overview of dssat growth and phenology -...
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AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
Overview of DSSAT Growth
and Phenology
AgMIP Multiple Crop Model Training
March 18-22, 2013 (CIMMYT-Nepal)
March 25-29, 2013 (ICRISAT-India)
1
2
3 actual
attainable
potential
Yield increasing
measures
Yield protecting measures
defining factors:
reducing factors:
limiting factors:
CO2
radiation
Temperature,Day length
crop characteristics
-physiology, phenology
-canopy architecture
a: water
b: nutrients
-nitrogen
-phosphorous
Weeds
pests
diseases
pollutants
1500 10,000 5000 20,000 Production level (kg ha-1)
Production
situation
Source: World Food Production: Biophysical Factors of Agricultural Production, 1992.
Crop Model Concepts
Simulating Growth and Yield with Crop
Models
• Process-level: Simulate inputs, losses, & balance of C, N, and H2O.
• Dynamic: Predict daily growth and development.
• Integrate over multiple processes: But must honor C, N, & H2O balance and resource limitations.
• Based on understanding of crop-soil-weather relationships.
• Use to study effects of crop management, weather, fertilization, soil water deficit, and genetic improvement.
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
DSSAT v4.5 – Mar 2013
• Cropping System Model CSM (2004,06,10,12)
– Modular Crop Simulation Model
– Share soil water, N, SOM land unit
• CROPGRO module for soybean, peanut, dry
bean, faba bean, chickpea, cowpea, and other
grain legumes
• CERES module for maize, rice, wheat, barley,
sorghum, millet, and other cereal crops
• SUBSTOR module for potato
• CROPGRO module for cotton, tomato, bell
pepper, green bean, and cabbage
An Overview of the DSSAT Crop Models
CERES CROPGRO Maize, Sorghum, Millet, Rice Peanut, soybean, cowpea, etc.
RUE approach Leaf & Canopy Ps (hourly),IXIM*
DM (No respiration) Growth & Maint. Resp
Grain # =f(PCARB or stem
mass). Set at anthesis
Pod and seed cohorts add
slowly over pod adding phase
Similarities (Use Same)
Soil Water Balance and ET Methods
Soil N Balance (Century or Godwin)
*IXIM-maize is like CROPGRO, developed by J. Lizaso
CERES Models: Daily Plant Growth Rate (per plant basis)
2*
*1
*CO
LAIke
PLTPOP
PARRUEPCARB
PCARB - Potential growth rate, g/plant
RUE - Radiation use efficiency (g DM/MJ PAR)
PAR - Photosynthetically active radiation (MJ/m2/d)
PAR = SRAD*PARSR
PLTPOP - Plant population, pl/m2
k - Light extinction factor
LAI - Leaf area index
CO2 - CO2 modification factor
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 200 400 600 800 1000 1200
Re
lati
ve
gro
wth
CO2 concentration, ppm
Effect of CO2 in CERES models
Maize, Sorghum, Millet
Wheat, Barley, Rice
2008 (385)
330 ppm
AgMIP Multiple Crop Model Course
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Modify Growth Rate for Stress
CARBO = PCARB*AMIN1 (PRFT,SWFAC,NSTRES,
(1.0-SATFAC))*PGFAC3
CARBO - daily plant growth, g/plant
PRFT - temperature effect
SWFAC - drought effect
NSTRES - nitrogen effect
SATFAC - waterlogging effect
PGFAC3 - soil fertility factor effect (from soil.sol)
Wheat & Barley: Potential * TF * Min(WFAC, NFAC)
AgMIP Multiple Crop Model Course
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Growth Processes in CERES Models
• Computes daily growth per plant
• Radiation Use Efficiency (RUE) approach: Light
interception x RUE
• RUE varies with temperature, veg. N conc., water stress,
CO2 level and fertility
• Leaf area per plant as function of V stage (not connected
to DM balance or SLA)
• Partitions dry matter to vegetative components as a
function of growth stage (internal to code)
• Adds grain number at anthesis as culm mass at anthesis or PCARB during phase prior to anthesis.
• Grains grow at G2 rate (mg/grain/d) for 95% of P5
CSM-CROPGRO – a modular crop model
• Same source code for soybean, peanut,
dry bean, faba bean, cotton, tomato, &
forages.
