1. OWC/OWRF
Use of Sensors and Spectral Reflectance Water Indices to Select for Grain Yield in Wheat
Dr. Arthur Klatt
Dr. Ali Babar
Dr. B. Prasad
Mr. Mario Gutierrez R.

2. Plant Breeding Methodology
Selection in classical plant breeding is based on yield
Breeding and release of new wheat cultivars or varieties have been based on grain yield measurements
A large number of genotypes or advanced lines have to be evaluated for yield potential
Promising high yielding genotypes require repeated testing across locations and years to make a final selection decision = costly and time consuming
Yield has low heritability and a high genotype-environment interaction, which can lead to imprecise results
Breeders are searching for new indirect selection tools to improve efficiency of selecting for yield
(Richards, 1982)

3. Spectral Reflectance Technique
Spectral reflectance from the canopy provides information about several physiological traits of the wheat plant
Spectral reflectance measurements are convenient and can be used to screen a large number of genotypes, with limited cost and minimal time commitment
Many indices have been developed from spectral reflectance measurements of the canopy
Are there spectral reflectance indices (SRI) that differentiate wheat genotypes for yield potential, and do so consistently across years and environments?

4. THE LIGHT SPECTRUM Visible spectrum (400-700 nm)
The human eye is sensitive to this region
NIR (near infrared) ( nm)
Human eye not able to see it

5. Scattered by atmosphere (i.e., clouds, particles, smoke)
Energy
source
Absorbed by atmosphere
(CO2, H2O, etc.)
Reflected radiation
by ground surface
Plant canopy 1
Reflected radiation in
Visible region ( nm)
NIR region ( nm)
Absorbed radiation in the visible region ( nm)
Chlorophyll
Xanthophylls
Carotenoids 2 3
Transmitted radiation to the ground
Absorbed by ground surface

6. Canopy reflectance Radiometer computer EReflected Refλ= EIncident
Reflected radiation
a) Visible region ( nm)
b) NIR region ( nm)
Canopy reflectance
EReflected
EIncident
Refλ=
Radiometer
computer

7. Canopy Reflectance Soil reflectance Visible Green Blue Red
R900+R680
R900-R680
NDVI=
Soil reflectance
Visible
Green
Blue
Red
Near infrared
(Lillesand et al., 2004)

8. Canopy Reflectance Visible Near infrared Leaf pigments Cell structure
Chlorophyll
Water absorption band at 970 nm
Xanthophylls
Green biomass
Visible
Near infrared
(Lillesand et al., 2004)

9. Spectral Reflectance in Wheat
Well irrigated
Drought
Water stress deficit
Nitrogen status

10. Canopy Spectral Reflectance
Chlorophyll strongly absorbs radiation in the visible region
The absorbed radiation is influenced by
Overall area of leaves
Other photosynthetic organs (stem and spike)
Pigment concentration
Canopy spectral reflectance provides information to estimate other parameters
Green biomass
Leaf area index (photosynthetic area)
Absorbed radiation (photosynthetic potential)
Nutrient deficiencies
Chlorophyll content
(Knipling, 1970; Osborne et al., 2002)

11. Spectral reflectance indices
Vegetation indices
NDVI= /
RED-NDVI= /
GREEN-NDVI= /
NVI= /
NDVI-2= /
GNDVI= /
IVEST= /680
VI 700= /
SR=900/680
NR=550/850
NRVI= /
NDVI-3= /
Grain yield
Green Biomass
Leaf area index
Intercepted radiation
Nitrogen content
Photosynthetic capacity
Chlorophyll indices
RARSa=675/700
RARS2a=680/800
RARSb=675/650*700
RARSc=760/500)
Datt1= /
Datt2= /
mND= / *445
mSR= /
Gitelson1= /
Gitelson2=750/700
NPQI= /
SIPI= /
Chlorophyll a, b
Carotenoids
SPAD readings
Green biomass
Water indices
WI=900/970
NWI-1= /
NWI-2= /
NWI-3= /
NWI-4= /
Relative water content
Canopy temperature
(Aparicio et al., 2002; Araus et al., 2001; Babar et al., 2006; Knipling, 1970; Osborne et al., 2002; Prasad et al., 2007)

12. Water index Water index (WI)
Peñuelas et al. (1993) established a water index based on the water absorption at 900 and 970 nm (as a reference)
WI=R970/R900
It is related with
relative water content in canopy,
leaf water potential,
and canopy temperature
970 nm wavelength is a water absorption band
900 nm is used as reference
(Peñuelas et al., 1993)

13. Water index as selection criteria
Babar et al. (2006) proposed two normalized water indices
NWI-1=R970-R900/R970+R900
NWI-2=R970-R850/R970+R850
They showed high relationship with yield in spring wheat genotypes (r=-0.40 to -0.88)
Prasad et al. (2007) proposed other two normalized water indices
NWI-3=R970-R880/R970+R880
NWI-4=R970-R920/R970+R920
They found a strong correlation with yield (r=-0.40 to -0.86) in winter wheat
(Babar et al., 2006; Prasad et al., 2007)

