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Average maize yields in the US
1860 1880 1900 1920 1940 1960 1980
Year
0
2
4
6
8
10
Grain Yield (t/ha)
Open PollinatedPopulations
Doubled
Hybrids
F1 Hybrids
Average maize yields in the US
1860 1880 1900 1920 1940 1960 1980
Year
0
2
4
6
8
10
Grain Yield (t/ha)
Open PollinatedPopulations
Doubled
Hybrids
F1 Hybrids Maize US 1860-1990
Soybean US 1940-2000
Wheat 1866-1996
Genetic gains of major crops
Chrispeels MJ & DE Sadava.
Plants, Genes and AgriculturePlants, Genes and Crop BiotechnologyJones and Bartlett Publ.
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Yield production and drought stressYield production and drought stress
Redrawn from Calderini & Slafer 1998
Argentina
Australia
Canada
France
Germany
UKUSA
Countries where wheat is
mostly grown undermoderate to little or no
severe drought drought
0
2
4
6
8
0 20 40 60 80
Years (from 1900)
Grainyie
ld(Mgha
-1)
0
24
6
0060 80
100
Barley
Argentina
Australia
Canada
France
Germany
UKUSA
Countries where wheat is
mostly grown undermoderate to little or no
severe drough
t drought
0
2
4
6
8
0 20 40 60 80
Years (from 1900)
Grainyie
ld(Mgha
-1)
0
24
6
0060 80
100
Barley
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How increases in yield potential have
been attained in the past?
Breeding gains have been achieved selecting by yield itself
as the main (and frequently) the only trait evaluated
Shorter plants with a higher HI have been the responsibleof the increase in yield potential
Under drought this approach is complicated by theexistence of important GxE interactions and the higherwithin-site variability that also diminishes heritability
Physiological traits have been seldom used in the past astrue selection criteria The main reason was the difficulty oftheir measure on practical breeding programs
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drought is responsible for most losses in rainfed agriculture,particularly on Mediterranean climates
drought is frequently a combination of water, temperature
and radiation stressesdrought is the most widely stress studied (and then withmore information available to reach sound general
conclusions)
Physiology-aided breeding for stress
environments
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Genetic improvement in this context may be approached
through selection either:
EMPIRICAL : directly, for a primary trait (normallyyield) under the targeted environment (with the
specific stresses naturally occurring; Ceccarelli &Grando, 1996), or
ANALYTICAL or PHYSIOLOGICAL: indirectly, for asecondary trait, that must be putatively related to animproved behaviour of the crop when it is grown in astressful environment
Physiology-aided breeding for stress
environments
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The putative secondary traits for an analytical breedingprogram may be used:
Identifying prospective parents for crossing, independentlyof whether the subsequent selection is to be made byprimary (e.g. yield) or secondary traits
As a direct selection criteria in segregating generations
Physiology-aided breeding: secondary traits
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Regardless the actual contribution of analytical breeding, mostbreeders develop a profound understanding of their environments
and adaptation of their genetic materials.Physiological assessment of adaptation to the environment isneeded to complement breeders' impressions particularly in thefirst and late stages of a breeding program.
A germplasm strategy is also needed for breeding for drought aswell as for any other trait. Most breeders focus on just the elitegene pool, reflecting decades of crossing, selection and
recombination. In fact there is a significant gap between the eliteant the unimproved gene pools.
As empirical breeding seems to be reaching a plateau we mayneed different approaches to further improve grain yields.
Analytical and Empirical Breeding
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As crops experience in most growing areas the effect ofabiotic stresses, at least during part of its growing season,these approaches must improve the response of the crop tothese stresses.
In this context, more emphasis should be given to the useof new genetic variability particularly through the genetically
building of new parent for crosses, incorporating desiredtraits into the gene pool after a series of pre-breedingactivity.
The value of local landraces of many crops in breeding
programs for dry lands should not be underestimated
Development of modern apparatus and new analytical toolswill facilitate measurement ofphysiological traits in the field.
