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Simulation of biomass production
Introduction
Dr. Ep Heuvelink - Wageningen University
Simulation of biomass production– scope of the course -
Growth:
yieldleaf area
light interception
photosynthesis
biomass increase
partitioning
Uptake water & nutrients
Development: quality
seed
young vegetative plant
generative plant
harvestable product / seed
germinationemergence
flowerinduction
Influencing factors: light (amount/quality/day length), temperature (level/day & night
regime), CO2, humidity, EC, water, nutrients, plant density, pruning.Interactions between climate factors and crop in a greenhouse. Not considered: organic matter mineralization, specific nutrients, pests & diseases, plant breeding.
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Simulation of biomass production- contents -
Crop models: what are they and why useful/important?
General scheme of a photosynthesis-driven crop growth model
Details of processes: - light interception- photosynthesis and respiration- dry matter production- dry matter partitioning
(other subject - concept of sink strength)
Simulation of biomass production- contents (cont.) -
Examples from
- wheat
- fruit vegetables (tomato, sweet pepper, cucumber)
- cut chrysanthemum
Educational methods
- Theory: syllabus/hand-outs/examples/exercises
- Training assignments: LINTUL, TOMSIM
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Simulation of biomass productionAbout models
What is a model:
Simplified representation of a part of reality (= a system)
Mechanistic models enable us to represent and combine knowledge in a generic way
Why use models:
Research - testing hypotheses
Greenhouse climate control
Economic decision making
Education
Decision Support System (DSS)}
Simulation of biomass production- about models -
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Model use in climate and fertigation control
Descriptive: black box, regression, statistic, empirical direct relation between input and output
Explanatory: physiological, mechanistic quantitative description of mechanisms and processes contains sub-models at least one hierarchical level
deeper than the response to be described.
Simulation of biomass production- two types of models -
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Simulation of biomass production- example of black box model -
Data from experiments in climate chamber:Length of young tomato plants after 40 days of growing.
Model based on these data:
Length = 36.3 +4.48 * (Td-21) + 1.37 * (Tn-21)
Disadvantages: No extrapolation No new conditions Advantages: High predictive value
Td (°C)
Tn(°C)
Plant length (cm)
26 16 5224 18 5022 20 3420 22 3518 24 2816 26 2318 18 1824 24 5424 12 39
Leaf area index
Light interception
Photosynthesis Respiration
Dry mass production (Growth)
LeavesRoot Stem Flowers
Simulation of biomass production- Principles of crop growth model (mechanistic)-
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Advantages:
extrapolation is possible
separated in modules
give insight in plant reactions (‘emerging behavior’)
Disadvantages:
development is very time consuming
(team of researchers)
only known effects are in the model
Simulation of biomass production- Example of Mechanistic model-
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Simulation of biomass production– Compare two models -
Model 1: descriptive model: growth rate (g m2 d1) = 1 + 0.2 * LI
Model 2: explanatory model: Photosynthesis GPHOT (g m2 d1) = LI * LAI Maintenance respiration MAINT = 0.03 * TDWTotal Dry Weight TDW = TDW + 0.7 * (GPHOT - MAINT)Leaf Dry Weight LDW = 0.2 * TDWLAI = LDW * 0.02
Question: If plants are grown under the situations listed below: at a light intensity (LI) of 20 W m2
with an initial dry weight of 100 g m2
with an initial Leaf Area Index (LAI) of 0.4 m2 m2
Please calculate crop dry weight after 2 days of growth using the 2 models given above respectively, and then compare the differences.
Simulation of biomass production– Compare two models (results)-
Model 1: descriptive model: growth rate (g m2 d1) = 1 + 0.2 * LI = 1+0.2*20 =5 (g m2 d1)
crop dry weight after 2 days = 100 + 2*5 = 110 g m2
Model 2: explanatory model: Firstly to calculate results for day 1 and use these results for the calculation on day 2.
