and collaborators; and the 

scientific literature.
NCCPI User Guide, Version 3.0

vi 

Why the NCCPI Was Developed

The Conservation Reserve Program (CRP) of USDA provided one 

stimulus for the development of the NCCPI. The inherent capacity of soil 

to produce commodity crops is one factor needed to adjust average 

rental payments. A soil model that is consistent across political 

boundaries and over time is required for many uses. Crop varieties, 

management scenarios, and yields vary from place to place and over 

time, reflecting choices made by farmers and ranchers. These factors 

partially mask inherent soil quality. Except for the extreme

circumstances mentioned above, inherent soil quality or inherent soil 

productivity varies little over time or from place to place for a specific soil 

(map unit component) identified by the NCSS.

Staff of the National Soil Survey Center in Lincoln, Nebraska,

developed the NCCPI to use soil survey information that is accessible in

every county where commodity crops are grown. The system arrays 

soils according to their relative productivity for commodity crops and

avoids the inequities that are possible when yield data alone are used to 

rate soils. Although the immediate focus of the NCCPI is on commodity 

crops, productivity for certain other crops was considered in those areas 

where other crops dominate.

Why the NCCPI Uses the NASIS Database

The NCCPI uses soil data stored in NASIS to rate all soils that are 

used for the production of commodity crops. The NCCPI model data

parameters are items that are (1) uniformly available across the Nation,

(2) calculated and/or produced by the same standards throughout the 

Nation, (3) accessible electronically throughout the Nation, (4) capable

of producing results through a system that uses routinely maintained

data and information, and (5) detailed enough to accurately represent a 

few acres in highly variable soils. The NASIS database meets these 

criteria.

The NASIS database provides information and soil data for nearly all 

the Nation’s farmland. After soils and their properties in a specific field 

have been identified, recorded, stored in a database, and published, the

database can be queried to obtain data about soil properties and 

accessory information about landscape features, soil climate, soil parent 

material, and taxonomic classes.

Each database entity used in the calculation of NCCPI ratings is 

called a map unit component. A map unit component is a phase or type

of a soil series or higher taxonomic unit. Numerical values for each 

component stored in the database are typical values for each map unit 

component of the soil series or higher taxa within a soil survey area. 
NCCPI User Guide, Version 3.0

vii

Thus, the index for a soil series may differ from place to place because

of the geographic variability of the soils in the series. Many map units

consist of more than one component. Not every component name is 

included in map unit names, but components are identified by name in

map unit descriptions and within the database.

Some other attributes important to plant growth are not available in 

the NASIS database. These attributes include such information as day

length and photosynthetically active radiation. The United States was

therefore divided into areas that commonly produce specific commodity 

crops. These areas have climatic factors that remain fairly constant. In 

some cases, criteria from Soil Taxonomy (Soil Survey Staff, 1999) are 

used as effective surrogates for specific climatic variables.

Short-term soil variations caused by differences in land management 

are not yet in the database. Dynamic soil properties, the sampling 

protocol, and data storage are currently under discussion as parts of 

projected adjustments in standard NCSS protocols and in the structure

of the NASIS database. Results from soil survey data are applicable to

the current norms for each soil component of each map unit within the 

NCCPI.

As the NCCPI continues in development and is expanded to include 

other land uses, the expert knowledge of soil scientists who collected 

the data and populated the database will be used to calibrate the

NCCPI indices across the Nation. As known relative productivities and 

geographic relationships of soils and crop growth become better 

understood, new information will be added and used to refine aspects of 

the model.

Present Applicability

In version 3.0 of the NCCPI, the model arrays soils according to their 

productivity only for nonirrigated crops. The focus is on commodity 

crops in the United States, except in areas where commodity crops are 

not important. The best soils for the growth of commodity crops 

generally are the best soils for the growth of other crops. 

The NCCPI model focuses on a relative productivity index or ranking

over periods of years, not for a single year. Productivity during a single 

year may be dramatically affected by annual weather or changes in 

management practices.

This user guide is intended to provide a general overview of the 

NCCPI and is therefore not a rigorous scientific treatment of soil

productivity. A future series of peer- reviewed publications will provide 

more detailed background information.

1 

User Guide for the National 

Commodity Crop Productivity 

Index (NCCPI), Version 3.0

By M.A. Albers, R.R. Dobos, and M.P. Robotham, United 

States Department of Agriculture, Natural Resources

Conservation Service, Soil and Plant Science Division, 

Washington, DC

The National Commodity Crop Productivity Index is a national 

interpretation in the National Soil Information System (NASIS). It is not 

intended to replace models that are developed and maintained by 

individual States, such as the Iowa Corn Suitability Rating or the Storie

Index. It is for nonirrigated commodity crops only. The interpretation 

uses natural relationships of soil, landscape, and climate factors to

model the response of commodity crops. The NASIS interpretations 

generator uses a rule-based fuzzy logic system approach to modeling. 

Each soil, climate, or landscape characteristic is given a rating (score) 

by comparing its value to an empirical optimum value (see appendix 3).

These scores are manipulated in various ways to produce the index. 

The model only uses data available in the NASIS data structure. The 

following discussion will identify attribute data used and illuminate 

relationships among the soil, landscape, and climatic properties and the

productivity index for each commodity crop category.
NCCPI User Guide, Version 3.0

2 

Figure 1.—The main model of NCCPI, version 3.0.

The structure of the model consists of four main submodels (fig. 1), 

each of which represents the response of a suite of crops to soil,

landscape, and climatic conditions. The “OR” operator indicates that the 

highest of the ratings calculated by four main submodels is reported as 

the NCCPI for the map unit component. This system has the flexibility to 

add more crop submodels if needed. The four current submodels 

(categories) are Corn, Soybeans, Small Grains, and Cotton. These 

categories represent four major divisions of commodity crops, in terms

of climatic, landscape, and soil adaptation. Each of the four submodels 

will be examined in this publication. While the overall look of the four 

submodels is similar, the inner workings are somewhat different.

A fifth model has been added to identify Impacted Soil. Impacted soils 

are soils affected by surface mining and smelting activities, rendering

them unable to support the production of commodity crops. This model 

will ensure impacted soils do not receive a rating for the NCCPI.

Overall Crop Submodel Design

Figures 2 through 5 illustrate the different crop submodels. The 

overall design of each crop submodel is very similar. Five of the 

subrules in these submodels have reasonable fuzzy logic relationships, 

whereas the remaining subrules use crisp relationships. Crisp
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3 

relationships are used because the data available for specific 

parameters, such as those for erosion, are not of a continuous nature 

but rather are class data. This information is not of sufficient quality for 

the development of a fuzzy relationship, since the quantitative impact of 

an erosion class on crop productivity is generally unknown. The crisp 

factors are added to the score (positive attributes) or subtracted from 

1.0 and multiplied by the score (negative attributes), depending upon 

whether they enhance or detract from soil productivity.

In the calculation of the crop index, ratings from the chemical, water, 

physical, climate, and landscape subrules are multiplied together in a 

manner similar to that of the Storie Index model used in California 

(Storie, 1937, 1978). The Interpretable Component Subrule occurs in all 

four submodels. It is the method used to ensure that soil components 

that should not be rated are not rated. Without affecting the ratings of 

arable soils, it causes miscellaneous areas (nonsoil components) to be 

classed as “not rated.” Similarly, the Drainage Class Not Subaqueous 

Subrule ensures that subaqueous soils are not given ratings for 

subaerial soils and vice versa.

Figure 2.—The Corn Submodel. Many of the subrules in this 

submodel retain legacy naming conventions from the NCCPI, 

Version 2.0, Corn and Soybeans Submodel. 
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Figure 3.—The Soybeans Submodel. It is most similar to the Corn

Submodel.

Figure 4.—The Small Grains Submodel. It lacks a Crisp Positive

Attributes Subrule. 
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5 

Figure 5.—The Cotton Submodel.

History and Differences Between the Models

Initially, three crop models were used for the NCCPI to represent the 

variation of nonirrigated crops grown across the continental US: Corn

and Soybeans, Small Grains, and Cotton. There has been extensive 

work to develop land productivity ratings using corn and soybeans in the 

midwestern States. Small grains provide an analogous crop for cooler, 

drier climates and shorter growing seasons. Cotton is a unique crop

requiring a very warm climate (by U.S. standards), a climate significantly 

warmer than that required for the production of corn and soybeans and

much warmer than that required for the production of small grains.

Feedback from northern tier States indicated that the three-model 

NCCPI was disproportionately downgrading their soils. While corn

production followed the model ratings fairly well, soybeans performed 

better than predicted because the longer daylight hours partially

compensated for the shorter growing season. The fourth crop model, 

new in version 3.0, separates out soybeans to address this variation.

Rather than discussing each crop model independently, this guide

presents the models by their constituent subrules. Each subrule section

discusses the different properties used by the rules depending on the

importance of the property for crop production.
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6 

Fuzzy Logic Subrules

Soil Chemical Properties Subrule

Figures 6 through 9 display the Soil Chemical Properties Subrules for 

each submodel. The Corn, Soybeans, and Small Grains Submodels all

quantify the effects of pH, CEC (cation-exchange capacity), organic 

matter, and adverse chemical properties in the root zone (i.e., from the 

soil surface to a depth of 150 cm or to a restrictive layer). The CEC 

influence is based on the total exchange capacity found in a 1-cmsquare area extending from the surface to a depth of 150 cm or to a 

root- restricting layer. Root-limiting layers are identified by a physical 

restriction, electrical conductivity greater than 8 decisiemens per meter, 

pH less than 3.5, or high bulk densities for the texture class of the soil. 

Horizon CEC, rock fragment content, and bulk density are used in the 

calculation. A small input of CEC is allowed for soils that have a water 

table.

Since cotton is often grown in low CEC soils, management 

techniques largely mask responses related to clay activity. The effects 

of pH are considered for depths of 0 to 20 cm and 20 to 150 cm. The 

most limiting of the two scores is used for the calculation. The pH 

adaptation for cotton is broader than that for the submodel for corn and 

soybeans or for small grains and considers mean annual precipitation 

when evaluating the influence of pH. Organic matter also is considered

for the depth ranges of 0 to 20 cm and 20 to 150 cm; the average 

condition is then used in the calculation. The Cotton Submodel does not 

include organic matter. Adverse chemical properties, namely, sodium 

adsorption ratio (SAR), electrical conductivity (EC), and gypsum 

content, are considered in a third-level subrule, and the most limiting of 

the three is used in the chemical properties calculation. The salinity

tolerance of cotton is broader than that of corn and soybeans.
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Figure 6.—The Corn Submodel – Soil Chemical Properties Subrule

(Corn and Soybeans).

Figure 7.—The Soybeans Submodel – Soil Chemical Properties

Subrule (Soybeans). 
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Figure 8.—The Small Grains Submodel – Soil Chemical Properties

Subrule (Small Grains).

Figure 9.—The Cotton Submodel – Soil Chemical Properties Subrule

(Cotton).

Water Subrule

The Water Subrule, depicted in figures 10 through 13 for all four 

submodels, quantifies the capability of the soil, climate, and landscape 

to supply water for crop growth. Four sources of water are considered 

for the Corn, Soybeans, and Small Grains Submodels: available waterholding capacity in the root zone (RZ AWC), precipitation during the 

growing season (Precipitation Recharge), the effects of subirrigation 

(Water Table Recharge), and surface contributions (Water-Gathering 

Surface). The Cotton Submodel does not consider surface contributions. 

