• TABLE OF CONTENTS
HIDE
 Front Cover
 Abstract
 Acknowledgement
 Preface
 Other reports on farm legislat...
 Table of Contents
 Highlights
 Introduction
 Conceptual bases for program...
 U.S. farm program participation...
 Data assembly procedures
 Program participation and soil...
 Farmer characteristics by program...
 Summary and implications
 Reference
 Appendix A: USDA program consistency...
 Appendix B: Instructions for completing...
 Appendix C: Distribution of farm...
 Advertising
 Back Cover






Group Title: Agricultural economic report - United States Dept. of Agriculture - no. 532
Title: Do USDA farm program participants contribute to soil erosion?
CITATION PAGE IMAGE ZOOMABLE PAGE TEXT
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00056206/00001
 Material Information
Title: Do USDA farm program participants contribute to soil erosion?
Series Title: Agricultural economic report
Physical Description: iv, 74 p. : ill. ; 28 cm.
Language: English
Creator: Reichelderfer, K. H
United States -- Dept. of Agriculture. -- Economic Research Service
Publisher: U.S. Dept. of Agriculture, Economic Research Service
Place of Publication: Washington DC
Publication Date: 1985
 Subjects
Subject: Soil erosion -- United States   ( lcsh )
Soil conservation -- United States   ( lcsh )
Genre: federal government publication   ( marcgt )
bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Bibliography: p. 42.
Statement of Responsibility: Katherine H. Reichelderfer.
General Note: Cover title.
General Note: Distributed to depository libraries in microfiche.
General Note: "April 1985"--P. i.
Funding: Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.
 Record Information
Bibliographic ID: UF00056206
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: aleph - 001307278
oclc - 12604208
notis - AGF8089

Table of Contents
    Front Cover
        Front Cover 1
        Front Cover 2
    Abstract
        Page i
    Acknowledgement
        Page i
    Preface
        Page ii
    Other reports on farm legislation
        Page ii
    Table of Contents
        Page iii
    Highlights
        Page iv
    Introduction
        Page 1
    Conceptual bases for program inconsistency
        Page 2
        Page 3
        Page 4
    U.S. farm program participation and erosion problems
        Page 5
        Soil erosion in United States
            Page 6
            Page 7
        Do farm program participation and soil erosion overlap?
            Page 8
    Data assembly procedures
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
    Program participation and soil erosion in critical resource areas
        Page 14
        Program participation
            Page 15
            Page 16
            Page 17
            Page 18
            Page 19
            Page 20
            Page 21
            Page 22
            Page 23
            Page 24
        Soil erosion rates
            Page 25
            Page 26
            Page 27
        Relationships between program participation and soil erosion in critical resource areas
            Page 28
            Page 29
            Page 30
            Page 31
        Who contributes to the soil erosion problem?
            Page 32
            Page 33
    Farmer characteristics by program participation and soil erosion class
        Page 34
        Ownership - Tenure
            Page 35
        Off-farm work - Operator age - Operating loan source - Land capability class and subclass
            Page 36
    Summary and implications
        Page 37
        Page 38
        Page 39
        Page 40
        Page 41
    Reference
        Page 42
    Appendix A: USDA program consistency study sample counties
        Page 43
        Page 44
    Appendix B: Instructions for completing AD-862 forms for USDA program consistency study
        Page 45
        Page 46
        Page 47
        Page 48
        Page 49
        Page 50
        Page 51
    Appendix C: Distribution of farm and operator characteristics among program participation categories and soil erosion classes
        Page 52
        Page 53
        Page 54
        Page 55
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
        Page 63
        Page 64
        Page 65
        Page 66
        Page 67
        Page 68
        Page 69
        Page 70
        Page 71
        Page 72
        Page 73
        Page 74
    Advertising
        Page 75
        Page 76
    Back Cover
        Back Cover
Full Text


































































































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DO USDA FARM PROGRAM PARTICIPANTS CONTRIBUTE TO SOIL EROSION? By Katherine H.
Reichelderfer, Economic Research Service, U.S. Department of Agriculture.
Agricultural Economic Report No. 532.





ABSTRACT

Only about one-third of U.S. cropland with excessive soil erosion rates is
operated by farmers who might be influenced to reduce erosion if changes were
made in the U.S. Department of Agriculture's commodity and soil conservation
programs. The present commodity programs may conflict with conservation
programs by encouraging the cultivation of erosive crops. Efforts to increase
the consistency of USDA commodity and conservation programs would contribute
little to overcoming the Nation's total erosion problems. Such efforts,
however, should balance conservation objectives with objectives for farm income,
commodity prices, production, and export.


Keywords: Commodity programs, conservation, program consistency, soil erosion.


ACKNOWLEDGMENTS

Jerry Lee and Keith Schmude of SCS and Gordon Nebeker and Jack Webb of ASCS
made significant contributions to the development, planning, design, and
implementation of means of assembling data for the study. Special acknowledg-
ment goes to the ASCS and SCS county personnel who willingly and efficiently
assembled data from their county offices. Also, special thanks go to the
agricultural economists who coordinated and recorded the massive set of
assembled data: Lee Christensen, Richard Clark, Neil Cook, Merlin Erickson,
Andrew Hudson, James Kasal, Doug Lewis, Arnold Paulsen, Robert Pfeiffer,
Jesse Russell, Gordon Sloggett, Marlow Vesterby, and Jerry Williams.
Keith Schmude, Stephanie Taylor, and Jack Webb were particularly helpful in
designing and protesting the data assembly process. Joyce Su gave excellent
attention to editing, summarizing, and running computerized statistical tests
on the data set. Venita Dimmick and Wanda Nelson typed various drafts of this
report, as well as all material dealing with the USDA Program Consistency
Study. Dennie Burns, Lee Christensen, Richard Clark, Neil Cook, Thomas Hertel,
James Kasal, Tom McDonald, Arnold Paulsen, Jesse Russell, and Peter Tidd provided
helpful comments and suggestions on former drafts. Lindsay Mann edited the
manuscript.











Washington, DC 20250 April 1985






DO USDA FARM PROGRAM PARTICIPANTS CONTRIBUTE TO SOIL EROSION? By Katherine H.
Reichelderfer, Economic Research Service, U.S. Department of Agriculture.
Agricultural Economic Report No. 532.





ABSTRACT

Only about one-third of U.S. cropland with excessive soil erosion rates is
operated by farmers who might be influenced to reduce erosion if changes were
made in the U.S. Department of Agriculture's commodity and soil conservation
programs. The present commodity programs may conflict with conservation
programs by encouraging the cultivation of erosive crops. Efforts to increase
the consistency of USDA commodity and conservation programs would contribute
little to overcoming the Nation's total erosion problems. Such efforts,
however, should balance conservation objectives with objectives for farm income,
commodity prices, production, and export.


Keywords: Commodity programs, conservation, program consistency, soil erosion.


ACKNOWLEDGMENTS

Jerry Lee and Keith Schmude of SCS and Gordon Nebeker and Jack Webb of ASCS
made significant contributions to the development, planning, design, and
implementation of means of assembling data for the study. Special acknowledg-
ment goes to the ASCS and SCS county personnel who willingly and efficiently
assembled data from their county offices. Also, special thanks go to the
agricultural economists who coordinated and recorded the massive set of
assembled data: Lee Christensen, Richard Clark, Neil Cook, Merlin Erickson,
Andrew Hudson, James Kasal, Doug Lewis, Arnold Paulsen, Robert Pfeiffer,
Jesse Russell, Gordon Sloggett, Marlow Vesterby, and Jerry Williams.
Keith Schmude, Stephanie Taylor, and Jack Webb were particularly helpful in
designing and protesting the data assembly process. Joyce Su gave excellent
attention to editing, summarizing, and running computerized statistical tests
on the data set. Venita Dimmick and Wanda Nelson typed various drafts of this
report, as well as all material dealing with the USDA Program Consistency
Study. Dennie Burns, Lee Christensen, Richard Clark, Neil Cook, Thomas Hertel,
James Kasal, Tom McDonald, Arnold Paulsen, Jesse Russell, and Peter Tidd provided
helpful comments and suggestions on former drafts. Lindsay Mann edited the
manuscript.











Washington, DC 20250 April 1985





PREFACE


This report contributes that part of the U.S. Department of Agriculture's Program
Consistency Study dealing with a definition of problems. This study is one of a
series of tasks being undertaken by USDA agencies in support of Departmental
implementation of the 1977 Resource Conservation Act (RCA).

One goal of the National Conservation Program, which is required by the RCA, is
to obtain consistency of Departmental commodity and conservation programs and
objectives. There have been several Departmental attempts in this and previous
administrations to examine ways that commodity programs could be designed to
simultaneously achieve soil and water conservation objectives and meet farm price
and income support objectives.

An integrated program approach requires information on the erosion problem
on farms participating in commodity programs and the potential response of these
farm operators to integrated program alternatives. The "problem-definition
phase" of the USDA Program Consistency Study was initiated to fill this informa-
tion gap.

The USDA Program Consistency Study is an interagency effort. Participating
agencies include the Economic Research Service (ERS), the Soil Conservation
Service (SCS), the Agricultural Stabilization and Conservation Service (ASCS),
and other USDA agencies.

The Consistency Study is split into three phases. The first phase, and the subject
of this report, is an effort to define more adequately the nature and extent of
program consistency problems. In the second phase, farm programs have been
examined for their impacts on soil erosion. In the third phase, three alterna-
tive policies have been examined to assess their expected effects on soil and water
resources, agricultural production, farm income maintenance, and other important
Departmental goals. The analytical work for all three phases is complete, and
published reports on results are forthcoming.


OTHER REPORTS ON FARM LEGISLATION

Other USDA reports providing background for 1985 farm bill discussions deal with
the major program commodities, the farm industries that produce them, and the farm
programs under which they are produced. These commodity papers are available from
EMS Information, Room 0054-S, USDA, Washington, DC 20250, (202) 447-7255. They
include Honey (AIB-465), Wool and Mohair (AIB-466), Wheat (AIB-467), Tobacco
(AIB-468), Peanuts (AIB-469), Rice (AIB-470), Corn (AIB-471), Soybeans (AIB-472),
Oats (AIB-473), Dairy (AIB-474), Sorghum (AIB-475), Cotton (AIB-476), Barley
(AIB-477), and Sugar (AIB-478).

Background reports are also available on Federal Credit Programs for Agriculture
(AIB-483), the History of Agricultural Price Support and Adjustment Programs,
1933-84 (AIB-485), The Current Financial Condition of Farmers and Farm Lenders
(AIB-490), A Summary Report on the Financial Condition of Family-size Commercial
Farms. (AIB-492), Foreign Exchange Constraints to Trade and Development (FAER-209),
Financial Constraints to Trade and Growth: The World Debt Crisis and Its
Aftermath (FAER-211), Possible Economic Consequences of Reverting to Permanent
Legislation or Eliminating Price and Income Supports (AER-526), and the Impacts of
Policy on U.S. Agricultural Trade (ERS Staff Report No. AGES840802).





PREFACE


This report contributes that part of the U.S. Department of Agriculture's Program
Consistency Study dealing with a definition of problems. This study is one of a
series of tasks being undertaken by USDA agencies in support of Departmental
implementation of the 1977 Resource Conservation Act (RCA).

One goal of the National Conservation Program, which is required by the RCA, is
to obtain consistency of Departmental commodity and conservation programs and
objectives. There have been several Departmental attempts in this and previous
administrations to examine ways that commodity programs could be designed to
simultaneously achieve soil and water conservation objectives and meet farm price
and income support objectives.

An integrated program approach requires information on the erosion problem
on farms participating in commodity programs and the potential response of these
farm operators to integrated program alternatives. The "problem-definition
phase" of the USDA Program Consistency Study was initiated to fill this informa-
tion gap.

The USDA Program Consistency Study is an interagency effort. Participating
agencies include the Economic Research Service (ERS), the Soil Conservation
Service (SCS), the Agricultural Stabilization and Conservation Service (ASCS),
and other USDA agencies.

The Consistency Study is split into three phases. The first phase, and the subject
of this report, is an effort to define more adequately the nature and extent of
program consistency problems. In the second phase, farm programs have been
examined for their impacts on soil erosion. In the third phase, three alterna-
tive policies have been examined to assess their expected effects on soil and water
resources, agricultural production, farm income maintenance, and other important
Departmental goals. The analytical work for all three phases is complete, and
published reports on results are forthcoming.


OTHER REPORTS ON FARM LEGISLATION

Other USDA reports providing background for 1985 farm bill discussions deal with
the major program commodities, the farm industries that produce them, and the farm
programs under which they are produced. These commodity papers are available from
EMS Information, Room 0054-S, USDA, Washington, DC 20250, (202) 447-7255. They
include Honey (AIB-465), Wool and Mohair (AIB-466), Wheat (AIB-467), Tobacco
(AIB-468), Peanuts (AIB-469), Rice (AIB-470), Corn (AIB-471), Soybeans (AIB-472),
Oats (AIB-473), Dairy (AIB-474), Sorghum (AIB-475), Cotton (AIB-476), Barley
(AIB-477), and Sugar (AIB-478).

Background reports are also available on Federal Credit Programs for Agriculture
(AIB-483), the History of Agricultural Price Support and Adjustment Programs,
1933-84 (AIB-485), The Current Financial Condition of Farmers and Farm Lenders
(AIB-490), A Summary Report on the Financial Condition of Family-size Commercial
Farms. (AIB-492), Foreign Exchange Constraints to Trade and Development (FAER-209),
Financial Constraints to Trade and Growth: The World Debt Crisis and Its
Aftermath (FAER-211), Possible Economic Consequences of Reverting to Permanent
Legislation or Eliminating Price and Income Supports (AER-526), and the Impacts of
Policy on U.S. Agricultural Trade (ERS Staff Report No. AGES840802).







CONTENTS




HIGHLIGHTS. . . .. . . . *


INTRODUCTION. . . ...... .


CONCEPTUAL BASES FOR PROGRAM INCONSISTENCY. . . .


U.S. FARM PROGRAM PARTICIPATION AND EROSION PROBLEMS. . .
Soil Erosion in the United States. . . .
Do Farm Program Participation and Soil Erosion Overlap?. e


DATA ASSEMBLY PROCEDURES ...... . .......


PROGRAM PARTICIPATION AND SOIL EROSION IN CRITICAL RESOURCE AREAS
Program Participation . . . ....
Soil Erosion Rates . . . . .
Relationships Between Program Participation and Soil
Erosion in Critical Resource Areas . .
Who Contributes to the Soil Erosion Problem? ....


FARMER CHARACTERISTICS BY PROGRAM PARTICIPATION AND SOIL EROSION
Ownership . . . . . .
Tenure . . . . . *
Off-farm Work . . . .
Operator Age . . . . .
Operating Loan Source . . .
Land Capability Class and Subclass . . .


SUMMARY AND IMPLICATIONS. . . . .


REFERENCES. . . . . . .


APPENDIX A: USDA PROGRAM CONSISTENCY STUDY SAMPLE COUNTIES .

APPENDIX B: INSTRUCTIONS FOR COMPLETING AD-862 FORMS FOR USDA
PROGRAM CONSISTENCY STUDY ...... . . ..


APPENDIX C: DISTRIBUTION OF FARM AND OPERATOR CHARACTERISTICS
AMONG PROGRAM PARTICIPATION CATEGORIES AND SOIL EROSION CLASSES


. .


. .


S* *


* *
* .


* *


. .
CLASS
* *
* *
* *
* *
* *
* *


. .





. .


. .


Page

iv


t
r -
.I~ i





HIGHLIGHTS


The present commodity programs of USDA may conflict with conservation programs by
encouraging the cultivation of erosive crops. This study indicates that only
about one-third of U.S. cropland with excessive soil erosion rates is operated by
participants in the U.S. Department of Agriculture'* (USDA) commodity and soil
conservation programs. Nevertheless, efforts to increase the consistency among
USDA commodity and conservation programs should attempt to balance conservation
objectives with objectives concerning farm income, commodity prices, production,
and exports.

These findings and judgments are based on a study of 1982 data for 2,882 farms to
determine the extent to Which USDA's commodity and conservation programs may be
at odds with USDA's goals to control soil erosion.

The author concludes that modifying USDA commodity and conservation programs to
achieve greater consistency could affect one-third to one-half of the Nation's
cropland acres eroding at unacceptable rates. However, any changes in commodity
and conservation programs would also need to protect the program participants who
do maintain soil erosion at tolerable levels.

Commodity and related farm programs support prices and reduce risks of producing
crops that are relatively more erosive than nonsupported production activities.
However, because program crops such as cotton, soybeans, corn, and other food and
feed grains are vital to farm income and export, changes in farm programs must
jointly address these issues along with soil conservation goals.






Do USDA Farm Program

Participants Contribute to Soil Erosion?



Katherine H. Reichelderfer*









INTRODUCTION

National policy seeks, through a variety of Federal conservation programs, to
reduce excessive erosion on U.S. agricultural land to levels that maintain the
long-term productivity of our soil resources and improve water quality.

Another goal of Federal policy is to stabilize commodity prices and assure
adequate farm income. These goals are achieved primarily through commodity, loan,
and crop insurance programs that support market prices, reduce production risk,
or provide direct income payments to participants.

