quantification of resource capture for crops - decagon … · 2010-05-20 · quantification of...
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Quantification of Resource E l Capture Efficiency for Cultivar ImprovementImprovement
Gaylon S. CampbellDecagon Devices, Inc.g
Pullman, WA.
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Maximum Potential Potato Yields
Kunkel and Campbell (1987) Am. Potato J.
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Th M d lThe ModelYield = HI * B Hi is harvest index B is total crop biomass B is total crop biomass
B = RUE * FI * SB = RUE FI S RUE is Radiation Use Efficiency (biochemical) FI is intercepted radiation fraction (canopy structure) FI is intercepted radiation fraction (canopy structure) S is solar radiation incident on the crop (environment)
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We could conclude:We could conclude
Some of the plots yielded the maximum possible for that environment and growing season lengthg g g
Plots yielding less than modeled values Had low RUE (stress, disease...)or Had low FI (senescing canopy, poor canopy structure)
Even higher yields might come from Even higher yields might come from Potatoes with higher RUE Lengthening the growing seasong g g g
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O tliOutline
Resource capture concepts Light capture and biomass productionL g p m p Measuring light interception and radiation
use efficiency Water capture and biomass production Water capture and biomass production A bit of history Transpiration vs. total ETp Measuring transpiration efficiency Conclusions
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Si l M d l f R C tSimple Models for Resource Capture
Resource: A form of energy or of matter that plants need in order to grow or reproduce
Capture: The process by which any organ – Capture: The process by which any organ above or below ground removes a resource from its environment to maintain metabolismmetabolism
Monteith (1994)
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RResources
Carbon Dioxide
Light (solar radiation)
W Water
Nutrients Nutrients
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Li ht C t M d l tiLight Capture Model assumptions
B = RUE * FI * S Light limits production (light assumed to g p g
be total solar radiation) B includes only the above ground biomass RUE is conservative (genetically RUE is conservative (genetically
determined)
B d F * Caution: Since B and FI * S are sums, a linear relationship between them will always exist. This approach is only useful if S is limiting.
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V l d R f RUEValues and Ranges for RUE
C3 Annuals 1.2 – 1.7 g/MJ
C4 Annuals 1.7 – 2.0 g/MJ
C Oil 1 3 1 6 /MJ C3 Oil crops 1.3 – 1.6 g/MJ
Legumes 1 0 – 1 2 g/MJ Legumes 1.0 1.2 g/MJ
Tuber and root 1.6-1.9 g/MJ
Stockle and Kemanian (2009)
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S f t th t ff t RUESome factors that affect RUE
From the table C3 vs. C4 metabolism3 . 4 m m Legume High sink strength (tuber)
Others RUE decreases with increasing vapor RUE decreases with increasing vapor
deficit of the atmosphere RUE increases under strong diffuse
radiationradiation
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T d t i RUETo determine RUE
RUE = B/FI*S Measure B – harvest, weigh, dry, weigh
Measure S – pyranometer and data logger
Measure fractional interception
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For this canopy FI is close to 1.0 For th s canopy F s c os to . – you don’t need to measure it
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For this canopy FI is much smaller – it d t b dneeds to be measured
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M i li ht i t ti i iMeasuring light interception in canopies
Light below a canopy is extremely variable
Many measurements are required to accurately characterize light levelscharacter ze l ght levels
The AccuPAR LP-80 has 80 light sensors in a 80 cm long bar for rapid cm long bar for rapid below-canopy measurements
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St t FISteps to measure FI
Level the LP-80 above the canopy and make a measurement of incident light
Level the LP-80 below the canopy at several locations and measure transmitted light
The LP-80 shows fractional transmission (TR)
FI = 1 – TR (TR may need to be converted to a daily value as shown in the LP-80 manual)
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Interception vs. absorption of radiation by canopies
Campbell and van Evert (1994)
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Early Growth Analysis
Exponential growth at early time
300
400
Linear growth as canopy closes
200
300
Bio
mas
s
py(FI approaches 1)
0
100B
00 10 20 30 40
Time
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The Expolinear Growth Equation The Expolinear Growth Equation (Goudriaan and Monteith, 1990)
)]}(exp[1ln{ bm ttrcB 300
400
)]}(exp[1ln{ bmm
ttrr
B
cm is max. absolute 200
300
Bio
mas
s
Lost time
growth raterm is max. relative
growth rate 0
100
growth ratetb is represents “lost
time” from incomplete
0 10 20 30 40
Time
canopy closure
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M i FI l iMeasuring FI on sparse, low canopies
Digital images can be i processes using
