A model based on multiple regression was developed to estimate corn yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the corn-growing area. Predictor variables for the model were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. A trend variable was included for the years 1965 to 1980 since an increasing trend in yields due to technology was observed between these years

Callis, S. L.

Sakamoto, C.

English

NASA Technical Reports Server

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JANUARY 16, 1934 
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(E84- 10 105) A B G l i N T I N A  CCBN Y I E L C  HODEL b184-2 1923 -2 ; 
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5 CLARENCE SAKAMOTO 
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COLUMBIA, MO 6 201 
Lyndon B. Johnson Space Center 
Houston Texas 77058 
1 
https://ntrs.nasa.gov/search.jsp?R=19840013855 2020-03-20T23:59:43+00:00Z
ARGENTIKA CORX YIELD ?lODEL 
Susan L. Callis 
and 
Clarence Sakamoto 
A I S C  Models Branch 
January 16, 1984 
1. R@mrt No. 2. -t Atcamion No. 3. hapcant's Cltalog No. 
YM-N4-04456, JSC 18908 
Argentina Corn Y ie ld  Model I 6. M o r m ~ n g  Organ~zat~a 1 I
Susan L. Cal l  i s  and C l  arence Sa kamoto 
10. W a k  Unit No. 
9. Paforming Oqmiotion Nwm and Addms 
USDCINOAA 
C IADIModel s Branch 
Room 200, Federal Bldg, 608 Cherry S t .  
Columbia, MO 65201 
b 
16. Abrcrat 
A model based on mu l t i p l e  regression was developed t o  estimate corn y i e l d s  f o r  the 
country o f  Argentina. A meteorological data set  was obtained f o r  the country by 
averaging data f o r  s ta t ions  w i t h i n  the corn-growing area. Predictor var iab les 
I I 
19. Srwitv Clamif. (of this rrportl 1 10. SIcurity Classif. (of this prprl 1 21. NO. of pages 1 22. Rice* t 
i 
11. Contract or Grant NO. f 
13. T v ~  of ReW't and Puled Covrrd 
i 
i 1 
1 
12. slmnuwl~ A W ~ ~ V  N8llm Adbat 
National Aeronautics and Space Administrat ion 
Lyndon B. Johnson Space Center 14. ~oonwlng ~gency c06 
Houston, TX 77058 
17. Key Words ( S u w  bv AuMw(sl) 
Mu1 t i p l e  regression 
Predictor var iables 
*For rJI by thr Nationrl Technical Information Srvico. Springfield, Virginia 22161 
JSC Form 1424 (Rw M a  78) 
, 
18. Distribut~on Statement 
I Unclassi f ied 
NASA - S C  I 
f o r  the model uere derived from monthly t o t a l  p rec ip i t a t i on ,  average monthly mean 
temperature, and average monthly maximum temperature. A " t rend var iab le "  was 
included f o r  the years 1965 t o  1980 since an increasing trend i n  y i e l ds  due t o  
Unclass i f ied 1 13 I 
1 technology was observed between these years. t 
ORIG1MAL PAGE IS 
OF POOR QUALm 
I NTRODUCT I ON 
The purDose of tn?s stuay was t o  se lec t  monthly weatner var laa les t ha t  coula I C *  
De  used t o  estimate y i e l d s  of  the  Argentina corn crop. Tpe corn-grow~ng area 
i n  Argentina i s  ind icated i n  Figure 1. Most o f  Aryent lna's corn i s  grown i n  a  
concentrated area i n  cent ra l  Argentina where t he  c l imate  i s  predominantly humid I 
subtropical .  The western edge of t he  soybean area i n  cen t ra l  Cordoba i s  semi- 
a r i d  w i t h  warm t o  hot summers. There drought and h igh  temperatures can be a 
problem dur ing  t he  growing season which begins w i t h  p l a n t i n g  i n  October and 
