YM-N4-04455 J S C 18907 t

.* A Joir:t Program for Agriculture and Resources Inventory Surveys Through Aerospace -

Remote Sensing Yield Wocit:. Development

JANUARY 16, 1984

3P.r.Z 1 L SOYBEAN Y I E U ) COVARIANCE MODEL ( E $ 4 - 10 134) bE,Ai,IL S b Y t E A h Y I E L D C L V A E I M ~ C L hdk-2 1922 H(;XL (Natio:.al cceh1.i~ aria A t a o s p h e r i c A d m l c i s t r a t l o ~ ~ ) 25 p .;L i i 3 i / E k A 0 1 LSLL 02C Llnc id s

3 G O l i . 4

SUSAN L , CALL1 S CLARENCE SAKAMOTO

USDA/NOAA C I A /MODEL RANCH

b ROO 1 200 ? % E ERA BUIG fog CHERAY STREE! OLUMBIA, MO 65201

Lyndon B. Johnson Space Center Houston. Texas 77Q58

https://ntrs.nasa.gov/search.jsp?R=19840013854 2018-07-12T09:03:28+00:00Z

JSC Form 1424 (Rrv N m 75)

'For salo bv the Nat~onal Technical Information Smlce. Spcngfield. Virginia 22161

3. R m p a t ' s Catalog No.

5. Report Date

January 16, 1984 6. f'erformlng Organlratcon Code

8. Performcng 0rganczat:m Rcpat No.

10. Work Unct No.

11. Contract or Grant No.

13. Type of Repon and Percod Covered

14. Sponsoring A- code

1. Report No.

\IM-N4-04455, ;SC 18907

NASA - JSC

2. Gowrnment Accesscon No.

4. Tctle and Subtctle

B r a z i l 5ovbean Y ie l d Covariance Model

7. Author(s1

Susan L. C a l l i s and Clarence Sakamoto

9. Perforrncng Organlation hbme and Address

USDCjNOAA CIAD/Model s Branch Room 200, Federal Bldg, 608 Cherry S t - Columbia, MO 65201

12. Sponsor~ng Agency Name and Address

Na t ions l Aeronautics and Space Admin i s t r a t i on Lyndon B. Johnson Space Center Houston, TX 77058

IS. Supplementary Notes

16. Abmm

A model based on m u l t i p l e regress ion was developed t o es t imate soybean y i e l d s f o r t h e seven soybean-growing s t a t e s o f B r a z i l . The meteoro log ica l data o f these seven s ta tes were "pooled" and t h e years 1975 t o 1980 were used t o model s ince the re was no techno log ica l t rend i n the y i e l d s du r ing these years. P r e d i c t o r va r iab les were der ived from monthly t o t a l p r e c i p i t a t i o n and monthly aversge temperature.

17. Key Words (Suqgsstsd by Authu(s1)

Mu1 t i p 1 e regress i o n ana lys i s P red ic to r va r iab les

'8. D~sn~butcon Statement

19. ~ s c w ~ t y a m l f . (of thk myor*'

Unc lass i f i ed 1 A

21. No. of Papss

I 2 4

20. Secur~ty Classif. (of thls psg)

Unc lass i f i ed

22. Rice'

BRAZIL SOYBEAN Y I E L D COVARIANCE MODEL

Susan L. C a l l i s

and

C l arence Sakamoto

AISC Models Branch

January 16, 1984

I NTRODUCTI ON

The put-pose o f t h i s study was t o se lec t weather va r iab les t h a t could be

used t o estimate the soybean y i e l d s f o r t he country of B raz i l . Soybean produc-

t i o n i n Brazi 1 had i t s incep t ion i n t he s t a t e of R io Grande do Sul where

soybeans were f i r s t p lanted i n r o t a t i o n w i t h wheat. Present ly, soybeans a re

planted i n seven southern states: R io Grande do Sul, Santa Catarina, Parana,

Sao Paulo, Mato Grosso, Goias and Minas Gerais. This means t h a t a v a r i e t y o f

growing condi t ions are covered i n t h i s extensive area. F igure 1 i s a map o f

t he soybean-growi ng areas of Brazi 1.

