Dynamic crop growth modelling with AGROSIM Application on the Bad Lauchstädt site

Dynamic crop growth modelling with AGROSIM Application on the Bad Lauchstädt site

Wilfried Mirschel

International Workshop „Modelling soil processes in different time scales“, Halle, 19th –20th September 2005

Leibniz-Centre for Agricultural Landscape Research (ZALF) Müncheberg, Institute of Landscape Systems Analysis

Eberswalder Str.84, 15374 Müncheberg e-mail: [email protected]

mailto:[email protected]




ZALF, Institut für Landschaftssystemanalyse

1. Motivation

2. Agro-ecosystem model family AGROSIM

2.1. AGROSIM model for winter cereals

2.2. AGROSIM model for sugar beet

2.3. AGROSIM model applications

3. AGROSIM model workshop results for Bad Lauchstädt (short term experiment)

3.1. Without parameter adaptation

3.2 With parameter adaptation

5. AGROSIM model transfer to other geographical sites

6. Conclusions

Content


►Yield formation and biomass accumulation in agriculture play an essential role in water, energy and nutrient cycles in agro-ecosystems.

►While crop yield on farm level are mainly in the focus of interest because of economic considerations, the total biomass is in the focus of interest because of changed water, nutrient and carbon balances as consequence of land use and climate

changes.

►In agro-ecosystems biomass formation and turnover is influenced by different factors.

+ climate and weather, + site conditions (incl. water and nutrients supply ), + crop properties (incl. cultivars, plant physiology and genetics), + management and + influences from other system components (pests and diseases).

► Simulation models are powerful tools to investigate the effects of different land use options and/or climate changes on water and matter cycles as well as to bridge the gap between different temporal and spatial scales.

Motivation


Agro-ecosystem model family AGROSIM (1)

The model family AGROSIM which consists plant physiological based agro-ecosystem models for agricultural crops was developed in the Institute of Landscape Systems Analysis of the Leibniz-Centre for Agriucultural Landscape Research Müncheberg (Germany) beginning in the 1990th.


Agro-ecosystem model family AGROSIM (2)

The dynamic plant physiologically based AGROSIM models

►belong to the group of soil-plant-atmosphere-management models with the main focus on crop growth processes,

►were elaborated not for single plants, but for whole crop stands under field conditions,

►have a similar model structure on the basis of rate equations,

►describe the processes with a time step of 1 day,

►need only standard meteorological input values (minimum and maximum temperature, global radiation or sunshine duration, precipitation, CO2-

content in the atmosphere) as driving forces and generally available inputs and parameters,

► are validated for weather and soil conditions of different locations in North-East Germany.


AGROSIM model for winter cereals

- Model structure -


AGROSIM model for sugar beet

- Model structure -


0 5 10 15 200

5

10

15

20Model (t ha-1)

1:1

Winter rye Winter wheat Winter barley

Experiment (t ha-1)

1 Sep 1 Nov 1 Jan 1 Mrz 1 Mai 1 Jul 1 Sep0

50

100

150

200

250Soil water (mm)

0 - 30 cm 0 - 60 cm 0 - 90 cm

0

3

5

8

10

13

15 Biomass (t ha-1)

above-ground grain

0

20

40

60

80

1001 Sep 1 Nov 1 Jan 1 Mrz 1 Mai 1 Jul 1 Sep

Ontogenesis (DC)

AGROSIM model validation results

Model-experiment-comparison for winter barley, 1993/94, Müncheberg

Model-experiment-comparison for above-ground biomass, 1991-1995, Müncheberg


Influence of water supply on yield and biomass for winter wheat(1991/92, N-fertilization: 125 kg N ha-1, Hohenfinow, cultivar: Alcedo)

AGROSIM model applications

- Influence of water supply -

31.12.1991 09.04.1992 18.07.19920

5

10

15

20

6,76 t ha-1

4,18 t ha-1

grain biomass

above-ground biomass

Water supply during grain filling: without precipitation real precipitation 5 mm every 5 days 10 mm every 5 days 15 mm every 5 days 20 mm every 5 days

Bio

mass (

t h

a-1)


Influence of nitrogen supply on yield and biomass for winter wheat(1991/92, with irrigation, Hohenfinow, cultivar: Alcedo)


- Influence of nitrogen supply -

31.12.1991 09.04.1992 18.07.19920

5

10

15

20

25

7,00 t ha-1

2,71 t ha-1

grain biomass

above-groundbiomass

without N-fertilization

30 kg N ha-1

50 kg N ha-1 (30/20)

90 kg N ha-1 (40/50)

125 kg N ha-1 (65/60)