Beneficiaries of these federally subsidized credit, crop insurance, and commodity
programs include farmers who operate excessively eroding cropland as well as
noneroding cropland. The direct and indirect financial benefits of producing
commodity program crops are available to farmers regardless of the erosion on
their land. Some commodity program participants who have erosion problems apply
conservation measures to reduce erosion. Commodity program participants who till
erodible soils without employing adequate conservation measures contribute to the
erosion problem.

Current commodity program provisions discourage long-term conservation uses of
land by denying base acreage status to land not recently used to produce program
crops. Thus, some farmers who utilize long-term soil conservation measures may
be penalized by being declared ineligible for commodity program benefits.

The Secretary of Agriculture has called for investigations of measures to ensure
that programs aimed at commodity supply management and farm income support are as
consistent as possible with efforts to conserve soil and water resources. Until
now a lack of information has precluded addressing the question of how consistent
these programs are with soil conservation goals.

Knowledge of the extent to which commodity program participants currently contri-
bute to national soil erosion and how participants differ from nonparticipants in

The author is Chief, Western Hemisphere Branch, International Economics
Division, Economic Research Service (ERS). When this study was prepared, she
was Deputy Chief, Inputs and Productivity Branch, Natural Resource Economics
Division, ERS. She served as leader of the group assigned to prepare that
part of the USDA Program Consistency Study dealing with the definition of
problems.






this respect is a prerequisite to planning for better program integration. The
study summarized in this report was initiated to develop such information.

Specific questions addressed by this study include:

o Are there logical conceptual bases for hypothesizing that commodity and
related farm program activities are inconsistent with soil conservation
goals?

o What group or groups of agricultural producers are the largest contri-
butors to soil erosion problems?

o To what extent, and in what way, is commodity program participation
related to erosion problems?

o Do contributions of USDA program participants to soil erosion problems
differ from those of nonparticipants?

o How much cropland on which there is an erosion problem is operated by
individuals participating in USDA programs?

o What are the characteristics of farms and their operators that could
be affected by policies designed to integrate commodity, farm income,
and soil conservation objectives?

The answers to these questions should help provide the knowledge base required to
design and analyze policy options for resolving serious land use or erosion
problems on lands of farm program participants without placing a burden on
individuals not contributing to resource conservation problems.

The hypothesis was set forth that certain aspects of current commodity, credit,
and crop insurance programs may encourage production patterns that are incon-
sistent with soil conservation goals. Data assembled to test the accuracy of
this hypothesis indicate that USDA program participants in certain production
areas make significant contributions to soil erosion problems. Up to half of
cropland with high erosion rates in those areas may, in fact, be operated by
individuals who might be influenced to reduce erosion on their farms through
changes in USDA commodity and conservation programs. 1/

CONCEPTUAL BASES FOR PROGRAM INCONSISTENCY

There are logical bases for hypothesizing an inconsistency between commodity and
natural resource related goals, concludes.Osteen's exploration of the relation-
ships between current farm policies and soil conservation (7). 2/ The principal

1/ Some currently proposed congressional legislation (for example, H.R.
3457 and S. 663) would modify provisions of current commodity and related
farm programs to make them more consistent with soil conservation goals. The
bills propose a variety of methods to achieve this, such as basing eligibility
for farm program benefits on adherence to soil-conserving production activities
and allowing base acreage status for land in long-term conservation uses.
This report does not address specific program options. Rather, a data and
information base was developed that may be used for subsequent analyses of
specific alternative program changes.
2/ Underlined numbers in parentheses refer to sources cited in References at
end of report.






factor contributing to these inconsistencies is the positive effect of commodity,
credit, and crop insurance programs on the relative economic attractiveness of
crops covered by these programs.

As explained by Osteen, there are three factors that determine erosion
from a given site:

o The physical and climatic characteristics of the site.

o Planting and crop management decisions determining what and how crops
are cultivated on the site.

o Investments in long-term soil conservation practices applied on the site.

Farm policies and programs do not affect inherent physical and climatic factors.
Policies and programs that affect the relative price or relative production risk
of alternative crops do, however, strongly influence planting and crop management
decisions. Policies and programs that increase farm income and credit availability
may affect the extent to which long-term conservation investments are made.

Price supports, target prices, nonrecourse loans, acreage reduction programs,
subsidized crop insurance, disaster payments, Farmers Home Administration (FmHA)
emergency loans, and current commodity export policies all have one thing in
common: each increases the economic attractiveness of the commodities to which
it applies in relation to commodities not covered by the program or affected by
the policy. Price supports, acreage reduction programs, and export policies
increase the market prices of program commodities or reduce the price risk asso-
ciated with them. These program crops offer high and stable relative price
advantages to all farmers--not just to program participants. Target price pro-
grams create additional incentives for program crop production and participation.
Nonrecourse loans, the farmer-owned reserve, and other price support programs
help stablilize or guarantee the price received by complying farmers for commodi-
ties covered. Finally, subsidized crop insurance, disaster assistance, and FmHA
emergency loans help protect participating farmers producing crops covered under
specific programs from suffering financial disaster during bad crop years or
because of poor cropping conditions. These safeguards are available only
for production of certain commodities. Thus, farmers may find production of
these commodities more attractive than production of commodities which are not
covered by such programs.

If an individual farmer has the right to decide what, where, and how much of
various commodities to produce, he or she will naturally take program benefits
(direct and indirect) into consideration when making planting and crop management
decisions. Those decisions may subsequently affect soil erosion rates. It is
useful, therefore, to compare the likelihood of soil erosion resulting from
various production activities with those activities' eligibility for program
benefits. Given the same physical factors and the same level of management,
production of commodity program crops produces more erosion (table 1). None of
the "least erosive" activities shown in table 1 is eligible to receive deficiency,
diversion, or disaster payments, or obtain nonrecourse loans. 3/ Few are
eligible for coverage through federally subsidized crop insurance.


3/ The dairy program, which supports and stabilizes milk prices, does provide
additional benefits to some producers whose land is in grassland, hayland, and
pasture.





Farmers may be particularly encouraged to plant program crops during periods
of economic recession and recovery. Currently U.S. farmers have a high rate
of indebtedness, and loans have been made at high interest rates. Substantial
cash flow is required to pay off debts and other expenses. Production of
program crops helps assure cash flow is forthcoming.

Commodity programs create situations that are relatively profitable and that may
increase the likelihood that erosive crops will be planted on erodible land.
However, the income-enhancing effects of the programs may also encourage conser-
vation investments. Farmers are likely to invest in terraces and other costly
structures only when they perceive a long-term net benefit from the investment
and have a sufficiently high income to cover its cost. Commodity programs, by
offering relatively profitable and stable production possibilities, help pro-
ducers meet the criterion of adequate farm income for investment purposes.

FmHA ownership, operating, and soil and water loans increase the availability
of credit to farmers. As credit availability increases, the ability to invest
in long-term improvements, including conservation practices, also increases.
Translating credit availability into conservation investments, however, depends
upon the farm operator's perception that current soil erosion is reducing or


Table 1--Agricultural land uses, erosion
direct farm program benefits


potential, and eligibility for major


: :Eligibility to receive--
Relative Land use/ Deficiency, : : Federal
erosiveness production diversion, : Nonrecourse : all-risk
: activity 1/ and disaster : loans : crop
: : payments :: insurance 2/


Most erosive Cotton Eligible Eligible Eligible
Most erosive Soybeans Ineligible Eligible Eligible
Moderately erosive Corn Eligible Eligible Eligible
Moderately erosive Grain sorghum Eligible Eligible Eligible
Less erosive Wheat Eligible Eligible Eligible
Less erosive Barley Eligible Eligible Eligible
Less erosive Oats Eligible Eligible Eligible
Less erosive Rice Eligible Eligible Eligible
Least erosive Grassland Ineligible Ineligible Ineligible
Least erosive Hayland Ineligible Ineligible Ineligible 3/
Least erosive Range and pasture Ineligible Ineligible Ineligible
Least erosive Forest and tree crop Ineligible Ineligible Ineligible 3/

I/ Land use/production activities are grouped according to relative erosiveness.
Specialty and miscellaneous crop production is not included since the relative
erosiveness of crops in those categories can range from high (such as tobacco) to
low (such as vineyards).
2/ Federal Crop Insurance Corporation (FCIC) all-risk crop insurance is avail-
able in locations where disaster payments are not made in conjunction with com-
modity programs.
3/ FCIC all-risk crop insurance is available for some forage and seed enter-
prises and tree fruit producers in only a few U.S. counties.






will negatively affect the productivity of land. There is considerable contro-
versy concerning farmers' perceptions of the long-term benefits from soil conser-
vation investments.

Although there are reasonable bases for linking soil erosion with farm program
participation, such relationships remain questionable. This report presents
information which we assembled to address these questions.


U.S. FARM PROGRAM PARTICIPATION AND EROSION PROBLEMS

Studies characterizing participants in commodity programs throughout the
sixties and those participating in 1978 commodity programs consistently
reveal the following (3):

o Program participants typically have larger farms than do nonparticipants;
a larger proportion of operators of large farms participate in commodity
programs.

o Program participants typically have a larger portion of land in crops and
obtain a larger share of sales from crops than do nonparticipants.

o Farms of program participants tend to be cropped more intensively
and to include a high proportion of specialized cash crop operations.

These observations were reconfirmed in 1982. In addition, 1982 participation
by region was found to be (8):

o Highest in the Plains States, where most producers usually participate.

o High in the North Central and Southern States, which account for
most participating producers outside the Plains States.

o Low in the Northwest, Southwest, and Northeast, where fewer than 4
percent of producers participated in 1982 commodity programs.

Much of the reason for the variation in participation rates among regions is
the difference in crops produced. Most U.S. wheat is produced in the Plains
States, corn in the North Central States, and cotton and rice in the South.
The proportion of program crops produced in the Northwest, Southwest, and
Northeast is less than that for the other three regions. The participation
rates for specific commodity programs vary according to benefits offered in a
given year. In 1982, program participation rates were greatest, in order, for
wheat, corn, cotton, and sorghum.

More information is available on commodity program participation than on
the distribution of participation in federally subsidized credit and crop
insurance programs.

FmHA is the lender of last resort for many farm operators. It provides low-
interest credit to farmers who are unable to obtain financing from commercial
sources. FmHA subsidies per cropland acre are highest for States that have a
large proportion of small farms (5). By its very nature, FmHA is likely to
be the primary source of credit for lower income farmers. However, no current
data on the characteristics of FmHA borrowers are available to test this
hypothesis.






The Federal Crop Insurance Act of 1980 authorized the substitution of disaster
payments with the availability of federally subsidized all-risk crop insurance.
Any producer of an insurable crop in counties where the Federal Crop Insurance
Corporation (FCIC) coverage is available can obtain Federal crop insurance. FCIC
will insure some 41 crop programs for the 1985 crop year. Principal crops
insured through FCIC are corn, grain sorghum, oats, barley, and peanuts. No data
are available to describe the characteristics of individuals purchasing Federal
crop insurance.


Soil Erosion in the United States

Soil erosion is considered tolerable when annual rates are at or below the level
needed to maintain long-term soil productivity--popularly expressed as the "soil
loss tolerance," or "T value." In the United States, 67 percent of all soils
have a tolerance level, "T" value, of 5 tons per acre per year (TAY), which is the
highest permissible rate, but T is lower on many soils, going as low as 1 TAY on
some sensitive soils. In 1982, about 56 percent of all U.S. cropland was eroding
at levels equal to or less than T. About 44 percent of all cropland is eroding
at levels greater than T (4). Over 140 million acres of that land have sheet and
rill erosion (resulting from rainfall) plus wind erosion resulting in erosion of
more than 5 tons per acre per year (9).

Soil erosion rates vary significantly among and within regions as a result of
differences in physical and climatic characteristics, crop mix, crop management
practices, and extent of conservation investments. Bills and Heimlich report a
soil erosion taxonomic system that groups sheet and rill erosion factors into
fixed and manageable components (1). They derive four erosion classes:

1. Nonerosive land is land for which physical characteristics are such that
regardless of the crop or practices applied, it will not erode at rates
greater than 5 tons per acre per year (TAY).

2. Moderately erosive land managed below tolerance is land that has the
potential for erosion rates greater than 5 TAY but is managed (through
crop choice and practices) in a manner that keeps erosion below the 5
TAY maximum tolerance level.

3. Moderately erosive land managed above tolerance is land on which erosion
rates could be restricted to 5 TAY, but management practices cause
erosion rates greater than 5 TAY.

4. Highly erosive land is so inherently erosive that almost no management
except permanent sod cover can keep the erosion rate at 5 TAY or below.

This classification scheme is useful in defining two distinctly different types
of erosion problems:
0
The problem of moderately erodible land on which erosion can be reduced
to tolerable levels, but which is managed in ways that cause erosion
above tolerance.
0
The problem of land cultivated despite the fact that it is inherently
highly erodible and not subject to management below tolerance unless
placed in a noncultivated use, such as permanent sod or forest cover.





Poor management of moderately erodible soils results from economic barriers to
investment in soil conservation, lack of knowledge of ways to reduce erosion on
these manageable soils, lack of perception of a problem, or some combination of
these factors. On some land, erosion above 5 tons per acre per year is not
considered by farmers to be a problem because it will not result in productivity
losses within any foreseeable time frame, or it would cost more to reduce the
erosion than could be captured in returns.

Conversion of inherently erodible soils that are under grass to cropland occurs
in response to a variety of economic incentives, including high grain prices
relative to livestock prices, the increased land value and favorable tax
structure that allow capital gains to be made from plowing up grasslands, and
availability of commodity program benefits on cultivated land used for program
crop production. However, agricultural programs do not seem to be the driving
force behind observed plowing and conversion of western grasslands to cropland
(2, 11, 12).

Bills and Heimlich also examined the regional, crop-specific, and owner-operator
characteristics of land that in 1977 fell into each of their soil erosion classes
for sheet and rill erosion. Major findings include the following (1):

Land highly susceptible to sheet and rill erosion is relatively more
prevalent in the Northeast, Appalachian, and Corn Belt regions;
relatively large amounts of cropland moderately susceptible to sheet and
rill erosion is managed above tolerance in the Appalachian, Corn Belt,
Delta, and Southeast regions.

More than two-thirds of cropland most susceptible to sheet and rill
erosion was committed to the production of row crops during the 1977 crop
year; more than three-fourths of all cropland moderately susceptible to
sheet and rill erosion eroding at more than 5 TAY was planted to corn,
soybeans, and other row crops; while approximately half of cropland
moderately susceptible to sheet and rill erosion was committed to wheat,
hay, or other close-grown crops and grasses.
0
Small differences were found among soil erosion and size of landholding:
relatively large numbers of owners with cropland highly susceptible to
sheet and rill erosion have small farm landholdings; relatively large
numbers of owners situated on cropland not susceptible to sheet and rill
erosion have large landholdings.

o There are no obvious characteristics defining groups of owner-operators
of nonerodible, moderately erodible, or highly erodible cropland.
Owner-operators seem to be distributed fairly evenly among sheet and rill
soil erosion classes by age, net farm income, principal occupation, type
of ownership, and educational level. 4/

Rates of wind erosion on cropland are highest in the Southern Plains. However,
no information is available on the characteristics of farms or farm operators on
whose land wind erosion is a major soil erosion problem.


4/ The Bills and Heimlich study considered only owner-operators of cropland
and addressed only the problem of sheet and rill erosion. No data were available
to examine characteristics of tenants among soil erosion classes. Owner-
operators constitute about half of all farm operators (1).





Do Farm Program Participation and Soil Erosion Overlap?


A comparison of the characteristics of farm program participation and soil
erosion suggests the possibility of some overlap. Specifically, participation in
farm programs is fairly high among row crop producers, and row crop production is
closely related to erosion problems.

On the other hand, some evidence suggests that operators of eroding cropland and
participants in commodity programs are more distinctly grouped. While program
participation is skewed toward operators of larger farms, high levels of soil
erosion are more prevalent among small farms. Further, the most erodible soil
may not exist in regions of the country that typically have high rates of
commodity program participation. Ogg, Miller, and Clayton compare the proportion
of agricultural counties' cropland acreage eroding over 25 TAY with the propor-
tion of those counties' cropland enrolled in 1977 commodity programs (6). They
find no positive correlation between these proportions, and maps illustrating
their findings suggest a negative correlation.

Aggregate data on program participation and soil erosion, while enlightening, do
not adequately define the dimensions of the problems of consistency in farm
programs. There is a need to look specifically at regions and crop production
activities that make soil naturally prone to all forms of erosion before we can
judge whether farm programs affect observed erosion where erosion is most likely
to occur. For example, while program participation rates are relatively low in
most counties that experience excessive erosion, it is necessary to know for
purposes of policy analysis whether the excessive erosion in these areas is the
result of practices on program participants' land. A more disaggregated data set
is needed to answer the questions posed at the beginning of this report. This
report, therefore, brings together primary data directly relating farm program
participation to soil erosion in regions of the country that have critical
erosion problems.