The northern pa r t o f t h e soybean area, i nc l ud ing t he s ta tes o f Mato Grosso,

Goias, and Minas Gerais, a re character ized by a "wet-arad-dry" c l ima te w i t h a d r y

season o f two or more months. The r e s t of t he area i s subt rop ica l humid w i t h

r e l a t i v e l y abundant r a i n f a l l which i s we1 l - d i s t r i buted throughout t h e year bu t

w i t h s l i g h t l y more r a i n f a l l i n t he warm months. Summers are hot and w in te rs

r e l a t i v e l y mild. Temperatures i n t h e summer are above 400C i n t he p l a i n s o f

R io Grande do Sul when warm a i r masses penetrate t h e plains. Southern Sao

Paulo i s t he northern l i m i t f o r f r o s t occurrence.

The soybean growing season begins w i t h p l a n t i n g i n October and November and

runs through A p r i l and May when harvest s ta r ts .

METHOD

Mu1 t i p l e regression ana lys is o f y i e l d w i t h se lected agroc l imat ic ind ices

was used t c der i ve a su i t ab l e model. The f i r s t approach taken was t o develop a

model f o r each state. However, l a ck o f data f o r several s ta tes posed a problem

and no su i t ab l e models were derived. Therefore, i t was decided t o c rea te a

covariance model whereby a l l t h e ava i l ab l e data a re combined t o ob ta in a model

f o r the country.

ORIGINAL PAGE C9 9F POOR QUALITY

Figure 1. Soybean-proving a r e a of B r a z i l . (Pitcher, 1971)

Since moisture stress was considered a prime determinant o f y ie ld , the

index P-PET (p rec ip i t a t i on m i nus potent ia l evapotranspiration) was used i n the

regressions.

The regression equation i s : A Y = U + BID + B2RDFNi + B3(P-PET)i + E

where A Y = Estimated y ie ld ,

a = Constant,

B j = Coef f ic ients of the variables j = 1 - 3,

D = Dummy var iable t o adjust each state 's y i e l d t o a base y i e l d set by

Rio Grande's do Sul 's y ie ld ,

RDFNi = Deviat ion from normal o f t o t a l p rec ip i t a t i on f o r month i ,

P-PET1 = P rec ip i t a t i on minus PET f o r month i , and

E = Unexplained e r ro r

I n developing the model, various procedures o f the S t a t i s t i c a l Analysis

System (SAS I n s t i t u t e Inc., 1979) were used. The procedures used and the

operations performed w i t h each are summarized i n t he Appendix. The .elected

model had the highest R~ and included variables s i g n i f i c a n t a t (o r close t o )

the 10 per cent leve l and agronomically meaningful.

DATA

The Braz i l crop data f o r 1961 t h r u 1977 were obtained from the Foreign

Agr icu l tura l Service (Sam Ruff, personal communication, 1982). The data were

recorded w i th year o f y i e l d as year o f harvest, so the weather in f luencing

the crop occurred during year-1.

Meteorological data from 1975 through 1980 were used t o model because

there i s no apparent trend i n the y i e l d data during t h i s period. Table 1 1 i s t s

- ORIGINAL PAGE M' OF POOR QUALITY

a Montes Cleros

Figure 2. Location of Meteorological Stations Used to Derive Data Sets for the Brazil Soybean Model.

STATE - Goias

Mato Grosso

Minas Gerais

Parana

METEOROLOGICAL S TAT1 OK

Bras i l i a /Cruze i ro Goiania

Ponta Pora Pres Prudente Foz do Iguacu

Montes Claros Araxa

Londrina Santa Branca Foz do Iguacu Por to Uniao

Rio Grande do Sul Ij u i Veranopolis Farroupi iha Sao B ~ r j a Passo Fundo J u l i o de Cas t i lhas Santa Maria

Santa Ca ta r ina

Sao Paulo

Por to Uniao Sao Joaquim Vacaria

Pindorama Ribei ro Pre to Mococa Bauru Jau Limeira Camp i n a s Monte Alegre do S u l T i e t e Sao Pa7:lo Jundiqr

Table 1. Meteorological S t a t i o n s Used t o Derive Data S e t s f o r the B r a z i l Soybean Model.

t h e s t a t i ons used t o der i ve t he meteorological da ta se t f o r each state.