• 1-day time step, but hourly loop for leaf-
canopy photosynthesis – rubisco kinetics
• “Read-in” Species file for crop-specific
parameters and functions:
– Defines the sensitivity of crop processes
to climatic factors, such as temperature,
solar radiation, CO2 and photoperiod
– Defines plant composition, initializations,
photosynthesis traits, and parameters.
Cropping System Model (CSM)
• Ecotype coefficients – Defines coefficients for groups of cultivars
that show similar behavior and response to environmental conditions.
• Cultivar coefficients – Cultivar and variety-specific coefficients,
such as photothermal days to flowering & maturity, sensitivity to photoperiod, seed size, seed composition, etc.
AgMIP Multiple Crop Model Course
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CROPGRO – Species File Example – Cardinal Temp. for Phenology
• Species file defines Tb, Topt1, Topt2, and Tmax
for three different accumulators: 1) vegetative, 2)
reprod to 1st seed, 3) reprod after 1st seed.
• Tb Topt1 Topt2 Tmax Type
7.0 28.0 35.0 45.0 1 Veg
6.0 26.0 30.0 45.0 2 Rep-1
-15.0 26.0 34.0 45.0 3 Rep-2
AgMIP Multiple Crop Model Course
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CROPGRO-Soybean: Vegetative, Prior-R5, &
Post-R5 Reproductive Development Rate
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40 50
Temperature, C
De
ve
lopm
en
t R
ate
Vstage
Pre R5
Post R5
CROPGRO – Species File Example – Cardinal Temperatures for Reproductive
Processes
• Species file defines Tb, Topt1, Topt2, Tmax, and shape of functions for temperature effects on a processes, such as pod addition or single seed growth rate.
• Tb Topt1 Topt2 Tmax Shape
14.0 21.0 26.5 40.0 QDR Pod Addition
6.0 21.0 23.5 41.0 QDR Seed Growth
AgMIP Multiple Crop Model Course
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CROPGRO-Soybean: Rate of Pod Addition
and Relative Rate of Single Seed Growth
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40 50
Temperature, C
Re
lati
ve R
ate
Pod Addition
Seed Growth
Podset: 14, 21, 26.5, 40 C
Seed growth rate (Egli &
Wardlaw): 6, 21, 23.5, 41 C
Daily Crop Photosynthesis (C option)
• Pg = Kp * PGMAX(PAR) * fL * f0 * fN * fT
• where:
PGMAX(PAR) = response to Photosynthetic Active Radiation
Kp = adjustment (soil fertility) = SLPF
fL = 0 to 1 response to LAI
f0 = 0 to 1 response to H20 supply
fN = 0 to 1 response to leaf N concentration
fT = 0 to 1 response to day temperature
Growth & Maintenance Respiration
• Growth Respiration: Conversion efficiency to Dry Matter. Glucose equivalent needed for biosynthesis depends on chemical composition of tissue, and biochemical pathways of synthesis. (Penning de Vries, J. Theor. Biol 45:339).
• Maintenance Respiration: provides energy for maintaining existing tissues (maintain ion concentration gradients, re-synthesize proteins, membranes, DNA, RNA).
– These processes increase as a function of plant activity and temperature.
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Partitioning of assimilate in CSM-
CROPGRO uses a combination approach
1. Partitioning calendar as function of thermal time (Vegetative stage).
2. At beginning pod, pods & seeds are added daily, with explicit sink strength.
3. Seed & pod have priority over vegetative. Stop adding when “carrying capacity” reached.
0
0.1
0.2
0.3
0.4
0.5
0.6
0 5 10 15 20 25
Vegetative Stage, No.
Fra
cti
on
Part
itio
nin
g
Leaf
Stem
Root
Priority for assimilate is seeds first,
then shells. Remaining assimilate is
shared among leaf, stem, and root
according to “partitioning calendar”
rules.
