@119”-

DECEMBER 1959

111c11111s11111c1111a11x11s 11111111 11111111111111 1>111c1s

AND INDEX BIIMPUTATIN METIIIIIIS, 1910-58

 

  

R. D. LEWIS. DI“ R E C T O R,

C LLLL s:

STATION, TEXAS


PERCENT O
09°96 ‘$1 3*’ ==CASH RECEIPTS
~ »O‘%»,(‘?a¢ K30 ”' 9* FROM TEXAS FARM
30o 90$ 94;’? g o ,9 y‘ MARKETlNGS,l947-56
P 3U 9o
G618 8% I '2' O g\(;q 
._ 4., 5; 9N‘
HOLESQLE 
2s 5% “'

- CATTLE AND COTTON /

 29g /\ CALVES 31%

 1 23% -==11\101-:x 01=
 "‘ PRICES RECEIVED
  I /\ BY U.S. FARMERS,
A / V V‘\\# 1910-14=100
 1910 1920 1930 1940 1950 1900

TEXAS AGRICULTURAL EXPERIMENT STATION

IN COOPERATION WITH THE U. S. DEPARTMENT OF AGRICULTURE

FOREWORD

This publication provides price information for
farmers, workers in agriculture who are concerned
with operations and for persons in business and
industry associated with farming.

 

Agriculture today includes the farmers as well
as the industries which supply productive resources
to farmers and the industries which assemble, proc-
ess, market and distribute farm products. It is vast-
ly different from the agriculture of a century ago
when most commodities consumed by farmers were
grown or produced on the farm and most products
sold commercially went to the consumer in virtually
the same form as when they left the farm. The in-
creasing complexity of farming and related busi-
nesses due to technological developments, and the
greater rewards from increasing specialization in
production, account for the changing nature of our
overall agricultural industry.

With the increasing complexity of modern agri-
culture, there has developed an increasing need
and use of basic price data, seasonal price patterns,
cyclical price behaviour and long-term price trends
for understanding problems the industry faces in
making more reliable decisions in operations. The
present publication is designed to provide workers
in agriculture in Texas with such basic data on fac-
tors influencing farm commodity prices and their
behaviour.

CONTENTS

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3

Seasonal Price Changes of Major
Farm Products in Texas . . . . . . . . . . . . . . . . . . .. 3
Definition of Zone of Price Expectancy. . . . 4
Marketing Farm Commodities . . . . . . . . . . .. 4

Seasonal Price Patterns . . . . . . . . . . . . . . . . .. 5

Livestock and Livestock Products , . . . . . . .. 5

Beef Cattle . . . . . . . . . . . . . . . . . . . . . . . . . .. 5

Calves , . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 6

Hogs . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . .. B

Wholesale Milk . . . . . . . . . . . . . . . . . . . . .. 6

Eggs . . . . . . . . . V . . . . . . . . . . . . . . . . . . . .. 7

Commercial Broilers . . . . . . . . . . . . . . . . .. 7

Wool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 8

Crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Cotton Lint . . . . . . . . . . . . . . . . . . . . . . . . . .. 8

Sorghum Grain . . . . . . . . . . . . . . . . . . . . .. 9

Wheat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 9

Rough Rice . . . . . . . . . . . . . . . . . . . . . . . . .. 9

Index of Prices Received by Texas Farmers. . .19

Selection of Commodities . . . . . . . . . . . . . . . ..10

Selection of Base . . . . . . . . . . . . . . . . . . . . . . ..ll]

Grouping of Commodities . . . . . . . . . . . . . . ..ll
Commodities Having Incomplete

Monthly Price Estimates . . . . . . . . . . . . . . ..ll

Computation of Subgroup Indexes . . . . . . . .12

Selection of Link Dates . . . . . . . . . . . . . . . . ..12

Linking and Conversion Procedures . . . . . ..l2

Combining Subgroup Indexes . . . . . . . . . . ..l4

Adding Commodities . . . . . . . . . . . . . . . . . . . ..l4 '

Acknowledgments . . . . . . . .__ . . . . . . . . . . . . . . . . . . . .16

 
 

 

Factors Affecting Texas Farm Commodity Prices
and Index Computation Methods, |9|0-58

l. A. Kineannon and G. B. 5trong*

ICE RELATIONSHIPS GREATLY CONCERN THE
' farmer. Each farmer produces a product or
t ral products that he hopes will give him an
me at some level above the cost of production.

,In recent years, the impact of vertical inte-
tion in almost all phases of agriculture and
s importance of agribusiness expansion and de-
gpment have necessitated a clear, extensive
'5; readily available program for the entire field
‘prices.

‘The principal objective of this study is to con-

ute to a better understanding of some of the

tors that affect prices received for certain

5: as farm commodities important to the agri-

‘ural industry of the State. An index of sea-
* v l variation of farm prices for these commod-
f; and a zone of price expectancy were estab-
ed. With a knowledge of these two concepts,
8 oducer might, by comparison with the estab-
,1 ed norm of the period on which these indexes
easonal variations and price expectancies were
_ puted, be in a better position to approximate
~ price range of a commodity for any particular
_ th. It often is profitable to know what the
Psonal pattern might be for a particular com-
ity in a certain month and have an estimate
‘the expected price. Some of the possible causes
i, these price behaviors are given. Reasonable
3 for determining the best time to store or
_ a product are presented for 11 major Texas
A commodities.

  
  
  
  
  
  
   
   
  
   
 
  
    
   
  
    
   
  
 
 
 
   
 
  

» To supplement seasonal price behavior and
f e expectancy for these commodities, which ac-
tfor most of the farm income of the State,
'ces received and price indexes also were com-
 ed for all important Texas farm commodities.
ese are presented in MP-401, “Prices Received
Texas Farmers and Price Index Numbers,
16-58.”

H!

l Texas and U. S. index numbers of prices re-
ved for all farm products, index numbers of
 farm prices received for crops and for all
“estock and livestock products and index num-
s: s of prices received" and U. S. prices paid are

spectively, assistant professor, Department of Agri-
iltural Economics and Sociology, Texas Agricultural
periment Station, College Station, Texas; and agri-
A tural statistician, Agricultural Estimates Division,
i cultural Marketing Service, U. S. Department of
.; iculture, Austin, Texas.

shown graphically. Figures 1 through 3 illustrate
the relative changes that have taken place during
the past five decades and indicate prewar and
postwar trends and the seasonal price patterns
which have prevailed for agricultural commodi-
ties in Texas and the United States since 1910.

