Improving Crop Models: Incorporating New Processes, New Approaches, and Better Datasets Jon I....

Improving Crop Models: Improving Crop Models: Incorporating New Incorporating New Processes, New Processes, New Approaches, Approaches, and Better Datasetsand Better Datasets

Jon I. Lizaso([email protected])

Technical University of Madrid

13th ESA Congress 25-29 August 2014, Debrecen, Hungary

2

OverviewOverview

Crop models improved in response to:o Better crop/environment understandingo New scientific questionso Need for better accuracy (especially under stress conditions)

Incorporating new processes o Anthesis-Silking Interval (ASI) in maize

Incorporating new approacheso Sink-limited kernel set in maize

The need for quality and diversity of datasets

3

Early crop modelsEarly crop models Early models described canopy light capture and

photosynthesiso De Wit, 1965; Monteith, 1965; Duncan et al., 1967

Personal computers not availableo Apple II released in 1977 o IBM PC released in 1981

Later models incorporated development, growth and partitioning, and yieldo Hesketh, Baker & Duncan 1971, 1972; Baker, Hesketh & Duncan,

1972

Almost 50 years of model improvement Better understanding New questions Better accuracy (stresses)

Review: Boote et al., 2013. Plant, Cell & Environment

4

Improving models: new Improving models: new processesprocesses Crop simulation models are a deliberate simplification of a

field grown crop

Modelers decide what process to include: Objectives

Models evolve:o Including new processeso Including new approaches (substitute/complement previous)o Re-parameterization or Re-calibration (quality datasets)

Example of incorporating a new process: Anthesis-Silking Interval (ASI) in maize

o Yield is sink-limitedo Kernel set is source-limited (under most field conditions)

5

Maize monoecious plantMaize monoecious plant

Pistillate flowers with stigmata

Staminate flowers shedding pollen

Monoecious:Monoecious:Separate male & Separate male & female flowers female flowers

in the plantin the plant

Grain yield depends on the synchrony between

Anthesis & Silking for adequate pollination and kernel set

ASI

6

Strong relationship of maize grain yield with ASI

Especially under water stress

Modern hybrids, with enhanced stress-tolerance, show similar trend

Incorporated ASI simulation into CERES-Maize

ASI (GDU 10-30º C)

-30 0 30 60 90 120

Gra

in Y

ield

(M

g h

a-1)

0

4

8

12

16

20ControlStress

Elite US Corn Belt hybrids(G. Edmeades, pers comm)

Bolaños & Edmeades, 1992SC403

ASI (d)

0 5 10 15 20

Gra

in Y

ield

(M

g h

a-1)

0

2

4

6

8Bolaños&Edmeades, 1993Managed Drought (SC403)Managed Drought (SC513)

(K. Tesfaye, pers comm)

Incorporating new processes: Incorporating new processes: ASIASI

7


BAGDD(SPE)

AAGDD(SPE)

1: Avg Shoot Growth Rate

(SGR)ASI

Barrenness

PSTR

SLPF

(MIN)

PAR

CO2

LAIPopDens

RowSpac

TEMP

WSTR

NSTR

KSTR

SRAD

k

RUE

Ear Growth

PartEar

Incorporated into CERES-Maize v4.5

Flowering event changed from silking to anthesis

The model calculates the average shoot growth rate (SGR) during a thermal time window around flowering

Thermal time window delimited by two user-specified parameters

8

Incorporating new processes: Incorporating new processes: ASIASI Model assumes no stress

when:

SGR > 5 g/plant day

Two new cultivar parameters:o ASNS (ASI under no stress)o ASEN (sensitivity to stress)

Under no stress: ASI=ASNS

Under stress silk extrusion is delayed according to ASEN

9


The model estimates kernel number as a function of ASI, according to Bolaños & Edmeades (1993)

For negative ASI values (protogyny), it uses a function calculated from Lizaso et al. (2003, 2007)

SGR 2: ASI

ASEN(CUL)

ASNS(CUL)

Onset Lin Grain Fill

KernelNumber

10


Model calculates barrenness as a function of SGR

Since kernels are set on ears, barren ears are checked with ASI

3: Kernel Number

(KN)

YieldG2(CUL)

SGR ASIOnset Lin Grain Fill

4: Barrenness

(EPP)

THRE(ECO)

PLTPOP

11


Finally, yield is calculated with:

o kernel number (KN)o ears/plant (EPP)o onset of linear grain fill

KN

EPP5:

Yield

G3(CUL)

P5(CUL)

Onset Lin Grain FillASI

12


2: ASI

ASEN(CUL)

ASNS(CUL)

6: Onset Lin Grain

Fill

DSGFT(ECO)

3: Kernel Number

(KN)

