Presentation done at CIAT on 25th April by James Cock and Julian Ramirez-Villegas about the cassava simulation model that is now part of the DSSAT modelling suite.

1. Plant Growth Models
Experiences with cassava: Highlighting
dangers and pitfalls.

2. What use are they?
• To ask difficult to answer questions of “What…if?”
• To predict production for management purposes.
• To understand better how little we know.
Lobell et al. (2014) NCC
B

3. Types of models?
• Statistical
• Mechanistic or process models.
Both have important but distinct roles to play, but none of the models are
totally trustworthy…..you must treat output with caution!
Hawkins et al. (2012) GCB
B

4. Difficult what if questions.
The questions may be difficult due to long time
frames, high costs, or technical difficulties of using
more traditional experimental methods. These models
are more interested in trends and tendencies than a
perfect estimate of production.
• How to manage difficult to reproduce situations like
climate change.
• What happens if I include a new trait into a breeding
programme.
• Long term fertility effects.
Ramirez-Villegas & Challinor (2016)
B

5. Early work on cassava models
JC

6. The Current Cassava Model
Objectives.
• To answer “What if?” questions, especially management and
climate change.
• Better understand the crop.
The model had to be process based or mechanistic to meet these
objectives.
JRV

7. What could we build on?
We would use, wherever possible existing code or subroutines.
• We decided to use the best currently available model, GUMCAS,
which had been converted (supposedly) to DSSAT format.
• The advantage of using a DSSAT based model is the existence of
routines already coded for many processes common to all plants
(e.g. water balance).
• There was little information on cassava grown outside the area o
12N and 12S so we limited the model to these limits, but made it
readily adaptable when information comes available.
JRV

8. Unpleasant surprises.
• The CSCAS model, supposedly based on GUMCAS, was a cereal
model converted to model cassava and had lost many of GUMCAS’s
features, but not all.
• The CSCAS model was poorly documented, not efficiently
programmed by an agronomist and difficult to understand.
• We were not quite sure what the best team composition should be
We now strongly suspect that we would have been better off
building a completely new cassava model, based on DSSAT
routines.
JRV

9. What’s different with cassava
Following the philosophy of buidling on code that already exists we
identified the following characteristics of cassava which distinguish
it from other crops and available routines:
1. Cassava does not have distinct growth phases like annual crops.
2. Cassava does not have a specific maturity date as in many crops.
3. Water conservation via stomatal response to both VPD and soil
water status rather than leaf water potential (φ).
4. Cassava restricts leaf area according to nutrient availability and
does not dilute nutrients when they are deficient.
JC

10. A very simple but
highly intelligent
plant!
Nodal units that consist of leaf
(inlcuding petiole), axillary buds,
node and internode, fibrous roots
and swollen roots that store starch.
Branching occurs as forking when
the terminal apex becomes
reproductive and the axillary bugs
immediately below the apex
develop as branches. (Note: the
model does not contemplate lateral
branches.)
JC

11. Model structure –cohorts
A group of nodes of the
same age, regardless of
branching
JC

12. The model structure to take account of
cassava’s specific traits.
• The basic structure is the nodal unit, which subtend leaves that
intercept incoming solar radiation.
• The total number of nodes is determined by the plant density and the
forking.
• Radiation use efficiency of the intercepted radation is used to determine
available carbohydrates for growth.
• The nodal units and the fibrous feeder roots have first call on
carbohydrate with excess going to the storage roots.
• Nodal unit number and development is restricted when insufficient
carbohydrate or nutrients are available and also by temperature and
water dficit. With nutrients (N) leaf expansion varies to maintain
constant leaf N content.
• The stomatal conductivity is reduced by reduced available water in the
soil and leaf to air vapour pressure déficit.
Rather than use black boxes we tried to use wherever possible
observable parameters.
JC

13. Model structure
Phenology
Time to branching
Growth
Node weight
Leafcanopy
Carbohydrate
production (RUE)
Feeder roots
Storage roots
Emergence
Temperature
(thermal time,
plant age)
Soil
water
balanceRainfall
Soil type
Soil
water
stress
Node number
Leaf area
Leaf weight
Leaf duration
Demand
Supply
SpilloverRadiation
VPD
Nitrogen
stress
JRV

14. Top growth preference over
storage root growh.
• Increasing assimilate by misting or decreasing it by
shading has minimal effect on top growth and very
large effects on root growth.
• Hence, we assume top growth has preference.
JC

15. The number of nodal
units per ápex and
branch number.
JC

16. Leaf area of
individual leaf
Data from Irikura et al. (1979)
JC

17. Evidence for constant leaf nutrient content
and photosynthesis.
Fertility
level
LAI Nitrogen per unit
area (mg-1 dm-2)
Nitrogen as %
dry matter of leaf
with petiole
Low 1.7 21.7 3.5
Medium 3.5 20.2 3.7
High 5.4 18.9 3.7
LAI and leaf nitrogen content of MMex 59 at three fertility levels.
Source: Cock and Parra (Cock 1984)
Photosynthetic rate with and without N.