SEASONAL PRICE CHANGES
OF MAJOR FARM PRODUCTS
IN TEXAS

Farm commodities produced by Texas farm-
ers are harvested seasonally, but consumers desire
a supply of these commodities the year round.
The seasonal market characteristics of farm pro-
ducts necessitate storage of nonperishable pro-
ducts to meet consumers’ needs throughout the
year. Some products can be stored easily and in-
expensively, while others that are highly perish-
able are more difficult and costly to store. Con-
sequently, prices of most farm commodities vary
throughout the year by the cost of storing from
one production season to the next and, in the case
of perishables, by variation in the cost of produc-
tion between areas and the cost of transportation
to distant markets. A reasonably clear knowl-
edge of the seasonal aspects of a particular com-
modity is essential; the producer can make wiser
decisions relative to the proper time to store or

   

 

PERCENT
TEXAS‘
300 -------
25o-----~~-~-~- ~-~-----~-
UNITED STATES
200 ,
I
:50 ~
/\\
I00 —~ Q,
‘7
lllllllll llllllll! lllllllll lllllllll llllllll
|9n> 1920 I930 |94o I950

Figure 1. Index numbers of prices received by farmers
ior all iarm products. Texas and United States, 910-58. Base
period: 1910-14 = 100.

3

PERCENT I
TEXAS ‘I
PRICES RECEIVED}
300
fvlr‘k_,/
25o l/v V!
U. S. I
p PRICES PAID l]
20o ,' ’
// n/ F
../ '~\
150 I T \jh‘\
x \ _/\__/
. l; l ‘J14
100 -<7’\_] V
I910 I920 I930 I940 I950

Figure 2. Index numbers of prices received by Texas
farmers for all farm products and index numbers of prices
paid by U.S. farmers, 1910-1958. Base period: 1910-14 = 100.

to market his products in the optimum price per-
iod. However, the seasonality of production and
marketing in the United States, or the world as
a whole, often has more influence on Texas farm
prices than does the Texas production and mar-
keting of these products. Consequently, geo-
graphic and climatic conditions in Texas may
make it possible for Texas farmers to market
their products in optimum high seasonal price
periods, or they may be compelled to market cer-
tain products during a period of low prices.

It is difficult to establish a set of price-de-
termining criteria for possible market reactions
and commodity movements in a free market, While
there exist federal price programs covering many
commodities produced. These controls alter in-
directly the amount of seasonal variation in prices
Texas farmers receive for their commodities when
price ceilings or price floors for farm commodities
are in effect. Nevertheless, with an understand-

PERCENT

LIVESTOCK

40o i7‘

30o f CROPS ' [V /‘ l

\
- /  .1 kl
\

T \
‘ I: \‘ f
I 1 I\ I
1 .1 \\ l!
\ /\/ /

200

I I
{Lun-
“i
A

 _] \ /‘-'\ y
O 11 1 l | 1 1| lllllllll lllllllll lllllllll llllllllt
l9l0 I920 I930 I940 I950

Figure 3. Index numbers of prices received by farmers
for all crops and {or all livestock and livestock products,
Texas, 1910-58. Base period: 1910-14 = 100.

4

   
  
  
    
    
  
   
    
  
   
    
 
  
   
    
  
    
  
  
   
   
 
   
   
   
  

i-ng of the seasonal pattern of farm comm
prices in the free market, supplemented by
isting price legislation and implications of fed
price programs, a farmer is in a much better
sition to make decisions relative to the pr
time to store and to sell the commodities he
duces. i’

Many factors, such asweather conditions, of
iness activities, trends in farm prices and 
policies here and abroad, alter the seasonal p
movement in any particular year. The varia
in price patterns for some commodities re 3
approximately the same year after year I
with other commodities the variation is prono
ced. Therefore, the average adjusted seas
variation for Texas farm commodities should-
applied in any one year only after adjustmj
for current as well as probable future change
economic conditions are made. Any partic 
analysis made on past years and a forecasti
the future made from the model of these r
years is a relative concept and should be re ’_
on with discretion. (Indexes of seasonal 
variation for 11 major Texas farm commodi
were developed using weighted seasonal aver,
monthly prices for the 1947-56 period, Figuresi
14. The prices were adjusted for cycles f
trends.)

The zone of price expectancy was calcula
for 1947-56 to afford a measure of the mont
variation in prices from the seasonal aver
price for the 10-year period. This zone gives t
range of the average seasonal price that can _
expected for any particular month, in approrll
mately 7 out of 10 years. 1

Definition of Zone of Price
Expectancy

The amount of variation in the price, forf
given month, from the average price prevail 1i
in that month for the 10 years, 1947-56, was co 
puted as follows: If the average price for it
month was 120 percent of the average ann,
price and the index of price variation was 5 l‘ 
cent, this would mean that the price in that mont
varied between an index of 115 and 125 in 
proximately 7 out of 10 years. The narrower th’
value of the index of the price zone, the great
is the stability and closeness of the monthly s =7
sonal price to the average price for the 
period. Conversely, if the value is large, t
monthly prices in individual years varied consi
erably from the average monthly prices.

Marketing Farm Commodities

Most farm commodities are produced and a
ready for market within a very short time af
harvesting. Prices usually are lowest at the time;
of harvest and gradually increase as the seas
progresses. This increase usually amounts to t

   
 
  
 
 
  
  
 
  
 
  
  
 
 
 
 
  
 
 
  
 
  
  
 
  
 
 
  
  
  
 
  
  
 
  
 
  
 
  
  
 
  
 
 
  
  
  
 
  
 
   
 
 
  
 
  
  
 
  

jf storage and other incidental expenses en-
F‘ in holding the commodity to the next sea-
"There is no definite rule regarding whether
re commodities in any 1 year because eco-
j conditions may vary from year to year.
 ost profitable time to store, however, is in
_s of rising prices or in seasons of high pro-
on. Before anyone can store a commodity,
 ate storage facilities must be available.
f facilities can be private or public; but re-
‘ess of the type, it may not pay to store un-
the anticipated price and possible future eco-
c conditions will be such that the cost of
‘ige, insurance and interest on the storage in-
f ent will be offset by an increase in the price
"ved at the time the commodity is marketed.

i Seasonal Price Patterns
he average seasonal variation of prices of
or farm commodities grown in Texas during
--56 are reviewed following. The broken lines
5 e zone) represent the zone of price expect-
which can be expected in about 7 out of 10
s. These indexes of seasonal variation for
Ecommodities discussed do not establish an ac-
te and foolproof guide in making decisions
5i the optimum time to sell or to store. Inas-
h as an index points out the months of rising
falling prices, prices above normal and prices
w normal, it does not necessarily reflect what
i happen now and in future months. However,
ther conditions, international relations, do-
tic demand, producers intentions and general
 omic conditions have to be compared with
‘itions existing during the base period (1947-
, if the index is to serve as a reasonably ac-
te guide for estimating future prices. The
ean war affected economic conditions and,
refore, prices of some farm commodities were
‘ch higher and perhaps others were not notice-
y affected. However, in establishing a 10-year
‘e period from the postwar years, the 1947-56
iod affords as good an average balance of eco-
l ic conditions and physical factors as any de-
‘e during 1946-58. Rice, for example, is de-
dent to a large extent upon foreign markets
l therefore rice prices were higher immediately
owing the Korean war than they perhaps
uld have been had rice production in Asia not
f dislocated by the Korean war. On the other
1 gl, many farm commodity prices were no
“1 er during and immediately after the Korean
than before the war.

