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Community
of Practice on
Crop Modeling
Newsletter
1: September'18
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Featuring:
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Welcome!
To
the Crop Modeling CoP
(@CoP_CM)
Dear
Members,
This is the first of many
updates detailing what the Crop
Modeling Community of
Practice is working on
and the progress we are
making. This newsletter also
aims to inform you about
important publications,
events and new resources.
We hope you find this
newsletter useful and that
together we can make our CoP
stronger by sharing ideas
and resources, identify
collaborators and
participate in relevant
discussions.
Enjoy reading,
Matthew
Reynolds
CoP on CM leader
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"Crop
modeling has the potential
to significantly
contribute to global food
and nutrition security"
Predictions of future crop
productivity and consumer
demand, respectively,
indicate that without
unprecedented efforts to
boost crop productivity, the
shortfall in food
availability will result in
higher food prices and
millions more hungry people
in the coming decades.
Multiple approaches must be
employed to improve leverage
of knowledge, expertise, and
data to achieve greater
returns on investment in
agriculture and maintain
global food security under
the added challenges of
climate change, resource
scarcity, growing
populations, and changing
demand.
Crop modeling has
the potential to
significantly contribute
to global food and
nutrition security.
Crop modeling has already
contributed to a better
understanding of crop
performance and yield gaps,
genetic gains, better
prediction of pest and
insect outbreaks, more
efficient irrigation
systems, and optimized
planting dates. Newer
developments include the use
of remote-sensed data and
mobile phone technology
linked to simulation models,
improved geographic
information system (GIS)
techniques, strategic
crossing and selection
models making use of
high-throughput genotyping
and phenotyping to increase
genetic gains, as well as
data sharing and
standardization in the new
era of big data. Renewed
interest in crop modeling
that addresses the
challenges of climate change
has already led to
collaborative initiatives
such as the Agricultural
Model Intercomparison and
Improvement Project
(AgMIP), the CGIAR Platform
for Big Data in
Agriculture (launched
in 2017), as well as other
initiatives for dataset
standardization.
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Using crop
modeling to achieve
strong global
development impacts
depends on the
contribution of a wide
range of stakeholders
including academia,
private companies and
the use of large,
multi-location datasets.
While the expansion of
crop modeling approaches
is still somewhat limited
by the availability of
quality data for
calibration, validation,
and evaluation, recent
progress in the
development of large-scale
agro-ecological datasets
are helping fill the data
gap. One example is the
(Big Data Platform led)
open access, searchable
data harvester GARDIAN (Global
Agriculture Research Data
Innovation and
Acceleration Network),
which enables the
discovery of
agricultural datasets
and publications across
the CGIAR system and
beyond.
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Modeling
innovations can address
increasing concerns on
sustainable food
production, nutrition,
and natural resource
management challenges,
and their consequences
on closely associated
socioeconomic issues
such as conflict,
migration, human health,
and gender inequality.
Achieving these kinds of
objectives will be
facilitated by the CoPs
through linking
different groups,
initiatives and
disciplines involved in
modeling and sharing
data sets, including
through use of tools
such as GARDIAN.
The CoP on CM will also
serve and interact with
other CoPs that are part
of the CGIAR Platform
for Big Data in
Agriculture, including:
- Data-Driven
Agronomy CoP:
aims to
collectively
strengthen the
innovation of
technology and big
data to tackle an
array of
agricultural
challenges –
including the
closing of
yield gaps – to
reduce hunger and
poverty and
transform global
agriculture
- Socio-Economic
Data CoP:
aims at bringing
together CGIAR
centers, academia,
not-for-profit
research and
development
organizations and
private sector
partners willing to
tackle major issues
related to
socio-economic data.
- Geospatial
Data CoP:
facilitates CGIAR’s
research using
geospatial data and
analysis,
undertaking
activities to bring
spatial scientists
together through
coordinated
communication,
community-developed
products, and the
convening of members
at various events to
represent CGIAR in
the geospatial
domain of expertise.
- Ontologies
Data CoP:
supports the
harmonization and
interoperability of
data needed by the
platform and the
CoPs by establishing
best practices,
guidelines, use and
application of
semantics for data
harmonization at the
levels of collection
and storage, for
data
interoperability and
data discovery
following the FAIR
principles.
- Livestock
Data CoP:
aims to drive
informed livestock
decision-making
through the better
use of existing data
and analyses
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AgMIP
Approach to Big Data
and Analytics:
Are
we able to sustainably
produce enough food in
the future under
climate and
socioeconomic change?