This calculation uses the sum of a proportion of each of the four factors 

for the Corn and Soybeans Submodels and a sum of a proportion of

three factors for the Small Grains and Cotton Submodels. 
NCCPI User Guide, Version 3.0

9 

RZ AWC is the amount of plant-available water a soil can store

between the surface and a depth of 200 cm. Water Table Recharge 

quantifies effects of a saturated zone deep within the root zone where

roots can access water during summer or during other parts of the 

growing season. Water-Gathering Surface accounts for additions of 

water (run-on) resulting from the position of the soil on the landform.

Precipitation Recharge is intended to represent the effect of rainfall 

during the growing season. The amount of rainfall indicated for a 

component in the database is decreased because of the effects of

temperature, and a proportion is assigned for crop use. For example, 

consider two soils, both receiving 1,000 mm of rainfall per year. One soil 

is hot thermic, and the other is cool mesic. The rainfall on the cooler site 

is more readily available for crop growth, and so the cool mesic 

component receives a higher “precipitation recharge” score. This 

adjustment lessens the negative effect on crop yields that is observed in 

soils having low RZ AWC. For soils that receive timely rainfall, AWC is 

less important. Therefore, if the soil component occurs in an area of 

high rainfall, the “precipitation recharge” becomes important if the 

temperature is not too high. Conceptually, “precipitation recharge” is an

oversimplified Thornthwaite-style calculation (Thornthwaite, 1948) using 

available NASIS data fields.

In the Small Grains Submodel, the various subrule components are

tailored to the response of small grains to water. This calculation uses 

the highest score of either the water table or precipitation recharge,

unlike the Corn and Soybeans Submodels, which combine both water 

table and recharge influences. This score is added to the RZ AWC and

Water-Gathering Surface scores. About 25 percent of the water available 

for crop growth is attributed to water table or precipitation recharge, and 

75 percent is attributed to RZ AWC.
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Figure 10.—The Corn Submodel – The Water Subrule (Corn and

Soybeans).

Figure 11.—The Water Subrule (Soybeans).

Figure 12.—The Water Subrule (Small Grains). 
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Figure 13.—The Water Subrule (Cotton).

Soil Physical Properties Subrule

Figures 14 through 17 show the Soil Physical Properties Subrule for

each of the four crop submodels, which quantify the effects of saturated 

hydraulic conductivity (Ksat), linear extensibility percent (LEP), content of 

rock fragments, and soil depth on soil productivity. The actual entity 

used in the Ksat calculation is the logarithm of saturated hydraulic

conductivity multiplied by LEP, which is used to account for the effects of

cracks on aeration and water movement in highly expansive soils. The

effect of this product is estimated for soil materials between depths of 0 

to 50 cm, 50 to 100 cm, and 100 to 150 cm that are not root-limiting. 

The populated LEP value is used for the calculation of the LEP Subrule. 

The score for the content of rock fragments is based on the weighted

average of the volumetric estimates for each soil horizon of the map unit 

component in the root zone, with greater emphasis on the 0 to 20 cm 

depth. The Soil Depth Subrule examines the thickness of soil material 

over a root-restricting horizon (see appendix 2 for the data elements 

used in the subrule and appendix 5 for a discussion of other root-limiting 

layers).
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Figure 14.—The Soil Physical Properties Subrule (Corn and

Soybeans).

Figure 15.—The Soil Physical Properties Subrule (Soybeans).
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Figure 16.—The Soil Physical Properties Subrule (Small Grains).

Figure 17.—The Soil Physical Properties Subrule (Cotton).

Soil Climate Subrule

In the Soil Climate Subrule, depicted in figures 18 through 21, two 

major aspects of soil climate are considered: frost-free days and

precipitation. Data for both of these are extracted from the Component 

Table of NASIS. Multiplying the scores together determines the soil

climate rating. The Soil Climate Subrule for the Cotton Submodel,

shown in figure 21, also considers the effect of mean annual air

temperature on cotton yields. The index is calculated by multiplying the 

precipitation score by the score for the mean annual air temperature or 

for frost-free days, whichever is lower.
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Figure 18.—The Soil Climate Subrule (Corn and Soybeans).

Figure 19.—The Soil Climate Subrule (Soybeans).

Figure 20.—The Soil Climate Subrule (Small Grains).

Figure 21.—The Soil Climate Subrule (Cotton).
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Soil Landscape Subrule

The Soil Landscape Subrule calculates an index for contributions of 

the soil landscape to crop productivity. The Corn, Soybeans, and Cotton

Submodels (figs. 22, 23, and 25) all use slope gradient, depth to a water 

table during the growing season (Growing Season Wetness for Corn 

and Soybeans Submodels), and the occurrence of ponding and flooding 

during the growing season. Determining the actual depth to a water 

table is difficult when soils are drained. To adjust for artificial drainage, 

the Component Local Phase is queried for the word “drained.” If a 

component is listed as drained, the water table is assumed to be at a 

depth of 160 cm. In addition, Land Capability Class and Subclass are 

used as indicators of the presence or absence of artificial drainage.

Another difficulty is determining when the growing season actually 

occurs. This time period is based on the taxonomic temperature regime,

populated in the Component Table, and is the same as the growing

season used by the hydric soil calculation.

The Soil Landscape Subrule for the Small Grains Submodel is shown 

in figure 24. The Effective Slope Subrule quantifies the effect of slope

gradient. The Excess Water Subrule calculates the combined effects of

a water table, ponding, and flooding in the context of a crop that is often 

seeded in the fall and begins to grow very early in the growing season. 

This result is multiplied by the results of the Fragments on Surface and 

Effective Slope Subrules to obtain the score for landscape factors.

Figure 22.—The Soil Landscape Subrule (Corn and Soybeans). 
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Figure 23.—The Soil Landscape Subrule (Soybeans).

Figure 24.—The Soil Landscape Subrule (Small Grains). 
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Figure 25.—The Soil Landscape Subrule (Cotton).

Crisp Attributes Subrules

Crisp Positive Attributes

The Crisp Positive Attributes Subrule quantifies soil attributes that 

foster high productivity but cannot, at present, be reasonably 

represented by fuzzy set methods. Currently, the Corn and Soybeans

Submodels (figs. 26 and 27) recognize the benefits of loess as a parent 

material, which is observed for at least Wisconsinan-age loess material. 

This relationship may or may not hold true for older loess deposits. 

There are no crisp positive soil attributes in the Small Grains Submodel. 

The Crisp Positive Attributes Subrule for the Cotton Submodel (fig. 28) 

quantifies the beneficial effect of the landscape position where rare

flooding is expected. The result of the crisp positive attributes score is 

added to the fuzzy score.

Figure 26.—The Crisp Positive Attributes Subrule (Corn and

Soybeans).
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Figure 27.—The Crisp Positive Attributes Subrule (Soybeans).

Figure 28.—The Crisp Positive Attributes Subrule (Cotton).

Crisp Negative Attributes

The Crisp Negative Attributes Subrule quantifies the effect of 

detrimental soil conditions for which obtaining a fuzzy set is difficult. This 

difficulty may result from the lack of consistent NASIS data or from the 

need for more time to analyze existing data. Surface rock fragments, 

erosion class of the component or map unit, rock outcrop present in the

map unit, surface degradation, and lack of a surface outlet are the 

current crisp negative attributes considered for the Corn, Soybeans, and 

Cotton Submodels (figs. 29, 30 and 32).

The Crisp Negative Attributes Subrule for the Small Grains Submodel 

is shown in figure 31. The most intriguing feature of this subrule is the 

Not Xeric Climate Subrule. A Mediterranean climate (wet winters and 

dry summers) has been shown to be highly conducive to winter and

spring wheat growth. When a soil component does not have a xeric soil 

moisture regime, its score is lowered by a given amount.

The Degraded Surface Component Subrule provides a stored rating 

for such characteristics as “channeling,” “gullies,” “impacted,” or 

“undrained,” as indicated in the map unit name. Erosion and rock 

outcrop are also known to negatively impact crop yields, but the 

relationships are not well quantified. The rating for each of these factors 

is summed to obtain the crisp negative attributes score. The crisp 

negative attributes score is subtracted from 1.0, and the difference is

multiplied by the fuzzy soil properties score.

Negative attributes are summed, and the summed total is subtracted

from 1.0 (the “NOT” square at the bottom of figures 2 through 5 

indicates subtraction). The difference is used to proportionately

decrease the sum of the fuzzy and crisp positive features.
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Figure 29.—The Crisp Negative Attributes Subrule (Corn and

Soybeans).

Figure 30.—The Crisp Negative Attributes Subrule (Soybeans). 
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Figure 31.—The Crisp Negative Attributes Subrule (Small Grains).

Figure 32.—The Crisp Negative Attributes Subrule (Cotton).

Discussion

While the submodels for the types of crops are generally similar, 

differences in the physiology of the crops and the geography of the soils 

in which the crops are grown cause some variations in the details of the 

submodels. For example, it was initially thought that only the climatic

parameters of the model would be substantially different among the four 
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21

submodels. The climatic adaptation of the small grains is quite broad in

comparison with that of cotton and that of corn and soybeans. The

diverse levels of salt tolerance, pH, and other properties became 

evident upon further study.

Version 3.0 of the NCCPI further explores the interaction of soil 

properties and climate and their effects on crop physiology and 

productivity. An examination of cotton production, for example, shows 

that the effect of linear extensibility percentage (LEP), or shrinking and 

swelling, is dependent upon the climate. In areas of low rainfall (less 

than 700 mm), the optimal LEP is about 5 percent; in areas where 

rainfall is more than 1000 mm, the optimal LEP is about 2.5 percent. A 

distinct maximum pH of 7.5 is typical in areas where rainfall is less than

700 mm, but pH seems to have little effect on cotton production in areas 

where rainfall exceeds 1000 mm; in these areas, pH ranges from 4.5 to

8.0. Cation-exchange capacity also shows a geographic tendency in its

impact on cotton production.

The impact of loess as a parent material is an interplay of geography 

and crop physiology. In areas where small grains are dominant, even 

though many of the soils formed in loess, the growing season is too 

short for crop roots to fully exploit the favorable characteristics of the 

material. Where corn is grown, the benefits of loess can be exploited by 

crop roots since the growing season is longer. Where cotton is grown, 

the soils generally did not form in Wisconsinan-age loess, so no side-byside comparison was possible. Also, more strongly weathered (preWisconsinan) loess has developed characteristics that are less 

favorable for plant growth.

The curves shown in appendix 3 are the result of several lines of

thought. Many of them are renderings of spline curves fit through 

scatterplots of the various soil, landscape, and climate factors against 

the populated yields for each crop in the NASIS database. When 

available, these curves are influenced by data from the soil productivity

literature. Since performance data for some soil properties are not

available in a geographic quantity that fits the scope of this model, the 

“boundary line model” is used as yet a third interpretation applied to the

bivariate plots in some cases. This method seemed particularly 

applicable since the crux of the model is biological rather than statistical 

(Milne et al., 2006).

23

Appendices
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Appendix 1.—What Is Different?

Version 3.0 of the NCCPI differs from version 2.0 in several respects.

Soybeans Model

1. A separate model was developed for soybeans. The original

corn and soybeans model was based on data from the midlatitude States of Iowa and Illinois where productivity does 

not differ greatly between the two crops.

Corn Model

1. Based on comments from users in several States, the

deleterious effects of moderate erosion on deep and very

deep soils have been decreased.

2. Based on comments from users, some poorly drained, 

flood-plain soils were rating too high. This was traced to the 

mechanisms used to tell whether or not it is likely a soil 

has been drained. One of the clues was whether or not the 

soil occurred in a glaciated MLRA (major land resource 

area). However, because some soil map units transcend

MLRA boundaries, an additional discriminator is now

employed, namely, location on flood plains, depressions, or

drainageways, which are generally thought to be 

undrainable.