‘In the final analysis economic, physical and
‘logical factors muist be weighed and adjusted
_ the basis of acquired knowledge, experience
d value judgments when comparing time per-
J as a basis for making decisions.

lJVGSfOCk and Livestock Products

,_ The livestock industry plays a vital and neces-
try part in maintaining the favorable economic
tus of the agricultural industry of the State.

Cash receipts from livestock and livestock pro-
ducts during 1947-56 amounted to about 43 per-
cent of total cash receipts from all farm com-
modities in Texas. They averaged a little over
870 million dollars annually. Beef cattle, calves,
hogs, wholesale milk, eggs, commercial broilers
and wool were the livestock and livestock pro-
ducts considered in this seasonal variation study.
These seven commodities accounted for 89 per-
cent of total receipts from livestock and livestock
products and 39 percent of cash receipts from
crops, livestock and livestock products. Out of
the 89 percent which the 7 livestock commodities
accounted for, cattle and calves accounted for 60
percent, wholesale milk for 14 percent, eggs for
9 percent, hogs for 8 percent, wool for 4 percent
and commercial broilers for 5 percent.

The seasonal variation for prices and the zone
of price expectancy for livestock and livestock
products, are discussed singly following. The dif-
ference between the lowest and highest index
numbers of seasonal variation in prices for each
commodity is converted to dollars by multiplying
this difference by the centered average annual
price.

BEEF CATTLE

Beef cattle prices followed a uniform pattern
throughout 1947-56, Figure 4, remaining above
normal for the first half of the year and below
normal for the last half. Prices rose consistently
from November to April, after which they de-
clined steadily through October. Prices varied
less from November through January than for
any other period, reflecting a slowing down of
beef cattle moving into the market as compared
with the peak marketing months just preceding
November. The months of largest variation in
prices are from May through November, thus re-
flecting large supplies being marketed, but rather
irregularly. The movement of cattle into the
markets is influenced largely by ranchmen’s de-
cisions as to the optimum time to feed and mar-

PERCENT
l l l l
BEEF CATTLE

" i-SEASONAL INDEX
I20 ---PRICE ZONE
no ,'--—; ‘* _\

(I }__-\\ ~_\\\

/ [/1 \\~\ \\
I00 _ ’ \ \"" \*\

\ \
._ \\ \\ f!‘
\ ,"—-—~__ ’
9O \/ \\ 
\\ /,

_ \/
8Q

J F M A M J J A S O N D

Figure 4. Beef cattle: index of seasonal variation oi

Texas farm prices and zone of price expectancy, 1947-56.

5

PERCENT I I I I
CALVES
" i-‘SEASONAL INDEX
no _ ,A ___
ll ~__,/ ~____
7/ (‘if “\\\___\
  

9o ' \\.\ ,____
- \\/'1

8O
J F M A M J J A S O N D

Figure 5. Calves: index of seasonal variation of Texas
farm prices and zone of price expectancy, 1947-56.

ket and by the composition of marketings. The
largest drops in prices were between mid-May
and mid-June and again between mid-September
and mid-October, which were an indication of the
peak marketings of spring and fall cattle. Prices
of beef cattle usually reached a low in November
of 91 and advanced to a high of 109 in April, a
difference of 18 points, or $3.62 per hundred-
weight.

CALVES

Calf prices followed practically the same pat-
tern as beef cattle prices, but with much less vari-
ation, Figure 5. Calf prices usually reached a low
of 92 in November and a high of 105 in April,
a difference of 13 points, or $2.92 per hundred-
weight. This was only about two-thirds of the
variation in cattle prices. The possible differ-
ences between the price pattern followed by calf
prices and that followed for beef cattle was the
large price drop between mid-May and mid-June
for beef cattle while the only perceptible drop

PERCENT I l l I
HOGS
_ "iSEASONAL lNDEX
 -‘“"' PRICE ZONE
_ /"' ‘“\
1/ / \\
H0 "___, kg \
._ ”‘,4” / \\\
____.a’ _ ____ __ \
 / I "‘ _\\ \\___
Li“ "II \-~\\ \
9O v”"—___——”’ ‘x
_ \
8O
J F M A M J J A 3 O N D

Figure 6. Hogs: index oi seasonal variation of Texas
{arm prices and zone oi price expectancy. 1947-56.

6

  
   
  
   
 
 
  
  
  
  
   
   
   
   
 
 
   
  
  
   
    
 
  
    
 
 
 
 
 
  
   
    
  
  

"from the average in calf prices was between
September and mid-October. This indicat
the heavier supply of calves moved into the
ket in the fall, with the largest numbers re '
the markets in October. It is not neces
true that a similar price pattern prevailed i
tober in any particular year or will prevail '
future. One other charaqltlteristic of calf l?
that differed from beef cattle prices is tha
variation in prices for calves in December
considerably more than beef cattle prices. 
smallest variation in prices for calves was i»
vember, January and March. Beef cattlfi
calf prices for Texas had only one seasonal

and one seasonal low. This is particularly W0
of note inasmuch as cattle and calf prices us
are considered to have two seasonal lows and)’
seasonal highs. This shift in the pattern was Y
haps attributed to the strong demand for 
and stocker cattle and the desire to enlarge b jj
ing stocks during this period. These factors;
the strong domestic demand for consume
stabilized, and in some instances prevented,
treme price fluctuations which had previo
prevailed in the cattle industry.

HOGS

Hog prices followed a uniform pattern thro“
out the year. They varied more than calf p j
but less than beef cattle prices, Figure 6. _
variation differed from that of cattle and ca
in that hog prices were above normal during l
summer and below normal in the late fall T,
early spring. Hog prices had one seasonal l;
and two seasonal lows. Hog prices varied les
October, November and June and had the gr
est variations in April and August, which re 5
heavier fall farrowings and consequently l 
spring marketings. 

