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no.610
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UNIVERSITY OF
ILLINOIS LIBRARY
AI URBANA-CHAMPAIGN
AGRICULTURE - - •

VARIABILITY OF YIELDS AND

INCOME FROM MAJOR ILLINOIS

CROPS 1927-1953

By Earl R. Swan$on

Counties with high year-
to-year crop-yield varia-
bility are heavily shaded.
Lighter shading indicates
less variability.

BULLETIN 610

University of Illinois Agricultural Experiment Station

f "

•*>••

CONTENTS

Part | — Yield Variability 4

Part II — Effect of Crop Diversification

on Farm Income and Income Variability 14

Summary 27

Access to the facilities of the Illinois
Electronic Digital Computer (Illiac) sub-
stantially reduced the computational
burden involved in this study.

Urbana, Illinois April, 1957

Publications in the Bulletin series report the results of investigations made
or sponsored by the Experiment Station

no. <*'0
. JT

VARIABILITY OF YIELDS AND INCOME FROM MAJOR
ILLINOIS CROPS 1927-1953

By EARL R. SWANSON, Associate Professor of Agricultural Economics

FARM BUSINESS decisions are based on expectations for the fu-
ture which in turn are largely founded on past experience. An
unusual experience may obscure the more common events which may
be equally relevant to wise planning. Therefore to gain a consistent,
long-range view, the following study of crop yield and income experi-
ence in Illinois reviews the 27-year period 1927-1953.

Such a review may be used as a guide in making decisions on
land valuation, crop insurance, choice of crops, and related farm busi-
ness matters.

Part I of the report contains average per-acre yields for five crops
and an estimate of their variation in each county and for the state as a
whole during the period of the study (1927-1953). By comparing
county figures with each other and with those for the entire state, the
reader may have a rough guide useful in land appraisal and crop
insurance programs. Such a guide constructed from county averages
cannot, of course, be considered to reflect accurately expected yields
on any given farm. The more homogeneous the county, however, the
more closely such averages may approach likely experience on individ-
ual farms in the county. A customary procedure in land valuation is
to use average yields in determining the annual income which is used as
a basis for estimating the value of the farm. In addition to considera-
tion of the average level of yields, attention should be given to 'the
variability of such yields. For example, an adjustment should be made
in the values of farms in different counties that have the same average
yields but are expected to differ in the stability of these yields.

Lending agencies may also use yield variability in adjusting the
amount that will be loaned to allow for differences in such variability
among farms in different areas. Appraisal of land for tax purposes
might take differences in yield dependability into account along with
average productivity.

The data in Tables 1-5 are significant to all-risk crop insurance
programs featuring premium rates based on normal county yields.
Premiums for such programs are determined in the following manner:
Let us say that the long-time county average corn yield is 50 bushels
per acre. If the farmer wishes to insure for 80 percent of the county
average, his premium would be based on 40 bushels. If the actual aver-

4 BULLETIN No. 610 [April,

age yield for the county for that year is 35 bushels, the claim is 5
bushels per acre irrespective of the insured farmer's yield.

Farmers considering such insurance will want to know if the
premiums are set on current figures which take into account upward
trends in yield averages. It is to the advantage of commercial insur-
ance agencies to keep premiums in line to encourage farmers to buy
their insurance. If the premiums are set on yield averages which are
unrealistically low, premiums over a period of years would so greatly
exceed claims as to discourage farmers from purchasing this insurance.

The data in Tables 1-5 may also be helpful in differentiating
between high- and low-risk counties in establishing premium rates.

In Part II, the effect on income and income variability of various
degrees of specialization in certain crops is examined. Such figures
can be useful to the farmer who does not want to move to another
county with less variability than the location he is presently farming.
He may wish to consider reducing uncertainty by diversification of
crops. (The undesirable consequences of yield variability may, of
course, also be met by crop insurance and by the maintenance of cash
reserves large enough to tide him over unfavorable years.)

In areas such as Illinois where there are rather stable yields for all
crops, it is believed that the proportion of total land in each of the
three classes of crops, (1) cultivated, (2) small grain, and (3) meadow,
will be determined chiefly by considerations other than reduction of
income variability. Thus the expected effect of meadow on succeeding
corn crops, maintenance of physical properties of the soil, distribution
of labor throughout the season, and considerations of livestock feed
are likely to be more important than income variability in choosing
the proportion of the three classes of crops. However, choices writhin
each of these three classes might be made with a view toward reducing
income variability.

Specifically, Part II seeks to find which combinations of corn and
soybeans and of wheat and oats minimize income variability from land
devoted to these crops.

Part I — YIELD VARIABILITY

One of the components of year-to-year income variability is the
year-to-year fluctuation in crop yields. Average yield data for counties
are published by the Illinois Cooperative Crop Reporting Service
and are the sole source of yield data used in this study. Use of county
average yields tends to underestimate the variation for any particular
farm or field within the county. Ideally, crop yields for a particular

1957]
80
70
60

VARIABILITY OF YIELDS AND INCOME FROM CROPS

;S'I2.5

WARREN COUNTY CORN YIELDS
(1927- 1953)

1927 "30 '35 40 55 50 1953

Yield variability measured from average and trend. (Fig. 1)

farm over a long period of time would provide a basis for a more
precise investigation of yield fluctuations. However, such data are not
available for all areas of the state for the length of time comparable
to that of the county average data of the Illinois Cooperative Crop
Reporting Service. Yield data for townships would also be more spe-
cific and therefore more suitable than county averages but are likewise
not available.

During the 1927-1953 period there has been, almost without excep-
tion, an upward trend in county yields for the five crops studied —
corn, soybeans, oats, wheat, and hay. New crop varieties, improved
machinery, increased fertilizer use, and other technological advances
are responsible for this upward yield trend. Our focus in measuring
yield variation is, however, to estimate the influence of such natural
causes as varying weather conditions. Therefore it is desirable to
measure yield fluctuation as independently as possible of the long-time
trend in yields over the years.

The procedure is illustrated in Figure 1. The Warren county
average corn yields are plotted for the 27-year period 1927-1953. The
average yield for the entire period is 50.0 bushels per acre. The
standard deviation1 S, is 12.5 bushels. The range from the average
minus one standard deviation to the average plus one standard devia-
tion will include approximately two-thirds of the annual yields. Tn
this case, 18 yields fall in the range 37.5 bushels to 62.5 bushels.

Yield variability is also shown measured about a trend line.2 The
yield range between the two lines drawn parallel to the trend line —

1 For method of computation see any standard statistical text, e.g., Snedecor,
G. W., Statistical Methods, Ames, Iowa State College Press, 1946.
1 Fitted by the method of least squares. See Snedecor, Chapter 6.

6 BULLETIN No. 610 [April,

one 9.4 bushels above and the other 9.4 bushels below — also includes
approximately two-thirds of the yields. The standard deviation about
the trend line (9.4 bushels) is called the standard error of estimate,
Sy.x. Use of a straight line instead of a curve to approximate a trend
line from which to measure yield variability may tend to cause over-
estimation of the yield variability. Corn, for example, shows evidence
that increases in yield have not been at a uniform rate.

A measure of variability should also be related to the average level
of yield. It might be expected that the actual variations in bushels
would be greater in a county that averages 60 bushels of corn per acre
than in a county which averages only 40 bushels. A measure of vari-
ability that is expressed as a percent of average yield would therefore
be more useful in comparing areas than one expressed in absolute
terms.

Further, if such a measure of relative variation is to be used to
compare crops or counties for forming future expectations, it may be
desirable to express the relative variability as a percent of recent
average yields. Accordingly, the standard error of estimate was
divided by the average yields for the five-year period 1949-1953. The
resulting measure of yield variability, expressed as a percent, is in the
third column of Tables 1 through 5. (A high value indicates high
variability.) The first column in these tables gives the average yield
for the 27-year period and the second column the standard error of
estimate based on the trend line for the 27 years.

In addition to the likely underestimation of variability for a spe-
cific farm or field when county data are used (see page 4), two more
limitations of the basic data should be noted.

First, the yield data reported by the Cooperative Crop Reporting
Service are based on harvested acres rather than planted acres. This
tends to overestimate yields of planted acres in years of crop abandon-
ment. A reduction in year-to-year variation may be expected as
a result of using yields based on harvested acreages. A second limita-
tion of the basic data is the measurement of hay yields. Since few farm-
ers actually weigh the hay produced, considerable errors may occur
in reporting hay yields. Without additional information, the effect of
such individual reporting errors cannot be estimated.

Certain regional differences within the state are apparent when
each county is given a weighted rank according to its average yield
variability. The following method was used to rank the counties:

First each crop was weighted according to the fraction it repre-
sented of the total county acreage devoted during 1949-1953 to the

(Text continued on page 12)

1957}

VARIABILITY OF YIELDS AND INCOME FROM CROPS

Table 1. — Average Corn Yields in Illinois Counties and Their Variation, 1927-1953

County

Average
yield in
bushels
per acre
1927-53

Standard
error of
estimate
about
trend
1927-53

Standard
error in
column 2
expressed
as a per-
cent of
1949-53
average
yield

County

Average
yield in
bushels
per acre
1927-53

Standard
error of
estimate
about
trend
1927-53

Standard
error in
column 2
expressed
as a per-
cent of
1949-53
average
yield

Adams

41.9

11.9

21.2

Lee

51.2

6.3

9.7

Alexander

. 29.2

5.8

18.6

Livingston . . .