175 kg N ha-1 (60/60/55)

Bio

mass (

t h

a-1)


Basis: + influence of CO2 on photosynthesis and respiration processes (not on stomata level)

+ Michaelis-Menten-equation for C3-plants, basis level: 350 ppm

GSc

GSk

036.080

158.0220

0

1

with:

CO2 - CO2-content in the atmosphere

GS - global radiation

01

0

01

0

2

350

3502

2

ck

ccCOk

cCO

KCO


- Influence of increased CO2 in the atmosphere on biomass accumulation (1) -



- Influence of increased CO2 in the atmosphere on biomass accumulation (2) -

Sugar beet, 2001,N: 126 kg N ha-1, with irrigation, Simulation with AGROSIM-ZR

300 350 400 450 500 550 6000

2

4

6

8

10

12

14

16

18

root

grain

above-ground 547 ppm 378 ppm

Bio

mas

s (t

ha-1

)

Day of the year (since 01.01.2002)

150 200 250 3000

4

8

12

16

Bio

mas

s (t

ha

-1)

370 ppm550 ppm

370 ppm550 ppm

leafe

root

Day of the year (since 01.01.2001)

Winter barley, 2002/03,N: 179 kg N ha-1, with irrigation, Simulation with AGROSIM-WG

Data base: FACE – experiment ( 1999 – 2005) of the Federal Agricultural Research Centre Braunschweig, Germany



- Influence of CO2 and temperature on biomass accumulation -

300

400

500

600

700

0.00.5

1.01.5

2.02.5

3.0

10

12

14

16

18

ab

ove-g

rou

nd

bio

mass (

t h

a-1)

Influence of temperature and CO2 increase on biomass accumulation of winter rye



- Climate change effect assessment for winter rye biomass and yield: 1994 vs. 2034 -

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

1000

2000

3000

4000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

20

40

60

80

100Precipitation (mm) 1994 (517 mma

-1)

2034 (447 mm a-1)

Global radiation (J/cm2/d) 1994 (100 %) 2034 (104 %)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

10

20

30

(Müncheberg, Germany, monthly)Climate

Temperature (°C) 1994 (8.34 °C) 2034 (9.58 °C)

Basis: climate model ECHAM1/LSG of the Max-Planck-Institute for Meteorology Hamburg, Scenario: “business as usual“

0

3

5

8 Grain biomass (t ha-1)

0

5

10

15

Climate 1994 Climate 2034

Above-ground biomass (t ha-1)0

20

40

60

80

100

yield loss by 0.8 t ha-1

increase by 0.5 t ha-1

grain filling period shorter by 7 days

JulMayMarJanNovSep

Ontogenesis (DC)


AGROSIM model workshop results for Bad Lauchstädt

► AGROSIM models run for the short time experiment (1999-2004).

► Because of not availability of AGROSIM models for potatoes and spring barley model runs for sugar beet in 1999 and 2003, and winter wheat in 2001/02 were realized only.

1. Without any parameter adaptation

original model parameter set for Müncheberg was used

2. With parameter adaptation

cultivar dependent model parameters were adapted only


AGROSIM model workshop results for Bad Lauchstädt – without parameter adaptation – (1)

0500

1000150020002500

02000400060008000

0

50

100

150

0

50

100

150

02468

Biomass (g/m2) root leaf

Leaf fresh biomass (g/m2)

Soil water in 45 cm depth (mm)

OSAA MMF JJJ1999


Leaf area index [LAI] (m2/m2)

► root and leaf biomass estimation with a good accuracy (light underestimation at harvest time)

► soil water is overestimated, especially in 90 cm depth during summer and late summer

Sugar beet, 1999



► root and leaf biomasses are under- and overestimated, respectively

► soil water estimation with a good accuracy (light overestimation especially in 90 cm

depth during summer and late summer)

Sugar beet, 2003

0

10

20

30

0

10

20

30

050

100150200

0200400600800

100002468


JAMFJ 2003


SAJ

Biomass (dt/ha) root

leaf

Leaf fresh biomass (dt/ha)




► significant overesti-mation in above-ground biomass and N-uptake during grain filling period

► soil water estimation in 45 cm and 90 cm depth is a little bit underesti-

mated, especially in 45 cm depth during spring and summer

Winter wheat, 2001/02

020406080

100

0300600900

1200

05

101520

010203040

010203040

Ontogenesis (DC)

Biomass (g/m2) above-ground grain

Nitrogen uptake (g/m2)


AJJMAMFJDNO20022001



AGROSIM model workshop results for Bad Lauchstädt – with parameter adaptation – (1)

► after adaptation of cultivar model parameters (distribution ratio between leaf and root) the biomasses can be estimated with a higher accuracy