3. In the Crisp Negative Attributes Subrule, the stoniness 

hedge was changed from 1 to 0.8 and the rock outcrop 

hedge was changed from 0.5 to 0.3. The changes will

lessen the deleterious impacts of these two features. Also

reduced is the no surface drain hedge, from 1 to 0.4.

4. The frost-free days evaluation was changed to decrease

the negative impact of growing seasons exceeding 195 

days on the corn index.

5. The evaluation in the Soil Depth Subrule was changed

because soils that are less than very deep were being 

rated too low.

6. The Bulk Density Subrule was removed from the Physical 

Properties Subrule because it never worked as expected.

Small Grains Model

1. The hedge on the Soil Chemical Properties Subrule was 

changed from “multiply(1)” to “somewhat.” The somewhat 

hedge applies a square root to the fuzzy number. This has

the effect of contracting the range of the fuzzy numbers. 

The effect on values close to 1 is slight, but values much 

smaller than 1, such as 0.01, are noticeably higher.
NCCPI User Guide, Version 3.0

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2. The pH evaluations were adjusted to allow pH around 5 

and pH over 7 to return higher fuzzy numbers. They now 

better reflect the physiology of the crop.

3. The dense layer criteria were removed from the Soil Depth 

Subrule. Some components had very high bulk density

populated at the surface such that these soils were being 

considered as having a limiting layer at the surface.

4. The Bulk Density Subrule was removed.

5. The membership function of the Frost-Free Days Subrule

was adjusted to lessen the impact of shorter growing 

seasons.

6. The stones on the surface curve was adjusted to lessen the

impact of stones on the surface.

7. Syntax was added to the water table property scripts to try

to distinguish drained versus undrained components.

8. The subrules for crisp negative attributes were rearranged

because erosion effects were being considered twice.

Cotton Model

1. The water table membership function was adjusted to

decrease the rating for soils that have near-surface 

wetness more than 2 months during the cotton growing 

season.

2. The Bulk Density Subrule was removed from the Soil

Physical Properties Rule.
NCCPI User Guide, Version 3.0

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Appendix 2.—Reports and Data

Measured data in NASIS are populated with high, low, and 

representative values. The NCCPI uses the representative value (rv). 

Character data typically are populated as one value only. A 

representative value is indicated if more than one character value 

describes an attribute.

The data elements currently used in the NCCPI are listed below.

Specific elements may vary depending on the crop model. They are

listed here in aggregate.

Impacted Soil Subrule

Local phase Component, Local Phase

Not Subaqueous Subrule

Drainage class Component, Component Name, Drainage Class

Soil Chemical Properties Subrule

pH Component, Component Name, Component 

Restriction, Horizon, Horizon Bottom Depth rv, Horizon 

Texture, Horizon Top Depth rv, In Lieu of Texture, 

MAP rv, pH .01M CaCl2 low-rv-high, pH 1:1 water lowrv-high, Restriction Kind, Restriction Top Depth low-rvhigh, Taxonomic Order, Texture, Texture Group

CEC Bulk Density 0.33 bar H2O rv, CEC-7 rv, Clay % rv,

Component, Component Restriction, ECEC rv, 

Electrical Conductivity rv, Fragment Volume rv, 

Horizon, Horizon Bottom Depth low-rv- high, Horizon 

Designation, Horizon Fragments, Horizon Top Depth

low-rv-high, MAP rv, Master Horizon Designation,

Month, Organic Matter % rv, pH 1:1 water rv,

Restriction Top Depth low- rv-high, Sand % rv, Silt % 

rv, Soil Moisture, Taxonomic Order, Taxonomic 

Subgroup, Texture Group

OM Component, Component Name, Component 

Restriction, Horizon, Horizon Bottom Depth rv, Horizon 

Texture, Horizon Top Depth rv, In Lieu of Texture,

Organic Matter % low-rv-high, Restriction Kind,

Restriction Top Depth low-rv-high, Taxonomic Order, 

Texture, Texture Group

Adverse Chemistry – SAR, EC, gypsum 

Component, Electrical Conductivity low-rv-high, 
NCCPI User Guide, Version 3.0

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Gypsum % low- rv-high, Horizon, Horizon Bottom

Depth rv, Horizon Top Depth rv, Sodium Adsorption 

Ratio rv

Water Subrule

RZ AWC AWC rv, Component, Horizon, Horizon Bottom Depth 

rv, Horizon Top Depth rv, MAP rv

Water Table Recharge 

Component, Local Phase, Month, SIR Phase, Soil

Moisture, Soil Moisture Status, Soil Moisture Top 

Depth low-rv-high

Mean Annual Air Temperature (MAAT) – Corn and Soybeans Submodels

only 

Component, MAAT low-rv-high, Taxonomic Temp

Regime

Precipitation Recharge

Bulk Density 0.1 bar H2O rv, Bulk Density 0.33 bar 

H2O rv, Component, Component Restriction,

Fragment Volume low-rv- high, Horizon, Horizon 

Bottom Depth rv, Horizon Designation, Horizon

Fragments, Horizon Texture, Horizon Top Depth rv, In 

Lieu of Texture, Ksat low-rv-high, LEP low-rv-high, 

MAAT low- rv-high, MAP low-rv-high, Organic Matter

% rv, Restriction Top Depth low-rv-high, Taxonomic 

Order, Taxonomic Subgroup, Taxonomic Suborder,

Taxonomic Temp Regime, Texture, Texture Group

Water-Gathering Surface – Corn, Soybeans, and Small Grains

Submodels 

Component, Geomorphic Description, Slope Shape, 

Slope Shape Across, Slope Shape Up/Down,

Taxonomic Moisture Class, Taxonomic Order

Physical Properties Subrule

Ksat Bulk Density 0.33 bar H2O rv, Clay % rv, Component, 

Component Restriction, Electrical Conductivity rv, 

Horizon, Horizon Bottom Depth rv, Horizon 

Designation, Horizon Texture, Horizon Top Depth lowrv-high, In Lieu of Texture,Ksat low-rv- high, LEP low-rvhigh, MAP rv, Master Horizon Designation, Organic 

Matter % rv, pH 1:1 water rv, Restriction Top Depth 

low- rv-high, Sand % rv, Silt % rv, Taxonomic Order, 

Texture, Texture Group
NCCPI User Guide, Version 3.0

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LEP Component, Component Name, Component 

Restriction, Horizon, Horizon Bottom Depth rv, Horizon 

Texture, Horizon Top Depth rv, In Lieu of Texture, LEP 

low-rv-high, MAP rv, Restriction Kind, Restriction Top 

Depth low-rv-high, Taxonomic Order, Texture, Texture 

Group

Rock Fragments

Component, Component Restriction, Fragment 

Volume rv, Horizon, Horizon Bottom Depth low-rv-high, 

Horizon Designation, Horizon Fragments, Horizon Top 

Depth rv, Master Horizon Designation, Restriction Top 

Depth low-rv-high, Taxonomic Order, Taxonomic 

Subgroup

Soil Depth Component, Component Restriction, Electrical 

Conductivity rv, Horizon, Horizon Bottom Depth low-rvhigh, Horizon Texture, Horizon Top Depth low-rv-high, 

In Lieu of Texture, Master Horizon Designation, pH 1:1

water rv, Restriction Kind, Restriction Top Depth lowrv-high, Taxonomic Order, Taxonomic Subgroup, 

Texture, Texture Group

Soil Climate Subrule

Frost-Free Days

Component, Frost Free Days low-rv-high

Daylength – Soybeans Submodel only

Area Symbol, Component

Precipitation Component, MAP low-rv-high, MAAT rv

Mean Annual Air Temperature (MAAT) – Cotton Submodel only

Component, MAAT low-rv-high, Taxonomic Temp

Regime

Soil Landscape Subrule

Growing Season Wetness – Corn and Soybeans Submodels only

Area Symbol (MLRA), Component, Drainage Class, 

Frost Free Days low-rv-high, Geomorphic Description,

Geomorphic Feature, Geomorphic Feature Name, 

Geomorphic Feature Type, Legend Area Overlap, 

Local Phase, Map unit Area Overlap, Month, NonIrrigated LCC, Non-Irrigated LCC Subclass, Parent 

Material, Parent Material Group, Parent Material Kind, 

SIR Phase, Soil Moisture, Soil Moisture Status, Soil 

Moisture Top Depth rv, Taxonomic Family Temp 
NCCPI User Guide, Version 3.0

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Class, Taxonomic Temp Regime

Effective Slope Component, Slope % low-rv-high, Taxonomic

Subgroup, Taxonomic Suborder

Ponding – Not considered for Small Grains Submodel

Area Symbol (SSA), Component, Drainage Class, 

Local Phase, Map unit Name, Month, Non-Irrigated 

LCC, Non-Irrigated LCC Subclass, Ponding Duration, 

Ponding Frequency, SIR Phase, Taxonomic Temp 

Regime

Flooding – Not considered for Small Grains Submodel

Area Symbol (SSA), Component, Component Name, 

Flooding Duration, Flooding Frequency, Map unit

Name, Frost Free Days rv, Month, Taxonomic Temp 

Regime

Fragments on Surface – Small Grains Submodel only

% Cover Surface Fragments low-rv-high, Component,

Fragment Volume rv, Fragment Size rv, Horizon, 

Horizon Fragments, Horizon Top Depth rv, Surface 

Fragment Size, Surface Fragments

Excess Water – Small Grains Submodel only

Area Symbol (MLRA), Component, Drainage Class, 

Flooding Duration, Flooding Frequency, Frost Free 

Days low-rv-high, Geomorphic Description, 

Geomorphic Feature, Geomorphic Feature Name,

Geomorphic Feature Type, Legend Area Overlap, 

Local Phase, Map unit Area Overlap, Frost Free Days 

rv, Month, Non-Irrigated LCC, Non-Irrigated LCC 

Subclass, Ponding Duration, Ponding Frequency, SIR 

Phase, Soil Moisture, Soil Moisture Status, Soil 

Moisture Top Depth rv, Taxonomic Family Temp 

Class, Taxonomic Temp Regime

Water Table – Cotton Submodel only

Component, Local Phase, Month, SIR Phase, Soil

Moisture, Soil Moisture Status, Soil Moisture Top 

Depth rv, Taxonomic Family Temp Class, Taxonomic 

Temp Regime

Crisp Positive Attributes Subrule

Soil Fabric – Corn and Soybeans Submodels only

Component, Parent Material Kind
NCCPI User Guide, Version 3.0

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Water From Rare Flood – Cotton Submodel only

Component, Drainage Class, Flooding Frequency,

Month

Crisp Negative Attributes Subrule

Fragments on Surface – Not used for Small Grains Submodel

% Cover Surface Fragments low-rv-high, Component,

Surface Fragment Size, Surface Fragments

Erosion Class – Not Used for Soybeans Submodel

Component, Component Restriction, Erosion Class, 

Local Phase, Map unit Name, Restriction Kind,

Restriction Top Depth low-rv-high, SIR Phase

Not Xeric – Small Grains Submodel only

Area Symbol (MLRA), Component, Taxonomic 

Subgroup, Taxonomic Suborder

Rock Outcrop Map unit Name

Degraded Surface

Component, Component Name, Local Phase, Map

unit Name

No Surface Outlet – Not used for Small Grains Submodel

Component, Drainage Class, Map unit Name, NonIrrigated LCC, Non-Irrigated LCC Subclass
NCCPI User Guide, Version 3.0

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Appendix 3.—Evaluations

This appendix shows the evaluations used in the calculation of the 

NCCPI. The evaluations indicate the range of property data used to 

produce the fuzzy numbers. The functions rarely go to zero. Using the 

lowest value of a variable was found to be a convenient way to weight

the variables. Properties that are more closely correlated with yields are 

given more impact than factors that are not so closely correlated.

