1. History of Predicting Yield Potential TEAM VRT Oklahoma State University TEAM VRT Oklahoma State University

2. Outline  Yield Goals and Potential Yield  Soil Test vs. Sensor Based  Sufficiency: Mobile vs. Immobile Nutrients  Bray’s mobility concept  How to generate nutrient recommendations  What should we learn from soil testing  Subsoil nutrient availability  Soil Testing: Correlation/Calibration/Recommendation  Models for Interpretation of Response  Interfering agronomic factors  Yield Goals and Potential Yield  Soil Test vs. Sensor Based  Sufficiency: Mobile vs. Immobile Nutrients  Bray’s mobility concept  How to generate nutrient recommendations  What should we learn from soil testing  Subsoil nutrient availability  Soil Testing: Correlation/Calibration/Recommendation  Models for Interpretation of Response  Interfering agronomic factors

3. Yield Goal/Potential Yield z Yield Goal: yield per acre you hope to grow (Dahnke et al., 1988) z Potential yield: highest possible yield obtainable with ideal management, FOR specific soil and weather conditions z Maximum Yield: grain yield achievable when all manageable growth factors (nutrients, insects, disease, and weeds) are nonlimiting and the environment is ideal z Yield Goal: yield per acre you hope to grow (Dahnke et al., 1988) z Potential yield: highest possible yield obtainable with ideal management, FOR specific soil and weather conditions z Maximum Yield: grain yield achievable when all manageable growth factors (nutrients, insects, disease, and weeds) are nonlimiting and the environment is ideal

4. Yield Goals in the Literature z Yield per acre you hope to grow (Dahnke et al. (1988). z Highest yield attained in the last 4-5 years and that is usually 30-33% higher than avg. yield (J. Goos, 1998). z Aim for a 10-20% increase over the recent average (Rehm and Schmitt, 1989). z Yield goal should be based on how much water is available (stored soil water to 1.5m, Black and Bauer, 1988). z When Yield Goals are used it explicitly places the risk of predicting the environment (good or bad) on the producer. z Yield per acre you hope to grow (Dahnke et al. (1988). z Highest yield attained in the last 4-5 years and that is usually 30-33% higher than avg. yield (J. Goos, 1998). z Aim for a 10-20% increase over the recent average (Rehm and Schmitt, 1989). z Yield goal should be based on how much water is available (stored soil water to 1.5m, Black and Bauer, 1988). z When Yield Goals are used it explicitly places the risk of predicting the environment (good or bad) on the producer.

5. Value of Using Yield Goals  Nutrient removal can be reliably estimated for a given yield level in specific crops.  Selected Yield Goal defines the risk the producer is willing to take.  Yield Goal can define the limits in terms of economic inputs when considering herbicides, insecticides, etc.  Nutrient removal can be reliably estimated for a given yield level in specific crops.  Selected Yield Goal defines the risk the producer is willing to take.  Yield Goal can define the limits in terms of economic inputs when considering herbicides, insecticides, etc.

6. Importance of Predicting Potential Yield  Seasonal N need directly related to observed yield.  NUE decreases with increasing N rate.  Known Potential Yield = Known N Input = Highest NUE.  Seasonal N need directly related to observed yield.  NUE decreases with increasing N rate.  Known Potential Yield = Known N Input = Highest NUE.

7. Max Yield YP MAX Average Yield + 30 % Yield Goal Potential Yield YP 0 Potential Yield with N, YP N Bound by Environment and Management Grain yield

8. Predicting N Needs  Use of Yield Goals.  Based on past season yields.  May take into account current-year preplant conditions of available moisture and residual N.  Seldom is adjusted for midseason conditions to alter N inputs.  Use of Potential Yield.  Reliability of predicting final yield (and N requirement) from existing soil and crop conditions should increase as harvest approaches.  Use of Yield Goals.  Based on past season yields.  May take into account current-year preplant conditions of available moisture and residual N.  Seldom is adjusted for midseason conditions to alter N inputs.  Use of Potential Yield.  Reliability of predicting final yield (and N requirement) from existing soil and crop conditions should increase as harvest approaches.

9.  Significant soil variability at distances less than 30 m apart (Lengnick, 1997)  In order to describe the variability encountered in field experiments, soil, plant and indirect measures should be made at the 1m or submeter resolution  Significant differences in soil test P, organic C, and pH were found at distances <0.30m (OSU)  Significant soil variability at distances less than 30 m apart (Lengnick, 1997)  In order to describe the variability encountered in field experiments, soil, plant and indirect measures should be made at the 1m or submeter resolution  Significant differences in soil test P, organic C, and pH were found at distances <0.30m (OSU) Spatial Variability and Yield Potential

10. Crop Response/Models to Predict Yield (N need)  CERES (Crop-Environment Resource Synthesis) crop response model was not useful in predicting wheat grain yield (Moulin and Beckie, 1993)  Complicated.  Total N uptake at Feekes growth stage 5 was found to be a good predictor of yield (Reeves et al., 1993)  Worked some, but not all years.  CERES (Crop-Environment Resource Synthesis) crop response model was not useful in predicting wheat grain yield (Moulin and Beckie, 1993)  Complicated.  Total N uptake at Feekes growth stage 5 was found to be a good predictor of yield (Reeves et al., 1993)  Worked some, but not all years.