November and extends t o  harvest i n  A p r i l  through June. 
METHOD 
M u l t i p l e  regression analys is  o f  y i e l d  w i t h  selected agrocl imat i c  ind ices 
was used t o  der i ve  a su i tab le  model. The regression equation f o r  t he  corn model I 
der ived i s :  
where 
PI 
Y = Es t ima tedy ie l d  
a = Constant 
B j  Coe f f i c ien ts  o f  the var iab les  j = 1 - 3 
T = Trend 1965-1980 
A A - -  
ETMETHi = ET - ET f o r  month i: where E T  = K * P E T  and K = ET/PET. 
T X ~  = Maximum temperature f o r  month i, and 
E = Unexplained er ror .  
\ 
Trend between the  years 1965 and 1980 was determined from a p l o t  o f  y i e l d s  as 
shown i n  Figure 2. ET (evapotranspirat ion) minus & ( c l i m a t i c a l l y  appropr iate I 
evapotranspirat ion) was used as an index represent ing so i  1  moisture fo r  p l an t  1 :  
growth. A more complete d e f i n i t i o n  i s  given i n  t he  Appendix. Large p o s i t i v e  
A 
E T  - ET values suggest wet condi t ions. 
- - . . - -- - - - , - - - - -. -- - -- - - - - 
, , - -&--&--ma;- m :*--.- , ; , , - l*&*..i *-= ---  - -  ' , - ( .  - _  
i m 
,., tmr-maul bun- 
- --- ~ " , , , u l & o o C q  I 
- P w i  b C y  
p- C ~ r r  
b p l e  Wac ~CPtat 
Figure 1 .  DENSITY OF P U V T E D  AREA IN CORS 1977178 CROP YEAR 
(Source: "Agronomic Characcerimtlon o f  the Arren- 
cina Indicator  Regiun") 
ORlGlNIL PAGG f3 
OF POOR QUALITY 
v 1 
6 I I I I I  , I . , .  . . , I  m . . , , , ,  
Y E A R  60 6s 7 0  
CORN PRODUCTION 
Figure 2. Plots of Production and Yield Versus Year for 
Argentina Corn. 
~ - 
CORN YLtLDS 
x" :~ '~ - - . - - -  - 
z 
,, , . '..,.. , . 
10 developing t he  models, var ious procedures o f  t he  S t a t i s t i c a l  Analysis 
System ! S A S  I n s t i t u t e ,  Inc., !o?9!  were used. The proceCures used and the  
. L,,,,, -a* C , I O ? ~  - - ?errf3~rted ~ f t n  eacn are sumnari zec i n  tne L.?;lcncrx. Tne selectec 
;nose; :;ic trle R: g w s t  22 ana inciuded v a r i ~ b i e s  tha; were s i g r l i f i can t  a t  t n e  
10 per cent l e v e l  and were agronomically meaningful. 
DATA 
Crop data f o r  Argentina from 1960 t o  1979 was obtained from the  Foreign 
Ag r i cu l t u ra l  Service (A1 an Vandagriih, personal communication, 1982). A data 
set  was created w i t h  year o f  y i e l d  as year o f  p lant ing.  Since the  grawing Y d  
season spans two numerical years, t he  crop da ta  was expressed i n  terns o f  "year 
+ 1" (o r  harvested year)  and the  meteoro log ica l  data  corresponding t o  any y i e l d  
included data f o r  t h a t  year and "lagged" da ta  f o r  the  previous year. 
The meteor01 og ica l  data was created us ing  t h e  general Argentina 
meteorological  s t a t i o n  f i l e .  This f i l e  was composed o f  data from several 
d i f f e r e n t  sources, i nc l ud ing  t he  Monthly C l ima t i c  Data f o r  t he  World and t h e  
Se rv i c i o  Meteorological  Nacional i n  Argent ina (R.E. Jensen, C.M. Sakamoto, and 
S.E. Mummert; August, 1974). S ta t ions  i n s i d e  t h e  corn-producing area were 
averaged by prov ince and weighted according t o  t h e  percentage t h a t  t h e i r  pro- 
v ince con t r ibu ted  t o  the  e n t i r e  count ry 's  product ion.  F igure 3 shows t h e  corn- 
