Overview of DSSAT Growth and Phenology -...

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.



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



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)



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.



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



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



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.



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.



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.



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.



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.



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)



• 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.



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



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



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


– Degree-day = 0


– Degree-day = Tavg – Tbase

• If Temperature > Topt

– Degree-day = Topt - Tbase



Degree-Day Approach



• 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

ICRISAT, Hyderabad (Oct 2010)

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



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



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


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


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


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)



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



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 %



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



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



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.



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, ...



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



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)



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)



Overview of DSSAT Growth and Phenology -...

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