The
Agricultural Model
Intercomparison and
Improvement Project
(AgMIP) is a community
of more than 1000
international scientists
that address this
question by combining
multiple climate, crop,
economic, and nutrition
models with vast
harmonized data sets to
explore solution options
under current conditions
and alternate future
scenarios. New data from
genetics, phenotyping,
high-resolution
satellites, drones,
robots, soil sensors,
and crowd-sourcing will
increase our
understanding of
sustainable food systems
and the capabilities of
models that simulate
them. AgMIP’s rigorous
multi-model integrated
projections create
credible new information
for district, national,
and global decisions
around the world.
AgMIP recognizes the
value of massive new
data and analytics, and
embraces the challenges
of harmonizing and
integrating them through
the Community of
Practice on Crop
Modeling as part of the
CGIAR Platform for Big
Data in Agriculture.
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Senthold
Asseng (top) and Jim W.
Jones (bottom), on
behalf of the AgMIP
Executive Committee and
Coordination Unit.
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Graeme
Hammer (The
University of
Queensland,
Brisbane) and
Charlie Messina (Corteva
Agriscience, Iowa)
explain what they
believe a Community
of Practice can
achieve and what is
its value to the
crop modelling
community?
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Graeme
Hammer, The University
of Queensland,
Brisbane, Queensland,
Australia (top); and
Charlie Messina,
Corteva Agriscience,
Johnston, Iowa, USA
(bottom)
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A community of practice on crop
modelling can be
particularly valuable as it
provides potential avenues
for:
- Effective
communication of current
R&D activities
utilising crop modelling
(agronomy, breeding,
phenotyping, etc)
provided it includes
successes and failures
- Sharing
of data sets and
approaches (including
code) relevant to
modelling crop growth
and development
processes. This requires
development of flexible
universal protocols for
interoperability of
systems and data.
- Potential
to establish R&D
collaborations around
specific case studies
for individuals with
shared goals
- Influence
on curricula and
training of graduate
students with diverse
disciplinary backgrounds
in next generation
concepts utilising
modelling and data
science in agronomy and
breeding
Of
course, potential value is
all fine, but real value to
the crop modelling community
can only arise by getting
beyond talk and actually
doing something. Initial
financial support to conduct
workshops/symposia to foster
interaction is a start, but
it must lead to more
targeted and substantive
interactions that attract
sufficient investment to
generate value.
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The importance of
this CoP for the
Platform
Brain
King | Coordinator of the Platform for Big Data in
Agriculture
Crop
Models are a key
link between data
and impact. Indeed,
one of the ways we
talk about our
mission to "big data
enable" the
agriculture
development
enterprise is to
make data "model
ready." Crop
models, something
CGIAR researchers
have developed and
improved for
decades, are at the
analytic heart of
the current wave of
digital
agriculture. A lot
has been learned,
but still there is a
significant
opportunity—and
need—to improve the
global coordination
of crop modeling
efforts in
agricultural
research, which in
turn will greatly
improve the world's
ability to develop
more adaptive,
resilient crops and
cropping systems.
The Community of
Practice on Crop
Modeling, hosted at
the CGIAR Platform
for Big Data in
Agriculture, will
diagnose and address
some of the gaps
that may slow
adoption, will help
avert duplicate
efforts, and strive
to enhance the
impact of
investments in crop
improvement.
Better-coordinated
and more
standardized
approaches to crop
modeling, sharing of
expertise and
resources--and
"model
ready" data--stand
to have a
transformative
effect on digital
agriculture and
agriculture
production overall,
and the CoP on Crop
Modeling is here to
achieve that.
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The
Community of Practice on
Crop Modeling (@CoP_CM)
was created to initiate
a better-coordinated and
more standardized
approach to crop
modeling challenges in
agricultural research to
underpin global food
security. It includes over
200 members
from CGIAR centers,
research institutions,
universities and private
sector and it is open to
new members. Our long-term
goals are:
- Define
and prioritize crop
improvement challenges
- Accelerate
dissemination of new
crop information and
technologies
- Enhance
collaboration among crop
improvement experts
- Increase
visibility and
accessibility of crop
research data
- Develop
overreaching proposals
for fundraising
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We
are starting a database
of Crop Modeling related
papers to share the main
achievements among the
Crop Modeling Community.
To be able to have an
up-to-date data base we
need your support. Do
you have some recent
publications or news you
would like to share
among the CoP?
PLEASE ADD THEM
HERE.
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The
Community of Practice on
Crop Modeling (CoP CM)
started a mini-grant
program in 2017
to provide our CoP members
from CGIAR and external
partners with mini-grants
(10K-20K) to
facilitate the
development of key
activities, tools,
datasets, and model
analysis that can
facilitate CGIAR’s crop
modeling research
and could achieve some
boost/impact and promote
collaborations between
CGIAR centers and
partners.