This appendix does not contain an all-inclusive list, but it does display 

the evaluations for properties where a reasonable fuzzy relationship has

been established. The graphs represent the best fit curves and are

based on the observed data. Relating the response of yields to one 

independent variable is nearly impossible because of covariance and 

interaction. The fuzzy relationships occasionally exhibit a function that is

being influenced by variables other than what is being modeled. The

resulting curves may look unexpected, but they fit well in the empirical 

model.

Soil Chemical Properties Subrules

1. RZ pH Evaluation Curves

a. Corn Submodel

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 4.3 5.2 5.5 6 6.18 7 7.5 8.2

Y Value 0.7 0.81 0.93 0.99 1 0.9 0.77 0.7

Units are pH units. 
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(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 4.3 5 5.2 6 6.6 7 7.6 8 8.3

Y Value 0.63 0.8 0.88 1 1 0.9 0.77 0.7 0.7

Units are pH units.

b. Soybeans Submodel

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 3.8 4.3 5.2 5.5 6 6.5 7 7.5 8.2 9

Y Value 0.5 0.55 0.7 0.85 0.97 1 1 0.99 0.92 0.7

Units are pH units. 

(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 4.3 5 5.2 6 6.5 7 7.6 8 8.3 9

Y Value 0.58 0.77 0.85 0.97 1 1 0.97 0.93 0.8 0.7

Units are pH units. 
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c. Small Grains Submodel

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 4 4.5 5 5.5 6 6.6 7.2 7.5 8.2 8.5 8.8 9.3

Y Value 0.6 0.9 0.97 0.98 1 1 0.9 0.8 0.6 0.5 0.4 0.2

Units are pH units. 

(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 4 4.5 5 5.8 6 6.7 7 7.7 8 8.3 9 9.3

Y Value 0.6 0.95 1 1 1 1 0.91 0.7 0.6 0.5 0.25 0.2

Units are pH units. 

d. Cotton Submodel – pH evaluation is dependent on mean annual

precipitation (MAP).

(i) MAP <700 mm

(1) 0-20 cm depth

Arbitrary curve, control points:

X Value 5 6 6.5 7 7.6 8.1 9 9.5 10

Y Value 0.03 0.7 0.8 0.92 1 0.85 0.75 0.6 0.05

Units are pH units. 
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(2) 20-150 cm depth

Arbitrary curve, control points:

X Value 4.5 5 6.5 7 7.7 8 8.3 8.5 9 9.5

Y Value 0.05 0.45 0.7 0.81 1 0.85 0.73 0.7 0.65 0.2

Units are pH units. 

(ii) MAP 700-1000 mm

(1) 0-20 cm depth

Arbitrary curve, control points:

X Value 4.5 5 6 6.5 7 7.7 8.1 9 9.5 10

Y Value 0.03 0.7 0.8 0.92 1 0.85 0.75 0.6 0.05 0.05

Units are pH units.

(2) 20-150 cm depth

Arbitrary curve, control points:

X Value 4.5 5 6.5 7 7.7 7.9 8 8.3 8.5 9 9.5

Y Value 0.63 0.65 0.78 0.83 0.98 1 0.98 0.82 0.72 0.4 0.2

Units are pH units. 
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(iii) MAP 1000-1500 mm

(1) 0-20 cm depth

Arbitrary curve, control points:

X Value 3.5 4 4.5 5.5 7.7 8.2 9 9.5 10

Y Value 0.7 1 1 1 1 0.95 0.5 0.22 0.05

Units are pH units. 

(2) 20-150 cm depth

Arbitrary curve, control points:

X Value 3.55 4 4.5 5 6.5 7 7.9 8 8.3 8.5 9 9.5

Y Value 0.6 0.7 0.95 1 1 1 1 1 0.9 0.72 0.4 0.2

Units are pH units.

(iv) MAP >1500 mm

(1) 0-20 cm depth

Arbitrary curve, control points:

X Value 3.5 4 4.5 5.8 3 8.2 9 9.5 10

Y Value 0.5 0.8 0.96 1 0.2 1 0.7 0.22 0.05

Units are pH units. 
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(2) 20-150 cm depth

Arbitrary curve, control points:

X Value 3 4.5 5.1 7 7.3 8 8.5 9

Y Value 0.6 0.9 1 1 1 1 0.8 0.4

Units are pH units.

2. RZ CEC Evaluation Curves

a. All four models include a slight contribution to CEC for soils

having a water table:

Arbitrary curve, control points:

X Value 0 1 2 3 7

Y Value 0 1 0.75 0.5 0

Units are a count of the months a water table is present in the soil. 
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b. Corn Submodel

Arbitrary curve, control points:

X Value 0 10 30 40

Y Value 0.7 0.9 0.97 1

Units are meq/cm2. Restriction based on Component restrictive

layer, pH<=3.5, EC>=8.01 or Bulk Density too high. 

c. Soybeans Submodel

Arbitrary curve, control points:

X Value 0 10 20 30

Y Value 0.6 0.8 0.95 1

Units are meq/cm2. Restriction based on Component restrictive

layer, pH<=3.5, EC>=8.01 or Bulk Density too high.

d. Small Grains Submodel

Arbitrary curve, control points:

X Value 0 5 10 13 20

Y Value 0.7 0.77 0.85 0.89 1

Units are meq/cm2. Restriction based on Component restrictive 

layer, pH<=3.5, EC>=16.01 or Bulk Density too high. 
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e. Cotton Submodel – Four arbitrary curves based on mean

annual precipitation (MAP).

(i) MAP <700 mm

Arbitrary curve, control points:

X Value 0 10 20 30 40 50

Y Value 0.6 0.72 0.83 0.9 0.98 1

Units are meq/cm2. Restriction based on Component restrictive

layer.

(ii) MAP 700-1000 mm

Arbitrary curve, control points:

X Value 0 10 20 35

Y Value 0.5 0.8 0.95 1

Units are meq/cm2. Restriction based on Component restrictive 

layer.
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(iii) MAP 1000-1500 mm

Arbitrary curve, control points:

X Value 0 10 11

Y Value 0.6 0.99 1

Units are meq/cm2. Restriction based on Component restrictive

layer.

(iv) MAP >1500 mm

Arbitrary curve, control points:

X Value 0 2 10 13

Y Value 0.7 0.82 0.95 1

Units are meq/cm2. Restriction based on Component restrictive

layer. 
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3. RZ OM Evaluation Curves – Corn, Soybeans, and Small Grains

Submodels. Cotton Submodel does not consider organic matter.

a. Corn and Small Grains use the same generic evaluations:

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 0 0.3 0.6 1.5

Y Value 0.8 0.87 0.94 1

Units are percent by weight.

(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 0 0.5 2

Y Value 0.8 0.9 1

Units are percent by weight. 
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b. Soybeans Submodel evaluations:

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 0 0.3 0.6 3.5

Y Value 0.7 0.75 0.8 1

Units are percent by weight.

(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 0 0.5 2

Y Value 0.7 0.85 1

Units are percent by weight.

4. Adverse RZ SAR Evaluation Curves

a. Corn Submodel

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 0 4 8 12 16 20

Y Value 1 0.7 0.45 0.25 0.1 0.02

SAR is a unitless ratio.
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(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 0 3 6 10 15 18 23

Y Value 1 0.8 0.6 0.4 0.2 0.1 0.05

SAR is a unitless ratio. 

b. Soybeans Submodel

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 1 2 8 12 16 20

Y Value 1 0.6 0.35 0.2 0.08 0.05

SAR is a unitless ratio.

(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 1 2 6 10 15 18 23

Y Value 1 0.9 0.6 0.44 0.27 0.21 0.1

SAR is a unitless ratio.
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c. Small Grains Submodel

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 0 4 8 15 20 30 60

Y Value 1 0.8 0.7 0.6 0.545 0.41 0.02

SAR is a unitless ratio. 

(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 0 3 6 10 30 40 60

Y Value 1 0.92 0.84 0.75 0.47 0.33 0.05

SAR is a unitless ratio.

d. Cotton Submodel

(i) 0-20 cm depth

Arbitrary curve, control points:

X Value 0 4 8 12 16 20 1.5

Y Value 1 0.7 .45 0.3 0.15 0.02 1

SAR is a unitless ratio. 
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(ii) 20-150 cm depth

Arbitrary curve, control points:

X Value 0 3 6 10 15 18 23 2.5 4

Y Value 1 0.9 0.62 0.45 0.3 0.2 0.05 0.98 0.8

SAR is a unitless ratio. 

5. Adverse RZ EC Evaluation Curves

a. Corn Submodel

(i) 0-10 cm depth (germination) Arbitrary curve, control points:

X Value 2 4 10 7

Y Value 1 0.85 0.05 0.3

EC units are mmhos/cm.

(ii) 0-150 cm depth

Arbitrary curve, control points:

X Value 2 4 8 12 16 20

Y Value 1 0.96 0.8 0.5 0.3 0.2

EC units are mmhos/cm. Restriction based on Component 

restrictive layer, pH<=3.5, EC >=16, or Bulk Density too high. 
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b. Soybeans Submodel

(i) 0-10 cm depth (germination) 

Arbitrary curve, control points:

X Value 2 8 12 16

Y Value 1 0.5 0.3 0.05

EC units are mmhos/cm.

(ii) 0-150 cm depth

Arbitrary curve, control points:

X Value 2 8 12 16 20 24

Y Value 1 0.7 0.4 0.2 0.15 0.1

EC units are mmhos/cm. Restriction based on Component 

restrictive layer, pH<=3.5, EC >=16, or Bulk Density too high.

c. Small Grains Submodel

(i) 0-10 cm depth (germination) 

Arbitrary curve, control points:

X Value 4 6 10 16

Y Value 1 0.93 0.72 0.02

EC units are mmhos/cm. 
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(ii) 0-150 cm depth

Arbitrary curve, control points:

X Value 4 6 10 16

Y Value 1 0.96 0.8 0.05

EC units are mmhos/cm. Restriction based on Component 

restrictive layer, pH<=3.5, EC >=16, or Bulk Density too high.

d. Cotton Submodel

(i) 0-10 cm depth (germination) Arbitrary curve, control points:

X Value 0 1 4 9 12 18

Y Value 1 0.98 0.9 0.5 0.35 0.2

EC units are mmhos/cm. 

(ii) 0-150 cm depth

Arbitrary curve, control points:

X Value 0 1 2 3 6 12 18

Y Value 1 1 0.82 0.7 0.5 0.35 0.2

EC units are mmhos/cm. Restriction based on Component 

restrictive layer, pH<=3.5, EC >=16, or Bulk Density too high.
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6. Adverse RZ Gypsum – The same generic evaluation is used for all

four crop submodels and applied to two depth ranges, 0-20 cm 

and 20-150 cm.

Linear curve, low value = 10, high value = 30.

Units are percent by weight of material less than 20 mm in size.