AgMIP Multiple Crop Model Course
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0
500
1000
1500
2000
2500
3000
3500
4000
20 40 60 80 100 120 140
Days after Planting
Yie
ld (
kg
/ha),
sd
#/m
2, p
d #
/m2
Grain # Grain Yield Pod # Grain # - Obs Grain Yield - Obs Pod # - Obs
Simulated pod numbers over time (green
line) showing over-set and abortion, along
with simulated seed numbers, and simulated
seed yield (Cobb soybean 1981 – FL).
CROPGRO: Hourly Leaf Version:
Scales to Canopy Assimilation (L option)
• Leaf-level photosynthesis, with sensitivity to [CO2], [O2], and temperature is modeled with simplified rubisco kinetics of Farquhar and von Caemmerer.
• Hedgerow light interception (hourly), using ellipsoidal canopy envelope (ht & width), using direct & diffuse beam capture of Spitters, and computing leaf Ps rate for sunlit and shaded leaves. Integrates to daily Pg.
• Half-sine function to distribute hourly PPFD
• Tested against experimental data on leaf and canopy assimilation response to PPFD and response to CO2.
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Leaf Response to PPFD, at 330 vpm CO2
-5
0
5
10
15
20
25
30
0 200 400 600 800 1000 1200
PPFD, umol/m2/s
Le
af
CE
R,
um
ol/
m2
/s
Obs - 330 vpm
Leaf CER
Ps = Amax(1-exp(-QE*PPFD/Amax))
QE & Amax affected by f(T), f(CO2)
Simulated leaf response to [CO2] at 1500 µmol
PPFD m–2 s-1 for leaf acclimated at 350 vpm [CO2].
Data of Griffin and Luo (1999). Actual SLA and N.
Growth CO2 - 350
0
5
10
15
20
25
30
35
0 300 600 900 1200 1500
Ambient CO2 (umol/mol)
Ne
t P
hoto
syn
the
sis
(um
ol/
m2
/s)
CROPGRO-Soybean: Relative Leaf ETR,
Relative QE, and Tmin effect on Asat
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0 10 20 30 40 50
Temperature, C
Re
lati
ve
Ra
te
Leaf-ETR
Rel QE
Tmin-Asat
Rubisco kinetics
on QE Harley via Asat
Lit. & Solved
Parmeterizing Temp-dependence of Ps in
CROPGRO-Soybean: Canopy Ps with LAI=5.0 (at
1470 umol PPFD/m2/s, leaf Ps at 1000 umol/m2/s)
0
10
20
30
40
50
0 10 20 30 40 50
Temperature, C
CE
R, u
mo
l/m
2/s
Leaf Ps
Canopy
Assimilati
on
Leaf and canopy assimilation response to temperature is
emergent outcome of several processes and efficiencies.
Canopy assimilation response integrated over whole day is
even flatter, within 2% of maximum from 25 to 37 C.
Gainesville, FL
1978
Yield
0
2000
4000
6000
8000
175 200 225 250 275 300
Day of Year
Grain - IRRIGATED Total Crop - IRRIGATED
Total Crop - NOT IRRIGATED Grain - NOT IRRIGATED
Simulated and Measured Soybean
0
1
2
3
4
5
6
7
20 40 60 80 100 120 140
Days after Planting
Leaf
Are
a I
nd
ex
LAI (81 COBB, IRRIG) TURFAC (81 COBB, IRRIG)
LAI (81 COBB, VEG STRESS) TURFAC (81 COBB, VEG STRESS)
LAI - Obs - Irrig. LAI - Obs - Veg Stress
Leaf area index & TURFAC signals of irrigated vs. vegetative
water deficit on Cobb soybean at Gainesville, FL in 1981.
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
20 40 60 80 100 120 140
Days after Planting
To
tal
Cro
p B
iom
ass,
kg
/ha
Crop mass (81 Cobb, Irrig) SWFAC (81 Cobb, Irrig)
Crop Mass (81 Cobb, Veg Stress) SWFAC (81 Cobb, Veg Stress)
Crop Mass - Obs - Irrig Crop Mass - Obs - Veg Stress
Crop Biomass & SWFAC signals of irrigated vs. vegetative
water deficit on Cobb soybean at Gainesville, FL in 1981.
