Presentation during African Cassava Agronomy Initiative (ACAI) Second Annual Review Meeting and Planning Workshop on 11 – 15 Dec. 2017 at Gold Crest Hotel, Mwanza, Tanzania. Presented by Patricia Moreno, Gerrit Hoogenboom, Senthold Asseng, James Cock, Myles Fisher, Julian Ramirez-Villegas & Luis Augusto Becerra

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Cassava model in DSSAT to support scheduled
planting and high starch content use cases
African Cassava Agronomy Initiative (ACAI)
Second Annual Review Meeting
and Planning Workshop
11 – 15 Dec. 2017
Gold Crest Hotel, Mwanza, Tanzania
Patricia Moreno, Gerrit Hoogenboom, SentholdAsseng, James Cock
Myles Fisher, Julian Ramirez-Villegas & Luis Augusto Becerra

2. www.iita.orgA member of CGIAR consortium
Cassava model in DSSAT to support
scheduled planting and high starch
content use cases
Patricia Moreno
Gerrit Hoogenboom
Senthold Asseng
James Cock
Myles Fisher
Julian Ramirez-Villegas
Luis Augusto Becerra

3. • Traditional agronomic approach:
– Experimental trial and error
Why Crop Models in Agriculture?

4. • Traditional agronomic approach:
– Experimental trial and error
• Systems Approach
– Computer models
– Experimental data
• Understand  Predict Control & Manage
– (H. Nix, 1983)
•  Options for adaptive management and
risk reduction
Why Crop Models in Agriculture?

5. Application/
Analysis
Control/
Management/
Decision Support
DesignResearch
Model
Development
Increased
Understanding
Model
Test Predictions
Prediction
Research for
Understanding
Problem Solving
Systems Approach
Crop Simulation Model

6. DSSAT Crop Simulation Model
5
Net Income Resource useEnvironmental
Plant growth
(grain, biomass,
roots, etc.)
Plant
development
(time to flowering,
maturity, etc.)
Yield
Soil conditions
(physical & chemical
properties by layer)
Weather (daily rainfall,
solar radiation, max & min
temperatures, …)
Management events
(sowing, irrigation, fertilizer,
organic matter, tillage,
harvest)
Genetics (cultivar-
specific parameters
controlling growth and
development)
Crop Model
Simulation

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Level 1: Running the model
1) Daily weather data: temperature, rainfall and solar radiation.
2) Soil data: Texture, % stones, bulk density, water retention (wilting
point, field capacity, saturation), hydraulic conductivity, slope, color,
organic carbon (if N is simulated).
3) Initial conditions: soil water content. If N is simulated: previous crop,
N content of residues, depth and % incorporation of residues, N
content in the soil.
4) Management: Planting date, plant density, cultivar characteristics,
irrigation amount, fertilizer amount, organic manure composition.
Data requirements

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Level 2: Model evaluation
Level 1 plus:
1) Treatments.
2) Yield and yield components.
3) General observations: Weed management, pest and disease
occurrence, extreme weather events.
Level 3: Model development
Level 2 plus:
1) Growth analysis measurements: Biomass partitioning, number of
leaves, LAI, nutrient concentrations in plant parts.
2) Soil water content.
3) Soil fertility: Organic carbon, N content.
Data requirements

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1) Define different planting and harvesting dates.
2) Simulate quality of planting material  Initial weight of the planting
stick.
3) Simulate varieties with different branching patterns and leaf
development.
4) Growth of the plant under contrasting temperatures and rainfall
patterns.
5) Effect of water stress on:
• Development: branching and leaf formation.
• Growth: Leaf area, biomass accumulation.
6) Simulations with nitrogen restrictions (under evaluation).
What can we do with the model?

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How does the model work?
First aboveground growth
• Priority leaf and stem growth.
• After supply demand of carbohydrates to aboveground growth
Additional carbohydrates accumulated in the storage roots

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Cardinal
point
Whole plant &
Germination
Branching
Potential
Leaf size
(1st part)
Potential
leaf size
(2nd part)
Leaf life Leaf
expansion
Tbase 13 13 13 13
Ecotype
specific*
Ecotype
specific*
Topmin 30 24 30 20 30 24
Topmax 35 24 35 35 30 24
Tmax 42.5 42.5 42.5 42.5 - -
How does the model work?
They change based on the process
modelled (see table).
Optimum maximum and maximum
temperatures require revision 
more trials at high temperatures.