Water Subrules

7. RZ AWC Sufficiency

a. Corn Submodel

Arbitrary curve, control points:

X Value 0 0.1 0.3 0.5 0.8 1 1.1

Y Value 0.23 0.23 0.42 0.62 0.75 0.94 1

AWC is in cm. 

b. Soybeans Submodel

Arbitrary linear, control points:

X Value 0 0.15 0.3 1.1

Y Value 0.3 0.4 0.5 1

AWC is in cm. 
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c. Small Grains Submodel

Arbitrary curve, control points:

X Value 0 0.2 0.3 0.6 0.8 0.9 1.1

Y Value 0.23 0.35 0.42 0.62 0.76 0.84 1

AWC is in cm. 

d. Cotton Submodel – Four arbitrary curves based on mean

annual precipitation (MAP).

(i) MAP <700 mm

Arbitrary curve, control points:

X Value 0.20 0.3 0.4 0.5 0.6 1 1.1

Y Value 0.2 0.35 0.45 0.56 0.73 1 1

AWC is in cm. 

(ii) MAP 700-1000 mm

Arbitrary curve, control points:

X Value 0.2 0.4 0.5 0.6 0.7 0.8 1 1.1

Y Value 0.3 0.42 0.5 0.65 0.8 0.9 1 1

AWC is in cm. 
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(iii) MAP 1000-1500 mm

Arbitrary curve, control points:

X Value 0.2 0.3 0.45 0.5 0.6 0.8 0.9 1 1.1

Y Value 0.3 0.5 0.7 0.76 0.86 0.92 0.95 0.98 1

AWC is in cm. 

(iv) MAP >1500 mm

Arbitrary curve, control points:

X Value 0.2 0.4 0.6 0.8 0.9 1.1

Y Value 0.3 0.47 0.65 0.82 0.9 1

AWC is in cm. 

8. Water Table Recharge

a. Corn Submodel

Arbitrary curve, control points:

X Value 0 75 85 100 136 175 190 200

Y Value 0.2 0.95 1 0.95 0.8 1 0.8 0.2

Depth is in cm. 
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b. Soybeans Submodel

Arbitrary curve, control points:

X Value 0 75 90 100 136 175 190 200 250

Y Value 0.7 0.97 1 0.98 0.95 0.85 0.8 0.75 0.2

Depth is in cm. 

c. Small Grains Submodel

Arbitrary curve, control points:

X Value 0 75 85 100 120 200

Y Value 0.2 0.95 0.99 1 0.9 0.5

Depth is in cm.

d. Cotton Submodel

Arbitrary curve, control points:

X Value 0 75 85 100

Y Value 0.2 0.95 0.98 1

Depth is in cm. 
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9. Precipitation Recharge – All four crop submodels consider

precipitation recharge separately but also take into account the 

influence of hydraulic conductivity (Ksat) and organic matter in the 

soil profile.

a. Precipitation Recharge

(i) Corn Submodel

Arbitrary curve, control points:

X Value -5 15 23 28 50 70 90

Y Value 0.2 0.77 1 0.95 0.85 0.8 7

Precipitation is in mm. 

(ii) Soybeans Submodel

Arbitrary curve, control points:

X Value -30 -10 -5 15 28 30 31 32

Y Value 0.7 0.73 0.74 0.9 0.99 1 1 1

Precipitation is in mm. 
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(iii) Small Grains Submodel 

Arbitrary curve, control points:

X Value -40 50 0

Y Value 0.2 1 0.55

Precipitation is in mm. 

(iv) Cotton Submodel

Arbitrary curve, control points:

X Value -210 -170 -100 -90 -75 -20 0 -50

Y Value 0.25 0.33 0.55 0.6 0.7 1 1 0.9

Precipitation is in mm. 

b. Ksat Minimum within 150 cm

(i) Corn, Small Grains, and Cotton Submodels Sigmoid, low 

value 80, high value 90:

Ksat times LEP. Units are μm/sec.
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(ii) Soybeans Submodel

Sigmoid, low value 80, high value 185:

Ksat times LEP. Units are μm/sec.

c. Organic matter

Arbitrary linear, control points:

X Value 0 4 6 40 100 260

Y Value 0 0 1 1 0.5 0

Units are kilograms of OC per square meter of soil. 

10. Water-Gathering Surface (not used in the Cotton Submodel)

a. Corn and Soybeans Submodels Arbitrary curve, control points:

X Value 0 0.2 0.4 0.6

Y Value 0 0 0.3 0

Unitless multiplier to adjust the effect of slope shape for differing 

rainfall. Property is the same as for wheat, but evaluation is

adjusted for corn and soybeans.
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b. Small Grains Submodel

Arbitrary curve, control points:

X Value 0 0.2 0.42 0.6

Y Value 0.5 0.8 1 0.3

Unitless multiplier to adjust the effect of slope shape for differing 

rainfall. 

11. Mean Annual Air Temperature (MAT) (not used in the Small

Grains or Cotton Submodel)

a. Small Grains Submodel

Arbitrary linear, control points:

X Value 8 15

Y Value 0.5 1

Mean annual air temperature in degrees Celsius. 
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Soil Physical Properties Subrules

12. Ksat Minimum – Considered for three depth ranges (0-50, 50-100,

and 100-150 cm) for each submodel.

a. Corn Submodel

(i) 0-50 cm

Arbitrary curve, control points:

X Value -4 0.99 1.1 1.6 3

Y Value 0.6 0.99 1 0.99 0.5

Log (Ksat times LEP) from 0-50 cm evaluation. Units are log(μ/sec). 

Restrictive layer is Component Restriction, EC >=16.01, or pH

<=3.5. 

(ii) 50-100 cm

Arbitrary curve, control points:

X Value -4 0 1 1.1 1.6 2.7 3

Y Value 0.6 0.9 0.995 1 0.95 0.6 0.5

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec). Restrictive layer is Component Restriction, bulk density 

too high, EC >=16.01, or pH <=3.5. 
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(iii) 100-150 cm

Arbitrary curve, control points:

X Value -4 1 1.5 1.8 3

Y Value 0.6 0.995 1 0.97 0.7

Log (Ksat times LEP) from 100-150 cm evaluation. Units are 

log(μ/sec). Restrictive layer is Component Restriction, bulk density 

too high, EC >=16.01, or pH <=3.5. 

b. Soybeans Submodel

(i) 0-50 cm

Arbitrary curve, control points:

X Value -4 0.99 1.1 1.6 2.4 3

Y Value 0.6 0.99 1 0.99 0.65 0.4

Log (Ksat times LEP) from 0-50 cm evaluation. Units are log(μ/sec). 

Restrictive layer is Component Restriction, EC >=16.01, or pH

<=3.5. 
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(ii) 50-100 cm

Arbitrary curve, control points:

X Value -4 0 1 1.1 1.6 2.7 3

Y Value 0.6 0.9 0.995 1 0.95 0.5 0.4

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec). Restrictive layer is Component Restriction, bulk density 

too high, EC >=16.01, or pH <=3.5. 

(iii) 100-150 cm

Arbitrary curve, control points:

X Value -4 1 1.1 1.8 2.4 3

Y Value 0.6 0.995 1 0.97 0.7 0.5

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec). Restrictive layer is Component Restriction, bulk density 

too high, EC >=16.01, or pH <=3.5. 
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c. Small Grains Submodel

(i) 0-50 cm

Arbitrary curve, control points:

X Value -0.5 0.55 1.2 1.6 3

Y Value 0.6 0.99 1 0.95 0.5

Log (Ksat times LEP) from 0-50 cm evaluation. Units are log(μ/sec).

Restrictive layer is Component Restriction, bulk density too high,

EC >=16.01, or pH <=3.5. 

(ii) 50-100 cm

(1) Restrictive layer is Component Restriction or pH <=3.5.

Arbitrary curve, control points:

X Value -1.5 0 0.6 1.4 1.5 1.6 2.7 3

Y Value 0.6 0.8 1 1 1 0.99 0.6 0.5

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec).
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(2) Restrictive layer is bulk density too high or EC >= 16.01.

Arbitrary curve, control points:

X Value -1.5 0 0.6 1.5 1.6 2.7 3

Y Value 0.6 0.8 1 1 0.99 0.6 0.5

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec). 

(iii) 100-150 cm

Arbitrary curve, control points:

X Value -2 0.5 1.5 1.8 3

Y Value 0.6 1 0.98 0.95 0.7

Log (Ksat times LEP) from 100-150 cm evaluation. Units are 

log(μ/sec). Restrictive layer is Component Restriction, bulk density 

too high, EC >=16.01, or pH <=3.5. 
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d. Cotton Submodel – Evaluates soils differently based on mean

annual precipitation (MAP). Restriction types are Component 

Restrictions.

(i) MAP <700 mm

(1) 0-50 cm

Arbitrary curve, control points:

X Value -1 0 0.6 0.9 1.2 1.4 1.5 1.8 2.2 3.5

Y Value 0.05 0.75 0.92 1 1 1 1 1 0.85 0.05

Log (Ksat times LEP) from 0-50 cm evaluation. Units are are 

log(μ/sec).

(2) 50-100 cm

Arbitrary curve, control points:

X Value -1 0 0.7 1.7 2 3 1 1.4

Y Value 0.05 0.75 0.95 1 0.9 0.05 1 1

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec).
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(3) 100-150 cm

Arbitrary curve, control points:

X Value -1 0 0.8 1.7 2 3.5 0.5 2.5

Y Value 0.05 0.8 1 1 0.9 0.05 0.95 0.75

Log (Ksat times LEP) from 100-150 cm evaluation. Units are 

log(μ/sec).

(ii) MAP 700-1000 mm

(1) 0-50 cm

Arbitrary curve, control points:

X Value -1 -0.5 0.9 1.4 1.9 2 2.5 3 0

Y Value 0.05 0.6 0.98 1 1 0.97 0.7 0.05 0.8

Log (Ksat times LEP) from 0-50 cm evaluation. Units are log(μ/sec). 
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(2) 50-100 cm

Arbitrary curve, control points:

X Value -1 0.5 0.8 1 1.5 2 2.5 3 0

Y Value 0.05 0.6 0.9 0.95 1 0.97 0.7 0.05 0.7

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec). 

(3) 100-150 cm

Arbitrary curve, control points:

X Value -1 -0.5 0.5 1.3 1.9 2 2.5 3 0

Y Value 0.05 0.62 0.8 1 1 0.97 0.7 0.05 0.7

Log (Ksat times LEP) from 100-150 cm evaluation. Units are 

log(μ/sec). 
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(iii) MAP 1000-1500 mm

(1) 0-50 cm

Arbitrary curve, control points:

X Value -2 -0.5 0.5 0.92 2 2.5 3 4 1.9

Y Value 0.05 0.6 0.85 1 0.99 0.85 0.6 0.05 1

Log (Ksat times LEP) from 0-50 cm evaluation. Units are log(μ/sec). 

(2) 50-100 cm

Arbitrary curve, control points:

X Value -2 -0.5 1.3 0.5 0.9 1.8 2.5 3 4 -1

Y Value 0.05 0.72 1 0.83 0.96 1 0.8 0.6 0.05 0.67

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec). 
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(3) 100-150 cm

Arbitrary curve, control points:

X Value -2 -0.5 1.3 0.5 0.9 1.9 2.5 3 4 -3 -4

Y Value 0.7 0.75 1 0.85 0.97 1 0.8 0.5 0.05 0.6 0.05

Log (Ksat times LEP) from 100-150 cm evaluation. Units are 

log(μ/sec). 

