lNDEXES OF THE MFLUEBKE OF WEATHER
ON AGRECULTURAL OUTPUT

Thesis fo: the Degree cf Ph. D.
MICHIGAN SYATE UNIVERSETY:
James Larkén StaEEings
1958

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thesis entitled
INDEXES OF THE INFLUENCE OF WEATHER
ON AGRICULTURAL OUTPUT

presented by

James Larkin Stal 1 ings

has been accepted towards fulfillment
of the requirements for

Ph.D degree in Agricultural Economics

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INDEXES OF THE INFLUENCE OF HEATHER CN AGRICULTURAL OUTPUT

By

James Larkin Stallings

AN ABSTRACT

Submitted to the School of Advanced Graduate Studies of
Michigan State University of Agriculture and Applied Science
in partial fulfillment of the requirements
for the degree of

DOCTOR OF IHILCSCPHY

Department of Agricultural Economics

Year 1958

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.ABSTRACT James Larkin Stallings

In this thesis indexes of the influence of weather on
yields of corn, oats, barley, wheat, soybeans, cotton, and
tobacco are constructed. Indexes are also constructed for
the influence of weather on some important aggregate measures
of U. S. agricultural production and yields including the
indexes of CrOp Production, Gross Farm Production, Farm Out—
put, Marketings and Home Consumption, and Crop Yields per
Harvested Acre. In addition, indexes were constructed for
the feed grain components of the indexes of CrOp Production,
Farm Output, Marketings and Home Consumption, and Yields per
Harvested Acre.

These indexes of the influence of weather were computed
from time series of experimental plot data for the various
creps located in the more concentrated areas of production.
Series were obtained where as many variables as possible had
been constant. The general procedure was as follows:

1. Trend was removed from each separate series far each
crOp at each location by fitting a linear regression
line to the data. This was done to remove the influ-
ence of increases or decreases in soil fertility due
to the particular treatment for each experimental plot.

2. Indexes for each series were computed as the ratio
of the actual to the computed yields.

5. Indexes for each series for each crop at each loca—
tion were averaged for overlapping years to get an

index for each crOp at each location.

ABSTRACT James Larkin Stallings

h.'Indexes for each crop at each location were weighted
together into an index for the particular crop for
the United States using average production for the
area to be represented by the index at each location
during:the base period 19u7-h9.

5. Indexes for the seven crOps were weighted together
into indexes of the influence of weather on various
aggregate neasures of production and yields using
value of production during the base period 19h7—h9.
The Indexes of Range Conditions as presented in vari-
ous U.S.D.A. publications were also combined into
an index and used in two cases.

An evaluation of the sixteen indexes by various formal
and informal technioues indicated that, in all but two cases,
variations in the U. S. average yields of the seven crops
and in the indexes of the various aggregate measures were
highly associated with variations in the reapective weather
indexes. There was also an indication that an important
amount of the variation in these crop yields and aggregate
production and yield measures was due to the influence of
weather. It was concluded that all but two of the indexes
of the influence of weather are valuable measures to include
in various econometric models where a weather variable is
needed; and to use in a less formal manner to help explain

and hypothesize about various relationships.

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INDEXES OF THE INFLUENCE OF WEATHSR ON AGRICULTURAL OUTTUT

BY

James Larkin Stallings

A THESIS

Submitted to the School of Advanced Graduate Studies of
Michigan State University of Agriculture and Applied Science
in partial fulfillment of the requirements .

for the degree of

DOCTOR OF PHILOSOPHY

Department of Agricultural Economics

1958

ACKNOWLEDGMENTS

It is a pleasure to acknowledge those individuals who
helped make this finesis possible. The author wishes to ex-
press his sincere appreciation to:

Dr. Glenn L. Johnson, under whose direction this thesis
was written, for his guidance and constant encouragement;

Dr. L. L. Boger for his part in providing financial as-
sistance;

Doctors C. G. Hildreth, K. J. Arnold, and V. E. Smith,
who served on the author's guidance committee,for their sug-
gestions and advice;

The personnel in the various agronomy departments through-
out the United States, who gave their time and energy in sup-
plying data;

A capable and cooperative staff of secretarial and cleri-
cal personnel, who relieved the author of a trenendous amount
of work;

Mrs. Donald Hillman, who typed the final draft; and

His wife, for her patience and encouragement during

graduate school.

‘»

V‘~

CHAPTER
I

II

III

IV

TABLE OF CONTENTS

IIITRODUCT IO}? 0 O O O O O C C O O C C O 0
Objectives 0 O O O C O C O O O O O 0

Review of Literature . . . . . . . . . .
COICEPTUAL FRAIEJURA AID FBASURBLBUT
TLCthgbnS . . . . . . . . .

General Conceptual Framework Used . . . .

Techniques Used in This Study . . . . . .

Some Preliminary Considerations . . .
Choosing the General Approach . . .
Deciding upon the CrOps to Include
Deciding upon the Time Period . . .
Procurement of Data . . . . . . . . .
Treatment of Data . . . . . . . . . .
INDEXES OF THE I"PLU‘TCE OF WEATHER ON
SPECIFIC CROPS . . . . . . . . . . . . . . .

Corn . . . . . . . . . . . . . . . . . .

Oats . . . . . . . . . . . . . . . . . .

Barley . . . . . . . . . . . . . . . . .

Wheat . . . . . . . . . . . . . . . . . .

Soybeans . . . . . . . . . . . . . . .

Cotton . . . . . . . . . . . . . . . . .

Tobacco . . . . . . . . . . . . . . . . .
INDEXES OF THE INILUBTCE OF HEATHER ON AGGRE-
GATE KBASURBS OF U. S. AGRICULTURAL PRODUCTION
AND YIELDS . . . . . . . . . . . . . . . . .
EVAIJUATIUI‘.‘ o o o o o e o o o o o o 0

Statistical Evaluation . . . . . . . . .

Sources of Error . . . . . . . . . .

Implications of Table 10 . . . . . . . .

Possibilities for Further Study . . . . .

BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . .
APPBITDIXA . . . . . . . . . . . . . . . . . . . . .
APPENDIX B . . . . . . . . . . . . . . . . . . . . .

_ ii _

22
25
51
59
45
61
65

82
103
105
107
108
109
114
117

154

LIST 0F TABLE

TABLE PAGE

1 Computation of the Index of the Influence of
{leather on Corn 0 O O O O O O O O O O O O O 26

2 Computation of the Index of the Influence of
'iveather On oats o o o o o o o o o o o o o o 54

5 Computation of the Index of the Influence of
Heather on Barley . . . . . . . . . . . . . 42

4 Computation of the Index of the Influence of
|IveatIJeI. 01] Wheat 0 o o o o o o o o o o o o 49

5 Index of the Influence of Weather on Soybeans. 65

6 Computation of the Index of the Influence of
Heather on Cotton . . . . . . . . . . . . . 69

7 Computation of the Index of the Influence of
Heather on TObaCCO o o o o o o o o o o o o 79

8 Percentage of Total Value Comparisons and Com-
puted Weights for Use in Constructing In-
dexes of the Influence of Weather on Vari-
ous Aggregate fieasures, 1947-49 . . . . . . 86

9 Indexes of the Influence of Weather on Various
Aggregate Measures and Comparisons with
These measures . . . . . . . . . . . . . . 89

10 Comparison of the Estimated Parameters for the
Regression of the Residuals About an Eleven—
Year Moving Average of the Various U. S.
Average Yields and Aggregate Indexes on the
Corresponding Computed Heather Indexes . . 105

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LIST OF FIGURES

FIGURE PAGE

1 Location of Corn Production and Average Per-
centage of Total Production by States,
1946-55 0 o o o o o o o o o o o o o o o o o 24

2 Comparison of the Index of the Influence of
Weather on Corn with U. S. Average Yield
or corn 0 O O O O O O O O O O O O O O O O O 50

3 Location of Cats Production and Average Per-
centage of Total Production by States,
1944-55 0 o o o o o o o o o o o c o o o o o 52

4 Comparison of the Index of the Influence of
Weather on Cats with U. S. Average Yield
Of oats O I O O O I O O O I C O O O O O O O 58

5 Location of Barley Production and Average Per-
centage of Total Production by States,
1944-55 0 o o o o o o o o o o o o o o o o 0 4O

6 Comparison of the Index of the Influence of
Weather on Barley with U. S. Average Yield
Of Barley O O O C O O O O O O O O O O O O O 44

7 Location of Wheat Production and Average Per-
centage of Total Production by States,
1946-55 0 o o o o o o o o o o o o o o o o o 46

8 Comparison of the Index of the Influence of
Weather on Wheat with U. S. Average Yield
Of Timeat O O O O I O O O O O O O O O O O 0 6O

9 Location of Soybean Production and Average
Percentage of Total Production by States,
1946-55 0 o o o o o o o o o o o o o o o o o 62

10 Comparison of the Index of the Influence of
Weather on Soybeans with U. S. Average
Yield of Soybeans . . . . . . . . . . . . . 64

11 Location of Cotton Production and Average Per-

centage of Total Production by States,
19M-55 o o o o o o o o o o o o o o o o o o 67

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15

14

15

l6

17

18

19

20

21

Comparison of the Index of the Influence of
Weather on Cotton with U. S. Average
Yield 0f 001713011 0 o o o o o o o o o o o o 75

Location of Tobacco Production and Average
Percentage of Total Production by States,
1944-53 0 o o o o o o o o o o o o o o o o 75

Comparison of the Index of the Influence of
Heather on Tobacco with U. S. Average
Yield of Tobacco . . . . . . . . . . . . 81

Comparison of the Index of the Influence of
Jeather on the Index of CrOp Production
with the Index of CrOp Production . . . . 96

Comparison of the Index of the Influence of
Weather on the Feed Grain Component of
the Index of Crop Production with the Feed
Grain Component of the Index of CrOp Pro-
duction . . . . . . . . . . . . . . . . . 97

Comparison of the Index of the Influence of
Weather on the Index of Gross Farm Pro-
duction with the Index of Gross Farm Pro-
duction . . . . . . . . . . . . . . . . . 98

Comparison of the Index of the Influence of
Weather on the Index of Farm Output and
on the Feed Grain Component of the Index
of Farm Output with the Index of Farm
Output . . . . . . . . . . . . . . . . . 99

Comparison of the Index of the Influence of
Heather on the Index of Farm Marketings
and Home Consumption with the Index of .
Farm Marketings and Home Consumption . . lOO

Comparison of the Index of the Influence of
Weather on the Peed Grain Component of
the Index of Farm Harketings and Home
Consumption with the Feed Grain Component
of the Index of Farm harketings and Home
Consumption . . . . . . . . . . . . . . . lOl

Comparison of the Index of the Influence of
Weather on the Index of CrOp Yields per
Harvested Acre and on the Feed Grain
Component of the Index of CrOp Yields per
Harvested Acre with the Index of CrOp
Yields per Harvested Acre . . . . . . . . 102

CHAPTER I
INTRODUCTION

This thesis reports an attempt to measure the influence
of weather on the yields of specific crOps and various aggre-
gate measures of agricultural production and yields. The
purpose is to aid and improve analysis and estimation of
economic relationships in agriculture. The need for a
study of this type was brought to the author's attention
primarily in connection with a study being carried on at
the Michigan Agricultural Experiment Station, now completed,
by W. A. Cromarty.l The objectives of that project were to
specify and compute quantitative measurements of the struc-
tural economic relationships present in the agricultural sec-
tor of the economy. The two main purposes were "to contri-
bute to economic models which are being develOped at the
University of Michigan by specifying in more detail the role
which agriculture plays and to aid in agricultural outlook
work." Categories of commodities studied by Cromarty in-
cluded wheat, feed grains, soybeans, tobacco, cotton, dairy,
beef cattle, hogs, eggs, poultry meats, potatoes and truck

crOps and all remaining commodities as a group. Cromarty

 

l. Cromarty, h. A., Economic Structure in American Mg -
culture, Unpublished Ph. D. Thesis, Michigan State University,

specified the relationships he believed to hold for these
commodities in the form of simultaneous equations for price
and supply. In his models for supply of wheat, feed grains,
soybeans, tobacco, and cotton, one of his predetermined vari-
ables included some measure of weather. In a preliminary re-
port on his project} he had this to say: "While it is real-
ized that a separate study concentrated on the influence of
weather on yields should be undertaken, such a study will not
be completed in time to be an integral part of this model.
Adjustments may be made at a later date." His suggestion for
the approach to this weather study was: "to cooperate with
state experiment stations in getting plot yield data on spe-
cific crops. If fairly constant production techniques have
been employed in growing like varieties of a crop over a
period of years, the effect of yield changes could be attri-
buted to weather. Area data could be compiled and aggregated
to get a national series. It is believed that such an ap-
proach is to be preferred to using specific climatic vari-
ables such as temperature or rainfall at critical periods.
However, these specific climatic variables may have to be
used until an index of weather is computed." Another sugges-
tion for wheat was "to combine such climatic influences as:

June temperature, April-May precipitation, July temperature,

 

’1.ICromarty, W. A., The Economic Structure of A icul-
ture ig_§gg_United States, A summary of’work staFEea in East
Lansing, Mich., and carried on in.Washington D. C. during the
summer of 19Sh. mimeographed.

July precipitation, and September-October precipitation of
the previous year into a weather index."

Going further back than Cromarty's study, the idea of
using plot data to construct a weather index was used by G.
L. Johnson in his study of burley tobacco control programs1
and D. E. Hathaway in his study of the dry bean industry.2

The fact that indexes of the influence of weather were
needed in Cromarty's study would probably be justification
enough for this project. However, it is believed that an
index of this type will be valuable when used for similar
types of studies in the future or for general appraisal of
the agricultural economy. This study should give an indica-
tion as to whether it is feasible to construct indexes of the
SCOpe computed in this study. Their use in particular stud-
ies will indicate whether or not they contribute to the study
of the particular relationships being considered. There are,

no doubt, other uses for such indexes which are not yet ap-

parent.
Objectives

Considering the study from the standpoint of: (l) the
interest and qualifications of the personnel, (2) the facili-

ties available, (5) the budget, (4) the time available to

 

l. thnson, G. L., Burley Tobacco Control Programs,
Ky. A. E. S. Bul. 580, Feb., 1952.

2. Hathaway, D. E., The Effects 9f the Price Support
Program 9g the Dry Bean Industry 1g Michigan, Mich. A.E.S.
Tech. Bul. 250, Apr., 1955.

complete the study, (5) the appropriateness of the subject,
and (6) the accuracy necessary and other considerations, the
following more specific objectives were decided upon.

To construct indexes of the influence of weather on:

1. Yields of specific crOps for the United
States.
2. Important aggregate measures of U. S. agri—
cultural production and yields.

It was believed that, considering the time and personnel
available, it would be best to restrict this study to a few
of the more important crOps. The crops chosen to study were:
corn, oats, barley, wheat, soybeans, cotton, and tobacco.
Cotton and tobacco are important crops alone. Corn, oats,
and barley are combined to give an indication of the influence
of weather on feed grains. Wheat represents food grains, and
soybeans represent oil crOps. Thus, indexes are computed which
give an indication of the influence of weather on some of the
more important groups of crOps. Even though not all crops
in each group are included, the crOps studied make up a large
percentage of the group in each case. Table 8, page 86, gives
an indication of the relative importance of the crOps included
in this study both individually and in total. It will be
noted in the Index of CrOp Production column that crOps included
in this study account for 65.6 percent of all crops. Several
important crOps, from a total value standpoint, have been left
out as have many of lesser importance but which make up a large

part of all crOps when grouped together. Hay and forages are

one important graip of crops not accounted for directly al-
though that group accounted for 11.5h percent of all crops

in 1947-49.1 Hay and forages were left out of this study
partly because no way could be thought of to construct accu-
rate indexes with.the chosen method. A preliminary review of
literature indicated that little plot data were available for
constructing indexes by this method and that data available
were mostly for alfalfa, which might not represent very well
all hay and forages. Another reason for not computing the
index for this important group of crops is the availability
of indexes of range and pasture conditions published by the
U.S.D.A. Vegetables, fruits and nuts, and sugar crops were
also left out of this study although these are important
groups of crops in total, and certain craps within these
groups such as oranges and apples are important individually.
Most crops left out were left out either because the method
used to construct the weather indexes does not work well be-
cause of lack of appropriate data or because the crop is rela-

tively unimportant from a total value standpoint.
Review 22 Literature

A review of literature was undertaken with two purposes

in mind. One purpose was to review any literature dealing

I. See TEEle 19, U.S.D.A. Agriculture Handbook No. 118,
Vol. 2, Agricultural Production and Efficiency.

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C X .r“ A a T. e as :. so C .2 U- at 3 .mg L. r 01 30
J a S L. .. . v“ .4 £4 .5 Tu . e. a; .C to si e . 2x Au 9..
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with studies of the same nature or studies which had something
to contribute in the way of methodology and suggestions for
this study. The other reason was to locate raw data to be
used in this study.

No study was found which had the particular objectives
or SCOpe of this study. In the few studies found where an
index of the influence of weather of the type to be construc-
ted here was used, it was only for one crOp or for a specific
region. Most other studies found were interested in correl-
ating rainfall or various components of weather with county,
state, or United States average yields.

Johnson, in his study of burley tobacco control programs,
used an index of the influence of weather on burley tobacco
constructed from plot data at various locations in the burley
growing areas. Hathaway2 used an index of the same type in
his study of the dry bean industry in Richigan.

Studies which were mainly concerned with correlating vari-
ous components of weather such as rainfall at various times,
temperature, etc. with yields of crOps and the year published
included: a study of the relation between precipitation, tem-
perature and yield of corn on the agronomy farm, Urbana, Illi-
nois, by Runge, 1957;5 a study of climatic factors and corn

yields in Texas blacklands by Bates, 1954;4 an analysis of

 

1. Johnson, G. 0., 9p. gi§., p. 5.

2. Hathaway, D. E., 9p. gi§., p. 5. _ .

5. Runge, E. C. A., The Relation Between Precipitation,
Temperature, and Yield of Corn 9p the Agronomy South Farm,
Urbana, Illinois, Unpublished M.S. Thesis, Agronomy Dept.,
U. of Illinois, 1957.

4. Bates, R. P., "Climatic Factors and Corn Yields in

Texas Blacklands," Agron. Abs., 46:85, 1954.

 

 

factors influencing cotton yields and their variability by
Fulmer'and Botts, 1951;1 a study of range forage conditions

in relation to annual precipitation by Clawson, l9h8;2 a

study of the comparative effects of season, location and va-
riety on the yield and quality of North Dakota hard red spring
wheat by Harris, 9.13.- 51” 19h7;3 a study of the techniques
for measuring joint relationships of temperature and precipi-
tation on corn yields by Hendricks and Scholl, 19143;)4 a study
of climatological measurements for use in the prediction of
maize yields by Bair, l9h2;S a study of crop yields and weafimr
by Bean, 19h2;6 a study of the relation of weather and its
distribution to corn yields by Davis and Harrel, l9h2;7 a

study of methods of computing a regression of yield on weathmr

 

I. FuImer, J. L. and R. R. Botts, Analysis 23 Factors
Influencing Cotton Yields and Their Variability, U.§.5.K.,
Tech. Bul. 1552, Uct. I951.

2. Clawson, M., Range Forage Conditions in Relation to
ggfiugé Precipitation," 3. g. 95 Land Econ., Aug., 19h8, pp.

- o, "“

3. Harris, R. H., L. D. Sibbitt, L. R. Waldron, and T.

E. Stoa, Compgrative Effects 2§_Seasons, Location, and Vari-
'fifiz gn_thg Yield and' ualit ‘3; North Dakota Hard 329 Spring
eat, N. D—TE. A. .ST—BuLi."3'H%, Jan—U 19127.

. Hendricks, W. A. and J. C. Scholl, Techni ues ig.Meas-
urin Joint Relationships: The Joint Effects 2:_%emperature
an Precipitation gg_ orn.Yields, N. C. A.E.S. Tech. Bul. 7h,
Apr., 19h30

5. Bair, R. A., "Climatological Measurements for Use in
the Prediction of Maize Yield," Ecolo , 23:79-88, l9h2.

6. Bean, L., Crop Yields gg_DWeat er, U.S.D.A. Misc. Pub.
h71. 19h2.

7. Davis, F. E. and G. D. Harrell, Relation of Weather
and Its Distribution 3.3 Corn Yields, U.s"".D.'A'.""—’Tecfi7 W1. 6,
FEET-Tens.

 

by Houseman, 19h2;1 a study of the influence of distribution
of rainfall and temperature on corn yields in western Iowa

by Houseman and Davis, 19h232 a study of the effect of the
amount and distribution of rainfall and evaporation during
the growing season on yields of corn and spring wheat by
Davis and Palleson, l9h0;3 a study of weather influences on
crOp yields by Visher, 19u0;u a study of growth and yield in
wheat, oats, flax, and corn as related to environment by Dun-
han, 1938;5 a study of the influence of rainfall on the yield
of cereals in relation to manurial treatment by Cochran, 19353‘5
a study of the relation between crop yields and precipitation

in the great plains area by Chilcott, 1931;7

a study of fore-
casting wheat yields from the weather by Alsberg and Griffing,

1928;8 a study of the relationship of weather to crops in the

 

41. Houseman, E. E., Methods of Com utin a Regression
of Yield on Weather, Iowa I. E. 3. R35. BuI. 352, June, 19H2.

2. Houseman, E. E. and F. E. Davis, "Influence of Distri-
bution of Rainfall and Temperature on Corn Yields in Western
Iowa," Jour. 55;. Res., 65: 533- 5A5, 19h2.