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n-2
n-2
n
n
n-1 n-1
Growingdegreedays
accumulated(DABR)
1 2 3
Branching level
How does the model work?
B01ND: Difference between early (↓
value) and late (↑ value) branching
varieties.
B12ND: Constant slope of branching
formation after the second branch.

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(a) (b)
How does the model work?
Observed number of leaves at 3 different temperatures using
chronological time (a) and thermal time (b). Source: Irikura ,1979

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How does the model work?
Simulated leaf
appearance with
different leaf
formation rate
(coefficient
LNSLP), and model
equation.

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How does the model work?
Individual leaf size versus thermal time for 4 varieties in 3 temperatures. Source:
Irikura ,1979
• LAXS: Maximum individual leaf
size (coefficient).
• LAXS reached at 900 GDD.
• Optimum temperature changes:
initially 24 °C until 900 GDD are
accumulated.
• After 900 GDD the optimum
temperature is 20 °C.

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𝐼𝐼
𝐼𝐼0
= 1 − 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃 = 𝑒𝑒−𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘
Senescence due to shading
How does the model work?
Leaf duration is
defined in 3 phases:
Growth, active and
senescence.

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A. Unit node
weight per canopy
level (each 20
nodes) for 3
varieties. Source:
Lian & Cock, 1979
Individualnodeweight(g)
Canopy level
How does the model work?
A
B
B. Difference of unit node weight per
canopy level (each 20 nodes) at 12
months after planting. Source: Lian &
Cock, 1979

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Individualnodeweight(g)
Days after planting
How does the model work?
C. Logistic curve used to simulate node
weight. Node weight is defined as a ratio
of the maximum value observed for each
variety. Source: Lian & Cock, 1979
C
D. Simulated node weight for nodes
in different cohorts.
D

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Publication Topic Location DSSAT Trial Varieties
Veltkamp (1986) Growth and development different
varieties
Palmira (Colombia) CCPA7801 MCol-1684
MVen-77
MPtr-26
MCol-22
Veltkamp (1986) Growth and development different
varieties
Palmira (Colombia) CCPA8001 MMex-59
MCo-l638
Manrique (1992) Growth and development different
temperatures (640 m)
Mt. Haleakala, Maui
(Hawai)
HIMA8801 Ceiba
Schulthess (1987)
Gutierrez et al. (1988)
Growth and development: water and N
stress
Ibadan (Nigeria) IIIB8301 TMS 30572
Lian & Cock (1979b) Growth and development different
varieties
Palmira (Colombia) CCPA7601 MCol-22
MMex-59
CMC40
El Sharkawy et al. (1998)
El-Sharkawy & Cadavid
(2002)
Water stress (control treatment year 1-
year 2)
Santander de Quilichao
(Colombia)
CCQU9101 MCol-1684
CMC-40
MCol523-7
MCol507-37
Mejía et al. (1997) Water stress (Control treatment) Santander de Quilichao
(Colombia)
CCQU9402 MCol-1684
Porto (1983) Water stress (control treatments) Palmira, Santander de
Quilichao (Colombia)
CCPA8201
CCQU8201
MCol-1684
Data sets for calibration

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Publication Topic Location DSSAT Trial Varieties
Veltkamp (1986) Growth and development
different varieties
Palmira (Colombia) CCPA7901
CCPA8001
MCol-1684
MVen-77
MPtr-26
MCol-22
Porto (1983) Water stress Palmira, Santander de
Quilichao (Colombia)
CCPA8201
CCQU8201
MCol-1684
Connor & Palta (1981)
Connor & Cock (1981)
Connor et al. (1981)
Water stress Santander de
Quilichao (Colombia)
CCQU7901 MCol 22
MMex 59
Manrique (1992) Growth and development
different temperatures
(282,1097 m)
Mt. Haleakala, Maui
(Hawai)
HIMA8801 Ceiba
El-Sharkawy & Mejia de Tafur
(2010)
Branching habit Santander de
Quilichao (Colombia)
CCQU9401 MCol22
Mejía et al. (1997) Water stress Santander de
Quilichao (Colombia)
CCQU9402 MCol-1684
El Sharkawy et al. (1998)
El-Sharkawy & Cadavid (2002)
Water stress (year 2) Santander de
Quilichao (Colombia)
CCQU9201 MCol-1684
CMC-40
MCol523-7
MCol507-37
Data sets for evaluation