suitable algorithms to determine green gcover
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O tliOutline
Resource capture concepts Light capture and biomass productionL g p m p Measuring light interception and radiation
use efficiency Water capture and biomass production Water capture and biomass production A bit of history Transpiration vs. total ETp Measuring transpiration efficiency Conclusions
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Wh l t d t t tWhy plants need to capture water
Taiz and Zeiger (1991)
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Photosynthesis (P) and Transpiration Photosynthesis (P) and Transpiration (T) for a Leaf
PhotosynthesisTranspirationDgCCgT
CCgP
i
cicac
)()(
p
Transpiration efficiencyDk
DCCg
TP
DgCCgT
cicac
vvaviv
)(
)(
Transpiration efficiency
Normalized efficiency CCkDDgT
cica
v
7.0 Normalized efficiency
Water capture modelDkTP
Water capture modelD
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C l i f th l f l iConclusions from the leaf analysis
Th i P/T i ll d i i ffi i The ratio, P/T is called transpiration efficiency (TE). It is the mass of carbon fixed per unit mass of water used (typically a small number b 0 1 d 1%) between 0.1 and 1%)
Photosynthesis (and therefore dry matter Photosynthesis (and therefore dry matter production) is proportional to transpiration
Ph i li i h i d Physics puts limits on what genetics can do to improve water use efficiency of plants
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M C l i f th l f l iMore Conclusions from the leaf analysis
T i i ffi i d d Transpiration efficiency depends on environment (vapor deficit of air)
Arid regions (with high vapor deficit) have lower dry matter production per unit water used than do humid regions (P = T*k/D)used than do humid regions (P = T k/D)
Transpiration efficiency varies dramatically di l l d over a diurnal cycle and over a season as vapor
deficit changes
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M C l i f th l f l iMore Conclusions from the leaf analysis
T i i ffi i d d h Transpiration efficiency depends on the difference between external and internal CO2concentration [k = 0.7*(Cair – Cint)]
Species which maintain low internal CO2concentration have high transpiration efficiencyff y
C4 species, maintain lower internal CO2concentrations than do C3, so have higher transpiration efficienciestranspiration efficiencies
As atmospheric CO2 concentrations increase transpiration efficiency increases
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V l /R f k (D*TE)Values/Ranges for k (D*TE)
Corn 9 – 12 Pa
Potato 7 Pa
Alf lf 4 P Alfalfa 4 Pa
Soybean 4 Pa Soybean 4 Pa
Tanner & Sinclair (1983)
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Plants differ in water use efficiency: An ld told concept
F H Ki (U f Wi i ) 1890 1902 F. H. King (U. of Wisconsin) 1890-1902
J A Widtsoe (Utah State U ) 1902 J. A. Widtsoe (Utah State U.) 1902
T. A. Kiesselbach (U. of Nebraska)
Briggs and Shantz (1913)
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B i d S h t (1913)Briggs and Schantz (1913)
“One of the most striking features of water requirement measurements is the marked qdifference in efficiency exhibited by different plants in the use of water. The millet, sorghum, and corn groups have g g pbeen found the most efficient, while alfalfa, and sweet clover are the least efficient in producing dry matter with a p g ygiven amount of water. The small-grain crops have a water requirement intermediate between the legumes and gcorn.”
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B i d S h t (1913) tBriggs and Schantz (1913) cont.
“M bl diff i th t “Measurable differences in the water requirement also exist between different varieties of the same crop, and this
t th ibilit f d l i suggests the possibility of developing through selection strains which are still more efficient in the use of water.”
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Widt ’ WUE i t d 1902 Widtsoe’s WUE experiments around 1902
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H t d t i TEHow to determine TE
Measure B – Harvest, weigh, dry, weigh
Measure T by weighing containers or monitoring soil
istmoisture
But weight loss is transpiration But we ght loss s transp rat on and soil evaporation – biomass is proportional just to transpirationtranspiration
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How do we compute Transpiration in the Fi ldField
Total water loss is transpiration (T) plus evaporation (E)
When soil water is not limiting PET = PT + PE When soil water is not limiting PET = PT + PE PT = FI * PET PE = (1 – FI) * PET PET computed from weather records (Penman-
Monteith Formula)
Again, measurements of FI are required
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B tt t d t i k D * TEBetter to determine k = D * TE
Calculate D from temperature and humidity data
D = es – ea = es(1 – RH)
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Daily course of vapor deficit
25
30
2
2.5
15
20
ratu
re (C
)
1.5
2
efic
it (k
Pa)
Daytime ave1.6 kPa
5
10
15
Tem
per
0.5
1
Vapo
r De
vapor
TempDaily ave.1.2 kPa
0
5
2AM
4AM
6AM
8AM
10AM
12PM2P
M4P
M6P
M8P
M10
PM12
AM
0
vapordeficit
2 4 6 8 10 12 2 4 6 8 10 12
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C ld TE di tl ?Could we measure TE more directly?
