F UNDAMENTALSOF THE OSU A LGORITHM T RAINING

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F UNDAMENTALSOF THE OSU A LGORITHM

F UNDAMENTALSOF THE OSU A LGORITHM

T RAINING Farmer training, Ciudad Obregon, Mexico, January 2007

T RAINING Farmer training, Ciudad Obregon, Mexico, January 2007

0 1971 1972 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984

0 1971 1972 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 0 Response Index 70 1971 1972 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Grain yield, bu/ac V ARIABLE N R ESPONSE 90 80 Exp. 502, 1971 -2009 0 -40 -60 100 -40 -60 60 50 40 30 20 10 4 3 2 1

G LOBAL I MPORTANCEOF F ERTILIZER N Malakoff (Science, 1998) $750, 000, excess N

G LOBAL I MPORTANCEOF F ERTILIZER N Malakoff (Science, 1998) $750, 000, excess N flowing down the Mississippi River Africa expenditure on fertilizer N, cereals $706, 000 Nitrogen Use Efficiency (NUE) World 33% 20% increase Worth $10. 8 billion US annually

8. 0 50 Locations, 1998 -2009 7. 0 YP 0 = 0. 409 e

8. 0 50 Locations, 1998 -2009 7. 0 YP 0 = 0. 409 e 258. 2 INSEY R 2=0. 50 YP 0 + 1 Std Dev = 0. 590 e 258. 2 INSEY Grain yield, Mg/ha 6. 0 5. 0 4. 0 3. 0 2. 0 1. 0 0 0. 002 0. 004 0. 006 INSEY 0. 008 0. 01 PKNP 1998 PKSN 1998 TPSN 1998 PKNP 1999 222 1999 301 1999 EFAA 1999 801 1999 502 1999 PKNP 2000 222 2000 301 2000 EFAA 2000 801 2000 502 2000 HNAA 2000 PKNP 2001 222 2001 301 2001 EFAA 2001 801 2001 PKNP 2002 222 2002 301 2002 EFAA 2002 801 2002 HNAA 2002 502 2003 222 2003 EFAA 2003 HNAA 2003 PKNP 2004 222 2004 301 2004 502 2004 2005 2006

S UB-S AHARAN A FRICA SAA USA Population, million 700 300 Cereals, million ha

S UB-S AHARAN A FRICA 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

YPN YP 0 Grain yield YPMAX. 0 2 RI= . 5 1 RI= INSEY

YPN YP 0 Grain yield YPMAX. 0 2 RI= . 5 1 RI= INSEY (NDVI/days from planting to sensing) YP 0 = (NDVI / Days, GDD>0) YP 0 = INSEY YPN = (YP 0*RI) Nf = (YP 0*RI) – YP 0))/Ef A RI-NFOA YPN=YP 0 * RI

YP 0 Grain yield YPMAX B INSEY (NDVI/days from planting to sensing) Nf =

YP 0 Grain yield YPMAX B INSEY (NDVI/days from planting to sensing) Nf = (YPMAX-YP 0)/Ef Max Yield-NFOA

RI=2. 0 YPN YP 0 Grain yield YPMAX CV CV C RICV-NFOA INSEY (NDVI/days

RI=2. 0 YPN YP 0 Grain yield YPMAX CV CV C RICV-NFOA INSEY (NDVI/days from planting to sensing) Nf = ((YP 0*RI)*(65 -CV/65 -Cr. CV)) – YP 0/Ef 65? Limit of CV data Critical CV or Cr. CV, changes for different crops Corn Wheat

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

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

V ARIABLE R ATE T ECHNOLOGY T R E A T T E M

V ARIABLE R ATE T ECHNOLOGY T R E A T T E M P O R A LA N D SPATIAL VARIABILITY R E T U R N SA R E H I G H E RB U T R E Q U I R EL A R G E RI N V E S T M E N T

Y IELD P OTENTIAL P REDICTION, C ORN, O HIO

Y IELD P OTENTIAL P REDICTION, C ORN, O HIO

Y IELD P OTENTIAL P REDICTION, W INTER W HEAT, O KLAHOMA

Y IELD P OTENTIAL P REDICTION, W INTER W HEAT, O KLAHOMA

P REDICTING N R ESPONSIVENESS

P REDICTING N R ESPONSIVENESS

R ESPONSE I NDEX T HEORY FOR F ERTILIZER N R ESPONSE

R ESPONSE I NDEX T HEORY FOR F ERTILIZER N R ESPONSE