View
44
Download
2
Category
Preview:
DESCRIPTION
Late-Season Prediction of Wheat Grain Yield and Protein. K.W. Freeman, W.E. Thomason, E.V.Lukina, G.V. Johnson, K.J. Wynn, J.B. Solie, M.L. Stone, and W.R. Raun. Oklahoma State University Department of Plant and Soil Sciences. Introduction. - PowerPoint PPT Presentation
Citation preview
Late-Season Prediction of Wheat Grain Yield and Protein
Late-Season Prediction of Wheat Grain Yield and Protein
K.W. Freeman, W.E. Thomason, E.V.Lukina,G.V. Johnson, K.J. Wynn, J.B. Solie,
M.L. Stone, and W.R. Raun
K.W. Freeman, W.E. Thomason, E.V.Lukina,G.V. Johnson, K.J. Wynn, J.B. Solie,
M.L. Stone, and W.R. Raun
Oklahoma State UniversityDepartment of Plant and Soil SciencesOklahoma State UniversityDepartment of Plant and Soil Sciences
IntroductionIntroduction Pre-harvest prediction of wheat yield
will assist producers• Provide more reliable field maps• Assist in pre-harvest marketing
Pre-harvest prediction of grain protein• Strong correlation between plant N and NDVI (Stone,
1996)• Determine whether or not to apply late-season N
Pre-harvest prediction of wheat yield will assist producers• Provide more reliable field maps• Assist in pre-harvest marketing
Pre-harvest prediction of grain protein• Strong correlation between plant N and NDVI (Stone,
1996)• Determine whether or not to apply late-season N
• In order to describe the variability encountered in the field, soil, plant, and indirect measurements should be made at the meter or submeter level (Solie et al., 1999).
• Field element size: area that provides the most precise measure of the available nutrient where the level of that nutrient changes with distance (Solie et al. 1996).
• Willis (1999) defined yield maps as tools used by producers to look for general patterns and trends, and that yield monitor data could be corrected using remotely sensed data.
• In order to describe the variability encountered in the field, soil, plant, and indirect measurements should be made at the meter or submeter level (Solie et al., 1999).
• Field element size: area that provides the most precise measure of the available nutrient where the level of that nutrient changes with distance (Solie et al. 1996).
• Willis (1999) defined yield maps as tools used by producers to look for general patterns and trends, and that yield monitor data could be corrected using remotely sensed data.
IntroductionIntroduction
ObjectivesObjectives
• To determine the relationship between spectral measurements taken from Feekes growth stages 9 to physiological maturity and grain yield and grain protein.
• To determine the relationship between spectral measurements taken from Feekes growth stages 9 to physiological maturity and grain yield and grain protein.
Growth Stages in Growth Stages in CerealsCereals
Growth Stages in Growth Stages in CerealsCereals
TilleringTilleringTilleringTillering
Stem ExtensionStem ExtensionStem ExtensionStem Extension HeadingHeadingHeadingHeadingRipeningRipeningStageStageRipeningRipeningStageStage
Materials and MethodsMaterials and Methods Seven experimental sites:
- Stillwater, Lahoma, Hennessey, Perkins and Haskell, OK Experimental design:
- 4 experiments in long-term fertility trials, 2 anhydrous ammonia NUE trials and a Sewage Sludge loading experiment
- 2 x 2 m subplots placed in existing experiments with differing N rates- Spectral reflectance readings taken with photodiode-based sensor with
interference filters for red at 671±6 and near infrared (NIR) at 780±6 nm wavelengths
Seven experimental sites:- Stillwater, Lahoma, Hennessey, Perkins and Haskell, OK
Experimental design: - 4 experiments in long-term fertility trials, 2 anhydrous ammonia NUE trials and a
Sewage Sludge loading experiment- 2 x 2 m subplots placed in existing experiments with differing N rates- Spectral reflectance readings taken with photodiode-based sensor with
interference filters for red at 671±6 and near infrared (NIR) at 780±6 nm wavelengths
Materials and MethodsMaterials and Methods Experimental design (cont):
- Readings were taken at Feekes growth stages 9, 10.5, 11.2, and 11.4
- Spectral indices were calculated for each subplot at all growth stages
Grain Production - Harvest of 2 x 2 m area with a self-propelled combine- Grain samples were ground to pass 120-mesh screen and analyzed for
total N using Carlo-Erba 1500 dry combustion analyzer (Schepers et al., 1989)