11.  1. SF45 = (NDVI4 + NDVI5)/days from F4 to F5  INSEY (in-season-estimated-yield)  GDD = (Tmin + Tmax)/2 – 4.4°C  2. EY = (NDVI4 + NDVI5)/GDD from F4 to F5  3. INSEY = (NDVI)/days from planting to sensing  4. INSEY = (NDVI)/days from planting to sensing where (GDD>0)  1. SF45 = (NDVI4 + NDVI5)/days from F4 to F5  INSEY (in-season-estimated-yield)  GDD = (Tmin + Tmax)/2 – 4.4°C  2. EY = (NDVI4 + NDVI5)/GDD from F4 to F5  3. INSEY = (NDVI)/days from planting to sensing  4. INSEY = (NDVI)/days from planting to sensing where (GDD>0) History of Predicting Potential Yield

12. OctoberFebruaryJune 0120240 days 50 lb N /ac 100 lb N/ac 75 lb N/ac N uptake, lb/ac INSEY: Rate of N uptake over 120 days, > ½ of the total growing days and should be a good predictor of grain yield INSEY: Rate of N uptake over 120 days, > ½ of the total growing days and should be a good predictor of grain yield 45 bu/ac, 2.5% N in the grain days with GDD>0?

13. October 1 Benchmark Planting Date October 1 Benchmark Planting Date Planting Date F4 Date F5 Date 20 143 185 6 116 145 Perkins Tipton 42 29 Adj. Index 42+20=62 29+6=35 Adj. Index 42+20=62 29+6=35 Adjusting Yield Potential

14. Feekes growth stage F4 F5Maturity  growth NDVI NDVI min SF45 = (NDVI4 + NDVI5)/days from F4 to F5 YIELD POTENTIAL

15. Feekes 4Feekes 5Grain Yield 40 2050 Total N Uptake

16. 0 1000 2000 3000 4000 5000 6000 0.010.020.030.040.050.060.07 NDVI F4+NDVI F5/days from F4 to F5 Grain Yield Perkins, N*P Perkins, S*N Tipton, S*N y = 1E+06x2 - 12974x + 951.24 R 2 = 0.89

17. 05001000150020002500 05001000150020002500 Above ground dryweight Above ground dryweight Cumulative growing degree days  NDVI T1 T2  NDVI T1 T2 Rickman, R.W., Sue E. Waldman and Betty Klepper. 1996. MODWht3: A development-driven wheat growth simulation. Agron J. 88:176-185. Rickman, R.W., Sue E. Waldman and Betty Klepper. 1996. MODWht3: A development-driven wheat growth simulation. Agron J. 88:176-185. 14 12 10 8 6 4 2 0 14 12 10 8 6 4 2 0 INSEY= (NDVI T1 + NDVI T2 )/GDD T1 to T2 INSEY= (NDVI T1 + NDVI T2 )/GDD T1 to T2 GDD

18. 9 experiments (NDVI F4 + NDVI F5/GDD from F4 to F5

19. 6 experiments (NDVI F4 + NDVI F5/GDD from F4 to F5

20. Field Experiments, 1998-2000. _____________________________________________________________________________________________ ExperimentLocationYearDatePlantingHarvestVarietyPlanting to Senseddatedatesensing, D/M/YD/M/YD/M/Ydays S*N§Perkins, OK19986/4/9821/10/9715/6/98Tonkawa167 S*N§Tipton, OK199826/2/987/10/973/6/98Tonkawa142 N*P¶Perkins, OK19982/4/9821/10/9715/6/98Tonkawa163 N*P¶Perkins, OK19994/3/9912/10/989/6/99Tonkawa143 Experiment 222Stillwater, OK199924/2/9913/10/9815/6/99Tonkawa134 Experiment 301Efaw, OK199924/3/9915/10/9815/6/99Tonkawa160 Efaw AAEfaw, OK199924/3/999/11/9815/6/99Tonkawa135 Experiment 502Lahoma, OK19995/3/999/10/9830/6/99Tonkawa147 Experiment 801Haskell, OK199923/3/9916/10/986/7/992163158 N*PPerkins, OK20008/2/008/10/9930/5/00Custer123 Experiment 222Stillwater, OK20006/3/007/10/996/7/00Custer151 Experiment 301Efaw, OK20006/3/007/10/992/6/00Custer151 Efaw AAEfaw, OK20006/3/007/10/997/7/00Custer151 Experiment 801Haskell, OK200014/3/008/10/992/6/002137158 Experiment 502Lahoma, OK200013/3/0012/10/9913/6/00Custer153 Hennessey AAHennessey, OK200013/3/007/10/997/6/00Custer158 Field Experiments, 1998-2000. _____________________________________________________________________________________________ ExperimentLocationYearDatePlantingHarvestVarietyPlanting to Senseddatedatesensing, D/M/YD/M/YD/M/Ydays S*N§Perkins, OK19986/4/9821/10/9715/6/98Tonkawa167 S*N§Tipton, OK199826/2/987/10/973/6/98Tonkawa142 N*P¶Perkins, OK19982/4/9821/10/9715/6/98Tonkawa163 N*P¶Perkins, OK19994/3/9912/10/989/6/99Tonkawa143 Experiment 222Stillwater, OK199924/2/9913/10/9815/6/99Tonkawa134 Experiment 301Efaw, OK199924/3/9915/10/9815/6/99Tonkawa160 Efaw AAEfaw, OK199924/3/999/11/9815/6/99Tonkawa135 Experiment 502Lahoma, OK19995/3/999/10/9830/6/99Tonkawa147 Experiment 801Haskell, OK199923/3/9916/10/986/7/992163158 N*PPerkins, OK20008/2/008/10/9930/5/00Custer123 Experiment 222Stillwater, OK20006/3/007/10/996/7/00Custer151 Experiment 301Efaw, OK20006/3/007/10/992/6/00Custer151 Efaw AAEfaw, OK20006/3/007/10/997/7/00Custer151 Experiment 801Haskell, OK200014/3/008/10/992/6/002137158 Experiment 502Lahoma, OK200013/3/0012/10/9913/6/00Custer153 Hennessey AAHennessey, OK200013/3/007/10/997/6/00Custer158