Analytical and Empirical Breeding
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Yield
Physiology
Growth
Development
Carbon economy
Nitrogen economyWater economy
Physiological Plant Breeding
Photoperiod
Vernalization
Intrinsic earliness
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DroughtDrought -- adaptive traits in C3 cerealsadaptive traits in C3 cereals
1. Early Growth. Pre-Grain filling Early vigor/ground cover Stem carbohydrates reserve
2. Access to water High relative leaf water content Low canopy temperature Osmotic adjustment
3. Water use efficiency
High Harvest index Spike/awn photosynthesis Low 13C
4. Photo-protection Leaf morphology
Pale color Wax/pubescence Posture/rolling
Mathew Reynolds, CIMMYT
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Physiological tools Integrative
- Carbon Isotope Discrimination (13C)
- Canopy Temperature Difference
- Spectroradiometrical Reflectance
- Fluorescence
- Near Infrared Reflectance Spectroscopy (NIRS)
- Ash content
Others
- Phenology- Chlorophyll fluorescence- SPAD and SLDW
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Selection by Secondary Traits
How to choose
a trait ?
How to
evaluate it ?
Requirements
and implications ?Usefullness in Breeding
and Crop Management
What traits
should be used ?
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Selection by Secondary Traits
Limitations
of molecular
or biochemical
aproaches
Yield is a
quantitative
character
Basic
Determinants
of Yield ?
Integrative trait
Genetic correlation
with yield
Heritability > yield
Directly related
with yield
Productivity
or survival ?
Stress scape,
avoidance or
tolerance ?
Negative
interaction
GxE
Ecological approach
Previous definiton
of target environment
How to choose
a trait ?
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Selection by Secondary TraitsSelection by Secondary Traits
Limited utility
of traditionalphysiological
methods
Emergency
of alternativemethods
(ecophysiological...)
Spectroradiometrics
Canopy Temperature
Fluorescence
Remote Sensing
13C/12C and
its surrogates
18O/16O
Stable Isotopes ............ .............
Quick
Easy
Non-destructive
Low cost
How to evaluate a trait ?
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Plant Breeding
Fertilization
Seed density
Phenology Adjustment
Chemical control
Mecanization
Crop Rotations
Manure
Organic matter
Pests
Soil Erosion
Other negatives
0 10 20 30 40 50 60-10-20-30
Relative Contribution (%)
Agriculture and the Environmental Challenge
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Response to stress
Accumulation of ABA (enhance survival but reduces
productivity => tolerance versus avoidanceProtection cellular membranes
Ability of the plant to capture water from a drying soilthrough
a) deeper and/or more thorough root explorationb) through osmotic adjustment
Delay senescence
Many of these mechanisms favor survival but mayhave limited value in enhancing grain yield under avarying levels of water stress
Bruce et al. 2002 J. Exp. Bot. 53: 13-15
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Understanding of crop responses to
environment =>Ideotype approach to plant breeding
Ideotype: plan of the phenotype of a cultivarthat will perform optimally in a specific set of
climatic, soil, biotic and socio-culturalconditions (Hall 2001)
(Hall 2001)
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Ideotype
Yield potential is important in determining yieldunder moderate stress, with yields beyond50% of potential
Grain yield is normally highly correlated withkernel number per unit area and per plantrather than with weight per kernel =>
Factors affecting grain set under drought
Bruce et al. 2002 J. Exp. Bot. 53: 13-15
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Ideotype
Under Water Stress
High grain yield
Small ASI
Stay green
Under well-watered conditions
Adequate yield
Small tassels Upright leave
Bruce et al. 2002 J. Exp. Bot. 53: 13-15
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Changes associated with selection
Reduced barrenness under drought (associated with
more rapid ear growth Not too increased biomass production
Slightly earlier anthesis date
Root biomass in the top 50 cm of soil declined by35%, but there was not change in any trait indicativeindicative of plant water status.
Reduced number of spikelets per ear (=> more earlyvigorous silking under drought) => more successful in
forming grain under drought at flowering These mechanism leading to improved yield under
drought also appear to hold under low N
Bruce et al. 2002 J. Exp. Bot. 53: 13-15
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Remote sensing techniquesRemote sensing techniques
Canopy temperature
Spectroradiometrical Reflectance Indices
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Canopy TemperatureCanopy Temperature
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Canopy Temperature DepressionCanopy Temperature Depression
CTD = TCTD = T airair-- TT plantplant
16.4 a16.1 b15.1 c13
C
2.16a1.94 ab1.68bCTD (C)
ModernVarieties
Old VarietiesLandraces
Landrace Old Varieties Modern
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CTD and YieldCTD and Yield
____Correlation of CTD with yieldAerial Hand-held
Trial n Phenotypic Genetic Phenotypic Genetic
RILs (Seri82*7C66)81 0.40** 0.63** 0.50** 0.78**
Advanced lines 58 0.34** - 0.44** -
**statistical significance at 0.01 level of probability- genetic correlations not calculated due to design restrictions
Reynolds etal., 1999
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Results of a stepwise regression with grain yield as dependent variable and the combination ofphysiological traits as independent variables performed across 12 trials assayed. Traits studied were:DISCR kernel carbon isotope discrimination; TKW, thousand kernel weight; CTDA, canopy temperaturedepression at anthesis; CTDM, CTD at milk grain stage; C, kernel carbon content).