Results DAY 1 DAY2GPHOT 8 8.2MAINT 3 3.1TDW 103.5 107.1LDW 20.7 21.4LAI 0.41 0.43
Compare models: 110 g m2 (Model 1) VS 107.1 g m 2 (Model2)
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Simulation of biomass productionLight interception
The processes: step by step - Light interception
Leaf area index
Light interception
Photosynthesis Respiration
Dry mass production (Growth)
LeavesRoot Stem Flowers
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Influence of Leaf Area Index (LAI) on the fraction of light intercepted by a tomato crop (k = extinction coefficient = 0.8)
Simulation of biomass production– Light on top of canopy -
Wavelength :
Global radiation : 300-3000 nm
PAR: Photosynthetically Active Radiation: 400-700 nm
Energy:
In day light PAR outside 0.5 x Global radiation
In a glasshouse: PAR 0.7 x PAR outside
Example:
20 MJ day1 global radiation outside would result in about
20 x 0.5 x 0.7 = 7 MJ (PAR) day1 inside a glasshouse.
NB: Absorbed Intercepted
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Light use efficiency (LUE, g MJ1) in winter: 3 plant densities
and - or + assimilation light for cut chrysanthemum
Simulation of biomass production– Dry matter production: a simple LUE model –
dW/dt = LUE (1 e k * LAI) I
dW/dt = growth rate [g(DM)m2 d1]
LUE = light use efficiency [g(DM) MJ1(PAR)]
K = extinction coefficient
LAI = Leaf area index
I = Photosynthetic Active Radiation (PAR) incident on crop
[MJ(PAR)m2 d1]
Assumes constant LUE !
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Simulation of biomass productionPhotosynthesis
The processes: step by step - Photosynthesis
Leaf area index
Light interception
Photosynthesis Respiration
Dry mass production (Growth)
LeavesRoot Stem Flowers
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Simulation of biomass productionInfluence of Leaf Area Index (LAI) on crop photosynthesis
LAI amount of light intercepted amount of Photosynthesis
Simulation of biomass productionInfluence of CO2 concentration (ppm) on crop photosynthesis
CO2 Light Use Efficiency amount of Photosynthesis
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Simulation of biomass productionInfluence of temperature (oC) on crop photosynthesis
Temperature Gross Photosynthesis (≠Crop growth/ yield)
Simulation of biomass production– Photosynthesis modules (1) –
Negative-exponential response curve
1
Non-rectangular hyperbola (not compulsory)
[ is between 0 and 1; determines the shape; ]
Rectangular hyperbola (not compulsory)
•
NB: Temperature and CO2 affect and Pgmax
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Simulation of biomass productionPhotosynthesis - light response curves (values for )
Simulation of biomass productionLeaf photosynthesis: Pg = Pg,max (1-exp(- H/Pg,max))
Absorbed PAR
Le
af
ph
oto
syn
the
sis
ra
te
Shade leaf
Sun leaf
α
Pg,max
Pg,max
Sun Shade Effect on Pg,max
but not α Positions in the
canopy or type of plants
NB: H= Iabs
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Simulation of biomass production– Resistance chain for CO2 transport and binding –
Total resistance = R(boundary layer)+ R(Stomata)+R(mesophyll)
Simulation of biomass production– Resistance chain for CO2 transport and binding –Inside leaves:
rm: Mesophyll resistance gm: Mesophyll conductance Г: CO2 compensation point
Internal CO2 concentration
Phot
osyn
thes
is
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Simulation of biomass production– Photosynthesis modules (2) –
Biochemical model (Farquhar & Von Caemmerer type)Leaf photosynthesis is described as either Rubisco-limited (CO2) or electron transport rate limited(light) .
Canopy photosynthesis: big leaf approach exponential radiation profile (light extinction in crop)
and integration of leaf photosynthesis over the leaf layers
Influence of sink demandon photosynthesis ?
Cucumber leaves
Only reduced photosynthesis after 20 days when all fruits were removed
Marcelis (1991). J.Exp.Bot. 42: 1387-1392.