(iv) MAP >1500 mm

(1) 0-50 cm

Arbitrary curve, control points:

X Value -2 -0.5 1.35 0.5 0.9 2 2.5 3 4 1.5

Y Value 0.05 0.65 1 0.87 0.97 0.92 0.75 0.5 0.05 1

Log (Ksat times LEP) from 0-50 cm evaluation. Units are log(μ/sec). 
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(2) 50-100 cm

Arbitrary curve, control points:

X Value -2 -0.5 1.4 0.5 0.9 2 2.5 3 4

Y Value 0.05 0.6 1 0.95 0.98 0.87 0.68 0.5 0.05

Log (Ksat times LEP) from 50-100 cm evaluation. Units are 

log(μ/sec).

(3) 100-150 cm

Arbitrary curve, control points:

X Value -2 -0.5 1.43 0.5 0.9 2 2.5 3 4 0 -3 -4

Y Value 0.7 0.8 1 0.92 0.97 0.95 0.85 0.75 0.05 0.85 0.6 0.05

Log (Ksat times LEP) from 100-150 cm evaluation. Units are 

log(μ/sec).

13. RZ LEP – Restrictions are Component Restrictions.

a. Corn Submodel

Arbitrary curve, control points:

X Value 0 0.5 1.5 3.2 4.5 7 9

Y Value 0.8 0.88 0.97 1 1 0.77 0.65

LEP from 0-150 cm evaluation. Units are cm3/cm3. 
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b. Soybeans Submodel

Arbitrary curve, control points:

X Value 0 0.5 1.1 3.5 4 7 9

Y Value 0.7 0.78 0.87 1 0.95 0.83 0.8

LEP from 0-150 cm evaluation. Units are cm3/cm3. 

c. Small Grains Submodel

Arbitrary curve, control points:

X Value 0 0.5 1.5 2 3.4 4.5 7 9

Y Value 0.8 0.85 0.93 0.97 1 0.95 0.9 0.85

LEP from 0-150 cm evaluation. Units are cm3/cm3. 
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d. Cotton Submodel - Evaluates soils differently based on mean

annual precipitation (MAP). Restriction types are Component

Restrictions.

(i) MAP <700 mm

Arbitrary curve, control points:

X Value 0 2 8 17

Y Value 0.7 1 1 0.8

LEP from 0-150 cm evaluation. Units are cm3/cm3. 

(ii) MAP 700-1000 mm

Arbitrary curve, control points:

X Value 0 1.3 2 6.7 9 12 14 17 22

Y Value 0.7 1 0.9 0.85 0.75 0.75 1 1 0.7

LEP from 0-150 cm evaluation. Units are cm3/cm3. 

(iii) MAP 1000-1500 mm

Arbitrary curve, control points:

X Value 0 1.5 5 8 11 13 18 4

Y Value 0.7 1 0.95 0.9 0.9 0.85 0.75 0.98

LEP from 0-150 cm evaluation. Units are cm3/cm3. 
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(iv) MAP >1500 mm

Arbitrary curve, control points:

X Value 0 1.5 5 10 20 3

Y Value 0.7 1 0.9 0.8 0.75 1

LEP from 0-150 cm evaluation. Units are cm3/cm3. 

14. RZ Rock Fragment – Same for all four crop submodels.

a. 0-20 cm

Arbitrary curve, control points:

X Value 2 20 35 65 70 85

Y Value 1 0.97 0.95 0.8 0.75 0.5

Evaluation of rock fragment volume 0-20 cm. Units are % by

volume. 

b. 0-150 cm

Arbitrary curve, control points:

X Value 2 20 35 65 75 85

Y Value 1 0.97 0.95 0.83 0.75 0.5

Evaluation of rock fragment volume 0-20 cm. Units are % by

volume. 
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15. RZ Soil Depth – Same for Corn, Soybeans, and Cotton; different

for Small Grains.

a. Corn, Soybeans, and Cotton Submodels Arbitrary curve, control 

points:

X Value 0 25 50 65 100 130 150

Y Value 0.45 0.6 0.75 0.8 0.9 0.95 1

Soil depth evaluation. Units are cm. 

b. Small Grains Submodel – Restriction is Component Restriction,

pH <3.5, or EC>=16.01.

Arbitrary curve, control points:

X Value 0 25 50 100 150

Y Value 0 0.5 0.8 0.97 1

Soil depth evaluation. Units are cm. 
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Soil Climate Subrules

16. Precipitation

a. Corn

Arbitrary curve, control points:

X Value 300 450 600 700 750 800

Y Value 0 0.45 0.8 0.96 1 1

Mean annual precipitation from high and low values. Units are mm. 

b. Soybeans

Arbitrary curve, control points:

X Value 300 400 450 600 700 750 800 900 1000

Y Value 0.1 0.65 0.75 0.9 0.955 0.97 0.99 1 1

X Value 1250 1400 1500 1700

Y Value 0.99 0.95 0.9 0.8

Mean annual precipitation from high and low values. Units are mm. 
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c. Small Grains

Arbitrary curve, control points:

X Value 0 200 300 430 600 700 900 1200 1300 1400 1700

Y Value 0.1 0.65 0.75 0.9 0.955 0.97 0.99 1 1 0.99 0.95

Mean annual precipitation from high and low values. Units are mm.

d. Cotton Submodel – Evaluates precipitation influence overall and

depending on mean annual air temperature (MAAT).

(i) Overall

Arbitrary curve, control points:

X Value 200 300 400 600 800 900 1000 1200 1300 1400

Y Value 0.05 0.25 0.35 0.42 0.52 0.58 0.67 0.88 0.96 1

Mean annual precipitation from high and low values. Units are mm.

(ii) MAAT <14

Arbitrary curve, control points:

X Value 200 300 600 800 900 1050

Y Value 0.05 0.35 0.7 0.8 0.9 1

Mean annual precipitation from high and low values. Units are mm.
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(iii) MAAT 14-17

Arbitrary curve, control points:

X Value 200 300 600 800 1000 1300 1100

Y Value 0.05 0.35 0.65 0.74 0.85 1 0.95

Mean annual precipitation from high and low values. Units are mm.

(iv) MAAT 17-20

Arbitrary curve, control points:

X Value 200 300 600 800 1070 1200 1400 1000

Y Value 0.05 0.35 0.65 0.7 0.91 0.97 1 0.82

Mean annual precipitation from high and low values. Units are mm.

(v) MAAT >20

Arbitrary curve, control points:

X Value 200 300 600 800 1000 1200 1700 400 350

Y Value 0.05 0.5 0.6 0.65 0.7 0.75 1 0.55 0.53

Mean annual precipitation from high and low values. Units are mm.
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17. Frost-Free Days – Corn and Small Grains consider frost-free

days alone, while Soybeans adjusts for daylength and Cotton

incorporates mean annual air temperature (MAAT).

a. Corn

Arbitrary curve, control points:

X Value 75 80 90 100 110 120 150 165

Y Value 0 0.4 0.62 0.67 0.73 0.79 0.92 1

X Value 185 195 200 247 265 273 300

Y Value 1 0.95 0.94 0.85 0.8 0.77 0.72

Frost-free days evaluation. Units are frost-free days/year.

b. Soybeans

(i) Frost-Free Days

Arbitrary curve, control points:

X Value 70 75 80 90 100 110 120 150

Y Value 0.1 0.4 0.5 0.61 0.68 0.74 0.79 0.95

X Value 165 185 195 220 247 263 273 300

Y Value 1 1 0.97 0.88 0.83 0.81 0.79 0.77

Frost-free days evaluation. Units are frost-free days/year.
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(ii) Daylength

Arbitrary curve, control points:

X Value 45 47 49 50

Y Value 0 0.2 0.7 1

Latitude estimator. Units are degrees north of the equator.

c. Small Grains

Arbitrary curve, control points:

X Value 50 80 90 110 120 160 195 220

Y Value 0.1 0.55 0.65 0.8 0.85 0.98 1 0.9

Frost-free days evaluation. Units are frost-free days/year.

d. Cotton

(i) Frost-Free Days

Arbitrary curve, control points:

X Value 160 170 190 180 250 305 330 350 200 270

Y Value 0 0.3 0.98 0.82 1 0.9 0.6 0.4 0.99 0.99

Frost-free days evaluation. Units are frost-free days/year.
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(ii) Mean Annual Air Temperature

Arbitrary linear, control points:

X Value 12 13 14 17.5 18 19.2 19.5 20 21 23 24

Y Value 0 0.93 0.95 1 1 0.98 0.95 0.88 0.74 0.47 0.3

Units are degrees Celsius. 

Soil Landscape Subrules

18. Effective Slope – Corn, Soybeans, Small Grains, Cotton

a. Corn and Soybeans

Arbitrary curve, control points:

X Value 1 2 8 14 20 22 25

Y Value 1 0.99 0.95 0.83 0.65 0.5 0.05

Units are percent slope. 

b. Small Grains

(i) Not Xeric

Arbitrary curve, control points:

X Value 0 5 10 15 20 22 25

Y Value 1 0.98 0.92 0.8 0.52 0.32 0.05

Units are percent slope. 
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(ii) Xeric

Arbitrary curve, control points:

X Value 0 5 10 17 20 30 35

Y Value 1 0.98 0.94 0.8 0.69 0.3 0.05

Units are percent slope. 

c. Cotton

Arbitrary curve, control points:

X Value 1 5 10 15 20 22 25 0 2

Y Value 1 0.98 0.92 0.83 0.65 0.5 0.05 0.97 1

Units are percent slope. 
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19. Ponding – Same for Corn, Soybeans, and Cotton Submodels.

Not considered for Small Grains Submodel.

Arbitrary curve, control points:

X Value 0 3 4 7.5 8 10 10.5

Y Value 1 1 0.95 0.25 0.2 0.05 0.02

Evaluation of ponding during the growing season. Units are 

(days)*(inundations)*(months). 

20. Flooding – Corn, Soybeans, and Cotton Submodels; not

considered for Small Grains Submodel.

a. Corn and Soybeans Submodels Arbitrary curve, control points:

X Value 0 1 2 4 5.4 6 7

Y Value 1 1 0.93 0.65 0.25 0.1 0.01

Evaluation of flooding during the growing season. Units are

(days)*(inundations/month)*(months). 
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b. Cotton Submodel – Evaluated differently depending on growing

season Frost-Free Days (FFD).

(i) <185 FFD

Arbitrary curve, control points:

X Value 0 1 7 8 12 14

Y Value 1 1 0.8 0.75 0.4 0.05

Evaluation of flooding during the growing season. Units are

(days)*(inundations/month)*(months). 

(ii) 185-215 FFD

Arbitrary curve, control points:

X Value 0 1.5 4 8 12 14

Y Value 1 1 0.92 0.75 0.3 0.05

Evaluation of flooding during the growing season. Units are

(days)*(inundations/month)*(months). 
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(iii) 215-245 FFD

Arbitrary curve, control points:

X Value 0 1.5 4 7 12 20

Y Value 1 1 0.95 0.85 0.7 0.05

Evaluation of flooding during the growing season. Units are

(days)*(inundations/month)*(months).

(iv) 245-278 FFD

Arbitrary curve, control points:

X Value 0 2 6 10 14 20

Y Value 1 1 0.94 0.8 0.5 0.05

Evaluation of flooding during the growing season. Units are

(days)*(inundations/month)*(months). 

(v) 278-305 FFD

Arbitrary curve, control points:

X Value 0 1.5 7 8 14 25

Y Value 1 1 0.93 0.9 0.65 0.05

Evaluation of flooding during the growing season. Units are

(days)*(inundations/month)*(months). 
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(vi) >305 FFD

Arbitrary curve, control points:

X Value 0 1.5 4 8 14

Y Value 1 1 0.95 0.85 0.05

Evaluation of flooding during the growing season. Units are

(days)*(inundations/month)*(months). 