3. Davis, F. E. and J. E. Palleson, "Effect of the Amount
and Distribution of Rainfall and Evaporation During the Grow-
ing Season on Yields of Corn and Spring‘Wheat," Journ. 553.
R040, 6031-23, 111118., Jan., 19140.

h. Visher, S. 8., "Weather Influences on CrOp Yields,"
Econ. G00 0, 16: 1‘37‘1‘1‘3, 19u00

nham, R. 8., "Growth and Yield in Wheat, Oats, Flax,
and Corn as Related to Environment," Amer. Soc. Agron. Journ.,
30:895-908, 1938.

6. Cochran, W. G., "A Note on the Influence of Rainfall
on the Yield of Cereals in Relation to Manurial Treatment,"
J. A r. Science, 25: 510- 522, 1935.

_57; Chilcott, E. G., The Relation Between Crop Yields and

Precigitation_ in the Great Plains Area, U. S. D. A. Misc. Cir.
8. Alsberg, C. L. and E. P. Griffing, "Forecasting Wheat

Yields from.the Weather, 1928," Stanford Univ. Food Research
Institute, Wheat Studies, 5: l-hh.

 

plains region of Montana by Patton, 1927;1 a study of the ef-
fect of climatic conditions on the growth of barley by Gregory,
1926;2 a study of coefficients of correlation between May and
June rainfall and the yield of wheat from 1911 to 1926 by
Willard;3 a study of the influence of rainfall on the yield
of wheat at Rothamsted, England, by Fisher, 192u;u a study of
forecasting crops from.the weather by Brooks, 1922;5 a mathe-
matical inquiry into the effect of weather on corn yields in
eight corn belt states by Wallace, 1920;6 a study of the re-
lation of moisture to yield of winter wheat in western Kansas
by Call and Hallsted, 1915;7 and a study of the relationship
of precipitation to yield of corn by Smith, 1903.8

Though the above is certainly not an exhaustive list of
all the studies concerned with the influence of weather on

crops, it covers a good prOportion of them from which cross

 

1. Patton, P., Relationship of Weather to Cro s in the
Plains Region_ of Montana, Mont: ATE.S. Bul. 256,192?
. Gregory, F. G., "The Effect of Climatic Conditions
on the Growth of Barley," Am. BLt., h0:1-26, 1926.
3. Willard, R. E., Co-fficients of Correlation Between
Ma and JUne Rainfall and the Yield BETW'heat froml 191 1 to 1926,
N. DT—K'.E. S. Bul. 212 ,—351y, 1927.
h. Fisher, R. A., "The Influence of Rainfall on the Yield
of Wheat at Rothamsted, " Roy. Soc. (London) Phil. Trans. SLr. B,
213:89-lh2,1llu8., 192%.
S. GBrooks, C. F., Forecasting the Crops from the Weather,
1922," Geo . RLv., 12. 305- 307.
alace, H. A., "Mathematical Inquiry Into the Effect
of Weather on Corn Yield in the Eight Corn Belt States," U. S.
Mo. Weather RLv., hB: h39-hu6, Aug., 1920.
a l, L. E. and A. L. Hallsted, The Relation of Mois-
ture to Yield of Winter WhLat in Western Kansas, Kan. A. E. S.
BET“ 2—6.19157_
8. Smith, J. W., "Relation of Precipitation to Yield of
Corn," E.§,2.§, Yearbook, 1993, 215-22u.

 

 

 

 

 

 

10

references to other studies can be found. Although much work
appears to have been done on the influence of weather on crops,
no study was found which actually provided indexes of the to-
tal influence of weather for the major crOps and for the whole
United States in a form that could be used in econometric
models such as Cromarty's. Cromarty did use various measures
of weather in his system of equations such as rainfall, un-
harvested acres and others but eXpressed the conviction that

these might be improved upon.1

 

l. Cromarty, op. cit., p. 2.

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CHAPTE II
CONCEPTUAL FRAKENORK AND MEASURJKEKT TECHNIQUES
General Conceptual Framework Used

The conceptual framework for measuring the influence of
weather on crOps in this study is similar to that used by John-
son1 and Hathaway2 and discussed by Cromarty.3 It is hypoth-
esized that if time series of yields for the studied crOps
can be obtained from experimental plots in the areas where
the particular crOps are grown and where as many variables
as possible have been held constant, the remaining variation
in yield from year to year should give an indication of the
influence of "weather" after trend has been removed to account
for increases or decreases in fertility level in the soil.

Actually, only part of the variation in plot yields can
be explained by direct weather influences, even after trend
has been removed to account for increases and decreases in
soil fertility. The part not due directly to weather influ-
ences can be further classified as variation which is corre-
lated with weather influences and variation not correlated
with weather influences. Examples of factors causing varia-

tion in yields which may be correlated with weather influences

 

I. Johnson, G. L., 9p. ci .,
2. Hathaway, D. E., 9p. Cit., p. 5.
5. Cromarty, N. A., pp. c1 ., p. 2.

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12

include such things as insect damage, plant disease, and
soil moisture levels. Examples of factors causing variation
in yields which may not be correlated withueather influences
include such things as uncontrolable variations in seed and
fertilizer application, cultural practices, crop damage by
various pests, various accidental occurrances and other fac-
tors which cannot be accounted for. All direct and indirect
influences of weather will be called the influence of
"weather" in this study. It will be assumed that all vari-
ations in plot yield due to non-weather factors not corre-
lated with weather are randomly and normally distributed
with an expected value of zero. It will be further assumed
that the trend due to fertility increases or decreases is
linear and can be removed by the standard statistical

method of fitting a regression line of yield on tine and
measuring deviations about the computed yield for each

year. Indexes can be computed for specific crOps at par-
ticular locations by dividing the actual by the computed
yield each year. Indexes at each location can then be
weighted tOgether using production figures for the area
represented by each location into an index for the whole
United States for each crOp. Indexes for various aggre-
gate measures can then be constructed by weighting indexes
for each crOp contained therein by the value of production

of each.

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Techniques Used in This Study
Some Preliminary Considerations

Choosing the General Approach

Various alternatives for constructing indexes of the in-
fluence of weather on crOp yields were considered. The tech-
nique used by Johnson and Hathaway, as discussed in Chapter I,
was decided upon. One method considered was to use some of
the measures Cromarty used such as rainfall and unharvested
acreage. Another method considered was to combine various
components of weather into an index in some manner. There
are some important difficulties in using rainfall, however.
These include the fact that annual rainfall alone is fre-
quently not the only important determinant of yield; the
time when rainfall occurs is also important. Another diffi-
culty is that rainfall alone is not the only component of
weather affecting yield. Other components of the weather such
as wind, sunlight, temperature, relative humidity, level of
the water table affected by prolonged drouth and possibly
other factors enter into the total effect of weather. A re-
view of the literature mentioned earlier revealed that con-
struction of a model to measure the vario 8 components of
weather would be difficult and expensive to work with empir-
ically since the interrelationships between the various com-
ponents are very complicated. Although there appeared to be

several studies on the influence of various components of

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weather on various crOps, the results were, in general, in-
complete in that usually no more than one or two components
were considered at a time and these were usually for a spe-
cific crOp in a specific location.

The main objections to using unharvested acreage and/or
yields attained by farmers as an indication of the influence
of weather on particular crOps would appear to be that they
are related to each other and that important non-random
variables other than weather influence unharvested acreage
and yield. A rather important variable in some years and
for some crOps would be price. In some years prices are low
enough to discourage fertilization and good cr0pping prac-
tices in addition to encouraging abandonment of a crOp. In
other years, high prices stimulate use of yield increasing
practices and harvesting of poor acreages. This is verified
empirically for burley tobacco by Johnson in his study of
burley tobacco control prOgrams.l It is desirable to leave
such influences out of an index of the influence of weather
as it is the interest of economists to analyze these sepa-
rately.

After the advantages and disadvantages of various al-
ternatives were considered, it was decided to use the experi-
mental plot method. The main consideration for using this
method was that it was felt that, from a theoretical stand-

point, it should measure what it was desired to measure.

 

1. Johnson, G. L., gp. cit., p. 5.

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.Another important consideration was that this general techni-
que had been used with apparent success by Johnson and Hath-

away 0

Deciding upon §h2_gggp§h£3 include.

Crops selected for this study were corn, oats, barley,
wheat, soybeans, cotton, and tobacco. The selection of these
particular crops was based on such criteria as relative im-
portance from a total value standpoint of each crop both in-
dividually and within important groups of crops, the time and
resources available and availability of data. It has already
been mentioned in Chapter I why no attempt was made to compute
indexes for hay, forage, or silage. For one reason it was
felt that appropriate data of the type needed could not be
obtained and, on the other hand, a substitute which could be
used for such an index was already available as indexes of
pasture and range condition published in various U.S.D.A.
publications. For some irrigated crops it was felt that no
index of the influence of weather was needed since weather
would cause little variation in the yields of these crops.
Also, most individual irrigated crops do not make up a very
high percentage of the total value of crop production in the
United States.

Roughly, the most important criterion for selection of
individual crOps was to select those which made up five per-
cent or more of the total value of all craps produced in the

United States over a recent time period except in the case

16

of hay and forage crOps, soybeans and barley. Barley was
chosen because the data were readily available and the barley
index would contribute to the computation of the feed grain
index. Soybeans were chosen because some measure of the in-
fluence of weather on oil crOps was desired and soybeans was
the most important oil crOp. (See Table 8, page 86). Some
fruits were relatively important (individually) from a total
value standpoint such as citrus fruits and apples, but the
proposed method of constructing the indexes would be diffi-

cultt>use on these because of the nature of their production.

Deciding Upon the Timg Period

The time period for this study was chosen considering
availability of data and possible uses of the indexes. A
review of the available data suggested that it would be
difficult to construct an index further back than 1900 and
that, even with this rather recent starting point, data
would be scarce until approximately 1950. Also, most
econometric studies using historical data do not go back
further than 1950. It was decided, however, to construct
the various indexes back to 1900 and present them along

with their limitations.
Procurement of Data

The first step was to decide where in the United States
plot data were needed. These decisions were based on concen-

tration of production of the crOp under consideration, the

need for homogeneous areas with respect to weather and geo-

17

graphic characteristics and the availability of data. In
many cases, it became a matter of deciding where data could
be obtained and then deciding whether or not they were adequate
for the purpose for which they were to be used. A large part
of the time spent reviewing literature was spent searching
for experimental plot data already published. It soon became
apparent, however, that an adequate amount of data could not
be obtained from published sources. To supp ement published
material, it was decided to write to the various agronomy de-
partments where data were needed. Directors of the various
experiment stations were contacted in order to explain the
project and to facilitate c00peration and understanding in
obtaining data from their respective agronomy departments.

A letter was then sent to the heads of the various agronomy
departments asking for the data needed. The responses to
these letters were, in most cases, rapid and fruitful. How-
ever, in many important locations it was indicated that data
would be difficult to obtain without additional personnel and
reimbursement, the chief reason in most cases being that the
data were not in the form needed and that a considerable
amount of work would be required to obtain it from the
records. In some cases budget limitations for this project
made data of the type needed simply not available for this
study. Funds were not available to obtain data that could
not be obtained either in published form or through corres-

pondence by letter or by telephone.

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In order to do a more thorough job on a project of this

sort, more time, personnel, and money would be needed to aid

in obtaining data from certain areas where it is desired and

where it

is more difficult to obtain. If there is a demand

for more accuracy than is afforded by the indexes in this

study,or if it is desired to keep these up to date, a more

comprehensive project should be undertaken, possibly by the

United States Department of Agriculture.

Treatment of Data

As data were obtained from various sources, each sepa-

rate series was cOpied onto a form especially designed for

the purposes of removing the trend and computing the index.

(See form in Appendix B). The general technique for compu-

tation of the various indexes in order of their computation

is outlined as follows:

1.

Indexes for each series, each.crop 33 each location:

 

These were computed directly on the form mentioned

above.

Indexes for each crop g£_each.location: The indexes

for each series were averaged together into an index
for each crop at each location. This involved much
subjective screening of data, consideration of whether
or not there were weather cycles, splicing shorter
series together and other difficulties. Construction

of these indexes involved making various rules to follow

l9

and assumptions as difficulties arose. Some of these

were as follows:

a. If series were no longer than five years, trend

b.

C.

was not removed. Deviations from the average
were used instead. Such series were used only
when longer series could not be found for the
particular purpose.

If weather cycles appeared to exist when detected
by computing moving averages at various locations,
the beginning and ending points for each series
were chosen so as to connect similar stages of
the cycle or, in general, avoid short run trends
due to cycles which did not reflect the longer
run trend of increases or decreases in soil fer-
tility.

Indexes for each year at each location were chamed
against each other, against county, state, or

U. 8. average yields and against various other
measures such as unharvested acreage, rainfall,
etc. which would reflect the influence of weather
on yield to some extent. When individual figures
looked irregular by comparison, the original
source of data was rechecked for mistakes or for
various disturbances at each location which might
have caused the suspected irregularity. Many
such irregularities were eliminated from the data

by this me thOd e

20

3. Indexes for each crop for the whole United States:

These were computed by taking the indexes for each
crop at each location and weighting them into a series
for the whole United States using average production
in the area represented by each location during a re-
cent period as weights. See Tables 1, 2, 3, u, S, 6,
and 7, Chapter 3, for computations of weights and in-
dexes for each crop.

Indexes for igportant aggregate measures 2: E. S.

 

 

ggricultural production and yields: These measures
include important series currently published and main-
tained by the United States Department of Agriculture.
In this study an index of the influence of weather
was computed for four production measures including
the Index of Crop Production, the Index of Cross Farm
Production, the Index of Farm Output, and the Index
of Farm Marketings and Home Consumption. An index
of the influence of weather on the Index of Crop
Yields Per Harvested Acre was also computed. In ad-
dition, indexes of the influence of weather on the
feed grain components of the Index of Crop Production,
the Index of Farm Output, and the Index of Farm Mar-
ketings and Home Consumption were computed.

In computing these indexes, indexes for the sep-
arate crOps for the United States were weighted to-

gether according to their relative dollar value of

21

production during 19h7-h9 as presented in Tables 19,
27, 31, and BA in U.S.D.A. Agriculture Handbook No.
118.1 A more detailed description of the various
measures and the methods used are presented in Chap-

ter IV.

 

I. U.S.D.A. Agriculture Handbook No. 118, pp. cit., p. 5.

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59 t: al

CHAPTER III
INDEXES OF THE INFLUENCE OF WEATHER ON SPECIFIC CROPS

This chapter explains the computation of the indexes of
the influence of weather on individual crops including corn,
oats, barley, wheat, soybeans, cotton, and tobacco. An evalu-
ation of all indexes is presented in Chapter V.

In computing these indexes, trend was removed from each
separate series of raw yields at each location by estimating
the parameters of a linear regression equation of yield on
time except in the case of very short series of five years
or less. Indexes were then computed as the ratio of the
actual to the computed yields in each year. In the case of
the shorter series, indexes were computed as the ratio of the
actual yield to the means However, these shorter series were
only used when no other data were available for the particu-
lar years. These separate indexes of varying lengths were
then combined at each location into one index. This involved
averaging the overlapping years of each separate index. The
indexes for each location were then weighted together into an
index for the whole U. S. according to production represented
by the index at each location. Further detail as to the com-
putation of each index is discussed in the following sections

of this chapter pertaining to each crop.

-22..

23

In computing the weights for each location, homogeneous
areas with respect to weather characteristics were mainly
arrived at by considering maps and data presented in Climate
QQQ.M§E.1 Similar characteristics with respect to rainfall,

growing season, and other relevent factors were considered.
Corn

Corn is the most important feed grain as well as the
most important crOp, from a total value standpoint, grown in
the United States. It accounted for about seventy-five per-
cent of the value of all feed grains and for nearly a fourth
of the total value of crOp production in 1947-49 (See Table
8, page 86). Figure 1 gives an indication of the location of
corn production. Important areas of corn production are in-
dicated as well as the average percentage production by states
for 1946-55.

An attempt was made to obtain time series plot data for
constructing the index from states having approximately five
percent or more of the total corn production and from loca-
tions representing the highest concentration of production.
Usable data were obtained from Urbana, Illinois; Ames, Iowa;
Columbia, iissouri; Lincoln, Nebraska; North Platte, Nebraska;
and Wooster, Ohio. Gaps include data from Minnesota, Wiscon-

sin, and Indiana; but these were not considered serious enough

 

1. U.S.D.A., Climate and Man, Yearbook of Agriculture,
1941.

11:11

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24

Figure 1: Location of Con Productim and Average Percentage of Total

Production by States, 1946-515]

 

 

lighter areas

‘5'” heavier areas

 

5/

Average percentage production caputed frcn production for 1946-55

reported in Crop Production, USDA, ANS, Nov. 12, 1957. General

boundaries of greatest corn production derived in. Van Royen, W.,
ricultux 1 Resources of the World, Prentice-Hall, 1951., Vol. 1.

 

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Ames, Iowa
Colulbia, Ho.
Lincoln, Nah.
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25

to warrant expending extra time and money in further attempts
to Obtain data from these states. Chief difficulties in ob-

taining data from these locations were: data were in a form

which made time series difficult to extract, or time and per-
sonnel were not available to obtain such data, or there were

no adequate data available.

In constructing the index of the influence of weather on
corn, weights were computed by determining the area to be re-
presented by each location and computing the percent of total
production represented by that area. Although it was recog-
nized that, ideally, areas of production to be represented
by each location would not necessarily follow state boundaries,
it was felt that in the case of corn the possible added accu-
racy which might be obtained by divisions smaller than state
boundaries was not worth the extra time and inconvenience.
This is in accord with.one criterion of the scientific method
which states that "there is no point in sharpening precision
to a higher degree than the problem at hand requires. (You
need no razor to cut butter.)"l

Considering the areas of production and the weather
characteristics of the various areas, the areas to be repre-
sented by each location were decided upon. The computation
of the index for the whole United States including computed
indexes at each location and weights is presented in Table

l. A comparison of the index with United States average

yields is presented in Figure 2.

 

l. Feigl, H., "The Scientific Outlook: Naturalism.and
Humanism," Readings $2 the Philosophyg§_Science, Appleton-

Century-Crofts, p. 12.

 

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31

Oats

 

Oats is an important single crOp from.a total value
standpoint in the United States. It is the second most im-
portant feed grain accounting for 16.7 percent of the total
value of that group and accounting for :.26 percent of the
total value of all craps produced in 19h7-h9 (See Table 8,
page 86). Oats production is centered in the North Central
United States. Some production exists in almost every state
but the bulk of the production is concentrated in Iowa,
southern Minnesota, southern Wisconsin, and northern Illinois
(See Figure 3). These four states account for approximately
50 percent of the cats production. Over 80 percent of the
oats production is accounted for if some of the states sur-
rounding these four states are added including North Dakota,
South Dakota, Nebraska, Missouri, Indiana, Michigan, and
Ohio.

Oats data were desired from Iowa, Minnesota, Wisconsin,
and Illinois as well as for some of the surrounding states
mentioned above. Usable data were obtained from‘Urbana,
Illinois; Ames, Iowa; Columbia, Missouri; Lincoln, Nebraska;
North Platte, Nebraska; Dickinson, North Dakota; and Fargo,
Norfln Dakota. Important gaps in the data from.Minnesota and
Wisconsin were not considered serious enough to warrant fur-

ther effort at this time.

32'

Figure 3: Location of Oats Production and Average Percentage of
Total Production by States, 1944-53.;l

[:::3 lighter areas
heavier areas
3/

   

  
  

a»
$3.. N

“1%

    
 
  

 

 

Average percentage producti- cup-ted fr- producti- ter ”Mt-.53
reported in Crops and Markets, USDA, DIS, 1956 editiu, Vol. 33.
General boundaries of greatest productius derived It. You Boyer.

H., ricoltural Resources of t d, Prentice-Hall, 19M, “1. 1.

 

Urbana, 111.
Ales, Iowa
Columbia, Mo.
Lincoln, Neb.
N. Platte, Heb.
Dickinson, I. D.

Fargo, N. D.

lccati
son of

sented

The constructed index along with the indexes at each
location and weights are presented in Table 2. A compari-
son of the index with United States average yields is pre-

sented in Figure h.

33

TABLE 2:

COMPUTATION OF THE INDEX OF THE INFLUENCE

fi‘WMEERONONBa

3h

 

Year

 

1900
1901
1902
1903
190k

1905
1906
1907
1908
1909

1910
1911
1912
1913
191a

1915
1916
1917
1918
1919

1920
1921
1922
1923
192k

1925
1926
1927
1928
1929

1930
1931
1932
1933
193k

1935
1936
1937
- 1938
1939

Urbana, I11.

