Improving Crop Models:
Incorporating New Processes,
New Approaches,
and Better Datasets
Jon I. Lizaso
(jon.lizaso@upm.es)
Technical University of Madrid
13th ESA Congress
25-29 August 2014, Debrecen, Hungary
Overview

Crop models improved in response to:
o
o
o

Incorporating new processes
o

Anthesis-Silking Interval (ASI) in maize
Incorporating new approaches
o

Better crop/environment understanding
New scientific questions
Need for better accuracy (especially under stress conditions)
Sink-limited kernel set in maize
The need for quality and diversity of datasets
2
Early crop models

Early models described canopy light capture and
photosynthesis
o

Personal computers not available
o
o

Hesketh, Baker & Duncan 1971, 1972; Baker, Hesketh & Duncan,
1972
Almost 50 years of model improvement




Apple II released in 1977
IBM PC released in 1981
Later models incorporated development, growth and
partitioning, and yield
o

De Wit, 1965; Monteith, 1965; Duncan et al., 1967
Better understanding
New questions
Better accuracy (stresses)
Review: Boote et al., 2013. Plant, Cell & Environment
3
Improving models: new processes

Crop simulation models are a deliberate simplification of a
field grown crop

Modelers decide what process to include: Objectives

Models evolve:
o
o
o

Including new processes
Including new approaches (substitute/complement previous)
Re-parameterization or Re-calibration (quality datasets)
Example of incorporating a new process:
Anthesis-Silking Interval (ASI) in maize
o
o
Yield is sink-limited
Kernel set is source-limited (under most field conditions)
4
Maize monoecious plant
Staminate flowers shedding
pollen
ASI
Monoecious:
Separate male &
female flowers in
the plant
Grain yield depends on the
synchrony between
Anthesis & Silking for adequate
pollination and kernel set
Pistillate flowers with
stigmata
5
Grain Yield (Mg ha-1)
Incorporating new processes: ASI
8
6
Bolaños&Edmeades, 1993
Bolaños & Edmeades, 1992
Managed Drought (SC403)
SC403
Managed Drought (SC513)

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 CERESMaize
(K. Tesfaye, pers comm)
4
2
0
0
5
10
15
20
Grain Yield (Mg ha-1)
ASI (d)
20
Control
Stress
16
12
8
4
Elite US Corn Belt hybrids
(G. Edmeades, pers comm)
0
-30
0
30
60
90
120
ASI (GDU 10-30º C)
6
Incorporating new processes: ASI


PAR
Incorporated into CERESMaize v4.5
CO2
LAI
k
Flowering event changed
from silking to anthesis
RUE
BAGDD
(SPE)

The model calculates the
average shoot growth
rate (SGR) during a
thermal time window
around flowering
(MIN)
Thermal time window
delimited by two userspecified parameters
Pop
Dens
Row
Spac
AAGDD
(SPE)
1: Avg Shoot
Growth Rate
(SGR)
Ear
Growth
Part
Ear
Barrenness
ASI
TEMP
WSTR

SRAD
SLPF
NSTR
PSTR
KSTR
7
Incorporating new processes: ASI

Model assumes no stress
when:
ASI vs. SGR
SGR > 5 g/plant day
18
16

Rel ASI (d)
14
12
10
8
Two new cultivar parameters:
ASEN=3
o
ASEN=7
o
ASNS (ASI under no stress)
ASEN (sensitivity to stress)
ASEN=11
6
ASEN=15
4
ASEN=19
2

Under no stress: ASI=ASNS

Under stress silk extrusion is
delayed according to ASEN
0
0
2
4
6
8
Shoot Growth Rate (g/pl d)
10
8
Incorporating new processes: ASI
Kernel
Number
ASNS
(CUL)

For negative ASI values
(protogyny), it uses a
function calculated from
Lizaso et al. (2003, 2007)
ASEN
(CUL)
Kernel Number
1
B&E 1993
400
0.8
CERES-ASI
300
0.6
200
0.4
100
0.2
0
Relative kernel set
500
Kernel Set
The model estimates
kernel number as a
function of ASI, according
to Bolaños & Edmeades
(1993)
Onset Lin
Grain Fill
2: ASI
SGR