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Simulation of biomass production– Effect of sink demand on photosynthesis? –
60 100 140 180
Day of year
0.0
0.4
0.8
1.2T
otal
dry
wei
ght (
g m
-2)
3 fruits per truss
7 fruits per truss
Influence of number of fruits per truss on total dry matter production in tomato. A reduction in fruit number (sink strength) by more than 50% did not influence total dry matter production. Since leaf area index was identical for both treatments, one can conclude that
effects of sink demand on leaf photosynthetic rate in commercial tomato production can be ignored.
Simulation of biomass productionRespiration
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The processes: step by step - Respiration
Leaf area index
Light interception
Photosynthesis Respiration
Dry mass production (Growth)
LeavesRoot Stem Flowers
Simulation of biomass productionDry matter production: explanatory crop growth model
dW/dt = Cf (Pgd - Rm)
dW/dt = Crop Growth Rate (g DM m2 d 1)
Pgd = Crop gross Assimilation Rate (g CH2O m2 d 1)
(In the model all leaves have identical photosynthetic properties)
R m = Maintenance Respiration Rate (g CH2O m2 d 1)
(Depends on Organ Dry Weight, Temperature, RGR)
Cf = Conversion Efficiency (g DM g1CH2O)
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Simulation of biomass production– Maintenance respiration (Rm) –
dW/dt = Cf (Pgd - Rm)
Rm = Coef. dry weightCoef. = Coef. at 25°C Q10
{(temp.-25)}/10
Example:
Question: Assume at 25°C, Coef. is 0.014 g(CH2O)g1(DM) and Q10=2.0. What is Coef. at 15°C ? And at 20°C ?
Answer: 0.007g(CH2O)g1(DM) and 0.010g(CH2O)g1(DM)
Simulation of biomass production– Maintenance respiration (Rm) cont. –
dW/dt = Cf (Pgd - Rm)
Rm is assumed to have priority above growth
Rm results from maintenance processes likeprotein turnover and ion fluxes over membranes
If Rm > Pgd then Rm is set equal to Pgd; growth is zero.
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Simulation of biomass production– Growth respiration –
dW/dt = Cf (Pgd - Rm)
Cf = growth conversion efficiency (g DM g1 CH2O)
Growth respiration depends on chemical composition
(costs of turning sugars into fats, organic acids, etc., & costs for ion uptake)
Simulation of biomass production– Glucose requirements –
Average amount of glucose required (G; gCH2O g1DM) to produce 1 gram of the following biochemical groups
GCarbohydrates 1.24Proteins 2.70Lipids 3.11Lignin 2.17Organic acids 0.93Minerals 0.05
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Simulation of biomass production– Glucose requirements (cont.) –
Glucose requirement (G; gCH2O g1DM) and conversion efficiency (Cf; gDM g1CH2O)
G CfYoung leaf(carbohydrates + proteins)
1.656 0.60
Oil rich seed(fats and proteins)
2.572 0.39
Woody stem(carbohydrates + lignin)
1.569 0.64
Root sugar beet(carbohydrates)
1.271 0.79
NB: G * Cf = 1
Simulation of biomass productionDry Mass Production
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The processes: step by step - Growth
Leaf area index
Light interception
Photosynthesis Respiration
Dry mass production (Growth)
LeavesRoot Stem Flowers
Light use efficiency (LUE, g MJ1) in winter: 3 plant densities
and - or + assimilation light for cut chrysanthemum
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Simulation of biomass production– Dry matter production: a simple LUE model –
dW/dt = LUE (1-e k LAI) I
dW/dt = growth rate ( g DM m 2 d 1)
LUE = light use efficiency ( g DM MJ 1PAR)
k = extinction coefficient
LAI = Leaf area index
I = Photosynthetic Active Radiation (PAR) incident on crop
(MJ m 2 d 1)
Assumes constant LUE !