0
200
400
600
800
1000
1200
1400
1600
1800
2000
20 40 60 80 100 120 140
Days after Planting
Lea
f M
as
s (
kg
/ha
)
Ma
ss
per
See
d (
mg
x 1
0)
LEAF (81 COBB, IRRI) Wt per Seed (81 COBB, IRRI)
LEAF (81 COBB, VEG STRESS) Wt per Seed (81 COBB, VEG. STRESS)
SWFAC (81 COBB, VEG. STRESS) LEAF - Obs (IRRI)
Wt per Seed - Obs (IRRI) LEAF - Obs (Veg Stress)
Wt per Seed - Obs (Veg Stress)
Leaf Mass, Wt per Seed, & SWFAC signals of irrigated vs.
vegetative water deficit on Cobb soybean at Gainesville in 1981.
What is Crop Development
• Development – Rate of progress through an organism’s life cycle.
• Ontogeny – The time course of development through phases of the life cycle.
• Phenology – The timing of visual events which mark the transition from one phase to the next.
AgMIP Multiple Crop Model Course
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Crop Development
Why Important?
• Timing of flowering, seed-set, & maturity are
important to productivity and to fit crop to
season length.
• Partitioning among organs changes with crop
phenological stage. Tied to progress in
Vegetative and Reproductive stage
development.
• Developmental modifiers, f(T), f(DL), affect
seed addition, seed growth rate, N
mobilization, V stage progression, rooting.
AgMIP Multiple Crop Model Course
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What Drives Development?
• What is the State Variable? V or R stage, HU
• Driving Variables:
– Temperature
– Photoperiod
• Limiting Factors:
– Food (CO2) supply (only weakly)
– Water status (low turgor limits expansion)
– Nutrient (N, P, K) status (only weakly)
AgMIP Multiple Crop Model Course
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• Stresses (water, N, K, or P deficits) DELAY onset
of reproductive growth, but the same stresses
ACCELERATE maturity. In some crops: water
stress accelerates reproductive onset after anth.
• Development is NOT very dependent on growth
rate.
AgMIP Multiple Crop Model Course
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Rate of
Development
vs.
Temperature
Tbase – Base temperature, below which development rate is
zero.
Topt1 – Lowest optimum temp. at which rate is most rapid
Topt2 – Highest optimum temperature at which rate is still at
its maximum
Tmax – Maximum temperature, at which rate is zero
Rate of Leaf Appearance (V-stage)
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50
Mean Temperature, C
Lea
f A
pp
ea
ranc
e R
ate
soybean
7, 28, 35, 45
Hesketh et al.
Short Day Plants or Long Day
Cultivar Coefficients
0.0
0.2
0.4
0.6
0.8
1.0
4 6 8 10 12 14 16 18 20 22 24
Day Length
Rela
tive R
ate
CSDL (11.5 h)
PP–SEN = Slope
CLDL (24 h)
Long Day?
CLDL=24 &
neg PP-SEN
• PD = Physiological days, (1.0 PD per calendar day if at opt. temperature & day length < CSDL, or >CLDL)
• Presently Multiplicative
– PD = f(T) x f(DL) x f(H20, N stress)
– T = Temperature
– DL = Day length
PROG(J) = FT(J)*FDL(J)*(1+(1-SWFAC)*WSENP(J))
• Each phase (J = 1 to 13) in crop life cycle has a given # of PD to be accumulated prior to triggering a phenological event, then the next phase begins.