0
-10
0
10
ASI
20
30
9
Incorporating new processes: ASI
Barrenness
THRE
(ECO)
1.2
PLTPOP
1
4:
Barrenness
(EPP)
Ears/pl
0.8
0.6
0.4
0.2
3: Kernel
Number
(KN)
0
0
2
4
6
8
Shoot Growth Rate (g/pl)
10
KN & Ear/plant
SGR
1
Relative KN & Ears/pl
Yield
G2
(CUL)
ASI
1
Relative KN
0.8
0.8
Ears per plant
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0
5
10
15
ASI (d)
20
25
Onset Lin
Grain Fill


Model calculates barrenness as a
function of SGR
Since kernels are set on ears,
barren ears are checked with ASI
30
10
Incorporating new processes: ASI
P5
(CUL)
G3
(CUL)

EPP
5:
Yield
KN
ASI
Onset Lin
Grain Fill
Finally, yield is calculated
with:
o kernel number (KN)
o ears/plant (EPP)
o onset of linear grain fill
11
Incorporating new processes: ASI
THRE
(ECO)
P5
(CUL)
G3
(CUL)
4:
Barrenness
(EPP)
7: Ear
Growth
5: Yield
BAGDD
(SPE)
AAGDD
(SPE)
Part
Ear
G2
(CUL)
1: Avg Shoot
Growth Rate
(SGR)
3: Kernel
Number
(KN)
6: Onset
Lin Grain
Fill
2: ASI
ASNS
(CUL)
ASEN
(CUL)
DSGFT
(ECO)
12
Incorporating new processes: ASI
Simulated / Measured
Kernel Number
Yield
1.4
1.2
CERES
ASI
1.0
0.8
0.6
0.4
0.2
0.0
Rain n Rain N
Irr n
Irr N
Rain n Rain N
Irr n
Irr N

Some preliminary results indicate the new model is working
reasonably well

Additional testing is required under various conditions and
stresses
13
Improving models: new approaches

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
o
o

Edmeades and Daynard, 1979
Light captured
Photosynthetic rate
Growth rate
Example of incorporating a new approach:
Sink-limited kernel set in maize
14
Simulating kernel set in maize

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 set
o
o
o
Pollen dynamics
Silk dynamics
Relationship linking pollen & silks
15
J. Lizaso, 2005
Dynamics of pollen shed: measuring
pollen rates
8
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)
53
212
16
J. Lizaso, 2007
Dynamics of pollen shed: measuring
pollen rates
500
Gauss functions
adequately
describe daily
pollen rates for
hybrids and inbreds
300
200
100
0
202
204
206
208
210
212
214
D a y o f th e ye a r
600
P3489
-1
200
d )
198
500
-2
196
P o lle n ra te (g ra in s c m
P o lle n ra te (g ra in s c m
-2
-1
d )
P3394
400
400
300
200
100
0
198
200
202
204
206
208
210
212
214
D a y o f th e ye a r
17
J. Lizaso, 2007
Dynamics of pollen shed: simulating
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)
Total pollen produced per tassel can be field
measured or estimated from tassel morphology
(Fonseca et al., 2003)
18
J. Lizaso, 2007
Dynamics of pollen shed: simulating
pollen rates
S im u laPteod
pno
neis
ra te
A
thlle
llen
sehs
d
A n th e s is
100
60
Poenlle
rc
en
tsc m
(% ))n s )
P
ra
P o lle
illio
gn
ra
intte
sopf(g
lapra
nla
tinns(m
40
-2
P e rc e n t o f p la n ts (% )
1
3
00.80
80
20
0
195
200
205
210
215
220
225
-1
D a y o f th e y e a r
P o lle n s h e d
0 .6
P o lle n g ra in s p la n t
-1
(m illio n s )
0 .8
0 .4
0 .2
0 .0
0
1
2
3
4
5
D a ys a fte r a n th e s is
6
7
8
2.64
millions
Dekalb
611
8 days
8 pl m-2
250
80
0 .6
Dekalb
Dekalb
611 611
-2
8
pl
8 pl m-2 m
200
60
105.40
40
100
0 .2
20
50
0
0 .00
11099 05
1 2 0109 5 2
205 2
30 0
2 140
215
205 5
62 1202 0
7
222155 8
DDaDays
aofte
ff thr eea nye
yth
eaaer rs is
ayy o
19
Dynamics of silk appearance:
measuring silk extrusion