 

Index 7Weight

.35

e
00001» 0000000000 wwwww 0102000900 0:

\RU'lU'lU‘l
P‘

UIU‘lU'lUIU‘l \RU'IVLUI‘JI UIUIUIUIUI U1

5"

.3

Arms, Iowa

 

Index 'weight

Lincoln, Neb.

 

Columbia,:Mb.
Index Wéighf
199.0 1.00b
8.2 1.00
105.h 1.00
81:1 .6o°
153.3 'gfid
135:8 :196
82.2 .19
63.8 .19
76.2 .19
57.21 .19
138.6 .19
25.9 .19f
19.2 .12
135.3 .OuS
120.2 .08
231.8 .Oh
101.6 .08
99.h .Oh
lOl.h .Oh
163.0 .01
57.8 .Oh
133oh .Ou
115.5 .Oh
122.6 .Ou
126.2 .Oh
133.8 .Oh
151.8 .Ou
91.14 e0“.
67.6 .Oh
129.6 .0h
57.0 .Oh
96. .Oh
13. .0h
99.9 .ouh
50.0 .Oh
166.1 .011
hB-S .08

Index DWeight

.26d

.266
.26
.26

.26
.26

.26
.15f

.07g
.07
.07
.07
.07

.07
.07
.07
.07
.07

.07
.07
.07
.07
.07

.07
.07
.07
.07
.07

.11h
.11
.071
.07
.073

Table--Continued.
Urbana, 111.

 

 

Kmes, Iowa Columbia,Mo.

 

35

 

fincoln, Neb.

 

 

 

 

 

Year Index weight Index Weight Index Weight Index ‘Weight
1981 95.2 .36 119.8 .38 92.5 .07
1982 80.2 .36 122.2 .38 121.0 .07
1983 85.9 .36 122.7 .38 161.2 .07
1988 68.1 .36 88.8 .38 77.8 .07
1985 130.2 .36 122.0 .38 115.8 .07
1986 121.7 .36 76.6 .38 58.8 .07
1987 168.2 .36 108.2 .38 108. .07
1988 130.7 .36 118.8 .38 127. .07
1989 68.0 .36 70.2 .38 65.1 .07
1950 76.9 .36k 99.0 .38 126.7 .07k
1951 .65.7 .36 78.7 .38k 99.6 .11
1952 92.1 . 81
Table 2--Continued.

N. §1§tte1 Neb. 2ickinson,_N. D. Fargo, N. D. Computed
Year ‘Index Weight Index Weight Index Weight Index
1900 199.0
1901 8.2
1902 105.8
1903 ---
1908 70.3
1905 116.8
1906 109.6
1907 122.8 .086 188.2 .119 101.5
1908 237.3 .08 158.8 .11 99.7
1909 86.8 .08 173.5 .11 109.2
1910 50.9 .08 113.2 .11 101.2
1911 0.0 .08 80.7 .11 91.8
1912 88.3 .08 0.0 .11 108.0
1913 0.0 .08 125.6 .11 69.5
1918 32.8 .ouf 73.2 .06f 77.8 .23f 86.9
1915 237.6 .088 202.0 .068 187.5 .13g 136.0
1916 158.1 .08 168.2 .06 132.2 .13 118.5
1917 35.3 .08 83.3 .06 93.3 .13 118.8
1918 30.8 .08 25.1 .06 19.1 .13 76.5
1919 182.0 .08 1.8 .06 59.1 .13 78.5

 

 

 

 

36

Table 2--Continued.

 

 

 

 

 

 

 

N. Flatte, Neb. {Eickinson, N. D. Eargo, N. D. Computed'

Year 'Index Weight Index Weight Index weight Index
1920 182.5 .08 115. .06 138.3 .13 109.8
1921 80.5 .08 18. .06 82.5 .13 85.0
1922 58.1 .08 207.2 .06 125.8 .13 100.9
1923 198.1 .08 1 8.6 .06 103.6 .13 117.5
1928 178.8 .08 1 0.6 .06 182.3 .13 132.2
1925 68.6 .08 81.3 .06 132.6 .13 83.2
1926 9.3 .08 30.8 .06 103.8 .13 99.6
1927 13 .3 .08 181.6 .06 107.2 .13 109.8
1928 18 .8 .08 175.6 .06 118.5 .13 118.7
1929 111.8 .08 72.0 .06 108.5 .13 116.5
1930 199.8 .08 90.2 .06 129.5 .13 110.8
1931 112.8 .08 6.8 .06 86.6 .13 93.0
1932 55.5 .08 115.1 .06 108.8 .13 119.3
1933 71.3 .08 53.0 .06 81.8 .13 68.3
1938 2.9 .08 89.6 .06 68.1 .13 32.8
1935 --- --- 79.1 .06h 123.7 .13h 118.2
1936 76.9 .08 0.0 .061 18.8 .13 73.9
1937 60.5 .08 16.2 .06 71.0 .131 120.6
1938 182.7 .08 83.0 .06 101.8 .13 85.6
1939 138.5 .08 178.6 .06j 87.0 .13j 85.9
1980 0.0 .08 73.6 .06 82.6 .13 88.8
1981 183.7 .08 51.2 .06 79.3 .13 102.2
1982 117.1 .08 191.0 .06 115.8 .13 110.0
1983 0.0 .08 ‘ 188.3 .06 138.1 .13 110.3
1988 182.0 .08 198.8 .06 111.3 .13 78.2
1985 181.7 .08 125.7 .06 121.8 .13 125.

1986 125.9 .08 116.6 .06 135.5 .13 103.

1987 117.7 .08 181.5 .06 105.5 .13 130.8
1988 83.5 .08 128.1 .06 105.1 .13 121.1
1989 92.0 .08 51.9 .06 120.8 .13 75.8
1950 31.3 .08 117.2 .06k 88.5 .13 90.0
1951 130.9 .06 130.9 .13k 86.2
1952 68.6 .121 78.5 . 1 82.8
1953 87.3 1.00m 87.3

 

a. Indexes at each location were computéd’uSTng the procedure
indicated in Chapter II. Raw data used are presented in Appendix A.
b. 1900-02: Columbia, Mo., weight includes 100 percent of

production.

37

Table 2--footnotes--continued.

 

c. 1908; Urbana, 111., weight includes productioné?rom
I11., Wisc., Ind., Mich., Ohio, N. Y., Pa., Mo., Vt., N. J.,

Md., W. Va., Va., N. G., S. 0., Ga., Ala., Ky., and Tenn. Co-
lumbia, Mo., weight includes production from Mo., Ark., La.,
Miss., Kan., Okl., Texas, N. M., 001., Utah, Calif., Wash., Ore”
Idaho, Mont., Wyo., N. D., S. D., Neb., Minn., and Iowa.

d. 1905-06: Urbana, 111., weight same as 1908 (See c).
Columbia, Mo., weight includes production from Mo., Ark., La.,
Miss., Minn., and Iowa. Lincoln, Neb., weight includes produc-
tion from Neb., N. D., S. D., wasn., Ore., Idaho, Mont., Wyo.,
001., Utah, Calif., Kan., Ok1., Texas, and N. M.

e. 1907-13: Urbana, 111., weight same as 1908 (See c).
Columbia, Mo., weight includes production from % Minn., % Iowa,
Mo., Ark., La., and Miss. Lincoln, Neb., weight includes pro-
duction from 3/8 Neb.. % S. D., % Minn., % Iowa, 3/8 Kan., 3/8
Ok1., and 3/8 Texas. N. Platt, Neb., weight includes production
from.§ Neb., % Kan., % Okl., % Texas, 001., N. M., Utah, and
Calif. Dickinson, N. D., weight includes production from % S. D.,
N. D., Mont., Wash., Ore., Idaho, and wyo.

f. 1918: Urbana, 111., weight same as 1908 (See c). Colum-
bia, Mo., weight same as 1918 except minus % Minn. (See f).
Lincoln, Neb., weight includes production from 3/8 Neb., % Iowa,
3/8 Kan., 3/8 Ok1., and 3/8 Texas. N. Platte, Neb., weight same
as 1907-13 (See f). Dickinson, N. D., weight includes produc-
tion from } N. D., % S. D., Mont., Wash., Ore., Idaho, and Wyo.
Fargo, N. D., weight includes production from 3/8 N. D., 3/8 S. D.
and Minn.

3. 1915-38: Urbana, I11., weight same as 1908 minus % Wisc.
(See 0). Ames, Iowa, weight includes production from % Wisc.,

/8 Minn., and Iowa. Columbia, Mo., weight same as 1918 minus

Iowa (See f). Lincoln, Neb., weight same as 1918 minus 3 Iowa
(See f). N. Platte, Neb., and Dickinson, N. D., weights same as
1918 (See f). Fargo, N. D., weight includes production from 3/8
N. D., M. s. D., and 1/8 Minn.

h. 1935-36: All weights same as 1915-38 (See g), except
that N. Platte, Neb., and Lincoln, Neb., weights are now combined.

1. 1937-38: All weights same as 1915-38 (See g).

J. 1939-50: Urbana, 111., weight same as 1915-38 except now
contains Miss. Ames, Iowa, weight same as 1915-38 except now
contains Mo., Ark., and La. Lincoln, Neb., and N. Platte, Neb.
Dickinson, N. D., and Fargo, N. D., weights same as 1915-38

See g .

k. 1951: All weights same as 1939-50 (See 3) except that
N. Platte, Neb., and Lincoln, Neb., weights are now combined.

1. 1952: Urbana, 111., weight same as 1908 (See 0) plus
Mo., Ark., La., and Miss. Dickinson, N. D., weight includes
production from i N. D., i S. D., % Neb., % Kan., % Ok1., % Texas,
Wash., Ore., Idaho, Mont., Wyo., Calif., Utah, 001., and N. M.
Fargo, N. D., weight includes production from 3/8 N. D., 3/8 S. D.,

Neb., % Kan., % Ok1., % Tbxas, Minn. and Iowa.

m. 1953: Fargo, N. D., weight includes 100 percent of pro—

duction.

58

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nu
ON

nu

nn
cc
me
an
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59

Barley

Most of the barley in the United States is produced in
the western states, upper Minnesota, and North Dakota. In
most U.S.D.A. publications it is classified as a feed grain;
but, actually, approximately 55 percent of the barley is used
in producing malt as compared to approximately 50 percent

which is used for livestock feed.1

The rest goes mostly for
seed and export.

Almost 90 percent of the barley is grown west of the
Mississippi River (See Figure 5). The area of greatest con-
centration is in eastern North Dakota and the adjacent Red
River Valley area of Minnesota. From a total value standpoint
barley is less important than the other crOps in this study,
making up only 1.83 percent of the total value of all creps
and 5.8 percent of the value of feed grains in 1947-49 (See
Table 8, Page 86). However, in the process of obtaining other
data, data on barley were found from Alliance, Nebraska: N.
Platte, Nebraska; and Dickinson, North Dakota; and it was felt
that an index should be computed. Admittedly more data would
be desired. Data from eastern North Dakota, southeastern
South Dakota, and California would be especially desirable but
were considered not worth the time and effort at this time
considering the importance of barley compared to all creps.

1. Estimated for 1947-49 from Agricultural Statistics,
USDA, 1955.

Figure 5: Location of Barley Production and Ave7age Percentage of
Total Production by States, 1944-53.£

 

 

 

 

 

 

IS
63
95 _
37
4.3 BA 2' I 4 '9 :
l6
3 ob c 2 LB
3 .3 2
2.3 I .l
5'3 :9 e e '0
'32 4
' .5
2.0 .2 — .2
_ _ J
.9 _

m lighter areas
m heavier areas

5/ Average percentage of production from production for 1944-53
reported in Crops and Markets, USDA, ANS, 1956 edition, '01. 33.
General boundaries of greatest production derived fro. Van loyen,

H., Agricultural Resources of the World, Prentice-Hall, 1954,
V01. 1.

2/ Alliance, Neb.

g/ N. Platte, Neb.

g/ Dickinson, N. D.

41

Computations of the index for the United States includ-
ing computed indexes at each location and weights are pre-
sented in Table 5. A comparison of the index with United
States average yield is presented in Figure 6. It will be
noted that the weather index for barley fluctuates through
a rather wide range compared to the indexes for other creps
in this chapter. Part of this wide fluctuation may be due
to rather scanty data; but it is also felt that this should
be expected due to the high variability of weather in the

areas from which the series were obtained.

82

TABLE 3: COMPUTATION OF THE INDEX OF THE INFLUENCE

OF WATER}? ON BARmYa

 

 

Alliance, Neb. N.PIatte, Neb. Dickinson, N.ID. Computed

 

 

 

 

Index Weight Index Weight Index Weight Index
166.2 .82b 170.8 .58b 168.6
157.6 .82 130. .58 181.8
85.8 .82 182. .58 181.9
68.5 . 2 109.0 .58 92.0
0.0 .82 57.8 .58 33.5
70.5 .82 0.0 .58 29.6
88.0 .82 118.3 .58 88.8
53.8 .82 125.3 .58 95.
189.2 .82 238.2 .58 200.
135.0 .82 130.3 .58 132.3
95.2 .82 80.2 .58 63.3
57e8 euz lush e58 32e6
152.8 .82 8.8 .58 66.7
107.0 .82 127.3 .58 118.8
108.0 .82 32.3 .58 68.1
85.6 .82 183.6 .58 125.6
158.8 0’42 131.2 e58 1’42e8
189.6 .82 118.1 .58 129.0
78.0 .82 87.8 .58 60.3
17.8 .82 18.0 .58 15.6
150.1 .82 128.2 .58 137.8
205.3 .82 151.5 .58 178.1
181.2 .82 82.8 .58 83.9
169.8 .82 106.8 .58 133.0
99.1 .82 10.0 .58 87.8
68.2 .82 159.3 .58 121.0
81.8 .82 86.2 .58 61.0
6.0 .82 39.8 .58 25.8
--- --- 110.5 1.000 110.5
92.2 .82 87.0 .58b 66.0
66.9 .82 62.6 .58 68.8
123.7 .82 199.2 .58 167.5
0.0 .82d 112.8 .58d 65.2
120.5 .31d 188.7 .13 57.2 .56 88.7
70.6 .826 207.0 .58° 189.7
80.0 .82 131.8 .58 93.2
15.9 .82 188.7 .58 90.6
197.1 .82 99.6 .58 180.6
128.1 .82 89.2 .58 105.5

83

Table 3--Continued.

 

Alliance, Neb. N. Platte, Neb. Dickinson, N. D. Computed

 

 

 

 

Year Index Weight lfidex Weiggt Index Weiggt Index
1987 168.3 .82 129.8 .58 18 .3
1988 99.8 .82 185.6 .58 12 .2
lghg 12707 0&2 ' 3802 .Saf 7ge8
1950 --- --- 120.2 1.00 120.2
1951 100.7 .826 179.2 .586 186.2
1952 80.0 .82 82.8 .58 81.6
1953 55.7 1.005 55.7

 

a. Indexes at each location were computed using the procedure
indicated in Chapter II. Raw data used are presented in Appendix
A.

.b. 1907-38, 1937-80: N. Platte, Neb., weight contains pro-
duction from.Neb., Kan., Okl., Texas, Col., Utah, Nev., Calif.,
Aril., Ne Me, Iowa, M00, Ille' Inde, Ohio, Pa., Mde, Ne Joy Dale,
W. Va., Va., N. 0., S. 0., Ga., Ky., and Tenn. Dickinson, N. D.,
weight includes production from N. D., S. D., Wash., Ore., Idaho,
Mont., Wyo., Minn., Wisc., Mich., N. Y., and Nb.

0. 1935-36: Dickinson, N. D., weight contains 100 percent
of all production.

d. 1981: Alliance, Neb., weight includes production from-%
Neb., Wyo., Col., Utah, Nev., Calif., Ariz., and N. M. N. Platte,
Neb., weight includes production from 3/h Neb., Kan., Okl., Texas,
Iowa, 111., Ind., Ohio, Mo., Ky., Tenn., Pa., N. J., Del., Md.,

W. Va., N. G., S. 0., and Ga. Dickinson, N. D., weight includes
production from Wash., Ore., Idaho, Mont., N. D., S. D., Minn.,
Wisc., Mich., N. Y., and M6.

0. 1982-89; 1951-52: Alliance, Neb., weight same as N. Platte,
Neb., weight of 1907-38 and 1937-80 (See b). Dickinson, N. D.,
weight same as 1907-38 and 1937-80 (See b).

f. 1950: Dickinson, N. D., weight contains 100 percent of
all production.

g. 1953: Alliance, Neb., weight contains 100 percent of all
production.

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8.5

Wheat

Wheat is the most important food grain. It accounted
for ninety-one percent of all food grains and over ten per-
cent of all crops from a total value standpoint in 1987-89
(See Table 8, page 86). Sore wheat is grown in almost every
state of the United States and production is not as highly
concentrated in one large area as is the case with some other
crOps such as corn. Also, there are several types of wheat
grown including white wheat, hard red spring, durum, hard
red winter, soft red winter and other less important types.
Important areas of production can be isolated, however (See
Figure 7). The most important area is the Great Plains re-
gion which can be further divided into two distinct areas.
These include a heavy concentration in Kansas, western Okla-
homa, and the Texas Panhandle; and another distinct concen-
tration in North Dakota, northern South Dakota, and Montana.
The first area is mostly hard red winter wheat while the lat-
ter is made up mostly of hard red spring and durum wheats.
Another important concentration is found in the Pacific North-
west with the heart in southeast Washington, northeast Oregon,
and northwest Idaho. This is mostly white wheat. The re-
maining regions are more scattered. There is, however, a
rather concentrated area d? soft red winter wheat extending
through Illinois, Indiana, and Ohio and smaller areas of white

wheat in Michigan and northwestern New York.

46

Figure 7: Location of Wheat Production and Average Percentage of
Total Production by States, 1946-55..2/
6A
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- lighter areas
- heavier areas

Average percentage production cuputed fr. productiu for 1946-55
reported inC Crgg hm USDA, ANS, lov. 12, 1957. General
boundaries of greatest production derived (to Van

Agricultural Esau-tee of the World, Prentice-Ian, 1990, Vol. 1.
Akron, Col.

Urbana, Ill.

Colby, Kan.

Garden City, Kan.

Days, Kan.

Cola-bis, 19.

Lincoln, lab.

N. Platte, lab.

Dickinson, l. D.

Fargo, N. D.

Honda, H. D.

Stillwater, 0:1.

Woodward, (kl.

Pull-an, Hash.

Prod
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117

An attempt was made to obtain data which best represented
the various areas, type of wheat, and production practices.
This amounted roughly to an attempt to obtain data from states
with five or more percent of the total value of wheat produc-
tion in 1987-89. Usable data obtained included series from
Akron, Colorado; Urbana, Illinois; Colby, Kansas; Garden City,
Kansas; Hays, Kansas; Columbia, Missouri; Lincoln, Nebraska;
North Platte, Nebraska; Dickinson, North Dakota; Fargo, North
Dakota; Mandan, North Dakota; Stillwater, Oklahoma; Woodward,
Oklahoma; and Pullman, Washington. It was decided that these
data would be adequate for this study. There are, however,
some weaknesses which should be nentioned. In the first place,
as was mentioned earlier, total wheat production is not as
localized as some other crops such as corn. One important
gap in the data were series to represent wheat production for
both soft red winter and white wheat east of the Mississippi
River. It will be noted in Figure 7 that this large area in-
cludes over twenty percent of the wheat production, but that
production is widely scattered. It was felt that dauifor
Illinois, Indiana, and Ohio would be adequate if they could
be Obtained sires these states account for approximately sixty
percent of the wheat produced in that area. Data were ob-
tained, however, only for Urbana, Illinois. Considering the
low variability of yields in this area as compared to the
Great Plains and considering the time and resources needed
for obtaining additional data, it was decided that Urbana,

Illinois, would be used to represent this area. Data were

88

considered more adequate for the two regions in the Great
Plains. This was fortunate since the two regions in that area
represented over half of the production of wheat and are the
principle source of variation in the U. S. yield of wheat.
It was felt that it would have been desirable if adequate
series could have been Obtained for a few other areas in-
cluding regions in northwestern Montana, southeastern Idaho,
and the Texas Panhandle. A series for the Pacific Northwest
covering more years than that at Pullman, Washington, would
also have been desirable.

The computation of the index for the whole United States
including computed indexes at each 1ozation and weights is
presented in Table 8. A comparison of the index with the

United States average yield is presented in Figure 8.

89

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61

Soybeans

Soybeans are the most important oil crOp from a total
value standpoint. Although they are relatively unimportant
as a percentage of total value of all crOps they accounted
for over 50 percent of the value of all oil creps in 1947-49
(See Table 8, page 86). The index for soybeans was computed
as a single index from Urbana, Illinois. Admittedly other
data would be desirable but were not readily available at
this time. Data from Iowa, Indiana, and Ohio would have
been especially desirable. However, if any one series could
be chosen to best represent all soybean production, Urbana,
Illinois, would appear to be a very good choice since it is
in the center of the highest concentration of production
(See Figure 9). In choosing a measure to account for weather
in soybean production, Cromartyl used rainfall at Urbana,
Illinois, with some success which should give some indica-
tion that the location may be satisfactory.