14. Water Index as Selection Criteria
They compared the water indices at booting compared with heading-grain filling
They determined that using the average SRI from the heading and grain filling stages can be used for predicting yield of the individual genotypes
The normalization removes
Soil interference
Position of sun (illumination)
Angle of view

15. Reflectance Calibration from a Radiometer
Handheld
sensor B)
Maximum reflectance
Foreoptic
Growth stages
Booting
Heading (anthesis)
Grain filling
50 cm
25o A)
Portable spectroradiometer (UV/VNIR FieldSpec)
No radiation
(dark)
Barium sulfate panel

16. HTWYT Yield (n=18) Grain yield (Kg ha-1) range 2006 2007
Well-irrigated
Drought -
High temperature

17. Heading-Grain filling
HTWYT Well-irrigated (n=18)
Relationship between spectral indices and grain yield
2006
2007
Booting
Heading-Grain filling
Vegetation indices
RNDVI
0.10
0.39
0.38
0.27
GNDVI
0.21
0.42
0.19 SR
0.01
0.49
0.40
Water indices WI
-0.18
-0.35
-0.69**
-0.61**
NWI1
-0.33
-0.62**
NWI2
-0.16
-0.38
-0.65**
-0.59**
NWI3
-0.36
-0.64**
-0.55*
*Significant at p=0.05
**Significant at p=0.01

18. Relationship Between Spectral Indices and Grain Yield
Low water index value means high
Yield
Water content in canopy
Transpiration rate
Photosynthesis rate
Cooler canopies

19. Heading-Grain filling
HTWYT-Drought
Relationship between spectral indices and grain yield
2007
Booting
Heading-Grain filling
Vegetation indices
RNDVI
0.62**
-0.05
GNDVI
0.33
-0.12 SR
0.57**
-0.03
Water indices WI
-0.26
-0.57*
NWI1
-0.52*
NWI2
-0.37
-0.63**
NWI3
-0.32
-0.61**
*Significant at p=0.05
**Significant at p=0.01

20. HTWYT-High Temperature (n=18) Heading-Grain filling
(> 39oC at midday)
Relationship between spectral indices and grain yield
2006
2007
Booting
Heading-Grain filling
Vegetation indices
RNDVI
0.69**
0.63**
0.75**
0.52*
GNDVI
0.62**
0.78**
0.51* SR
0.68*
0.58**
0.56*
0.44
Water indices WI
-0.76**
-0.51*
-0.85**
-0.78**
NWI1
-0.77**
-0.50*
NWI2
-0.42
-0.89**
-0.71**
NWI3
-0.75**
-0.48*
-0.88**
*Significant at p=0.05
**Significant at p=0.01

21. SAWYT Yield (n=50) Grain yield (Kg ha-1) range 2006 2007
Well-irrigated
Drought

22. SAWYT-well irrigated (n=50) Heading-Grain filling
Relationship between spectral indices and grain yield
2006
2007
Booting
Heading-Grain filling
Vegetation indices
RNDVI
-0.04
-0.09
-0.01
-0.10
GNDVI
0.04
0.03
-0.07 SR
-0.02
-0.13
0.19
-0.20
Water indices WI
-0.31*
-0.43**
-0.17
-0.55**
NWI1
-0.16
NWI2
-0.33*
-0.41**
-0.19
-0.53**
NWI3
-0.32*
-0.42**
-0.18
-0.52**
*Significant at p=0.05
**Significant at p=0.01

23. Heading-Grain filling
SAWYT-Drought (n=50)
Relationship between spectral indices and grain yield
2006
2007
Booting
Heading-Grain filling
Vegetation indices
RNDVI
-0.08
0.26
-0.17
0.18
GNDVI
-0.05
0.01
0.05 SR
-0.16
0.06
Water indices WI
-0.32*
-0.41**
-0.50**
-0.51**
NWI1
-0.40**
-0.49**
NWI2
-0.43**
-0.48**
NWI3
-0.44*
*Significant at p=0.05
**Significant at p=0.01

24. CONCLUSIONS
The potential of the water indices for predicting grain yield in wheat has been demonstrated repeatedly in diverse environments
The water indices have an inverse relationship with grain yield
The water indices have shown a consistently good association with yield under well-irrigated, drought and high temperature conditions
There is essentially no difference in accuracy between the different water indices in predicting yield and total biomass
These indices may prove to be an efficient and low cost indirect selection tool for wheat breeders
May be especially effective in selecting at the preliminary yield trial level—testing is now being done!

25. THE NEWEST HAND-HELD SENSOR