However, the variation in prices for hogs ‘
nearly as large in December and January as a
August. Hog prices, based on the 10-year a
age for the seasonal index of prices, reach
low of 92 in December and a high of 108 in 5;
gust, a difference of 16 points or $3.23 per h’
dredweight. The high prices for hogs du l
July, August and September seemed to be m
consistently true in any particular year than t1
for cattle and calves. This perhaps is a result
fewer hogs being marketed during the sum i
irrespective of the supply that may be availa
for the market in any 1 year. Too, hog produc
can go in and out of production more rapidly |»~_
cattle producers. Consequently, control over z
supply of calves or slaughter cattle available l
market in any particular year or month is p -
nearly so flexible as the control of hogs availa
for market in any particular season. _

WHOLESALE MILK

Wholesale milk prices followed a uniform s
sonal price pattern, having two seasonal hi;
and one seasonal low, Figure 7. The prices-p

W 1 1 1 1

MILK, WHOLESALE
——SEASONAL INDEX
—-—PR|CE ZONE

f "*—_

 

x / ,
\\\~\\ /%”
\ \ zl/ l’

\ / '
Q \\\ ”,//
_ ‘\_____*__a’§-'/‘/I
\

~
s I
\_

  
  
 
 
  
 
 
  
  
 
 
  
  
  
  
 
 
  
  
 
  
 
  
  
  
  
 
  
 
 
  
 
  
 
  
 
  
  
 
  
  

LJFMAMJJASOND

lI-‘igure 7. Wholesale milk: index of seasonal variation
‘exas iarm prices and zone oi price expectancy, 1947-56.

‘in above normal from September through Feb-
ry. Milk prices declined uniformly from a
h 0f 109 in November to a low of 91 in June,
aifference of 18 points or $1.08 per hundred-
iight. An interesting characteristic of the zone
‘price expectancy for wholesale milk is the fact
to the zone is extremely narrow and uniform
l oughout the year with the exception of May,
_ e and October. This perhaps is attributed to
l strong postwar demand for milk. The tech-
ogical innovations in production, high propor-
in of total sales by Texas producers made un-
i federal order regulations during recent years,
~ nagement practices within dairy organizations
d the overall efficiency of state and national
lk marketing organizations also have contribu-
.1 to the uniform variation in milk prices. Other
f tors which caused the supply of milk to be held
j ormly at the level of consumer demand were
iciency in milk production and marketing, and
- base plans used in the six federal orders in
xas. Percentage utilization of milk according
 fluid and manufacturing also is an important
ctor affecting month-to-month milk prices.

AGGS

" Egg prices followed closely the pattern of
“holesale milk, but with considerably more varia-
11 from the average than did milk prices, Fig-
e S. Egg prices also had two seasonal highs
‘dlone seasonal lOW. Prices remained above
lerage from August through January and be-
w normal for the remaining months. Prices in-
eased uniformly from June until January, fall-
1 rapidly throughzMarch and declining grad-
 from March through June. The index of
“sonal price variation for eggs reached a low
5 83 in June and a high of 129 in December, a
'fference of 46 points, or 19 cents per dozen.
he wide variation in egg prices is related di-
l- tly to the lack of control over supply relative
.1 domestic demand. This relationship between
1 pply and demand is complicated because of the

PERCENT EGGS
i-SEASONAL INDEX

———PRICE ZONE l
I40 l.
l/
I30 l;
w’,
12o ,1’ ,
\v\ l/f/y x”
11o \ ,.» , f,
\ / I
\\ I /’/
100 \ l.’ , ,1
\ \ \ / /
\ \ ~\\ J I/
9O \\ \ ___ \\ w” ’ z"
\\‘ mg 4/
'“\ /
80 ar_________,
J F NA 1A BA J J A1 S C) N D

Figure 8. Eggs: index of seasonal variation of Texas
farm prices and zone of price expectancy. 1947-56.

risk and uncertainty in egg production and many
firms leaving and entering the industry. Another
important factor contributing to the variation in
egg prices is related directly to feed supply and
prices in any particular season or year.

High operating costs, storage facilities and a
guaranteed market for eggs as they are produced
should be recognized before large-scale egg pro-
ducing operations are undertaken.

COMMERCIAL BROILERS

Broiler prices followed an irregular pattern,
having two seasonal highs and three seasonal
lows, Figure 9. Prices, however, did remain above
normal from March through September, reaching
a low of 93 in December and a high of 108 in
August. The difference in the lOW and high of
these seasonal indexes of price amounted to 15
points or 4 cents per pound. The strongest fac-
tors, perhaps, accounting for the irregular pat-
tern of broiler prices are: variations in the sup-

PERCENT I ‘ \ l
l COMMERCIAL BROILERS
- -——SEASONAL INDEX
11o //~~-- __ ___/,-:<\
:__ //  —_’\\ \\.,_--1-___
10o -/ l,‘ ~.\ \

,_\\ ,’_'/‘ \ \
90X ,/" " \\ \
____// ‘\\\\

8O
J F M (A M J J A S O N D

Figure 9. Commercial broilers: index of seasonal vari-
ation oi Texas farm prices and zone of price expectancy.
1947-56.

7

PERCENT { l l I
A WOOL
- / \ »\ -——SEASONAL moex
_/ \\ ,’ \\ ---PRICE ZONE
llO ," ~a I‘ \
\/
b]! \\
l /\ \
\
IO5 Z \

IOO
_ /"'“'\ \
/ . \ g

--__- __- f

9O

JFMAMJJASOND

Figure 10. Wool: index of seasonal variation of Texas
farm prices and zone of price expectancy, 1947-56.

ply available for the market, an irregular supply
of available feed, fluctuations in feed prices and
many substitute foods for chicken. The widest
zone of price expectancy occurred from October
through May, reflecting extreme variation in do-
mestic demand through the winter and early
spring. This was, perhaps, attributed to a
stronger demand for beef and pork during the
colder months.

WOOL

The average seasonal variation in wool prices
followed a symmetrical pattern, remaining above
normal for the first half of the year and below
normal for the last half, Figure 10. The index of
average seasonal variation of wool prices ranged
from a low of 96 in September to a high of 106
in June. This difference amounted to 10 points
or 6 cents per pound. The zone of price expect-
ancy remained uniform and relatively narrow
from June through December, but Wide and
irregular from January through June. Wool
prices had two seasonal highs in the first half
of the year with a seasonal low in September.
The wide zone of price expectancy and irregu-
larity of prices from January through June is a
direct reflection of heavy marketings during the
early winter and spring accompanied by a varia-
tion in the number of sheep shorn for the mar-
ket. Spring wool prices represent sale of 12-
month wool and, consequently, bring higher prices
than the shorter staple sold from fall clips.

Crops

A seasonal index of price variation and zone
of price expectancy for the 10-year period, 1947-
56, was computed for four Texas crops. These
were cotton lint, sorghum grain, wheat and rice
in order of their importance relative to cash re-
ceipts from marketings by Texas farmers. These
four cash crops accounted for 76 percent of the
total receipts from crops marketed in Texas dur-

8

‘ing the 10-year period. Cotton amounted.
percent of the total cash receipts from farm.
ketings of crops, livestock and livestock pr

sorghum grain 5 percent, wheat 4 percen
rice 3 percent. i

The index of seasonal variation and th?
of price expectancy for these four comm ,

are discussed following. "f.