► the cultivar parameter change does not influence the soil water course

Sugar beet, 1999

0500

1000150020002500

02000400060008000

0

50

100

150

0

50

100

150

02468

Biomass (g/m2) root leaf

Leaf fresh biomass (g/m2)


OSAA MMF JJJ1999





► here also the same parameter adaptation (distribution ratio function between leaf and root)

► adapted variant (dotted lines) has a better agreement with the measured biomasses over the time

Sugar beet, 2003

0

10

20

30

0

10

20

30

050

100150200

0200400600800

100002468


SAJJAMF 2003J


Biomass (dt/ha) root

leaf

Leaf fresh biomass (dt/ha)




► adaptation of cultivar model parameters gives significant better results in biomass accumulation (dotted lines)

► ontogenesis and soil water are not changed significant

Winter wheat, 2001/02

020406080

0300600900

1200

05

101520

010203040

010203040

Ontogenesis (DC)

Biomass (g/m2) above-ground grain

Nitrogen uptake (g/m2)


AA MMF JJJDNO20022001



AGROSIM model transfer to other geographical sites (1)

► To transfer crop growth and ecosystem models from one geographical site to another successfully it means to recalibrate model parameter in every case, more or less intensive! This is shown by

1. workshop results with the Bad Lauchstädt data set from the short time experiment

2. transfer investigations with the AGROSIM model for winter wheat to different European sites

Russia

parameter maximum ontogenesis rate gross photosynthetic rate

tillering shooting grain filling tillering shooting grain filling

country

France 0.10...0.11 0.37 0.07 0.95...1.10 0.25...0.31 0.03

Germany 0.17 0.40 0.035 0.90 0.245 0.055

Hungary 0.10 0.45 0.07 0.90 0.245 0.055

Italy 0.10 0.34 0.12 0.90 0.26 0.03

Netherlands 0.12 0.60 0.035 0.96 0.27 0.055

Poland 0.07 0.45 0.08 1.10 0.31 0.03

0.07...0.09 0.45 0.08 1.10 0.31...0.46 0.03...0.055


AGROSIM model transfer to other geographical sites (2) – AGROSIM-WW transfer to European sites -

Model-experiment-comparison for winter wheat grain yield (simulation with AGROSIM-WW)

0 2 4 6 8 100

2

4

6

8

1:1

MOD = 3,414 + 0.930 EXP

R2 = 0,858N = 128

Netherlands (17) France (11) Poland (5) Germany (57) Hungary (2) Italy (31) Russia (5)

sim

ula

ted

gra

in y

ield

-M

OD

- (t

ha

-1)

measured grain yield -EXP- (t ha-1)

Latitude:

39.3° ... 55.0°

Experimental sites: 24

Growing periods:

1957 ... 1997

different Cultivars: 29


Conclusions

► The AGROSIM models for sugar beet and winter wheat can describe the real situation on the Bad Lauchstädt experimental station for 1999, 2001/02 and 2003 with a sufficient accuracy only after a recalibration of cultivar model parameters.

►The workshop results show that a model transfer to other geographical and sits conditions model parameters representing crop, site and other properties must be re-estimated or newly derived.

A model transfer without any adaptation is not useful !

► The better considered the influence of site, weather, agro-management and cultivar properties the more accurate the simulation results and the greater the possibilities to transfer a model from one geographical site to another and from one time period to another.

► The chances of a broad model application increase if model adaptation could be limeted to weather and soil information and only a few clearly defined parameters. For this

coherent data series are needed.


Conclusions

What is the parameter situation of crop growth models within long-term simulations ?

► In opposite to the soil processes with more or less constant laws of soil physics and more or less constant process parameters, the crop growth processes are adaptable processes controlled by genetic memory and genetic information, i.e with changeable process parameters (ontogenetic rates, shoot-root-ratio, straw-grain-ratio ...) over a long time. On the one hand there are anthropogenious reasons like plant breeding, and on the other hand there are natural reasons like the self adaptation of plants to changing environmental factors.

► Investigation results that the CO2-reaction of old winter wheat cultivars from the 1930th differ from that of modern winter wheat cultivars underlines this fact (R. Manderscheid, Federal Agricultural Research Centre Brunswick, Germany).

► Changing genetic plant-own reactions from plant generation to plant generation make it necessary to adapt parameters in crop growth models anew for different time periods. So it is necessary to adapt these parameters any times for long-term simulation runs, like for the about one hundred years experiment here in Bad Lauchstädt.


Thank you for your attention !

Dynamic crop growth modelling with AGROSIM Application on the Bad Lauchstädt site

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