0
10
20
30
40
50
60
70
80
90
DISCR TKW CTDA CTDM C
Percentage
Percentage of environments where thetrait significantly entered the model
Percentage (mean across environments)of model-explained yield attributed to thetrait
Royo et al. 2002 (Aust. J. Agric. Res)
Integrative BreedingIntegrative Breeding
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SpectroradiometricalSpectroradiometrical ReflectanceReflectance IndicesIndices
Different levels of assessment:
- Canopy
- Seedlings- Leaves
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SpectroradiometricalSpectroradiometrical ReflectanceReflectance IndicesIndices
Remote determination by instantaneous, non-invasivemethods of the pigment content of leaf canopies, the
status of these pigments and the crops ability tointercept radiation and photosynthesize is a good way ofdetecting physiological status and stresses in plants.
The use of portable narrow-bandwidth visible/nearinfrared spectroradiometers provides a lot of informationthat can be summarized in a set of indices calculatedfrom formulations based on simple operations betweenthe reflectances at specific wavelengths, such as ratiosand differences.
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H
A
Visible (VIS) Near Infrared (NIR)
Blue Green Red
M
PM
Soil
SpectroradiometricalSpectroradiometrical ReflectanceReflectance IndicesIndices
0
0.1
0.2
0.3
0.4
0.5
300 500 700 900 1100Wavelength (nm)
Reflectance
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Wavelength, nm400 500 600 700 800 900 1000
Refle
ctance
0.0
0.2
0.4
0.6
0.8
1.0
Control
N-deficient
Spectroradiometrics and Nitrogen Status
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SpectroradiometrySpectroradiometry and Green Biomassand Green Biomass
Wavelength, nm400 500 600 700 800 900
Reflecta
nce
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Irrigated
Rainfed-2
Rainfed-1
Bare soil
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Spectroradiometrical Indices
Some indices for remote sensing of crop status.Physiological
parameter
Radiometric
Index
NDVIR R
R R
NIR d
NIR red
=
+
Re
Leaf area, [Chl],
Green Biomass, etc. SRR
RNIR red=
Water Content WIR
R=
900
970
Are they able to detect true genotypic differences,or are they only valuable to discriminate across
major environmental effects?
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Spectroradiometrical Indices
Some indices for remote sensing of crop status.
Physiological parameter Radiometric Index
NDVI
R R
R R
NIR d
NIR red=
+
Re
Leaf area,
[Chl],Green Biomass, etc.
SRR
RNIR
red
=
SAVIR R
R R L
LNIR d
NIR red
=
+ +
+Re
( )1
(where L=0.5 for most crops)
Chl degradationNPQI
R R
R R=
+
415 435
415 435
Car/ChlSIPI
R R
R R=
+
800 435
415 435
PRUEPRI
R R
R R=
+
531 570
531 570
Water ContentWI
R
R=
900
970
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Yield Components
General determinants Yield = IR x AR x PE x HI
IR, Incident Radiation
AR, Absorbed Radiation
PE, Photosynthetic Efficiency HI, Harvest Index
In water-limiting conditions (Passioura 1977) Yield = W x WUE x HI
W, Water used
WUE, Water Use Efficiency
HI, Harvest Index
S di i lS t di t i l i di ii di i t
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0.2
0.4
0.6
0.8
1.0
0 2 4 6 8 10 12
LAI anthesis
NDVI
y0.5
= 0.95 - 0.68 e-x
r 2 = 0.93**
0
2
4
6
8
10
0.2 0.4 0.6 0.8 1.0NDVI
Yield(t/ha
y = 92 + 116 exp (x / 0.23)
r 2 = 0.86**
0
2
4
6
8
10
0.7 0.8 0.9 1.0 1.1
WI
Y
ield(tm/ha
y0.5
= 27 - 363 x2
lnxr 2 = 0.93 ***
SpectroradiometricalSpectroradiometrical indices across environmentsindices across environments
0
1
2
34
5
6
7
8
9
0 5 10 15 20 25 30 35
SR
Yield(t/ha)
y = -3579 + 3258 x 0.34
r2
= 0.86**
Royo et al. 2003. Int. J. Remote Sensing, 24:1-16
Aparicio et al. 2002. Crop Sci., 42: 1547-1555
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WIWI vs.vs. waterwater statusstatus
Relationship between WI andeither grain yield, carbon isotopediscrimination (13C ) and canopytemperature difference betweenthe canopy and the air (T) forbarley cultivated under differentlevels of salinity.