Light use efficiency (LUE, g MJ-1) in summer at 3 light levels
(shade screens) for cut chrysanthemum
A fixed value for the LUE, will not work
over the whole
season/year, because LUE is depend on LIGHT LEVEL
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Simulation of biomass production– Simulated light use efficiency for crop with LAI = 3 –
–
Very high light
intensity will give
lower LUE because of
light saturation
Simulation of biomass productionDry matter production: explanatory crop growth model
dW/dt = Cf (Pgd - Rm)
dW/dt = Crop Growth Rate (g DM m-2 d-1)
Pgd = Crop gross Assimilation Rate (g CH2O m-2 d-1)
(In the model all leaves have identical photosynthetic properties)
R m = Maintenance Respiration Rate (g CH2O m-2 d-1)
f (Organ Dry Weight, Temperature, RGR)
Cf = Conversion Efficiency (g DM g-1CH2O)
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Simulation of biomass productionDry matter production: explanatory crop growth model
Why can we expect that the relative effect of temperature on crop growth is larger at low light than at high light ???
In other words: crop growth rate changes with a different percentage when temperature rises from 15 to 25oC at low light, compared to high light levels. Why ?
Simulation of biomass productionDry matter production: explanatory crop growth model
Temp Low light intensity High light intensityPg Rm dW/dt=Cf(Pgd Rm) Pg Rm dW/dt=Cf(Pgd Rm)
15°C 10 4 6*Cf 30 4 26*Cf25°C 10 8 2*Cf 30 8 22*Cf
% decrease (6-2)/6=66% % decrease (26-22)/26=16%
At higher temperature gross assimilation rate (Pg) doesn’t change much, but 10°C difference will lead to twice as much as respiration rate (Rm).
Light intensity doesn’t have effect on Rm, but will influence Pg. From the table, we can see that crop growth rate changes with a
different percentage (66% at low light and 16% at high light) Rm is not depend on light level. At low light intensity, Rm is the main
part of gross assimilation. You can choose any number (logical), many numerical examples are
possible, but they will give you the same conclusion
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Simulation of biomass production– ‘Homework’ question –
Assume that salinity stress increases maintenance respiration rate.
Why can this explain that elevated CO2 reduces salt stress in wheat?
Please answer this question by yourself, and you can refer to the answers from previous question.
Simulation of biomass production– Dry matter production: a simple LUE model –
dW/dt = LUE (1-e -k LAI) I
dW/dt = growth rate ( g DM m-2 d -1)
LUE = light use efficiency ( g DM MJ -1 PAR)
k = extinction coefficient
LAI = Leaf area index
I = Photosynthetic Active Radiation (PAR) incident on crop
(MJ m-2 d-1)
Assumes constant LUE !
Be aware of differences in dimensions
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Simulation of biomass production- Be aware of differences in units !!! -
For example:A radiation use efficiency of 3.0 g MJ-1 PAR
is the same asA radiation use efficiency of ?? g MJ-1 global
radiation
1 PAR = 2 global radiation
Global radiation PAR LUE2 1 31 0.5 1.5
Simulation of biomass production- Leaf area development -
Function of plant developmental stage (temperature)[works reasonably well in the field, not in greenhouses]
Predicted from simulated leaf dry weightSLA needed (Specific Leaf Area = leaf area/leaf mass; cm2 g-1)• constant• function of plant age/physiological age• function of season• environmental conditions
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Simulation of biomass production- Leaf area development (cont.) -
In model LINTUL:When LAI < 0.75 m2 m-2 and TSUM < 330 oCdLeaf growth exponential and RGRL depends on
temperature
ElseSLA = constant and increase in LAI calculated as
SLA increase in leaf dry weight (g m-2)
Simulation of biomass productionSeasonal effect on SLA (Leaf area/Leaf mass)
(regression on 12 experiments)
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Simulation of biomass production- From fruit dry weight to fresh weight -
Fig.4. Time course of measured(symbols) and simulated (lines) DMcontent of harvestable cucumberfruits during a growing season in agreenhouse at a daily temperatureof 23.2±0.4°C. DM content wassimulated as a function of fruit age,temperature and fresh weightaccording to Marcelis (1992b) (——)or a positive linear relationship tothe source/sink ratio of the past 5days was added to the formula (----);this relation was derived from theexperiment shown. Experimentalconditions and calculation ofsource/sink ratio are described byMarcelis (1992a, 1994).