DATA ASSEMBLY PROCEDURES

Characterizing producers by joint soil erosion and farm program participation
requires observations linking physical, demographic, and farm program partici-
pation information on an appropriate sample of farming operations. Data
collected for this study linked preliminary physical data on erosion from the
1982 Soil Conservation Service's (SCS) National Resources Inventory (NRI) with
information supplied by the Agricultural Stabilization and Conservation Service
on the farm and the operator of the land on which sampled NRI cropland points
fall.

National Resources Inventories of soil and water conservation needs, conducted
every 5 years by SCS, provide detailed data on land capability, land use, sheet
and rill erosion, wind erosion, soil quality, and conservation needs for various
land uses. The 1982 NRI contains information on 328,014 specific points of land
across the United States. SCS field personnel conducted onsite inspections of
each point and recorded resource data, including the information needed to esti-
mate annual soil erosion from the point.

The data base for this study was derived by first selecting a sample of NRI
cropland points in regions of the country that have critical soil erosion
problems.





Do Farm Program Participation and Soil Erosion Overlap?


A comparison of the characteristics of farm program participation and soil
erosion suggests the possibility of some overlap. Specifically, participation in
farm programs is fairly high among row crop producers, and row crop production is
closely related to erosion problems.

On the other hand, some evidence suggests that operators of eroding cropland and
participants in commodity programs are more distinctly grouped. While program
participation is skewed toward operators of larger farms, high levels of soil
erosion are more prevalent among small farms. Further, the most erodible soil
may not exist in regions of the country that typically have high rates of
commodity program participation. Ogg, Miller, and Clayton compare the proportion
of agricultural counties' cropland acreage eroding over 25 TAY with the propor-
tion of those counties' cropland enrolled in 1977 commodity programs (6). They
find no positive correlation between these proportions, and maps illustrating
their findings suggest a negative correlation.

Aggregate data on program participation and soil erosion, while enlightening, do
not adequately define the dimensions of the problems of consistency in farm
programs. There is a need to look specifically at regions and crop production
activities that make soil naturally prone to all forms of erosion before we can
judge whether farm programs affect observed erosion where erosion is most likely
to occur. For example, while program participation rates are relatively low in
most counties that experience excessive erosion, it is necessary to know for
purposes of policy analysis whether the excessive erosion in these areas is the
result of practices on program participants' land. A more disaggregated data set
is needed to answer the questions posed at the beginning of this report. This
report, therefore, brings together primary data directly relating farm program
participation to soil erosion in regions of the country that have critical
erosion problems.

DATA ASSEMBLY PROCEDURES

Characterizing producers by joint soil erosion and farm program participation
requires observations linking physical, demographic, and farm program partici-
pation information on an appropriate sample of farming operations. Data
collected for this study linked preliminary physical data on erosion from the
1982 Soil Conservation Service's (SCS) National Resources Inventory (NRI) with
information supplied by the Agricultural Stabilization and Conservation Service
on the farm and the operator of the land on which sampled NRI cropland points
fall.

National Resources Inventories of soil and water conservation needs, conducted
every 5 years by SCS, provide detailed data on land capability, land use, sheet
and rill erosion, wind erosion, soil quality, and conservation needs for various
land uses. The 1982 NRI contains information on 328,014 specific points of land
across the United States. SCS field personnel conducted onsite inspections of
each point and recorded resource data, including the information needed to esti-
mate annual soil erosion from the point.

The data base for this study was derived by first selecting a sample of NRI
cropland points in regions of the country that have critical soil erosion
problems.






Major land resource areas (MLRA's) were chosen as the units of regional delinea-
tion. An MLRA is a geographically associated land resource unit within which
physical and climatic conditions are relatively homogeneous (9). Eleven MLRA's
were selected from among those having high concentrations of critical soil erosion
problems. The criterion used for identifying such an MLRA was that USDA had
targeted a high proportion of counties in the MLRA to receive increased conserva-
tion technical assistance because of cropland or rangeland erosion or water
conservation and salinity control problems or both. The areas targeted for water
conservation and salinity control were considered representative of critical wind
erosion areas. Critical erosion control target areas were selected as represent-
ative of sheet and rill erosion problem areas. The five individual and three
paired sets of MLRA's designated as study areas are illustrated in figure 1 and
are further described by figures 3 to 10. The study areas, or 11 MLRA's
comprising them, contain 41 percent of all U.S. counties identified by SCS as
critical "cropland erosion control" counties. They also include 31 percent of
those identified as critical "water conservation and salinity control" counties,
that is, areas in which wind erosion is expected to occur at critical rates.

Sample counties within study areas were chosen on the basis of whether a county
was included in SCS's set of sample counties for its Conservation Technical
Assistance (CTA) Program Evaluation. Those counties were previously selected to
provide a nationally valid sample of agricultural counties for SCS and ASCS pro-
gram evaluation purposes. The advantage of using a subsample of those counties
for the USDA Program Consistency Study is that it allowed for coordination and
supplementation of data sets collected for other related studies. The con-
sistency study data, however, are not subject to evaluation within the framework
of the CTA evaluation sample frame. The two separate samples were designed to
intersect one another, but the characteristics of the two samples are distinct.
Some 66 counties were CTA counties within the eight study areas. Two additional
counties were randomly selected from study areas that had too few CTA counties to
provide a sample of reasonable size. The 68 counties selected from among the
eight study areas for the consistency study sampling are listed in appendix A and
illustrated in figure 2. The set of sample counties represents over 6 percent of
Conservation Technical Assistance Target Areas.

A random sample of NRI cropland points was drawn, according to standardized
procedures, for each of the 68 sample counties. SCS personnel in the sample
counties indicated the location of each randomly selected NRI cropland point on
county mapping materials. The land capability class and subclass and the sum of
wind and water erosion rates estimated for each selected point were also recorded
alongside the point's location on the mapping material.

SCS mapping materials were then lent to ASCS county offices where ASCS county
personnel identified the farm operation on which each mapped NRI cropland point
was located. ASCS county office records, supplemented by personal knowledge of
ASCS county personnel, were used to assemble the following information on the
operation associated with each selected sample NRI point:
0
Farm identifier;
0
Farm type;
0
Farm size;
Cropland acreage;
Cropland acreage;





Figure 1


Location of Study Areas Covered by USDA Program Consistency Study Data Assembly


Figure 2
Consistency Study Sample Counties


Flaure 1






Commodity and conservation program participation for 1978, 1982, and
1983;
0
Type of farm ownership;
0
Farm tenure arrangement;
0
Primary source of operating loans to farm operator;

0
Off-farm work status of farm operator;

Age class of operator; and
0
Base acreage and effective yield of program crops produced.

Data on farm size, farm type, cropland acreage, cropping information, and ASCS
program participation were available on filed farm record cards. Participation
in SCS programs was determined through consultation with SCS personnel or
examination of SCS file records. Type of ownership, classification of tenure
arrangements, loan sources, off-farm work status, and age class of operator were
determined on the basis of personal knowledge of ASCS county personnel. Because
ASCS personnel are generally very familiar with their local farming communities
and since they had the option of stating whether or not they are aware of a
particular characteristic of a given farm operator, the data based on personal
knowledge appear to be very reliable.

Conservation program participation for a given year was defined as the receipt of
payments in that year for a farm operator's participation in any ASCS-administered
conservation program 5/ or any SCS-administered program 6/, and the submission of
a conservation plan to SCS in that year or action taken that year in accordance
with a previously filed plan. Thus, the study considers only the financial and
technical assistance aspects of USDA's overall conservation goals. Appendix B
provides definitions of other variables.

Data were assembled between October 1983 and January 1984. A total of 2,882
observations linking soil erosion, farm type, and operator characteristics were
collected. The distributions of sample data among study areas, farm types, and
land capability classes are shown in tables 2-4.

The data set assembled to relate soil erosion and farm program participation
in critical resource areas of the country represents a merger of systematically
selected subsets of 1982 data from the Natural Resources Inventory and ASCS
farm records that randomly overlap with data independently collected for other
SCS conservation program evaluation purposes. The nature of the data assembly
process and the multiple objectives for which the data originally were collected
impose several limitations on the value of the data.

5/ ASCS-administered conservation programs include the Agricultural Conservation
Program, Emergency Conservation Program, Rural Clean Water Program, Water Bank,
and Forestry Incentives Program.
6/ Formalized SCS conservation programs include Great Plains, Rural Abandoned
Mines Program, Resource Conservation and Development, PL-566 (Small Watersheds),
and the Conservation Technical Assistance Program.





First, the study sample is purposefully biased to reflect the erosion rates and
program participation situations in specific areas of the country. The sample
was derived strictly from areas of the country where observed sheet and rill or
wind erosion rates are highest. Not all cropland within these areas erodes at
high rates; much of it erodes at far lower than tolerable levels. However, the
study areas are characterized by greater than average soil erosion rates and
the sample data reflect this. As a result, the data from the geographically
biased sample universe do not present a description that can be applied to
other regions of the country or to the Nation as a whole. (See "Study Areas
Are Not Representative of the United States," page 15.)

The study data are not derived from a probability-based sampling technique.
Although observations were randomly selected, statistically determined confi-
dence limits cannot be placed on derived numerical averages.

These limitations restrict the use of our study area sample data to the pro-
vision of descriptive information on the study areas only. In the sections
immediately following, the sample data on erosion, program participation, and
farm characteristics are compared to derive generally observed relationships
among relevant factors in the study areas.


Table 2--Distribution of sample data among study areas

Study area : Observations : Proportion of
: : total sample

Number Percent
Columbia Plateau and Palouse and
Nez Perce Prairies (MLRA's 8
and 9) 1/ 320 11.1

Central High Plains and Upper
Arkansas Valley Rolling Plains
(MLRA's 67 and 69) 261 9.1

Southern High Plains (MLRA 77) 300 10.4

Loess Uplands and Till Plains and
Nebraska and Kansas Loess-Drift
Hills (MLRA's 102B and 106) 291 10.1

Iowa and Missouri Deep
Loess Hills (MLRA 107) 304 10.6

Southern Coastal Plains (MLRA 133A) 730 25.3

Southern Mississippi Valley Silty
Uplands (MLRA 134) 491 17.0

Southern Piedmont (MLRA 136) 185 6.4

Total 2,882 100.0

1/ Major land resource area (MLRA) numbers are those designated by the U.S.
Soil Conservation Service (10).






Results apply only to the critical resource study areas front which the data were
drawn. Subsequently, we used these study area-specific descriptions to derive
broad implications for U.S. conservation and commodity program interrelationships.


Table 3--Distribution of sample data among farm types

Farm type : Observations : Proportion of
: : total sample

Number Percent

Cash grain farms 1,321 45.8
Cotton farms 219 7.6
Tobacco farms 37 1.3
Other field crop farms 111 3.9
Vegetable and melon farms 17 .6
Fruit, berry, and nut farms 18 .6
Livestock farms and ranches 142 4.9
Miscellaneous farms 1/ 14 .5
Combination livestock and grain farms 986 34.2
Tree farms 17 .6

Total 2,882 100.0

I/ Includes fur-bearing animals, bees, grass, and seed, among other types
of farming.

Table 4--Distribution of sample data by USDA land capability class


Land capability class 1/ : Percent of total sample


I 5.8
II 42.9
III 32.9
IV 14.0
V 1.0
VI 2.7
VII .8
VIII 0

Total 100.0

Land in subclass e 61.3

Land in classes IVe, VI, and VII 14.8

Note: Total does not.add to: 100.0 due to rounding.
1/ The SCS land capability classification system groups soils at several
levels. The capability class, designated by Roman numerals, indicates progres-
sively greater limitations and narrower choices for land uses. The capability
subclasses, designated by letters, are subgroups of soils that have particular
limitations due to climate or soil characteristics. Risk of erosion is designated
by subclass e.





Table 5--Program participation rates for sample points by year


Program participation : 1978 : 1982 :1983


Number Percent Number Percent Number Percent

Land of operators
participating in
neither commodity
nor conservation
programs 1,236 43.4 1,188 41.2 777 27.0

Land of operators
participating only
in commodity programs 861 30.2 799 27.7 1,093 37.9

Land of operators
participating only
in conservation
programs 294 10.3 405 14.1 254 8.8

Land of operators
participating in
both commodity and
conservation programs 459 16.1 490 17.0 758 26.3



Total 2,850 100.0 2,882 100.0 2,882 100.0


PROGRAM PARTICIPATION AND SOIL EROSION IN CRITICAL RESOURCE AREAS

All data are aggregated to study area levels. Each study area represents
an area of the country that experiences unique and relatively critical soil
resource problems.

Using 1982 data, four different program participation classes as specified for
the sample as follows:
0
Land operated by individuals participating in neither commodity nor
financial and technical assistance conservation programs.
0
Land operated by individuals participating only in commodity programs.
0
Land operated by individuals participating only in financial or technical
assistance conservation programs.
0
Land operated by individuals jointly participating in both commodity
and financial or technical assistance conservation programs.







STUDY AREAS ARE NOT REPRESENTATIVE
OF THE UNITED STATES


The areas of the country that were sampled for purposes of the USDA
Program Consistency Study are among those that have the most severe
soil erosion problems. Consequently, the erosion and land use
characteristics of the study areas differ from national averages for
erosion and land use.

The 1982 average annual soil erosion rate estimated for
cropland samples in the study area (11.4 tons per acre
per year) is 56 percent greater than the average rate
of cropland erosion nationwide (7.3 tons per acre per
year) (4).

While 50.5 percent of U.S. cropland is designated by SCS
as being subject to erosion hazard (categorized in SCS
land capability subclass e), a greater proportion of
cropland in the study area sample (61.3 percent) is
classified as erosion prone.
0
The average size of farms sampled in the study areas is
1,187 acres, which is 2.7 times larger than the national
average of 433 acres.

Program participation characteristics also differ between the study
areas and the Nation. For instance, 45 percent of the study area
was enrolled in 1982 commodity programs as compared with the national
average of 23 percent of total cropland reported by ASCS as having
been enrolled in 1982 commodity programs. Because of these differences,
results specific to the study areas cannot be applied to the Nation as a
whole.


Figures 3-10 describe the study areas sampled and summarize area-specific findings
regarding commodity program participation, farm-type distribution, land capa-
bility classes, and soil erosion. The study areas differ in many respects.
Wheat is the principal crop in the Columbia Plateau and Palouse area. Operators
in the north-central study areas are primarily livestock and grain producers, and
the southeastern study areas farmers are more diversified. In the study area
sample, average estimated erosion rates range from a high of 30.7 tons per acre
per year in the Southern High Plains to a low of 6.3 tons per acre per year in
the Southern Coastal Plains. Program participation patterns vary greatly among
the study areas. In the Southern Piedmont, roughly half of the sample points fall
on land of individuals who do not participate in either commodity or conservation
programs. By contrast, nearly all land sampled in the Southern High Plains is
operated by commodity program participants. Program participation patterns for
the entire sample are reviewed below.

Program Participation

Commodity program participation data assembled from the eight study areas reflect
the popularity of the programs in 1978, 1982, and 1983 (table 5). While 1978
showed moderate commodity program participation rates, 1982 showed relatively low
participation, and 1983 showed high program participation. Operators of land





Figure 3

Columbia Plateau and Palouse and Nez Perce Prairies


* Area located in Northwestern Wheat and Range
Region.
More than 80 percent of study area land is in farms
and ranches.
* About half of agricultural land is cropland.
* Principal production activity is dryland wheat.
* Average farm size is 3,035 acres.
* Average estimated soil erosion rate Is 7.25 tons per
acre per year.


Farm Types

Percent


Cash grain 78.4


Combination
livestock
and grain


18.4


Livestock 2.2





Miscellaneous 1.9


Program Participation Pattern

Program
programs 1978 1982 1983
participation

Percent
Neither
program 57.8 41.9 29.1

Commodity
only 30.3 42.8 50.3

Conservation
only 4.4 3.4 3.1

Both program
types, jointly 7.5 11.9 17.5


Land Capability Class D
Class Percent

I 1.0D

II 14.7

III 42.8

IV 36.3

V 0o

VI 3.9

VII 1.3


distribution





Figure 4

Central High Plains and Upper Arkansas Valley Rolling Plains


* Area is located in Western Great Plains region.
* Most of study area is in farms and ranches.
* About 65 percent of agricultural land is in range of
native grasses grazed by cattle and sheep.
* About 20 percent of agricultural land is dry-farmed to
wheat and other grains.
* Average farm size is 2,343 acres.
* Average estimated soil erosion rate is 7.25 tons per
acre per year.


Farm Types
Percent


Cash grain 55.6


36.4


Program Participation Pattern

Program gg ^
Program .1978 1982 1983
participation
Percent
Neither
program 33.7 48.7 34.1

Commodity
only 51.3 39.1 54.8

Conservation
only 2.3 4.2 2.7

Both program
types, jointly 12.6 8.0 2.9


Land Capability Class Distribution


Class Percent


Combination
livestock
and grain


I 8.0

II 29.5

III 29.5


Livestock 5.7





Miscellaneous 2.3


IV 26.8


V 01

VI 4.6

VII 1.5


I I





Figure 5
Southern High Plains


* Area is in Central Great Plains region.
* Almost all of study area is in farms and ranches.
* More than 40 percent of land area is range of
native grasses and shrubs grazed by beef cattle.
* About one-third of land area is dry-farmed, nearly
one-third is irrigated.
* Principal crops are winter wheat, grain sorghum, and
cotton.
* Average farm size is 1,322 acres.
* Average estimated erosion rate is 30.71 tons per
acre per year.

