predicting yield potential, 2007
DESCRIPTION
Predicting Yield Potential, 2007. Can Yield Potential (similar to “yield goals”) be Predicted MID-SEASON? Better than a preplant N decision?. NDVI at F5. =. INSEY. Days from planting to sensing, GDD>0. Winter Wheat. Units: biomass, kg/ha/day, where GDD>0. - PowerPoint PPT PresentationTRANSCRIPT
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Predicting Yield Potential, 2007Predicting Yield Potential, 2007
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YCan Yield Potential (similar to “yield goals”) be Predicted MID-SEASON?Better than a preplant N decision?
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43 Locations, 1998-2006
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01
INSEY
Gra
in y
ield
, M
g/h
a
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 2003PKNP 2004222 2004301 2004502 200420052006
YP0 = 0.409e258.2 INSEY R2=0.50
YP0 + 1Std Dev = 0.590 e258.2 INSEY
NDVI at F5 INSEY
Days from planting to sensing, GDD>0
Units: biomass, kg/ha/day, where GDD>0
Winter WheatWinter Wheat
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Predicting Yield Potential in Corn NDVI, V8 to V10
INSEY Days from planting to sensing
20 Locations, 2002-2005Hybrid Corn, Mexico, Nebraska, Iowa,
Oklahoma, Virginia, OhioV8-V10 (44 to 69 days)
y = 19583x1.7916
R2 = 0.71
0
2
4
6
8
10
12
14
16
18
20
0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018
INSEY
Gra
in y
ield
, M
g h
a-1
104-day (2003)
107-day (2003)
111-day (2003)
99-day (2004)
113-day (2004)
105-day (2002)
109-day (2002)
113-day (2002)
113-day (OFIT)
108-day (OFIT)
Efaw (2003)
LCB (2003)
Efaw (2004)
LCB 2004
Mexico (2002)
Shelton (2004)
Ames (2004)
OhioCORNCORN
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Exp. 502, 1971-2006
0
10
20
30
40
50
60
70
80
901
97
11
97
21
97
41
97
51
97
61
97
71
97
81
97
91
98
01
98
11
98
21
98
31
98
41
98
51
98
61
98
71
98
81
98
91
99
01
99
11
99
21
99
31
99
41
99
51
99
61
99
71
99
81
99
92
00
02
00
12
00
22
00
32
00
42
00
52
00
6
Gra
in y
ield
, b
u/a
c
0-40-60
100-40-60
Long-Term Winter Wheat Grain Yields, Lahoma, OK
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0
20
40
60
80
100
120
140
1971
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Op
tim
um
N R
ate,
lb
/ac
Exp. 502, 1971-2006
Optimum N Rate Max YieldAvg. 49 lb N/ac +/- 39 Avg. 43 bu/ac +/- 13
Response to Fertilizer N, Long-Term Winter Wheat Experiment, 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
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Can RI be Predicted in Wheat?.... YES
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75RINDVI
RI H
arve
st
67 Locations, 1998-2004y= -0.70 + 1.69X (x<1.72)y= 1.13 + 0.45X (x>1.72)
R2 = 0.53
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Can RI Be Predicted in Corn?... YES
MullenAgronomy Journal 95:347-351 (2003)Winter Wheat
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Improved Prediction of Yield Potential
SuperPete to the Rescue
Improved Prediction of Yield Potential
SuperPete to the Rescue
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YPMAX
INSEY (NDVI/days from planting to sensing)
Gra
in y
ield
YP0YPN YPN
RI=2.0RI=2.0
RI=1.5RI=1.5
RI-NFOAYPN=YP0 * RI
Nf = (YP0*RI) – YP0))/Ef
The mechanics of how N rates are computed are really very simple
1. Yield potential is predicted without N
2. The yield achievable with added N is #1 times the RI
3. Grain N uptake for #2 minus #1 = Predicted Additional N Need
4. Fertilizer Rate = #3/ efficiency factor (usually 0.5 to 0.7)
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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)
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0. 10
0. 75
1. 39
2. 04
ndvi
47
84
121
158
days
yl d
0. 70
22. 59
44. 49
66. 38
NDVI and days from planting to sensing where GDD>0 interact with one another
Model includes > 2800 observations (1996 to present)
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Response Mean 2.427489
Root MSE 1.383095
R-Square 0.5857
Coefficient of Variation 56.9764
Type I Sum
Regression DF of Squares R-Square F Value Pr > F
Linear 2 2148.878927 0.1731 561.67 <.0001
Quadratic 2 5084.614676 0.4095 1329.00 <.0001
Crossproduct 1 37.934739 0.0031 19.83 <.0001
Total Model 5 7271.428342 0.5857 760.23 <.0001
Sum of
Residual DF Squares Mean Square
Total Error 2689 5143.930740 1.912953
Standard from Coded
Parameter DF Estimate Error t Value Pr > |t| Data
Intercept 1 6.841693 0.488513 14.01 <.0001 10.500821
days 1 0.017736 0.007928 2.24 0.0254 -1.395237
ndvi 1 -23.847124 0.819606 -29.10 <.0001 30.358037
days*days 1 -0.000065116 0.000036672 -1.78 0.0759 -0.200572
ndvi*days 1 -0.027534 0.006183 -4.45 <.0001 -1.478405
ndvi*ndvi 1 27.065163 0.524480 51.60 <.0001 25.332527
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47-66 days, GDD>0
Group 1, Mg/ha47 - 66 days, GDD>0
y = 2.2372e0.5494x
R2 = 0.04
0
1
2
3
4
5
6
7
8
0 0.2 0.4 0.6 0.8 1
NDVI
Yie
ld,
Mg
/ha
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67-95 days, GDD>0
Group 2, Mg/ha67 - 95 days, GDD>0
y = 1.22e1.5446x
R2 = 0.18
0
1
2
3
4
5
6
7
8
9
10
0.00 0.20 0.40 0.60 0.80 1.00
NDVI
Yie
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Mg
/ha
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96-119 days, GDD>0
Group 396 - 119 days, GDD>0
y = 0.8192e2.0179x
R2 = 0.42
0
1
2
3
4
5
6
7
8
0 0.2 0.4 0.6 0.8 1
NDVI
Yie
ld,
Mg
/ha
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120-139 days, GDD>0
Group 4120 - 139 days, GDD>0
y = 0.7067e2.0896x
R2 = 0.46
0
1
2
3
4
5
6
7
8
0 0.2 0.4 0.6 0.8 1
NDVI
Yie
ld,
Mg
/ha
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146-158 days, GDD>0
Group 5146 - 158 days, GDD>0
y = 1.0949e1.5846x
R2 = 0.45
0
1
2
3
4
5
6
7
0 0.2 0.4 0.6 0.8 1
NDVI
Yie
ld,
Mg
/ha
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Yield Prediction Curve Coefficients, kg/ha
y = -0.0003x2 + 0.0816x - 2.7337R2 = 0.99
y = 0.3231x2 - 77.8x + 5405.7R2 = 0.99
0
500
1000
1500
2000
2500
0 20 40 60 80 100 120 140 160 180
GDD
"A"
0
0.5
1
1.5
2
2.5
"B"
"A"
"B"
GDD "A" "B"52 2232 0.59481 1222 1.544
105 819 2.018125 707 2.09154 1094 1.584
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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)
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Variable Rate Technology Treat Temporal and Spatial Variability Returns are higher but require larger investment
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Just remember boys, you can always trust SuperPete!
Just remember boys, you can always trust SuperPete!