Presentation during final project workshop in Hanoi, Vietnam. A summary of the main results for the GCFSI-funded project on cassava modelling is presented.

1. Summary of project results
Julian Ramirez-Villegas, Jonatan Soto, Daniel Amariles, Patricia
Moreno, Tin M. Aye, Myles Fisher, James Cock
Toward an Improved Cassava Simulation Model to Aid Management Decisions in the Tropics

2. Cassava is suitable to be grown across the global tropics
Ceballos et al. (2011)

3. 10
t/ha
19
t/ha12
t/ha
Latin America Africa Asia
FAO (2016)
Cassava yield is low in most areas where it is grown

4. Towards an improved cassava simulation model to aid
management decisions in the tropics
Objective
To improve the existing DSSAT Cassava simulation model using existing
literature data, and new data from field experiments in Vietnam so that
management recommendations can be simulated, tailored to SEA
conditions

5. Four major activities
1. Design of non-destructive methodology
2. Recovery of existing trial data
3. New field trials
4. Development of model functions and coding
5. Model testing

6. 1. Design and implementation of
sampling strategy
• Completely non-destructive, easy to implement, and cost-
effective
• Measures phyllochron, leaf longevity, leaf area, canopy
cover (%), LAI, stem diameter and length, and overall plant
structure
• Adaptable to high-intensity and low-intensity sampling
situations

7. 2. Recovery of existing trial data
• Hundreds of experiments in various countries
• Little systematic recording of them, so we know little about which are
useful for modelling and which are not

8. 2. Recovery of existing trial data

9. Why recover old data?
Data from Irikura et al. (1979)

10. CIAT HQ experiment for varieties evaluation
 To evaluate the growth and development of three cassava
varieties (MCOL22; CM523-7; MPER183) under normal
environmental conditions and management.
 To evaluate the water dynamics in these trials.
 Calibrate and evaluate the model for these varieties.
Leaf area measurement with ImageJ
WHY?
3. New field trials: Colombia

11. 3. New field trials: Vietnam
KM21-12
KM94
SM937-26
KM60

12. Vietnam field trials
Trt. ID Trt. name
N
(kg ha1
)
P
(kg ha1
)
K
(kg ha1
)
Level of
detail
1 N0P0K0 0 0 0 High
2 N0P2K2 0 40 80 High
3 N1P2K2 40 40 80 Low
4 N2P2K2 80 40 80 High
5 N3P2K2 160 40 80 Low
6 N2P0K2 80 0 80 High
7 N2P1K2 80 20 80 Low
8 N2P3K2 80 80 80 Low
9 N2P2K0 80 40 0 High
10 N2P2K1 80 40 40 Low
11 N2P2K3 80 40 160 Low
12 N3P3K3 160 80 160 High
Non – Destructive methodology
• It provides a means of development of cassava under different
biophysical conditions.
• No needs big plots and allows to follow up the development of the
same plants.
• To generate, validate and improve the robustness of simulation
cassava models.
Level of detail Variables to measure
High All (fallen tags, branching, length,
diameter, etc)
Low Fallen tags and leaf area

13. What kinds of comparisons are we interested in?
• Overall response to the 3 nutrients
N0P0K0 (T1)
N2P2K2 (T4)
N3P3K3 (T12)
• Response to N
N0P2K2 (T2) N2P2K2 (T4)
N1P2K2 (T3) N3P2K2 (T5)
• Response to P
N2P0K2 (T6) N2P2K2 (T4)
N2P1K2 (T7) N2P3K2 (T8)
• Response to K
N2P2K0 (T9) N2P2K2 (T4)
N2P2K1 (T10) N2P2K3 (T11)
We can investigate paired responses
(e.g. N0 vs. N1) with other nutrients
held constant, or can investigate
response curves (using up to 4
treatments).

14. Results – Yen Bai
Leaf area (cm2)
N0P2K2 N2P2K2
Instead of having yellowing leaves due to N, cassava lowers leaf area
maintaining leaf N concentration

15. Results – Dong Nai

16. 4. The MANIHOT model –overview
1. Built within DSSAT –commonly used suite of models
2. The nodal unit: node, internode, bud that develops into
leaf, and axillary bud that may develop into new branch
3. Uses cohorts of nodal units of the same age, so that
many branches can be considered simultaneously.
4. Two types of roots: feeder and storage
5. Assimilation through Radiation-Use-Efficiency approach
6. Accounts for water, nitrogen, and air humidity
responses on growth and development
7. Spill-over model: Potential growth of aboveground
organs is compared with available carbohydrate pool.
Surplus goes to storage roots.

17. 4. The MANIHOT model (progress)

18. 4. Other model-related
achievements
• Current version of DSSAT (v4.7)
contains latest version of model
• Positioned group as main
developers for cassava model
• Explored collaboration with
Poram’s group at KKU
• PhD student with Embrapa in
Brazil
• Participated in 3 DSSAT
development ”sprints”

19. Model testing against observations
(temperature response)
• Data from Manrique (1992)
• Mt. Haleakala, Maui (Hawai), at 3 elevations
• Cultivar: Ceiba
282 m
640 m
1047 m
282 m
640 m
1047 m
Moreno et al. (2018)

20. Model applications
• Identify areas with high difference between potential
and farmer yield
• Identify most suitable cultivars at site-specific level
• Determine optimum planting dates to maximize yield
• Optimum levels and timing of N application to achieve
maximum yield
• If any negative effect of water stress exist, assess
strategies to reduce them
Models are useful to answer “what if” questions

21. Future directions
• For cassava in Asia, response to K is
important, as it determines starch
content.
• Starch content response to climate
and management need to be
investigated and implemented in the
model
• We know little about response to
high [CO2], important if we want to
look at climate change
• Connecting to pest/disease models is
also important in SEA
3.5 months of growth
Rosenthal et al. (2012)

22. Thank you!