Farm Types


Perc

Cash grain 14.0

Combination
livestock 44.7
and grain


ent

1


Cotton farms 41.3


Program Participation Pattern

Program
Prt n 1978 1982 1983
participation

Neither Perent
program 1.0 1.0 0.7
Commodity
only 55.3 48.0 48.3
Conservation
only 0 1.3 0
Both program
types, jointly 43.7 49.7 51.0


Land Capability Class Distribution
Class Percent
I 0 1
II 38.2 I
III 50.5
IV 9.6 I I
V 01
VI 1.3 0
VII 0.3 |


I I


I


am" -





Figure 6
Loess Uplands and Till Plains and Nebraska and Kansas Loess-Drift Hills


* Area is in the Central Feed Grains and Livestock
region.
* Most of study area is in farms, and 60-70 percent
is cropland.
* Corn, soybeans, and other feed grains are the
principal crops grown; much of crops grown is fed
to beef cattle and hogs on farms where it is grown.
* Average farm size is 322 acres.
* Average estimated erosion rate is 8.1 tons per acre
per year.

Farm Types
Percent


Cash grain 21.6


w


Program Participation Pattern

Program 1978 1982 1983
participation
Percent
Neither
program 30.6 53.3 25.4

Commodity
only 47.8 25.8 45.0

Conservation
only 8.6 14.8 4.8

Both program
types, jointly 13.1 6.2 24.7


Land Capability Class Distribution
Class Percent


Combination
livestock
and grain



Miscellaneous


77.3


I 17.3
II 36.0 I
III 32.91
IV 12.8 I
V 01
VI 1.0 0
VII 01


1.1


z~z~zI




Figure 7

Iowa and Missouri Deep Loess Hills


* Area is in the Central Feed Grains and Livestock
region.
* Most of the area is in farms, and about 60 percent,
is cropland, about 20 percent of the area is in
permanent pasture.
* Corn, soybeans, and hay are the principal crops.
* Beef cattle and hog production are important,
enterprises on many farms in the area.
* Average farm size is 414 acres.
* Average estimated erosion rate is 18 tons per acre
per year.


Fanm Types

Percent


Cash grain 28


Combination
livestock
and grain


Program Participation Pattern

Program
participation 1978 1982 1983

Neither Percent
program 41.8 45.1 17.8

Commodity
only 27.0 24.7 44.7

Conservation
only 18.1 19.7 8.2

Both program
types, jointly 13.2 10.5 29.3


Land Capability Class Distribution
Class Percent

I 8.6

II 41.4

III 42.8

IV' 5.9 |

V 01

VI 1.3f

VII 01


J





Figure 8

Southern Coastal Plain


* Area is in the South Atlantic and Gulf Slope
region.
* The area is about 69 percent woodland, 17 percent
cropland, 11 percent pastureland, and about 3
percent urban and other land uses.
* Cash crops include soybeans, corn, peanuts and
cotton.
* Average farm size is 925 acres.
* Average estimated erosion rate for cropland is 6.26
tons per acre per year.


Farm Types
Percent


Program Participation Pattern

Program
participation
Percent
Neither
program 55.5 44.1 30.3
Commodity
only 18.0 24.1 33.0
Conservation
only 12.1 17.5 11.0
Both program
types, jointly 14.5 14.3 25.7


Cash grain 53.1 L
Combination
livestock 18.2[
and grain
Livestock 4.4


Cotton


2.7


Other 14.
field crops 14.9


Miscellaneous 6.6


D1


Land Capability Class Distribution


Class Percent
I 7.2 I
II 57.5 1
III 18.5 I I
IV 10.6 I
V 1.2 0
VI 6.4 -I
VII 3.2 [


I


I





Figure 9

Southern Mississippi Valley Silty Uplands


* Area Is in the South Atlantic and Gulf Slope
region.
* Most of the area is in farms; about 46 percent of
land area is in forests; 35 percent is cropland; and
16 percent is in pasture or hay.
* Urban development is infringing on farmland in some
locations; In other locations, pasture and forests
are being converted to cropland.
* Cash-cropping of cotton, corn, soybeans, and wheat
are major enterprises; feed grains and forage are
grown on dairy farms.
* Average farm size is 984 acres.
* Average estimated erosion rate Is 9.88 tons per acre
per year.

Farm Types
Percent

Cash grain 68.6


Combination
livestock
and grain

Livestock


Cotton


4.1


9.6


15.3


Miscellaneous 2.4


Program Participation Pattern


Program 1978 1982 1983
participation

Percent
Neither
program 51.7 41.5 31.8

Commodity
only 19.6 15.7 23.2

Conservation
only 15.5 20.4 15.3

Both program
types, jointly 13.2 22.4 29.7


Land Capability Class Distribution
Class Percent


I 4.3 L

II 49.1
III 32.6

IV 8.0

V 3.7

VI 2.1 0

VII 0.2





Figure 10

Southern Piedmont


* Area is in the South Atlantic and Gulf Slope region.
* Area is characterized by small farms and
considerable residential development; sizeable
acreage is wooded.
* Most of the open land is pasture, but some
soybeans, small grain, corn, cotton, wheat, and
tobacco are grown.
* Average farm size is 329 acres.
* Average estimated erosion rate is 9.29 tons per
acre per year.

Farm Types
Percent


Cash grain 32.4


27.6


Program Participation Pattern

Program 1978 1982 1983
participation
Percent
Neither
program 55.6 57.3 47.6

Commodity
only 10.5 7.0 11.9

Conservation
only 19.6 26.0 23.2

Both program
types, jointly 14.4 9.7 17.3


Land Capability Class Distribution
Class Percent


Combination
livestock
and grain


Livestock 23.8


1 o0

II 55.7


III 31.3

IV 9.7

V 0.5

VI 2.7

VII 0


Miscellaneous 16.2


I


I 1





sampled in the critical resource study areas had a higher rate of commodity pro-
gram participation than the Nation as a whole. In 1978, 35 percent of U.S.
farmers were enrolled in commodity programs, but 46 percent of farm operators in
our sample participated in 1978 commodity programs. National commodity program
participation was 19 percent in 1982, compared with our sample's participation
rate of 45 percent.

Conservation program participation by farm operators in the eight study areas
seems to have increased between 1978 and 1983. Conservation program participa-
tion rates for the sample population were 26.4, 31.1, and 35.1 percent for 1978,
1982, and 1983, respectively. In all three sample years, most conservation
program participants (62, 55, and 75 percent for 1978, 1982, and 1983, respec-
tively) also participated in commodity programs.

Bayes Theorum is used to examine the expected overlap of commodity and conserva-
tion program participation. 7/ Given that a farm participated in commodity
programs, the probability that it also participated in a conservation program is
35, 38, and 41 percent for 1978, 1982, and 1983, respectively. By contrast, the
probability that a farm was involved in conservation programs given that it was
not involved in commodity programs is 19, 25, and 25 percent for 1978, 1982, and
1983, respectively. This suggests that commodity program participants are more
inclined to participate in conservation programs than are other farm operators.
Similarly, conservation program participants are more likely to participate in
commodity programs than are those who do not participate in conservation
programs. These findings suggest that the two program types are complementary.

Commodity and conservation program participation rates vary greatly by farm type.
Within the study areas, cotton has the highest commodity program participation
rate; nearly 100 percent of sampled cotton farms participated in commodity pro-
grams. The second highest commodity program participation rate from the sample
is for farms classified as "other field crop farms." Almost all of these farms
are located in the Southern Coastal Plains and produce a mixture of peanuts,
soybeans, corn, and wheat. The commodity program participation rate for "other
field crop farms" is over 50 percent for all three sample years. Also in
relatively high commodity program participation categories are cash grain farms
and combination livestock and grain farms. Commodity program participation is
relatively low among sampled farms classified as "specialty and miscellaneous
farms." These farms are not represented in commodity programs because of the
crops produced on such farms, only tobacco has a commodity program. Almost none
of the sampled farms classified as "livestock farms or ranches" participated in
commodity programs. This is to be expected since there are no programs for
cattle or hog operations, the primary livestock production activities in the
sample study areas. Conservation program participation is highest for "other
field crop farms," ranging between 26 and 35 percent for other sampled farm
types in all three sample years. Table 6 shows 1982 program participation by
farm type and serves as an illustration of variation among farm types.

7/ Bayes Theorum tells us that:

Pr (RIC) Pr(Rf C)
Pr (C)

Where: Pr (RIC) is the probability that a farm participates in conservation
programs (R), given that it participates in commodity programs (C); Pr (RfC) is
the proportion of farms participating jointly in commodity and conservation pro-
grams; and Pr(C) is the proportion of farms participating in commodity programs.






The sample data reflect national variation in program participation by farm size
(table 7). Nonparticipants' average farm size is significantly smaller than that
for farms participating in either program type. Average cropland acreage is
significantly larger for commodity program participants than for nonparticipants.

Finally, commodity and conservation program participation rates vary
among the study areas sampled. This variation, illustrated by comparison of
information in area-specific figures 3 to 10, is primarily a function of the
distribution of farm types and farm sizes among the study areas, but may
partially be explained by differences in regional preferences for participation.
For example, the consistently high commodity program participation rates
observed for the Southern High Plains are due in large part to the fact
that over 40 percent of sample observations from that study area are cotton
farms. However, the finding that over 98 percent of grain farmers in the
Southern High Plains participate in commodity programs (versus less than 50
percent for the total sample) indicates a regional preference for participation.
The Southern Piedmont, which has the lowest program participation rates, also
has the smallest average farm size.


Soil Erosion Rates

Soil erosion rates obtained for sample points are those estimated by SCS on a
subset of sites sampled for that agency's 1982 National Resources Inventory. Sheet
and rill erosion were estimated by SCS by use of the Universal Soil Loss Equation
(USLE). Wind erosion is estimated by the wind erosion equation (WEE). Our erosion
data relating to each sample point was restricted to the sum of sheet and rill plus
wind erosion. We did not obtain values of the specific USLE or WEE components.

We assume, for analytical purposes, that the point sampled for erosion is repre-
sentative of the total cropland acreage surrounding the point and farmed by the

Table 6--Study sample program participation rate, by farm type, 1982

: Land of operators participating in --
Farm type : Neither : Commodity : Conservation : Both : Total
: program : program only : program only : programs :


Percent

Cash grain farms 45.8 26.6 13.4 14.2 100
Cotton farms 1.0 64.4 2.3 32.4 100
Other field crops 1/ 27.9 17.1 12.6 42.3 100
Livestock farms and
ranches 65.5 1.4 31.0 2.1 100
Combination live-
stock and grain
farms 40.6 27.0 15.3 17.1 100
Specialty and mis-
cellaneous 2/ 55.3 18.4 13.6 12.6 100


and other


Note: Totals may not add due to rounding.
1/ Includes sugar, potatoes, and peanuts, among other field crops.
2/ Tobacco; vegetable and melon; fruit, berry, and nut; tree farms;
miscellaneous farms.





operator on whose cropland the point falls. While this assumption will not hold
true for individual landholdings, the number of samples obtained for a given MLRA
or farm type is sufficiently large for the assumption to be reasonable when
aggregated to the level of an MLRA or farm type.

The average 1982 estimate of sheet and rill plus wind erosion obtained from study
area sample points is 11.4 tons per acre per year (TAY). This estimate is higher
than the national average erosion rate for cropland (7.3 TAY) obtained from
analysis of the full 1982 NRI data set (4). The difference is explained by the
origin of this study's sample points--they are derived from areas of the country
that have the highest erosion rates.

Soil erosion estimates vary significantly among farm types (table 8). Average
erosion rates are highest for points sampled from cotton farms. Sample points
from cash grain and combination livestock and grain farms have the next highest
rates of erosion in the eight study areas. These farms produce most of the corn,
soybean, wheat, and small grains represented in the sample. Points from "other
field crop farms" exhibit a moderate rate of erosion. Sample average estimated
erosion rates in the critical erosion study areas are near the 5-TAY level only
on livestock farms and ranches and specialty and miscellaneous farms.

Average estimated erosion rates vary among study areas (table 9). This variance
is due both to differences in the physical and climatic characteristics of the



Table 7-Average farm size and cropland acreage for sample farms, by program
participation category, 1982


Program participation : Average farm : Average cropland
category : size 1/ : per farm 1/


Acres

Land of operators participating
in neither commodity nor con-
servation programs 835.2 B 450.4 B
Land of operators participating
only in commodity programs 1,400.6 A 932.6 A
Land of operators participating
only in conservation programs 1,425.0 A 502.7 B
Land of operators participating
in both program types 1,495.3 A 875.6 A

Total sample 1,187.1 663.8

1/ Numbers followed by the same letter in a given column exhibit no
statistically significant differences from one another at a 95-percent
confidence level. (For example, under the "Average cropland per farm"
column, the 932.6- and 875.6-acre averages are not statistically different
from one another; neither are the 450.4- and 502.7-acre averages signifi-
cantly different from one another. However, the two averages followed by
the letter "A" are significantly greater than the two averages followed
by the letter "B.")






Table 8--Estimated soil erosion rates for sample points, by farm type from which
points were sampled, 1982


Farm type Average soil erosion rate


Tons per acre per year 1/

Cotton farms 33.6 A
Combination livestock and grain farms 11.3 B
Cash grain farms 9.4 B,C
Other field crop farms 2/ 6.0 C,D
Livestock farms and ranches 5.0 D
Specialty and miscellaneous 3/ 4.9 D

1/ Numbers followed by the same letter exhibit no statistically significant
differences at a 95-percent confidence level. (For example, the average rate
for cotton farms is significantly greater than average rates from any other farm
type. The average rate from combination livestock and grain farms is not
significantly different from the average obtained for cash grain farms, but it is
significantly greater than the average rate for other field crop farms.)
2/ Includes sugar, potatoes, and peanuts.
3/ Tobacco; vegetable and melon; fruit, berry, and nut; tree farms; and
other miscellaneous farms.



Table 9--Estimated soil erosion rates for sample points, by study area, 1982


Study area Average soil erosion rate


Tons per acre per year 1/

Southern High Plains 30.7 A
Iowa and Missouri Deep Loess Hills 18.0 B
Southern Mississippi Valley Silty Uplands 9.9 C
Southern Piedmont 9.3 C,D
Central High Plains and Upper Arkansas Valley
Rolling Plains 8.7 C,D
Loess Uplands and Till Plains and Nebraska
and Kansas Loess-Drift Hills 8.1 C,D
Columbia Plateau and Palouse and Nez Perce
Prairies 7.5 C,D
Southern Coastal Plains 6.3 D

1/ Numbers followed by the same letter exhibit no statistically significant
differences from one another at a 95-percent confidence level. (For example,
the average rates for the Southern Mississippi Valley Silty Uplands and the
Southern Coastal Plains are significantly different from one another. However,
neither of these is significantly different from the rates shown for study areas
listed between them in the soil erosion rate column.)





study areas and differences in crop production patterns. Table 10 shows erosion
rates by farm type and study area. Several things are suggested by this tabular
information:

o Production of livestock or specialty and miscellaneous crops results in
relatively low rates of soil erosion regardless of the region in which
production takes place.

o Where production is concentrated in cash grain and combination livestock
and grain operations (Columbia Plateau and Palouse; Central High Plains;
and study areas in the north-central region), erosion rates do not vary
significantly among the farm types that were sampled.

o Differences in average estimated erosion rates from samples on cash
grain and combination livestock and grain farms reflect regional
differences in erosion.

o Erosion rates for samples from cotton farms are significantly higher
than those from other farm types in every study area where cotton is
grown. Cotton farm samples exhibit highest erosion rates in the
Southern High Plains, where wind erosion presents a greater threat
than in any other area where cotton is produced.

o Average soil erosion rates obtained from specialty and miscellaneous farm
samples are lower than those for any other farm types in the Southern
Coastal Plains and Southern Mississippi Valley Silty Uplands. But that
observation is not true in the Southern Piedmont where many of the
specialty farms grow tobacco.

Relationships Between Program Participation and Soil Erosion in
Critical Resource Areas

Average estimated soil erosion rates from land of sample operators participating
in commodity programs are significantly higher than rates observed for land of
those who participate only in conservation programs or for that of those who
participate in neither program. However, erosion levels by farm type do not
differ significantly among categories of program participation (table 11).
Erosion rates for a given farm type are similar for 1982 regardless of whether
the operator participated in a commodity program in 1982. The one exception is
for the farms raising "other field crops," where erosion rates for the land of
those who participated solely in conservation programs are significantly higher
than erosion rates for farmland of those in other groups. This may be because
field crop farmers with severe erosion problems seek or are more receptive to
assistance in conserving soil.

Only in the Southern Coastal Plains and Southern Mississippi Valley Silty Uplands
are there significant differences in erosion rates among categories of program
participation (table 12). These two study areas have the greatest diversity in
farm types. When erosion rates for these study areas are further broken down by
farm type, again there is no difference in erosion rates among categories of
program participation.