Peuelas et al. 1996
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Measuring spectral reflectance of crop
canopies
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Handheld instrument
NTech Industries, Inc.
740 South State StreetUkiah, CA 95482
GreenSeeker Hand-Held Unit
f f
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Measuring spectral reflectance of
crop canopies
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Spectroradiometrics for individual plants
Spectrorradiometer
Tube with reflecting walls
Halogen lamp
Fiberoptic
Diffuser
Remote Cosine Receptor
Aluminium foil
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Potted DW plantsBIOMASS / NDVI
y = 79,802x - 8,6683
R2
= 0,8845
0,00
10,00
20,00
30,00
40,00
50,00
60,00
0,000 0,100 0,200 0,300 0,400 0,500 0,600 0,700 0,800 0,900
NDVI
SHOOT(gr.DW)
E l i / dE l i / d
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Early vigor/ground coverEarly vigor/ground cover
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Stress Management
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1. Estimate N response in-season.1. Estimate N response in-season.
45 N Preplant45 N Preplant45 N Preplant
90 N Preplant90 N Preplant90 N Preplant
RINDVI = 1.46RIRINDVINDVI = 1.46= 1.46
SolutionsSolutionsSolutions
N stress Management
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Management Solutions1. Estimate N response in-season.
29 Locations, 1998-2002
y = -0,22x2
+ 1,86x - 0,50
R2
= 0,68
0
1
2
3
4
0 1 2 3 4 5
RINDVI
RIHarves
t
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0
1
2
3
4
5
6
0 0.002 0.004 0.006 0.008 0.01
INSEY=NDVI/Days f rom planting to sensing GDD>0
Grainyield,
Mg/h
a
Perkins N&P, 1998
Perkins S&N, 1998
Tipton S&N, 1998
Perkins N&P, 1999
Experiment 222, 1999
Experiment 301, 1999
Efaw AA , 1999
Experiment 801, 1999Experiment 502, 1999
Perkins N&P, 2000
Experiment 222, 2000
Experiment 301, 2000
Efaw AA , 2000
Experiment 801, 2000
Experiment 502, 2000
Hennessey, AA, 2000
Perkins N&P, 2001
Experiment 222, 2001
Experiment 301, 2001
Efaw AA , 2001
Experiment 801, 2001
Experiment 502, 2001
Hennessey, AA, 2001
y=0.4593e246.3x
R2=0.55
YP0YP0
Management Solutions2. Provide in-season estimate of yield(INSEY)
YPNYPN
YPMAXYPMAX
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3. Measure and treat spatial variability, in-seasonManagement Solutions
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Management: Conclusions
Temporal variability can be managed. Create N-Rich Strip in each field.
Evaluate yield potential and N responsiveness in-season using sensor.
Spatial variability can be managed on a fine
resolution (
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Use of Spectral Reflectance inBreeding
Some case studies
Use of Spectral Reflectance in
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Use of Spectral Reflectance in
Breeding Early prediction of crop yield can be an
important tool for identifying promisinggenotypes in breeding programmes.
Some results indicate that, for durum wheat,
milk-grain stage is the most appropriatedevelopment stage for yield assessment.
However, some indices are also sensitive to
yield variations when determined at anthesis oreven heading or booting.
C. ROYO, N. APARICIO, D. VILLEGAS, J. CASADESUS, P. MONNEVEUX and J. L. ARAUSUsefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions
INT. J. REMOTE SENSING, 20 NOVEMBER, 2003,VOL. 24, NO. 22, 44034419
Use of Spectral Reflectance in
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Use of Spectral Reflectance in
Breeding
C. ROYO, N. APARICIO, D. VILLEGAS, J. CASADESUS, P. MONNEVEUX and J. L. ARAUSUsefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions
INT. J. REMOTE SENSING, 20 NOVEMBER, 2003,VOL. 24, NO. 22, 44034419
Use of Spectral Reflectance in
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Use of Spectral Reflectance in
Breeding The capacity of spectral reflectance indices to
forecast grain yield in durum wheat increased onlocations that allowed genotypes to express theiryield potentiality.