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Priorit
y
Parameter Output variable
2 B01ND: Thermal time from germination to
first branching
B01ND
LAI
Stem weight
Aboveground biomass
3 B12ND: Mean thermal time between
branches formation (after the first branch)
B12ND
LAI
Stem weight
Aboveground biomass
5 LAXS: Maximum individual leaf size
during the growing period.
LAI
Aboveground biomass
Harvest
6 SLAS: Specific leaf area. Leaf weight
LAI
Aboveground biomass
7 LLIFA: Active leaf duration. Leaf weight
LAI
1 LNSLP: Leaf formation rate. Leaf number
4 NODWT: Individual node weight for the
first stem of the shoot before branching at
3400 ˚Cd.
Leaf weight
Stem weight
Aboveground biomass
Harvest
8 NODLT: Mean internode length (cm) for
the first stem of the shoot before
branching
Plant height
Genetic coefficients
= species + ecotype
+ cultivar
Adjustment of
parameters modifying
cultivar and ecotype.
Genetic coefficients

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Priority Parameter Output variable
1 PARUE: PAR
conversion factor (g dry
matter/MJ)
Leaf weight
Stem weight
Aboveground biomass
Harvest
4 TBLSZ: Base
temperature for leaf
development (˚C)
LAI
2 BRxF: Branch number
per fork at fork x (1-4).
Leaf weight
LAI
Aboveground biomass
3 KCAN: PAR extinction
coefficient
Leaf weight
LAI
Genetic
coefficients =
species + ecotype
+ cultivar
Adjustment of
parameters
modifying cultivar
and ecotype.
Genetic coefficients

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(a) LAI, (b) Total weight
(kg/ha), (c)
aboveground biomass
(kg/ha), (d) yield (kg/ha)
at 282 m (orange), 640
(blue), 1097 m (green)
(Data from Manrique
(1992))
a b
c d
Evaluating the model

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Trigger: Water content < Field capacity
Evaluating the model

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Evaluating the model

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↑ altitude ↓ temperature ↓ leaf area ↑ # branches ↑ shading ↓ leaf duration
Evaluating the model

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Variety: TMS 30572
Location: IITA, Ibadan
Source: Schulthess,1987,
Gutierrez, 1988
The model in Nigeria
Data collected: Individual leaf size,
number of leaves, biomass
partitioning and growth rates.
Part I PhD thesis: Analysis of
cassava growth and development in
Nigeria.

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Variety: TMS 30572
Location: IITA, Ibadan
Source: Schulthess,1987, Gutierrez, 1988
The model in Nigeria
Dry seasonDry season

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Variety: TMS 30572
Location: IITA, Ibadan
Source: Schulthess,1987
The model in Nigeria
Dry season
Dry season

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Variety: TMS 30572
Location: IITA, Ibadan
Source: Schulthess,1987
The model in Nigeria
Dry season
Dry season
Dry season
Dry season
Dry season
Dry season
Different planting dates

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LAI
Importance quality of data
Observed data of LAI and leaf weight do not show similar
tendency although it would be expected.
Leaf weight

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Further work
• Evaluate performance of the model with data collected
on years 1 & 2.
• Challenge: Weather data collection and soil analysis.
• Model is simulating dry matter, what about fresh weight
and dry matter content variability.
• Develop algorithms to represent starch content
dynamics.
• Data workshop: What do we have available for the crop
modeling activity in the ACAI project?

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PhD Objectives Patricia Moreno
1. Understand the dynamics and
mechanisms of modeling the dry
matter distribution and starch
content in cassava and other storage
crops.
2. Determine the relationships between starch
and dry matter accumulation with environmental
and management variables in cassava.

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PhD Objectives Patricia Moreno
3. Develop a module that
simulates the dynamics of dry matter
and starch content for cassava as a
function of environmental variables
and management
4. Identify best management practices for small-
holder farmers in East and West Africa that optimize
dry matter and starch content in cassava

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