Large differences in inherent erodibility of cropland and in crop production
practices exist among study areas, and commodity program participation also
varies by farm type and study area. These data imply that location and type of
crop production have a greater effect on soil erosion than any other variable





Table 10--Estimated soil erosion rate, by study area and farm type, 1982


: Average soil erosion rate from sample points on--
Study area : : Specialty : Other:Livestock:Combination
:Cash grain:Cotton: and : field: farms : livestock
: farms : farms:miscellaneous: crop : and : and grain
: : : farms : farms: ranches : farms


Tons per acre per year 1/


Columbia Plateau and
Palouse and Nez
Perce Prairies 2/

Central High Plains
and Upper Arkansas
Valley Rolling
Plains 2/


Southern High
Plains 4/


7.1 C 3/


7.9 C 3/


25.4 A 50.6 A


2.3 A 3/ 2.0 A 8.6 C


1.7 A 3/


2.7 A 10.1 B,C


14.0 A,B


Loess Uplands and
Till Plains and
Nebraska and Kansas
Loess-Drift Hills 2/

Iowa and Missouri
Deep Loess
Hills 2/

Southern Coastal
Plains 4/

Southern Mississippi
Valley Silty
Uplands 4/

Southern
Piedmont 5/


8.3 C 3/



16.3 B 3/


6.5 C 9.2 B 4.6 A 6.1


10.1 C 12,2 B 4.4 A 3/ 5.8 A 10.9 B,C


15.3 B 3/ 6.9 A 3/ 4.7 A


1/ Mean erosion values followed by the same letter in a farm type column are not
significantly different from one another at the 95-percent confidence level.
2/ Mean erosion values for farm types within study area exhibit no statistically
significant differences from one another at the 95-percent confidence level.
3/ One or no observations of specified farm type in specified study area.
4/ Erosion from points sampled on cotton farms is significantly higher than that
sampled from other farm types in the Southern High Plains, Southern Coastal Plains,
and Southern Mississippi Valley study areas.
5/ Erosion from points sampled on cash grain farms is significantly higher than
that sampled from other farm types in the Southern Piedmont.


3/



3/


6.0 A


8.1 C



18.7 A


5.8 A


7.6 C











Table 11--Estimated soil erosion rates for study sample points, by program participation category and
farm type, 1982


: Average soil erosion rate on land sampled from-- :Average,
Program participation : : Specialty : Other :Livestock :Combination:all farm
: Cash : : and : field :farms : livestock : types
: grain : Cotton :miscellaneous: crop : and : and grain :
: farms : farms : farms 1/ farms : ranches : farms

Tons per acre per year 2/
Operators participating
in neither commodity 9.5 A 3/ 4.9 A 5.6 B 5.2 A 10.7 A 9.2 B
nor conservation programs

Operators participating only
in commodity programs 9.3 A 36.5 A 4.3 A 4.2 B 3/ 11.9 A 14.7 A

Operators participating only
in conservation programs 8.9 A 3/ 4.1 A 11.2 A 4.9 A 11.8 A 9.4 B

Operators participating
in both program types 9.6 A 30.7 A 6.8 A 5.6 B 3/ 11.4 A 12.8 A

1/ Includes tobacco; vegetable and melon; fruit, berry, and nut; and tree farms as well as other
miscellaneous farm types.
2/ Mean erosion values followed by the same letter in a farm-type column exhibit no statistically
significant differences from one another at the 95-percent confidence level.
3/ Less than 3 percent of farm types sampled fell in specified program participation category.











Table 12--Estimated soil erosion rates
study area, 1982


for study sample, by program participation category and


: Average soil erosion rate from major land resource area :Average,
Program participation :8 & 9: 67 & 69 : 77 : 102B : 107 :133A : 134 :136 :all study
: : & 106 ::: : :areas

Tons per acre per year 1/
Land of operators
participating in
neither commodity
nor conservation 6.9 A 8.1 A 2/ 9.3 A 17.9 A 6.5 A,B 10.0 A,B 9.1 A 9.2 A
programs

Land of operators
participating only
in commodity 8.4 A 9.0 A 38.8 A 7.7 A 18.7 A 5.8 A,B 12.2 A 11.1 A 14.7 B
programs

Land of operators
participating only
in conservation 5.4 A 5.5 A 2/ 5.4 A 19.4 A 7.6 A 9.5 A,B 7.7 A 9.4 A
programs

Land of operators
participating in
both program types 4.9 A 13.0 A 24.1 A 5.8 A 14.2 A 4.7 B 8.3 B 13.4 A 12.8 B

1/ Mean erosion values followed by the same letter within a single MLRA column exhibit no


statistically significant differences from one
(See table 2 for identification of MLRA's.)
2/ Less than 2 percent of study area sample


another at the 95-percent confidence level.

falls in specified program participation category.





considered in this study. The crop type and location factors completely override
any contribution of participation in either or both commodity and conservation
technical or financial assistance programs in explaining erosion from cropland in
critical erosion areas.

Who Contributes to the Soil Erosion Problem?

To the extent that USDA program participants contribute to the cause or to the
solution of soil erosion problems, there may be ways to encourage them to further
reduce erosion while continuing to enjoy program benefits. For example, commodity
program participants might have their benefits reduced if they did not manage
croplands so as to conserve soil. However, farmers who are not in USDA programs
are not vulnerable to the exertion of financial leverage as a way to influence
soil conservation.

Farm operators can be classified into eight groups with respect to their vulner-
ability to and target potential for policies to integrate commodity and conser-
vation programs. Operators in each of the four previously defined program
participation categories are further categorized on the basis of whether erosion
rates for their land meet or exceed the tolerance level or 5 tons per acre per
year (table 13).

Tables 13-15 show that in the critical resource study areas:

0 More than half of the sampled cropland erodes at rates greater than 5
tons per acre per year. (Only about 34 percent of total U.S. cropland
erodes at rates above that level.)

Table 13--Participation in USDA commodity and conservation programs in critical
resource study areas, by soil erosion levels, 1982 1/

: Land eroding at rates : Land eroding at rates :Total
Program participation : less than 5 tons per : of 5 or more tons per : all
: acre per year : acre per year :cropland

Percent of total sample

Land of operators partici-
pating in neither commodity
nor conservation programs 19.0 22.1 41.1

Land of operators partici-
pating in commodity
programs only 11.6 16.2 27.8

Land of operators partici-
pating in conservation
programs only 7.1 7.0 14.1

Land of operators partici-
pating in both commodity
and conservation programs 8.0 9.0 17.0

Total 45.7 54.3 100.0

1/ See app. table 10 for distribution among more finely detailed soil erosion
classes.





-- Almost 62 percent of 1982 cropland eroding at rates above 5 TAY was
operated by individuals participating in USDA commodity and/or financial
or technical assistance conservation programs in that same year.


-- The other
participated
conservation


38 percent of operators of land eroding above 5 TAY
in neither commodity nor financial or technical assistance
programs.


In the critical resource areas studied, land eroding above and below 5 TAY is
distributed fairly evenly between the commodity and conservation program parti-
cipation categories. Concurrent participation of commodity program participants
in conservation programs is only slightly higher (41 percent) on land eroding at
or below 5 TAY than on land eroding above 5 TAY (37 percent). Table 13 also
shows that in the study areas:

0 About 40 to 45 percent of the cropland sampled is operated by commodity
program participants (some of whom also are conservation program
participants).

-- Of the land operated by commodity program participants, 58 percent
erodes above 5 TAY.








Table 14--Participation in USDA commodity and conservation programs for
cropland eroding at or above 5 tons per acre per year in critical
resource study areas, 1982

: Cropland eroding at rates of
Program participation : 5 or more tons per acre per year
:_ in critical resource study areas

Percent

Land of operators participating
neither in commodity nor conservation
programs 38

Land of operators participating in
commodity programs only 28

Land of operators participating in
conservation programs only 16

Land of operators participating in
both commodity and conservation
programs 18

Total 100





-- 42 percent of commodity program participants maintain rates of
erosion at or below 5 TAY, even though the majority of these
operators do not also participate in financial or technical assistance
conservation programs.

o About one-third of the cropland sampled is operated by financial or
technical assistance conservation program participants (some of whom also
are commodity program participants).

-- Of the land operated by financial or technical assistance conservation
program participants, about half erodes at a rate of more than 5 TAY and
half erodes at or below that maximum T level.

Availability of 1982 National Resources Inventory data will permit further
classification of USDA program participation by the extent of erosion. It will
then be possible to provide estimates of the degree to which land operated by
program participants and eroding at excessive rates is either moderately erodible
and managed so that erosion is above tolerance or is inherently erodible land
placed into cultivation.

FARMER CHARACTERISTICS BY PROGRAM PARTICIPATION AND SOIL EROSION CLASS

Factors other than farm type and region may influence the behavior
of farmers with respect to farm programs and soil conservation. This section
of the report reviews analyses of the distribution of farm ownership, tenure,
off-farm work, loan source, and land capability classes among program
participation and soil erosion classes.



Table 15--Participation in USDA commodity and conservation programs for
cropland eroding below 5 tons per acre per year in critical
resource study areas, 1982

: Cropland eroding at less than
Program participation : 5 tons per acre per year in
: critical resource study areas

Percent

Land of operators participating neither
in commodity nor conservation programs 41

Land of operators participating in
commodity programs only 25

Land of operators participating in
conservation programs only 16

Land of operators participating in
both commodity and conservation
programs 17

Total 100

Note: Total does not add due to rounding.





Program participation classes are the same as those defined previously. Sample
results were grouped for analytical purposes according to land falling into
classes 8/:

Low rate of erosion = Less than 5 TAY

Medium-low rate of erosion = 5-13 TAY

Medium-high rate of erosion = 14-24 TAY

High rate of erosion = 25 TAY and above

We conducted chi-square statistical tests to determine whether the distribution
of selected farm and operator characteristics among operators of land in
different soil erosion classes (or program participation classes) are the same
as for the whole sample. Differences in the distributions provide clues as to
what factors may be related to soil erosion and program participation. Identi-
fication of important characteristics also provides information that can be used
to describe various groups that could be affected by changes in commodity or
conservation programs.


Ownership

Individuals or families own 92 percent of the sample farms. Cropland owned by
either individuals or families is fairly evenly distributed among program
participation and soil erosion classes. Nonfamily partnerships or corporations
own 3 percent of the sample farms. These few farms fall with disproportionately
high frequency in the lowest and highest erosion classes. Operators of the
nonfamily and corporate farms have significantly lower participation in conserv-
ation planning and cost-sharing programs, but appear with relatively high
frequency in the "commodity program only" participation class. (See app. tables
2 and 3 for details.)

Tenure

Half of the sample farms are operated by the owners. Statistical tests indicate
that owner-operators and tenants with long-term leases are similarly distributed
among program participation and soil erosion classes. The distribution of these
groups contrasts with that of tenants with short-term leases in several ways.
First, owner-operators and tenants with long-term leases appear with relatively
greater frequency in the low rate of erosion class and with relatively less
frequency in the high rate of erosion class than do tenants with annual leases.
Owner-operators and tenants with long-term leases are more likely to be
participants in conservation .programs only, or to be general program nonpar-
ticipants than are tenants with annual leases. These findings lend some support
to the theory.that operators with long-term interests in the land tend to better
protect and conserve soil resources. (See app. tables 4 and 5 for details.)


8/ Bills and Heimlich find that virtually all of the land estimated to erode
at 25 or more TAY in 1977 fell into their "highly erosive" category, as
did 63 percent of the land eroding at 14-24 TAY; most of the land eroding
at 5-13 TAY fell into their "moderately erosive--managed above tolerance"
category; land eroding at less than 5 TAY fell strictly in the "nonerosive"
and "moderately erosive--managed below tolerance" categories (1).





Off-farm Work

The distributions among off-farm work categories do not significantly differ
among soil erosion classes, implying off-farm work is not a determinant of
erosion probability. However, differences among program participation cate-
gories are highly significant. As time off the farm increases, participation
in commodity programs (solely or in conjunction with conservation programs)
decreases. (See app. table 6 for details.)

Operator Age

Age seems to be a significant determinant of operator behavior in the sample
areas. With respect to soil erosion, a relatively greater proportion of
operators of seriously eroding cropland are 21-40 years old. And a relatively
greater proportion of operators of cropland eroding under 5 TAY are over 50 years
old. A possible explanation for this finding is that young operators are less
financially able to purchase or rent good farmland. They may, consequently, be
operating a greater proportion of marginal, erodible land. Older operators who
obtained land at a time when land prices were lower may be farming better land.
With respect to program participation, age differences are highly significant.
Operators 31 to 60 years of age are heavier participants in conservation programs
(both solely and in conjunction with commodity programs) than the younger and
older operators. Participation in commodity programs only is relatively
higher in the 21-30 and 61-70 age classes. Operators over 70 years of age
are far more likely to be program nonparticipants. (See app. tables 7 and
8 for details.)

Operating Loan Source

The principal source of operating loans is unknown for 40 percent of the sample
population. Of the remaining sample, operators whose primary source of operating
loans is FmHA or the Production Credit Association (PCA) fall with relatively
greater frequency in the group whose land has a high rate of erosion than do
those who borrow primarily from commercial banks and insurance companies. This
finding suggests that FmHA and PCA may be providing credit for operators whose
land is more highly erodible, who plant high percentages of their land to erosive
crops, or who are less able to invest in conservation measures. (See app. table 9
for details.) Differences in principal loan sources among program participation
classes suggest only that FmHA- and PCA-financed operators are more likely than
others to be in commodity programs. There is no evidence to suggest that
commercial banks are more likely to lend to operators who participate in com-
modity programs. Operators borrowing primarily from commercial banks and
insurance companies are evenly distributed among program participation categories.

Land Capability Class and Subclass

Our study data lend some support to the theory that commodity program benefits
encourage cultivation of marginal, erodible cropland. At least that appears
to be the case in the sample areas. Erodible cropland, with the greatest
erosion hazard, defined either as all cropland in land capability subclass e,
or all cropland in land capability classes 4e, 6e, and 7, is represented at a
greater than proportionate level in the "commodity program only" participation
class. This holds true for 1982 and 1983, but it is more pronounced in 1982.
(See app. tables 11 through 14 for details.)





The effect on soil erosion of relatively higher program crop production on
erodible lands is not clear. Table 16 shows average estimated erosion rates by
program participation category and land capability class. In land capability
classes II, III, and IV, average erosion rates are significantly higher for sole
commodity program participants. This, again, is a function of the fact that
program crops tend to be more erosive than other commodities. It implies that
the distribution of program crop production among land classes may have an
important effect on soil erosion.

SUMMARY AND IMPLICATIONS

USDA influences farmer decisions to produce or not produce particular commodities
through credit, crop insurance, commodity, and other agricultural programs.
Federal programs outside USDA, such as tax provisions, further influence farmers'
choices regarding type and intensity of operation. Along with general economic
conditions, each of these factors affects what is produced, how agricultural land
is used, and, consequently, the amount of soil erosion on agricultural land.

The availability of commodity program benefits, in particular, may make the
production of certain crops more attractive to farmers. Farmers who are in debt
or face a cost-price squeeze are especially likely to be attracted to the
advantages of producing program crops. Because program crops are relatively more
erodible than other production activities, increased soil erosion may uninten-
tionally result from farmers' response to farm program incentives.

Any commodity or conservation technical or financial assistance program change
designed to make the goals of price support and farm income more consistent
with soil conservation objectives would primarily affect the farm operators who
participate in the programs. The size and characteristics of potentially
affected groups have important implications for the success of program integra-
tion efforts.

In the critical resource areas of the United States, more than half of the crop-
land eroding above 5 TAY is cultivated by participants in either USDA conservation
financial or technical assistance or commodity programs or both. Thus, program
changes could have a positive effect on a major portion of the erosion problem in
these areas.

Various groups of operators in critical resource areas could be affected by
program changes. Changes in commodity program provisions or policies applying to
highly erodible cropland would have a greater relative effect on nonfamily and
corporate farms than on family-owned farms in generally erodible areas of the
country. Similarly, changes in approaches to commodity program implementation and
erosion problems would have a relatively greater effect on short-term tenants
than on owner-operators or long-term tenants in the critical erosion study areas.
Changes in conservation programs will affect mainly middle-aged owner-operators
and long-term tenants, the most predominant participants in conservation programs
in these areas. The age distribution of U.S. farmers is highly skewed to older
individuals. The few young individuals currently operating farms would be
relatively more affected by changes in commodity programs or policies aimed at
soil conservation in critical erosion areas than would middle-aged or older
farmers.

Both the proportion of land eroding above tolerance and the percentage of
cropland operated by commodity program participants are greater in the critical
resource study areas than in the rest of the country. Because the critical










Table 16--Estimated erosion rates for consistency study sample, by
participation category, 1982


land capability class and program


Program participation Average erosion rate for land capability class 1/

: : II : III : IV : V : VI : VII


Tons per acre per year 2/

Land of operators participating in
neither commodity nor conservation
programs 4.1 A 6.4 B,C 12.2 B 11.9 B 2.8 A 21.4 A 14.0 A

Land of operators participating in
commodity programs only 4.4 A 8.6 A 17.9 A 23.2 A 1.0 A 24.0 A 15.1 A

Land of operators participating in
conservation programs only 3.4 A 5.9 C 11.8 B 20.9 A,B 5.4 A 20.9 A 29.0 A

Land of operators participating in
both commodity and conservation
programs 3.8 A 7.7 A,B 14.8 A,B 18.1 A 2.1 A 28.4 A

-- = No observations.
1/ The SCS land capability classification system groups soils at several levels. The capability
class, designated by Roman numerals, indicates progressively greater limitations and narrower choices
for land uses. Capability class I has the least limitations, and VII represents the class of soils
with greatest limitations.
2/ Average values followed by the same letter within the column exhibit no statistically significant
differences from one another at the 95-percent confidence level.





resource areas are targeted for extra conservation cost-share funding and
technical assistance, these areas also are likely to contain a greater proportion
of conservation program participants than many other areas of the country.

