F UNDAMENTALS OF THE OSU A LGORITHM

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

V ARIABLE N R ESPONSE

G LOBAL I MPORTANCE OF F ERTILIZER 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

S UB -S AHARAN A FRICA SAAUSA  Population, million  Cereals, million ha 8856  Production, million tons97364  Yield, tons/ha  Fertilizer N, million tons  Avg. N rate, kg/ha452  % of world N consumed1.413  % of world population104

YP MAX INSEY (NDVI/days from planting to sensing) Grain yield YP 0 YP N RI=2.0 RI=1.5 RI-NFOA YP N =YP 0 * RI YP 0 = (NDVI / Days, GDD>0) YP 0 = INSEY YP N = (YP0*RI) Nf = (YP 0 *RI) – YP 0 ))/Ef YP 0 = (NDVI / Days, GDD>0) YP 0 = INSEY YP N = (YP0*RI) Nf = (YP 0 *RI) – YP 0 ))/Ef A

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

YP MAX INSEY (NDVI/days from planting to sensing) Grain yield YP 0 YP N RI=2.0 RICV-NFOA CV Nf = ((YP 0 *RI)*(65-CV/65-CrCV)) – YP 0 /Ef 65? Limit of CV data Critical CV or CrCV, changes for different crops Nf = ((YP 0 *RI)*(65-CV/65-CrCV)) – YP 0 /Ef 65? Limit of CV data Critical CV or CrCV, changes for different crops C Wheat Corn

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

V ARIABLE R ATE T ECHNOLOGY T REAT T EMPORAL AND S PATIAL V ARIABILITY R ETURNS ARE HIGHER BUT REQUIRE LARGER INVESTMENT

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

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

P REDICTING N R ESPONSIVENESS

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