44.1

8.6

16.2

Bond

. 31.6

8.3

19.5

Logan

48.3

10.6

16.7

Boone

. 48.6

8.0

12.2

McDonough

47.1

11.7

20.2

Brown

. 39.9

11.9

22.9

McHenry . . . .

46.2

8 0

13 3

Bureau

. 52.7

7.7

12.1

McLean

48.0

8 0

13 9

Calhoun . . .

. 44.0

10.2

20.0

Macon

46 6

10 5

17 7

Carroll

. 54.2

6.4

9.1

Macoupin . . .

38.3

9.9

18.8

Cass . .

. 45 2

9 8

17 5

Madison

37 2

9 6

20 9

Champaign . .

. 48.2

9.0

15.3

Marion

24.4

7.9

23.7

Christian . . . .

. 44.3

10.8

17.9

Marshall ....

46.8

9.2

15.7

Clark

. 37.1

5.6

12.6

Mason

. 36.9

8.5

17.8

Clay . .

. 25.8

8.1

23.5

Massac

31.4

5.8

16.7

Clinton

. 31.2

10.3

26.5

Menard ....

44.5

9.6

16.7

Coles

. 42.9

8.6

16.3

Mercer

49.3

8.6

14.7

Cook

. 41.3

8.0

16 3

Monroe . . .

. 38 0

8.9

20.4

Crawford . . . .

. 35.7

6.4

14.7

Montgomery.

37.3

9.4

17.4

Cumberland . .

. 33.9

7.2

15.9

Morgan

46.3

10.9

18.3

DeKalb

. 53.7

7.8

11.8

Moultrie. . . .

43.7

9.7

17.3

DeWitt

. 45.9

9.2

16.3

Ogle

51.9

6.2

9.3

Douglas

. 47.0

8.6

14.7

Peoria

46.5

9.2

15.8

DuPage

. 44.8

7.4

12.8

Perry

24.1

6.4

20.4

Edgar

. 46.9

7.9

14 6

Piatt

49.2

10.0

15.8

Edwards

. 33.4

8.1

20 0

Pike

. 41.9

11.3

20.9

Effingham. . . .

. 30.3

6 4

15 0

Pope ....

. 27.3

6.2

19.0

Fayette

. 28.8

8.1

20.5

Pulaski

29.5

5.1

17.6

Ford

44 2

7 9

14 8

Putnam

51 2

8.6

14.2

Franklin

. 24.5

6.3

20.2

Randolph . . .

33.5

9.8

25.7

Fulton

. 45.9

10.2

18.1

Richland ....

. 27.4

8.0

22.5

Gallatin

. 35.4

7.2

17.7

Rock Island .

51.5

7.3

12.2

Greene

. 43.1

10.8

19.9

St. Clair

37.4

9.5

21.3

Grundy

. 43.3

8.4

15 8

Saline

. 31.5

6.6

18.2

Hamilton . . . .

. 26.3

6 0

18 5

Sangamon . . .

. 44.4

10.0

17.4

Hancock

. 44.4

12.2

22 1

Schuyler ....

43.0

11.7

21.0

Hardin

. 28.1

6 9

21 4

Scott

. 43.9

11.1

21.2

Henderson . . .

. 48.3

8.7

15.5

Shelby

. 38.7

8.4

16.6

Henry

. 51.4

7.8

12 6

Stark

. 49 1

9.4

15.7

Iroquois . .

. 42.9

7 9

15 5

Stephenson

. 52.7

6.8

10.2

Jackson

. 31.9

7 1

20 5

Tazewell

. 48 6

8.4

13.7

Tasoer. .

. 31.4

6 9

16 6

Union

30 1

6.3

19.8

Jefferson

. 24.4

6.5

20 2

Vermilion

44 0

7.8

14.2

Tersev .

. 41.3

10.4

20 6

Wabash

. 38.1

8.1

21.2

Jo Daviess . . .

. 50.1

7.2

11.4

Warren

. 50.0

9.4

15.9

Johnson

. 25.5

5.4

20 3

Washington. .

. 25.9

9.0

26.3

Kane

. 51.4

7.5

11 7

Wayne

. 25 4

7.1

21.4

Kankakee. . . .

. 41.9

8.1

15 4

White

. 32.8

7.3

19.0

Kendall

. 46.3

10.1

17.6

Whiteside . . .

. 52.6

6.5

10.2

Knox

. 48.3

8.9

14.8

Will

. 40.7

8.3

16.4

Lake

. 42.0

7.4

13.6

Williamson . .

. 25.5

6.4

21.6

LaSalle

. 48.7

9.1

15 5

Winnebago

. 49.2

6.1

9.6

Lawrence

. 32 4

6.3

16 2

Wood ford

. 50.6

9.0

5.1

State8 . .

43.9

7.2

13.2

• Based on average annual yields for the state.

Table 2. — Average Soybean Yields in Illinois Counties and Their Variation, 1927-1953

County

Average
yield in
bushels
per acre
1927-53

Standard
error of
estimate
about
trend
1927-53

Standard
error in
column 2
expressed
as a per-
cent of
1949-53
average
yield

County

Average
yield in
bushels
per acre
1927-53

Standard
error of
estimate
about
trend
1927-53

Standard
error in
column 2
expressed
as a per-
cent of
1949-53
average
yield

Adams

19.9

3.3

12.3

Lee

20.9

2.0

7.7

Alexander8 . . .
Bond

. 15.2
. 13.8

2.9

2.7

14.6
14.7

Livingston . . .
Logan

20.7
22.1

2.7
2.6

10.1
9.0

Booneb

. 19.3

1.9

7.7

McDonough .

22.0

3.2

11 0

Brown

. 18.5

3.1

13.0

McHenryd. . .

18.5

2.4

10 0

Bureau

. 21.9

2.2

7.6

McLean

. 22.6

2.9

10 0

Calhoun

. 18.4

2.9

12.7

Macon

22 9

2.9

10 1

Carroll0

. 19.4

2.7

11.6

Macoupin

18 9

2.3

9 3

Cass

. 19.8

3.1

12.0

Madison

17 5

2 7

12 4

Champaign . .

. 22.9

2.9

10.3

Marion

11.9

3.1

21.2

Christian . . . .

. 22.0

2.7

9.9

Marshall ....

21.6

2.9

10.4

Clark

. 15.5

1 7

8 1

Mason

17 6

3 2

13 0

Clay. .

. 11.9

2.4

16.9

Massac0

. 12.8

3.5

21.6

Clinton

. 14.8

3.5

19.7

Menard

20.1

3.0

11.0

Coles

. 20.1

2.6

9.8

Mercerh

21 2

2.7

10 5

Cookd

. 19.0

2.0

8.5

Monroe*

16 1

3 6

17 0

Crawford . . .

. 14.0

2.5

14 0

Montgomery.

18 5

2 4

9 9

Cumberland. .

. 14.9

2.1

10.5

Morgan

21.6

2.8

9.9

DeKalb

. 22 0

2 0

7 4

Moultrie

21 9

3 5

12 2

DeWitt

22 4

2 9

10 4

Ogleh

22 1

6 0

21 1

Douglas

. 22.6

2.8

9.9

Peoria

. 22 1

3.1

10.3

DuPaged

. 19.4

2.1

8.5

Perry

11 4

3.4

23 6

Edgar

. 21.6

2.6

9.7

Piatt

23 6

2 8

9.7

Edwards

. 14.0

2.7

16 1

Pike

18 7

2 8

11 2

Effingham. . . .

. 14.0

2 4

13 0

Popec

12 8

3 1

18 9

Fayette

. 13.2

2.9

16.7

Pulaskid

13.8

2.8

15.7

Ford

. 20.8

2.8

10.6

Putnam

. 20.9

2.1

7.3

Franklin

. 11.3

2.7

19.0

Randolph . .

14 1

3.5

18.6

Fulton

. 21.0

3.2

12.0

Richland . . .

11 8

2 2

14.7

Gallatin

. 14.2

2.3

12 5

Rock Island

22 3

2.6

9 9

Greene . . .

. 19 3

3 1

12 2

St Clair

17 1

3 4

15 5

Grundy6

. 20.2

2.1

8.1

Saline

14.0

2.4

13.3

Hamilton . . . .

. 12.2

2.6

16.7

Sangamon . . .

21.6

2.8

10.1

Hancock

. 20.9

4.0

15.2

Schuyler . . .

19.5

3.5

13.8

Hardinf

. 12.9

2.4

17.4

Scott

19 5

3.1

12.4

Henderson . . .

. 21.0

2.5

9.4

Shelby

18 3

2.9

12.2

Henry

. 21.9

2 4

8.3

Starkh

22 4

2 5

8.7

Iroquois . .

. 20 4

2 7

10 4

Stephenson .

19 6

2 2

9 4

Jackson . . .

. 13 5

2 9

16 9

Tazewell

22 6

2 6

8 7

Tasoer .

12 9

2 2

12 4

Union0

13 7

3 0

17 4

Jefferson

. 12.2

2.9

18.8

Vermilion . . .

21 0

2.5

9.7

Jersey. .

. 19.7

3.8

15.3

Wabashd . .

15 5

2.9

18.4

Jo Daviess8. .