growing area i n  Argentina and associated s ta t ions ;  Table 1 l i s t s  t he  s t a t i ons  - 
used and t h e i r  weights. 
* 
The years between 1961 and 1980 (year o f  harvest )  were used i n  t he  model 
s ince these years contained t he  most complete meteorological  data. 
PROCEDURES 
Weather va r iab les  were selected from c o r r e l a t i o n s  and p l o t s  w i t h  y i e l d .  
These var iab les,  along w i t h  TREND, were t r i e d  i n  regress ion equations. The 
1 i ln i ted number of data years severely l i m i t s  t he  nunber o f  var iab les t h a t  
.--. ] .  - -*.- - - -"a'-.. vr- 
ORIGINAL PAGE IS 
OF POOR QUALlW 
C A 
b 
B e l l  V I I ie  l . 
Rio C u r r t o  
Trenque Lauquen 
i BUENOS AIRES 
M8e8ehln l 
- 
J 
BUENOS AIRES 
oRlOMAL ? A M  IS 
OF POOR QUALW 
Pe rgamino 
Junin 
Nueve de Julio 
CORDOBA 
Rio Cuarto 
Bel l  Vi l le  
SANTA FE 
Rosario 
Casilda 
Las Delicias 
Parana 
Pe rgamino 
Table 1. Meteorological Stations Used 
for the Argentina Corn Model. 
ORIGINAL PAGE 121 
OF POOR QUALITY 
can be included i n  a regression model by l i m i t i n g  t h e  number o f  degrees o f  
freedom. This i ~ f l  uenced the  choice o f  model considered "best" f o r  soybeans. 
Tnree weather var iab les plus THEND were considered the  optimum moael w i t h  19 
years o f  data. Tne tnree weatner va r iab les  are: 
A 
ETMETH11.. ......... ...ET . ET f o r  November 
TX12 ............... ...Maximum temperature f o r  December 
Txl................... Maximum temperature f o r  January. 
The c o e f f i c i e n t  f o r  t he  November va r i ab l e  was p o s i t i v e  which r e f l e c t s  
moisture requirements j u s t  a f t e r  p lant ing.  The c o e f f i c i e n t s  f o r  maximum 
temperature f o r  Decenber and January were negat ive i n d i c a t i n g  t h a t  temperatures 
t oo  h igh i n  t he  peak summer months du r i ng  t h e  t a s s e l l i n g  and s i l k i n g  stages lead 
t o  s t ress on t he  plant.  There i s  a c r i t i c a l  growth per iod f o r  corn. The 
s t a t i s t i c s  o f  the  selected model are summarized i n  Table 2. 
TEST RESULTS 
A jackkn i fe  t e s t  was run  on t he  model. I n  t h i s  t e s t  a year was e l im ina ted  
from the crop data and t he  model was used t o  p r e d i c t  t h a t  year ' s  y i e l d .  This 
process was dvv .  f o r  each successive year beginning w i t h  1961. The r e s u l t s  were 
reasonable w i t h  the  greatest  d i f fe rence  (3.24 qu in t  31 s) between p red ic ted  and 
actua l  y i e l d  i n  1976. That year severe drought badly damaged the  corn crop and 
harvest was hampered by ra ins.  Test r e s u l t s  a re  p r i n t ed  on Table 3 and p l o t t e d  
on Figure 4. 
. . 
-- 
(I, *-- 
APPEND I X 
De f in i t l on  o f  Variables 
Tne d i  f iorence between E i  and E f  1 s  an index o f  tne amount o f  moisture 
avai l ab le  fo r  p lant  growth. Soi 1 moisture aepiet ion i s  base0 on evapo- 
t ransp i ra t ion  (ET) estimates, determined as f o l  lows: 
where 
(ET), = "Actual" evapotranspiration, 
( 1  = Available moisture a t  end o f  n-1 month, 
AWC = Maximum water holding capacity, 
(P), = Prec ip i ta t ion  f o r  month n, and 
(PET), = Potenti a1 evapotranspi ra"ion f o r  month n. 