Assessment of differences between genotypesin specific environments reduced the percentageof yield variability explained by those indices.
C. ROYO, N. APARICIO, D. VILLEGAS, J. CASADESUS, P. MONNEVEUX and J. L. ARAUSUsefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions
INT. J. REMOTE SENSING, 20 NOVEMBER, 2003,VOL. 24, NO. 22, 44034419
Association between canopy reflectance indices and
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yield and physiological traits in bread wheat under
drought and well-irrigated conditions
The relationship of SR indices with grain yield and biomass fittedbest with a linear model. NDVI and GNDVI showed positiverelationships with grain yield and biomass under well-irrigated
conditions (r= 0.350.92), whereas NDVI showed a strongerassociation with yield under drought conditions (r= 0.54).
The 6 chlorophyll indices showed significant association with yieldand biomass of wheat genotypes grown under well-irrigatedconditions (r= 0.390.90).
WI showed a significant relationship with grain yield in wheatgenotypes grown under drought stress conditions (r= 0.60) as wellas with grain yield and biomass under well-irrigated conditions (r=0.520.91).
The relationship between WI and CTD was significant (P 0.05) in
both environments (r= 0.440.84). In conclusion, the SR showed potential for identifying higher-yielding
genotypes in a breeding program under dry or irrigated conditions,as well as for estimating some physiological parameters.
Mario Gutirrez-Rodrguez, Matthew Paul Reynolds,, Jos Alberto Escalante-Estrada and Mara Teresa Rodrguez-GonzlezAssociation between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions
Australian Journal of Agricultural Research 55(11) 11391147 (2004)
Yield predicting attributes of
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Yield predicting attributes of
spectral reflectance indices Photosynthetic area indices and senescence
indices were good indicators, of biomass and
phenology, respectively, when comparing awetter site with a drier site.
When crop development rate was acceleratedby growing plants under high temperature,provided by a spring-sown trial underMediterranean conditions, all spectral indicesshowed significant variation within a period of
one week through grain filling, reflecting thechanges in crop phenology and the onset ofsenescence.
J. BORT, J. CASADESUS, M. M. NACHIT and J. L. ARAUS
Factors affecting the grain yield predicting attributes of spectral reflectance indices in durum wheat: growing conditions, genotypevariability and date of measurement
International Journal of Remote Sensing Vol. 26, No. 11, 10 June 2005, 23372358
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Yield predicting attributes
J. BORT, J. CASADESUS, M. M. NACHIT and J. L. ARAUS
Factors affecting the grain yield predicting attributes of spectral reflectance indices in durum wheat: growing conditions, genotypevariability and date of measurement
International Journal of Remote Sensing Vol. 26, No. 11, 10 June 2005, 23372358
Yield predicting attrib tes
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Yield predicting attributes
Both the sign of the correlation coefficients betweengrain yield and some spectral reflectance indices, and
the changes of those signs throughout the grain fillingperiod of durum wheat canopies, were tracking thecapacities of those canopies to obtain higher yieldsthrough adjustment of phenology, mainly by avoidance
of late grain filling temperatures and drought. Spectral reflectance data give clues to understand which
phenological characteristics of durum wheat canopiescan be selected to improve yield. The results also
illustrated how important it is to define carefully the dateduring the crop cycle when spectral reflectance is to bemeasured.
J. BORT, J. CASADESUS, M. M. NACHIT and J. L. ARAUS
Factors affecting the grain yield predicting attributes of spectral reflectance indices in durum wheat: growing conditions, genotypevariability and date of measurement
International Journal of Remote Sensing Vol. 26, No. 11, 10 June 2005, 23372358
Spectral Reflectance Indices
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Spectral Reflectance Indices
SRI have been suggested as indirect selection criteria byreporting genetic variation for SRI among genotypes, the
effect of phenology and year on SRI and their interactionwith genotypes, and the correlations between SRI andgrain yield and yield components of wheat.
A clear trend for higher association between grain yield
and the NIR-based indices was observed at heading andgrain filling than at booting. Overall, NIR-based indiceswere more consistent and differentiated grain yield moreeffectively compared to the other indices. The results
demonstrated the potential of using SRI as a tool inbreeding programs for selecting for increased geneticgains for yield.