. 19.5

2.4

10 5

Warren .

23 2

2.8

9.3

Johnson . . . .

. 12.3

2.7

19 6

Washington.

12 6

3 7

21.3

Kane

. 20.4

1 5

5 9

Wayne . .

11 7

2 4

16.4

Kankakee. . . .

20.0

2 3

9 0

White

13 7

2 6

14.6

Kendall... .

20 4

2 3

8 3

Whiteside

19 9

2 4

9.4

Knox ....

22 1

2 6

8 8

Will

19 4

2 0

8.3

Lake"

. 18.4

2.1

9.7

Williamson . .

11.5

3.3

23.9

LaSalle . .

21 9

2 6

9 1

\Vinnebago

18 2

2 1

9.1

Lawrence. . . .

. 13.1

2.5

15.2

Wood ford

22 9

3.1

10.1

State} . .

20.0

2.1

9.3

• No data reported for 1927,

1928, 1932, 1933, and 1935.

b No data reported for 1928
and 1934.

0 No data reported for 1927
and 1928.

d No data reported for 1928.

e No data reported for 1934.

f No data reported for 1927,

1928, 1930, 1931, 1932, 1933,
1934, 1940. and 1950.

8 No data reported for 1927,
1928, and 1934.

h No data reported for 1927.

1 No data reported for 1933.
i Based on average annual
yields for the state.

VARIABILITY OF YIELDS AND INCOME FROM CROPS

Table 3. — Average Oats Yields in Illinois Counties and Their Variation, 1927-1953

County

Average
yield in
bushels
per acre
1927-53

Standard
error of
estimate
about
trend
1927-53

Standard
error in
column 2
expressed
as a per-
cent of
1949-53
average
yield

County

Average
yield in
bushels
per acre
1927-53

Standard
error of
estimate
about
trend
1927-53

Standard
error in
column 2
expressed
as a per-
cent of
1949-53
average
yield

Adams

30.7

8.5

25.6

Lee

41.2

8.2

17 9

Alexander. . . .

. 23.9

5.7

27.7

Livingston. . . .

33.8

9 2

25 8

Bond

. 24.0

9.9

41.9

Logan

37.0

9 0

21 3

Boone

. 40.4

8.3

17.4

McDonough . .

35.9

9 2

24 7

Brown

. 28.7

9.3

31.0

McHenry

42 4

8 4

17 6

Bureau

. 40.5

7.4

16.7

McLean . ...

36 2

8 7

22 4

Calhoun . . . .

. 26.8

6.3

26 5

Macon . . .

36 2

10 3

25 2

Carroll . . .

. 40.5

7.3

16 1

Macoupin . .

30 3

7 8

22 4

Cass

. 32 6

7 3

19 9

Madison

26 6

7 1

27 5

Champaign. .

. 35.1

8.9

23.8

Marion

20.4

6.3

32 1

Christian . . . .

. 35.0

9.6

23.0

Marshall

35.9

8.4

20.7

Clark

. 22.5

7.6

35.2

Mason

28.1

7.5

24 5

Clay. .

. 18.5

6.2

36.5

Massac

23.8

5.3

24 3

Clinton

. 28.6

8.0

33.9

Menard

34.4

8 2

20 4

Coles

. 32.1

8.2

24 1

Mercer . . .

34 6

7 9

21 1

Cook

. 40.1

9.5

22 1

Monroe

28 7

5 8

24 4

Crawford . .

. 21 4

7.5

37 5

Montgomery.

28 3

8 0

23 3

Cumberland. .

. 21.7

7.8

36.1

Morgan ....

35.8

7.9

18.6

DeKalb . . .

46 9

8 8

17 1

Moultrie

34 0

9 2

25 3

DeWitt

. 34.5

9.4

24.7

Ogle

40.3

8.5

19.0

Douglas

. 35.3

9.2

24.3

Peoria . . .

35.1

9.4

24.2

DuPage

. 43.3

9.7

19 6

Perry

19 5

5.3

29.8

Edgar

. 33.3

8.2

24 3

Piatt

36 6

9.7

23.2

Ed wards

. 22.6

8 0

41 7

Pike

28 7

7.8

26 9

Kffingham. . . .

. 22.1

7 6

34 2

Pope

21 3

5 6

29 8

Fayette

. 22.3

6.7

27.9

Pulaski

24.3

4.8

23.1

Ford

. 33.0

8.5

24.4

Putnam

41.2

10.3

23.1

Franklin

. 20.2

5.6

29.2

Randolph . . .

26.9

6.3

26.5

Fulton

. 34.4

8.9

24.9

Richland . . .

19.5

7.1

34.8

Gallatin

. 22.0

6.8

40.0

Rock Island

41.5

10.3

27.0

Greene

. 30.2

6.5

20 6

St Clair . . .

29.3

6.2

24.6

Grundy

. 35.3

9.7

25 3

Saline . . .

21.7

6.6

38.8

Hamilton . . . .

. 19.6

5.7

32 8

Sangamon

37 2

8.8

20.3

Hancock

. 33.5

7.6

22 1

Schuyler

31 3

9.7

28.5

Hardina

. 18.9

6 1

36 3

Scott

31 3

7.6

22.8

Henderson . . .

. 34.0

7.6

21.1

Shelby

27.6

8.2

27.3

Henry

. 39.0

8 2

19 6

Stark

37 0

9.3

22.6

Iroquois

. 31.7

8 2

24 3

Stephenson

41 5

8.7

18.0

Jackson

. 25.4

5.7

28 5

Tazewell

36 8

8 5

21.9

Jasper

. 20.0

6.7

32 5

Union

24 0

4 8

21.8

Jefferson

. 18.9

5.3

31 9

Vermilion

32 2

8 8

25.3

Jersey

. 28.0

7.8

27 5

\Vabash

24 4

7.7

49.4

Jo Daviess. . .

. 39.8

8.5

18.6

Warren . . .

36.3

8.7

21.8

Johnson

. 22.5

5.5

30 2

\Vashington

25 5

6 4

27.6

Kane

. 46.5

9 2

18 3

\Vayne

19 1

6 3

37.5

Kankakee. . . .

. 34.4

9.0

22.4

White

21.7

6.0

31.6

Kendall

. 42 2

8 8

18 8

\Vhiteside

40 9

7.8

17.3

Knox

. 35.8

9.1

23.8

Will

37.5

9.5

22.4

Lake

. 42.4

8.6

18.9

Williamson . . .

20.9

5.0

27.5

LaSalle

. 39 9

9.3

21 4

Winnebago

38 5

8.1

18.2

Lawrence . .

. 22 4

6.6

33 0

W'oodford

37 1

9.0

23.0

Stateb..,

35.5

7.1

18.1

" No data reported for 1934.

b Based on average annual yields for the state.

10

BULLETIN No. 610

[April,

Table 4. — Average Wheat Yields" in Illinois Counties and Their Variation, 1927-1953

County

Average
yield in
bushels
per acre
1927-53

Standard
error of
estimate
about
trend
1927-53

Standard
error in
column 2
expressed
as a per-
cent of
1949-53
average
yield

County

Average
yield in
bushels
per acre
1927-53

Standard
error of
estimate
about
trend
1927-53

Standard
error in
column 2
expressed
as a per-
cent of
1949-53
average
yield

^dams

18.3

3.1

12.8

Lee . .

24 5

4 1

14 7

\lexander. . .

.. 15.8

3.4

18 9

Livingston

22 2

3 5

13 5

Bond

. . 16.3

3.2

15 5

Logan

22 6

4 8

17 5

Boone

.. 21.9

4.8

17.5

McDonough .

21.1

3.6

14.2

Brown

.. 17.8

3.9

16 1

McHenry

23 3

4 7

16 8

Bureau

. . 24.5

3.2

11.8

McLean

. 22.6

3.7

13.8

3alhoun ....

. . 19.1

3.7

16.2

Macon

22 7

4.3

15 4

"arroll

. . 23.3

3.6

14.1

Macoupin

. 18 3

4 5

19 4

3ass

. . 20.6

3.9

14 9

Madison

18 3

3.7

17 6

Champaign . .

.. 22.6

3.6

12.9

Marion

. 14.5

3.1

19.1

Christian .

.. 21.7

4.1

14 5

Marshall

21 9

4 0

14 4

~lark

. . 16.0

3.3

20 1

Mason

18 3

3 8

16 4

31ay. .

.. 13.2

3.1

21.5

Massac

. 14.9

2.5

13.9

Zlinton

.. 16.9

4.2

22.3

Menard ....

. 20.6

4.3

16 4

~oles

. . 19.9

3.3

13.4

Mercer

. 21 8

3.4

15 9

3ook

. . 22.9

4.2

16 3

Monroe

18 6

4.7

21 6

Zrawford

. . 15.3

3.2

20 3

Montgomery

18 5

4 1

16 5

Cumberland .

.. 16.0

3.7

19.5

Morgan ....

. 21.7

4.3

15.6

DeKalb

. . 24 4

5 2

17 6

Moultrie

21 1

4 0

15 4

DeWitt

.. 21.9

4.0

14.9

Ogle ....

. 22.8

4.5

17.3

Douglas . . .

.. 21.7

4.3

15.8

Peoria . .

21 9

3.6

12 9

DuPage

. . 24.3

5.0

18 1

Perry

13 3

3.5

21 6

Sdgar ....