21. Growing Season Wetness – Corn, Soybeans

a. Corn

(i) <=135 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6

Y Value 1 1 1 0.95 0.94 0.93 0.8 0.95 0.9 0.85

X Value 1.9 2.1 2.2 2.5 2.7 2.8 3.1 3.2 3.5 4.5

Y Value 0.7 0.5 0.9 0.35 0.25 0.2 0.1 0.75 0.1 0.02

Water Table Index <135 Frost-Free Days (unitless).
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(ii) 135-160 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.4 1.6

Y Value 1 1 1 0.95 0.94 0.93 0.8 0.99 0.95 0.87

X Value 1.9 2.1 2.2 2.6 2.8 3.1 3.2 3.5 3.7 4.5

Y Value 0.7 0.6 0.9 0.3 0.2 0.15 0.8 0.3 0.15 0.02

Water Table Index 135-160 Frost-Free Days (unitless).

(iii) 160-185 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6

Y Value 1 1 1 1 1 0.93 0.8 0.99 0.98 0.94

X Value 1.9 2.1 2.2 2.6 2.8 3.1 3.2 3.5 3.7 4

Y Value 0.9 0.65 0.97 0.6 0.4 0.2 0.85 0.5 0.3 0.02

Water Table Index 160-185 Frost-Free Days (unitless).
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(iv) 185-210 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6

Y Value 1 1 1 0.95 0.94 0.93 0.8 0.99 0.96 0.94

X Value 1.9 2.1 2.2 2.6 2.8 3.1 3.2 3.5 3.7 4.3

Y Value 0.9 0.65 0.95 0.7 0.55 0.2 0.85 0.4 0.25 0.02

Water Table Index 185-210 Frost-Free Days (unitless).

(v) >210 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6 1.9

Y Value 1 1 1 0.95 0.94 0.93 0.8 0.99 0.96 0.94 0.9

X Value 2.1 2.2 2.5 2.7 2.8 3.1 3.2 3.5 3.7 3.9 4.3

Y Value 0.65 0.95 0.8 0.65 0.55 0.3 0.85 0.3 0.1 0.05 0.02

Water Table Index 185-210 Frost-Free Days (unitless).
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b. Soybeans

(i) <100 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6

Y Value 1 1 1 1 1 0.93 0.8 1 0.97 0.93

X Value 1.9 2.1 2.2 2.5 2.7 2.8 3.1 3.2 3.5 4.5

Y Value 0.75 0.6 0.95 0.7 0.5 0.4 0.2 0.75 0.4 0.1

Water Table Index <100 Frost-Free Days (unitless).

(ii) 100-135 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6

Y Value 1 1 1 1 0.97 0.93 0.8 1 0.97 0.93

X Value 1.9 2.1 2.2 2.5 2.7 2.8 3.1 3.2 3.5 4.5

Y Value 0.8 0.5 0.8 0.65 0.5 0.4 0.3 0.6 0.4 0.1

Water Table Index 100-135 Frost-Free Days (unitless).
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(iii) 135-160 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.4 1.6

Y Value 1 1 1 0.95 0.94 0.93 0.8 0.99 0.95 0.87

X Value 1.9 2.1 2.2 2.6 2.8 3.1 3.2 3.5 3.7 4.5

Y Value 0.75 0.65 0.8 0.65 0.5 0.3 0.7 0.6 0.5 0.2

Water Table Index 135-160 Frost-Free Days (unitless).

(iv) 160-185 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6

Y Value 1 1 1 1 1 0.93 0.8 0.99 0.95 0.94

X Value 1.9 2.1 2.2 2.6 2.8 3.1 3.2 3.5 3.7 4

Y Value 0.9 0.65 0.95 0.8 0.7 0.4 0.9 0.7 0.5 0.2

Water Table Index 160-185 Frost-Free Days (unitless).
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(v) 185-210 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6

Y Value 1 1 1 0.95 0.94 0.93 0.8 0.99 0.96 0.94

X Value 1.9 2.1 2.2 2.6 2.8 3.1 3.2 3.5 3.7 4.3

Y Value 0.9 0.65 0.95 0.8 0.75 0.55 0.9 0.7 0.5 0.2

Water Table Index 185-210 Frost-Free Days (unitless).

(vi) >210 FFD

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6 1.9

Y Value 1 1 1 0.95 0.94 0.93 0.8 0.99 0.94 0.9 0.65

X Value 2.1 2.2 2.5 2.7 2.8 3.1 3.2 3.5 3.7 3.9 4.3

Y Value 0.65 0.95 0.8 0.7 0.6 0.4 0.9 0.7 0.55 0.4 0.2

Water Table Index >210 Frost-Free Days (unitless).
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22. Fragments on Surface – Small Grains

a. Fragments >250 mm on Surface

Arbitrary linear, control points:

X Value 0 6

Y Value 1 0.05

Fragments on soil surface greater than 250 mm in diameter. Units

are percent of ground surface area. 

b. Fragments in Surface

Arbitrary curve, control points:

X Value 0 10 20 35 50 70

Y Value 1 0.98 0.95 0.9 0.8 0.1

Fragments in soil surface layer greater than 4 mm in diameter.

Units are percent of whole soil layer by volume. 
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23. Excess Water – Small Grains

a. Water Table

(i) <=90 FFD

Arbitrary curve, control points:

X Value 0 50 100

Y Value 1 0.8 0.05

Evaluation of Wetness During FFD <= 90 (Wheat). Units are cm.

90-105 FFD.

Arbitrary curve, control points:

X Value 0 50 140 180 200

Y Value 1 0.9 0.7 0.4 0.05

Evaluation of Wetness During FFD 90-105 (Wheat). Units are cm.

(ii) 105-120 FFD

Arbitrary curve, control points:

X Value 0 200 300 400 600 700 800 900 1100 1300

Y Value 1 1 0.95 0.8 0.6 0.54 0.5 0.45 0.3 0.1

Evaluation of Wetness During FFD 105-120 (Wheat). Units are cm.
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(iii) >120 FFD

Arbitrary curve, control points:

X Value 0 201 400 500 600 700 1000 1500 1700

Y Value 1 0.95 0.85 0.8 0.75 0.72 0.6 0.1 0.05

Evaluation of Wetness During FFD >120 (Wheat). Units are cm.

b. Ponding

(i) <90 FFD

Arbitrary curve, control points:

X Value 0 1 2

Y Value 1 0.5 0.05

Ponding During Growing Season <90 FFD (Wheat). Units are

(days)*(inundations)*(months).

(ii) 90-105 FFD

Arbitrary linear, control points:

X Value 0 1 2

Y Value 1 1 0.05

Ponding During Growing Season 90-105 FFD (Wheat). Units are 

(days)*(inundations)*(months). 
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(iii) 105-120 FFD

Arbitrary curve, control points:

X Value 0 6 10

Y Value 1 0.8 0.05

Ponding During Growing Season 105-120 FFD (Wheat). Units are 

(days)*(inundations)*(months).

(iv) 120-145 FFD

Arbitrary linear, control points:

X Value 0 2 10

Y Value 1 1 0.05

Ponding During Growing Season 120-145 FFD (Wheat). Units are 

(days)*(inundations)*(months).

(v) >145 FFD

Arbitrary linear, control points:

X Value 0 2 8

Y Value 1 1 0.2

Ponding During Growing Season >145 FFD (Wheat). Units are

(days)*(inundations)*(months). 
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c. Flooding

(i) <90 FFD

Arbitrary curve, control points:

X Value 0 1 2.5 1.8

Y Value 1 0.84 0.05 0.6

Flooding During Growing Season <90 FFD (Wheat). Units are

(days)*(inundations/month)*(months).

(ii) 90-105 FFD

Arbitrary curve, control points:

X Value 0 1 2 3 3.5 4

Y Value 1 0.87 0.8 0.6 0.3 0.05

Flooding During Growing Season 90-105 FFD (Wheat). Units are 

(days)*(inundations/month)*(months). 

(iii) 105-120 FFD

Arbitrary curve, control points:

X Value 0 1 2 4 5 6

Y Value 1 1 0.9 0.7 0.4 0.05

Flooding During Growing Season 105-120 FFD (Wheat). Units are 

(days)*(inundations/month)*(months).
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(iv) >120 FFD

Arbitrary curve, control points:

X Value 0 1 4 11 7

Y Value 1 1 0.85 0.05 0.6

Flooding During Growing Season >120 FFD (Wheat). Units are

(days)*(inundations/month)*(months). 

24. Water Table – Cotton

Arbitrary linear, control points:

X Value -1.1 0 0.3 0.6 0.7 0.9 1 1.1 1.2 1.6

Y Value 1 1 1 0.95 0.94 0.93 0.8 0.99 0.96 0.94

X Value 1.9 2.1 2.2 2.6 2.8 3.1 3.2 3.5 3.75 4.4

Y Value 0.9 0.65 0.95 0.8 0.7 0.5 0.8 0.6 0.45 0.3

Evaluation of Wetness Index During Growing Season (Cotton);

unitless.
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Appendix 4.—Properties With a Possible Result of “Null 

Not Rated”

The representative value (rv) for each is used in the derivation of the

index.

Slope

Frost-Free Days Ksat

Mean Annual Precipitation Mean Annual Air Temperature

Available Water-Holding Capacity

Bulk Density 1/3 Bar

Linear Extensibility Percent

Organic Matter

pH 1:1 H2O or CaCl2 CEC

Component Kind

Horizon Depth Top and Bottom
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Appendix 5.—Root-Limiting Layers in the NCCPI

The depth (and therefore volume) of soil that roots can access 

contributes significantly to soil productivity. Many calculations of soil 

capacity factors, such as available water-holding capacity (AWC) and

cation-exchange capacity, stop at a root- limiting layer. Recognition of 

root-limiting layers involves not only physical but also chemical barriers 

to root growth.

The NCCPI recognizes four kinds of root-limiting layers. The first kind 

is the typical root-limiting layers populated in NASIS, including hard 

bedrock, soft bedrock, a fragipan, a duripan, sulfuric material, and a 

dense layer. The second kind is a layer having a pH of less than 3.5,

and the third is a layer having an electrical conductivity of more than 12.

The fourth kind is a possible dense layer determined by an examination 

of the differential between the populated bulk density of a layer and a 

theoretical optimum density. If the differential reaches a threshold, then 

the layer is considered to stop root growth. If no root-restricting zone is 

identified, a depth of 150 cm is used to approximate the maximum 

rooting depth. For available water storage, a maximum depth of 150 cm 

is examined for small grains, cotton, and soybeans. The corn model 

examines the available water storage of soils to 200 cm to acknowledge 

the deepest rooting potential in growing seasons long enough for the

roots to use the deeply stored water.
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Appendix 6.—Calculations Used to Manipulate Data

Typically, soil and site data are rated in the evaluations just as they

occur in the database. In some cases, however, a more meaningful 

relationship between soil properties and soil productivity can be derived 

by combining some attributes or by performing some manipulation of the

data. The relationships used in the model generally are the best fit after 

several possibilities were considered. This section discusses these 

relationships and considerations.

Available Water-Holding Capacity

Available water-holding capacity (AWC) is represented in the 

database as simply cm of available water per centimeter of soil. 