The computed index is presented in Table 5. A compari-
son of the index with United States average yield is pre-

sented in Figure 10.

 

l. Cromarty, W. A., pp. cit., p. l.

62

Figure 9: Location of Soybean Production and Average Percentage of
Total Production by States, 1946-55.!

 

 

E: lighter areas
heavier are”

v.
2/ Average percentage production caputed fru probed- hr ”“65
reported in Cro Productiu, USDA, AIS, III. 12, 1957. Mel
boundaries of grenteet production derived 11‘- VII “1., H.,

riculturel Resources the H 1 Prentice-I111, 1’“, Vol. 1.

 

2/ Urbana, 111 .

 

TABLE 5: INDEX OF THE INFLUENCE OF WEATHER ON SOYBEANSa
Year Index Year Index
1909 120.6 1954 85.9
1910 126.2 1955 97.7
1911 119.6 1956 115.6
1912 91.1 1957 82.5
1915 77.0 1958 128.0
1914 97.4 1959 92.4
1915 101.0 1940 85.9
1916 54.0 1941 125.4
1917 88.2 1942 105.7
1918 79.9 1945 90.5
1919 98.8 1944 115.5
1920 95.5 1945 95.1
1921 157.5 1946 91.0
1922 95.6 1947 59.2
1925 64.6 1948 116.0
1924 80.5 1949 115.9
1925 155.2 1950 95.7
1926 92.8 1951 108.5
1927 85.1 1952 101.7
1928 115.4 1955 82.5
1929 114.2 1954 96.0
1950 85.4 1955 81.0
1931 95.7 1956 125.9
1952 127.7 1957 110.8
1955 101.6

 

a. Computed from a singlelocation at
Urbana, I11. Three segments were computed
separately due to variety changes. These
segments were 1909-25, 1926-49, and 1950-57.
The first two segments were computed by the
usual procedure of removing trend and com-
puting the index about this trend as des-
cribed in Chapter II. The 1950-57 segment
was computed as percent of average yield
since it was felt the series was too short
for a trend to be meaningful.

63

 

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65

Cotton

Cotton ranks next to corn among all crops in total value
of production for the United States} One of the chief pro-
ducing areas includes what is called the "old cotton south"
which is a belt extending across the southeastern part of the
United States including chiefly North Carolina, South Carolina,
Georgia, Alabama, Mississippi, Tennessee, Arkansas, Louisiana,
and parts of surrounding states. Other major areas include
the humid area of the eastern part and the Gulf coast of
Texas, subhumid areas in the Panhandle and Rio Grande Valley
of Texas and the irrigated areas of New Mexico, Arizona, and
California.

Most of the cotton grown is the short staple type, but
relatively small acreages of American Egyptian or long staple
cotton are grown in Texas, New Mexico, and Arizona.2 The
bulk of the cotton is grown without irrigation but some is
grown on irrigated land in the southwestern United States.
Over 1/3 of the cotton in the high plains of northwestern
Texas and.aboat 80 percent of the cotton grown in the upper
and lower Rio Grande Valley of Texas is irrigated.3 This is
roughly ho percent of the cotton acreage of Texas. Approxi-

mately 80 percent of the acreage of New Mexico, Arizona, and

 

1. See Table 19, U.S.D.A., Agriculture Handbook No. 118,
Vol. 2, Agricultural Production and Efficiency.

2. See U.S.D.A., Agricultural Statistics, 1956.

3. Estimated from.§.‘§.50ensus 2; Agriculture, 195M, U. 8.
Bureau of the Census, Vol. III, Special Reports, Part 9, Cot-
ton Producers and Cotton Production and from U.S.D.A. Crops
gnghMarkets.

 

 

66

California combined is irrigated also.1 In constructing the
index for the Whole United States, it was assumed that weather
has little or no influence on irrigated acreage; thus, the
New Mexico, Arizona, and California areas are assigned an in-
dex of 100 each year along with to percent of the Texas acre-
age. Location of cotton production in the United States is
presented in Figure 11.

An attempt was made to obtain plot data for cotton re-
presenting the bulk of the non-irrigated acreage. This would
include roughly North Carolina, South Carolina, Georgia, Ala-
bama, Mississippi, Tennessee, Arkansas, Louisiana, Oklahoma,
and 60 percent of Texas. Data were obtained for Stoneville,
Mississippi; Jackson, Tennessee; and several locations in
Alabama. Admittedly more data would be desirable but it was
felt that an index constructed from these three locations
should give a good indication of the influence of weather on
cotton production. Figure 11 indicates that Alabama data
might represent, fairly well, the influence of weather on
most of the acreage in Alabama and Georgia which accounts for
a large percentage of the non-irrigated cotton production.
Jackson, Tennessee, can represent a large part of the acre-
age in Tennessee, Missouri, and part of Arkansas, while Stone-
ville, Mississippi, can represent, fairly well, production in
the Mississippi-Arkansas-Louisiana region. Major gaps include
data from Texas, Oklahoma, and the Piedmont region of the

southeastern United States.

 

1. See footnote 3, p. 65 of this study.

67

Figure 11: Location of Cotton Production and Average Percentage of
Total Production by States, 1944-53.:

 

lighter areas

heavier areas
5/

Average percentage production computed fr- production for 1946-53
as reported in Cry: and Marketa, USDA, AIS, 1956 editiu, '01. 33.
General boundariea of greateat productiu derived fr- Yen

m
H., Agricultural leacurcea of the World, Prentice-Hall, 1956, Vol. 1.
33/ Auburn, Ala.
El Jackson, Tenn.

2/ Stoneville, mu. (Delta Branch kperi-nt sun-a)

68

Considering the fact that the data were less than could
be desired but also considering the importance such an index
might have in various studies on cotton, it was decided to
construct the index from these data. Construction of the
index with the separate indexes and weights used is presented
in Table 6. A comparison of the index to United States

average yields is presented in Figure 12.

69

 

 

 

 

 

 

 

 

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.poSCHpcooulmopocpcohnno canoe

73

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74

Tobacco

Tobacco is an important crop in the United States. Al-
though the acreage is small it has a high value per acre.
From a total value standpoint, it accounted for approximately
five percent of the total value of crop production in the
United States in l9h7-h9 (See Table 8, pageEMQ. In six
states, tobacco contributed 15 percent or vmre of the cash
farm income in l9§hol These were Connecticut, 15 percent;
Tennessee, 17 percent; Virginia, 18 percent; South Carolina,
23 percent; Kentucky, 45 percent; and North Carolina, 5h per-
cent.

In general, tobacco is grown in rather distinct and re-
stricted areas (See Figure 13). States with the largest
acreage are North Carolina, Kentucky, Tennessee, Virginia,
South Carolina, and Georgia in that order of importance.
Other states with important areas in tobacco are Maryland,
Pennsylvania, Ohio, Connecticut, Wisconsin, and Florida.
Tobacco grown in one area possesses characteristics that dis-
tinguishes it from.tobacco grown in another area. These
characteristics result from the combination of soil and cli-
matic conditions, variety of seed, methods of cultivation
and fertilization, and methods of harvesting and curing.

Even tobacco in the sane general class sometimes differs

greatly because of these factors.

 

l. U. 8. Bureau of the Census, Census of Agriculture:
195 , Vol. III Special Reports, Chap. III, "Tobacco and Peanut
ro ucers and Production.

75

Figure 13: Location of Tobacco Production and Average Percentage
‘ of Total Production by Statea, 1944-53.}!

 

 

D Flue-cured

[:3 Light air—cured (neatly barley)
All other
_a_/

Average percentage of production curated fr- produeti- for 1964-
53 reported in Crgpa and Herketa, USIA, ANS, 1956 editin, V01. 33.
General boundariea of greateat production derived ir- Van

2.7.,
w., Agricultural geaourcea of the 351‘, Pr-tice-lall, 1954, Vol. 1.

E/ Canpbellaville, Ky.
£/ Lexington, Ky.
2/ Greenville, Tenn.

2/ Blackaburg, Va .

76

In recognition of these differences, tobacco in the sev-
eral producing regions has been grouped into classes and types.
A general classification is as follows:1

I. Cigarette, smoking and chewing types.

A. Class 1, flue-cured types.
B. Class 2, fire-cured types.
C. Class 3-A, light air-cured types (includes burley).
D. Class 3-B, dark air-cured types.
II. Cigar types.
A. Class h, cigar—filler types.
B. Class 5, cigar-binder types.
C. Class 6, cigar-wrapper types.

III. Miscellaneous.

The two most important classes of tobacco are flue-cured
and burley comprising the bulk of the production of tobacco
in this country. Flue-cured is produced in Virginia, North
Carolina, South Carolina, Georgia, Florida, and to a small
extent in Alabama. Burley, classed as light air-cured type,
is the second most important type of tobacco grown in the
United States. The important states in burley production are
Kentucky, Tennessee, Virginia, and North Carolina, in that
order of importance. Other less important areas are in Ohio,
Indiana, West Virginia, Kansas, and Missouri. The most inten-
sive areas of production for burley are the Kentucky Blue

grass subregion with its center approximately at Lexington,

 

1. U. S. Bureau of the Census, 22, cit., p. 74.

77

Kentucky, the eastern and western Highland Rim of Kentucky
and Tennessee and the Southern Appalacian Ridge.l

Other types andsubtypes of tobacco are relatively less
important than flue-cured and burley. The relative percentage
of the total acreage of the various types during the 1950-5h
period1 were flue-cured, 62 percent; burley, 26 percent;
southern Maryland, 3 percent; dark—fired and air-cured types,
h percent; and cigar types, 5 percent.

Thus it can be seen that series for the flue-cured and
burley tobacco growing areas would account for approximately
88 percent of all tobacco production. In the collection of
data it was attempted to obtain data for these two major
classes. Data were obtained for burley from Lexington, Ken-
tucky; Campbellesville, Kentucky; and Greenville, Tennessee.
These are fairly well located to represent all burley produc-
tion. Data for flue-cured tobacco, however, could be ob-
tained at this time only at Blacksburg, Virginia, which lies
at the extreme northwest edge of the more important flue-cured
producing area. The lack of more data for flue-cured tobacco
from the heart of the flue-cured producing region in North
Carolina is certainly a major gap. However, no further at-
tempt will be made to obtain more data at this time.

In computing the index it was recognized that these data
actually can only be taken to represent burley and flue-cured

production. Thus, only burley and flue-cured production are

 

1. U. S.’Bureau of the Census, pp, cit., p. 74.

78

used in constructing weights. However, due to the fact that
these types account for a large percentage of all tobacco pro-
duction, the computed index should represent all tobacco pro—
duction fairly well. If desired for certain purposes, the
Blacksburg, Virginia, index could be considered an irdex of
the influence of weather on flue-dured tobacco and the indexes
for the Kentucky and Tennessee locations could be combined
into an index of the influence of weather on burley.
Computation of the index for tobacco for the whole United
States along with the individual indexes and weights is pre—
sented in Table 7. A comparison of the index with the United

States average yield is presented in Figure 1h.

79

 

 

 

 

 

 

 

 

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«N mqmde

80

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81

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CHAPTER IV

INDEXES OF THE INFLUENCE OF WEATHER ON AGGREGATE MEASURES

OF U. S. AGRICULTURAL PRODWSTION AND YIELDS

In this chapter the indexes of the influence of weather
on individual crops computed in Chapter III are used to con-
struct indexes of the influence of weather on some of the
more important aggregate measures of U. S. agricultural pro-
duztion and yields computed by the United States Department
of Agriculture. In addition to the individual indexes com-
puted in this study, an index computed from indexes of pas-
ture conditions as reported in Agricultural Statistics was
also used when appropriate.

The measures referred to above include the Index of
Crop Production, the Index of Gross Farm Prodmztion, the In-
dex of Farm Output, the Index of Farm Marketings and Home
Consumption, the Index of Crop Yields per Harvested Acre, and

1 The Index of Crop Pro-

the Feed Grain Component of these.
duction is a measure of year-to-year changes in total pro-
duction of food, feed, and nonfood creps. It is used as a
neasure of crop production during the current year regard—
less of its eventual use. The Index of Gross Farm Production

is a measure of the year-to-year changes in the combined vol-

une of total crop prodmztion, product added by all livestock

 

1. See U.S.D.A., Agricultural Handbook No. 118, Vol. 2,
Agricultural Production and Efficiency, for a detailed dis-
cussion of each series.

 

 

 

-82-

83

and pasture consumed by all livestock. This measure includes
the total contribution of farm labor and farmland, since it
includes the production of commodities for eventual human use
as well as "producer goods," such as farm produced power, hay-
seeds, pasture seeds and cover-crOp seeds. The Index of Farm
Output is a measure of the current year's production of cem-
modities for human use, even though some of the output may be
sold or consumed in succeeding years. In a sense, it measures
the end product of the total agricultural effort during the
current year since hay and concentrates fed by horses and
mules (farm produced power) hayseeds, pasture seeds, and cover—
crop seeds are subtracted. The Index of Farm Marketings and
Home Consumption is a measure of changes in the quantities

of farm production entering the marketing system in the form
of sales by farmers or as direct consumption in farmer's house-
holds. All the commodities that are included in the Index

of Gross Farm Production are included in this index except

a few that are estimated on a value basis and for which no
quantity data are available. In comparison to the above

three indexes which measure production when it is produced,
this index measures production when it enters the marketing
system. The Index of Crop Yields Per Harvested Acre measures
year-to-year changes in average level of yields of 28 crops.
It is further subdivided into yield indexes of 18 field and

10 fruit cr0ps. Only the index for 18 field crOps will be

considered in this study.

84

In constructing the indexes of the influence of weather
on the various aggregate measures, the weather indexes for
individual crOps and the index of pasture conditions were
weighted according to value of production of each during
1947-49 as presented in Tables 18, 27, 21, and 55 in U.S.D.A.,
Agricultural Handbook No. 81.1 These weights are presented
in Table 8. The computed weather indexes and comparisons
with the apprOpriate U.S.D.A. indexes are presented in Table
9. For a graphic comparison of some of these see Figures
15, l6, l7, l8, 19, 20, and 21. Conceptually, some objection
may be raised to using 1947-49 weights exclusively since the
weights of the crOps entering into each index may have changed
since 1900. This is, of course, true especially in the case
of the Index of Farm Output where hay and concentrates fed
to horses and mules (farm produced power) are subtracted.
Historically, farm-produced power has decreased from a fifth
of gross farm production in the United States in 1910-14, to
a tenth in 1955-59, and to about two percent in recent years.2
In the past, the reduction in farm-produced power has been
an important factor in the increase in farm output. Weights
for other crops have changed also. A good example is the
weight for soybeans. Recently soybeans has become a rela-
tively important cr0p (See Table 8) yet production was very
small in the early 1900's. It was not included in most
m” Agricultural Handbook No. 118, Feed Con-
sumed.py Livestock, p. 82.

2. U.S.D.A., Agricultural Handbook No. 118, 92. cit.,
p. 82.

 

85

U.S.D.A. publications until approximately the 1920's. Ad-
mitting the conceptual difficulties, however, it was decided
that only the one set of weights for 1947-49 would be used.
This was decided upon partly because it was assumed that the
added accuracy from more detailed weights would not be worth
the extra time involved, and partly because the 1947-49
weights should not cause substantial biases in the weather
indexes for the Span of recent years in which most persons

using these indexes would be interested.

m-}l¢uuull.i‘ .II’ l's‘I-IIVI b. Hts: . .l“h

86

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on pogo pooh
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aas maosomwas
up: Ho; ho omopCooaom mpanoz no omopCooaom

pompoo Epom Hopoe no KopGH
possppcoolum oanoe

88

 

 

 

 

 

 

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.ooosapcoo--o oases

89

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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m.mm -- a.mo .. m.mm -u m.ama aoma
o.moa .. m.aaa -- m.ooa .. oo.oma ooma
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CHAPTER V
EVALUATION

This chapter presents a statistical evaluation of the
various weather indexes, discusses sources of error and

suggests further study.
Statistical Evaluation

In evaluating the various indexes, special emphasis was
given to determining the reliability and usefulness of the
indexes in estimating structural relationships. An indica-
tion of the reliability and usefulness of each was obtained
by computing a regression of deviations about an eleven
year moving average of each of the U. S. average yields and
aggregate indexes on the corresponding computed weather
index. This regression was chosen because it was felt that
it was reasonable considering the probable uses of the
weather indexes. It was felt that much of the analysis
involving the use of these indexes would cover a rather
short recent time period. It was assumed that non-weather
variables, such as technology, change slowly and that a
regression in which an attempt is made to eliminate these
variables would be more useful than a more direct one in-

volving the raw data over the longer period covered by

-105—

104

the indexes. The parameters of the above mentioned re-

gression and some other statistics are presented in Table

10.

In interpreting Table 10 for both the individual crops

and aggregate indexes let:

Yt

Y"

value of a "true" U. S. average yield or aggregate
index in period t.

value of a "true" U. S. average yield or aggre-
gate index in period t when weather and non—weather
variables not associated with time are "normal."

Thus variations in Y ' in different time periods

t
are due only to non-weather variables associated
with time.

Yt - Yt' = variation in a "true" U. S. average
yield or aggregate index due to weather variables
and non-weather variables not associated with

time in period t.

value of a "true" index of the influence of
weather in period t.

value of a published U. S. average yield or aggre-

gate index in period t which is presumably the

best available measurement of Yt'

t + 5
Vll :E yt which is presumed to be the best
t-S

available measurement of Yt'.
yt - yt' which is presumed to be the best available

measurement of Yt"'

 

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105

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omhxmmha mmmamma ahebhzoo whamzothMhoo mma ho mMNMQZH heawmmood.

mz¢ mQQMHM mw<mm>< .m .D mDOHm¢> WEB hO mw<MQ>4 GZH>OE mdmwlzm>mgm 2d BDOM<
deDQHmmm mme ho ZOHmmmmwflm mme mOh mmflemudm<m mmedeBmm mma ho ZOWHmdeOO "OH mfimde

106

zt = value of a computed weather index in period t
which is presumed to be the best available mea-
surement of Zt'

Consider the following relationships:

(1) It" .. o4 *7th + U This is the relationship which would,
presumably, give the "true" relationship between weather
and U. S. average yields or aggregate indexes.

But Yt" and Zt are not observable. Therefore, consider the

relationships:

(2) Y£" = Yt" + u1 and

(3) 2t 2 Zt + u2 in which yt" and 2t are observable and ul

and u2 are independent normal deviates with means zero

and variances cria and Crag. Then the best estimate of
2
It" is given by the regression of yt" on zt as follows:1

(4) yt" = a + bzt + u'.

A linear relationship was used because it was felt, upon
observation of the plotted points of the Yt"'s and zt's that
this relationship fitted the data best in most cases.

In order to obtain unbiased estimates of a, b, and R2
it must be assumed that u' is also independently and normally
distributed with mean zero and variance Cru?’ To the extent

that these assumptions cannot reasonably be made the above

 

I. For a discussion of this see Winsor, C. P. "Which
Regression?" Biometrics, Vol. 2, No. 6, Dec. 1946. See also
Goulden, C. H., Methods of Statistical Analysis, Wiley and

Sons, New York, 13'5'2, p' 7108-9.

107

statistics will not provide a very good indication of the re-
liability and usefulness of the weather indexes. Although
it is felt that such assumptions can reasonably be made in
this case, some sources of error are discussed in the fol-

lowing section.
Sources gf Error

In using equation (4) to derive Table 10 it was assumed
that u' was independently and normally distributed with mean
zero and variance (rh? in each case. Contained in u' are
deviations due to errors of observation of both Yt" and Zt
and due to non-weather variables not associated with time.
All contain possible sources of error. Errors of observa-
tion for Yt" include errors in the published U. S. average
yields and aggregate indexes as well as errors due to the
methodology used in its computation. Errors of observation
for Zt include errors of observation in the original plot
data at each location for each crOp as well as errors due to
methodology used in its computation. The more important of
these latter errors include errors due to:

(1) Choice of time series of yields at particular

locations for each crOp.

(2) Choice of the method of computing an index for

each time series at each location for each crop.

(3) Choice of locations of data for each crOp.

108

(4) Computation of weights for combining indexes at

each location into national indexes for each cr0p.

(5) Computation of weights for combining national

indexes for each crop into the various aggregate
indexes.

Published U. S. average yields and aggregate indexes
were taken as data as was the individual plot yields. It
was felt that the necessary assumptions about the errors
of observation of these were reasonable considering the
fact that the published U. S. average yields and aggregate
indexes are adjusted after the actual yields and production
is known and considering the nature of the design of crOp
experiments. The errors which appear to be the greatest
are those associated with the methodolOgy involved in com-

puting yt" and 2 These are also the errors which should

t.
probably receive the greatest attention if further study

were carried on in the construction of weather indexes.
Implications g; Table ;9

The statistics in Table 10 give some indication of the
usefulness and reliability of the various weather indexes
for use in regression analysis. As for usefulness, persons
interested in using these indexes would be particularly in-

2". This statistic gives an

terested in the statistic, "R
indication of the percentage of year to year variations in

particular dependent variables such as U. S. Average Yields,

109

Production Measures and Production.per Unit that are asso-
ciated with changes in the available weather indexes. For

example, by considering the R2'

8, one would expect the
weather index for corn to be useful and the weather indexes
for farm marketings and home consumption to be of little or
no use.