COTTON LINT

Cotton lint prices followed a uniform pal
remaining above normal from April throug
tember and below normal from October t _
March, Figure 11. Cotton lint prices had on,‘
sonal high and two seasonal lows. The ind‘
seasonal variation of cotton lint prices re
from a low of 96 in January to a high of 1
August, a difference of 7 points or 2.4 cen i
pound for the 10-year average. The 2.4 cen
pound for cotton lint, which represented th
gest range in price below and above the s’
average, was small relative to the averagef
sonal price at which cotton sold during the
considered. The widest range of price vari
below and above the average for cotton lint*
from July to October. The wider range an
higher degree of irregularity in the variatif
prices from the average during July to Ja '
would be expected for cotton since these wer”
months of cotton harvesting in Texas. The,‘
sonal price drop from the months of early ha
is attributable largely to the shift from the-
quality cotton of the Lower Valley in July,,?
gressing through the clean-up of harvesti 1
shorter staple and often weather-damaged c’
on the High Plains. The smallest variation ip,
average price was in February, March and
also in October, with the smallest in March. A.
are many factors, such as international i "
and export laws, foreign demand, synth
wool, mohair, weather, economic conditions, ’
ness activities and government programs, w,
can weaken this seasonal index pattern as a _

PERCENT i I I
COTTON um?
- ——SEASONAL mos
—--PRICE ZONE
llO
/ \\_
_ ’____-_"// \\
I
I05 l, ~\ I
I \ I
l, y? r
_»’ / \
 ---_ K '.”\\ N
a’ \

F / /’ fly‘; \\

95 "" " \
l’ \\ ll’

>— q, 

9O

JFMAMJJASO

Figure ll. Cotton lint: index of seasonal vari '

Texas farm prices and zone of price expectancy, 1947

"T | 1 1 1

SORGHUM GRAIN
i SEASONAL INDEX
--- PRICE ZONE
-///\\\
/ \
' s
\___LW\ \\ /
/
\ /
/_——>-__ \ ,,r”'h__——-i"' I
4 \‘\ \ x’ I //
/ \\\ y ,
~V/ \\ \___-——-——'-'_“ ///
\ ‘I
\ V
\ /
\\____,"//

FMAMJJASOND

A gure l2. Sorghum grain: index of seasonal variation
Al» as iarm prices and zone oi price expectancy, 1947-56.

of future price trends and as a guide in as-
_,j:ining the optional time to store or sell cot-

GHUM GRAIN
rghum grain prices followed a uniform pat-
s, remaining above normal from mid-Novem-
lto mid-June and below normal for the remain-
‘ months, Figure 12. Sorghum grain prices
P- at normal only three times during this per-
 These three normal periods of seasonal var-
fv in prices were in February, between mid-
__- and mid-July and between mid-November
mid-December. Sorghum grain prices had
1 seasonal highs and one seasonal low. The
_‘ seasonal highs were in January and May,
' e the low was in August.

 
 
  
  
  
 
 
  
 
  

‘The index of seasonal variation in prices of
hum grain reached a low of 93 in August and
"gh of 109 in May. This was a difference of
points or 36 cents per hundredweight. The
est spread above and below the index of sea-
‘a1 price variation was from August through
ber. This was to be expected in view of the
vy marketings of sorghum grain during these
nths and competitive feed grains reaching the
l ket. This relation will vary depending on the
ply of sorghum grain as well as the potential
lies of competing grains. The smallest vari-
n in price is in March when the smallest quan-
 is moving into the market and the demand
i elatively stable. Usually the most profitable
e to store sorghum grain is when the supply
large or prices are rising. Therefore, the best
l e to store wouldibe between mid-October and
-December and to sell between mid-February
y. mid-May. The least profitable time to store
ighum grain, relative to the seasonal price pat-
_ , would be between mid-May and mid-August,
 though the farmer has to store at the time
. harvesting. Decisions concerning when to
re or to sell should be compared with average

 
 
  
  
 
  
 
 
  
 
  
 
  

conditions prevailing relative to the general econ-
omy, general price levels, economic considera-
tions, climatic conditions an-d government pro-
grams closely approaching those that prevailed
during the period in which this seasonal varia-
tion study was based.

WH EAT

Wheat prices followed a pattern similar to
that for sorghum grain, Figure 13, except that
the index reached a low in June and a high in De-
cember, as compared with a low in August and
a high in March for sorghum grain. Wheat prices
remained above normal from October through
May and below normal from May to October.
Wheat prices had two seasonal highs and one sea-
sonal low. The widest variation in wheat prices
usually occurred from November through April
with the widest occurring in March and a small
variation from April to October. The variation
in wheat prices would be expected to be some-
what more uniform and smaller than with sor-
ghum grain because of marketing quotas and
acreage allotments which have been in force for
wheat for most of the time during 1947-56 and
the fact that wheat is sold primarily for human
consumption, while sorghum grain is sold mostly
for livestock feed. Consequently, variations in
the demand for wheat for human consumption
during this period were much less subjected to
variations in demand than was true for sorghum
grain as feed for livestock.

ROUGH RICE

Rough rice prices did not follow as uniform
a pattern as did wheat and sorghum grain, Fig-
ure 14. The zone of price expectancy particularly
was wider and more irregular than it was with
wheat. This would be expected for rice during
this period since rice was subject to acreage al-
lotments and marketing quotas in only 2 years,
1955-56, during the period considered. The sta-

PERCENT \ I I I
WHEAT

" , i SEASONAL INDEX

 --- PRICE ZONE

llO \
\\ l, \\~\ ’¢""
_ \\ I/ ‘\\ r,’

I00 \\‘~. /’ Z

y?_§__ \-a—x v-’,_-’- ’ ’—u—”
:—--.___/ \ l'-——P
\\\ WWW/l’
9O ‘a/"I-
8O

J F M ‘ A M J J A S O N D

Figure 13. Wheat: index oi seasonal variation of Texas
farm prices and zone oi price expectancy, 1947-56.

