fundamentals of the osu algorithm

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FUNDAMENTALS OF THE OSU ALGORITHM

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Fundamentals of the OSU Algorithm. Training. Farmer training, Ciudad Obregon, Mexico, January 2007. Variable N Response. Global Importance of Fertilizer N. Malakoff (Science, 1998) $750,000,000, excess N flowing down the Mississippi River Africa expenditure on fertilizer N, cereals - PowerPoint PPT Presentation

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Page 1: Fundamentals of the OSU Algorithm

FUNDAMENTALS OF THE OSU ALGORITHM

FUNDAMENTALS OF THE OSU ALGORITHM

Page 2: Fundamentals of the OSU Algorithm

TRAINING

Farmer training, Ciudad Obregon, Mexico, January 2007

Page 3: Fundamentals of the OSU Algorithm

VARIABLE N RESPONSE

1971

1972

1974

1975

1976

1977

1978

1979

1980

1981

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1989

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2009

0

10

20

30

40

50

60

70

80

90 Exp. 502, 1971-2009

0-40-60 100-40-60

Gra

in y

ield

, bu/

ac

1971

1972

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

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1989

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1991

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1998

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2001

2002

2003

2004

2005

2006

2007

2008

2009

0

1

2

3

4

Resp

onse

Inde

x

Page 4: Fundamentals of the OSU Algorithm

GLOBAL IMPORTANCE OF FERTILIZER N

Malakoff (Science, 1998)

$750,000,000, excess N flowing down the Mississippi River

Africa expenditure on fertilizer N, cereals

$706,000,000

Nitrogen Use Efficiency (NUE) World 33%

20% increase

Worth $10.8 billion US annually

Page 5: Fundamentals of the OSU Algorithm

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

50 Locations, 1998-2009

PKNP 1998PKSN 1998TPSN 1998PKNP 1999222 1999301 1999EFAA 1999801 1999502 1999PKNP 2000222 2000301 2000EFAA 2000801 2000502 2000HNAA 2000PKNP 2001222 2001301 2001EFAA 2001801 2001PKNP 2002222 2002301 2002EFAA 2002801 2002HNAA 2002502 2003222 2003EFAA 2003HNAA 2003PKNP 2004222 2004301 2004502 2004200520062009INSEY

Gra

in y

ield

, Mg/

ha

YP0 = 0.409e258.2 INSEY R2=0.50YP0 + 1Std Dev = 0.590 e258.2 INSEY

Page 6: Fundamentals of the OSU Algorithm

SUB-SAHARAN AFRICA

SAA USA

Population, million 700 300

Cereals, million ha 88 56

Production, million tons 97 364

Yield, tons/ha 1.1 6.5

Fertilizer N, million tons 1.3 10.9

Avg. N rate, kg/ha 4 52

% of world N consumed 1.4 13

% of world population 10 4

Page 7: Fundamentals of the OSU Algorithm
Page 8: Fundamentals of the OSU Algorithm
Page 9: Fundamentals of the OSU Algorithm
Page 10: Fundamentals of the OSU Algorithm
Page 11: Fundamentals of the OSU Algorithm

YPMAX

INSEY (NDVI/days from planting to sensing)

Gra

in y

ield

YP0YPN YPN

RI=2.0RI=1.5 RI-NFOA

YPN=YP0 * RIRI-NFOAYPN=YP0 * RI

YP0 = (NDVI / Days, GDD>0)YP0 = INSEYYPN = (YP0*RI) Nf = (YP0*RI) – YP0))/Ef

YP0 = (NDVI / Days, GDD>0)YP0 = INSEYYPN = (YP0*RI) Nf = (YP0*RI) – YP0))/Ef

A

Page 12: Fundamentals of the OSU Algorithm

YPMAX

INSEY (NDVI/days from planting to sensing)

Gra

in y

ield

YP0

Max Yield-NFOAMax Yield-NFOA

Nf = (YPMAX-YP0)/EfNf = (YPMAX-YP0)/Ef

B

Page 13: Fundamentals of the OSU Algorithm

YPMAX

INSEY (NDVI/days from planting to sensing)

Gra

in y

ield

YP0YPN

RI=2.0

RICV-NFOARICV-NFOACVCV

Nf = ((YP0*RI)*(65-CV/65-CrCV)) – YP0/Ef65? Limit of CV dataCritical CV or CrCV, changes for different crops

Nf = ((YP0*RI)*(65-CV/65-CrCV)) – YP0/Ef65? Limit of CV dataCritical CV or CrCV, changes for different crops

C

0

100

200

300

400

500

600

0 10 20 30 40 50CV

Pla

nts

m2

-1

WheatWheat

y = 163862x-0.4636R2 = 0.65

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

0 10 20 30 40 50 60

RCV

Pla

nt

pop

ula

tion

(p

lan

ts h

a -1

)

113-day

99-day

104-day

107-day

111-day

CornCorn

Page 14: Fundamentals of the OSU Algorithm

Data compiled by Dr. Robert Mullen, The Ohio State University

Page 15: Fundamentals of the OSU Algorithm

VARIABLE RATE TECHNOLOGY TREAT TEMPORAL AND SPATIAL VARIABILITY

RETURNS ARE HIGHER BUT REQUIRE LARGER INVESTMENT

Page 16: Fundamentals of the OSU Algorithm

YIELD POTENTIAL PREDICTION, CORN, OHIO

Ohio y = 0.9689e2655.7x

R2 = 0.73

0

2

4

6

8

10

12

14

16

18

20

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014

INSEY, NDVI / cum GDD

Yie

ld, M

g/h

a

Page 17: Fundamentals of the OSU Algorithm

YIELD POTENTIAL PREDICTION, WINTER

WHEAT, OKLAHOMA

Page 18: Fundamentals of the OSU Algorithm

PREDICTING N RESPONSIVENESS

Page 19: Fundamentals of the OSU Algorithm

0

0.5

1

1.5

2

2.5

3

3.5

0 0.2 0.4 0.6 0.8 1

Field Rate NDVI

Po

ten

tial

Yie

ld,

Mg

/ha

YP0

YPN = RINDVI YP0

YPN = YPmax

Soil/Crop Divide

RI/YPmax Divide

Yield with additional N predicted by Response Index

Yield increase with additional N limited to the maximum potential yield

RESPONSE INDEX THEORY FOR FERTILIZER N RESPONSE

ttanConsRI

YPNRIYPN

NDVI

NDVI

maxYPYPN

NDVI

2aNDVI

NDVI Fp1acosh

Fp0aRI