Presentation at Khon Kaen University on modelling efforts for cassava at the global and regional (Asia) scale.

1. Julian Ramirez-Villegas
Tin M. Aye
Looking forward to the next
5 years of cassava
modelling

2. Provides critical research support and scientific
guidance on the development and application of
agricultural and climate models using cutting-edge
science and approaches
Agriculture and climate modelling team
Crop modelling (30 / 70)
Agro-climatic modelling (20 / 80)
Socio-economic modelling and ex-
ante impact assessment (20 / 80)
Team composition
• 16 members
• 2 PhD level scientists
(co-leaders)
• 3 MSc level research
associates
• 11 BSc level research
assistants

3. Adaptation across timescales
= F(t)
Ramirez-Villegas and Khoury (2013) Climatic Change

4. Using seasonal forecasts
for addressing climate
variability impacts

5. Generating agroclimatic seasonal forecast for rice
productive regions in Colombia
How do we do it?
Establish agro-climatic forecasts using seasonal climate prediction models and crop
models (mechanistic models) to inform when and what to plant?

6. What do farmers need to know?
Agroclimatic forecast: Cordoba case study
Identify the most appropriate
planting date (with best
environmental supply) for rice crop
in the period May - Dec 2014.
Actions
to implement
Implement seasonal weather forecasts
+ mechanistic crop models
0
5000
10000
15000
115 165 215
Biomass(kg/ha)
Day in year
Projected crop performance to
future climate conditions

7. Monteria (May – Dec)
Pronóstico de precipitación
Mayo – Diciembre de 2014
Precipitación(mm)
Decreased monthly rainfall
Increased monthly temperatures and solar
radiation
Fecha de siembra
5 May 25 May 19 Jun 14 Jul 08 Aug
Rendimiento(kg/ha)
3500
4000
4500
Select the best planting date,
as a preventive measure.
If farmers make the decision to plant by
June 20, the yield obtained can be around
4500 kg/ha.
If the crop sowings are delayed, yields will
decrease.
With this measure:
 Great economic losses to 170 rice farmers
were avoided.
 1,800 hectares of rice were saved from being
destroyed by the intense drought.

8. Automated web interface to generate and communicate
forecasts at local scales
• Funded by USAID, under the
Climate Services for Resilient
Development (CSRD)
programme.
• Farmer focus groups to
understand information and
format requirements.
• Intense web development
and automation of crop-
climate model runs
• Beta version in Dec (!), and
official release in April 2017

9. Cassava modelling work

10. Cassava in Southeast Asia and the world
• Second most important food crop in the least-developed countries (LDCs),
and the fourth most important in developing countries, with total
production (218 MT) (RTB, 2016)
• ~8 million farmers growing cassava in Southeast Asia
More than 3 million farmers in Greater Mekong Subregion (Myanmar unknown)
Another 1.5 million households in southern China
Another 3 million households in Indonesia
• ~4 million ha
More than 1 million ha in each of Thailand and Indonesia
>500,000 ha in Vietnam
~ 500,000 ha in China
~ 350,000 ha in Cambodia
• > US $3.5 billion / year in GMS
Thailand: industry ~US$ 1.5 billion
Eco-Efficient Agriculture for the Poor

11. 10
t/ha
19
t/ha12
t/ha
Latin America Africa Asia

12. CIAT cassava modelling activities
1. Suitability modelling (2008-present) using niche-based
models (EcoCrop)
2. Process-based modelling (2011-present): develop and
maintain cassava model in DSSAT.
3. Opportunities and knowledge gaps
1. Modelling of pests: greenmite, mealybug, whitefly
2. Climate change effects

13. Current suitability
Growing season (days) 240 Killing temperature (°C) 0
Minimum absolute
temperature (°C)
15.0
Minimum optimum
temperature (°C)
22.0
Maximum optimum
temperature (°C)
32.0
Maximum absolute
temperature (°C)
45.0
Minimum absolute
rainfall (mm)
300
Minimum optimum
rainfall (mm)
800
Maximum optimum
rainfall (mm)
2200
Maximum absolute
rainfall (mm)
2800
Ceballos et al. (2011)

14. Future suitability and change (2020s)
Ceballos et al. (2011)

15. Why a process-based approach?
 Cassava does not have a robust model (Legg et al., 2014)
 Short list of models describing the basic processes of growth
and development (Cock et al. 1979; Manrique 1992; Matthews & Hunt 1994; Gray 2000,2003; Mithra et
al.2013; Gabriel et al. 2014; Gutierrez et al. 1988).
 Models answer what if? questions  Ex-ante evaluation of
technologies and/or environmental conditions expensive or
impossible to evaluate (Cock & Moreno, 2013).