• If 10 PD required: at 0.5 PD/d, takes 20 calendar days to trigger
Daylength-Sensitive Plants:
Both short- or long-day plants
Simulation of plant responses to temperature
and photoperiod (use both species and cultivar GC)
1.0
Temperature (°C) Tb TM
T01 T02
Daylength (h) CSDL
PPSEN
Model 1/d =f(T) x f(D)
Stagei = f(photothermal days)
Cultivar
Coefficients
Species
Coefficients (Do not vary)
Phenology in CERES-Models
• Thermal Time (C-day)
• Base and optimum temperature concept
• GDD = Tavg - Tbase
• GDD = Min(GDD, Topt - Tbase)
• Tavg may be air or soil temperature
• No penalty for temperatures > optimum
• No consideration for other stresses
• Temperature sensitivity defined in Species file
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Rate
Simple Function
Temperature
Tb
Degree-Day Approach
No Topt
Degree-Day Approach
• If Temperature > Tbase
– Degree-day = Tavg – Tbase
• If Temperature < Tbase
– Degree-day = 0
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Rate
Simple Function
Temperature
Tb
1PD/CD, 1 PD is
same as 22 GDD
if Tavg = 30 C, and
Tb = 8 C and
Topt = 30C
Topt
Degree-Day Approach
No Topt
Degree-Day Approach
• If Temperature < Tbase
– Degree-day = 0
• If Temperature > Tbase
– Degree-day = Tavg – Tbase
• If Temperature > Topt
– Degree-day = Topt - Tbase
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Degree-Day Approach
• If Temperature < Tbase
• If Temperature > Tbase
• If Temperature > Topt
• Tbase = 8
• Topt = 30
• Avg. Temp. Degree Day – 7 0
– 15 7
– 30 22
– 40 22
Growing Degree Days for Vegetative Development
0
5
10
15
20
25
30
0 5 10 15 20 25 30 35 40 45
Average Temperature, C
Gro
win
g D
eg
ree
Da
y,
C
Maize and Sorghum
Millet
Wheat and Barley
Tbase
Topt
Growing Degree Days for Reproductive Development
0
5
10
15
20
25
30
0 5 10 15 20 25 30 35 40 45
Mean Temperature, C
Gro
win
g D
eg
ree
Da
ys,
C
Maize and Sorghum
Millet
Wheat and Barley
Tbase
Topt
ISTAGE = 3, 4, 5
= 34C
Course on Cropping System Models
ICRISAT, Hyderabad (Oct 2010)
Vernalization Units
0
0.2
0.4
0.6
0.8
1
1.2
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Temperature (oC)
V U
nit
s
Wheat & Barley
During Emergence to End-Juvenile,
“winter” type cultivars accumulate
vernalization units if <15C
May require 30 to 50 V-units prior
to readiness for floral induction
Course on Cropping System Models
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Daylength Factor
0
0.2
0.4
0.6
0.8
1
1.2
10 11 12 13 14 15 16 17 18 19 20
Daylength (hour)
Facto
r (0
-1)
6.40% 12.80%
Wheat & Barley
Wheat and Barley are Long-Day
plants: CLDL=20h. Example has
two different curvature slopes
Developmental Life Cycle Maize, Sorghum and Millet
Stage
No. Stage Name
7 Sowing date
8 Sowing to Germination
9 Germination to Emergence
1 Emergence to End of Juvenile
2 End of Juvenile to Tassel Initiation
3 Tassel Initiation to End of Leaf Growth and Silking
4 Silking to beginning of effective filling period
5 Effective grain filling (EGF) period
6 End EGF to Physiological maturity
Growth stage determines
which components are
growing and may be
experiencing stress
Developmental Life Cycle Rice
Stage # Stage Name
8 Sowing
9 Germination
1 Emergence
2 End juvenile
3 Panicle initiation
4 Heading
5 Begin grain fill
6 End grain fill, main culm
7 Harvest
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Developmental Life Cycle Wheat and Barley
Stage
No. Stage Name
8 Sowing date
9 Sowing to Germination
1 Germination to Emergence
2 Emergence to End of Spikelet
3 End of leaf growth
4 End of Spikelet Growth
5 End of Lag Phase
6 End of Grain Filling
7 Harvest
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CSM-CERES- Maize
(6 cultivar traits; 2 more for IXIM –Maize*)
• P1: 100-400 Juvenile Phase Duration (Cd)
• P2: 0.0-4.0 Photoperiod Sensitivity
• P5: 600-990 Grain Filling Duration (Cd)
• G2: 620-910 Kernel Number per Plant
• G3: 5.0-12.0 Kernel Growth Rate (mg/d)
• PHINT:39-49 Cd, between leaf tip
appearance
• AX*: 650-880 Area (cm2/leaf) of largest leaf
• LX*: 650-930 Leaf longevity (Cd)
Table 6. Definitions of cultivar coefficients for the CERES-Millet and CERES-Sorghum models with example values
CC Name Cultivar Coefficient Definition Millet CZP-84
Sorghum N. Amer
P1 Thermal time from emergence to end of juvenile phase during which plant is not sensitive to photoperiod (GDD†)
138.0 360.0
P2O Critical short photoperiod or the longest day length at which development occurs at a maximum rate (h)
12.20 12.50
P2R Extent to which phasic development leading to panicle initiation is delayed per hour increase in photoperiod above P2O (GDD)
128.0 30.0
P5 Thermal time from beginning of grain filling to physiological maturity (GDD)
360 540
G1 Scaler for relative leaf size 2.3 0.0
G2 (G4-millet)
Scaler for partitioning of assimilates to the panicle (head)
0.50 6.0
PHINT Phylochron interval, thermal time between successive leaf tip appearances (GDD)
43.0 49.0
† Degree days, above Tb = 8 ºC for sorghum, Tb=10 ºC for millet.