Table 17 shows the approximate number of acres known to have been in commodity
programs in 1982 and those eroding above 5 TAY in the study area and in the
Nation. Assumptions regarding the distribution of cropland eroding above 5 tons
per acre per year among program participation categories (table 17) are made on
the basis of national statistics and the relationships observed from the study
areas.

The assumptions shown in table 17 are derived from 1982 information on
commodity program participation. To compensate for the fact that commodity
program participation rates vary significantly from year to year, we assume a
20-percent standard deviation is associated with the average rates shown in
table 17. This series of assumptions leads us to conclude the following:

o Roughly 40 to 65 million acres of U.S. cropland eroding above 5 tons per
acre per year are operated by participants in USDA commodity or con-
servation financial and technical assistance programs or both.

o Between 75 and 110 million acres of cropland eroding at rates greater
than 5 tons per acre per year are operated by individuals who parti-
cipate in neither commodity nor conservation financial and technical
assistance programs.

o Roughly 65 to 105 million acres of U.S. cropland eroding at rates
below 5 tons per acre per year are operated by USDA program participants.

o Between 150 and 230 million acres of U.S. cropland eroding at rates
below 5 tons per acre per year are operated by individuals participating
in neither USDA commodity nor conservation financial and technical
assistance programs.

These figures are very rough estimates. They are based on deductive logic rather
than on the basis of a probability sample. While the absolute values should not
be too literally interpreted, the relative magnitude of values in each group is
useful in examining the extent to which soil erosion problems might be addressed
through program modifications. 9/

Between one-fourth and one-half of the total erosion apparently could be addressed
through modifications in USDA commodity or conservation financial and technical
assistance programs or both. Barriers to designing conservation-oriented program
changes include the need to assure that the programs will be relatively low cost
and that they will not impair farm income, commodity price, production, or export
objectives.

In a given year, from 45 to 80 percent of the cropland enrolled in prevailing
USDA conservation and commodity programs may be maintained so that soil erosion
rates are under 5 tons per acre per year. This outcome, like the proportion of
program participants whose land erodes above tolerance, varies from year to year


9/ Values reported here differ from preliminary estimates published earlier
in the Journal of Soil and Water Conservation due to the later availability
of better data on regional and national ASCS commodity program participation
rates.





Table 17--Assumed quantities of cropland acreage by program participation,
soil erosion levels, and proportions of cropland in and outside
of study areas

: Study : All other : United States
Cropland groups areas : areas
: (A) : (B) :(C)

Million acres

Total cropland 55 366 421

Cropland eroding above
5 tons per acre per year 1/ 30 2/ 114 3/ 144

Cropland in 1982 commodity
programs 1/ 25 2/ 72 4/ 97

Cropland in 1982 commodity
programs and eroding above
5 tons per acre per year 1/ 14 5/ 22 6/ 36

Approximate acres in
Conservation Technical
Assistance Targeted Areas 50 84 134

Cropland in 1982 conservation
programs 1/ 17 7/ 73 6/ 90

Cropland in 1982 conservation
programs and eroding above
5 tons per acre per year 1/ 9 8/ 23 6/ 32

Cropland in either or both
program type 1/ 32 9/ 105 6/ 137

Cropland in either or both
program type and eroding 1/ 18 5/ 8/ 33 6/ 51
above 5 tons per acre
per year
1/ Based on percentage observations from study.
2/ Value in column A subtracted from value in column C.
3/ Source: (9)
4/ Source: Agricultural Stabilization and Conservation Service
5/ Assumes land of commodity program participants is distributed among land
eroding above and below 5 tons per acre per year in same proportions as land is
generally distributed. (This holds true for the study areas.)
6/ Value in column A plus value in column B.
7/ Assumes conservation program participation is twice as high on targeted
acreage as on nontargeted acreage.
8/ Assumes land of conservation program participants is distributed among
land eroding above and below 5 tons per acre per year in same proportion as land
is generally distributed. (This holds true for the study areas.)
9/ Assumes the same Bayes probability of commodity program participation given
conservation program participation in nonstudy areas as was determined for study
areas.





according to program availability, provisions, and participation rates. The
intermittent nature of some commodity programs and consequential variance in
program participation rates pose additional problems regarding the potential
effectiveness of conservation-oriented program changes.

Finally, operators of from one-half to three-fourths of cropland eroding above
the 5-ton-per-acre maximum tolerance level do not participate in USDA commodity
or conservation financial or technical assistance programs. These farmers would
not be directly influenced by program changes designed to reduce erosion. They
might, however, be influenced by State or local programs.

Because there are differences among the characteristics of farmers participating
in various programs and operating different types of land, program changes could
have differential effects on large and small, young and old, or family and
nonfamily farmers. Program alternatives for reducing inconsistencies between
farm programs and conservation goals need to be specifically formulated, their
costs estimated, and their effects on soil erosion, agricultural production, and
farm income estimated before the feasibility or desirability of any one option
can be judged.





REFERENCES


1. Bills, Nelson L., and Ralph Heimlich. Assessing Erosion on U.S. Cropland:
Land Management and Physical Features. AER-513. U.S. Dept. Agr., Econ. Res.
Serv., July 1984.

2. Huszar, Paul C., Kenneth Nobe, and John E. Young. "Policy Assessment and
Implications of the Grasslands Plowout Problem in Eastern Colorado,"
Proceedings of Howard E. Conklin Conference on Rural Land Use and Public
Policy, Ithaca, NY: Cornell Univ., 1984.

3. Johnson, James D., and Sara D. Short. Commodity Programs: Who Has Received
the Benefits? Paper presented at 1983 annual meeting, American Agricultural
Economics Association, West Lafayette, IN, Aug. 1983.

4. Lee, Linda K. "Land Use and Soil Loss: A 1982 Update," Journal of Soil and
Water Conservation, Vol. 39, No. 4 (July-Aug., 1984), 226-28.

5. Ogg, Clayton. Cross-Compliance Proposals and Fragile Croplands. Paper
presented at 1983 annual meeting, American Agricultural Economics Associa-
tion, West Lafayette, IN, Aug. 1983.

6. Arnold B. Miller, and Kenneth C. Clayton. Agricultural Program
Integration to Achieve Soil Conservation. Unpublished working
paper. U.S. Dept. Agr., Econ. Res. Serv., 1984.

7. Osteen, Craig. The Impacts of Farm Policies on Soil Erosion: A Problem
Definition Paper. ERS Staff Report AGES841109. U.S. Dept. Agr., Econ. Res.
Serv., Jan. 1985.

8. U.S. Senate, Committee on the Budget, 98th Congress, 2d Session. The
Distribution of Benefits from the 1982 Federal Crop Programs. Committee
print 98-238, Nov. 1984.

9. U.S. Department of Agriculture, Soil Conservation Service. Basic Statistics:
1977 National Resources Inventory. SB-686. Dec. 1982.

10. Soil Conservation Service. Land Resource Regions and Major Land
Resource Areas of the United States. AH-296. Dec. 1981.

11. Watts, Myles J., Lloyd D. Bender, and James B. Johnson. Economic Incentives
for Converting Rangeland to Cropland. Bulletin 1302. Montana State Univ.,
Coop. Ext. Serv., Nov. 1983.

12. Young, John Edward. "Economics of Grassland Plowing and Its Regulation."
M.S. thesis. Colorado State Univ., 1984.





Appendix A: Appendix table 1--USDA program consistency study sample counties

: County categories,
Major land resource areas and counties : other than CTA 1/

Columbia Plateau:
Adams, WA 5
Gilliam, OR 5
Umatilla, OR 5, 6

Palouse and Nez Perce Prairies:
Spokane, WA 4, 5
Whitman, WA 5

Central High Plains and
Upper Arkansas Valley Rolling Plains:
Kit Carson, CO 2/
Otero, CO 1, 6
Prowers, CO 6
Weld, CO 2/
Platte, WY 6

Southern High Plains:
Carson, TX 6
Dallam, TX 6
Hale, TX 6
Hockley, TX 6
Terry, TX

Loess Uplands and Till Plains and
Nebraska and Kansas Loess-Drift Hills:
Lyon, IA 2/
Leavenworth, KS 4, 5
Lancaster, NE 2/ 5
Pierce, NE 5
Bon Homme, SD

Iowa and Missouri Deep Loess Hills:
Carroll, IA 5
Ida, IA 2/ 5
Mills, IA 5
Page, IA 5
Holt, MO 5

Southern Coastal Plains:
Clarke, AL
Dale, AL 5, 9
Elmore, AL
Franklin, AL
Mobile, AL 5
Pike, AL 5

See footnotes at end of table. --Continued





Appendix A:


Appendix table 1--USDA program
counties--Continued


consistency study sample


: County categories
Major land resource areas and counties : other than CTA 1/


Southern Coastal Plains continued:
Bulloch, GA
Emanuel, GA
Irwin, GA
Mitchell, GA
Screven, GA
Stewart, GA
Tooabs, GA
Alcorn, MS
Itawamba, MS
Lamar, MS
Pontotoc, MS
Simpson, MS
Stone, MS
Tippah, MS
Edgecombe, NC
Decatur, TN
Southern Mississippi Valley Silty Uplands:
Arkansas, AR
Calloway, KY
Fulton, KY
E. Baton Rouge, LA
E. Feliciana, LA
Copiah, MS
Jefferson Davis, MS
Warren, MS
Crockett, TN
Shelby, TN
Weakley, TN
Southern Piedmont:
Gwinnett, GA
Henry, GA
Monroe, GA
Cleveland, NC
Vance, NC
Greenville, SC
McCormick, SC
Cumberland, VA
Halifax, VA
Louisa, VA


5
1, 5


5
3
5
5
5

5


3, 5
4
3, 5

1, 4
5
5, 9
4

4
4

5
5
5





4
1, 5
1, 3, 4

1
1, 3, 5


- County not categorized beyond its having been a CTA county.
1/ All but five counties (those indicated below) are CTA counties. Codes
indicate the following: 1 Original Study Evaluation County; 3 = Variable
Cost-Share Level Counties; 4 Nationally Funded Special Projects; 5 Critical
Erosion Target Areas; 6 Critical Water Storage Targeted Areas; 9 = Rural Clean
Water Program.
2/ Not a CTA county. These counties were included in the sample to assure that
sufficient data points were collected for critical MLRA's





APPRNDIX B:


INSTRUCTIONS FOR COMPLETING AD-862 FORMS FOR USDA PROGRAM CONSISTENCY STUDY

(The following instructions for completing the data assembly form are included
to give the reader a better idea of what information was collected and how it
was collected.)

Form AD-862 shall be completed as prescribed below for identified sample
farms. Leave all other portions of form AD-862 blank. The circled numbers
below identify or locate specific sections on the attached blank copy of
AD-862. Following the blank copy, you will find a copy of a completed form
(from a Carson County, Texas pretest).

SECTION A GENERAL INFORMATION

Complete the data items indicated below as follows:


1 A-i (Date)
the month,
entered as


Enter the date form is prepared. Use 2 digits each to enter
day, and calendar year. For example,-September 1, 1983, is
090183.


2 A-2 (Reporting Location) Enter the FIPS code in blocks 2a and 2b.
Enter a check digit in right-hand block of 2b. Enter the SCS 3-digit
field office code in block 2c. (County personnel will provide these
codes. The reporting location code will be the same for all observations
in a given county.)

3 A-3 (Farm identifier) Enter the ASCS Community Code in block 3a.
Enter the farm number in block 3b, using right-justification of the
digits. (NOTE: If the county only has one community, enter an "A"
Sin the farthest right-hand cell of block 3a.)


4 A-4 (Data Code) Enter code "6"
Study Data.

5 A-5 Enter one of the following
single) lender of producer, for
provide this information on the


for identification as ERS Consistency


codes to indicate
operating loans.
basis of personal


primary (largest
(ASCS personnel will
knowledge.)


CODE

1 FmHA

3 PCA


4 Insurance companies

5 Commercial banks

6 Other, or no lender

7 Unknown





6 A-7 (Program Code) Enter one of the following codes in: 7a for 1978;
7b for 1982; and 7c for 1983.

1 Neither commodity nor conservation program* participation

2 Farm participated in commodity program only

3 Farm participated in conservation program* only

4 Farm participated in both commodityand conservation* programs

*NOTE: Conservation programs include:

a. ASCS-administrated programs:
Agricultural Conservation Program (ACP)
Emergency Conservation Program (ECP)
Rural Clean Water Programl (RCWP)
Water Bank
Forestry Incentives Program (FIP)

ASCS conservation program participation for a given year is defined by
the operator's having received payment in that year for one of the
above.

b. SCS-administrated programs:
Great Plains
Rural Abandoned Mines Prqgram (RAMP)
Resource Conservation and Development (RC&D)
PL-566 (Small Watersheds)
Operator filing of a conservation plan with SCS

Determine from a follow-up visit with SCS District Conservationist
in county whether specific individual operators who did not participate
in ASCS conservation programs in given years did participate in SCS
conservation programs during those years. Revise codes previously
filled in as "1" or "2" in Block A-7, as appropriate after follow-up.

7 A-9 Enter SCS code, recorded on mapping material, in 9b. For one-digit
codes, record as two-digit number, with a "0" preceding digit (For example,
record SCS code 4 as 04).

8 A-10 (Farm Type) Enter the appropriate code for farm type. If a variety
of commodities are grown, choose the code that represents the predominant
commodity. (NOTE; ASCS personnel will classify the farms from their
knowledge of the operations. Farm record cards do not provide enough
information to independently determine farm type.)

Code Type

1 Cash grain farms (includes grain sorghum, corn, soybeans, rice,
wheat, oats, barley, among others; crops can be in rotation with
hay or pasture or can be on a continuous basis).






2 Cotton farms.

3 Tobacco farms.

4 Other field crop farms (includes sugar, potatoes, peanuts, among
others)

5 Vegetable and melon farms.

6 Fruit, berry, and nut farms.

7 Livestock farms and ranches (includes sheep, cattle, dairy, swine,
and poultry.)

8 Miscellaneous farms (includes fur-bearing animals, bees, grass,
seed, among others)

9 Combination livestock aid grain farms.

10 Tree farms.


9 A-11 (Farm Size) Enter in whole numbers the total acres in this farm
operating unit. (The operating unit may include State or Federal land.)
Total acres are shown on the farm record card as the first whole number
to the right of the operator's name.

THE PURPOSE OF ITEMS A-12 and A-14 TO A-16 IS TO IDENTIFY SOME OF THE
MAJOR CHARACTERISTICS OF THE OWNERS AND DECISIONMAKERS OF THE SELECTED
FARM UNITS. THESE ITEMS ARE TO BE ANSWERED USING PERSONAL KNOWLEDGE
AND JUDGMENT OF ASCS PERSONNEL. IF UNABLE TO ANSWER THESE ITEMS USING
EXISTING KNOWLEDGE AND JUDGMENT, ENTER THE CODE FOR UNKNOWN.

NOTE: ITEM A-12, BELOW, REFERS TO THE OWNER OF THE FARM UNIT, WHO MAY OR
MAY NOT BE THE DECISIONMAKER.

10 A-12 (Type of Ownership) Enter the appropriate code.

Code Type

1 Individual or family (including husband-wife, family partnership,
or family corporation).

2 Nonfamily partnership or corporation.

3 Government other than Federal (State, county, municipal).

4 Federal Government.

5 Indian tribal trust lands.






6 Combination of Federal-State-private, Federal-private, or State-
private.

7 Any other combination.

8 Other (estates, trusts, institutions, foundations, among others).

9 Unknown.

ITEMS A-14 to A-16 REFER TO THE DECISIONMAKER (OPERATOR) FOR THIS FARM.

11 A-14 (Type of Decisionmaker) Enter the appropriate code.

Code Type

1 Ag-owner operator the decisionmaker owns and operates all
the land in the unit for agricultural purposes.

2 Ag-tenant operator the decisionmaker rents or leases all the
land in the unit from others on an annual basis for agricul-
tural purposes.

3 Ag-tenant operator the decisionmaker rents or leases all the
land in the unit from others for more than 1 year for agricul-
tural purposes.

4 Ag-landlord the decisionmaker owns all the land in the unit
but rents or leases it to others for agricultural purposes.

5 Nonag operator the decisionmaker owns and operates all of
the land in the unit for NONFARM PURPOSES such as an airport,
golf course, industrial site, or for other use.

6 Nonag operator the decisionmaker rents or leases all of the
land in the unit from others on an' annual basis for NONFARM
PURPOSES such as an airport, golf course, industrial site, or
for other use.