M. A. Babar, M. P. Reynolds, M. van Ginkel, A. R. Klatta, W. R. Raun and M. L. StoneSpectral Reflectance Indices as a Potential Indirect Selection Criteria for Wheat Yield under IrrigationCrop Sci 46:578-588 (2006)
Spectral Reflectance Indices
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Spectral Reflectance Indices
Spectral indices as a selection tool in plant
breeding could improve genetic gains for
different important traits:
estimate genetic variation for in-season biomass
production, leaf chlorophyll, and canopy temperature
(CT) in wheat (Triticum aestivumL.) under irrigatedconditions.
the potential of using SRI as a breeding tool to select
for increased genetic gains in biomass andchlorophyll content, plus for cooler canopies.
M. A. Babar, M. P. Reynolds,*, M. van Ginkel, A. R. Klatt, W. R. Raun and M. L. StoneSpectral Reflectance to Estimate Genetic Variation for In-Season Biomass, Leaf Chlorophyll, and Canopy Temperature in WheatCrop Sci 46:1046-1057 (2006)
GY vs NDVI phenology(1)
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GY vs NDVI, phenology(1)
IDUWUE Gimenells RILs 2005
R2
= 0,2238
R2
= 0,3254
R2 = 0,041
0
500
10001500
2000
2500
3000
0 0,2 0,4 0,6 0,8 1
NDVI
G
Y22/04/2005
10/05/200531/05/2005
GY vs NDVI phenology(2)
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GY vs NDVI, phenology(2)
IDUWUE Gimenells RILs 2005
R2 = 0,1969
R2
= 0,0979
R2
= 0,2413
0
500
10001500
2000
2500
3000
-0,2 0,0 0,2 0,4 0,6
Decrease in NDVI
G
YApril-June
April-MayMay-June
GY vs NDVI phenology(3)
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GY vs NDVI, phenology(3)
Spectral reflectance data may help to
understand phenological characteristics ofdurum wheat canopies, such as crop
duration, provided the date of
measurement is well chosen.
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Digital photography as a
screening tool for cerealbreeding.
Jaume Casadess1, Jordi Bort2 and Jos Lus Araus2.
1Institut de Recerca i Tecnologia Agroalimentries (IRTA), Spain.
2Dept. Biologia Vegetal, Universitat de Barcelona, Spain.
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IntroductionDigital Cameras are very popular devices that can be useful
for field data acquisition.
-affordable-portable
-ease of use
Analysis of digital images can bring in many variables,
allowing-objective estimation of some vegetation traits
-gathering of data for statistical analysis
Numerical representation of color
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IHS
Intensity, Hue, SaturationPractical for image analysis
0
120
240
Hue wheel:
Numerical representation of color
RGB: related with color reproduction by computer screens, etc.
CIE-XYZ~ sensitivity of human visual system
Consistent distance
practical for arithmetics
CIE-Lab
There are a number of different systems for representing a given color.All them use 3 quantities (different meaning for each system)
Color data from each image
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Average color:RGB 92.0 91.3 45.8
g
RGB 92.0 91.3 45.8
XYZ 0.096 0.109 0.033
IHS 0.299 59.3 0.401
CIE-Lab 37.1 -10.0 31.233.2-18.140.232.1-11.038.731.2-10.037.1CIE-Lab
0.36375.20.3300.39460.70.3140.40159.30.299IHS
0.0450.1410.1140.0370.1210.1050.0330.1090.096XYZ
53.6106.692.248.596.195.545.891.392.0RGB
Green AreaSoil coverField of view
Beyond averages: histograms for color components
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Histogram of Hue
0
0.05
0.1
0.15
0.2
0.25
2 32 62 92 122 152 182
Hue (0-360)
frequency
Histogram of Hue
0
0.02
0.04
0.06
0.08
0.1
0.12
2 32 62 92 122 152 182
Hue (0-360)
freq
uency
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Howeverunsteady yield of conventional digital cameras.
Due to the cameras self-adjustments, the same object can be recorded
with different colors depending on the general brightness of the scene.
Alternative ways to cope with
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cameras self-adjustments.
Fix the settings and characterize the camera in laboratory
Use reference panels to recalculate the colors.
Select robust parameters, least affected by self-adjustments. R, G, B, I, H, S, X, Y, Z, L, a, b,...