Additional Cultivar Coefficients for Rice
Var Definition Units Typical
Range
P1
P2O
P2R
Juvenile Phase Duration (Cd)
Critical Short Photoperiod (h)
Delay per h increase in DL above P2O
G1 Potential spikelet number coefficient
estimated from the number of spikelets per g
of main culm dry weight less leaf blades and
sheaths plus spikes at anthesis
50-65
G2 Single grain weight under ideal growing
conditions, i.e. non-limiting light, water,
nutrients, and absence of pests and diseases
g 0.022-
0.03
Additional Cultivar Coefficients for Rice
Var Definition Units Typical
Range
G3 Scalar Vegetative Growth Coefficient for
tillering relative to IR64 cultivar under ideal
conditions. A higher tillering cultivar would
have a coefficient greater than 1.0
0.6-1.5
G4 Temperature “cold” tolerance (to <18C)
scalar coefficient. Usually 1.0 for varieties
grown in normal environments
0.8-1.25
Var Definition Units Typical
Range
P1V Vernalization time required to
complete vernalization when
temperatures are within
optimum range
Days 0-60
P1D Reduction in development rate
when daylength is 1 hour less
than the threshold 20h
%*10 0-150
P5 Thermal time from onset of
linear grain fill to physiological
maturity
Degree day
above base
of 0oC
600-900
Wheat and Barley
Var Definition Units Typical
Range
G1 Number of kernels set per unit
canopy weight at anthesis
kernels/g
dry
weight
15-30
G2 Genetic potential kernel size
under optimum conditions
mg dry
matter
20-60
G3 Standard non-stressed weight
of a single tiller, including
grain at maturity
g dry
weight
1.0-2.5
PHINT Phyllochron interval. Interval
in thermal time between
successive leaf tip appearances
oC
days/tip
60-100
Wheat and Barley
CROPGRO Cultivar Traits: Differ among
cultivars within species.
Users will change Cultivar traits
• Traits that determine life cycle and phase durations, sensitivity to day length (phase modifiers)
• Reproductive traits such as seed fill duration, duration of pod addition, seed size, # seeds/pod
• Veg. & growth traits such as: SLA, determinacy, leaf photosynthesis rate
• Seed composition (oil, protein)
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CROPGRO (18 cultivar traits) (Below are life cycle related traits)
• CSDL: 13.4 Critical Short Daylength (h)
• PPSEN: .285 Slope, Sensitivity to DL (1/h)
• EM-FL: 19.0 PD, Emergence to 1st flower
• FL-SH: 8.6 PD, 1st flower to begin pod
• FL-SD: 14.2 PD, 1st flower to begin seed
• SD-PM: 33.5 PD, 1st seed to Phy. Mat.
• FL-LF: 26.0 PD, 1st flower to end leaf exp
• FL-VS: 26.0 PD, 1st flower to end MS node
• PODUR: 11.0 PD, Pod adding duration
• SFDUR: 25.0 PD, Single seed growth duration
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Day
s A
fter
Sow
ing
0
20
40
60
80
100
120
140
Sowing
Emergence
V1 Stage End Juvenile.
Floral Ind.
EM - FL
1st Flower
FL-SH
First Pod
FL-SD
End MS
FL - LF
End Leaf Exp. First Seed
End Pod Add.
SD-PM
Physiological Mat.