. . 20.7

3.1

13 0

Piatt

22 8

4.3

15 0

Edwards ....

.. 15.9

4.1

23.3

Pike

. 17.5

3.5

16.2

Effingham . . .

.. 15.8

3.8

21.3

Pope

. 13.9

2.8

16.5

Payette

.. 15.0

3.2

18.4

Pulaski ....

. 15.2

3.4

18.3

Ford

. . 22.2

4.2

15.8

Putnam . .

. 24.5

4.4

17.2

^ranklin ....

. . 14.7

3.4

20 7

Randolph

16 2

3.7

19 1

^ulton

. . 20.5

3.8

16 0

Richland

14 5

3.8

25 0

liallatin ....

. . 16.2

3.9

20.5

Rock Island .

. 23.5

4.5

20.1

jreene

. . 18.7

4.0

17.4

St. Clair

. 18.3

4.1

19.3

3rundyb. . . .

. . 22.6

3.3

12.4

Saline

. 15.5

3.0

17.2

Hamilton . . .

. . 14.5

3.1

19.4

Sangamon . . .

. 21.7

4.5

16.8

"lancock. . . .

.. 19.7

3.5

14.2

Schuyler

. 19.2

4.2

16.4

Htardin"

. . 14.1

3.8

23 5

Scott

. 19 4

3.9

17.0

lenderson . .

. . 20.7

3.6

15.3

Shelby

. 18.3

3.0

13.5

fienry

. . 23.5

3.3

13.2

Stark

. 23.4

3.8

12.8

roquois ....

., 22.6

3.8

13.6

Stephenson . .

. 20.8

4.2

16.2

] ackson

.. 16.1

4.0

23.0

Tazewell

. 21.5

3.8

15.1

asper

. . 14.5

3.3

20.4

Union . ...

. 16 3

3.9

20.7

efferson ....

.. 14.6

4.0

25 3

Vermilion

21 6

3.9

14.9

ersey

. . 19.0

4.2

19 8

Wabash

. 16 7

3.8

28.8

o Daviess . .

. . 20.3

4.3

18.4

Warren

. 22.5

4.4

17.1

ohnson ....

. . 13.9

3.2

19.3

Washington. .

. 15.2

4.2

23.3

•Cane

. . 25.1

4 9

16 9

Wayne

14 1

3.5

22.4

•Cankakee. . .

.. 21.7

3.9

14.9

White

. 14.9

3.8

22.6

Kendall

. . 24.3

3.8

13.3

Whiteside

23.3

3.9

15.2

•Cnox

. . 22.0

3.8

14.2

Will

. 23.4

3.9

13.7

Lake

.. 23.5

4.4

15.3

Williamson

14.1

3.1

18.5

LaSalle

. . 23.0

4.8

17 0

Winnebago

21.6

4.1

15.9

^awrence. . .

.. 15.1

3.0

17 2

Wood ford

22.3

3.9

14.7

Stated . .

18.7

2.8

12.6

• 1927-1949 — Winter wheat
only; 1950-1953 — All wheat.

b No data reported for 1934.
c No data reported for 1950.

d Based on average annual yields
for the state.

1957]

VARIABILITY OF YIELDS AND INCOME FROM CROPS

11

Table 5. — Average Hay Yields in Illinois Counties and Their Variation, 1927-1953

Standard

Standard

Average
yield in
County tons

Standard
error of
estimate
about

error in
column 2
expressed
as a per-

Average
yield in
County tons

Standard
error of
estimate
about

error in
column 2
expressed
as a per-

per acre
1927-53

trend
1927-53

cent of
1949-53

per acre
1927-53

trend
1927-53

cent of
1949-53

average

average

yield

yield

Adams

1 35

218

13 9

Lee

1.55

139

7 1

Alexander. . . .

1.55

.234

14.3

Livingston ....

1.51

.211

12.3

Bond

1.19

.183

13.9

Logan

1.35

.173

12 2

Boone

1.79

.245

10.0

McDonough . .

1.39

.190

12.0

Brown

1.36

.222

14.3

McHenry

1.72

.239

11 1

Bureau

1 54

.150

8.3

McLean

1.52

.190

11 0

Calhoun

1 62

.237

12.6

Macon

1.37

.192

13 9

Carroll

1.56

.135

7.1

Macoupin ....

1.39

.175

11.2

Cass

1 35

207

14 4

Madison

1.57

.180

10 4

Champaign

1 47

198

12 4

Marion

.84

055

5 9

Christian . . .

1.32

.186

13.2

Marshall

1.51

.205

11.5

Clark

1.15

.123

9.2

Mason

1.43

.210

12.4

Clay

.80

.180

20.7

Massac

1.10

.184

14.8

Clinton

1 39

209

12.6

Menard

1.38

.178

11.9

Coles

1 32

173

12 4

Mercer

1.47

.161

9.5

Cook

1 49

186

9 9

Monroe ....

1.60

.271

14 9

Crawford ....

1.13

.161

12.6

Montgomery. .

1.29

.144

10.1

Cumberland . .

1.09

.139

11.0

Morgan

1.40

.209

14.4

DeKalb

1.60

.194

10.1

Moultrie

1.31

.174

12.9

DeWitt

1 50

.176

11.1

Ogle .

1.54

.180

9.5

Douglas

1 38

.268

17.0

Peoria

1.46

.179

10.8

DuPage

1 53

182

9.3

Perry

1.03

.243

21.9

Edgar

1 29

188

13.9

Piatt

1.40

.193

13.5

Edwards

1 01

178

16 2

Pike

1.50

.205

11.6

Effingham

1 05

142

11 6

Pope

.97

.166

15.2

Fayette

1.02

.159

13.7

Pulaski

1.16

.168

13.3

Ford

1.44

.226

14.0

Putnam

1.55

.146

7.8

Franklin . ...

.97

.193

18.6

Randolph ....

1.45

.245

15.6

Fulton

1 45

209

12.3

Richland

.82

.167

18.6

Gallatin

1.18

.205

15.3

Rock Island . .

1.58

.128

6.1

Greene

1 51

180

10 8

St. Clair

1.60

.185

10.3

Grundy

1 46

210

11 9

Saline

1.07

.175

15.0

Hamilton ....

.88

.197

20.1

Sangamon ....

1.38

.159

10.5

Hancock

1.32

.179

12.2

Schuyler

1.33

.237

16.5

Hardin

1.02

.228

21.5

Scott

1.56

.178

9.9

Henderson . .

1.47

.171

9.9

Shelby

1.21

.134

10.2

Henry

1.62

.144

7.4

Stark

1.48

.160

9.5

Iroquois . .

1.51

.222

13.7

Stephenson . . .

1.70

.240

10.9

Jackson

1.26

.171

11.2

Tazewell

1.57

.149

8.3

Tasoer. .

.91

.135

13.0

Union

1.17

.208

16.4

Jefferson ....

.87

.177

17.7

Vermilion . . .

1.38

.179

12.1

Tersev .

1.65

.189

9.9

Wabash

1.27

.195

17.9

Jo Daviess . .

1.63

.235

11.6

Warren . ...

1.40

.179

11.6

Johnson

.96

.169

16.7

Washington. . .

1.15

.181

13.4

Kane . . .

1 68

.193

9.5

Wayne

.80

.186

20.9

Kankakee. .

1 39

183

11.2

White

1.09

.187

15.2

Kendall

1.52

.178

10.1

Whiteside ....

1.62

.192

9.5

Knox .

1 48

.171

9 6

Will

1.47

.189

10.7

Lake

1.63

.250

12.1

Williamson . . .

1.00

.186

16.9

LaSalle

1.56

.163

8.7

Winnebago . . .

1.65

.171

8.1

Lawrence ....

1.09

.162

14.5

Wood ford ....

1.61

.214

11.1

State8..

1.36

.120

7.3

a Based on average annual yields for the state.

12

BULLETIN No. 610

(April,

five crops studied. For example, in Clinton county during the period
1949-1953, of the land devoted to the five crops, corn represented 27.43
percent; wheat, 24.39 percent; soybeans, 21.97 percent; oats, 13.90 per-
cent and hay, 12.31 percent.

The second step was to multiply these percentages by their respec-
tive yield variabilities (Col. 3, Tables 1-5). Thus (still using Clinton
county as an example), we multiplied 27.43% by 26.5 (weight for
corn X Col. 3 of Table 1) = 7.27, 21.97% X 19.7 = 4.33 for soybeans,
etc.

Finally these weighted yield variabilities for each crop are added
(7.27 -f- 4.33, etc.) to get a county average yield variability — in the
case of Clinton county, 23.30.

A general pattern of increasing yield variability occurs as we move
from the northern part of the state to the southern part (Fig. 2). Coun-
ties in northwestern Illinois are characterized by high yield stability
while in the counties in southern Illinois (excluding the extreme
southern tip of the state) a higher degree of variability is noted.

