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)