Harvest Maturity
Example of
phase durations
in cultivar file
FL - VS
CROPGRO (18 cultivar traits) (4 vegetative and 5 reproductive)
• LFMAX: 1.03 Light-saturated leaf photosynthesis
• SLAVAR: 375 Spec. Leaf Area (under opt. cond.)
• SIZLF: 180 Used for early Veg. Vigor
• XFRUIT: 1.0 Max partitioning to reproductive
• WTPSD: 0.18 Potential seed size (under opt.)
• SDPDV: 2.4 Seeds per pod
• THRSH: 77.0 Threshing percentage
• SDPRO: 40.5 Seed protein %
• SDLIP: 20.5 Seed oil %
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
Soil Profile
Irrigation
Infiltration
Precipitation
Runoff
Soil
Evaporation Transpiration
Drainage
Hydrologic Cycle Processes Simulated in CSM
Water Redistribution
Flow(1)
Flow(2)
Flow(3)
Flow(4)
D
W(1)
W(2)
W(3)
W(4)
W(5)
Ep Es
Water and Nitrogen Balance
Water and N demand
State variables include
• water content
• nitrate content
• ammonia content
• organic matter
Important Soil Profile Parameters
• L = Layer number
• Z(L) = Depth of layer L, mm
• LL(L) = Lower limit of water availability, in units mm3 [water] mm-3[soil], ~ Wilting Point
• DUL(L) = Drained upper limit of water content, ~ Field Capacity
• SAT(L) = Saturated soil water content
• WR(L) = Root preference factor, for distributing new root
growth with depth in soil. In soil file = SRGF
State Variables • SW(L) = Soil water content in each layer L on day t
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
Potential ET (ETo)
• Priestley-Taylor Equation – Inputs:
• Total Solar Radiation
• Tmax and Tmin
– ETo = DELTA/(DELTA+GAMMA) * Rnet* C
(i.e., Equilibrium Evaporation *C)
• Penman-Monteith Equation following (FAO 56)
– Eto=DELTA/(DELTA+GAMMA) * Rnet* f(WIND,HUM)
– Requires additional weather inputs (wind speed & HUM=Tdew)
where • DELTA = Slope of vapor pressure and temperature curve
• GAMMA = psychometric constant
• Rnet = Net radiation, MJ m-2 d-1
• C = Coefficient depending on temperature
• Net Radiation computed from SRAD, Total Solar Radiation Flux
Evapotranspiration Compute Soil-Limited Evaporation Rate, ES0
Uses new Ritchie et al. (2009) Soil Evaporation model (paper provided)
No longer uses stage 1 and stage 2 evaporation
Primary equation derived from diffusion theory by
Suleiman and Ritchie (2003):
• Where = max daily change in volumetric
water content at depth z, due to evaporation on day
t (cm3cm-3d-1)
• = volumetric water content at depth z, day t
• = air dry vol. water content at depth z (cm3cm-3)
• = transfer coefficient for soil at depth z (d-1). Fz
is estimated empirically from water holding limits
• z = mean depth of soil layer (cm)
ES0
zzADtzES Ftz
)( ,,,
tzES ,
tz ,
zAD ,
zF
Root Water Uptake
Evaporation Root water uptake
RWU computed by radial flow eq.
Function of
• root length in each soil layer
• water available in each soil layer
• resistance to water flow in roots
Summary of Water Balance Calculations
• Compute potential ET
• Partition into potential plant transpiration and potential
soil water evaporation (KEP and KSEP)
• Compute soil limitations to surface evaporation
• Actual soil evaporation is minimum of the two
• Compute soil-root system limitations to root water uptake
• Actual plant transpiration is minimum of the two
• Actual ET = sum of actual transpiration and evaporation
• Water Stress calculations are based on ratio of root
water uptake maximum to atmospheric demand of water
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
Physiological Effects of Soil Water Deficits
1.0
1.5 1.0
RWU
EPo
No
Stress High Stress
N Balance & Increasing Complexity
Increasing
Demand
for
Inputs
Potential Water Water, N Water, N, P Production Balance Balance Balance Solar Radiation Solar Radiation Solar Radiation Solar Radiation Max/Min T Max/Min T Max/Min T Max/Min T Precipitation Precipitation Precipitation Cultivar Cultivar Cultivar Cultivar Characteristics Characteristics Characteristics Characteristics Management Management Management Management Practices Practices Practices Practices Irrigation Irrigation Irrigation Management Management Management Soil Profile Soil Profile Soil Profile Physical Physical Physical Properties Properties Properties Management of Management of N Fert. and N Fert. and Residues Residues Soil Profile Soil Profile Chemical Prop. Chemical Prop. Management of P Fert. And Residues
Soil N Supply to Plants
• Plants use inorganic N (NH4+, NO3
-);
• Mineralization means loss of organic N as it is converted to inorganic N;
• Mineralization is independent of plant need (it is a microbiological process);
• Inorganic N not used by plants can be lost from the system, e.g. leaching, denitrification, volatilization.