1900% AND OVER

Average crop-yield variability. (Fig. 2)

1957]

VARIABILITY OF YIELDS AND INCOME FROM CROPS

13

Following is the ranking of counties in order of decreasing
variability:

Rank County

Average
crop yield
variability

1 Clinton 23.30

2 Washington 22.56

3 Coles 22.43

4 Wabash 22.42

5 Perry 22.27

6 Hardin 21.49

7 Randolph 21.30

8 Clay 21.09

9 Brown 20.65

10 Jefferson 20.58

11 Richland 20.35

12 Williamson 20.24

13 Monroe 20.16

14 Marion 20.05

15 Franklin 19.95

16 Edwards 19.83

17 Wayne 19.53

18 Hamilton 19.06

19 Schuyler 18.95

20 Bond 18.90

21 Jackson 18.77

22 White 18.76

23 Johnson 18.72

24 Fayette 18.66

25 Hancock 18.47

26 St. Clair 18.44

27 Union 18.43

28 Pike 18.35

29 McDonough 18.20

30 Gallatin 18.10

31 Jersey 18.10

32 Livingston 17.87

33 Adams 17.83

34 Calhoun 17.76

35 Massac 17.66

36 Scott 17.53

37 Fulton 17.31

38 Saline 17.16

39 Madison 16.95

40 Peoria 16.94

41 Pope 16.82

42 Grundy 16.79

43 Mason 16.79

44 Ford 16.78

45 Greene 16.76

46 Alexander 16. 70

47 Pulaski 16.47

48 Effingham 16.45

49 Kendall 16.42

50 Moultrie 16.32

51 Will.. 16.31

Rank County

Average
crop yield
variability

52 Lawrence 16. 25

53 Stark 16.25

54 Warren 16.15

55 Iroquois 16. 11

56 Jasper 16.03

57 Macon 16.03

58 Cook 15.90

59 LaSalle 15.89

60 Marshall 15.86

61 DeWitt 15.75

62 Crawford 15.69

63 Logan 15.57

64 Cass 15.44

65 Knox 15.44

66 Shelby 15.43

67 McLean 15.37

68 Morgan 15.35

69 Cumberland 15.25

70 Kankakee 15.21

71 Christian 15.20

72 Menard. 15.15

73 Champaign 15. 13

74 Mercer 15.09

75 Putnam 15.07

76 Henderson 15.05

77 Piatt 15.00

78 Sangamon 15.00

79 Montgomery 14.85

80 Douglas 14.68

81 Lake 14.59

82 Macoupin 14.43

83 Edgar 13.99

84 Vermilion 13 . 99

85 DuPage 13.84

86 Tazewell 13.83

87 McHenry 13.81

88 Henry 13.51

89 Jo Daviess 13.40

90 Boone 13.15

91 Kane 13.02

92 DeKalb.. ... 12.95

93 Clark 12.94

94 Stephenson 12. 85

95 Ogle 12.73

96 Bureau 12.63

97 Rock Island 12.33

98 Whiteside 12.13

99 Winnebago 1 1 . 86

100 Lee 11.61

101 Woodford 11.38

102 Carroll.. 10.62

The relative variability of crops within any county may be com-
pared by using Column 3 of Tables 1-5.

14 BULLETIN No. 610 [April,

A comparison of the relative variability of crop yields over the
state may be obtained from the state data given in the last line of
Tables 1 through 5. Using these state averages we find that oats have
the most variable yields; corn is second; wheat, third; soybeans,
fourth; and hay the most stable.

Part II — EFFECT OF CROP DIVERSIFICATION ON
FARM INCOME AND INCOME VARIABILITY

In Part I our primary purpose was the comparison of counties
with respect to crop yield variabilities. To make such a comparison
meaningful, yield variabilities were expressed as percentages of an
average yield (standard error of estimate of yield per acre divided by
average yield per acre, 1949-1953).

In Part II, however, interest is in the effect of crop diversification
on (a) average level of income per acre, and (b) variability of income
per acre in a particular county. Since we do not attempt to compare
counties in this analysis, the measure of variability is expressed in
actual dollars per acre and not as a percent of the average level of
income per acre for any period.

In considering the income1 and income variability effects of diversi-
fication, two pairs of crops are studied. The first of these is corn and
soybeans, frequent competitors for the acreage allotted to cultivated
crops. The following analysis shows the effect on income variability
of dividing an acreage in different proportions between these two
crops. The choice of a small-grain crop usually lies between oats and
wheat. Accordingly, the effect of diversification upon year-to-year
income variability of these crops is also compared.

Gross income is used because both crops considered within a class
(cultivated or small grain) require approximately the same tillage and
harvesting operations. Seed and fertilizer costs may vary slightly
between crops within a class but it is not believed that omission of
these costs along with other operating costs will alter the conclusions
of the analysis. Appropriate cost-of-production data are not available
for a time period of sufficient length to display the variability which is
our focus of attention in this study.2

1 Prices used to compute gross incomes are those reported by the Cooperative
Crop Reporting Service. Prices for Crop Reporting Districts were used for
counties within that district.

2 Although cost-of-production studies have been conducted by the Illinois
Agricultural Experiment Station since 1913, the period of time the study has
been located in any one area of the state, almost without exception, has been
quite limited.

1957}

VARIABILITY OF YIELDS AND INCOME FROM CROPS

15

The applicability of the results of the following analysis based on
county yield data to any particular farm within a county depends, of
course, on the similarity between conditions on that farm and the
average of the county.

Corn and soybeans. What has been the relation between income
variability and diversification between corn and soybeans? Using the

INCOME VARIABILITY

15

ui

£ 14

tc.

LJ
0.

OT

5.3

12

u.
O

g

<r
<r
ui

Q
(£

<t 10

CO

ADAMS

WOODFORO

JOHNSON

100 PERCENT CORN- SOYBEANS
ACREAGE IN CORN

50 PERCENT CORN AND
50 PERCENT SOYBEANS

100 PERCENT CORN-SOYBEANS
ACREAGE IN SOYBEANS

20 3O 40 50

GROSS INCOME (DOLLARS PER ACRE)

60

Effect of corn-soybeans combination on income and income variability
in three counties, 1927-1953. (See Table 6.) (Fig. 3)

16 BULLETIN No. 610 [April,

standard error of estimate about the trend1 as a measure of income
variability, the effect on income and its variability of substituting soy-
beans for corn is presented in Table 6. Income variability weighed
against average level of income may be an aid in selecting the best
crop combination for a particular farmer.

Three general types of relationships are noted between income
variability and the manner in which corn-soybean land is divided
between these two crops. In Figure 3, data are presented for three
counties representing these general relationships. Adams county repre-
sents the most common relationship — a shift from 100 percent corn to
100 percent soybeans reduces both gross income and income variability
throughout the entire range of combinations. Seventy-nine of the 102
counties had a relationship of this nature during the period studied.

Woodford county displays a U-shaped relationship. A combination
of corn and soybeans resulted in less income variability than either
extreme of specialization. In the case of Woodford county, 38 percent
of the corn-soybean acreage devoted to corn would have minimized
income variability in the 27-year period considered. Twenty-one coun-
ties had income data corresponding to this general U-shaped pattern
(Table 6). Substitution of soybeans for corn beyond the minimum
variability point not only increased variability, but generally decreased
income in these counties.2

The twenty-one counties displaying the U-shaped relationship be-
tween average level of income and its variability may be further sub-
divided into two groups based on whether minimum variance occurred
with more or less than 50 percent of the corn-soybean acreage in corn.
Following are the counties in each of these groups.

Minimum income variability Minimum income •variability
between 50 percent and
100 percent corn

Grundy Alexander McLean

Massac Coles Marshall

Moultrie Douglas Mason

Ogle Edgar Monroe

Pulaski Jackson St. Clair

Union Jasper Warren

Williamson Livingston Woodford

1 The standard error of estimate of income about the trend (Sy.t) is the square
root of [q2Sc.?+(l — q)2Sb.?+2rcb.tq(l — q)Sc.tSb.t] where q is the proportion of the land
devoted to corn, Sc.t and Sb-t are the standard error of estimate about trend of gross
income from corn and soybeans respectively, and rcb-t is the partial correlation
coefficient between gross incomes from corn and soybeans.

2 In Alexander county, gross income per acre increased slightly as soybeans
replaced corn.

1957] VARIABILITY OF YIELDS AND INCOME FROM CROPS 17

Table 6. — Average Gross Incomes per Acre and Standard Errors of

Estimate per Acre With Various Combinations of Corn and

Soybeans in Illinois Counties, 1927-1953

(Average gross income figures in bold-face type)

Percent

Corn 100

75

50

25

0

County

Soybeans 0

25

50

75

100

Adams

. £41.94

£39.96

£37.99

£36.01

£34.03

15.59

14.43

13.40

12.41

11.59

Alexander"