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
20
J. Lizaso, 2007
Dynamics of silk appearance:
simulating silk extrusion

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)
21
J. Lizaso, 2007
Dynamics of silk appearance:
simulating silk extrusion
S im u la
ilk
pn
e a ra n c e
ilk
inasgpio
S te
ilkdS
esx
tru
107
80
610006
S ilk in g
100
Asgrow
Asgow740
740
Asgrow
740
-2
8 8plplmm
-2
8 pl m -2
80
60
40
20
0
185
190
195
105
60
200
205
210
215
D a y o f th e y e a r
410004
40
103
S ilk e x tru s io n
800
-1
210002
20
N u m b e r o f s ilk s e x p o s e d e a r
-1
Nu
rm
oefnestrg
ilk
pts
oss(%
ehda) e adr
em
wPb
lyeerc
e
ilk
o fesd
pe
lasxn
-1
-1
8
811000 0
101
1 0000
1108855
600
400
200
0
0
0
1 9 01 9 2
195
1 9 52 040
220005
6 2 1200 5
2 1 58 2 1 0 2 2 0
1201252 5
2
4
6
8
D a ys a fte r s ilk in g
D aDD
ys
rthseeilk
aayyaofte
ye
aag
rr
off th
yein
22
10
Linking pollen & silks: kernel set
relationship
SSim
ilk
a pnp era
a te
ra n c e
imuulalateteddsp
o lle
S im u la te d s ilk a p p e a ra n c e
S im u la te d p o lle n ra te
310008
-1
108
107
d
250
150
100
107
250
Asgrow 740
8 pl m-2
Dekalb 611
8 pl m-2
-2
100
-1
rtio
n
o fe(g
kdera
rn
e lsssce
ta (%
P
)d) -1
olyo
lle
te
in
NPero
wp
enmra
e rg
s ilk
hm
200
50
0
190
195
200
205
D a y o f th e ye a r
210
215
N e w ly e m e rg e d s ilk s h a
-1
-2
P o lle n ra te (g ra in s c m )
300
6
8100
106
105
104
103
102
101
100
200
105
185
190
195
200
205
210
215
D a y o f th e ye a r
60
115004
3
4100
100
B a s s e tti a n d W e s tg a te , 1 9 9 4
102
20
50
101
y = 0 .9 6 x
0 < x < 100
y = 96
100 < x
2
r = 0 .9 7
10000
1
019805
11
90
0 01 9 5 1 9 5 2 0 0 220000
320005
2 0 52 140 0 0
D
Dlle
a yanyosfohfth
e
aarinr s cm
R a te o f p o
eth
deye
(gye
ra
2 1251 0 5 0 02 2 0
-2
2 12652050
-1
d )
23
220
225
Simulated kernels plant-1
Evaluating a complementary approach
Lizaso et al., 2007
800
r2C = 0.17; MSDC = 15084
r2M = 0.73; MSDM = 6664
600
400
200
CERES
Modified
0
0
200
400
600
800
Measured kernels plant-1
Simulated kernels plant-1
Results from seed production
fields show the processes are
quite predictable and our
procedures capture them
1600
Yet useful for seed industry
r2C = 0.77; MSDC = 23127
r2M = 0.87; MSDM = 8350
1200
800
400
Too many inputs from male
and female inbreds
CERES
Modified
When both, source- and sinklimited conditions were
simulated the new model
showed excellent accuracy
0
0
400
800
1200
1600
Measured kernels plant-1
24
J. Lizaso, 2005
Towards the future: the quest for
quality & diverse datasets
A number of current efforts to improve crop models:
o
o
o
AgMIP Program: Pilot studies on wheat, maize, rice, and
ongoing work on sugarcane, potato, sorghum-millet, peanut,
soybean
MACSUR Project: Focusing on European agriculture, more
interested in crop rotations, pastures, and livestock
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
25
J. Lizaso, 2005
Towards the future: the quest for
quality & diverse datasets
Bassu et al., 2014
Relative variation of
simulated yield
0.4
Morogoro, TZ
Lusignan, FR
Rio Verde, BR
Ames, US
0.3
0.2
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?
0.1
0.0
0
4
8
12
16
20
Number of models averaged
As the ensemble size increased,
relative variation dropped
differently for each site
26
J. Lizaso, 2005
27