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
Soil profile:
6
7
8
surface litter
9
….
2 H2O, Urea, NO3-, NH4
+ SOM
H2O, Urea, NO3-, NH4
+ SOM
1 SOM H2O, Urea, NO3-, NH4
+
5 …. ….
4 SOM H2O, Urea, NO3-, NH4
+
3
SOM = Soil Organic Matter,
composed of C, N, P, ...
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
Nitrogen Uptake
• Mineral N in root zone:
• Ammoniacal-N soil solution concentration
• Nitrate-N soil solution concentration
• Root length density
• Maximum N uptake per unit root length
• Moisture availability
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
Two Options in DSSAT for SOM Modeling
• G – CERES organic matter module, D. Godwin
modified Papran model (from Seligman)
• P – CENTURY organic matter module (Parton et al.)
• Both models have the same mineral N module (N
uptake, N leaching, etc.)
• CENTURY option (P) is recommended for in low
input conditions and in crop rotations, but …
Processes Simulated in the Mineral Nitrogen
Module
Process Simulated Main Factors Influencing Process
SOIL N SUPPLY
Mineralization/Immobilization Soil Temperature, Soil Water, C/N Ratio
Nitrification Soil Temperature, Soil Water, Soil pH, NH4
+ Concentration
Denitrification Soil Temperature, Soil Water, Soil pH, Soil C
NO3- Leaching Drainage, Nitrate Adsorption
Volatilization Soil Temperature, Soil pH, Surface Evap., NH3 Conc.
Urea Hydrolysis Soil Temperature, Soil Water, Soil pH, Soil C
Uptake Soil Water, Inorganic N, Crop Demand, Root Length Density, Root Uptake Efficiency
*
• In addition to inputs needed for simulating the water
balance, the following need to be provided for each soil
layer:
– Bulk density (g/cm3)
– Organic carbon (%)
– Texture (silt and clay %)
– Total nitrogen (%)
• Calculated using C:N ratio of 10:1 if not known or if
ratio from C and N inputs in files is unrealistic
– Soil pH (in water)
Inputs for soil N processes
(Soil profile)
Inputs for soil C & N processes
(File X)
• N balance switch turned on (simulation options)
• Select which SOM model to use (G=CERES, P=CENTURY)
• In initial conditions, specify initial ammonium (NH4+), nitrate
(NO3-) for each soil layer as g/Mg
• In initial conditions:
– Root weight of previous crop (dry weight as kg/ha)
– Previous crop residues: dry weight (kg/ha), N content (%),
proportion incorporated (%), and incorporation depth (cm)
• In soil analysis: Soil organic C for each soil layer (% mass
basis) and passive soil organic C for each layer (SOM3) –
optional (Note: Soil Analysis inputs over ride Soil.sol)
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)
Inputs for soil C & N processes
(File X)
• In Field: Crop history (management intensity, years
produced in this way, and status of field prior to this history
(degraded, cultivated with good management, cultivated
with low input, grassland or forest, or degraded)
• In organic amendments: date of application, dry weight
(kg/ha), N content (%), proportion incorporated (%), and
incorporation depth (cm)
• In fertilizer: date of application, fertilizer material (e.g.
urea), application method (e.g. banded), nature of
incorporation, application depth (cm), and quantity of N
applied (as N, not material)
AgMIP Multiple Crop Model Course
CIMMYT (Nepal), ICRISAT (India)