9

PERCENT h I I I I

RICE ROUGH

t. ——'$EASONAL lNDEX
---PRICE ZONE
IZO
 l, \\‘ “,7 x‘
t’ ___""4 \\ /»___ "a
w/g \\ I ~
~__*\ \ \ // Z"?
IOO ‘ ~s \

___.! f
. \ /____
7 x z’
.\ ,
90 \ — '

so “~\,’

JFMAMJJASOND

Figure 14. Rough rice: index of seasonal variation of
Texas {arm prices and zone oi price expectancy, 1947-56.

bility in rice prices during this period hinged al-
most entirely 0n the stability of foreign demand
for United States rice. This is particularly true
in view 0f the constant level of domestic demand.
Rice Was under government support prices dur-
ing 1947-56 and the free market price was higher
than the support price except in 1951, 1954 and
1955. The wider variation in the zone of price
expectancy for rice as compared with wheat was
primarily a result of the difficulty of adjusting
supply to the variation in foreign demand for
United States rice. Another factor contributing
to this difference is the greater number of bal-
ancing demand forces for wheat as a consequence
of the many wheat by-products as compared with
rice. The index of seasonal variation in rice
prices reached a low in September of 89 points,
and a high in February of 106 points. This dif-
ference amounted to 17 points or 90 cents per
hundredweight for rice. This amounts to $1.46
per barrel which can reduce profit relative to the
high cost of production for rice in periods of ris-
ing costs of production. On the basis of the es-
tablished model for the index of seasonal varia-
tion of rice prices, the most profitable time to
store rice relative to the seasonal price pattern
would be from September through February and
the least profitable time to store would be be-
tween February and September, even though the
farmer has to store at the time of harvesting.
Rice prices had two seasonal highs, one in Feb-
ruary and another in May with one seasonal low
in September. Rice prices reached a normal be-
tween October, November and June and rose to a
high in February and fell slowly reaching a nor-
mal in June.

INDEX OF PRICES RECEIVED BY
TEXAS FARMERS

An index number is a useful means of reduc-
ing large numbers to simple fractions with a com-
mon base or denominator. An index number is

10

   
   
  
  
  
   
    
   
   
  
   
    
    
   
    
 
 
  
  
   
 
 
 
   
   
  

merely a ratio of the magnitude of a variabl
one time or place or position to its magnitude:
another. Index numbers are valuable for
termining quickly significant changes in pri
sales, volumes, Wage rate, employment, inco
and many other factors used in developing sta
tical measures. The computational methods 
shown in this publication‘ afnd the actual pri
received and index numbersare presented in a
401, “Prices Received by Texas Farmers and P y
Index Numbers, 1910-58.”

Selection of Commodities

A basic factor in constructing the index
prices received was the inclusion of commodit
of major importance. However, only commodit
for which price and sales data are available co i
be included. Pecans were omitted from the ind
because price data are limited to the season av
age price which does not become available un
December. Much of the crop has been sold I
that date. Prices received for forest products a
greenhouse and nursery products could not be i
cluded since basic price and sales data are lac
mg.

Additional commodities were brought into t
index as they became important and price 
sales data were available. For example, sorgh
grain prices were brought into the feed grain a“ A
hay index in January 1917. Sub-crop grou
were expanded at the beginning of 1924 to inclu
fruits and commercial vegetables for fresh mi
ket. In the latter group, winter lettuce W
added in October 1946 and the early fall lettu
crop was picked up in January 1948. *

Turkey prices were first included in the p0
try and egg index in June 1933. For earlier yea ,
turkey was a Thanksgiving or Christmas dis
and prices were available for only a few mont A ~
Because of diminishing importance, butter WP
dropped from the dairy index in October 1946. p

Selection of Base

Amendments to the parity legislation includ
in the Agricultural Acts of 1948 and 1949 requ'
ed shifting the base period for the index of pric
received by farmers from August 1909-July 191,
to January 1910-December 1914. This shift W
made to coincide with the base used in construe
ing the national prices paid index. Procedur
used in constructing the national index were f0 _
lowed in revising the index of prices received "
Texas farmers. 

Structurally, the revised index of prices g’
ceived by Texas farmers is a modified fix"
weight aggregative type with the base peri
January 1910-December 1914 taken as 100 pe
cent. Three weight base periods were used: 192
29, 1935-39 and 1948-53.

For January 1910 to June 1933, prices we‘
Weighted by average sales during the 6 year

  
  
  
 
 
 
  
  
 
   
 
  
 
 
   
  
  
  
 
 
  
  
  
 
   
  
  
  
   
   
   
  
 
   
  
   
  
  
  
  
   

4-29. Prices and cash receipts were more
‘ii during 1924-29 than in any other period of
parable length during 1910-35. From June
ii through September 1946, prices were
'ghted by average sales in 1935-39. This is
j period used in constructing the original index
, prices received by Texas farmers. Beginning
October 1946 and continuing to the present
' ; e, prices were weighted by average sales for
8-53. This period was taken as indicative of
. twar agriculture and was chosen to provide
, even distribution between pre-drouth and
l» years. A more recent period would have
 too heavily weighted with drouth years.

. Use of shifting weight base periods is a com-
 mise between fixed weights and the necessity
r recognizing long-time shifts in agricultural
voduction. Indexes computed using the various
eight base periods are linked together to pro-
5» a continuous series with 1910-14 = 100.
ethods used in linking these indexes are ex-
ined on page 12.

Weight base periods used in constructing the
tional index of prices received by farmers are:
24-29, from January 1910 to January 1935;
137-41 from January 1935 to August 1952; and
53-57 from September 1952 to date.

Grouping of Commodities

Y Farm products can be grouped on the basis of
Y eir general use or production requirements. An
h ‘t-erall picture of agricultural price movement
mes into focus by observing price changes for
few major groups of products. The more de-
diled task of analyzing price movement for each
mmodity is time consuming and fails to give
'ther the trend or level of prices received for
ll farm products.

. Groups and subgroups used in the original in-
ex follow the groupings for the national index
_. nd generally were retained. Instead of a “woo1”
l oup as carried in the original index, “wool and
A ohair” now comprise the comparable livestock
‘roducts group. The “truck crops” index is now
deferred to as “commercial vegetables for fresh
1 arket” in keeping with the terminology used
urrently in referring to these crops.

, Commodities were brought into the index when
heir importance in relation to cash income suffi-
iient to warrant their inclusion and when ade-
uate price and marketing weight data became
‘vailable. For methods used in adding commodi-
lies, see page 14.

j Following are the two major groups and 11
ubgroups used in the revised index of prices re-
eived by Texas farmers with the commodities
n each:

GROUP COMMODITY
Crops

Cotton .................................. ._Cotton lint

Food grains ......................... ..Wheat, rice

Feed grains and hay ........ .-Corn, oats, barley, sorghum

grain, hay
Oil-bearing crops ............... _.Cottonseed, peanuts
Potatoes and
sweet potatoes ................ ..Potatoes, sweet potatoes
Fruits ................................... _.Oranges, grapefruit, peaches
Commercial vegetables Cabbage, winter; carrots, win-
for fresh market ............ ..ter; onions, early spring, late

spring; spinach, winter; toma-
toes, early spring, late spring,
late fall; watermelons, early
summer; lettuce, winter, early
fall

Livestock and Livestock Products

Meat animals ...................... ..Beef cattle, calves, hogs, sheep,
lambs

Poultry and eggs ............... ..Chickens, turkeys, eggs

Dairy products ................... "Milk, wholesale; milk, retail;

butterfat in cream, butter (drop-
ped in October 1946)

Wool and mohair ............... ..Woo1, mohair

Thus, prices received for 26 crops are com-
bined into an all-crops index. In like manner,
prices received for 14 livestock and livestock pro-
ducts items were combined. These two major
groups—al1 crops, livestock and livestock pro-
ducts-—were combined into an all-farm products
index. Commodities used in constructing this in-
dex account for more than 95 percent of the
State's cash income.