Experimental design (cont):- Readings were taken at Feekes growth stages 9, 10.5,
11.2, and 11.4- Spectral indices were calculated for each subplot
at all growth stages
Grain Production - Harvest of 2 x 2 m area with a self-propelled combine- Grain samples were ground to pass 120-mesh screen and analyzed for
total N using Carlo-Erba 1500 dry combustion analyzer (Schepers et al., 1989)
Materials and MethodsMaterials and Methods
Formulas for Spectral Indices
NDVI = NIR ref – red ref / NIR ref + red ref
INSEY = NDVI (each date) / days from planting
RI = DM yield of highest yielding plots / DM yield of check
ISRI = Highest NDVI / NDVI from check
Formulas for Spectral Indices
NDVI = NIR ref – red ref / NIR ref + red ref
INSEY = NDVI (each date) / days from planting
RI = DM yield of highest yielding plots / DM yield of check
ISRI = Highest NDVI / NDVI from check
Feekes 9Feekes 9
y = 2188.5xy = 2188.5x22 + 778.72x + 711.53 + 778.72x + 711.53
RR22 = 0.46 = 0.46
00
10001000
20002000
30003000
40004000
50005000
60006000
00 0.10.1 0.20.2 0.30.3 0.40.4 0.50.5 0.60.6 0.70.7 0.80.8 0.90.9 11
NDVINDVI
Yie
ld k
g h
a-1Y
ield
kg
ha-1
Feekes 9Feekes 9
R.I.>1.5R.I.>1.5
y = 1628.6xy = 1628.6x 22 + 1731.1x + 443.84 + 1731.1x + 443.84
RR22 = 0.69 = 0.69
00
10001000
20002000
30003000
40004000
50005000
60006000
00 0.20.2 0.40.4 0.60.6 0.80.8 11
NDVINDVINDVINDVI
R.I.<1.5R.I.<1.5
y = -2530.4xy = -2530.4x 22 + 8204.1x - 2054.1 + 8204.1x - 2054.1
RR22 = 0.12 = 0.12
00
10001000
20002000
30003000
40004000
50005000
60006000
00 0.20.2 0.40.4 0.60.6 0.80.8 11
Yie
ld k
g h
a-1
Yie
ld k
g h
a-1
Feekes 10.5Feekes 10.5
- 2310.5x + 1504.5 - 2310.5x + 1504.5y = 5379.6xy = 5379.6x 2 2
RR22 = 0.5943 = 0.5943
00
10001000
20002000
30003000
40004000
50005000
60006000
00 0.10.1 0.20.2 0.30.3 0.40.4 0.50.5 0.60.6 0.70.7 0.80.8 0.90.9 11
NDVINDVI
Yie
ld k
g h
a-1Y
ield
kg
ha-1
Feekes 10.5Feekes 10.5
R.I.<1.5R.I.<1.5
10001000
20002000
30003000
40004000
50005000
60006000
Yie
ld k
g h
a-1
Yie
ld k
g h
a-1
y = 2344xy = 2344x 22 + 1844.5x + 270.99 + 1844.5x + 270.99RR22 = 0.23= 0.23
0000 0.20.2 0.40.4 0.60.6 0.80.8
NDVINDVI
11
R.I.>1.5R.I.>1.5
y = 2188xy = 2188x 22 + 1336.7x + 708.19+ 1336.7x + 708.19RR 22 = 0.72= 0.72
00 0.20.2 0.40.4 0.60.6 0.80.8 11
NDVINDVI
10001000
20002000
30003000
40004000
50005000
60006000
00
NDVI and INSEY vs. Yield, Feekes 10.5NDVI and INSEY vs. Yield, Feekes 10.5
y = 700.86ey = 700.86e1.815x1.815xRR 22 = 0.6039 = 0.6039
00
10001000
20002000
30003000
40004000
50005000
60006000
00 0.10.1 0.20.2 0.30.3 0.40.4 0.50.5 0.60.6 0.70.7 0.80.8 0.90.9 11
NDVINDVI
Yie
ld k
g h
a-1
Yie
ld k
g h
a-1
y = 696.59ey = 696.59e 365.08x365.08x
RR 22 = 0.6009 = 0.6009
00
10001000
20002000
30003000
40004000
50005000
60006000
00 0.00050.0005 0.0010.001 0.00150.0015 0.0020.002 0.00250.0025 0.0030.003 0.00350.0035 0.0040.004 0.00450.0045 0.0050.005
INSEYINSEY
Response IndicesResponse IndicesFeekes 9Feekes 9
y = -0.0403xy = -0.0403x22+ 1.1861x - 0.0877+ 1.1861x - 0.0877
RR 22 = 0.98= 0.98
000.50.5
111.51.5
222.52.5
333.53.5
44
00 0.50.5 11 1.51.5 22 2.52.5 33 3.53.5 44
ISRIISRI
RI
RI
• Strong correlation between ISRI and RI determined at harvest
• Accurately predict the crop’s ability to respond to N
• RI can refine whether or not N should be applied, how much, and expected NUE
• Strong correlation between ISRI and RI determined at harvest
• Accurately predict the crop’s ability to respond to N
• RI can refine whether or not N should be applied, how much, and expected NUE
Feekes 10.5Feekes 10.5
y = -0.1467xy = -0.1467x22 + 1.5816x - 0.4018 + 1.5816x - 0.4018
RR 22 = 0.93 = 0.93
000.50.5
111.51.5
222.52.5
333.53.5
44
00 11 22 33 44 55ISRIISRI
RI
RI
ConclusionConclusion
• Grain yield was highly correlated with NDVI and INSEY
• Grain yield could be accurately predicted using NDVI readings at Feekes growth stages 9 and 10.5
• ISRI can accurately predict response to N• NDVI readings (Feekes 9 and 10.5) at locations
with high RI (>1.5) showed higher correlation with grain yield than those with low RI (<1.5)
• Grain yield was highly correlated with NDVI and INSEY
• Grain yield could be accurately predicted using NDVI readings at Feekes growth stages 9 and 10.5
• ISRI can accurately predict response to N• NDVI readings (Feekes 9 and 10.5) at locations
with high RI (>1.5) showed higher correlation with grain yield than those with low RI (<1.5)
Recommended