28.99

29.01

29.03

29.04

29.06

12.85

11.10

9.83

9.24

9.48

Bond

31.61

29.71

27.81

25.91

24.01

12.74

11.62

10.82

10.04

9.55

Booneb

50.79

46.64

42.48

38.33

34.18

15.23

13.86

12.61

11.51

10.61

Brown

39.80

37.79

35.77

33.76

31.74

15.09

14.02

13.12

12.27

11.67

Bureau

52.76

49.20

45.63

42.07

38.50

15.48

14.53

13.72

13.10

12.63

Calhoun

43 . 58

40.47

37.36

34.24

31.13

14.76

13.35

12.21

11.17

10.53

Carroll0

55.05

49.74

44.42

39.10

33.79

16.43

14.87

13.41

12.07

10.89

Cass

45.97

42.98

40.00

37.01

34.02

15.36

14.22

13.25

12.42

11.83

Champaign

48.45

45.91

43.38

40.84

38.30

13.69

13.05

12.56

12.26

12.15

Christian

45.77

43.57

41.36

39.16

36.96

15.05

13.88

12.93

12.25

11.89

Clark

36.97

34.51

32.06

29.60

27.14

10.86

10.32

9.98

9.63

9.51

Clay

26.65

25.11

23.57

22.03

20.49

13.15

11.96

10.90

9.94

9.19

Clinton

31.59

30.10

28.60

27.11

25.62

13.31

12.41

11.68

11.12

10.77

Coles

43 . 19

41.01

38.84

36.66

34.48

11.99

11.32

10.91

10.73

10.88

Cookd

42.03

39.69

37.36

35.02

32.68

12.97

11.89

10.97

10.27

9.84

Crawford

36.00

33.03

30.06

27.10

24.13

13.05

11.90

10.93

9.96

9.25

Cumberland

34.61

32.52

30.42

28.33

26.24

11.78

11.02

10.39

9.87

9.53

DeKalb

54.77

50.54

46.30

42.07

37.83

15.24

14.30

13.54

12.85

12.40

DeWitt

46.19

44.05

41.92

39.78

37.64

13.08

12.44

12.00

11.75

11.74

Douglas

47.47

45.03

42.58

40.14

37.70

12.80

12.04

11.67

11.48

11.73

DuPaged

46.34

43.20

40.06

36.91

33.77

14.47

13.23

12.13

11.20

10.50

Edgar

46.29

43.80

41.31

38.82

36.33

12.57

12.00

11.72

11.56

11.74

Edwards

34.66

32.20

29.74

27.28

24.82

14.77

13.52

12.41

11.52

10.88

Effingham

31.28

29.58

27.88

26.17

24.47

12.06

11.38

10.86

10.44

10.21

Fayette

29.43

27.90

26.36

24.83

23.30

11.81

11.24

10.77

10.37

10.18

(See page 20 for footnotes)

18 BULLETIN No. 610 [April,

Table 6. — Continued

Percent

Corn 100

75

50

25

0

County

Soybeans 0

25

50

75

100

Ford

44.43

42.14

39.86

37.58

35.29

12.87

12.37

12.00

11.74

11.64

Franklin

25.50

24.16

22.82

21.47

20.13

9.84

9.47

9.20

9.04

8.98

Fulton

45.71

43.17

40.62

38.08

35.53

14.54

13.68

12.93

12.24

11.71

Gallatin

36.25

33.45

30.65

27.85

25.05

12.56

11.62

10.83

10.22

9.81

Greene

43.48

40.93

38.38

35.84

33.29

15.64

14.69

14.00

13.32

12.94

Grundy8

43.93

38.78

33.64

28.50

23.36

12.38

12.23

12.33

12.67

13.22

Hamilton

27.39

25.92

24.46

22.99

21.52

11.30

10.57

9.92

9.37

8.95

Hancock

44.05

41.81

39.57

37.32

35.08

15.05

13.95

12.95

12.15

11.50

Hardin'

29.02

28.11

27.20

26.29

25.38

11.01

10.04

9.32

8.88

8.79

Henderson

47.77

44.68

41.59

38.50

35.41

14.31

13.38

12.62

12.05

11.71

Henry

50.98

47.82

44.66

41.50

38.34

13.79

13.29

12.92

12.74

12.71

Iroquois

43.18

41.04

38.90

36.75

34.61

13.45

12.66

12.04

11.64

11.46

Jackson

32.24

30.24

28.24

26.25

24.25

10.70

10.36

10.19

10.18

10.34

Jasper

32.49

30.14

27.79

25.44

23.09

13.41

10.78

9.20

9.11

10.65

Jefferson

25.57

24.56

23.54

22.52

21.51

10.81

10.49

10.21

10.01

9.87

Jersey

41.47

39.56

37.64

35.73

33.82

13.87

13.18

12.61

12.16

11.86

Jo Daviess8

50.29

46.27

42.24

38.22

34.20

14.96

13.36

11.93

10.75

9.89

Johnson

25.56

24.56

23.57

22.58

21.58

8.89

8.96

9.15

9.47

9.91

Kane

52.64

48.33

44.03

39.72

35.41

14.41

13.43

12.40

11.85

11.32

Kankakee

42.68

40.54

38.40

36.25

34.11

13.28

12.71

12.27

11.94

11.75

Kendall

47.42

44.53

41.64

38.74

35.85

13.48

12.55

11.84

11.30

11.05

Knox

48.19

45.66

43.13

40.59

38.06

13.87

13.36

13.05

12.85

12.76

Lake0

43 . 86

40.72

37.58

34.44

31.30

13.88

12.58

11.42

10.46

9.75

LaSalle

49.22

46.47

43.73

40.98

38.23

13.81

13.16

12.68

12.41

12.34

Lawrence

32.72

30.22

27.71

25.20

22.70

13.15

11.94

10.97

10.10

9.58

(See page 20 for footnotes)

7957] VARIABILITY OF YIELDS AND INCOME FROM CROPS 19

Table 6. — Continued

Percent

Corn 100

75

50

25

0

County

Soybeans 0

25

50

75

100

Lee

52.09

48.13

44.18

40.22

36.26

15.59

14.60

13.63

12.93

12.28

Livingston

44.02

41.87

39.72

37.56

35.41

12.49

12.15

11.94

11.85

11.91

Logan

48.90

46.04

43.18

40.33

37.47

15.40

14.32

13.39

12.62

12.07

McDonough

46.90

44.55

42.21

39.85

37.51

14.81

13.97

13.24

12.71

12.34

McHenryd

48.21

44.22

40.23

36.24

32.26

13.37

12.48

11.79

11.33

11.15

McLean

48.13

45.67

43.21

40.74

38.28

13.40

13.08

12.90

12.85

12.95

Macon

46.71

44.54

42.36

40.18

38.01

14.61

13.57

12.73

12.17

11.89

Macoupin

39.47

37.69

35.92

34.14

32.36

14.67

13.41

12.37

11.32

10.56

Madison

37.46

35.57

33.68

31.79

29.90

13.66

13.11

12.66

12.09

11.66

Marion

24.99

23.92

22.86

21.80

20.73

11.56

11.10

10.74

10.49

10.36

Marshall

47.11

44.56

42.01

39.46

36.91

13.75

13.39

13.18

13.10

13.16

Mason

36.93

35.36

33.78

32.21

30.64

12.32

12.07

11.92

11.89

11.98

Massacc

31.61

29.46

27.32

25.18

23.04

9.55

9.45

9.50

9.71

10.06

Menard

44.85

42.32

39.80

37.27

34.74

14.64

13.89

13.27

12.82

12.53

Mercerh

48.95

45.78

42.61

39.44

36.27

14.69

13.48

12.39

11.46

10.74

Monroe'

38.07

35.84

33.60

31.36

29.12

12.40

12.08

11.96

12.04

12.32

Montgomery. . . .

38.97

37.14

35.32

33.50

31.67

14.10

12.27

10.63

9.07

7.88

Morgan

46.96

44.44

41.92

39.39

36.87

15.55

14.59

13.82

13.19

12.80

Moultrie

43.71

42.03

40.35

38.67

36.99

11.86

11.63

11.71

11.93

12.42

Ogle"

52.93

49.43

45.94

42.44

38.94

15.74

15.73

16.07

16.71

17.65

Peoria

46.80

43.20

39.60

36.01

32.41

14.42

14.10

13.88

13.75

13.73

Perry

24.99

23.80

22.62

21.43

20.24

9.83

9.85

9.97

10.17

10.47

Piatt

49.76

47.14

44.52

41.89

39.27

14.28

13.60

13.05

12.67

12.45

Pike

42.26

39.84

37.42

35.01

32.59

15.14

14.11

13.32

12.53

12.04

Popec

28.27

26.84

25.42

23.99

22.57

10.23

9.72

9.34

9.10

9.01

Pulaskid

28.91

27.81

26.71

25.61

24.51

8.31

8.10

8.24

8.69

9.42

(See page 20 for footnotes)

20 BULLETIN No. 610 [April,

Table 6. — Concluded

Percent

Corn 100

75

50

25

0

County

Soybeans 0

25

50

75

100

Putnam

... 51.17

48.12

45.07

42.02

38.97

Randolph

14.84
34.10

14.04
31.76

13.36
29.41

12.88
27.06

12.56
24.72

Richland

13.03
27.98

12.06
26.13

11.32
24.28

10.84
22.43

10.66
20.58

Rock Island . .

12.69
50.54

11.62
46.81

10.64
43.08

9.78
39.35

9.08
35.62

St. Clair

15.88
38.14

14.84
36.16

13.85
34.18

13.07
32.20

12.38
30.22

Saline . . .

13.44
32.42

12.90
30.52

12.54
28.61

12.38
26.70

12.43
24.80

Sangamon

11.51
45 . 06

10.86
42.94

10.38
40.82

10.07
38.69

9.96
36.57

Schuyler

15.08
42.76

13.88
40.29

12.97
37.82

12.20
35.34

11.81
32.87

Scott

14.07
43.39

13.13
40.86

12.31
38.32

11.66
35.79

11.17
33.26

Shelby . . .