However, AWC in the surface soil horizons, where more plant roots 

occur, will contribute more toward crop productivity than AWC at 

greater depths. The calculation, based on one developed in Missouri, 

attempts to determine the contribution of AWC sufficiency for each 

centimeter of soil material to a depth of 200 cm (Kiniry et al., 1983).
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Bulk Density

Bulk density is represented in the database as simply grams per cubic 

centimeter. Bulk density may limit root growth. The calculation uses the 

difference between the bulk density of a given soil and the “ideal” bulk 

density for the given sand, silt, and clay content to determine if the given

bulk density is root-limiting. Organic soils are currently considered to 

have an optimum bulk density.
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Cation-Exchange Capacity (CEC)

Cation-exchange capacity is handled in a way analogous to root zone

AWC. It is basically root zone CEC. The density of the layer and the

content of rock fragments are considered as well as any root-limiting 

layer.
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Saturated Hydraulic Conductivity (Ksat) 

Saturated hydraulic conductivity is measured when the soil is 

saturated by water. In the case of Vertisols, this measurement pushes 

the Ksat to an unrealistically low level, in terms of what a growing root

senses. Vertisols are expansive and allow aeration and water movement 

primarily through cracks. A better measure of aeration is provided by 

multiplying the Ksat by the linear extensibility (LEP) rather than using the 

Ksat by itself. Using the log10 of the product also helps in graphing a value

that crosses several orders of magnitude. When mean annual 

precipitation (MAP) is important for crop productivity, the product of Ksat

and LEP is also doubled, e.g., define lepksat_r 2*lep_r*ksat_r.
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Flooding

Flooding calculations must consider duration, frequency, and timing. 

Flooding during the time of year when the crop is in the ground is 

detrimental. Flooding while the ground is unused is less detrimental. A 

month of flooding during a 200-day growing season is less detrimental 

than a month of flooding during a 90-day growing season. Rare flooding 

during the growing season is less detrimental than frequent flooding 

during the growing season. An index that integrates the timing,

frequency, and duration of flooding, called Flooding Severity, is used to 

assess the impact of flooding on productivity. In the following examples, 

wheat in a 90- to 105-day growing season is the crop of interest.

Flooding Duration
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Flooding Frequency
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Months of Flooding During the Growing Season
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Flooding Severity
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Ponding

Like flooding, ponding calculations must consider duration, frequency, 

and timing. Ponding during the time of year when the crop is in the 

ground is detrimental. Ponding while the ground is unused is less 

detrimental. A month of ponding during a 200-day growing season is 

less detrimental than a month of ponding during a 90-day growing 

season. Rare ponding during the growing season is less detrimental 

than frequent ponding during the growing season. An index that

integrates the timing, frequency, and duration of ponding, called 

Ponding Severity, is used to assess the impact of ponding on

productivity. In the following examples, wheat in a 90- to 105-day

growing season is the crop of interest.

Ponding Duration
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Ponding Frequency
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Months of Ponding During the Growing Season
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Ponding Severity
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Water Tables

Water table depth, duration, and timing are synthesized into a single

number called the “General Indicator of Soil Wetness” for various 

lengths of growing season.

An example of saturation during the growing season at a given depth

is given a rating according to this table:

Saturation depth (cm) Monthly rating

>200 0.08

100-200 1.0

50-100 13

15-50 157

0-15 1888
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The monthly ratings are summed to calculate the yearly index, and 

the logarithm of the yearly index is the indicator of soil wetness. Thus, a 

soil that does not become saturated within a depth of 200 cm during a 6-

month growing season would have an index of:

Log10(6*0.08) = -0.319.

This index was developed to create a relatively simple way to sum the 

depths of the water tables (actually, the thickness of dry soil) over time

without getting the same result for several moderately wet months as for 

one very wet month. A soil that is saturated to the surface for 2 months

of a 7-month growing season but is otherwise dry returns an index of 

3.58, whereas a soil that is saturated at a depth of 75 cm for the entire 

7-month growing season returns an index of 1.96. Depending on the 

physiology of the crop and on its required length of growing season, an 

index of 1.96 may be more limiting than an index of 3.58, and the 

associated evaluation can be constructed to indicate that relationship. 

The evaluations using the General Indicator often have a “sawtooth” 

look (see Evaluation Curve 21a(v) in appendix 3, for example).

Plot of cotton yield versus the General Indicator.

The figure above shows a spline curve fitted through cotton yield

plotted against the General Indicator. The fine structure of the curve 

shows the relationship of increasing wetness to yield even while the 

water table is relatively deep. This curve was used to create the 

evaluation depicted in Evaluation Curve 15b in appendix 3. 
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Precipitation Recharge

Corn, Cotton, and Soybean (Spring Planted)
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Fall Planted

Different crops have differing water-use patterns. Small grains are 

sufficiently different from corn, cotton, and soybeans for the relatively 

insensitive data available in NASIS to allow a different calculation of the 

effect of rainfall during the growing season.

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References

Kiniry, L.N., C.L. Scrivner, and M.E. Keener. 1983. A soil 

productivity index based upon predicted water depletion and root

growth. Research Bulletin 1051. University of Missouri–Columbia,

College of Agriculture, Agricultural Experiment Station.

Milne, A.E., H.C. Wheeler, and R.M. Lark. 2006. On testing

biological data for the presence of a boundary. Annals of Applied

Biology 149:213–223.

Soil Survey Staff. 1999. Soil Taxonomy: A Basic System of Soil

Classification for Making and Interpreting Soil Surveys. 2nd 

edition. United States Department of Agriculture Handbook 436.

Storie, R.E. 1937. An index for rating agricultural value of soils.

Bulletin 556. University of California, Berkeley.

Storie, R.E. 1978. Storie index soil rating (revised). Special

Publication 3203, Division of Agricultural Science, University of 

California, Berkeley.

Thornthwaite, C.W. 1948. An approach toward a rational

classification of climate. Geographic Review 38:55–94.

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Glossary

Available water capacity (available moisture capacity). The capacity 

of soils to hold water available for use by most plants. It is commonly 

defined as the difference between the amount of soil water at field 

moisture capacity and the amount at wilting point. It is commonly 

expressed as inches of water per inch of soil. The capacity, in 

inches, in a 60-inch profile or to a limiting layer is expressed as:

Very low..................................... 0 to 3 

Low............................................ 3 to 6 

Moderate ....................................... 6 to 9 

High........................................... 9 to 12

Very high ...................................more than 12

Bulk density. Soil bulk density is the ratio of the mass of dry solids to 

the bulk volume of the soil occupied by those dry solids. Bulk density

is an important site characterization parameter because it varies with 

the structural condition of the soil, particularly that related to packing.

Cation-exchange capacity (CEC). The total amount of exchangeable

cations that can be held by the soil, expressed in terms of 

milliequivalents per 100 grams of soil at neutrality (pH 7.0) or at 

some other stated pH value. The term, as applied to soils, is 

synonymous with base-exchange capacity but is more precise in 

meaning.

Clay. As a soil separate, the mineral soil particles less than 0.002 

millimeter in diameter. As a soil textural class, soil material that is 40

percent or more clay, less than 45 percent sand, and less than 40 

percent silt. 

Electrical conductivity. A measure of the concentration of watersoluble salts in soils. It is used to indicate saline soils. High 

concentrations of neutral salts, such as sodium chloride and sodium

sulfate, may interfere with the absorption of water by plants because 

the osmotic pressure in the soil solution is nearly as high as or

higher than that in the plant cells. Salts may also interfere with the

exchange capacity of nutrient ions, thereby resulting in nutritional

deficiencies in plants.
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Frost-free days. The expected number of days between the last

freezing temperature (0 degrees C) in spring (January-July) and the 

first freezing temperature in fall (August-December). The number of 

days is based on the probability that the values for the standard 

“normal” period of 1971 to 2000 will be exceeded in 5 years out of 

10.

Fuzzy system. A method of modeling that uses the degree of 

membership in a set, as a membership function between 0 and 1, to

quantify the impact of independent variables on dependent variables.

This method is particularly useful when complex nonlinear 

relationships are modeled.

Gypsum. A mineral that is partially soluble in water and can be

dissolved and removed by water. Soils with more than 10 percent 

gypsum may collapse if the gypsum is removed by percolating 

water. Dissolved gypsum reduces the amount of water available to 

plants by increasing the osmotic pressure of the soil solution.

Linear extensibility percentage (LEP). Measurement of the change in 

length of an unconfined clod as moisture content is decreased from 

a moist to a dry state. Linear extensibility is used to determine the 

shrink-swell potential of soils. It is an expression of the volume 

change between the water content of the clod at 1/3- or 1/10-bar 

tension (33kPa or 10kPa tension) and oven dryness. Volume change

is influenced by the amount and type of clay minerals in the soil. The 

volume change is the percent change for the whole soil. If it is 

expressed as a fraction, the resulting value is COLE, coefficient of 

linear extensibility.

Loess. Fine grained material, dominantly of silt-sized particles, that is

transported and deposited by wind.

Miscellaneous area. A kind of map unit that has little or no natural soil

and supports little or no vegetation.

Organic matter. Plant and animal residue in the soil in various stages 

of decomposition. The content of organic matter in the surface layer

is described as follows:

Very low ....................................less than 0.5 percent

Low............................................0.5 to 1.0 percent

Moderately low ..........................1.0 to 2.0 percent

Moderate ...................................2.0 to 4.0 percent

High...........................................4.0 to 8.0 percent

Very high ...................................more than 8.0 percent
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Parent material. The unconsolidated organic and mineral material in

which soil forms.

pH value. A numerical designation of acidity and alkalinity in soil. (See

Reaction, soil.)

Ponding. Standing water on soils in closed depressions. Unless the

soils are artificially drained, the water can be removed only by

percolation or evapotranspiration.

Productivity, soil. The capability of a soil for producing a specified plant

or sequence of plants under specific management.

Reaction, soil. A measure of acidity or alkalinity of a soil, expressed as 

pH values. A soil that tests to pH 7.0 is described as precisely neutral

in reaction because it is neither acid nor alkaline. The degrees of

acidity or alkalinity, expressed as pH values, are:

Ultra acid ...................................less than 3.5

Extremely acid...........................3.5 to 4.4

Very strongly acid ......................4.5 to 5.0

Strongly acid..............................5.1 to 5.5

Moderately acid ............................5.6 to 6.0

Slightly acid ...............................6.1 to 6.5

Neutral.......................................6.6 to 7.3

Slightly alkaline..........................7.4 to 7.8

Moderately alkaline......................7.9 to 8.4

Strongly alkaline ........................8.5 to 9.0

Very strongly alkaline.................9.1 and higher

Rock fragments. Rock or mineral fragments having a diameter of 2

millimeters or more; for example, pebbles, cobbles, stones, and 

boulders.

RZ AWC. Available water capacity in the root zone.

Sand. As a soil separate, individual rock or mineral fragments from 0.05

millimeter to 2.0 millimeters in diameter. Most sand grains consist of 

quartz. As a soil textural class, a soil that is 85 percent or more sand

and not more than 10 percent clay.

Saturated hydraulic conductivity (Ksat). The quality of the soil that 

enables water to move through the profile. Ksat is the reciprocal of

the resistance of soil to water movement. As the resistance 

increases, the hydraulic conductivity decreases. Resistance to water 

movement in saturated soil is primarily a function of the arrangement 

and size distribution of pores.
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Silt. As a soil separate, individual mineral particles that range in 

diameter from the upper limit of clay (0.002 millimeter) to the lower 

limit of very fine sand (0.05 millimeter). As a soil textural class, soil

that is 80 percent or more silt and less than 12 percent clay.

Sodium adsorption ratio (SAR). A measure of the amount of sodium

(Na) relative to calcium (Ca) and magnesium (Mg) in the water 

extract from saturated soil paste. It is the ratio of the Na 

concentration divided by the square root of one-half of the Ca + Mg 

concentration.

Water-gathering surface. A concave part of the landscape where 

runoff can accumulate.