7 Nonag operator the decisionmaker rents or leases all of the
land in the unit from others for more than 1 year for NONFARM
PURPOSES such as an airport, golf course, industrial site, or
for other use.

8 Nonag operator the decisionmaker owns all of the land in
the unit but rents or leases it to others for NONFARM PURPOSES
such as an airport, golf course, industrial site, or for other
use.

9 Part ag-owner operator the decisionmaker owns part of the
land in the unit and rents and leases on an annual basis part
of the land from others for agricultural purposes.





10 Part ag-owner operator the decisionmaker owns part of the land
in the unit and rents or leases for more than 1 year part of
the land from others for agricultural purposes.

11 Other

12 Unknown

12 A-15 (Off-Farm Work) Enter the appropriate code.

Code Item

1 Decisionmaker is a full-time operator with no off-farm work.

2 Decisionmaker is a farm operator with 1-99 days per year off-farm
employment.

3 Decisionmaker is a farm operator with 100 or more days per year
off-farm employment.

4 Decisionmaker is not a farm operator.

5 Unknown

13 A-16 (Age) Enter one of the following codes to most nearly approximate the
operator's age.

Code Range of age

1 Less than 21

2 21-30

3 31-40

4 41-50

5 .51-60

6 61-70

7 Over 70

8 Unknown





14 A-18 (Control Number) Enter 83 in block 18a. Enter unique "4000" series
numbers for each AD-862 submitted from a given county. For example:
834001, for the first form filled out in a county; 834002, for the second
form filled out in the same county, etc.

SECTION B CTU CHARACTERISTICS

Complete the data items indicated below as follows:

15 B-4 (Soil Record No.) This area of the form will be used to record total
cropland acres for the farm unit. Enter in whole numbers the total cropland
acres in the decision maker's operating unit. This may be found on the farm
record card as the second whole number to the right of the operator's name.

16 B-5 (Land Capability Class and Subclass) Enter land capability class and
subclass as recorded on SCS mapping materials.

17 B-8 This area of the form will be used to ascertain yield and allotment
data for up to 4 crops. Use blocks 8a, b, c, and d to list predominent
crops for this purpose.

Enter the appropriate crop code and most recently determined effective
yield from the following list in the first block under the "B" subheading.


Code


Crop

Oats


Barley


Corn


Grain sorghum

Rice


Units

bu/acre

bu/acre

bu/acre

bu/acre

cwt/acre


bu/acre (estimated)

cwt/acre

bu/acre

cwt/acre (estimated)

cwt/acre


Soybeans

Cotton

Wheat

Peanut

Tobacco





Enter the crop yield in the units identified above in the remaining blocks
under the "B" subheading. For example: Enter a 47 bushel/acre wheat yield as
B B
1W1014171. Enter a 3,450 pound/acre rice yield as ]R1013141.

NOTE: In some counties on some farms, yields will be given both for irrigated
and nonirrigated production of the same crop. Decide which of the two yields
to record in these instances on the basis of the land capability class and
subclass recorded for the cropland point from the farm. For example, if the
capability class and subclass is 2S, record effective yield for irrigated
production; if the capability class and subclass is 3E, record effective
yield for nonirrigated production.

Enter in whole numbers under the subheading "A" the base acreage for the crop
identified in subheading "B." For allotments exceeding 9,999, enter 9,999.
Enter 10 acres as 10101l|01. Commodity program nonparticipants for 1982-83
will not have an established base.

SECTION C APPLICATION OF RESOURCE MANAGEMENT SYSTEMS

18 Ib (Units) Enter the last five digits of the control number from AD-862's
previously submitted by ASCS for this farm unit. If more than one AD-862,
enter the last five digits of each control number up to 5 on line 2, 3, (and
so forth). No more than five previously submitted control numbers may be
listed. If CUA evaluation was submitted, be sure to include in lb.

SECTION D EROSION CONTROL

19 la(B) (Erosion) In the first three cells in Section D, enter in whole numbers
the sum of sheet and rill and wind erosion as recorded on SCS mapping materi-
als. Right-justify the erosion figure in space la(B).





APPENDIX C: DISTRIBUTION OF FARM AND OPERATOR


CHARACTERISTICS AMONG PROGRAM PARTICIPATION CATEGORIES

AND SOIL EROSION CLASSES


We conducted chi-square tests for differences in distributions using the
two-way tables in this appendix. The tables contain figures showing the
absolute and percentage distributions of the study sample operators among
various characteristic groupings, as well as the percentage distributions of
each grouping within the other.

Chi-square statistics are presented in table footnotes. The null hypotheses
tested through the chi-square procedure are that operators in specified soil
erosion or program participation classes have the same characteristics as all
sample operators.

The general formula used to calculate the chi-square statistics is:

n
x2 = E (f-F)2
i-1 F

where: f = the observed frequency in an erosion
class or program participation category;

F the frequency in the whole population
of operators; and

n the number of classes of the characteristic.






Appendix table 2--Farm ownership by program participation category, 1982 1/

: : Program participation category

Type of farm ownership : Unit : Neither : Com- : Conser- : Joint : Totals
: : program : modity : ovation :commodity and :
: : : only : only :conservation :


Individual or family:
Total observations
in program category
Proportion of total
sample
Proportion of
ownership category
Proportion of
program category

Nonfamily partnership
or corporation:
Total observations
in program category
Proportion of total
sample
Proportion of
ownership category
Proportion of
program category

Government, combi-
nation private and
Government, and
Indian tribes:
Total observations
in program category
Proportion of total
sample
Proportion of
ownership category
Proportion of
program category


Number 1,099.0


722.0


Percent 38.4 25.2


41.3 27.1

93.6 90.5


Number

Percent

do.

do.


Number

Percent

do.

do.


34.0 30.0


44.7 39.5


18.0 17.0


34.0 32.1


1.5


373.0

13.0

14.0

92.3


5.0

.2

6.6

1.2


53.0


13.0

.5

24.5

3.2


See footnotes at end of table.


467.0


16.3

17.6

95.9


7.0


2,661.0


92.9


76.0


9.2


--Continued





Appendix table 2--Farm ownership by program participation category,
1982 l/--Continued


Type of farm ownership


: Pr

: Unit : Neither
: : program


ogram participation category


Com-
modity
only


Conser-
vation
only


: Joint
:commodity and
:conservation


Other:
Total observations
in program category
Proportion of total
sample
Proportion of
ownership category
Proportion of
program category

Total:
Observations in this
program category
Proportion of
total sample


Number

Percent

do.

do.


23.0 29.0

.8 1.0

31.5 39.7


Number 1,174.0


3.6



798.0


Percent 41.0 27.9


degrees of freedom = 9;


: Totals


8.0


73.0


13.0

.5

17.8

3.2



404.0

14.1


11.0


1.6


487.0

17.0


NA = Not applicable.
1/ The statistics for this two-way table are as follows: d
chi-square = 25.159; probability of null hypothesis = 0.0028.


2,863.0

100.0





Appendix table 3--Farm ownership among soil erosion classes, 1982 V_
: : Soil erosion class:
: :Less than: 5-13 : : 25 or
Type of farm ownership : Unit : 5 tons/ :tons/acre/:14-24 tons/:more tons: Totals
: :acre/year: year : acre/year :acre/year:


Individual or family:
Total observations
in erosion class
Proportion of total
sample
Proportion of
ownership category
Proportion of
erosion class

Nonfamily partnership
or corporation:
Total observations
in erosion class
Proportion of total
sample
Proportion of
ownership category
Proportion of
erosion class

Government, combi-
nation private and
Government, and
Indian tribes:
Total observations
in erosion class
Proportion of total
sample
Proportion of
ownership category
Proportion of
erosion class


Number 1,204.0


Percent

do.

do.


Number

Percent

do.

do.






Number

Percent

do.

do.


42.1

45.3

92.2


44.0

1.5

57.9

3.4






24.0

.8

45.3

1.8


832.0

29.1

31.3

94.0


12.0

.4

15.8

1.4






20.0

.7

37.7

2.3


315.0 310.0 2,661.0


11.0

11.8

91.8


6.0

.2

7.9

1.8






8.0

.3

15.1

2.3


10.8

11.7

94.2


14.0


93.0


76.0


18.4

4.3


53.0


See footnotes at end of tables.


--Continued





Appendix table 3--Farm ownership among soil erosion classes, 1982 l/--Continued
: : Soil erosion class:
: :Less than: 5-13 : : 25 or
Type of farm ownership : Unit : 5 tons/ :tons/acre/:14-24 tons/:more tons: Totals
: :acre/year: year : acre/year :acre/year:

Other:
Total observations Number 34.0 21.0 14.0 4.0 73.0
in erosion class
Proportion of total Percent 1.2 .7 .5 .1 2.5
sample
Proportion of do. 46.6 28.8 19.2 5.5 NA
ownership category
Proportion of do. 2.6 2.4 4.1 1.2 NA
erosion class

Total:
Observations in Number 1,306.0 885.0 343.0 329.0 2,863.0
this erosion class
Proportion of Percent 45.6 30.9 12.0 11.5 100.0
total sample
NA Not applicable.
1/ The statistics for this two-way table are as follows: degrees of
freedom 9; chi-square 23.72; probability of null hypothesis = 0.0048.






Appendix table 4--Farm tenure among program participation categories, 1982 1/


: : Program participation category

Type of decisionmaker : Unit : Neither : Com- : Conser- : Joint Totals
: :program : modity : vation :commodity and:
: : : only : only :conservation :


Owner operator:
Total observations
in program category
Proportion of total
sample
Proportion of
decisionmaker
category
Proportion of
program category

Tenant with annual
lease:
Total observations
in program category
Proportion of total
sample
Proportion of
decisionmaker
category
Proportion of
program category

Tenant with long-
term lease:
Total observations
in program category
Proportion of total
sample
Proportion of
decisionmaker
category
Proportion of
program category

Landlord:
Total observations
in program category
Proportion of total
sample
Proportion of
decisionmaker
category
Proportion of
program category


Number 605.0 368.0


Percent 21.3


do.


do.




Number

Percent

do.


do.


42.7


52.2


12.9

26.0


46.2


279.0 222.0


9.8

38.9


24.1


7.8

31.0


27.9


Number 170.0 101.0


Percent

do.


do.



Number

Percent

do.


do.


6.0

44.2


14.7



30.0

1.1

42.3


2.6


3.5

26.2


12.7



8.0

.3

11.3


1.0


See footnotes at end of table.


1,417.0


49.8


229.0

8.1

16.2


47.1


215.0

7.5

15.2


53.2




91.0

3.2

12.7


22.5


717.0


25.2


17.4


25.7


125.0


59.0

2.1

15.3


14.6



20.0

.7

28.2


5.0


55.0


385.0

13.5


14.3


11.3


13.0


71.0


2.5


18.3


--Continued






Appendix table 4--Farm taeure among program pewbhapation categories,
1982 1/--Continued


: : Program participation category

Type of decisionmaker : Untt : Neither : Coa- Conser- : Joint : Totals
: : progrpan umodity t Vation :commodity and:
S. : : only ..; .ly -tonservation :


Part owner operator
with annual lease:
Total observations
in program category
Proportion of total.
sample
Proportion of
decisionmaker
category
Proportion of
program category

Part owner operator
with long-term lease:
Total observations
in program category
Proportion of total
sample
Proportion of
decisionmaker
category
Proportion of
program category

Other:
Total observations
in program category
Proportion of total
sample
Proportion of
decisionmaker
category
Proportion of
program category

Total:
Observations in
this program cate-
gory
Proportion of
total sample


Number

Percent

do.


do.


Number

Percent

do.


do.



Number

Percent

do.


do.


13.0

.5

28.3


1.1


54.0

1.9

29.7


4.7


16.0


34<8


2.0


7..0


15*2


1.7


71.0 61.0


2.5

39.0


8.9


8.0 11.0


.3

28.6


.4

39.3


.7 1.4


Number 1,159.0


Percent 40.7


.2

3.3


1.5


6.0

.2

21.4


1.5


797.0 404.0


28.0


14.2


10.0


46.0


21.7


51.0

1.8

28.0


10.5


3.0


182.0

6.4


28.0


10.7


486.0 2,846.0


17.1


100.0


freedom '


NA -V ot applieable.
1/ The statistics for this two-way table are as follows: degrees of
18; chi-square 78.172; probability of null hypothesis -0.0001.






Appendix table 5-Farm tenure by soil erosion class, 1982


e: s.. Soil erosion class
: :." Less : : :
Type of operator : Unit : than : 5-13 : 25.or :Totals
: 5 tons/ : tons/acre/: 14-24 tons/: more tons:
.: acre/.ar: year : acre/year : acre/year:


Owner operator:
Total observations
in erosion class
Proportion of total
sample
Proportion of
operator category
Proportion of
erosion class

Tenant with annual
lease:
Total observations
in erosion class
Proportion of total
sample
Proportion of
operator category
Proportion of
erosion class

Tenant with long-
term lease:
Total observations
in erosion class
Proportion of total
sample
Proportion of
operator category
Proportion of
erosion class

Landlord:
Total observations
in erosion class
Proportion of total
sample
Proportion of
operator category
Proportion of
erosion class


Number 691.0.


Percent

do.

do.


Number

Percent

do.

do.




Number

Percent

do.

do.



Number

Percent

do.

do.


24.3

48.8

53.5


274.0

9.6

38.2

21.2




200.0

7.0

52.0

15.5



32.0

1.1

45.1

2.5


414.0

14.6

29.2

46.9


256.0

9.0

35.7

29.0




98.0

3.4

25.5

11.1



24.0

.8

33.8

2.7


166.0

5.8

11.7

48.4


71.0

2.5

9.9

20.7




53.0

1.9

13.8

15.5



8.0

.3

11.3

2.3


146.0 1,417.0


49.8


10.3

44.5


116.0 717.0


25.2


16.2


35.4


34.0

1.2

8.8

10.4



7.0

.3

9.9

2.1;


385.0

13.5

NA

cNA



71.0

2.5

NA

NA


See footnotes at end of table.


--Continued





Appendix table 5-Farm tenure by soil erosion class, 1982--Continued


: _: Soil erosion class
: : Less : : :
Type of operator : Unit : than : 5-13 :: 25 or :Totals
: : 5 tons/ : tons/acre/: 14-24 tons/: more tons:
: : acre/year: year : acre/year : acre/year:

Part owner operator
with annual lease:
Total observations Number 25.0 14.0 2.0 5.0 46.0
in erosion class
Proportion of total Percent .9 .5 .1 .2 1.7
sample
Proportion of do. 54.4 30.4 4.4 10.9 NA
operator category
Proportion of do. 1.9 1.6 .6 1.5 NA
erosion class

Part owner operator
with long-term lease:
Total observations Number 59.0 66.0 39.0 18.0 182.0
in erosion class
Proportion of total Percent 2.1 2.3 1.4 .6 6.4
sample
Proportion of do. 32.4 36.3 21.4 9.9 NA
operator category
Proportion of do. 4.6 7.5 11.4 5.5 NA
erosion class

Other:
Total observations Number 11.0 11.0 4.0 2.0 28.0
in erosion class
Proportion of total Percent .4 .4 .1 .1 1.0
sample
Proportion of do. 39.3 39.3 14.3 7.1 NA
operator category
Proportion of do. .9 1.3 1.2 .6 NA
erosion class

Total:
Observations in Number 1,292.0 883.0 343.0 328.0 2,846.0
this erosion class
Proportion of Percent 45.4 31.0 12.1 11.6 100.1
total sample

NA Not applicable.
1/ The statistics for this two-way table are as follows: degrees of freedom -
18; chi-square 74.261; probability of null hypothesis- 0.0001.






Appendix table 6--Off-farm work by program participation category, 1982 1/


:: : Program participation category

Work status of operator :Unit :Neither: :Com- : Conser- : Joint : Totals
: :program: modity : vation :commodity and:
: : : only : only :conservation :


Full-time farm operator:
Total observations
in program category
proportion of total
sample
Proportion of
work status category
Proportion of
program category

Off-farm work 1-99
days per year:
Total observations
in program category
Proportion of total
sample
Proportion of work
status category
Proportion of
program category

Off-farm work 100
or more days
per year:
Total observations
in program category
Proportion of total
sample
Proportion of work
status category
Proportion of
program category


Number 788.0 618.0 291.0


Percent 30.0 23.5


37.5 29.4

75.5 85.6


Number 45.0 37.0


Percent 1.7


37.8 31.1


4.3


Number 175.0 54.0


Percent

do.

do.


52.2 16.1


16.8


See footnotes at end of table.


406.0

15.4

19.3

84.6


19.0


2,103.0

80.0


119.0


11.1

13.8

76.0




18.0

.7

15.1

4.7


60.0

2.3

17.9

15.7


4.5


16.0


335.0

12.9


46.0

1.8

13.7

9.6


--Continued





Appendix table 6--0ff-farm work by program participation category,
1982 1/--Continued


S : jProgram participation category

Work status of operator :Unit :Neither: Com- : Conser- : Joint :Totals
: :program: modity : vation :commodity and:
: : only : only :conservation :

Full-time off-farm
worker:
Total observations Number 36.0 13.0 14.0 9.0 72.0
in program category
Proportion of total Percent 1.4 .5 .5 .3 2.7
sample
Proportion of work do. 50.0 18.1 19.4 12.5 NA
status category
Proportion of do. 3.5 1.8 3.7 1.9 NA
program category

Total:
Observations in this Number 1,044.0 722.0 383.0 480.0 2/ 2,629.0
program category
Proportion of Percent 39.8 27.5 14.6 18.2 100.1
total sample


NA -Not applicable.
1/ The statistics for
9; chi-square 50.561;


this two-way table are as follows: degr
probability of null hypothesis = 0.0001.


ees of freedom =


2/ Off-farm work status is unknown for 9 percent of the sample.