13.93
39.23

13.25
37.21

12.75
35.19

12.53
33.17

12.52
31.15

Starkh

12.09
49.43

11.43
46.74

11.20
44.06

10.58
41.38

10.46
38.70

Stephenson. . .

14.86
53.67

14.11
48.63

13.48
43.58

12.97
38.54

12.61
33.49

Tazewell . . . .

15.72
49.15

14.34
46.54

13.12
43.94

11.96
41.33

11.05
38.72

Union0

14.84
29.46

14.09
28.21

13.47
26.97

12.99
25.72

12.67
24.48

Vermilion . . . .

8.43
44.93

8.30
42.58

8.41
40.22

8.77
37.87

9.34
35.52

Wabashd . . .

14.00
39.42

13.36
36.32

12.87
33.22

12.52
30.12

12.34
27.02

Warren

16.13
49.66

14.13
47.09

12.52
44.52

11.45
41.94

11.08
39.37

Washington . .

14.23
27.03

13.78
25.92

13.53
24.80

13.35
23.68

13.38
22.57

Wayne

12.57
26.77

12.02
25.24

11.60
23.70

11.32
22.17

11.20
20.64

White

12.72
33 . 77

11.52
31.38

10.42
28.99

9.48
26.60

8.73
24.21

Whiteside. . . ,

13.36
53.31

12.50
48.65

11.71
43.99

11.04
39.34

10.48
34.68

Will

16.60
42.01

15.23
39.87

14.00
37.72

12.92
35.58

12.13
33.43

Williamson . .

13.91
25.91

12.86
24.56

12.03
23.20

11.25
21.85

10.76
20.50

Winnebago .

9.75
50.36

9.71
45 55

9.81
40 89

10.06
36 15

10.46
31.41

Wood ford

14.71
50 33

13.47
47 60

12.46
44 86

11.43
42 12

10.71
39.39

13.89

13.57

13.40

13.40

13.58

Footnotes
a No data
b No data
c No data
d No data
e No data
* No data
s No data
h No data
1 No data

refer only to soybeans,
reported for 1927, 1928, 1932
reported for 1928 and 1934.
reported for 1927 and 1928.
reported for 1928.
reported for 1934.
reported for 1927, 1928, 1930,
reported for 1927, 1928, and
reported for 1927.
reported for 1933.

, 1933, and

1931, 1932,
1934.

1935.
1933, 1934,

1940, and

1950.

1957] VARIABILITY OF YIELDS AND INCOME FROM CROPS 21

Two counties — Johnson and Perry — experienced increasing vari-
ability and decreasing income as soybeans were substituted for corn
throughout the entire range. Soybeans would not appear to be a good
substitute for corn in terms of both income and its variability in these
two counties.

In about three-fourths of the counties increased income was ac-
companied by increased variability through the complete range of
alternative combinations from 100 percent soybeans to 100 percent
corn. However, variability increases at a more rapid rate than does
average income as corn is substituted for soybeans.

For example, in Adams county the increase in the standard error
of estimate per dollar of income gained by moving from 100 percent
soybeans to 25 percent corn — 75 percent soybeans is $0.41 ; from this
combination to 50 percent in each crop, $0.50; from 50 percent in each
to 75 percent corn — 25 percent soybeans, $0.52; and from the latter
to 100 percent corn, $0.59 (Fig. 3).

Whether the gains in increased stability from diversification be-
tween corn and soybeans are sufficient to offset the possible income
decrease is a matter that will depend on the ability and willingness of
the individual farmer to accept the consequences of income variability.
The livestock system, as well as the labor supply, will of course also
play a role in the selection of the cultivated crops.

Oats and wheat. In Table 7 the effect of substitution of oats for
wheat on income and its variability is shown. In the following counties
(see Fig. 4 for the example of St. Clair county), income variability
was minimized by a combination of approximately 75 percent oats and
25 percent wheat:

Edwards Lawrence Perry

Effingham Macoupin Pope

Gallatin Madison Randolph

Jackson Marion St. Clair

Jersey Menard Wabash

Johnson Monroe Washington

Lake Ogle White

In Clinton county, a 50-50 combination served to minimize income
variability during this period. In the remaining 80 counties, 100 percent
oats would have kept income variability at a minimum. An example of
this relationship is in Sangamon county (Fig. 4).

As in the case of corn and soybeans, a reduction of average income
accompanies the reduction of income variability in the majority of situ-

22

BULLETIN No. 610

(April,

ations. This sacrifice in income must be considered, as well as such fac-
tors as the relative desirability of oats and wheat as nurse crops.
Furthermore, government wheat acreage allotments have not permitted
free substitution of wheat for oats in recent years. Seeding winter
wheat on land in corn is not always possible due to the time of corn
harvest. This may further restrict the range of choice between wheat
and oats.

INCOME VARIABILITY

10

1 7

»-

(A
111

g 6
tr.
cc.
ui

o

cc

2

I

SANGAMON

ST. CLAIR

100 PERCENT WHEAT- OATS
ACREAGE IN WHEAT

50 PERCENT WHEAT AND
50 PERCENT OATS

100 PERCENT WHEAT-OATS
ACREAGE IN OATS

10 15 20 25

GROSS INCOME (DOLLARS PER ACRE)

30

Effect of wheat-oats combination on income and income variability
in two Illinois counties, 1927-1953. (See Table 7.) (Fig. 4)

1957] VARIABILITY OF YIELDS AND INCOME FROM CROPS 23

Table 7. — Average Gross Incomes per Acre and Standard Errors of

Estimate per Acre With Various Combinations of Wheat and

Oats in Illinois Counties, 1927-1953

(Average gross income figures in bold-face type)