As for reliability, an indication of whether or not
there is a relationship between a particular weather index
and a particular dependent variable can be obtained from
the "t-test." The null hypothesis, b = O, was tested and
rejected (one-tail test) at the 5 percent level of signifi-
cance for all but two cases and at the 1 percent level for
nine cases.

An indication of how much variation in the particular
dependent variable is, on the average, associated with a
1 unit change in the weather indexes can be obtained from
the estimates of "b". For instance, in the case of the
regression involving U. S. Average Yield of Corn, a 1 unit
change in the weather index is, on the average, associated

with a .12 bushel change in U. S. Average Yield of Corn and

the relationship is positive.
Possibilities for Further Study

In computing yt' the errors due to moving average con—
struction should be considered. It should be realized, for

instance, that a moving average tends to overestimate a

110

"best-fitting" line in some years and underestimate it in
others.' Another possibility would be to fit some equation
such as a polynomial to the data and measure deviations
about this. Another possibility for holding non-weather
variables due to time constant is to use the method of
"first differences." This would eliminate the necessity of
fitting either a moving average or some other line to the
data. However, two regressions were computed using this
method and the results appeared to have no advantage over
the method used.

The method of adjusting the original time series with
a linear trend in computing the individual weather indexes
could be further studied. Other methods than using experi-
mental plot data could also be explored. Although it was
found that combining measurements of the various compo-
nents of weather such as rainfall, sunlight and temperature
into a weather index become rather complicated, it might
prove to be a reliable method with further study.

The possibilities of bias due to using plots with
treatments atypical of the area to be represented in some
cases could be further studied. Plots involving atypical
treatments may be used if it can be assumed that there is
no sizeable weather-treatment interaction. To be useful,
data need only be representative in the sense of accurately
reflecting weather effects. Past studies contain some in-

formation on this matter. A limited review of the literature

111

on selected crOps indicate that, in some cases, there is a
significant relationship between certain factors and vari-
ability in yields due to weather. A specific example of
this is the interaction between fertilizer application and/or
inherent fertility of the soil and variability in yields due
to weather.1 Except in special cases, variability in yields
appeared to be inversely related to fertilizer applications
and/or inherent fertility level of the soils, up to certain
levels, beyond which variability again increased. The in-
plication of this would be that there may be more or less
important sources of error due to not using data which best
approximate fertilizer application or fertility conditions
in the area to be represented by the data. Other literature
indicated that there may be significant interrelationship
between such things as tillage methods, variety, etc. and

2 Several plots in-

variability in yields due to weather.
volving atypical treatment were used in this study. In

many cases they were the only series available. In other

 

1. For one illustration of this see Fulmer, J. L. and
R. R. Botts, g2. gi§., p. 7. See also Miller, L. B. and
F. C. Bauer, The Effect of Soil Treatment in Stabilizing
Yields of Corn," Jour. Am. Soc. Agron., Vol. 50, N. 8,
August, 1958.

2. See Miller, J. D. and W. W. Ross, "Relative Yields
of Varieties of Wheat on Fallow and on Cropland at Hays,
Kans., 1921-52," Agronomy Journal, Vol. 47, No. 7, July,

1955.

112

cases atypical series were used when it was felt that their
advantages out-weighed their possible disadvantages. Some
of these advantages included the fact that longer series
were sometimes available for atypical treatments. It was
assumed, however, that individual errors due to atypical
treatments were themselves, independently and normally dis-
tributed with an expected value of zero. This assumption
seems feasible when it is considered that a large number

of individual series were used.

Another source of further study could be on the results
of not including series for various locations for the indi-
vidual crOp weather indexes and of not including many
crOps in the various aggregate weather indexes. In this
study data were collected until it was felt that the added
returns in accuracy from collecting more data was no longer
worth the extra cost. Aggregate indexes, however, were
made up of the available individual indexes and no further
accuracy was possible. It appeared to be a reasonable as-
sumption for this study that the errors due to these sources
also meet the assumptions required in the regression used.

The choice of weights and base periods are two more
sources of error which could be further studied. It is
possible that there may be biases in some indexes due to
using a relatively recent base period as the source of
weights; but, it is felt that these become relatively unim-

portant in years near the base period. Since these are the

113

more recent years and ones which will probably be used the
most, it was felt that it was not worth the extra trouble
to either attempt another method of weighing or to use
more than one base period in this study.

In view of the possible usefulness of weather indexes
of the nature computed in this study, it is felt that it
would also be highly desirable for some agency to further
test and improve their accuracy. This would include a
study of the sources of bias and error mentioned above. It
would also be desirable to have some method of keeping the
indexes current from year to year. The above suggestions
would involve extracting some data known to exist which were
unavailable for this study. It would also involve locating
new data. After data for past years have been obtained, it
should be a less costly matter to arrange with various
sources to obtain yield data in future years as it becomes
available. Procedures could be worked out for incorporating
new data into these indexes as it becomes available. The
U.S.D.A. appears to be a reasonable choice for doing such
research. Possibly, the indexes could be published and
kept current in such publications as Agricultural Statistics
along with the indexes of pasture and range conditions

which are already maintained.

BIBLIOGRAPHY

Alsberg, C. L. and E. P. Griffing, "Forecasting Wheat Yields
from the Weather, 1928, " Standford Univ. Food Research
Institute, Wheat Studies, 5: l-hh.

 

Bair, R. A., "Climatological Measurements for Use in the Pre-
diction of Maize Yield," Ecologv, 23: 79- 88, 19u2.

Bates, R. P., "Climatic Factors and Corn Yields in Texas Black-
lands," Agron. Abs., h6:85, l95h.

Bean, L., Crgp_Yields and Weather, U.S.D.A. Misc. Pub. U71,
l9h2.

Brooks, C. F., "Forecasting the Crepe from the Weather, 1922,"
Geog. Rev., 12:305‘3070

Call, L. E. and A. L. Hallsted, The Relation of Moisture to‘
Yield of Winter Wheat in Western Kansas, Kan. A. E. 5. Bul.

QUE—‘I9IST__—_—~

Chilcott, E. C., The Relation Between Cro Yields and Precipi-
tation ;Q_the Great Plains Area, . .D.A. Misc. Cir. 81,
1931.

 

 

 

 

 

Clawson, M., "Range Forage Conditions in Relation to Annual
Preci itation," ‘Q. g. 2; Land Econ., Aug., l9h8, pp.
zen-2 o.

 

Cochran, W. G., "A Note on.the Influence of Rainfall on the
Yield of Cereals in Relation to Manurial Treatment," g.
Agr. Science, 25:510-522, 1935.

 

Cromarty, W. A., Economic Structure in American Agriculture,
Unpublished Ph. D. Thesis, Dept. of Ag. Econ., Michigan
State University, 1957.

 

Cromarty, W. A., The Economic Structure of Agriculture in the
United States, A summary of work started in East Lansing,
Mich., and carried on in Washington, D. C. during the
summer of l95u, mimeographed.

 

 

Davis, F. E. and G. D. Harrell, Relation of Weather and Its
Distribution to Corn Yields, U. S.D “A Tech. Bul. 806,
Feb., I9h2.

 

 

 

Davis, F. E. and J. B. Palleson, "Effect of the Amount and
Distribution of Rainfall and Evaporation During the
Growing Season on Yields of Corn and Spring Wheat,"
Journ. Agr. Res., 60: l- 23, illus., Jan., l9h0.

- 114 -

lull! Ill—lull]

 

115

Dunham, R. 8., "Growth and Yield in Wheat, Oats, Flax, and
Corn as Related to Environment," Amer. Soc. Agron. Journ.,

30: 895- 908 . 1938.

Feigle , H. and M. Prodebeck, Readings in the Philosophv of
Science, Appleton-Century-Crofts, 1953.

Fisher, R. A., "The Influence of Rainfall on the Yield of Wheat
at Rothamsted, " Roy. Soc. (London) Phil. Trens., Ser. g,
213. 89-1u2, illus., 1'7H'. "‘

Fulmer, J. L. and Potts, R. 3., Analysis of Factors Influenc-
ing Cotton Yields and Their Variability, U. S'.D. A. Tech.
Bul.10h2,0ct., 19 5 .

 

 

 

Gregory, F. G., "The Effect of Climatic Conditions on the
Growth of Barley," Am. Bot., hO:l-26, 1926.

Harris, R. H., L. D. Sibbitt, L. R. Waldron, and T. E. Stoa,
Comparative Effects of Seasons, Location, and Variety on
the Yield and Quality of North Dakota Hard Red Sori_g
vméat,*N. D., ITETET‘BGI'. 3u2, Jan., 19H7.

 

 

 

 

Hathaway, D. E., The Effects of the Price Support Proaram on
the Dr Rean Industry in Tfichisan, Mich. A.E.S. Tech.
Bul. 0, Apr., lgggo

 

 

 

Hendricks, W. A. and J} C. Scholl, Techniques in Measuring
Joint Relationships: The Joint Effects of Temperature
and Precipitation on Corn Yields,N .C.,— A.E. S. Tech.
Bul. 7h. Apr., 19A3.

 

 

 

 

 

 

Houseman, E. E., Methods of Computigg_a Regression of Yield
on Weather, Iowa A.ETS. Res. Bul. -352. JUne, 19E?—

 

 

Houseman, E. E. and F. E. Davis, "Influence of Distribution
of Rainfall and Temperature on Corn Yields in Western
Iowa," Jour. Agr. Res., 65:533-5u5, 19h2.

 

 

Johnson, G. L., Hurley Tbbacco Control Programs, Ky. A.E.S.
BUl. $80, Feb., 1932.

 

Patton, P., Pelationship_of Weather to Cross in the Plains
Region_ of Montana, Mont. A. E. 3. Tul 2 W6 1927.

 

Runge, E. C .A., The Relation Between Precipitation, Tempera—
ture and Yield of CErn or the Agronomy South Farm, Urbana,
Tllinois, Unpublished M. S. Thesis, Agronomy Department,

U. of Illinois, 1957.

 

 

 

Smith, J. W., "Relation of Precipitation to Yield of Corn,"
2. S. D. A. Yearbook, 1903, 215-22h.

 

116

U.S. Census Bureau, Census 3: Agriculture, l95u (Various spe-
cial reports).

U.S.D.A., Aariculture Handbook No. 118, Vol. 2, Agricultural
Production and Efficiency.

U.S.D.A., Agricultural Statistics (Various years from 1936-56).

U.S.D.A., A.M.S., Crop Production, Nov. 12, 1957.

 

U.S.D. A., A. M. 3., Feed and Grain Statistics Through 195;, U. S. D. A.
Stat. Bul. N0. E59, 19

U.S.D. A., A.E. 3., Feed Consumed by Livestock, U. S.D.A. Stat.

Bul. N0. 1U5,1995u.
U.S.D.A., Climate and Man, Yearbook of Agriculture, 19u1.

 

U.S.D.A., Crops and Markets, 1956.

 

VanRoyen, W., Agricultural Resources Lf the World, Prentice
Hall, 1951:, V01. 10

 

 

Visher, S. 8., "Weather Influences on Crop Yields," Econ. Geop.,

16: h37-hL3, l9h0o

Wallace, H. A., "Mathematical Inquiry Into the Effect of Weather
on Corn Yield in the Eight Corn Belt States,“ U. S. Mo.
Weaflaer Rev., 148316941146, Aug., 1920.

 

Willard, R. E., Coefficients of Correlation Between May and
JUne Rainfall and the—Yield of Wheat from 1911 to 1926,
N. D., AE .Bu 1. 212, July. _1927.

 

 

 

Other references used as a source of raw data are in-

cluded in the "Sources of Data" section of Appendix A.

117

APPENDIX A

BASIC TIME SERIES USED IN ANALYSIS

 

 

CORN RAW DATA

 

 

 

 

2118

 

 

3:13 (i) (5) (35 (1) (5V (6) 77) (87* Z95
1900 28.6
1901 11.3
1902 79.1
1903 39.7 37.2 55.5
1901 58.9 18.1 17.1 13.1
1905 68.8 51.9 31.1 61.2
1906 14-2011» 5701‘ 3508 "
1907 61.9 16.0 18.7 33.1
1908 68.7 31.3 28.0 11.7
1909 65.1 62.2 31.6 16.6
1910 76.0 66.5 51.6 6.5
1911 15.7 29.5 31.5 25.1
1912 72.1 72.1 61.2 30.8
1913 11.9 39.2 32.0 19.2
1911 50.0 16.8 39.1 33.9
1915 51.1 62.1 66.0 15.7
1917 65.8 69.0 67.0 35.6
1918 55.2 60.0 32.6 22.1
1919 16.0 57.3 13.1 16.1 62.5 66. 6 50.1 11.6 39.1
1920 71.1 66.1 51.1 18.7 57.0 70. 6 57.0 52.9 50.8
1921 67.0 60. 2 12.2 11.8 55.6 57.0 50.2 13.3 12.8
1922 59.9 17.7 38.9 16.1 55.6 59. 7 52.9 10.5 15.0
1923 66.1 58.2 31.1 37.8 58.2 .59.1 51.0 13.8 12.6
1921 51.1 16.3 38.0 36.1 13.3 11.9 37. 8 30.9 37.8
1925 59.7 58.2 15.1 57.6 60.2 61.0 52.5 33.3 31.0
1926 69.1 57.6 35.1 10.2 10.2 12.6 36.6 25.8 19.8
1927 78.3 80.6 11.0 11.0 12.3 11.8 39.7 33.9 21. 2
1928 62.1 12.8 32.1 51.7 55.6 52.0 12.6 36.6 35.6
1929 69.0 52.0 36.0 17.0 51.9 52.1 13. 8 38.2 7.8
1930 36.3 39.8 31.1 33.9 31.9 38.7 36 8 36.0 1.0
1931 19.1 62.7 19.2 13.1 52.7 71.112.7 37.5 50.0
1932 63.8 62.0 51.0 51.7 57.0 55. 8 18.0 39.1 38.3
1933 11.2 16.5 27.9 51.5 56.3 51.6 13.2 31.8 32.1
1931 53.5 58.2 51.0 25.1 25.7 21.2 25.3 22.1 -
1935 72.8 70.8 62.9 59.5 65.7 69.2 68.0 17.8 17.8
1936 17.0 30.0 16.6 26.6 27.5 30.1 29.1 23.1 -
1937 80.1 79.6 60.8 71.1 79.7 78.1 75.0 53.7 12.7
1938 89.6 73.3 62.3 17.5 55.6 58.6 50.2 29.7 11.8
1939 85.0 56.6 50.3 60.0 63.1 66.5 55.0 18.5

1910 71.8 51.1 13.7 16.6 51.7 57.2 18.9 11.7

1911 115.6 96.7 63.1 52.3 63.1 66.9 57.3 13.6

1912 103.2 91.0 61.3 50.2 61.1 55.8 53.3 52.5

1913 89.1 60.0 59.5 15.9 55. 8 55.2 51.2 12.5

1911 81.0 79.8 62.2 12.5 16.6 18.1 16.7 36.3

1915 91.2 75.9 70.7 39.6 55.2 55.1 12.6 32.0

1916 100.1 78.2 81.7 33.2 18.0 55.1 51.2 37.1

1917 72.9 62.3 37.9 27.6 19.0 23.1 21.8 23.1

1918 113.7 110.9 71.7 56.8 60.0 61.0 56.6 10.0

1919 100.9 86.0 63. 8 37.2 16.5 17. 6 36.8 27.5

1950 93.5 90.1 50. 8 39.5 55.8 60. 8 18.3 30.9

1951 102.1 85.1 65.3 26.8 38.1 37.7 29.1 22.3

1952 81.0 85.6 52.5

1953 91.6 72.9 53.2

1951 83.7 82.1 72.0

1955 87.1 81.2 78.8

1956 79.8 85.7 98.0

 

‘Il|lrn.u|'|l 5 I' it’o‘o .

CORN RKW'DATA

119

 

 

 

 

YEAR £19) (11) (12) (131 (111 (15) (16) £171 JIBL
1900

1901

1902 75

1903 75

nm1 62

585 72

1906 66

1907 66

1908 55

1909 11

1910 58

1911 13

1912 18

1913 8

1911 53

1915 75

1916 62

1917 17

1918 3

1919 21

1920 51

1921 65 50.1
1922 17 11.1
1923 61 16.3
1921 39 27.5
1925 38 3-7
1926 3 10.1
1927 66 11-3
1929 55 30.8
1930 9 8-1
1931 30 22.8
1932 17 15-9
1933 38 0
1931 . 0 0
1935 7 7.0
1936 0 0
1937 5

1938 1

1939 1

1910 18

1911 13

1912 37 16.8 51.0 13.9 38.2 39.6 18.1 39.9

1913 56 56.9 71.1 56.0 62.1 50.8 53.3 52.1

1911 57 77.3 75.0 71.2 66.0 66.5 62.8 57.9

1915 51 65.1 66.1 56.2 52.2 11.6 16.1 53.9

1916 51 82.0 81.6 77.1 71.8 51.5 19.0 61.1

1917 21 26.8 30.0 25.5 27.3 13.3 19.7 23.0

1918 73 75.6 89.6 66.1 80.0 51.6 61.6 69.2

1919 72.7 69.1 72.2 68.1 15.1 38.1 59.3

1950 120.8 113.2 96.0 98.0 61.9 73.2 71.6

1951 79.1 70.0 69.6 51.7 28.8 10.8 35.7

1952

1953

1951

1955

1956

CORN RAW DATA

 

$119. $192 ’“1202 #121) (22) (235* 121) (25) (26) (271::_

1901
1902
1903
1901
1905
1906
1907
1908
1909
1910
1911
1912
1913
1911
1915
1916
1917
1918
1919
1920
1921
1922
1923
1921
1925
1926
1927
1928
1929
1930
1931
1932
1933
1931
1935
1936
1937
1938
1939
1910
1911
1912
1913
1911
1915

1916 .

1917
1918
1919
1950
1951
1952
1953
1951
1955
1956

NNN
o o

N
O
OONO-QOU'IO‘WF’HN

{2'3" U1 UIO\O\O
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OOWHOHmwormerNH

n) FJh)
;-acr\o
C

1.5

8.5
.1

28.8

1.6
29.6
28.5
37.2
22.3
27.0
21.0
37.7
25.7

1.6

7.5
16.7
13.6
28.0
10.8

5.8
23.
11.3
31.
19.0
21.8
38.8
31.6
36.5

9.1

12.5
15.0

38.8

7.3
33.3
35.2
27.7
12.1
21.1
20.3
26.7
16.9

10.3

RS
0 o
OU'LO

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anrkp
oer-am \o
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CORN RAW DATA

12H.

 

 

 

 

7m Q87 129)
1900
1901
1902
1903
1901
1905
1906
1907
1908
1909
1910
1911
1912
1913
1915 71
9
1916 58
1917 71
1918 53
1919 91
1920 61
1921 66
1922 05
1923 60
1921 77
1925 109
1926 79
1927 71
1928 9
1929 51
1930 g2
1931 95
1932 58
1933 52
1931 77
1935 91
1936 3.2 70
1937 0 70
1938 5.8 78
1939 1.0 63
1910 3.0 65
1911 31.0 72
1912 8.1 05
1913 35.1 75
1911 10.0 39
1915 33.2 >6
1916 31.1 51
1917 22.9 57
1918 25.8 63
1919 33.3 93
1951 35.1 89
1952 18.7 111
1953 13.1 70
1951 13.0
1955 7.6

1956

1900
1901
1902
1903
1901
1905
1906
1907
1908
1909
1910
1911
1912
1913
1911
1915
1916
1917
1918
1919
1920
1921
1922
1923
1921
1925
1926
1927
1928
1929
1930
1931
1932
1933
1931
1935
1936
1937
1938
1939
1910
1911
1912
1913
1911
1915
1916
1917
1918
1919
1950
1951
1952
1953
1951
1955

25.1
59.2
58.5
35.2
29.0
13.1

{‘03
70.7
78.8
27.5
51.0
81.1
61.8
62.0
61.8
37.2
10.6
39.1
19.3
5807

71.0.

13.8
77.2
61.6
61.6
66.9
58.6
59.6
73.1
12.8
13.1
61.5
51.11
69.1
18.1
11.3
71.7
51.2
16508
19.2
39.1
75.0
70.3
95.1
75.9
39.6
11.9
38.5
51.1

harms-1:43) 1:1»
«memegooOOwowE-Twm
a I O 0 O O o O O O O 0 O O O O O O

~q-QkuerDChcrtrC3~au3
O O Q 0 O 0 O O

O
\OCDCDK‘DW‘OMUIUIODJZ'CDWt'C'HCDHWUIOJUILmNNEHO\OO)O-\)I\)\103

H
O‘W 001.40%-J
o

OATS RAW DATA

39.1
16.3
79.1
13.7
10.7
78.8
37.5
19.