F igure 2 shows the l oca t i on of each s ta t ion . The seven s ta tes o f R io Grande - 1

do Sul, Santa Catarina, Parana, Sao Paulo, Mato Grosso, Goias, and Minas Gerais i 8

were included i n the covariance model. 1

PROCEDURES ,

. The o r i g i n a l va r iab les used i n t h e regress ion equat ion included "dummy - .

v a r i ables" f o r s i x o f t he seven s ta tes (Rio Grande do Sul was t h e excep t i r k . ;

Thit "durnmy var iab les" ad jus t t h e con t r i bu t i ons t o y i e l d o f each o f t he s ta tes t

t o a base y i e l d which, i n t h i s case, i s Rio Grande do Su l ' s y i e l d . Weather

va r iab les selected r e f l e c t e d ava i l ab l e no i s t u re f o r t h e months December through

March. The var iab les which were s i g n i f i c a n t a t t he 10 per cent l e v e l were t h e

"dummy var iab les" f o r Sao Paulo, Parana, and Santa Catar ina and t h e P-PET fo r

January and February (see the Appendix f o r a d e f i n i t i o n o f P-PET). The coef-

f i c i e n t f o r the Santa Catar ina "dummy var iab le " was negative, i nd i ca t i ng ' i t s

y i e l d was below t h a t o f t h e norm set by R io Grande do Sul; t h e c o e f f i c i e n t s

f o r t he o ther dummy var iab les were pos i t i ve . The c o e f f i c i e n t s f o r January and

February P-PET were pos i t i ve , i n d i c a t i n g a need f o r mois ture du r i ng those

c r i t i c a l months. The s t a t i s t i c s o f t h e selected model a re summarized i n Table 2. I TEST RESULTS !

A j a ckkn i f e t e s t was run on t h e f i n a l model. I n t h i s tes 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 done f o r each successive year beginning w i t h 1975.

The t e s t had t o be run separately on each state. The r e s u l t s a re p r i n t ed on

Tables 3 through 9 and p l o t t e d on Figures 3 through 9.

APPEND I X

Def in i t ion o f Variables

P-PET, p rec ip i t a t i on minus potent ia l evapotranspiration, i s an index used

t o measure the amount o f moisture avai lab le f o r p lant growth. Potent ia l

evapotranspiration i s determi ned by the procedure devel oped by Thornthwai t e

(1948) which uses only temperature:

PET = \ A /

where I = Heat index, which i s the sum o f the 12 monthly i ndices i .

T = Monthly temperature i n OC, and

a = An empir ical exponent, 6.75 x 10-713 - 7.71 x 10'512 + 1.79 x 10'21

The durat ion o f day1 i g h t i s used t o adjust po ten t ia l evapotranspiration as a

por t ion o f 12 hours.

S t a t i s t i c a l Analysis System Procedures Used

PROC CORR

PROC PLOT

PROC STEPW I SE

PROC STEPWISE FORWARD

PROC STEPW I SE BACKWARD

Computes cor re la t ion coe f f i c i en ts between variables, inc lud ing Pearson product-moment and weighted proca.ct-moment corre lat ion.

Graphs one var iable against another, producing a p r i n t e r p lot .

Provides f i v e methods f o r stepwi se regression. Stepwi se i s useful when select ing variables t o be included i n a regression model from a co l l ec t i on o f independent variables.

Begins by f ind ing the one-variable model t ha t produces the highest ~ 2 . For each o f the other independent 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 cont r ibu t ion t o the model i f the var iable were t o be included.

Begins by ca lcu la t ing s t a t i s t i c s fo r a model inc luding a l l the independent variables. The variables are deleted from the model one by one u n t i l a l l the remaining variables produce F -s ta t i s t i cs s ign i f i can t a t the .10 level.

PRO6 STEPW I S E STEPW I SE

PHUC STEPWISE MAXK

PROC PETM

PROC ZINDEX

The stepwise method i s a modi f icat ion of the forward select ion technique, d i f f e r i n g i n t h a t var iables a1 ready i n the model do not necessari ly stay there. Af ter a variable i s added (as i n the forward select ion method) the stepwise method looks a t a11 tne variables alreaay lncluaea i n the moael and oeletes any var iable tha t does not produce an F-statistic s ign i f i can t a t the .10 level. Only a f te r t n i s check i s made and the necessary delet ions accomplished can another var iable be added t o the model.

(Maxlmum K 2 imp ~vement) Unl ike the three techniques above, t h i s method does not s e t t l e on a s ingle method. Instead i t looks f o r the "best' two-variable model, the "best" three var iab le model, and so for th.

Uses l a t i t u d e and mean monthly temperature t o ca lcu la te Thornthwai t e ' s po ten t ia l evapo- t ranspi r a t i o n f o r each month.

Uses monthly PET'S, p rec ip i ta t ion , SS (begi ning moisture i n surface layer), AWCS (avai lab le water capacity i n surface layer) , SU (beginning moisture i n the underlying 1 ayer ) , and AWCU (avai 1 able water capacity i n the underlying layer ) t o ca lculate Palmer's SON moisture budget, drought index Z, ET, and ET.

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