Simulation of biomass productionCompare model with experimental data
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Simulation of biomass production- Validation of dry matter production model -
Measured (symbols) and simulated (lines: model TOMSIM) dry matter production for tomato in Dutch greenhouses at 5 planting dates. Relevant model input: daily outside radiation integral, hourly greenhouse temperatures and CO2 concentrations, crop leaf area index.
(Heuvelink, 1995, Annals of Botany 75: 369-379).
Symbols and lines correlate
with each other Dry matter production is predicted well for all these
situations using the model
Simulation of biomass production- Measured and simulated total dry weights -
Logic patterns? Beginning:Plant density↑LAI ↑ light intercepted ↑ Crop growth rate ↑
In the end:LAI is not a limiting factor; amount of light intercepted are almost the same no big difference among crop growth rates parallel
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Simulation of biomass production- Examples of simulation of crop growth -
0
4
8
12
16
50 70 90 110 130
Day of year
Dry
we
igh
t (g
/ p
lan
t)
32 plants / m2
64 plants / m2
From: J‐H Lee, E. Heuvelink & L. Ortega, unpublished
Note: y-axis is per plant! Chrysanthemum
Total dry weight (g m2) = plant dry weight (g plant 1) * plant density (plants m 2)
Salinity Effects on Fruit Yield in Vegetable Crops: a simulation study
Ep Heuvelink, Cecilia Stanghellini and Menno Bakker
Wageningen University and Research Centre
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Salinity Effects on Fruit Yield in Vegetable Crops- via plant-water relations -
Increase fruit dry matter contentBut reduce fresh yield (less dilution with water)
Reduce leaf expansionNot enough turgor to extend leaf area
Less LAI, less light interception, less yield
Stomatal closure Stomata partly close, to reduce transpiration
Higher resistance for CO2, less Photosynthesis
Aim: Determine salinity impact on fruit yield of single and combined effects
Approach: Simulation study (model TOMSIM)
Nutrients in high salinity solution
reduce the water potential
at soil level, and consequently in the whole plant
(via osmotic potential of the soil solution)
Salinity Effects on Fruit Yield in Vegetable Crops - Crop growth: principles of the simulation model -
Leaf area index
Light interception
Photosynthesis Respiration
Dry mass production (Growth)
LeavesRoot Stem Fruits
SLA
DMC Yield
Model with NO salinity stress
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+
Salinity Effects on Fruit Yield in Vegetable Crops - Crop growth model: influence of salinity -
Leaf area index
Light interception
Photosynthesis Respiration
Dry mass production (Growth)
LeavesRoot Stem Fruits
SLA
Salinity
DMC Yield
Negative effect Positive effect
+
+
Salinity Effects on Fruit Yield in Vegetable Crops-Quantifying salinity effects -
Fruit dry matter content (DMC)5% at EC=2 dS m-1; linear increase by 0.2% per dS m-1
Leaf Area Expansion- SLA according to seasonal pattern- decrease 8% per dS m-1 (from threshold 3 or 6 dS m-1)
Stomatal resistance50 s m-1; increase with EC over the range 1 to 10 dS m-1
by either a factor 2 or a factor 4
Li et al. (2001); Li & Stanghellini (2001)
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Salinity effects on fruit yield in vegetative cropsDry matter production: explanatory crop growth model
dW/dt = Cf (Pgd - Rm)
dW/dt = Crop Growth Rate (g DM m2 d 1)
Pgd = Crop gross Assimilation Rate (g CH2O m2 d 1)
(In the model all leaves have identical photosynthetic properties)
R m = Maintenance Respiration Rate (g CH2O m2 d 1)
(Depends on Organ Dry Weight, Temperature, RGR)
Cf = Conversion Efficiency (g DM g1CH2O)
Salinity Effects on Fruit Yield in Vegetable Crops- the simulation model: Dry Matter partitioning -
Regulated by the sinks (fruit trusses and vegetative units)
Sink strength = demand for assimilates = potential growth rate