63.1
68.8

30.0
53.1
11.7
38.1
7608
5000
10.0
68.
30.6
21.6
15.0
50.0
66.9
3609
290h
37.5
58.8
60.0
53.8
27.
57.5
31.1
71.2
6.2
37.5
53.1
10.0

“3 cr

K)
o o
COi-‘OD

FJFJRJP'
. O O I O O

[0
o o o o o o o
WQ‘ONowHWWOONNWl—JW\»

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f‘CDAJynv1VJbJV)C35-uJV3v1%)n>c)rJ~a~0«aI (314:?
O . Q

\O\OO

21.5
21.1
28.2
31.8
32.8
17.9
16.1
31.5
9.6
20.6
3.1
33.1
7.7
60.
10.5

5

19.8

2.0
15.1
31.3
19.1
31.1
37.1
29.1
15.8

7)

83.3
10.9
16.7
61.1

73.0

51.8
39.7
3702
55.5
72.6
51.0
60.0
88.3
12.6

55.7
11.0
31.2
68.2
12.1
27.9
10.3
15.6
13.8
61.2

3.1.

(9

55.0
71.8
27.1
56.9
29.0
39.0
65.0
29.1
62.3
19.1

1122

c O t c

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o .. o a o 6
o v o I c o
c u

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421‘. ‘.!.l i .I!$V.‘In-.n .
r

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r

.41

OATS RAW DATA

 

mm MW

7111 ’12) 7137

(11)

(15)

(15)

(17)

(15)

 

1900
1901
1902
1903
1901
1905
1906
1907
1908
1909
1910
1911
1912
1913
1911
1915
1916
1917
1918
1919
1920
1921
1922
1923
1921
1925
1926
1927
1928
1929
1930
1931
1932
1933
1931
1935
1936
1937
1938
1939
1910
1911
1912
1913
1911
1915
1916
1917
1918
1919
1950
1951
1952
1953
1951
1955
1956

16.1
59.1
23.3
15.0
23.6
30.7
13.9
22.1
12.9
32.8

52.1
62.2
32.1
11.9
22.3
26.9
35.3
22.7
35.6
30.9

1
1
6
D.
2
8
1

mrrl—‘oom. box] VIE-40h)
0 O
nap-\os-oxot-iooowmwm

WU'IQONU'I WU‘L 13’me

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5"?er NU‘L

mu: H
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I

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N-QI-‘O‘xwwvl o~o
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° 0'0
1901
1902
1903
1901
1905
1906
1907
1908
1909
1910
1911
1912
1913
1911
1915
1916
1917
1918
1919
1920
1921
1922
1923
1921
1925
1926
1927
1928
1929
1930
1931
1932
1933
1931
1935
1936
1937
1938
1939
1910
1911
1912
1913

1915
1916
1917
1918
1919
1950
1951
1952
1953
1951
1955
1956

19

o o o o o
«P'HCDQQOOE’OOJE'H?

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19.
15.0

DJ
\0
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13'

OATS RAW DATA

l£¥+

 

 

20 (217’ (22 23 (21 25' __(26
51.5 19.1
61.9 18.1
66.6 5509
50.6 32.0
26.1 6.6
0 0
60.0 33.0
11.1 15.3 39.0 12.9 11.6 33.5’ 10.2
92.2 55.6 95.3 97.0 96.6 96.1 97.2
81.2 11.3. 63.3 65.5 71.1 70.9 71.1
27.5 7.2 15.2 19.2 13.0 50.0 60.0
21.3 0.9 10.8 9.8 11.0 10.3 9.1
1.7 0 29.1 31.1 29.8 31,8 36.1
10.9 38.7 67.3 75.9 78.0 79.3 70.5
16.9 0 37.3 39.1. 10.6 12.3 18.1
73.0 69.1 62.1 70.8 72.5 73.8 77.7
66.9 38.8 56.2 58.1. 60.5 61.1 61.1
81.9 19.1 81.6 76.6» 89.5 95.1 71.3
16.3 16.6 76.1 75.1 75.1 83.1 80.6
28.1 (3 59.0 60.2 59,7 63,8 65.7
51.1 11.1 61.2 60.5 68.2 61.5 66.
95.6 38.1 72.3 73.6 71.7 61.9 63.9
29.7 21.3 53.1 58.1. 73.0 79.7 73.3
5006 1808 730 7800 82.6 814.6 8703
6.3 C) 28.3 30.3 30.9 29.7 28.5
51.7 29.7 68.0 68.1 72.6 71.1 65.9
31.7 8.1 27.5 25.8 26.3 26.8 26.5
28.1 10.0 37.7 39.1 12.1 11.5 16.9
18.1 31.6 79.0 81.2 81,0 83,3 83.0
0 0 8.9 9.0 10.5 11.6 8.2
1.7 5.9 52.7 13.3 15.1 11.8 57.5
22.2 10.0 71.6 70.5 65.9 66.3 71.3
63.1 58.1 60.0 58.0 62.1 61.3 59.8
11.1 13.1 60.0 61.7 55.8 59.6 61.3
20.9 15.0 51.0 56.6 57. 58.5 57.2
71.1 58.1 81.0 87.1 81.2 81.2 75.9
51.6 18.1 91.1 102.2 102.0 92.0 93.6
88.8 . 51.6 81.6 81.1 82,0 81,6 76.3
59.7 31.9 76.1 81.7 92.8 96.2 102.1
71.9 20.3 91.9 98.6 103.1 105.3 103.0
61.7 37.2 67.0 76.7 85.5 83.8 85.0
62.2 31.6 75.1 80.3 '82.? 83.1 77.85
23.1 13.8 88.5 89.8 93.5 95.3 96.7
53.8 30.6 66.1 62.8 70.0 70.2 71.2
60.6 33.8 91.5 109.2 98.0 103.8 106.8
29.1 16.9 50.0 57.1. 65. 71,1 66.9
62.1 68.6 '70,1 72.5 71.9

-;

BARIEY AND SCYBEAT‘IS RAW DATA

12N5

 

 

 

 

 

YEAR (11’ f(2{____(31;3_ (11 (57’ (61_
‘1900 . """‘::

1901

1902

1903

1901

1905

1906

1907 37.7 37.3 15.8

1908 35-5 30.0 33.5

1909 19.1 50.0 39.8 23.1
1910 15.2 21.0 28.3 21.6
1911 0 19.1 9.6 23.1
1912 20 17.5 0 0 17.9
1913 17 7.8 36.9 19.2 15.2
1911 21 9-2 35.2 25.0 19.

1915 10 37.2 61.1 19.0 20.1
1917 35 16-8 10.8 8.1 17.7
1920 30 21-1 25.6 31.5 19.0
1922 12 10.5 11.3 38.7 19.6
1923 16 33-3 35.0 21.8 13.3
1921 18 27.8 29.6 21.9 16.6
1925 28 12.3 12.3 9.0 32.2
1926 9 ' 1-0 7.1 0 29.3
1927 ’37 33-9 33.8 .23.3 26.2
1928 60 39.5 52.1 18.1 35.7
1929 16 23.6 11.7 7.1 35.9
1930 11 35.3 23.5 21.9 26.2
1931 26 20-1 1.2 0.6 29.1
1932 29 6-3 17. 22.9 10.0
1933 23 11.9 16.5 1.6 31.8
1931 3 -2 11.2 3.8 26.2
1935 " 1902 25.0 3005
1936 - 0 0 35.1
1937 25 6.0 11.9 25.6
1938 18 18.3 8.5 39.8
1939 33 38.8 10.0 28.7
1910 0 26.7 19.0 26.0
1911 13.7 39 13.8 9.1 38.2
1912 25.5 18.5 31.1 32.7
1913 11.1 27.3 21.0 27.9
1911 5.7 10.2 19.2 35.0
1915 70.3 21.0 15.1 29.3
1916 15.5 27.3 9.8 28.0
1917 58.1 28.1 21.5 18.2
1918 35.0 33.8 22.3 35.6
1919 11.8 5.8 7.7 31.9
1950 - 25.1 19.1 31.7
1951 35.0 38.5 28.1 36.7
1952 27.7 18.1 12.6 31.1
1953 19.2 27.9
1951 32.5
1955 27.1
1956 12.6
1957 37.5

 

 

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WHEAT RAW DATA.

 

 

 

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000 23700 902
000 a... on.
660-26h3
2 2

221 3.83
0500 56200822030530 2570850
. .19?» .96.???
7 2
7 .EH6 D u 21 2232

0500 619050760 1570 6829090
0 000 00.0 000 000000
70 .uos h/OIU. -576 -023952

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21 3332
0200 5710000920 96h0 5091000
0 o. 000 on. o...
6 .80 03/0 .8 2 .623H3
l 2 l 112

000003 0730303307300
0 O C O 0 O O O O O O C C O O C O
68 862 .5573525832.” 2
ll 12 l 11 2 311

0500377228883093838050
.00 00000000000000. o
096 83158117337 236 5
1321 321 3 nun/.3

112
07196618 82269680hh27h33 3356863739296688853912603
O O O O O O O O O O O O O O O O O O O 0 O O O O I O O O O O O O O O O I O O O O O O O O O O O O
90822171.6800312hh6h853h.63726868732076h6660607132
13388h85 33588338333388.) 381432123 h33223233h83h335

11885876302362953370332092002
O O O O O O O O O O O O O O O O O O O O O O 0
5136.55

. C O .0 .
how/7. 2 33616 h272116712
M 3 221 lml 1n 1 111
76302362953370332092002
00000000000000.00000000
365512 3361611272116712
1 lul 11 1 111
1 38.5 7890 23 5678901238567890123 567.890 2 78
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1901 25.2
1902 27.1
1903 16.0
190h -
1905 15.h
1906 8.7
1907 10 .8
1908 16.1
1909 15 .2
1910 31.2 6.2
1911 .3 10.3
1912 15.7 12.5
1913 3.9 16.9
191k 23.h 26.h
1915 12.8 18.7
1916 20 .5 10 .7
1917 1.2 10.9
1918 16 .0 30.3
1919 22.5 19.5 17.3 18.h 12.0
1920 8.8 6.7 o 23.6 25.1
1921 6.2 5.8 3.8 22.3 16.9
1922 - - - 9.7 22.0 223h 28.2
1923 1.8 0 0 - - - 2h.1
192k 23.2 17.3 11.0 39.0 32.1 2h.h 21.5
1925 2.5 0 o 1. 6 6.2 7.7 31.3
1926 3.8 2.8 1.2 13 6 1h.7 1h.9 13 7
1927 o 0 o 5. 8 8.1 8.3 26. h
1928 - - - 38. h 33.1 29.2 15 .5
1929 16.7 18.0 12.6 20.5 22.9 20.5 25.u
1930 12.3 12.8 8.2 22.8 28.6 22.h 12.2
1931 23.2 21.3 21.8 25.2 20.8 19.9 25.2
1932 28.3 29.2 25.3 12.8
1933 12 .7 10.14 10.8 30,2
193h 0 2-h 5-1- 20.1
1935 0 -* " 15. O
1937 6.2 6-7 19.0
1938 15 .1: 16-0 19.0
1939 0.8 0.8

19h0 6.2 8.2

19h1 20.3 18.9

19h3 6.3 6.3

192124 23 .3 23 .7

1915 7 .5 11.3

1916 111.8 12.3

19h? 31.7 23.2

1918 20.6 18.5

191.9 2-9 0 ~0

1950 9.3 9.8

1951 23 .0 20 .5

1952 22 .0 20 .3

1953 2 .7 3 .1

195k

1955

MEAT RAM DATA

 

 

 

 

 

 

 

dd

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WHEAT RAW DATA

1128

 

 

 

 

YEAR (1% (21) (222 “(23) (2112 (252 (25) (NT
I900 19 .3 10 .0 16 .7 10 .5 19 .3 23 .5

1901 23.3 27.2 28.5 21.0 23.6 2h.1

1902 35.2 11.7 25.8 31.5 30.1 32.6

1903 1703 " 7.1L 1008 705 608

190k - - - - - -

1905 12.8 - 29.7 19.3 15.6 15.6

1906 7.2 8.7 11.3 8.0 11.8 7.2

1907 17.1 2h.8 11.1 9.5 23.1 28.1

1908 1h.5 16.1 19.1 16.8 2h.8 23.9

1909 - 2109 100,4 lOoLL 1.3.8 1 O7

1910 12.7 - - - - -

1911 6.5 20.2 18.1 19.7 28.6 30.6

1912 12.6 10.0 12.0 11.8 10.7 12.1

1913 15.9 20.9 22.5 16.8 17.3 17.h

191h 26.6 29.h 28.2 29.6 30.6 28.8

1915 18.2 17.8 11.2 8.h 18.h 22.2

1916 8.8 15.2 11.6 10.8 10.5 7.8

1917 8.3 8.6 1002 8.6 903 1’409

1918 35.2 33.8 26.2 19.5 27.9 314.1;

1919 13.5 12.8 11.8 5.7 12.8 18.3

1920 19.8 27.8 29.5 11.7 19.1 28.0

1921 12.6 15.2 1h.7 3.6 23.0 26.7 27.6 36.8
1922 26.7 15.0 20.0 21.0 21.9 27.3 23.5 31.1
1923 29.2 25.3 2h.h 13.0 25.6 32.8 21.8 23.7
192h 18.2 21.6 19.5 10.2 18.1 26.1 55.3 37.9
1925 31.3 29.6 31.7 33.h 26.h 29.6 13.5 12.9
1926 1307 12.5 907 307 501 1107 1205 1705
1927 21.0 22.5 22.0 8.3 18.2 23.0 22.0 hh.8
1928 12.8 20.h 1h.8 13.2 8.5 19.9 25.5 19.8
1929 21.6 12.5 2h.3 19.6 9.1 7.0 81.6 h8.3
193° 1002 1.11 2014 302 1.1 hfih 31409 3705
1931 28.1 15.6 25.7 32.9 25.1 28.1 39.8 h3.0
1932 9.0 12.6 5.1 1.2 12.6 19.1 27.1 8.3
1933 22.6 29.7 22.1 12.0 20.2 33.h 16.8 25.0
193k 9.3 22.2 7.9 2.6 11.9 2h.5 25.3 3h.8
1935 17.8 15.2 20.8 11.3 12.6 19.0 23.3 22.6
1936 12.8 120,4 1102 207 1‘03 802 2,407 2902
1937 22.5 2h.2 2h.6 16.8 16.7 22.2 15.h 15.1
1938 19.3 15.6 18.2 9.5 16.3 16.1 10.1 22.3
1939 11.1 10.5
19h0 21.1 20.7
19h1 0 17.0
19h2' 39.6 h3.3
19h3 36.9 80.1
1911 22.5 2h.5
1985

19h6

19h?

19h8

19h9

1950

1951

1952

1953

195h

1955

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O O I O I O C c O
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'YEER (28f335(29)

 

 

19h6
19h?
19h8
19h9
,1950
1951
1952
1953

. 1955
1956

32.1
27.3
25.3
61.5
12.6
21.1
38.5
19.1
h8.6
37.5
h7.3
2h.7
23.9
30.9
22.7
27.5
1h.7
18.

12.h
17.h
10.1
h1.h
h3.3
21.7

u1.5
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20.3
b5.7
13.6
16.6
36.2
21.?
10.6
31.3
14105
20.2
18.7
21.8

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29.7
35.5
31.9
35.1
39.6
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11.2
17.8
53.8

6.0

20.8
35.0
16.2
38.2
23.2

12.2
0

10.7
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7.6
35.6
30.7

6.7
21.9
28.3
22.9
13.1

9.8
32.
10.6

1.0
19.3
35.6
16.9
h2.3
33.1

.2

9.7

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WHEAT RAW DATA

'YEAR (372 (335 I392 (502 (hi)
T900

 

(H22 (h322 (hhf

1901
1902

1903'

190h
1905
1906
1907
1908
1909
1910
1911
1912
1913
191k
1915
1916
1917
1918
1919
1920
1921
1922
1923
192k
1925
1926
1927
1928
1929
1930
1931
1932
1933
193k
1935
1936
1937
1938
1939
l9h0
19h1
19h2
19h3
19hh
19h5
19h6
19h?
19h8
19h9
1950
1951
1952
1953
1958
1955
1956

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11.1

6.0
15.0
18.6
11.8
19.8
80.2

0
25.5
u8.8
3h.2
h6.5

' 39.8

21.2
25.8
28 .6
35.6
29.0
21.0
h6.6

17.0

23.0
33.7
13.7
21.8
36.7

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2h.8

55.0'

uu.7
50.8

- h2.8

18.5
29.7
7.7

22.0

12.5
19.5
0

15.5
28.0

10.2
10.2
12.7
26.0
16.5

6.2
15.0
10.8

WHEAT RAW DATA

 

 

 

 

 

1 9

1906 21.3 18.3

1907 37.0 28.3 50.0 21.6

1908 21.3 33.8 38.5 32.5

1909 26.8 35.7 - -

1910 17.1 26.8 31.3 26.0

1911 5.7 23.3 11.2 11.5

1912 0 0 — -

1913 13.5 27.0 32.1 31.7 31.3
1911 10.5 20.1 20.3 21.3 11.9
1915 2508 380 7 " " 21103
1916 16.7 22.8 10.0 7.6 8.6
1917 5.5 12.3 35.9 31.6 32.1
1918 3.5 19.5 39.3 29.1 18.8
1919 O 5.3 17.9 15.7 16.1
1920 15.6 21.1 26.5 12.2 28.7
1921 3.9 5.7 26.6 19.1 11.7
1922 23.5 27.6 22.8 26.6 21.8
1923 10.3 11.8 33.0 32.2 23.6
1921 18.0 27.0 13.3 15.3 35.8
1925 1.5 20.0 35.0 29.3 27.5
1926 0 11.0 35.3 21.2 21.7
1927 11.8 22.3 31.2 30.6 22.5
1928 12.5 29.2 11.7 28.9 23.8
1929 10.2 15.3 31.6 31. 2 27.8
1930 7.3 20.0 11. 7 11.1 27.7
1931 1.0 2.5 35.3 35. 0 31.2
1932 15.7 21.2 29. 0 33 3 29.7
1933 1.8 11.3 21.2 29.9 19.7
1931 1.3 8.0 17.6 11.2 21.1
1935 13.5 11.5 30.1 31.6 10.7
1936 16.8 15.5 21.8 20.8 0 0 5.8 10.0 8.7
1937 0 13.5 11.3 13.7 10.0 5.3 21.9 30.1 13.8
1938 9.7 21.5 21.8 26.7 1.3 7.7 37.3 30.7 38.3
1939 17.3 31.3 33.8 22.7 17.0 26.7 30.2 21.5 28.9
1910 0 22.3 20.5 25. 3 8.7 11.3 15.1 19.7 28.3
1911 16.3 22.7 20.5 27. 8 12.3 17.0 27 9 28.0 31.6
1912 17.8 11.3 28.2 51.3 28.7 18.0 51 3 18.0 16.9
1913 O 0 0 17.8 19.5 20.3 21.0 16.0
1911 2.5 27.2 25. 8 18. 8 23.2 39.5 21.1 23.1 21.7
1915 1.5 53.0 53. 8 58.0 13.2 26.7 32.2 27.7 29.0
1916 10.7 11.0 33.3 36.2 9.3 27.0 31.9 21.6 30.1
1917 20.2 15.3 13.5 52.7 15.0 18.5 28.9 23. 2 28.7
1918 6.8 18.0 19.2 17.7 21.7 31. 5 37.8 27.1 30.9
1919 2.7 19.3 19.7 17.2 3.2 7. 2 30.8 29.2 27.9
1950 7.5 39.3 35.8 32.2 11.5 17. 5 21.7 39.1 21.8
1951 7.8 31.7 30.3 35.2 13.7 22.2 31.7 39.7 28.0
1952 30.3 30.2 32.0 7.5 8.2 20.1 17.7 11.5
1953 33.8 33.0 29.9 13.1 25.6 11.1
1951 15.2 16.6 21.6 8.6 28.7

1955 60.6 61.2 53.5 21.3 27.7

1956 35.5 32.9

 

”MEAT RAW DATA

1132

 

 

 

 