Commodities Having Incomplete
Monthly Price Estimates

For short-season crops, such as commercial
vegetables and fruits, marketings occur in only
a few months. For potatoes and sweet potatoes,
prices are estimated only for months when sales
account for at least 1 percent of seasonal sales.
In recent years, nominal prices have been dis-
continued for cottonseed and peanuts.

A monthly price is needed for each commodity
in constructing the index. This involves supply-
ing prices for months having no estimate. In
the original index, the season average was sup-
plied for citrus and commercial vegetables. This
basic principle has been retained, but modified to
limit fictitious fluctuations in the index.

Under certain conditions, use of the price esti-
mated for the last month of the marketing sea-
son prevented fictitious advances or declines in
periods of no grower sales. For example, growers
receive 80 cents per hundredweight for cabbage
in May 1950. The season average price received
for that crop was only 60 cents. Growers re-
ceived $1.25 for marketings of new-crop cabbage
in October. Use of the May price for months

l1

with no estimated price prevented a fictitious
change in the index of prices received for com-
mercial vegetables. -

For potatoes, sweet potatoes, cottonseed and
peanuts, crops for which prices were available
for practically all months, the last monthly price
of the marketing season was used generally until
the new crop came into production. Historically,
the price adopted for the first month of the next
marketing year provided a helpful guide in de-
ciding Whether to supply the season average or
the last monthly price.

Computation of Subgroup Indexes

Table 1 shows basic data used in computing
subgroup indexes for January 1910 through De-
cember 1958. For each weight base period (1924-
29, 1935-39 and 1948-53), the average monthly
price was obtained first for each commodity as
well as the annual or season average quantity
sold. Thus, during the 7 years, 1924-29, the aver-
age price of wheat was $1.27 per bushel and the
average quantity sold amounted to 23,029,000
bushels. Comparable figures for rice, the other
component of the food grain index, are $2.58 per
hundredweight and 2,986,000 hundredweight. The
average annual aggregate value of wheat and
rice sales during this base period was $36,951,000.

Average quantities sold during 1924-29 Were
used for weighting prices each month from Jan-
uary 1910 through May 1933. For example, the
May 1933 computations are shown in Table 2.

Dividingthe May 1933 aggregate of $17,610,
000 by the base aggregate of $36,951,000 gives
an (1924-29=100) index of 47.7 percent. This
procedure was followed each month in the Jan-
uary 1910-May 1933 period. The objective, how-
ever, was to construct the series of index num-
bers so that 1910-14=100. Consequently, the in-
dex had to be converted from 1924-29=100 to
1910-14=100. This was done by obtaining the
January 1910 through December 1914 average of
the 1924-29=100 indexes, which was 75.295. To
place the 1924-29=100 indexes on the 1910-14:
100 level, 100 percent was divided by 75.295,
which resulted in a conversion factor of 1.3281.
Each of the monthly food grain indexes from
January 1910 through May 1933 was adjusted by
this factor. In May 1933, forexample, the 47.7
percent computed on the 1924-29=100 base was
adjusted to 63.4 percent (47.7 percent >< 1.3281).
A similar procedure was followed for other sub-
groups except cotton.

Since cotton comprises a separate subgroup,
a price relative was computed from the 1910-14
average price. This placed the cotton index on
the desired 1910-14=100 base and no adjustment
was necessary. Special handling was needed,
however, to combine cotton with other crops into
an all-crops index. This procedure is explained
in the section, “Combining subgroup indexes.”

12

 
   
   
   
 
  
  
   
  
   
   
   
    
  
  
  
  
  

From June 1933 through September 1946-
dexes for all subgroups, except cotton, were t
puted from 1935-39 base data. Starting in,
tober 1946 subgroup indexes were computed f
1948-53 base data. Indexes from each of 1
base periods were converted to a 1910-14=,
base by applying the appropriate conversion 
tor. These factors are shown in Table 3 and

explained in the following section. '

Selection of Lin/< Dates

As previously stated, three different wéi
base periods were used in constructing the
vised series of index numbers to reco 
changes in Texas’ agricultural price pattern.‘
was necessary to link index numbers compu
from these three weight base periods to effec
smooth shift when changing from one base?
another. Selecting the link date is import
Of primary importance is selection of a mo
when the two major groups, all crops and li
stock and livestock products, are in close a 1
ment.

Original plans were to shift in 1932 from .-_
1924-29 base to 1935-39. Throughout 1932, >
all-crops index was considerably lower than
livestock and livestock products index. It "
not until June 1933 that these two major if
indexes were within two points of each other. __
that time prices were just starting to turn t
ward following the prolonged depression.

October 1946 was chosen as the date for li _
ing indexes computed from 1935-39 to 194,
base. For that month, the all-crops index c‘
puted from the 1910-14 = 100 base was 278 
the livestock and livestock products index 2
This five-point spread is more than is desira
but is the minimum in the period between g
two base weight periods (1940-47).

Linking and Conversion Procedur

Procedures for converting subgroup inde
from the 1924-29=100 base to the 1910-14=1;
base have been explained. Similar procedures L
used for converting the all-crops, livestock 
livestock products and all-farm products inde
to the 1910-14=100 base. 1

Appropriate linkingfactors were computed f l,
connecting indexes calculated on a 1924-29  ~
with those developed on a 1935-39 base. In 1'
manner, factors were computed for linking " _
dexes from the 1935-39 base with those wor 
from the 1948-53 base. Table 4 shows the co‘ '
putations involved in developing food grain y‘
version factors for the 1935-39 and 1948-53 bi
periods. ‘

The indexes are derived from the “aggrega 
and the respective base aggregate shown for u»
grains in Table 1. 1

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TABLE 2. COMPIITATION OF AGGREGATE VALUE OF
FOOD GRAIN SOLD IN 1924-29 AT PRICES RECEIVED IN

 

MAY 1933
Prices Average
received quantity Aggregate
by farmers. sold, value
May 1933 1924-29
Thousand
Commodity Unit Dollars Thousand Dollars
Food grain group
Wheat Bushels y .60 23.029 13.818
Rice Cwt. 1.27 2.986 3.792
17.610
TABLE 3. FACTORS FOR CONVERTING FROM WEIGHT

BASE TO 1910-14 = 100

Conversion iactor

to to to
1910-14 1910-14 1910-14
Cotton lint 19.304‘ 9.945‘ 32.66‘
Food grains 1.3281 .88782 2.3754
Feed grains and hay 1.0154 .73382 1.6281
Oil-bearing crops 1.6145 1.2819 3.3892
Potatoes and
sweet potatoes 1.3669 .90147 2.3246
Commercial
vegetables’ 1.5928 1.0386 2.6281
Fruits’ 1.5928 .85551 2.3699
All crops 1.5928 .86741 2.6293
Meat animals 1.3598 1.1725 4.1246
Poultry and eggs 1.5877 1.1224 2.5849
Dairy products 1.5082 1.1648 3.0683
Wool and mohair 2.1620 1.5422 3.6285
All livestock
and products 1.4695 1.1541 3.4390
All commodities 1.5565 .97048 2.9363

‘Index is a price relative (1910-14 = 100) using 1910-14 aver-
age price oi 19.304 cents per pound. The 1935-39 average
is 9.945 cents per pound and the 1948-53 average is 32.66
cents.