4: Barrenness

(EPP)

THRE(ECO)

5: Yield

7: Ear Growth

BAGDD(SPE)

AAGDD(SPE)

1: Avg Shoot Growth Rate

(SGR)

PartEar

P5(CUL)

G3(CUL)

G2(CUL)

13


Some preliminary results indicate the new model is working reasonably well

Additional testing is required under various conditions and stresses

Kernel Number

Rain n Rain N Irr n Irr N

Sim

ula

ted

/ M

ea

su

red

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Yield

Rain n Rain N Irr n Irr N

CERESASI

14

Improving models: new Improving models: new approachesapproaches Maize grain yield is sink-limited. The potential size of the sink,

kernel set, is determined around flowering

However, maize kernel set is usually source-limited

Maize models simulate kernel numbers:o Light capturedo Photosynthetic rateo Growth rate

Example of incorporating a new approach: Sink-limited kernel set in maize

Edmeades and Daynard, 1979

15

Simulating kernel set in maize Simulating kernel set in maize

J. Lizaso, 2005

If pollen becomes limited, as in hybrid seed production, or there is poor synchrony between anthesis and silking, kernel set may be sink-limited

Example of incorporating a new approach that complements current procedure: sink-limited kernel seto Pollen dynamicso Silk dynamicso Relationship linking pollen & silks

16

8

53

212212

Self-adhesive traps are located daily at silks level. Fluorescence microscopy produces images that are processed with image-analysis software. This result in pollen counts as pollen grains cm-2 d-1 (Fonseca et al., 2002)

J. Lizaso, 2007

Dynamics of pollen shed: Dynamics of pollen shed: measuring pollen ratesmeasuring pollen rates

17

P3394

Day of the year

196 198 200 202 204 206 208 210 212 214

Po

lle

n r

ate

(g

rain

s c

m-2 d

-1)

0

100

200

300

400

500

P3489

Day of the year

198 200 202 204 206 208 210 212 214

Po

llen

rat

e (g

rain

s cm

-2 d

-1)

0

100

200

300

400

500

600

Gauss functions adequately describe daily pollen rates for hybrids and inbreds

J. Lizaso, 2007

Dynamics of pollen shed: Dynamics of pollen shed: measuring pollen ratesmeasuring pollen rates

18

Total pollen produced per tassel can be field measured or estimated from tassel morphology (Fonseca et al., 2003)

J. Lizaso, 2007

Dynamics of pollen shed: Dynamics of pollen shed: simulating pollen ratessimulating pollen rates

To simulate ear-level pollen rates (grain cm-2 d-1) 2 pieces of information are required:

Progression of population reaching anthesis (%) Daily pollen production from individual tassels (grain plant-1 d-1).

These values can be calculated from: Total pollen produced per tassel (million grains/tassel) Duration of pollen shed per tassel (d)

19

Day of the year

195 200 205 210 215 220 225

Per

cen

t o

f p

lan

ts (

%)

0

20

40

60

80

100

Anthesis

Dekalb 6118 pl m-2

Days after anthesis

0 1 2 3 4 5 6 7 8

Po

lle

n g

rain

s p

lan

t-1 (

millio

ns

)

0.0

0.2

0.4

0.6

0.8

Pollen shed

Dekalb 6118 pl m-2

2.64 millions

8 days

Dynamics of pollen shed: Dynamics of pollen shed: simulating pollen ratessimulating pollen ratesDynamics of pollen shed: Dynamics of pollen shed: simulating pollen ratessimulating pollen rates

Simulated pollen rate

Day of the year

190 195 200 205 210 215

Po

llen

ra

te (

gra

ins

cm-2

)

0

50

100

150

200

250

300

Dekalb 6118 pl m-2

Days after anthesis

0 1 2 3 4 5 6 7 8

Po

llen

gra

ins p

lan

t-1 (

millio

ns)

0.0

0.2

0.4

0.6

0.8

Pollen shed

Day of the year

195 200 205 210 215 220 225

Pe

rce

nt

of

pla

nts

(%

)

0

20

40

60

80

100

Anthesis

20J. Lizaso, 2007

Silks are cut and ears are covered with glassine bags to prevent pollination

Each day 2 cm pieces are cut from the silk bouquet and are kept in alcohol at 4º C

Silks are counted and monomolecular functions are fit

Dynamics of silk appearance:Dynamics of silk appearance:measuring silk extrusionmeasuring silk extrusion

21J. Lizaso, 2007

Silk simulation requires field measurements of: Progression of population reaching silking Pattern of silk extrusion from individual ears:

Number of silks per ear Duration of silk extrusion

Measurements of number of silks are facilitated by measuring the perimeter of the bouquet (Schneider, 2005)