One common assimilate pool
More information is given in Chapter 5, dry matter partitioning
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Experiments for Salinity Effects simulation settings
Crop: Start January 10; flowering of first tomato truss
End: September 7 (day 250)
Plant density 2.5 plants m-2
No side shoots retained; no decapitation
All trusses 7 fruits (no abortion simulated)
Greenhouse climate: 20°C; 350 ppm CO2;
average radiation The Netherlands
Experiments for salinity effects Results (1)
0
10
20
30
40
0 2 4 6 8 10 12
EC (dS/m)
Fru
it Y
ield
(kg
/m2 )
DMC
RS2
RS4
Both these two factors: Increased fruit dry matter content and stomatal resistance have modest impact on yield (2-3 % yield loss
per dS m-1)
Impact on fruit yield by Increased dry matter content (DMC) or Stomatalresistance (RS2 - factor 2; RS4 - factor 4)
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0
10
20
30
40
0 2 4 6 8 10 12
EC (dS/m)
Fru
it Y
ield
(kg
/m2 )
DMC
SLA3
SLA3+DMC
RS2
RS4
Strongest salinity effects result from effects on leaf expansion (reduced SLA) (12-17% yield loss per dS m-1)
Experiments for salinity effects Results (2)
Impact on fruit yield by reduced SLA • 8% per (dS m-1)• threshold 3 dS m-1
Influence of Leaf Area Index (LAI) on the fraction of light
intercepted by a tomato crop (k = extinction coefficient = 0.8)
•Higher EC at low LAI has bigger impact on light interception. •To reduce yield loss caused by high EC, high LAI is preferred
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Delay leaf picking
Increase plant density
What can a grower do to mitigate salinity
effect via leaf expansion ?
Salinity Effects on Fruit Yield in Vegetable Crops- how to mitigate salinity effect via leaf expansion -
2 Hypotheses:
• To verify the hypotheses, following experiments were conducted ...
Salinity Effects on Fruit Yield in Vegetable Cropsmitigate salinity effect - influencing Leaf Area Index (LAI)
0
1
2
3
4
1 3 5 7 9 11EC (dS/m)
LA
I (
m2 /m
2 )
control
delayed leaf picking
increased plantdensity
• LAI is drastically declined with high EC level, because of decreased SLA• Increased plant density and delayed leaf picking gave relatively higher
LAI
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Salinity Effects on Fruit Yield in Vegetable Cropsmitigate salinity effect – effect on fruit yield
0
10
20
30
40
0 2 4 6 8 10 12
EC (dS/m)
Fru
it Y
ield
(kg
/m2 )
control
delayed leaf picking
increased plantd itIncreased plant density
• Delayed leaf picking and increased plant density has positive
effect on reducing yield loss caused by high EC.
Conclusions:
Salinity Effects on Fruit Yield in Vegetable Crops- conclusions -
Increased fruit dry matter content or stomatal resistance
modest impact on yield (2-3 % yield loss per dS m1)
Delayed leaf picking or increased plant density mitigate
this negative effect
Strongest salinity effects result from effects on leaf
expansion (12-17 % yield loss per dS m 1)
Within the ranging of EC between 1 and 11
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Simulation of biomass productionConcluding remarks
Simulation of biomass productionSimulated effect of number of fruits per truss in tomato
0
1
2
3
4
5
0 5 10 15
# fruits per truss
Dry
we
igh
t (k
g/m
2)
or
LA
I or
Fra
cti
on av_LAI
W_total
W_fruit
fraction_fruits
1: There’s an optimum number of fruit per truss 2: The number of fruits per truss defines partitioning fraction3: More fruits per truss less to vegetative parts Lower LAI less photosynthesis less total dry weight
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Interaction (feedback) between assimilate production and assimilate partitioning
Simulation of biomass production- Conclusions on explanatory models -
Strong features: light interception gross photosynthesis
Weak features: leaf area development maintenance respiration organ initiation/abortion dry matter content product quality