“Commercial vegetables and fruits brought into index Ianu-
arY. 1924 at level of all crops.

TABLE 4. DATA USED TO COMPUTE FACTORS FOR LINK-
ING WEIGHT BASE PERIODS (FOOD GRAINS)

Old New
Month and vibealgdlt Aggtre‘ Index fee; toe‘: 3:11}:
Year period ga e sion sion
factor factor
lune 1933 1924-29 18.109 49.0
__:66.849 X 1.3281:.88782
lune 1933 1935-39 21.489 73.3
October 1946 1935-39 71.308 243.2
-_-::267.55 X .88782:2.3754
October 1946 1948-53 133.514 90.9

14

  
 
  
  
  
  
    
    
  
  
 
   
   
   
  
  
   
     
  
 
    
   

Similar procedures were used for each of
subgroup indexes (except cotton), the two l.
groups of all crops and livestock and live
products, and the “all-farm products” group.
tors used to convert the respective group ind
computed on each of the three different has
a 1910-14=100 base are shown in Table 3. C0
indexes are price relatives iiyvorked from the 

14 average price and no' conversion factors
needed.

Combining Subgroup Indexes I

Table 5 shows the basic data for combi
subgroup indexes into two major group inde
(all crops and livestock and livestock produc
Weights for combining these two major gro
into the all-farm products index also are shoe
Indexes for the two major groups (Figure 3) :
all farm products are computed from the ap‘
priate base before converting to the 1910-1’
100 base. Cash receipts are used for weights
combining subgroup indexes. ‘

Table 6 and Table 7 show the actual comp a1
tions for May 1933 for the all-crops (Table i
and the all-farm products indexes (Table 7). 

The all-crops conversion factor (1.5928) _
developed by first obtaining the January ‘,
through December 1914 average of the 1924-
100 indexes which was 62.782. Secondly, 100 l,
cent was divided by 62.782 to give the factor
1.5928. ’

Since cotton is treated as a separate gro
this crop required special handling in comput'-_
the all-crops index on the 1924-29, 1935-39
1948-53 base. In Table 6, the 41.4 percent is
price relative computed from the 1924-29 av
age $0.19304 ($0.08 + $0.19304 = 41.4). F
other months of the period studied, price re
tives computed from the respective base p 
shown in Table 3 were used in computing the 
crops index.

Adding Commodities

The following commodities were added t0 t
index during the revision at the time specified ;

COMMODITY OR GROUP DATE 

Sorghum grain ........................................ .. January 1911
Fruits ........................................................ .. January 19 ' j
Commercial vegetables for

fresh market ........................................ .. January 19 

Turkeys ..................................................... .. June 1933 . -
Lettuce, winter ........................................ .. October 1946= ,
Lettuce, early fall .................................. .. January 194$

The most favorable time to introduce a l;
modity into the index is at the beginning of ‘V
weight base period since the effect of the ne
commodity is taken care of in computing the n
weight base aggregate. Only turkeys and win a

5

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TABLE 6. COMPUTATION OF ALL-CROPS INDEX, MAY 1933

Percent-
"ge °* 11331:”
Subgroup cash 1924 29 _'_ Extension
receipts, {no _
1924-29
Cotton 77.3 41.4 3,200.22
Food grains 5.9 47.4 279.66
Feed grains and hay 3.7 48.0 177.60
Potatoes and sweet potatoes .8 39.1 31.28
Oil-bearing crops 8.2 31.9 261.58
Fruits ' .9 57.2 51.48
Commercial vegetables 3.2 82.7 264.64
100.0 xxxx 4,266.46
All crops index
1924-29 : 100 (4266.46 —I— 100) = 42.7
Factor (converts to 1910-14 = 100) = 1.5928
1910-14 = 100 (42.7 X 1.5928) : 68.0

lettuce, however, were added at link dates. Com-
modities or subgroups can be brought into the
index at any time. The following example shows

TABLE 7. COMPUTATION OF ALL FARM PRODUCTS IN-
DEX, MAY 1933

Percent-
. age °* 133311?’ .
Mayor group cash 192449 _'_ Extension
receipts, mo _-
1924-29
All crops 72.2 42.7 p 3,082.94
Livestock and livestock
products 27.8 50.3 1,398.34
Total 100.0 xxxx 4,481.28
A11 iarm products index
1924-29 = 100 (4481.28/100) = 44.8
Factor (converts to 1910-14 = 100) = 1.5565
1910-14 : 100 (44.8 X 1.5565) = 69.7

16

  
  
  
  
     
     
   
  
 
 
 
 
 
   
 
  
    
  

details of bringing sorghum grain prices into -_
feed grains and hay index in January 1917: .

A=Average 1917 monthly
feed grains and hay f
gregate including so A
ghum grain ($47,479,,

B=l924-29 base weight a
ivgregate ($27,949,000)

C==”.-'Average 1917 monthly
feed grain and hay i f
including sorghum g 1
on 1924-29 = 100 bas j

$30,396,000 = ($17,893,000) D=Average 1917 monthl»

(169.8773) feed grains and hayaa
' gregate excluding so!"
ghum grain. ‘

E=New base weight y‘
gate to be used 1910-1
for feed grains and ha

A=CB

$47,479,000 = (169.8773)
($27,949,000)
D =EC

The group aggregate of $17,893,000 is ~
before sorghum grain was added in January 19,
and $27,949,000 for other years prior to the J V
1933 link date.

A similar procedure was used for bringing A
fruit and commercial vegetables for fresh mar .-
groups into the all-crops index in January 19
Both groups were added at the same time ,
simplify computations.

ACKNOWLEDGMENTS

The authors express their appreciation to s
clerical staffs of the Agricultural Estimates K
vision, Agricultural Marketing Service, U. S. In
partment of Agriculture, Austin, Texas, the ._
Processing Center, Texas A&M College Syst
and the Department of Agricultural Econom
and Sociology of the Texas Agricultural Expe
ment Station for assistance in preparing this p p
lication. “