Copyright
E. R. (Earl Raymond) Swanson.

Variability of yields and income from major Illinois crops 1927-1953 online

. (page 1 of 3)
Online LibraryE. R. (Earl Raymond) SwansonVariability of yields and income from major Illinois crops 1927-1953 → online text (page 1 of 3)
Font size
QR-code for this ebook


630.7
Il6b
no.610
cop. 8



NOTICE: Return or renew all Library Materials! The Minimum Fee for
each Lost Book is $50.00.

The person charging this material is responsible for
its return to the library from which it was withdrawn
on or before the Latest Date stamped below.

Theft, mutilation, and underlining of books are reasons for discipli-
nary action and may result in dismissal from the University.
To renew call Telephone Center, 333-8400

UNIVERSITY OF ILLINOIS LIBRARY AT URBANA-CHAMPAIGN



FIB 24




L16I O-1096



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 w r ithin
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 deviation 1 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,
S y . 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


13 3


Bureau


. 52.7


7.7


12.1


McLean


48.0


8


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


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


Pike


. 41.9


11.3


20.9


Effingham. . . .


. 30.3


6 4


15


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


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


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










State 8 . .


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


Alexander 8 . . .
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


Boone b


. 19.3


1.9


7.7


McDonough .


22.0


3.2


11


Brown


. 18.5


3.1


13.0


McHenry d . . .


18.5


2.4


10


Bureau


. 21.9


2.2


7.6


McLean


. 22.6


2.9


10


Calhoun


. 18.4


2.9


12.7


Macon


22 9


2.9


10 1


Carroll


. 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


Clay. .


. 11.9


2.4


16.9


Massac


. 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


Mercer h


21 2


2.7


10 5


Cook d


. 19.0


2.0


8.5


Monroe*


16 1


3 6


17


Crawford . . .


. 14.0


2.5


14


Montgomery.


18 5


2 4


9 9


Cumberland. .


. 14.9


2.1


10.5


Morgan


21.6


2.8


9.9


DeKalb


. 22


2


7 4


Moultrie


21 9


3 5


12 2


DeWitt


22 4


2 9


10 4


Ogle h


22 1


6


21 1


Douglas


. 22.6


2.8


9.9


Peoria


. 22 1


3.1


10.3


DuPage d


. 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


Pope c


12 8


3 1


18 9


Fayette


. 13.2


2.9


16.7


Pulaski d


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


Grundy 6


. 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


Hardin f


. 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


Stark h


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


Union


13 7


3


17 4


Jefferson


. 12.2


2.9


18.8


Vermilion . . .


21


2.5


9.7


Jersey. .


. 19.7


3.8


15.3


Wabash d . .


15 5


2.9


18.4


Jo Daviess 8 . .


. 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


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


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.

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


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


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


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


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


Hardin a


. 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


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


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


W'oodford


37 1


9.0


23.0










State b ..,


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


1 3

Online LibraryE. R. (Earl Raymond) SwansonVariability of yields and income from major Illinois crops 1927-1953 → online text (page 1 of 3)