16. A brief history of our undertaking
 2011: Established a ”loose” consortium of cassava modellers, involving
CIAT, KKU, CSIRO, and DSSAT foundation.
 2012: Started model improvement with CCAFS support, but realised the
amount of work to be done was larger than expected
 2013-2014: Min. Agriculture Colombia supported some modelling
activities, but most importantly the development of the sampling
methodology
 2015: No formal support for cassava modelling activities, but continued
with small CCAFS funding
 2016: Secured support from MSU (USAID-funded GCFSI), and Gates
Foundation (IITA-led project)

17. Four major components
Design of non-destructive methodology
Recovery of existing trial data
New field trials
Development of model functions and coding
Model calibration and testing

18. 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

19. 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

20. Recovery of existing trial data

21. • Two trials in Vietnam (Yen Bai
and Dong Nai)
• 12 fertiliser treatments
• 2 varieties
• Measuring growth and
development non-destructively
• 1 trial in Colombia (Palmira)
• Lysimeter for water balance
• 3 varieties
• 100+ trials in Nigeria and
Tanzania
New field trials

22. Development of model functions and
coding: tapping into existing
knowledge
•Creation of “flow document”,
documenting more than 85 model
processes (and growing)
•Creation of development backlog
•Development scheduling
•Standard tests for ensuring progress
is being made
Representation of cohorts

23. Model development

24. Progress so far…
 From phenological phases to
an indeterminate crop
 Node cohorts (leaf and stem
growth, leaf life)
 Different Tbase for leaf
development and branching
 Spillover model
 VPD effects on RUE
 N distribution component to
deal with low fertility soils
Leaf appearance Branching
Leaf size
Moreno et al. (unpublished)

27. Opportunities and knowledge gaps
• Pests and diseases: early warning and forecast
system for P&D management
* Dry season data only Data: K. Wyckhuys

28. P. manihoti
Potential distribution of cassava mealybug
Parsa et al. (2012) PLoS ONE

29. M. tanajoa
M. mcgregori
Potential distribution of cassava greenmite
Parsa et al. (2015) Exp. Appl. Acarol.; Herrera Campo et al. (2011) Food Sec.

30. Towards a mechanistic simulation of
crop-pest-management interactions
Gutierrez et al. (1988)

31. Nothing new… simulating pest dynamics in
1982-1984 experiments in Nigeria
Mealybug with
biological control
Gutierrez et al. (1988)
Uncontrolled
mealybug
Root
weight
Mealybug
number
Coccinellid
A. lopezi
The knowledge is
there, but we need to
tap into it and put it at
work
But need data to test
modelling approach

32. • Group spatio-temporal variations in pest dynamics and
yield impacts to prioritise interventions and breeding
efforts (cf. Target Population of Environments)
• Develop management recommendations (e.g. best
planting periods, N application) to reduce pest damage…
and estimate tradeoffs.
• Early warning systems –for timely release of parasitoids, or
other type of control.
• Understand crop & associated pest expansion patterns
What can we do with this type of
approach?

33. Opportunities and knowledge gaps
• Climate change: where (niche) and how much (productivity)?
Rippke; Ramirez-Villegas et al. (2016) Nat. Clim. Chang.
Cassava is a fallback option under climate change for many farmers in
the developing world – is it?

34. Knowledge gaps
• We know little about yield response
under future climates, particularly under
varied management assumptions
• We know little about response to high
[CO2]
• We know little about starch content
response, and changes in postharvest
attributes
3.5 months of growth
Areas for research & development
• Identify promising growing areas for high starch content
• Identify climate-adapted varieties (advantage under high [CO2]
atmosphere) * management for promoting cassava to improve food
security
Rosenthal et al. (2012)

35. Julian Ramirez-Villegas
j.r.villegas@cgiar.org
Tin M. Aye
t.aye@cgiar.org
Thanks