12.19 (55; (562 (572 1582 (522 (602 1612 1621 (632
1900

1901

1902

1903

1901

1905

1906

1907

1908

1909

1910

1911

1912

1913 35.2 38.2 38.2 39.3 31.7 29.2

1911 15.5 16.1 11.2 15.2 21.3 13.3

1915 28.3 26.8 26.9 30.0 — -

1916 8.8 9.5 8.8 10.5 7.6 1.1

1917 36.8 37.9 37.0 35.8 31.6 31.7

1918 25.7 27.3 27.2 26.1 29.1 26.5

1919 16.1 16.3 16.5 15.7 15.7 10.6

1920 31.1 33.0 27.6 27.5 12.2 13.2

1921 13.0 13.2 13.2 13.2 19.1 13.1

1922 26.6 29.0 29.8 30.5 26.6 11.2

1923 26.3 27.0 27.2 26.7 25.5 20.5 32.2

1921 11.1 11.3 15.8 17.1 39.5 38.2 15.3

1925 33.1 33.6 33.5 33.2 23.9 17.8 29.3

1926 28.5 31.2 31.7 31.9 18.2 16.1 21.2

1927 23.1 27.1 26.2 26.3 26.2 20.2 30.6
538 29.0 30.8 30.6 27.6 27.0 32.2 28.9

1929 36.3 10.1 39.9 11.1 29.3 26.2 31.2

1930 37.0 12.7 11.7 10.7 38.0 38.8 39.2 11.1

1931 37.2 10-7 11.0 10.? 30.5 29.1 33.5 35.0

1932 29.0 30.1 31.0 32.7 28.6 26.3 29.6 33.3

1933 17.9 19.1 18.7 18.0 28.0 29.7 29.0 29.9

1931 23.0 25.3 25.6 28.0 11.8 15.3 11.2 17.3

1935 11.7 13.9 13.0 11.7 11.7 8.8 31.6 20.3

1936 10.1 10.1 11.5 12.3 9.0 7.6 9.7 10.0 10.1

1937 15.0 16.0 15.2 16.2 12.0 7.8 31.1 30.1 20.2

1938 38.5 37.5 10.9 10.9 17.8 6.1 37.1 30.7 23.2

1939 29.2 2.1 31.1 33.8 23.6 22.5 26.8 26.3 26.1

1910 26.6 26.9 27.2 25. 11.7 13.3 18.0 19.1 17.5

1911 39.9 11.0 10.8 11.9 17.2 17.1 28.0 22.0 23.6

1912 51.8 53.8 52.6 16.5 39.2 3._ 11.5 38.3 12.6

1913 20.1 18.1 19.2 22.1 16.2 11.7 19.9 21.1 17.2

1911 31.1 31.3 31.1 23.0 15.7 15.5 20.9 22.1 18.2

1915 35.6 38.0 39.8 10.9 23.0 2.6 27.8 26.7 21.3

1916 31.9 33.6 31.1 37.9, 26.2 25.6 28.1 26.5 27.1

1917 35.0 35.0 33.8 37.0 22.6 22.1 22.8 22.7 22.5“

1918 36.1 31.7 36.2 33.1

1919 31.1 35.0 31.1 31.7

1950 31.7 29.9 31.1 28.6

1951 38.9 37.7 38.1 13.2

1952 ‘ 17.1 23.9 21. 20.5

1953 17.2 18.9 18.0 18.1

 

EWEAT RAW DATA

 

15

 

 

YEAR (61) (65) (66) _(67)_ (68) (69) (70) (71) (72)
1900 12.1
1901 31.6
1902 20.3
1903 33.7
1901 15.6
1905 8.2
1906 15.2
1907 10.1
1908 11.2
1909 23.6
1910 27.0
1911 3.6
1912 12.9
1913 10.2
1911 27.1 28.1
1915 2.8 17.3 31.8 23.8 27.6 16.0 31.2 27.7
1916 15.3 10.3 2.8 1.2 1.5 2.2 1.8 3.3
1917 10.9 26.5 11.5 1.3 7.2 2.2 10.5 13.5
1918 8.5 20.0 9.8 6.0 6.0 0 9.2 8.3
1919 7.1 9.3 13.7 15.8 12.2 13.7 12.5 17.1
1920 7.0 30.7 12.5 19.5 9.8 9.3 12.3 21.7
1221 0.9 15.8 32.8 29.5 28.3 23-8 27.2 30.7
1922 18.9 6.3 31.5 28.5 27.2 13.3 30.3 36.2
1923 12.5 19.0 35.8 11.5 22.3 17.0 11.7 31.8
1921 20.2 10.7 7.5 13.5 10.3 13.7 9.3 20.5
1925 18.3 22.5 20.8 18.8 18.5 5.5 17.5 26.7
1926 17.0 18.1 29.7 20.5 20.8 12.5 25.5 28.8
1927 22.3 1.9 13.0 11.0 13.0 5.8 12.7 15.2
1928 16.6 22.7 19.7 11.0 19.5 5.3 22.0 30.8
1929 13.0 10.0 11.8 10.5 12.3 6.5 11.7 26.0
1930 10.8 20.1 22.2 16.3 20.2 6.8 18.7 28.5
1931 9.5 27.8 27.5 19.2 23.8 11.8 21.8 38.2
1932 22.1 21.9 26.7 21.3 20.5 13.0 19.7 32.2
1933 28.1 0 26.3 8.2 9.3 9.5 1.7 13.2 13.7
1931 16.1 0.9 31.3 23.3 13.7 15.0 1.8 21.0 16.3
1935 30.0 20.8 30.1 20.0 9.5 11.8 1.5 20.5 19.7
1936 8.6 0 18.9 20.8 17.2 19.3 12.0 17.5 23.3
1937 27.9 8.5 29.2 27.5 13.0 19.8 10.3 21.0 26.0
1938 33.8 11.5 21.9 39.8 31.; 29.7 27.5 33.7 17.7
1939 28.8 16.1 33.8 19.8 1€'3 16.8 8.5 16.7 26.2
1910 16.1 8.8 33.5 13.7 5.3 8.8 3.7 16.3 22.7
1911 29.1 22.5 17.1. 35.8 21.7 30.0 31.3 26.8 16.2
1912 17.3 21.2 18.8 18.0 16.2 13.5 9.7 26.0 26.5
1913 19.3 15.3 9.9 18.3 12.0 17.0 9.3. 16.5 28.8
1911 20.9 23.1 28.6 31.3 23.7 23.5 13.2 21.2 18.9
1915 26.8 21.5 20.7 26.2 19.8 16.8 13.0 19.7 32.0
1916 26.9 - 27.8 20.8 17.8 19.3 10.3 18.2 29.3
1917 21.9 17.1 21.1 17.5 10.8 10.8 15.2 11.3 31.2
1918 19.1 19.3 9.7 10.2 11.0 8.5 10.2 22.2
1919 21.6
1950 23.8
1951 16.8
1952 17.7
1953 31.5
1951 11.3
1955 6.2
'1956 16.1

 

 

 

 

i E.'.Iv‘v..

b

.1'.

 

TREAT RAW DATA

 

 

 

 

 

 

h3R3hJ£rro
\OCDwmmwNNU‘LWNO\
O O 0 0

HHHI—‘w
O O o O
MWHMQWNH C‘WC‘NMO‘J’JWOMVU‘L

“JRDFJRJUJ F4“)
wwwz-Hoooxn-I
O

YEAR-+ _£?3) (712 (752 (76) (77) (78) (79) (802 (81)
1900

1901

1902

1903

1901

1905

1906

1907

1908

1909

1910

1911

1912

1913

1911

1915

1916

1917

1918

1919

1920

1921

1922 31.5 35.3 26.5 29.2 33.0 30.1 30.6 29.0
1923 11.0 9.8 27.5 35.8 15.0 39.8 37.3 35.6
1921 17.8 22.3 31.5 30.1 23.8 29.9 18.7 17.5
1925 16.7 20.2 31.7 31.9 31.6 38.9 27.5 25.2
1926 20.7 16.7 35.1 11.1 27.5 12.1 22.5 21.2
1927 11.8 13.2 11.8 18.9 23.8 20.2 10.3 7.2
1928 16.8 21.8 39.8 16.0 62.3 57.1 30.1 26.9
1929 13.0 19.5 18. 13.9 15.3 17.6 10.1 15.2
1930 11.7 21.7 18.9 22.7 19.6 22.6 11.2 10.9
1931 22.3 25.7 29.5 27.6 10.5 13.8 15.6 12.8
1932 20.8 23.8 21.9 31.5 32.2 36.6 19.2 15.0
1933 10.2 11.7 7.8 7.3 1.1 10.8 5.2 1.2
1931 11.7 7.7 - - - - - _
1935 9.2 9.8 30.7 37.7 33.3 39.3 19.0 19.1
1936 11.3 18.7 19.1 30.6 28.8 29.7 12.2 9.0
1937 17.0 11.8 37.6 21.8 10.3 8.8 8.7 9.6
1938 33.8 37.2 27.7 35.1 36.9 37.2 26.7 26.8
1939 1808 1905 - - 2607 29.2 1907 1905
1910 10.7 8.3 17.3 19.9 11.1 21.0 13.9 12.8
1911 33.3 35.5 22.1 30.5 38.7 26.2 8.7 10.3
1912 15.5 21.8 30.3 36.3 27.6 33.8 21.5 22.7
1913 17.5 19.2 55.0 52.3 55.8 16.1 31.8 11;0
1911 27.2 30.8 26.6 29.7 31.0 28.6 23.2 21.5
1915 20.5 22.0 33.8 32.7 23.7 22.2 22.8 21.1
1916 20.2 18.0

1917 1112 17.3

1918 13.7 17.0

1919

1950

1951

1952

1953

1951

1955'

WHEAT RAN DATA

 

 

YEIR (82) (832 §§12 (85) T186) $872 (88) (89)

 

1900
1901
1902
1903
1901
1905
1906
1907
1908
1909
1910
1911
1912
1913
1911
1915
1916
1917
1918
1919
1920
1921
1922
1923
1921
1925
1926
1927
1928
1929
1930
1931
1932
1933
1931
1935
1936
1937
1938
1939
1910
1911
1912
1913
1911
1915
1916
1917
1918
1919
1950
1951
1952
1953
1951
1955
1956

\OO\\O\RI:'O\O\O\
o o o o

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15.1
37.0

A1109
38.8
33.8
31.

28.9
65.5
57.3
55.0
11.5
18.5
32.0
18.3

18.6
38.1

11.7
12.7
38.1
31.

31.2
61.9
55.1
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11.1
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30.6
15.3

16.1 36.8
31.9 36.5
13.2 38.7
15.8 33.2
17.1 16.0
36.7 15.1
21.7 15.1
68.5 52.3
50.0 15.3
16.3 31.8
38.5 33.1
11.9 37.3
29.0 26.6
19.8 39.7

KM

(90)

COTTON RAW DATA

 

562
901
1150
1091
1396
1528

1662
1537
1728
1181
1511
2025
1587
1515
1305

990
1536
1571
1311
1095
1590
1119
1836

738
1581

1077
1187
1110

812
1191

923
1112
1315
1331
1953
1511
2228
1831
1892
1955
1899
1537
1512
1816
1717
1818
1199

655

861

756
2512

811
1125
1291
1189
1113
1271
1268
1363
2152

731
1220
1113
16119
2117
2175
1877
1031
1151
1998
1159
2139
1810
1907
1987
2275
2901

1288
1100
1221
1517
1361
1589
1752
1652
1680
1258

960

817
1808

826
2189
1669

- 983

1756
2677
2111
1076
1922
1202
2181
1973
2905

556
1088
1626
1398
1110
1636
1126
1151
1557
1912
111,90
1685
1113
1601
2111
1762

752
1235
1912
1129
1852
1651
1390
1652
1253
3028

1318
2019
1857
2301
2013
2315
1523
2067
1978
2271
1783
2500
2111
1925
1852
2101
1998
1811
2302
1702
1011
2221
1761
1122
1573
1666

870
1683
1390
2016
1506
1097
1311
1663
1911
2266
1118
2216
1226
1191
1826
1618
1118
1693
1811
1862
2385
1136

861
1851

765
2023

 

1115

986
1330
1199
1121
1610
1778
2009
1813
1061
1717
1616
1620
2025
1539
1698
1719

835
1770

565
1583
1802
1182
2181
2801
2276

 

 

.J.

 

 

‘Fovt:‘.zt m $M.WEH¥U~{§IE . r.1¢\ l

COTTON RAW DATA

3L3?

 

 

 

Year (10) (11) (12) (13) (11) (15) (16) (17) (18)
1911 1750
1912 1230
1913 1810
1911 1200
1915 1010
1916 1810
1917 1760
1918 820
1919 2000 1600
1920 1330 655
1921 1381 1161 1562 1702 1723 1803 1705 1980 2080
1922 733 831 902 967 972 1008 1083 1180 1210
1923 157 622 768 879 991 958 871 885 1060
1921 1168 1190 1198 1309 1369 1170 1156 1550 1220
1925 2601 3060 2965 3020 2903 3010 3050 1511 1798
1926 2071 2216 2362 2551 2863 3112 3102 1752 2160
1927 1315 1503 1657 1927 2701 2780 2790 1618 1680
1928 1628 1838 1959 2023 2201 2313 2390 1216 1632
1929 1382 1513 1663 1820 2038 2127 2257 1536 1520
1930 801 853 893 1115 1113 1136 1119 1010 992
1931 1189 1326 1110 1111 1111 1556 1512 1196 1632
1932 615 711 828 889 963 1012 1052 1616 1600
1933 886 973 1091 1235 1193 1512 1656 1180 1121
1931 778 851 939 961 1012 1036 1075 1108 1056
1935 1018 1179 1207 1370 1131 1599 1651 968 610
1936 777 810 999 1082 1169 1085 1121 1720 1280
1937 902 1106 1257 1152 1620 1765 1798 2280 1520
1938 1167 1275 1117 1681 1781 1931 1999 1728 1600
1939 961 1036 1210 1211 1556 1515 1510 1032 1632
1910 919 1080 1252 1111 1519 1575 1586 1188 1536
1911 1101 1220 1135 1651 1875 2079 2236 1501 1120
1912 886 956 1198 1155 1187 1581 1791 2100 1888
1913 915 1015 1127 1217 1357 1121 1156 1696 1360
1911 836 923 1061 1281 1198 1137 1508 1180 1218
1915 651 869 1060 1121 1539 1763 1753 1816 1792
1916 736 817 1065 1116 1160 1331 1508 2311 1581’
1917 903 1313 1168 1285 1577 1671 1868 1600 1296
1918 866 981 1163 1136 1610 1880 1917 2280 1108
1919 871 965 1131 1213 1119 1515 1585 2320

1950 557 771 896 921 1035 1126 1076 1656

1951 678 687 816 921 1012 1079 1127 1560

1952 810 758 911 1050 1315 1168 1563 1296

1953 1151 1253 1658 1785 1915 2086 2156 1311

1951 995 1067 1267 1318 1115 1377 1381 1320

1955 1191 1161 1382 1583 1825 2122 2399 2008

1956 686 729 797 911 1161 1215 1237

 

gal-“3%.. ' .‘n.’ J‘ .c béWIFI-rultotxz... r. , .H'"\

 

COTTON RAW DATA

1553

 

 

 

Year (19)
1911 1120
1912 1012
1913 1610
1911 1110
1915 1970
1916 1520
1917 1200
1918 612
1919 1680
1920 1215
1921 1720
1922 1110
1923 875
1921 1260
1925 1199
1926 1632
1927 1288
1928 1101
1929 1061
1930 952
1931 1206
1932 1196
1933 1152
1931 1221
1935 818
1936 1560
19 37 1701
1938 1616
1939 1072
1910 1328
1911 1210
1912 1760
1913 1161
1911 1280
1915 1296
1916 2108
1917 1312
1918 1760
1919 1781
1950 1192
1951 1576
1952 1128
1953 1120
1951 1208
1955 1993

1956

70131000 RAW DATA '

139

 

 

 

 

 

 

 

 

 

 

 

 

 

 

YEAR (1) (2) (’32 (1) - (5) (61 (7) KB) (97
1935 508 1080 1158 1001

1936 116 982 1056 918

1937 871 1072 996 920

1938 868 898 806 852

1939 1332 1381 1301 1191

1910 1070 1100 900 782

1911 651 981 990 890

1912 670 981 1090 831

1913 506 602 501 532

1911 800 781 826 828

1915 1217 1665 1131 1310

1916 811 1119 1606 1161 2117 1610

1917 1120 1235 1305 1129 1771 1181 1576 1857 1728
1918 1171 1692 1777 - 1911 2099 1710 1791 1852
1919 815 1123 1215 1160 2318 2307 2171 2327 2101
1950 1729 1731 1783 1925 1839
1951 1750 1801 1589 1713 1550
1952 1595 1768 1381 1521 1110
1953 1913 2031 1656 1921 1612
1951 2081 2185 2019 1822 1637
1955 1895 1918 1779 1711 1618
1"‘2112 (10) (11; [pl ; (132 (11) (15) Q6) (17) (18)
1916 1705 1317 1190 , 1371 2036
1917 1705 1511 1587 1226 1067 1396 1276 1677 1523
1918 1903 1502 1587 1336 1371 1819 1788 1995 2181
1919 2088 1972 2108 1212 1201 1567 1557 1713 2273
1950 1715 1611 1663 811 928 1371 1108 1216 1652
1951 1656 1256 1298 978 1038 1351 1230 1680 1761
1952 1100 1212 1262 938 1131 1351 1177 1602 1508
1953 1880 1119 1612 980 950 1283 1388 1792 1868
1951 1826 1198 1680 1359 1217 1703 1767 1869 1879
1955 1831 1119 1686 967 1002 1150 1168 1718 1915
TEE 19 20 21 22 23 21 25 26

1916 1676 2119 1826 1870 1813 2017
1917 1151 1131 1589 1637 1272 1318 1268 1218 1577
1918 2083 1962 1871 2116 1562 2002 1996 1750 2158
1919 1778 2092 2009 2111 2016 2065 2031 2096 2135
1950 1661 1121 1551 1717 1609 1538 1138 1268 2193
1951 1506 1590 1861 1510 1191 1662 1735 1539 1592
1952 1555 1610 1810 1958 1653 1838 1817 1781 1866
1953 1736 1705 1578 2108 1619 1829 1977 1938 2289
1951 1788 1839 1882 2123 1586 1586 2011 2031 2110
1955 1715 1788 1819 1917 1525 1815 1712 1731 1912

 

TOBACCO RAW DATA 140

 

Ar

 

 

 

 

 

 

 

 

'1211 (2 (29 (30 31 32 33 (31 (355 I3§2
1935 1186 925 1002 1171 1318 1118 1227 1223 1168
1936 - - - _ - - - 789 951 778
1937 1285 1506 1330 1381 1355 1129 1575 1209 1365
1938 1632 1577 1610 1582 1621 1189 1821 1617 1581
1939 1182 1012 963 1063 1271 1255 1191 1160 1157
1910 1368 1393 1555 1167 1589 1531 1116 1386 1315
1911 1272 1373 1566 1289 1111 1313 1671 1173 1101
1912 1368 1375 1336 1310 1288 1256 1181 1311 1116
1913 1121 1571 1528 1813 1758 1662 1538 1532 1956
1911 1868 1851 2016 2170 1879 1981 1950 1951 1820
1915 1306 1121 1318 1212 1289 1232 15 23 1372 1210
1916 1771 1713 1626 1681 1981 1770 1578 1719 1722
1917 1332 1106 1257 1562 1170 1511 1111 1220 1286
1918 1765 1797 1835 1828 1782 17 85 2186 1955 1731
1919 1707 1607 1739 1812 2016 1902 1883 17 70 2136
'YEAR (37) (38) (39) (10) (11) (12) (13) (11) (15)
1932 1307 1338 1219 113:: 1171 1381 1381 1331 1283
193 - - - 7 - - - - -

1937 1116 1601 1538 1251 1126 1186 1327 1008 1166
1938 1691 1185 1875 1691 1739 1677 1599 1578 1615
1939 1075 1371 115 8 1192 1057 1106 1283 1197 1108
1910 1525 1138 1511 1177 1260 1387 1388 1026 1105
1911 1376 1091 1375 1186 1131 1119 1510 1060 1333
1912 1163 1535 1623 1621 1529 1690 1177 1318 1371
1913 1511 1981 1131 1765 1522 1689 1695 1859 1635
1911 2035 17 71 1932 1827 2071 1969 1827 1885 2013
1915 821 1133 1116 1178 1336 1597 1530 1278 1538
1916 1823 1575 1751 1900 2011 2028 1737 1592 1715
1917 1311 1160 1151 1109 1157 1127 1385 1587 1507
1918 1836 2006 1783 1518 1618 17 75 1519 1657 1981
1919 2015 2026 1900 1681 1997 1977 1771 1999

185 7

 

TOBACC 0 RAW DATA

 

 

 

'YEAR (16) (17) (18) (19) (50) (51) (52)
1921 113
1925 588
1926 751
1927 631
1928 631
1929 718
1930 600
1931 839
1932 1518 1235
1933 1318 1611 1318 821
1931 1510 . 1908 1510 1255
1935 1628 1539 1710 1539 1201
1936 1113 15 81 17 13 1581 929
1937 1051 1321 1151 1321 1088
1938 1560 1686 1791 1686 1068
1939 1221 1318 1310 1705 1318 1132
1910 1551 1375 1658 1375 1036
1911 1137 1159 1515 1159 1121
1912 1531 1361 1151 1361 1287
1913 1276 1537 1150 1537 1258
1911 828 1280 1115 1280 1315
1915 1751 1176 1710 1176 1308
1916 2097 3023 3023 - 1360
1917 1650 2162 2162 2016 1211
1918 2520 2207 2207 1695 1362
1919 1155 2107 2107 1100
1950 2072 2072 1511
1951 2221 2221 1101
1952 1192 1192 1513
1953 1836 1836 1512
1951 2089 2089 1131
1955 1333 1333 1115

H‘

 

 

 

 

142

CCATICN AND SOURCES OF DATA WITH SELECTED CORRENTS

The numbers to the left on the pages in this section re-

fer to the nuMhers at the top of the individual series of raw

data in the preceding sections for each crop. The location

and source of each series is presented with some comments to

identify the series in each case.