21. Normalized Difference Vegetation Index (NDVI) = NIR ref – red ref / NIR ref + red ref Normalized Difference Vegetation Index (NDVI) = NIR ref – red ref / NIR ref + red ref (up – down) excellent predictor of plant N uptake Units: N uptake, kg ha -1 Units: N uptake, kg ha -1

22. OctoberFebruaryJune 0120240 days 50 lb N /ac 100 lb N/ac 75 lb N/ac N uptake, lb/ac INSEY: Rate of N uptake over 120 days, > ½ of the total growing days and should be a good predictor of grain yield INSEY: Rate of N uptake over 120 days, > ½ of the total growing days and should be a good predictor of grain yield 45 bu/ac, 2.5% N in the grain days with GDD>0?

23. Normalized Difference Vegetation Index (NDVI) Reasonably good predictor of final grain yield Normalized Difference Vegetation Index (NDVI) Reasonably good predictor of final grain yield

24. T1 GDD from T1 to T2 NDVI T2 NDVI Estimated Yield (EY)     +Good predictor of final grain yield - Requires two sensor readings +GDD y = 0.4554e344.12x R2 = 0.62

25. NDVI at F5 In-Season Estimated Yield (INSEY)1   days from planting to F5 +Good predictor of final grain yield +Requires only one sensor reading Units: N uptake, kg ha -1 day -1 Units: N uptake, kg ha -1 day -1

26. NDVI at F5 In-Season Estimated Yield (INSEY)1   days from planting to F5 Hard Red Winter Wheat (Oklahoma) Soft White Winter Wheat (Virginia)

27. NDVI at F5 In-Season Estimated Yield (INSEY)2   days from planting to F5, GDD>0 +Good predictor of final grain yield +Requires only one sensor reading +Appears to work over different regions Units: N uptake, kg ha-1 day-1 where GDD>0 Units: N uptake, kg ha-1 day-1 where GDD>0

28. NDVI at F5 In-Season Estimated Yield (INSEY)2   days from planting to F5, GDD>0 Hard Red Winter Wheat (Oklahoma) Soft White Winter Wheat (Virginia)

29. Soft White Winter Wheat 7 locations in Virginia, 2001

31. n Can We Predict Yield with No Additional N Applied? YP 0 n Can We Predict The Yield Increase If We Apply N in a Given Year? YP N n Can We Predict if Harvested Yield will be Less than Predicted Yield? YP ? n Can We Predict Yield with No Additional N Applied? YP 0 n Can We Predict The Yield Increase If We Apply N in a Given Year? YP N n Can We Predict if Harvested Yield will be Less than Predicted Yield? YP ?

32. 12 10 8 6 4 2 0 12 10 8 6 4 2 0 Above ground dry weight Cumulative growing degree days Harves t Post-maturity yield loss

33. R-NH 2 NO 3 - + 2e (nitrate reductase) NO 2 - + 6e (nitrite reductase) NH 4 + NH 3 R-NH 2 NO 3 NH 4 R-NH 2 NO 3 NH 4 REPRODUCTIVE VEGETATIVE moisture heat Total N amino acids NH 3 3 nitrite reductase nitrate reductase NO 2 3 Safety valve

34. 12 10 8 6 4 2 0 12 10 8 6 4 2 0 Above ground dry weight Cumulative growing degree days Harvest Rainfall Disease Frost

35. OctoberFebruaryJune N uptake, lb/ac 40 N 0 N Predicting the Increase in Yield due to Applied N NEXT Section…… ????