Carbon isotope discrimination
Sim lt n s t nspi ti n nd Simultaneous transpiration and photosynthesis measurements
Measurement of stomatal conductance
Measurement of canopy temperature
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Di t f t t l d tDirect measure of stomatal conductance
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Steady State Porometerleaf
R1h1
sensorsR2
h2
211
RCC
RRCC vvvvL
Teflon filter
2
21 RRRvs
1211 RRhRvs
12
12 hhvs
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P t b d f Porometer can be used for
Measuring maximum daytime conductance (cultivars with high conductance likely have low TE)low TE)
Measuring dark conductance Water lost in the dark doesn’t contribute
to dry matter Low dark conductance correlates with low Low dark conductance correlates w th low
Cci in soybean (Earl and Walden, 2009)
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C l iConclusions
The resource capture paradigm can be useful for identifying and removing limitations to productionproduction
When water and nutrients are plentiful light is th li iti s It is l d i is the limiting resource. It is analyzed in terms of three factors: available solar radiation, interception by the crop canopy,
d th ffi i f i f di ti and the efficiency of conversion of radiation to biomass
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C l iConclusions
Both interception and radiation use efficiency are subject to genetic manipulation.
When water is limiting biomass production When water is limiting, biomass production can be predicted from a transpiration efficiency and a knowledge of the amount of water transpired water transpired.
The transpiration efficiency is not constant, but decreases as the vapor pressure deficit f th i iof the air increases
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C l iConclusions
Transpiration efficiency is subject to genetic manipulation and depends on internal CO2concentrationconcentration
Appropriate field instrumentation can help in identifying cultivars with high RUE or high TE
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For references, questions, or to access thi t ti t this presentation go to
www.decagon.com
or
www.junipersys.comj p y m
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ReferencesKunkel, R. and G. S. Campbell. 1987. Maximum potential potato yield in the
Columbia Basin USA: Model and measured values. Am. Potato J. 64:355Monteith, J. L. 1994. Principles of resource capture by crop stands. In Monteith, , p p y p ,
J. L., R. K. Scott and M. H. Unsworth eds. Resource Capture by Crops. Nottingham University Press.
Stöckle, C.O. and A.R. Kemanian. 2009. Crop radiation capture and use efficiency: A framework for crop growth analysis In Crop Physiology (Vefficiency: A framework for crop growth analysis. In Crop Physiology (V. Sadras and D. Calderini Eds). Academic Press, Elsevier Inc. p 145-170.
Campbell, G. S. and F. K. van Evert. 1994. Light interception by plant canopies: efficiency and architecture in J L R K Scott and M H Unsworth edsefficiency and architecture. in J. L., R. K. Scott and M. H. Unsworth eds. Resource Capture by Crops. Nottingham University Press.
Goudriaan, J. and J. L. Monteith. 1990. A mathematical function for crop growth. Annals of Botany 66:695-701.
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ReferencesTaiz, L., and E. Zeiger. 1991. Plant Physiology. Benjamin/Cummings, N. Y.Briggs, L. J. and H. L. Shantz. 1913. The water requirement of plants; II A
review of the literature. USDA Bureau Plant Industry Bull. 285.yEarl, H. J. and A. E. Walden. 2009. Why does dark adapted leaf conductance
predict water use efficiency of soybean? Agronomy Abstracts AnMtgsAbstracts2009.55299
Tanner, C. B. and T. R. Sinclair. 1983. Efficient water use in crop production: research or re-search? In H. M. Taylor et al. (ed.) Limitations to Efficient Water Use in Crop Production. ASA, Madison WI p. 1-27.
Richards R A G J Rebetzke M Watt A G Cordon W Spielmeyer and RRichards, R. A., G. J. Rebetzke, M. Watt, A. G. Cordon, W. Spielmeyer and R. Dolferus. 2010. Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment. Functional Plant Biology 37:85-97.