(1)

(2)

(3)

(1)

Corn Raw Data

 

 

Urbana, 111., Agronomy South Farm; Runge, E.C.A., $23 33-
lation Between Precipitation, Temperature, and Yield of
Corn on the Agronomy Scuth Farm, Urbana, Illinois, Unpub-
lished’M.S. Thesis, Agronomy Department, U. of Illinois,
1957. Open polinated corn was planted until 1939, hybred
from 1910-56. Drummer soil. First year corn, North Cen-
tral Rotation.

 

 

 

 

 

Same as (1) except second year corn, North Central Rota-
tion.

Urbana, 111., Morrow plots; Bauer, F. G., C. H. Farnham,
and L. B. Miller, The Morrow Plots. Dept. of Agron., U.
of 111., Mimeo., 1935. Fertilization MLP. continuous
corn.

 

Ames, Iowa, Obtained through correspondence with W. D.
Shrader, Department of Agronomy, Iowa State College, Ames,
Iowa. Nicollet loam soil. Check plot. Yields prior to
1936 adjusted for hybred seed by formula Y - 8.97 + .96X.

Sana as (1) except manure applied.

Same as (1) except manure and lime applied.

Same as (1) except lime applied.

Same as (1) except a different check plot.

Columbia, Mo.; Smith, G. E., Sandborn Field, Mo. A.E.S.

Bul. 158, 1912. Plot 18. 6 T. manure—Epplied annually.
Continuous corn.

 

Lincoln, Neb.; Iowa Res. Bul. 166. Open pollinated, Hague
Dent until 1933, Open pollinated Krug 1931-18.

(11)

(12)

(13)
(11)
(15)
(16)
(17)
(18)

(19)

(21)

(22)

(23)
(21)
(25)

(26)

(27)
(28)

145

Lincoln, Neb.; Kiesselbach, T.A. and W.E. Lyness, Crop
Rotation Experiments, Neb. A.E.S. Bul. 116, 1952. U.S.

 

13 until 1911, Ohio 0 92 from 1915-51 (both similar).
Rotation 9: W, with manure-C-O-chl-C-B.
Same as (11)

except Rotation 9: W, with manure-C-O-chl-

chl-C-B.

Same as (11) except Rotation 1: w-C-O-chl-chl-C-B.
Sane as (13) except different plot.

Same as (11) except Rotation 2: W-C-O-C-C-B.

Same as (15) except different plot.

Same as (15) except different plot.

Lincoln, Neb.; obtained through correspondence with D.

P. MCGill, Department of Agronomy, U. of Neb., Lincoln,
Neb. Mean yields in same series of plots. Check plots
in long term fertility study. Fields 2, 3, and 1, Agron-
omy Farm.

N. Platte, Neb.; Zook, L. L. and H. E. Weakly, Crop fig-
tation and Tillage Experiments at the North Platte (Neb.)

 

 

Substation, 1907331, U.S.D.A. 1631. Bul. 1007, 1950. Av.

 

on 3 experimental fields.

N. Platte, Neb.; obtained through correSpondence with

R. E. Ramig, College of Agriculture Experiment Substa-
tion, North Platte, Neb. Corn after oats, spring plowed,
3 yr. rotation, summer fallow-O-C. Rotation 50.

Same location and source as (20). Open pollinated,
Substation White, continuous corn, alt. fallow.

Same as (21) except continuous corn after fall blank
listing.

(21)
(21)
(21)

Same as except continuous corn after fall plowing.

Sane as except continuous corn after Spring plowing.

Same Rotation 171, alt. W and C, 2 yr.

rot.

as except

(21)
(26)

Same as (21)
Fallow-W-C-w.

Same as except Field C Check Bot., Fallow-W-C-C-W.

Same a

on

except different plot.

except Rotation 613, Field A, 1 yr. Rot.

(1)

(2)

(1)

(5)
(6)

(7)

(8)
(9)

(10)
(11)

114

Wooster, Ohio; Obtained through correspondence with J.
L. Haynes, Department of Agronomy, Ohio Agricultural
Experiment Station, Wooster, Ohio. Wooster silt loam
soil. Average of 8 rotations where corn follows legume.
Changed from Open pollinated to hybred, 1935. Changed
from broadcast to raw application of fertilizer, 1910.

Oats Raw Data

 

Urbana, Ill.; obtained through correspondence with L.
B. Miller, Department of Agronomy, U. of I11., Urbana,
111. Details of original plan described in Ill. A.E.S.
Bul 273 beginning on page 279. Crop residue and rock
phOSphate 1901-10. Crop residue, rock phosphate, and
limestone, 1910-52.

Ames, Iowa; obtained through correspondence with W. D.
Shrader, Department of Agronomy, Iowa State College,
Ames Iowa. Clarion and Nicollet soil. Check plots 805
and 811 average.

Same as (2) except a different single check plot.

Columbia, Mo.; Smith, G. E., Sandborn Field, No. A.E.S.
Bul. 158, 1912. No fertilization. Plot 16. Continuous
Data.

Sane as (1) except Plot 15 and 6 T. manure applied.

Lincoln, Neb.; Kiesselbach, T. A. and W. E. Lyness, Pro-
duction Practices for Spring Small Grains, Neb. A.E.ST—
BuL 106, 1951. Varieties: Kherson, 1917-31, Iogold,
1935-12, others 1913-50. Medium planting. Single field.
C-O-W rotation.

Lincoln, Neb.; obtained through correspondence with D.

P. McGill, Department of Agronomy, Univ. of Neb., Lincoln
3, Nebraska. Average of three varieties. Same field
(K), campus.

Same as (7) except fields 2, 3, and 1, agronomy farm.

Lincoln, Neb.; Kiesselbach, T. A. and W. E. Lyness, Crop
Rotation Experiments, Neb. A.E.S. Bul. 116, 1952. Rota-
tion 9,8W with manure-C-O and Sw.cl.-Sw.c1.-C-B. Vari-

eties: Trojan 1912, Cedar 1913-7, Clinton 1918, Nemaka

1919-51.

Same as (9) except Rotation 1, W-C-O-Sw.cl.-Sw.cl.-C-B.

 

Same as (9) except Rotation 2, WeC-O-C-C-B.

(12)

(13)

(11)
(15)

(16)
(17)

(23)
(21)
(2a)
(26)

(l)

145

N. Platte, Neb., Zook. L. L. and H. E. Weakly, Crop Ro-
tation and Tillaoe Experiments at the North Platte (Neb. )
substation,1907- 31., U}S. D. A., lbch.fiEul. 1007, 1950.
Average of 3 fields.

 

 

N. Platte, Neb.; obtained through correSpondence with
R. E. Ramig, Univ. of Neb. Exp. Substation, N. Platte,
Neb. Varieties: Neb. 21,1936-7, Brinker, 1938-18.
Continuous oats-~oats every other year on summer fallow

Same as (13) except continuous oats--after blank list-
ing in fall.

Same as (13) except continuous oats--after early fall
plowed.

Same as (13) except continuous oats--after Spring plowing.

Same location and source as (13). Rotation 9, 3 year
rotation C-O-W.

Same location and source as (13). Oats on fallow. 3 year
rotation, summer fallow-O-W. Rotation 8.

Same as (18) except Rotation SO.

Dickinson, N. D.; N. D. A.E.S. Bul. 383. Continuous oats--
alternated fallow.

Same as (20) except not alternate fallow.

Fargo, N. D.; obtained through correspondence with 12 E.
Stoa, Agronomy Dept., North Dakota Agricultural College,
State College Station, .argo, N. D. Check plot. Live-
stock series.

Same as (22) except fr.,man., livestock series.

Same as (22) except fr.,man., and P, livestock series.
Same as (22) except fr” man., P, and Ca, livestock series.
Same as (22) except fr” man., P, Ca, and K, livestock

series.

Barley and Soybeans Raw Data

 

Barley

Alliance, Neb.; obtained through correspondence with
Robert O'Keefe, Box Butte Experiment Farm; Alliance, Neb.,
average of all rotations.

(l)

(2)

(3)

(7)
(8)
(9)

(lo)
(11)
(12)

l#6

N. Platte, Neb.; Neb. A.E.S. Bul. 362. Fallow.

N. Platte, Neb.; U.S.D.A. Tech. Bul. 1007. Average of
three experimental fields.

Dickinson, N. D.; N. D. A.E.S. Bul. 383. Continuous bar-
ley--alternate fallow.

Same as (1) except not alternate fallow.

Soybeans
Urbana, I11.; obtained through correSpondence with L. B.

Miller, Agronomy Department, Univ. of 111., Urbana, 111.
South Central Rotation-C-C-C-SB.

Wheat Raw Data

 

Akron, Col.; U.S.D.A. Ciro. 700. Variety: Karkof. Av-
erage of all plots growing the particular variety.

Same as (1) except Turkey variety 1000-15 and Karkof 1916—
38 (i.e.--(l) is the same as the Karkof part of (2).)

Urbana, 111.; obtained through correspondence with L. B.
Miller, Agronomy Dept.,Univ of 111., Urbana, 111. C-0-
cl-W rotation.

Colby, Kan.; U.S.D.A. Tech. Bul. 761, Jan. 1911. C or D
fallow.

Colby, Kan.; Kan. A.E.S. Bul. 273. Average of late and
early plowed and fallow.

Garden City, Kan.; U.S.D.A. Tech. Bul. 761, 1911. Con-
tinuously crOpped. Winter wheat.

Same as (6) except average of all fallow.
Same as (6) except 0 or D fallow.

Garden City, Kan.; Kan. A.E.S. Bul. 262. Fallow, fall
listed. Winter wheat.

Same as (9) except early listed.
Same as (9) except subsoiled.

Same as (9) except late plowed.

(13)
(11)

(15)
(16)

(17)
(18)

(19)
(2o)
(21)
(22)
(23)
(21)
(25)
(26)

(27)
(28)
(29)

(10)

(31)

(32)

147

Same as (9) except early plowed.

Hays, Kan.; U.S.D.A. Tech. Bul. 761, 1911.

all fallow.

Average of

Same as (11) except continuously cropped.

Hays, Fan.; Kan. A.E.S. Tech. Bul. 85, 1956.

Fallow,

wheat, wheat, wheat rotation.

Same as (16) except fallow, Wheat, wheat rotation.

Columbia, Mo.; Mo. A.E.S. Bul. 158.

ous wheat.

Same as (18)

Same
Same
Same
Same
Same

Same

as

as

8.3

as

m
m

38

(18)
(18)
(18)

except
except
except

except

(18) except

(18)
(18)

except

except

Lincoln, Neb.; Neb.
Ave. planting date Oct. 8.

Plot
Plot
Plot
Plot
Plot
Plot

Plot

A.E.S. Bul. 3890

Plot 10. Continu-

5, continuous wheat.

2, continuous wheat.

36, continuous wheat.
30, continuous wheat.
29, continuous wheat.
21, continuous wheat.
21, continuous wheat.

Turkey variety.

Same as (26) except ave. planting date Sept. 23.

Save as (26) except ave. planting date Oct. 1.

Lincoln, Neb.; obtained by correspondence with D. P.
M00111, Dept. of Agronomy, Univ. of Neb., Lincoln 3,

Neb.

Mean yields in same series of plots.
in long term fertility study.

Agronomy Farm.

Lincoln, Neb.; Neb. A.E.S. Bul. 116.
with manure-C-O- and Sw.cl.-C-B.

1912; Pawnee, 1913-51.

Same as (30) except Rotation 9:

C-B.

Same as (30) except Rotation 2:

Check plots
Fields 2, 3, and 1.

Rotation 9: W
Nebred,

Varieties:

W-C-O with Sw.cl.-Sw.cl.-

W-C -O-C -C -E .

(33)

(31)
(35)
(36)

(37)

(38)

(19)

(50)
(51)

198

N. Platte, Neb.; U.S.D.A. Tech. Bul. 1007. Field 19.
Late plowing. Plot A.

Same as (33) except early plowing. Plot B.
Same as (33) except ave. of 3 fields.

Same as (33) except late plowing, Series I and III, plots
(4 and 16.

Same as (33) except field 12, early plowing, Series II
and IV, plots 1 and 16.

N. Platte, Neb., obtained through correSpondence with
R. E. Ramig, Univ. of Neb. Exp. Substation. N. Platte,
Neb. Rotation 17. Alternate W and C.

Same as (38) except Field 0, check rotation: Fallow-W-
o-o-w.

Same as (39) except mean of 6 replications.

Same as (38) except Rotation 613, Field A-—1 yr. rotation:
Fallow-W-C-W.

Same as (11) except different series.

Same as location and source of (38) except Cheyenne vari-
ety, alternate fallow.

Same as (13) except continuous wheat, early fall, blank
listed.

Same as (13) except continuous wheat, early fall plowed.
Same as (13) except continuous wheat, late fall plowed.
Same location and source as (38) except Rotation 269:
Alternate W-Fallow. 10 %. manure tepdressed in fall

after emergency.

Same as (17) except 10 T. manure plowed down every other
year, Rotation 268: Alternate W-Fallow.

Same as (17) except no fertilizer, Rotation 267: Alter-
nate W-Summer fallow. '

Dickinson, N. D., N. D. A.E.S. Bul. 383. Continuous wheat.

Same as (50) except alternate fallow.

(52)

(53)
(51)
(55)
(56)
(57)

(58)
(59)

(60)
(61)
(62)
(63‘)
(61)
(65)
(66)

(67)

(68)
(69)
(70)
(71)

119

Fargo, N. D.; obtained through correspondence with TB E.
Stoa, Agronomy Dept., North Dakota, Agricultural College,
State College Station, Fargo, N. D. Variety: Durum--
Kubanka 929, 1901-19; Mindum, 1920-S6.

Same as (52) except Hard Red Spring variety.

Same as (53) except Check, livestock series.

Same as (53) except Fr. Manure, livestock series.

Same as (53) except Fr. Manure, and P, livestock series.
Same as (53) except Fr. Manure, P, and Ca, livestock ser—
ies.

Same as (53) except Fr. Manure, P, Ca and K.

Fargo, N. D.; N. D. A.E.S. Bul. 350. Variety: Marqus.
Variety trials.

Same as (59) except Power Fife variety.

Save as (59) except Rival variety.

Same as (59) except Thatcher variety.

Same as (59) except Ceres variety.

Same as (59) except Pilot variety.

Mandan, N. D.; N. D. A.E.S. Bul. 362.

Stillwater, Okl.; obtained through correSpondence with
A.M. Schlehuber, Agronomy Dept., Oklahoma State Univ.,
Stillwater, Okl. Varieties: Local Turkey, 1900-01;
Fultz, 1905-7; Sibley's New Golden, 1908-ll; Karkof,
1912-l6; Turkey Red, 1917-20; Kanred, 1921-35; Tenmarq,
1936-1S; Pawnee, 1916-56. Ave. of manured and unmanured,
1906-20.

Field A.

Woodward, Okl.; U.S.D.A. Circ. 917. Alternate

cropped and fallowed.

Same as (67) except continuously cropped, early listed.
Same as (68) except early plowed, 8 in. deep.

Save as (68) except late plowed.

Save as (67) except ave. of unmanured rotations, Field A.

(72)
(73)
(71)
(75)

(76)
(77)
(78)
(79)
(80)
(81)
(82)
(83)
(81)

(85)
(86)
(87)
(88)
(89)
(90)

150

Same as (67) except ave. of manured rotatiwns, Field A.

Same as (68) except early disked, onewayed.

Same as (68) except early plowed 1 in. deep.

Pullman, Wash.; Wash. A.E.S. Bul. 176.

108,
Same
Same
Same
Same
Same
Same
Same

Save

Manure.
as (75)
as (75)
as (75)
as (75)
as (75)
as (75)
as (75)

except
except
except
except
except
except

except

Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot

107:
106,
105:
101,
103.
103:
101,

Field 3, Plot

straw and NHgNOB.

N N .
a 03
Straw and NaNO3.
‘no treatment.
straw.

straw and alf. hay.

alf. hay.

as (75) except 100, straw and NaNO3.

Pullman, Wash.: Wash. A.E.S. Bul. 207. Red

Russian. Field Plots.

Variety:

Same as (81) except Little Club variety.
as (81)
as (81)
as (81)
as (81)
as (81)

except Jones Fife variety.

except Hybred 123 variety.

except Hybred 128 variety.

except Hybred 113 variety.

except Forty fold variety.

Cotton Raw Data

 

Alexandria, Ala.; obtained through correspondence with

H. T. Rogers, Dept. of Agronomy and Soils, Alabama Poly-
technic Institute, Auburn, Ala. Change in fert. 1919.
Aliceville, Ala.; same as (1) otherwise.

Auburn, Ala.; same source as (1). One plot from 1900-55.

Brewton, Ala.; same as (1) otherwise.

(5)
(6)
(7)
(8)

(10)

(ll)
(19.)
(13)
(11)
(15)
(16)
(17)

(18)
(19)

(l)

(2)
(3)

(1)
(5)

151

Monroeville, Ala.; same as (1) otherwise.

Prattville, Ala.; same as (1) otherwise.

Sand Mountain, Ala.; same as (1) otherwise.

Tennessee Valley, Ala.; same as (1) otherwise.
Wiregrass, Ala.; sane as (1) otherwise.

Stoneville, Miss.; obtained through correspondence with
Perrin R. Grissom, Delta Branch Experiment Station, Stone-
ville, Miss. No nitrogen.

Same as (10) except 7.5 lb. nitrogen.

Same as (10) except 15.0 lb. nitrogen.

Same as (10) except 22.5 lb. nitrogen.

Same as (10) except 30.0 lb. nitrogen.

Same as (10) except 37.5 lb. nitrogen.

Same as (10) except 15.0 lb. nitrogen.

Jackson, Tenn.; obtained through correspondence with J.
R. verton, West Tennessee Experiment Station, Jackson,
Tenn. Limed.

Same as (17) except 35 1b. P205 etc.

Same as (17) except unlimed.

Tobacco Raw Data

 

Campbellesville, Ky.; obtained from G. L. Johnson, Dept.
of Ag. Economics, Michigan State Univ., E. Lansing, Mich.
Data obtained for his study of burley tobacco control
programs. Burley tobacco. Line 10.

Same as (1) except Line 7.
Same as (1) except Line 1.
Save as (1) except Line 1.

Lexington, Ky.; obtained through correspondence with C.

E. Bortner, Dept. of Agronomy, U. of Kentucky, Lexington
29, Ky. Plot 802. Variety: Ky. 16.

(29)
(30)

(31)
(32)

Same
Same
Same
Same
Same
Same
Same
Same
Same
Same

Same

Lexington, Ky., same source as (1).

Line

as
as
as
as
as
as
as
as
as
as
as
as
as
as
as
as
as
as
as
as
as

as

l.

(5)
(S)
(5)
(S)
(S)
(q)
(S)
(S)
(5)
(S)
(5)
(F)
(S)
(S)
(S)
(S)
(S)
(S)

(5)

except
except
except
except
except
except
except
except
except
except
except
except
except
except
except
except
except
except
except
except
except

except

Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Dlot

Plot

Same as (28) except Line
Same as (28) except Line
Save as (28) except Line

Same as (28) except Line

801.
h32.
h33.
u3h.
803.
h35.
80h.
h36.
805.
u37.
806.
h38.
807.
h39.
808.
uhO.
810.
hul-
800.
811.
812.
813.

2.

Burley tobacco.

152

(33)
(3b,)
(35)
(36)
(37)
(38)
(39)
(MO)
(1(1)
((42)

(hB) \

(nu)
(L15)
(1(6)

((47)
(’48)
(1(9)
(‘30)
(51)

155

(28)
(28)
(28)
(28)
(28)
(28)
(28)
(28)
(28)
(28)
(28)
(28)

as except Line

except Line 7.

as except Line

as except Line

as except Line

as except Line 11.

as except Line 12.

13.
1h.
15.
18.

as except Line

as except Line

as except Line

as except Line

as except Line 17.

Greenville, Tennessee; obtained from same source as (1).

Greenville, Tennessee; obtained through correspondence
with B. C. Nichols, Tchacco Experiment Station, Green-
ville, Tenn. Variety: Ky. 16. 3 yr. rotation.

Same as (ué) except Judy's Pride variety.

Same as (h?) except different plot.

Same as (ué) except different plot.

Same as (Q?) except different plot.

Blacksburg, V8.3 obtained through correspondence with

H. L. Dutcn, Agronomy Departwent, Virginia Polytechnic

Institute, Blacksburg, Va., Station Yield. Several vari-
eties.

FORM USED TO B

APPENDI X B

EMOVE TREND FROM BASIC DATA

154

 

 

CROP AND VARIETY

 

155

 

LOCATION

 

SOURCE OF INFO.

 

SOIL

 

FERTILIZAT ION

 

REMARKS

 

   

( )
(1) Y = a + bX

 

  

I.

.3

.ao:
V n
1: y- .2)
{2

JAN 6 1961'?

   

““3.me . s! @1342,
“~-"'« 4 ide. ‘-

 

   

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