62





Appendix table 7--Operator age by program participation category, 1982 1/


: : Program participation category :

Age of operator : Unit :Neither : Cor- : Conser- : Joint :Totals
: :program : modity : ovation : commodity and :
: : : only : only : conservation :


Less than 21 years:
Total observations
in program category
Proportion of total
sample
Proportion of
age category
Proportion of
program category

21-30 years:
Total observations
in program category
Proportion of total
sample
Proportion of
age category
Proportion of
.program category

31-40 years:
Total observations
in program category
Proportion of total
sample
Proportion of
age category
Proportion of
program category

41-50 years:
Total observations
in program category
Proportion of total
sample
Proportion of
age category
Proportion of
program category


Number


1.0


Percent <.1


16.7

.1


Number 61.0

Percent 2.3


42.0

5.8


Number 176.0 120.0


Percent 6.6


38.6

16.8


Number 285.0 180.0 112.0


Percent 10.8


39.9

27.3


See footnotes at end of table.


6.0


2.0

.1

33.3

.3



45.0

1.7

31.0

6.1


23.0


145.0


3.0

.1

50.0

.8



16.0

.6

11.0

4.1


70.0

2.6

15.4

17.9


5.5


15.9

4.8


4.5

26.3

16.3


456.0

17.1

NA


714.0

27.0


90.0

3.4

19.7

18.8


137.0

5.2

19.2

28.7


6.8

25.2

24.4


4.2

15.7

28.6


--Continued






Appendix table 7--Operator age by program. participation category,
1982 I/--Continued


: : Program participation category

Age of operator : Uit :Neither.: Cmr- : Conser- : Joint. :Totals
: :program : modity : nation : commodity and
: : : only : only : conservation :


51-60 years:
Total observations
in program category
Proportion of
total sample
Proportion of
age category,
Proportion of
program category

61-70 years:
Total observations
in program category
Proportion of total
sample
Proportion of
age category
Proportion of
program category

Over 70 years:
Total observations
in- program category
Proportion of total
sample
Proportion of
age category
Proportion of
program category

Total:
Observations in
this category
Proportion of sample


Number 294.0 231.0


Percent 11.1


36.7

28.1


Number 172.0 137.0


aPerent 6.5


do.

do.


40.4

16.5


Number 56.0

Percent 2.1


53.9

5.4


Number 1,045.0

Percent 39.4


8.7

28.8

31.3


155.0

5.8

19.4

32.4


121.0

4.6

15:. 1

30.9



58.0

2.2

13.6

14.8



12.0

.5

11.5

3.1



392.0

14.8


801.0

30.2


426.0

16.1


NA

NA


59.0

2. 2

13.9

12.3


5.2

32.2

18.6


22.0

.8

21.2

3.0



737.0

27.8


14.0

.5


104.0

3.9


13.5


S478.0

18.0


NA Not applicable.
1/ The statistics for this two-way table are as follows: degrees of
18; chi-square = 32.769; probability of null hypothesis = 0.0178.


2,652.0

100.0


freedom =


- --------






Appendix table 8--Operator age by soil erosion class, 1982 1/


: :Soil erosion class

Age of operator : Unit :Less than: 5-13 :14-24 tons/: 25 or : Totals
: : 5 tons/ :tons/acre/: acre/year :more tons/:
: :acre/year: year : :acre/year :


Less than 21 years:
Total observations
in erosion class
proportion of total
sample
Proportion of
age category
Proportion of
erosion class

21-30 years:
Total observations
in erosion class
Proportion of total
sample
Proportion of
age category
Proportion of
erosion class

31-40 years:
Total observations
in erosion class
Proportion of total
sample
Proportion of
age category
Proportion of
erosion class

41-50 years:
Total observations
in erosion class
Proportion of total
sample
Proportion of
age category
Proportion of
erosion class


Number

Percent


5.0

.2


do. 83.3


do.



Number

Percent


.4



52.0

2.0


do. 35.9


Number 193.0


Percent


7.3


do. 42.3

do. 16.0


Number 329.0

Percent 12.4

do. 46.1

do. 27.3


See footnotes at end of table.


6.0


0

0

0



28.0


<.3

NA

NA



145.0


1.0

< .1

16.7

.1



50.0

1.9

34.5

6.1


0

0

0

0



15.0

.6

10.3

4.7


139.0

5.2

30.5

16.9


229.0

8.6

32.1

27.8


19.3

9.3


56.0

2.1

12.3

17.5


86.0

3.2

12.0

26.9


68.0

2.6

14.9

22.5


70.0

2.6


456.0

17.2


714.0

26.8


23.2


--Continued





Appendix table 8--Operator age by soil erosion class, 1982 1/--Continued


: : Soil erosion class

Age of operator : Unit :Less than: 5-13 :14-24 tons/: 25 or : Totals
:5 tons/ :tons/aere/: acre/year :more tons/:
::acre/year: year ,- :acre/year :


51-60 years:
Total observations
in erosion class
Proportion of total
sample
Proportion of
age category
Proportion of
erosion class

61-70 years:
Total, observations
in erosion class
Proportion of total
sample
Proportion of,
age category
Proportion of
erosion class

Over 70 years:
Total observations
in erosion class
Proportion of total
sample
Proportion of
age category
Proportion of
erosion class

Total:
Observations in
this erosion class
Proportion of
total sample


Number 378.0 233.0


Percent

do.

do..


Number

Percent

do.

do.


Number

Percent

do.

do.


14.2

47.2

31.3


8.8

29.1

28.3


193.0 141.0


. 7.3

45.3

16.0


56.0

2.1

53.9

4.6


Number 1,206.0

Percent 45.5


5.3

33.1

17.1



31.0

1.2

29.8.

3.8


824.0

31.1


1004!0

3.8

12.5

31.3


53.0

2.0

12.4

16.6


10.0

.4

9.6

3.1


320.0

12.1


90.0

3.4

11.2

29.8


39.0

1.5


801.0

30.2


426.0

16.1


12.9


7.0


104.0


302.0 2/ 2,632.0


11.4


100.1


es of freedom -


NA = Not applicable.
1/ The statistics for this two-way table are as follows: degree
18; chi-square 31.735; probability of null hypothesis 0.0236.
2/ Operator age is unknown for 8 percent of sample.






Appendix table 9--Primary source of operating loan, by soil erosion class, 1982 1/

: : Soil erosion class
Primary source of : Unit :Less than: 5-13 : : 25 or : Totals
operating loans : : 5 tons/ :tons/acre/:14-24 tons/:more tons/:
: :acre/year: year : acre/year :acre/year :

Farmers Home Adminis-
tration:
Total observations Number 128.0 101.0 35.0 53.0 317.0
in erosion class
Proportion of total Percent 7.5 5.9 2.1 3.1 18.6
sample
Proportion of loan do. 40.4 31.9 11.0 16.7 NA
source category
proportion of do. 16.7 19.5 17.6 23.1 NA
erosion class

Production Credit
Association:
Total observations Number 87.0 90.0 24.0 39.0 240.0
in erosion class
Proportion of total Percent 5.1 5.3 1.4 2.3 14.1
sample
Proportion of do. 36.2 37.5 10.0 16.3 NA
loan source category
Proportion of do. 11.4 17.4 12.1 17.0 NA
erosion class

Commercial banks and
insurance companies:
Total observations Number 429.0 243.0 102.0 95.0 869.0
in erosion class
Proportion of total Percent 25.1 14.2 6.0 5.6 50.9
sample
Proportion of do. 49.4 28.0 11.7 10.9 NA
loan source category
Proportion of do. 56.1 47.0 51.3 41.5 NA
erosion class

See footnotes at end of table. --Continued





Appendix table 9-Primary source of operating loan, bysoil elvsion class,
1982 1/--Continued

: : 4SOil erosion class:
Primary source of : Unit :Less e han: 5-13 : : 25 or : Totals
operating loans : : 5 tons/ :tons/acre/:14-24 tons/:more tons/:
::acre/year: year : acre/year :acre/year :

Other 2/:
Total observations NuObea 121.0 83.0 36;'0 41.0 281.0
in erosion class
Proportion of total Percent 7.1 4.9 2.1 2.4 16.5
sample
Proportion of loan do. 43.1 29.5 12.8 14.6 NA
source category
Proportion of do. 15.8 16.1 18.1 17.9 NA
erosion class

Total:
Observation in this Number 765.0 517.0 197.0 228.0 3/1,707.0
erosion class
Proportion of Percent 44.8 30.3 11.5 13.4 100.0
total sample

NA Not applicable.
1/ The statistics for this two-way table are asefol-ows: deg-ree of freedom 18;
chi-square 45.972; probability of null hypothesis 0.0003.
2/ "Other" includes operators not borrowing operating funds.
3/ Total sample size is 2,882 observations. Primary operating loan source is
unknown for 40 percent of sample.





Appendix table 10--Program participation by soil erosion class, 1982 1/

: : Soil erosion class:
Program : : : : : :
participation : Unit : Less than: 5-13 : : 25 or :Totals
category : 5 tons/ :tons/acre/:14-24 tons/:more tons/:
: : acre/year: year : acre/year :acre/year :


Neither commodity
nor conservation
programs:
Total observations
in erosion class
Proportion of total
sample
Proportion of
program parti-
cipation category
Proportion of
erosion class

Commodity programs
only:
Total observations
in erosion class
Proportion of total
sample
Proportion of
program parti-
cipation category
Proportion of
erosion class

Conservation programs
only:
Total observations
in erosion class
Proportion of total
sample
Proportion of
program parti-
cipation category
Proportion of
erosion class


Number 548.0


Percent

do.


do.


19.0

46.1


41.6


Number 334.0

Percent 11.6


41.8


25.4


Number 204.0


Percent

do.


do.


7.1

50.4


15.5


See footnotes at end of table.


105.0

3.6


1,188.0

41.1


8.8


31.6


393.0

13.6

33.1


44.2




239.0

8.3

29.9


26.8


126.0

4.4

31.1


14.2


142.0

4.9

11.9


41.4




97.0

3.4

12.1


28.3


43.0

1.5

10.6


12.5


799.0

27.8


129.0

4.5

16.1


38.9


32.0

1.1


405.0

14.1


13.5


--Continued





Appendix table 10--Program participation by soil erosion class, 1982 I/--Continued

: : Soil erosion class:
Program : : :
participation : Unit : Less than: 5-13 : : 25 or :Totals
category : : 5 tons/ :tons/acre/:14-24 tons/:more tons/:
: : acre/year: year : acre/year :acre/year :

Both program types:
Total observations Number 231.0 132.0 61.0 66.0 490.0
in erosion class
Proportion of total Percent 8.0 4.6 2.1 2.3 17.0
sample
Proportion of do. 47.1 26.9 12.5 13.5 NA
program parti-
cipation category
Proportion of do. 17.5 14.8 17.8 19.9 NA
erosion class

Total:
Observations in this Number 1,317.0 890.0 343.0 332.0 2,882.0
erosion class
Proportion of Percent 45.7 30.9 11.9 11.5 100.0
total sample


degrees of freedom = 9;


NA Not applicable.
1/ The statistics for this two-way table are as follows: d
chi-square = 38.637; probability of null hypothesis = 0.0001.





Appendix table 11--Program participation categories, by land capability
subclass e, 1982 1/


: : Program participation category:

Land capability class : Unit : :: : Joint :Totals
: :Neither:Commodity:Conservation:commodity and:


: :program:


only


: only :conservation :


All land in subclass e:
Total observations
in program category
Proportion of total
sample
proportion of land
capability class
Proportion of
program category


Number 704.0 546.0


Percent 24.4


do.

do.


39.9

59.3


19.0

30.9

68.3


All land not in
subclass e:
Total observations Number
in program category
Proportion of total Percent
sample
Proportion of land do.
capability class
Proportion of do.
program category

Total:
Observations in this Number
program category
Proportion of total Percent
sample


484.0 253.0


16.8

43.4

40.7


1,188.0

41.2


8.8

22.7

31.7



799.0

27.8


279.0


1,766.0


237.0

8.2

13.4

58.5


61.3


15.8

56.9


168.0

5.8

15.1

41.5



405.0

14.0


211.0

7.3

18.9

43.1


490.0

17.0


1,116.0

38.7


2,882.0

100.0


NA = Not applicable.
1/ The statistics for this two-way table are as follows: degrees of freedom =
3; chi-square = 24; probability of null hypothesis = 0.0001. Subclass e indicates
erosive cropland.





Appendix table 12--Program participation
1983 1/


categories, by land capability subclass e,


: : Program participation
S: : Joint
Land capability class : Unit : : Conser- : commodity : Totals
: : Neither :2 Commodity: ovation : and
: program : only : only :conservation:


All land in subclass e:
Total observations
in program category
Proportion of total
sample
Proportion of
land capability class
Proportion of
program category

All land not in
subclass e:
Total observations
in program category
Proportion of total
sample
Proportion of
land capability class
Proportion of
program category


Number

Percent

do.

do.




Number

Percent

do.

do.


462.0

16.0

26.2

59.5




315.0

10.9

28.2

40.5


712.0

24.7

40.3

65.1




381.0

13.2

34.1

38.9


156.0

5.4

8.8

61.4




98.0

3.4

8.8

38.6


436.0

15.1

24.7

57.5


322.0

11.2


1,766.0


61.2


1,116.0


38.7


28.9

42.5


Total:
Observations in this Number
program category
Proportion of total Percent
sample


777.0 1,093.0


26.9


37.9


NA = Not applicable.
_/ The statistics for
3; chi-square = 12.474;


indicates erosive cropla


this two-way table are as follows:, degrees of freedom =
probability of null hypothesis = 0.0059. Subclass e
nd.


254.0

8.8


758.0

26.3


2,882.0

100.0,






Appendix table 13--Program participation categories by land capability classes,
1982 1/


: :, Program participation:
: : : Joint
Land capability class : Unit :: : Conser-: commodity : Totals
: : Neither : Commodity: ovation : and
: : program : only : only :conservation:


All land in classes
IV-e, VI-e, and VII:
Total observations
in program category
Proportion of total
sample
Proportion of land
capability class
Proportion of
program category

All land in classes I,
II, III, V, and IV
(not subclass e) or
VI (not subclass e):
Total observations
in program category
Proportion of total
sample
Proportion of land
capability class
Proportion of
program category

Total:
Observations in this
program category
Proportion of total
sample


Number

Percent

do.

do.


166.0

5.8

38.9

14.0


Number 1,022.0


Percent

do.

do.


35.5

41.6

86.0


Number 1,188.0


Percent


41.2


151.0

5.2

35.4

18.9


648.0

22.5

26.4

81.1



799.0

27.7


51.0

1.8

11.9

12.6


354.0

12.3

14.4

87.4



405.0

14.1


59.0

2.1

13.8

12.0


427,0

14.8


431.0 2,455.0


15.0

17.6

88.0


85.2


490.0 2,882.0


17.0


100.0


NA = Not applicable.
I/ The statistics for


this two-way table


are as follows: degrees of'freedom -


3; chi-square = 15.797; probability of null hypothesis 0.0012. The lo\
Roman numeral, the fewer are the physical limitations on the land's use.
e indicates erosive cropland.


wer a
Subclass






Appendix table 14--Program participation categories by land capability class,
1983 1/


: : Program participation :
: : Joint :Totals
Land capability class :Unit : : Conser- : commodity
: : Neither : Commodity: ovation : and
: : program : only : only :conservation:

All land in subclass
IV-e, VI-e, and VII:
Total observations
in program category Number 111.0 198.0 31.0 87.0 427.0
Proportion of total
sample Percent 3.9 6.9 1.1 3.0 14.8
Proportion of land
capability class do. 26.0 46.4 7.3 20.4 NA
Proportion of
program category do. 14.3 18.1 12.2 11.5 NA

All land in classes I,
II, III, V, and IV
(not subclass e) and
VI (not subclass e):
Total observations Number 666.0 895.0 223.0 671.0 2,455.0
in program category
Proportion of total Percent 23.1 31.1 7.7 23.3 85.2
sample
Proportion of land do. 27.1 36.5 9.1 27.3 NA
capability class
Proportion of do. 85.7 81.9 87.8 88.5 NA
program category

Total:
Observations in this Number 777.0 1,093.0 254.0 758.0 2,882.0
program category
Proportion of total Percent 27.0 37.9 8.8 26.3 100.0
sample


NA Not applicable.
_/ The statistics for this two-way table are as follows: degrees
chi-square = 17.666; probability of null hypothesis = 0.0005. The
numeral, the fewer are the physical limitations on the land's use.
indicates erosive cropland.


of freedom = 3;
Lower a Roman
Subclass e


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