OSU Corn Algorithm

Can Yield Potential (similar to “yield goals”) be Predicted MID-SEASON Can Yield Potential (similar to “yield goals”) be Predicted MID-SEASON? Is it better than a preplant N decision?

= Winter Wheat NDVI at F5 INSEY Days from planting to sensing, GDD>0 Winter Wheat Units: biomass, kg/ha/day, where GDD>0

Predicting Yield Potential in Corn NDVI, V8 to V10 = INSEY Days from planting to sensing CORN

Long-Term Winter Wheat Grain Yields, Lahoma, OK

Response to Fertilizer N, Long-Term Winter Wheat Experiment, Lahoma, OK “After the FACT” N Rate required for “MAX Yields” Ranged from 0 to 140 lbs N/ac

Can RI be Predicted in Wheat?.... YES

Can RI Be Predicted in Corn?... YES Mullen Agronomy Journal 95:347-351 (2003) Winter Wheat

Improved Prediction of Yield Potential SuperPete to the Rescue

All GDD Class Yield Prediction Equations for Corn

The mechanics of how N rates are computed are really very simple RI-NFOA YPN=YP0 * RI YPN YPN YP0 YPMAX RI=1.5 Grain yield RI=2.0 INSEY (NDVI/days from planting to sensing) Nf = (YP0*RI) – YP0))/Ef The mechanics of how N rates are computed are really very simple Yield potential is predicted without N The yield achievable with added N is #1 times the RI Grain N uptake for #2 minus #1 = Predicted Additional N Need Fertilizer Rate = #3/ efficiency factor (usually 0.5 to 0.7)

INSEY works, but needs to be more robust Problems: Extremely early season prediction of yield can be overestimated (Feekes 4, wheat) (V6, corn) Inability to reliably predict yield potential at early stages of growth should be accompanied by more risk averse prediction models (small slope)

Combined RI = (NDVI-N Rich Strip/NDVI-Farmer Practice) CoefA = (0.323123*Gdd2 - 77.8* Gdd + 5406) CoefB = -0.0003469*Gdd2 + 0.08159*Gdd - 2.73372 YP0 = (CoefA * exp(CoefB * NDVI-FP)) If ((NDVI-N Rich Strip/NDVI-FP)< 1.72) RI = (NDVI-N Rich Strip/NDVI-FP)*1.69 - 0.7 If (RI<1) RI=1 YPN = YP0*RI; NRate = ((YPN-YP0)*0.0239/0.6) Determine based on %N in the grain

Variable Rate Technology Treat Temporal and Spatial Variability Returns are higher but require larger investment

Just remember boys, you can always trust SuperPete!

OXIDATION STATES ATMOSPHERE N2O NO N2 INDUSTRIAL FIXATION LIGHTNING, GLOBAL WARMING 15-40 kg/ha N2O NO N2 INDUSTRIAL FIXATION LIGHTNING, RAINFALL N2 FIXATION PLANT AND ANIMAL RESIDUES HABER BOSCH 3H2 + N2 2NH3 (1200°C, 500 atm) SYMBIOTIC NON-SYMBIOTIC MESQUITE RHIZOBIUM ALFALFA SOYBEAN BLUE-GREEN ALGAE AZOTOBACTER CLOSTRIDIUM MATERIALS WITH N CONTENT > 1.5% (COW MANURE) MATERIALS WITH N CONTENT < 1.5% (WHEAT STRAW) 10-80 kg/ha FERTILIZATION PLANT LOSS AMINO ACIDS MICROBIAL DECOMPOSITION 0-50 kg/ha NH3 IMMOBILIZATION AMMONIA VOLATILIZATION AMINIZATION ORGANIC MATTER HETEROTROPHIC R-NH2 + ENERGY + CO2 BACTERIA (pH>6.0) FUNGI (pH<6.0) R-NH2 + H2O pH>7.0 NH2OH AMMONIFICATION FIXED ON EXCHANGE SITES IMMOBILIZATION R-OH + ENERGY + 2NH3 MICROBIAL/PLANT SINK Pseudomonas, Bacillus, Thiobacillus Denitrificans, and T. thioparus N2O2- 2NH4+ + 2OH- MINERALIZATION + NITRIFICATION +O2 NO2- Nitrosomonas DENITRIFICATION NO3- POOL NITRIFICATION OXIDATION STATES 2NO2- + H2O + 4H+ DENITRIFICATION LEACHING LEACHING VOLATILIZATION NITRIFICATION Nitrobacter + O2 NH3 AMMONIA -3 NH4+ AMMONIUM -3 N2 DIATOMIC N 0 N2O NITROUS OXIDE 1 NO NITRIC OXIDE 2 NO2- NITRITE 3 NO3- NITRATE 5 Joanne LaRuffa Wade Thomason Shannon Taylor Heather Lees Department of Plant and Soil Sciences Oklahoma State University ADDITIONS TEMP 50°F LEACHING LEACHING LOSSES LEACHING OXIDATION REACTIONS pH 7.0 REDUCTION REACTIONS 0-40 kg/ha