A 
ET - E T  measures the d i f ference between the actual evapotranspiration and the 
" c l  imat ica l  l y  appropriate" evapotranspiration, g i v ing  an i ndicat ion o f  s o i l  
moisture supply and demand. 
S t a t i s t i c a l  Analysis System Procedures Used 
PROC CORR 
PROC PLOT 
PROC STEPW I SE 
PROC STEPW I SE FORWARD 
Ccnputes co r re la t i on  coe f f i c i en ts  between 
variables, i ncl  udi ng Pearson product-moment 
and weighted product-moment correlat ion. 
Graphs one var iable against another, jroducing 
a p r i n te r  p l o t  . 
Provides f i v e  methods f o r  stepwise regression. 
Stepwi se i s  useful  when select ing variab,es t o  
be included i n  a regression model from a 
co l l ec t i on  o f  independent varlables. 
Begins by f i nd ing  the  one-variable model t ha t  
produces the  highest ~ 2 .  For each o f  the other 
i ndependent variables, FORWARD calculates F- 
s t a t i s t i c s  r e f l e c t i n g  the  contr ibut ion t o  the 
model i f  the  var iab le  were t o  be :ncluded. 
PROC STEPWISE MAXR 
PROC PETM 
PROC ZINDEX 
ORIGINAL ?Ad€ 1(1 
OF POOR QUALIW 
Begins by c a l c u l a t i n g  s t a t i s t i c s  f o r  a model 
i nc l ud ing  a1 1 t he  f nde9endent var iab les.  The 
var iab les are deletea f ro?  cne model one by 
one un:il a l l  t n c  r25z:r . ; r ;  v a r i a ~ l e s  produce 
i - s t a t i s t i c s  s i g n i + t c a n r  3: : le  .lil i eve l .  
Tne stepvJisr metnod i s  s moe: f~cat ior :  o f  t n r  
forwara se lec t ton  tecnnique, d l  f f e r i n g  i n  
t h a t  va r iab les  already i n  t h e  model do no t  
necessar i ly  stay there. A f t e r  a  va r i ab l e  i s  
added (as i n  t he  forward se lec t i on  method) t he  
stepwise method looks a t  a l l  t he  va r iab les  
a1 ready included i n  t h e  model and de le tes  any 
va r i ab l e  t h a t  does not  produce an F - s t a t i s t i c  
s i g n i f i c a n t  a t  t he  .10 leve l .  Only a f t e r  t h l s  * 
check i s  made and t h e  necessary de le t i ons  
accomplished can another va r i ab l e  be added t o  
t h e  model. 
(Yaximum ~2 improvement) Un l i ke  t h e  th ree  
techniques above, t h i s  method does no t  s e t t l e  
on a s i ng le  method. Instead i t  looks f o r  t h e  
"best"  two-var iab le  model, t he  "best8' t h ree  
va r i ab l e  model, and so f o r t h .  
Uses l a t i t u d e  and mean monthly temperature t o  
ca l cu l a te  Thornthwai t e ' s  po ten t i a l  evapo- 
t r a n s p i r a t i o n  f o r  each month. 
Uses monthly PET'S, p r t  . i p i t a t i o n ,  SS 
(beginning mois ture i n  surface layer ) ,  
AWCS ( ava i l ab l e  water capac i ty  i n  surface 
1 ayer, SU (beginn ing mois ture i n  t he  
under ly ing 1  ayer) , and AWCU (avai  1  ab le  water 
capac i ty  i n  t he  under l y ing  l aye r )  t o  c a l c u l a t e  
mois ture budget, drought index 

LC 'r S f , P  fi\*-&b*%.r&- 
t ze%=:us#xr  -522 it' r2?,3*?+?*>***2 - --***%9e* J F - = * ~  OWQHAL PA= 19 
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OF POOR QUALITY 
U t I t l l 1 l I l l l t t t t  
ORIGINAL PAGE 13 
OF POOR QUALITY 
I 6 
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Argentina corn yield model

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