18. Stomatal Conductance
• Stomatal conductance is affected by soil
water and VPD.
• The model uses percentage readily
available wate (RAW) as a measure of
soil water stress, and estimates VPD
from maximum and minimum daily
temperatures.
• There is no correlation between
photosynthesis and leaf water potential,
but a high correlation with leaf
conductance.
• The stomatal conductance is then used
to estimate the reduction in Radiation
Use Efficiency (RUE) on an hourly basis
to determine the assimilate produced
each day.
JC

19. Soil water stress
• Readily available soil water
and leaf growth.
JC

20. List of model features
JRV

21. Problems with developing the model
• Early on a clear picture of important cassava processes and their
reaction to stress.
• Difficulties in converting this into code in the CSCAS model due to:
• More expertise in physiology and agronomy than in programming.
• Complicated coding and poor documentation of the original model.
• Lack of clearly defined modules for individual processes in original code.
• Lack of confidence to eliminate original code and write new routines.
Conclusion: it is often better to write your own code than to try and
correct others, and one needs a well rounded team with programmers,
agronomists and physiologists working together.
Moraleja: Zapatero a su zapatos.....y es mejor hacer zapatos nuevos que
repararlos!
JC

22. ….more learning
• It is important to test the model, but testing has to be at the right
time. A critical number of features need to be developed before
testing.
• Although difficult, it is important to identify what parameters and
processes are most important. We spent too much time on fiddling
with unimportant parameters (eg. Length of leaf senescence
period and elongation of underground stems after germination.)
JC

23. Where are we now?
• Testing and refining the model.
• We abhor fudge factors and black boxes, so we try to refine the parameters
of the processes rather than putting in general “calibration” factors. This is
more complicated, but should lead to a more robust model.
JRV

24. Leaf number per apex with and without
water stress.
• We seem to be doing pretty
well, except we have too
many leaves formed in the
early period.
• We suspect we need to look
at the initial soil water status
in the experiment we are
simulating and the time to
germination. An extra few
days would make a big
difference!
JRV
Data from Connor & Cock (1981)
MCol-22
MMex-59
Santander de Quilichao, 1979, water stress experiment

25. Response to temperatures (Manrique, 1992),
cv. Ceiba, Hawaii
Temperature effect on RUE?
282 m
640 m
1,097 m
JRV

26. Detailed analysis of even 1 experiment can
be quite informative
Velktamp (1986) PhD Thesis. Experiment in 1978 to assess varietal differences.
Here we analyze MCol-1984
Branching times
Determined by 2 parameters
1. TT to first branch (B01ND)
2. TT from first to second branch
(B12ND)
Leaf number per stem
Determined by 1 parameter:
1. Slope of node production curve
(LNSLP)
Phenology
Period of
water stress
JRV

27. Detailed analysis of even 1 experiment can
be quite informative
Determined by 1 parameter
1. Weight of node (NODWT)
Stem weight
But also influenced by other parameters, including
(1) Radiation use efficiency (PARUE) –Ecotype
(2) Extinction coefficient (KCAN) –Ecotype
(3) Growth of other organs, esp. leaves
JRV

28. Canopy development
Branching pattern before DAS 150 seems ok
But… BR1FX, BR2FX, BR3FX are taken directly from measured
data (from Velktamp)
BR1FX=2.1 BR2FX=3.1 BR3FX=2.7 BR4FX=1.0
Apex death
??
JRV

29. Canopy development
BR4FX=1.5
Note LAI. Goes up but
doesn’t come down
as in observations
JRV

30. Canopy development
BR4FX=2.7 (=BR3FX)
Note issue with LAI
intensifies.
Remarks
• LAI is sensitive to
branching pattern
• Missing mechanism
for apex death?
JRV

31. Harvest (storage root) yield
Harvest weight
Heavily influenced by
1. Radiation use efficiency (PARUE) –Ecotype
2. Extinction coefficient (KCAN) –Ecotype
3. Growth of other organs, esp. leaves
And also sink size, i.e. carbohydrate going to stems
and leaves.
– Do we need stronger reduction of leaf size?
- Maybe missing water stress effects on RUE or
KCAN?
Yet, the end-of-season yield is ok.
JRV

32. Looking at the bigger picture
Node number
(calibration)
Node number
(evaluation)
Harvest yield
(calibration)
Harvest yield
(evaluation)
Moreno-Cadena (2018)
JRV

33. Final comments:
• We are getting there!
• Do not underestimate the difficulties of modifying someone elses
code. It may be better to write your own.
• You need a well rounded team with distinct strengths….Falcao
would be a hopeless goal keeper!
• Get the model as near complete as possible before trying to
further refine it by calibrating processes, not the whole model.
• Don’t waste time perfecting unimportant processes
JC