Robustness of different color parameters
A color chart (24 known colors) was recorded over 15 turfs of different color
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Red
0
60
120
180
240
300
0 60 120 180 240 300actual value
recordedvalue
Green
0
60
120
180
240
300
0 60 120 180 240 300actual value
recorde
dvalue
Blue
0
60
120
180
240
300
0 60 120 180 240 300actual value
recordedvalue
Intensity
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1actual value
recordedvalue
Hue
0
60
120
180
240
300
360
0 60 120 180 240 300 360actual value
record
edvalue
Saturation
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1actual value
record
edvalue
A color chart (24 known colors) was recorded over 15 turfs of different color.
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Digital photography in breeding programs
AffordableEasy to use
Ubiquitous
Can allow unexpensive field data acquisition
simultaneously at different sites.
Objectives
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Objectives
1. Derive vegetation indices from color
analysis of digital images.
2. Explore the potentialities beyond
vegetation indices: image features that
can contribute to the assessment of
physiological traits.
Materials and methods
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Pictures with conventional
digital cameraNDVI measurement with
GreenSeeker
As many field trials aspossible, showing a wide
range of environments
One NDVI mesurementvs.
One digital picture
Performance of Hue as a Vegetation Index
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e o ce o ue s Vege o deNDVI vs Hue
R2
= 0.90
30
40
50
60
70
80
90
100
110
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
NDVI
Hue
(0-360)
Irrigated
Rainfed
DrySev.Dry
Performance of %Green Area as a Vegetation Index
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NDVI vs. Green Area
R2
= 0.91
0
20
40
60
80
100
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
NDVI
GA,%
Irrigated
Rainfed
DrySev.Dry
Performance of a* as a Vegetation Index
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NDVI vs a* (from CIE-Lab color space)
R2 = 0.87
-35
-30
-25
-20
-15
-10
-5
0
5
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
NDVI
a*
Irrigated
Rainfed
DrySev.Dry
Results
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Example of NDVI and Hue covariation along a range of barley rainfed plots
30
40
50
60
70
80
90
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
plot number
Hue,
0-360
0.2
0.3
0.4
0.5
0.6
0.7
0.8
NDVI
Hue NDVI
NDVI vs color parameters in more trials
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iduwue RILs Gimenells 050510
30
34
38
42
46
50
0 50 100 150 200 250 300num plot
HU
E
0,1
0,15
0,2
0,25
0,3
0,35
NDVI
NDVI vs color parameters in more trials
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iduwue RILs Gimenells 050531
31,0
32,0
33,0
34,0
35,0
36,0
37,0
0 50 100 150 200 250 300num plot
HU
E
0
0,05
0,1
0,15
0,2
0,25
ND
VI
NDVI vs color parameters in more trials
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iduwue Gimenells RILs 050510
R2 = 0,3254
R2 = 0,2354
0
10
20
30
40
50
60
0 500 1000 1500 2000 2500 3000
GY
HUE
0
0,1
0,2
0,3
0,4
0,5
0,6
ND
VI
NDVI vs color parameters in more trials
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iduwue Gimenells RILs 050531
R2
= 0,041
R2
= 0,0197
30
31
32
3334
35
36
3738
0 500 1000 1500 2000 2500 3000
GY
HUE
0
0,1
0,2
0,30,4
0,5
0,6
0,70,8
ND
VI
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NDVI vs color parameters on each trial.
0.870.910.90all
0.600.670.62Barley arid (later)
0.620.710.67Barley arid
0.770.850.82Durum wheat-Rainfed
0.020.050.06Durum wheat-Irrig.
a*%GreenAreaHuetrial
(R2 for the relationship between NDVI and each color parameter)
Beyond vegetation indices
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Other parameters could be estimated from digital images.
Total soil cover(green+dry vegetation)
Physiological status
(N-content, Chl,...)?
from the color of the
green area only.
Conclusions
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Some color parameters derived from image analysis that
can be used as Vegetation Indices are: Hue, %Green
Area and a* (from CIE-Lab).
At least in the essayed sites, dry vegetation can be
distinguished from the soil and quantified.
These color parameters can be calculated separately for3 regions of the image : total field of view, soil cover and
green vegetation.
The image analysis was performed automatically at arate of 2.2 images / s, with a plain desktop PC.
As many ants as you may eventually find in this field are the many
hours, you need
NOTto be there measuring ecophysiology
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Many Thanks!
NOT
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