Dynamics of silk appearance:Dynamics of silk appearance:simulating silk extrusionsimulating silk extrusion

22

Silking

Day of the year

185 190 195 200 205 210 215

Pe

rce

nt

of

pla

nts

(%

)

0

20

40

60

80

100

Asgow 7408 pl m-2

Silk extrusion

Days after silking

0 2 4 6 8 10

Nu

mb

er o

f si

lks

ex

po

sed

ea

r-1

0

200

400

600

800

Asgrow 7408 pl m-2

Silk extrusion

Days after silking

0 2 4 6 8 10

Nu

mb

er

of

silk

s e

xp

os

ed

ea

r-1

0

200

400

600

800

Silking

Day of the year

185 190 195 200 205 210 2150

20

40

60

80

100

Simulated silk appearance

Day of the year

185 190 195 200 205 210 215 220 225

New

ly e

mer

ged

silk

s h

a-1

d-1

100

101

102

103

104

105

106

107

108

Asgrow 7408 pl m-2

Dynamics of silk appearance:Dynamics of silk appearance:simulating silk extrusionsimulating silk extrusionDynamics of silk appearance:Dynamics of silk appearance:simulating silk extrusionsimulating silk extrusion

23


Day of the year

190 195 200 205 210 215

Po

llen

rat

e (g

rain

s cm

-2)

0

50

100

150

200

250

300

Dekalb 6118 pl m-2


Day of the year

190 195 200 205 210 215

Po

llen

rat

e (g

rain

s cm

-2)

0

50

100

150

200

250

300


Day of the year

185 190 195 200 205 210 215 220 225

New

ly e

mer

ged

silk

s h

a-1

d-1

100

101

102

103

104

105

106

107

108


Day of the year

185 190 195 200 205 210 215 220 225

New

ly e

mer

ged

silk

s h

a-1

d-1

100

101

102

103

104

105

106

107

108

Asgrow 7408 pl m-2

Rate of pollen shed (grains cm-2 d-1)

0 100 200 300 400 500 600

Pro

po

rtio

n o

f ke

rnel

set

(%

)

0

20

40

60

80

100

y = 0.96 x 0 < x < 100

y = 96 100 < x

r2 = 0.97

Bassetti and Westgate, 1994

Linking pollen & silks: kernel Linking pollen & silks: kernel set relationshipset relationshipLinking pollen & silks: kernel Linking pollen & silks: kernel set relationshipset relationship

24J. Lizaso, 2005

Results from seed production fields show the processes are quite predictable and our procedures capture them

Too many inputs from male and female inbreds

Yet useful for seed industry

When both, source- and sink-limited conditions were simulated the new model showed excellent accuracy

Measured kernels plant-1

0 400 800 1200 1600

Sim

ula

ted

ker

nel

s p

lan

t-1

0

400

800

1200

1600

CERESModified

r2C = 0.77; MSDC = 23127

r2M = 0.87; MSDM = 8350

Measured kernels plant-1

0 200 400 600 800

Sim

ula

ted

ker

nel

s p

lan

t-1

0

200

400

600

800

CERESModified

r2C = 0.17; MSDC = 15084

r2M = 0.73; MSDM = 6664

Evaluating a complementary Evaluating a complementary approachapproachLizaso et al., 2007

25J. Lizaso, 2005

A number of current efforts to improve crop models:

o AgMIP Program: Pilot studies on wheat, maize, rice, and ongoing work on sugarcane, potato, sorghum-millet, peanut, soybean

o MACSUR Project: Focusing on European agriculture, more interested in crop rotations, pastures, and livestock

o Model packages: DSSAT, APSIM, CropSyst, STICS, EPIC, and others

Beyond the number of processes included, and the approach chosen, a permanent concern for model improvement/testing is the quality and diversity of datasets especially in areas and processes poorly represented

Towards the future: the Towards the future: the quest for quality & diverse quest for quality & diverse datasetsdatasets

26J. Lizaso, 2005

Field data collection must continue especially in areas and processes poorly represented

AgMIP maize team showed that an ensemble of 19 models was superior simulating maize yield than any single model

So, how many models are enough?

As the ensemble size increased, relative variation dropped differently for each site

Towards the future: the Towards the future: the quest for quality & diverse quest for quality & diverse datasetsdatasets

Number of models averaged

0 4 8 12 16 20

Rel

ativ

e va

riat

ion

of

sim

ula

ted

yie

ld

0.0

0.1

0.2

0.3

0.4Morogoro, TZ Lusignan, FRRio Verde, BRAmes, US

Bassu et al., 2014

Improving Crop Models: Incorporating New Processes, New Approaches, and Better Datasets Jon I....

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