Percent

Wheat 100

75

50

25

0

County

Oats 0

25

50

75

100

Adams

. J24.84

^22.43

320.02

317.61

315.20

8.29

7.04

5.98

5.20

4.89

Alexander

20.88

18.73

16.59

14.44

12.29

5.40

4.64

4.04

3.69

3.67

Bond

22.21

19.68

17.14

14.61

12.08

7.75

6.24

5.03

4.25

4.21

Boone

30.48

28.52

26.56

24.61

22.65

11.91

10.67

9.64

8.92

8.57

Brown

24.52

21.88

19.25

16.61

13.97

9.21

7.63

6.32

5.27

4.89

Bureau

32.48

29.64

26.81

23.97

21.13

10.77

10.96

8.57

7.68

7.06

Calhoun

25.45

22.37

19.29

16.21

13.13

9.12

7.28

5.66

4.40

3.99

Carroll

30.50

28.22

25.95

23.68

21.40

10.27

9.44

8.72

8.14

7.73

Cass

27.77

24.96

22.14

19.32

16.51

9.75

8.00

6.49

5.20

4.61

Champaign

30.64

27.31

23.98

20.65

17.32

10.43

8.90

7.10

6.14

5.04

Christian

29.19

26.49

23.80

21.10

18.40

9.44

7.95

6.79

6.01

5.92

Clark

20.45

18.15

15.85

13.55

11.25

7.91

6.64

6.25

4.88

4.69

Clay

17.47

15.42

13.36

11.31

9.26

6.40

5.51

4.75

4.07

3.67

Clinton

22.06

20.29

18.52

16.76

14.99

6.36

5.10

4.36

4.39

5.17

Coles

26.72

24.14

21.56

18.97

16.39

8.46

7.21

6.18

5.39

5.08

Cook

30.97

28.58

26.18

23.78

21.39

9.67

8.85

8.23

7.90

7.87

Crawford

19.64

17.38

15.12

12.85

10.59

5.69

4.87

4.30

4.95

3.99

Cumberland

21.33

18.76

16.18

13.61

11.04

7.59

6.37

5.31

4.47

4.09

DeKalb

33.08

31.03

28.98

26.93

24.88

11.36

9.91

8.77

7.97

7.72

DeWitt

29.67

26.55

23.42

20.30

17.18

11.34

9.43

7.70

6.26

5.37

Douglas

29.19

26.38

23.56

20.75

17.94

10.18

8.43

6.93

5.77

5.30

DuPage

32.95

30.59

28.22

25.86

23.50

11.36

10.21

9.26

8.59

8.25

Edgar

27.30

24.72

22.13

24.72

16.96

9.33

7.93

6.77

5.97

5.70

Edwards

20.58

18.38

16.18

13.98

11.78

5.36

4.63

4.10

3.87

3.96

Effingham

20.83

18.49

16.14

13.80

11.46

7.34

6.16

5.36

4.76

4.81

Fayette

20.05

17.96

15.37

13.78

11.69

7.73

6.51

5.50

4.84

4.68

24 BULLETIN No. 610 [April,

Table 7. — Continued

Percent

Wheat 100

75

50

25

0

County

Oats 0

25

50

75

100

Ford

30.03

26.60

23.18

19.75

16.32

10.54

8.68

6.99

5.61

4.80

Franklin

19.50

17.31

15.12

12.94

10.75

6.12

5.02

4.02

3.20

2.75

Fulton

27.21

24.67

22.14

19.60

17.06

9.89

8.44

7.16

6.12

5.52

Gallatin

21.43

18.94

16.44

13.94

11.45

6.18

5.34

4.70

4.39

4.46

Greene

24.71

22.32

19.92

17.53

15.14

8.22

6.68

5.38

4.48

4.30

Grundy*

31.22

28.05

24.88

21.70

18.53

11.27

9.54

8.01

6.78

6.05

Hamilton

19.07

16.88

14.68

12.49

10.30

5.61

4.86

4.24

3.81

3.62

Hancock

26.62

24.09

21.57

19.04

16.51

9.32

7.93

6.70

5.77

5.26

Hardinb

18.39

16.38

14.36

12.35

10.33

6.29

5.60

5.07

4.76

4.72

Henderson

27.36

24.73

22. 10

19.47

16.84

9.59

8.28

7.15

6.18

5.57

Henry

30.58

27.95

25.32

22.68

20.05

9.96

8.80

7.86

7.13

6.76

Iroquois

30.91

27.16

23.42

19.67

15.92

10.75

8.90

7.23

5.88

5.11

Jackson

21.07

19.11

17.15

15.19

13.23

5.65

4.97

4.50

4.32

4.47

Jasper

19.18

16.94

14.70

12.46

10.22

6.91

5.63

4.70

4.01

3.67

Jefferson

19.01

16.76

14.50

12.25

10.00

5.97

4.97

4.16

3.67

3.64

Jersey

24.73

22.01

19.30

16.59

13.88

8.00

6.28

4.86

4.04

4.26

Jo Daviess

27.23

25.67

24.12

22.56

21.00

10.24

9.17

8.29

7.64

7.30

Johnson

18.72

17.00

15.28

13.57

11.85

5.59

5.18

4.92

4.80

4.86

Kane

34.26

31.87

29.48

27.09

24.70

11.76

10.42

9.24

8.40

7.89

Kankakee

29.47

26.52

23.56

20.61

17.66

10.29

8.73

7.34

6.23

5.58

Kendall

33 . 26

30.54

27.82

25.10

22.38

10.47

9.22

8.15

7.37

6.94

Knox

29.85

26.86

23.87

20.87

17.88

8.07

7.24

6.55

6.02

5.71

Lake

32.30

29.94

27.58

25.22

22.86

10.09

9.49

9.13

9.05

9.25

LaSalle

31.68

29.00

26.31

23.63

20.94

11.22

10.92

8.31

7.30

6.82

Lawrence

19.80

17.66

15.53

13.40

11.26

6.17

5.28

4.67

4.49

4.74

a No wheat yield reported for 1934.

b No oats yield reported for 1934; no wheat yield reported for 1950.

1957] VARIABILITY OF YIELDS AND INCOME FROM CROPS 25

Table 7. — Continued

Percent

Wheat 100

75

50

25

0

County

Oats 0

25

50

75

100

Lee

32.95

30.15

27.34

24.54

21.73

11.39

10.14

9.04

7.95

7.11

Livingston

29.89

26.62

23.34

20.06

16.79

10.37

8.73

7.22

5.98

5.16

Logan

30.17

27.27

24.38

21.48

18.58

11.05

9.15

7.46

6.19

5.61

McDonough. . . .

28.21

25.57

22.92

20.28

17.63

9.64

8.15

6.84

5.85

5.34

McHenry

32.30

30.00

27.69

25.39

23.08

13.00

11.49

10.29

9.50

9.25

McLean

30.75

27.56

24.37

21.18

17.99

11.85

9.79

7.89

6.31

5.30

Macon

30.54

27.42

24.30

21.18

18.06

10.77

8.92

7.44

6.27

5.96

Macoupin

24.36

22.17

19.98

17.78

15.59

7.98

6.42

5.18

4.44

4.53

Madison

23.99

21.31

18.64

15.96

13.28

4.61

3.81

3.20

2.94

3.10

Marion

18.98

16.83

14.68

12.53

10.38

5.93

5.01

4.34

3.99

4.10

Marshall

30.03

27.14

24.25

21.36

18.47

10.98

9.47

8.11

7.06

6.41

Mason

24.32

21.71

19.10

16.48

13.87

8.14

6.74

5.58

4.80

4.61

Massac

19.84

18.02

16.20

14.37

12.55

5.49

4.79

4.25

3.94

3.92

Menard

27.76

25.16

22.57

19.98

17.38

10.28

8.39

6.86

5.95

5.98

Mercer

28.30

25.70

23.10

20.50

17.90

9.91

8.68

7.61

6.79

6.32

Monroe

24.70

22.31

19.92

17.53

15.14

6.07

5.26

4.78

4.75

5.17

Montgomery . . .

25.06

22.57

20.08

17.59

15.10

8.50

6.83

5.30

3.99

3.28

Morgan

28.81

26.20

23.59

20.98

18.37

10.05

8.36

6.94

5.87

5.48

Moultrie

28.38

25.62

22.86

20.09

17.33

10.00

8.42

7.07

5.98

5.46

Ogle

30.61

28.56

26.51

24.46

22.41

11.12

10.47

10.07

9.95

10.13

Peoria

30.10

27.03

23.96

20.89

17.82

10.98

9.49

8.15

7.06

6.33

Perry

17.94

16.09

14.24

12.39

10.54

6.04

5.03

4.27

3.96

4.16

Piatt

30.96

27.80

24.64

21.47

18.31

10.76

8.94

7.34

6.15

5.63

Pike

23.49

21.17

18.86

16.54

14.22

8.27

6.76

5.46

4.53

4.25

Pope

18.85

16.98

15.12

13.25

11.38

5.45

4.95

4.61

4.47

4.53

Pulaski

20.31

18.37

16.44

14.50

12.56

5.79

5.02

4.40

3.99

3.85

26 BULLETIN No. 610 [April,

Table 7. — Concluded

Percent

Wheat 100

75

50

25

0

County

Oats 0

25

50

75

100

Putnam

... 30.99

28.61

26.22

23.84

21.45

10.79

9.66

8.66

7.88

7.33

Randolph

21.71

19.87

18.02

16.18

14.34

6.11

5.14

4.54

4.44

4.88

Richland

18.75

16.49

14.22

11.96

9.70

5.74

4.96

4.28

3.74

3.42

Rock Island . .

28.44

25.98

23.51

21.05

18.58

10.32

9.14

8.14

7.26

6.71

St. Clair

24.29

22.06

19.82

17.58

15.35

6.35

5.29

4.59

4.38

4.75

Saline

20.43

18.14

15.85

13.56

11.27

5.71

5.05

4.57

4.29

4.26

Sangamon

28.90

26.46

24.03

21.60

19.16

10.12

8.36

6.90

5.87

5.60

Schuyler

25.92

23.30

20.67

18.05

15.42

9.42

7.80

6.44

5.45

5.15

Scott

25.31

22.89

20.47

18.05

15.63

7.92

6.45

5.23

4.50

4.46

Shelby

24.53

21.94

19.35

16.76

14.17

8.15

6.96

5.91

5.09

4.62

Stark

32.07

28.83

25.60

22.36

19.12

12.38

10.51

8.90

7.73

7.20

Stephenson

28.22

26.72

25.23

23.73

22.23

10.50

9.57

8.76

8.19

7.84

Tazewell

29.05

26.38

23.71

21.04

18.37

11.18

9.40

7.83

6.60

5.92

Union

21.52

19.29

17.07

14.84

12.61

6.03

5.15

4.44

4.01

3.93

Vermilion

29.25

25.96

22.67

19.38

16.09

10.25

8.48

6.90

5.70

5.13

Wabash

21.63

19.49

17.34

15.20

13.05

6.03

5.56

5.29

5.24

5.42

Warren

29.65

26.78

23.92

21.06

18.19

9.83

8.47

7.32

6.34

5.78

Washington

20.16

18.52

16.87

15.22

13.58

6.50

5.27

4.44

4.27

4.84

Wayne

18.41

16.30

14.19

12.08

9.97

5.27

4.57

3.97

3.54

3.33

White

19.60

17.58

15.56

13.53

11.51

5.45

4.83

4.38

4.19

4.26

Whiteside

30.74

28.42

26.09

23.76

21.44

10.13

9.21

8.37

7.75

7.35

Will

32.16

29.16

26.16

23.15

20.15

10.12

8.98

8.07

7.35

7.03

Williamson

19.07

17.09

15.11

13.13

11.15

5.68

5.02

4.50

4.19

4.14

Winnebago

28.90

26.82

24.74

22.66

20.58

8.96

8.89

7.84

7.52

7.39

Woodford

30.19

27.30

24.41

21.52

18.63

8.49

7.50

6.72

6.23

6.09

1957] VARIABILITY OF YIELDS AND INCOME FROM CROPS 27

SUMMARY

County average corn, soybean, wheat, oats, and hay yields were
studied for the period 1927-1953 to determine the relative variability
of yields among areas and among crops. Yield dependability affects
crop choice, land values, crop insurance programs, credit arrangements,
and other aspects of the farm business. The five major crops ranked as
follows in order of decreasing yield variability: oats, corn, wheat,
soybeans, and hay. In general, yield stability for all crops increased in
moving from southern Illinois to northern Illinois.

Diversification between corn and soybeans in acreage of cultivated
crops resulted in three patterns of effect on income variability. The
most common pattern was that of a reduction in both average income
and its variability as soybeans are substituted for corn. The gains from
diversification between wheat and oats were of two general types. The
large majority of the counties displayed a reduction in both income
variability and average income as oats replaced wheat in the land
devoted to small grain.

6M— 4-57— 62161

UNIVERSITY OF ILLINOIS-URBANA

Q.630.7IL6B COOS

BULLETIN. URBANA
6101957

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