The first call was sent
out on November 2nd
2017 within the CoP on CM
members and 5 competitive
proposals were received
and evaluated. 3 out of
the 5 proposals were
selected and have been
developing their
activities during 2018:
During
2018, due to funds
availability, two extra
projects were awarded with
the CoP CM mini-grant, one
from the 2017 call, and the
other from the GeoSpatial
CoP call:
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Project
description: The
main goal of this project,
led by Senthold Asseng from
the University of Florida in
partnership with Matthew
Reynolds from CIMMYT, is to
employ the current stage of
the search engine GARDIAN
to search, retrieve and
translate the data that have
been collected at the CGIAR
Centers since their
inception into AgMIP format
field experimental data sets
and then made available to
crop modelers through the
AgMIP-Wheat network. The
specific objective is to
develop an end-to-end case
to show that the data is
Findable, Accessible,
Interoperable and Re-usable
(FAIR). As a case study, the
CIMMYT wheat database for
field data on germplasm with
increased heat stress
tolerance was explored. To
be useful for crop modeling,
such field data need to
include information on crop
management (sowing, N
fertilizer, irrigation, any
effect from pest and
diseases, lodging), soil
(texture, soil organic
carbon, bulk density, any
constraints to root growth,
potential rooting depth, and
initial conditions),
germplasm characteristics
(phenology and yield
characteristics, and
specific traits, especially
those related to heat
tolerance), weather (daily
max and min temperature,
rainfall, solar radiation)
and crop measurements
(yield, yield components,
phenology stages, growth
patterns, soil water
dynamics). This project
started in January 2018 and
finished in June 2018.
Results:
This project highlights
three very different types
of datasets and the steps
involved to locate, access,
and prepare the data for
crop models using AgMIP data
translation tools. Three
datasets were retrieved from
GARDIAN
and evaluated for usefulness
in crop modeling
applications. The usefulness
of GARDIAN as a
user-friendly method of
discovering data was also
validated. A new application
was developed that allows
Excel spreadsheets to be
read directly and relational
connections be automatically
discovered by the
application. This allows the
data manager much greater
flexibility in organization
of the data and also removes
the burden of creating csv
files from the Excel
workbook prior to
translation. The field data
sets have been made
available to the
international crop modeling
community through upload to
the AgMIP Crop Site
Database. The impact of the
proposed project is
awareness to the crop
modeling community of the
CGIAR Big Data Platform and
the FAIR principles of CGIAR
data, and the broader
support and impact of the
CGIAR to science and policy.
The results of the project
can be found here.
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Project
description: With
this project, GEMS team (www.gems.icrisat.org)
and national collaborators
(IIMR) aim to develop
necessary tools to
accelerate production of
post-rainy (rabi) sorghum
yields in India. This will
involve using APSIM_sorghum
as a decision support tool
to design suitable crop and
management interventions for
particular locations and to
optimize rabi sorghum
systems productivity. The
validated modelling set-up
from prior work is being
used and being spatially
refined to suit this
particular purpose. The
model refinement includes
parameterization of the
elite sorghum material into
APSIM. This tool will
allow assessment of
particular GxM fitness to
particular production zones
(E), development of novel
approaches, and its
quantitative advantage over
standard Maldandi cultivar
(M35-1) and standar
management practices. Such
improved set-up will be
further used to design the
site-specific optimal
genotype x management (G×M)
options, which have higher
probability to increase
productivity in specific
regions of rabi-sorghum
production tract in India.
The most relevant of
predicted GxM options will
be tested in multi-location
trials in-vivo to clearly
demonstrate and validate the
tool for practical on-ground
use of such modeling
framework. The project is
planned for three years and
all the data obtained from
the project and
resulting publications will
be available open-source.
More information about the
project can be found here.
Results:
A project update can be
found here.
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Project
description: The
aim of this project, led by
Julian Ramirez-Villegas from
CIAT in partnership with
Andy Challinor, from the
University of Leeds, is to
identify heat stress
environments for bean
breeding in Colombia using
crop-climate modeling. More
specifically, they will
implement the Target Population
of Environments (TPEs)
methodology for key
vulnerable areas in
Colombia. The TPE analysis
is supported by modeling
abiotic stress under current
and future climates, itself
underpinned by data held at
CIAT. Outputs of the
project include a breeding
strategy that will
ultimately make the CIAT
bean breeding program more
targeted and efficient
for abiotic stress
tolerance, which will
provide wider societal
benefits for the Colombian
and other agricultural
economies. Success in this
project will provide an
exemplary case of how
breeders and modellers can
develop targeted work within
one of the Modelling CoP
areas, thus inspiring other
teams to do the same. The
project started in April
2018 and is planned for
one year. Do not miss the news’
entry about this
project published in our
website.
Results:
So far, already-existing
data for model calibration
and evaluation have been
collected and organized for
use in the bean model. The
SAB686 variety has been
calibrated using the genetic
Algorithm (GA) methodology,
and Sum square error (SSE)
and Distances as function to
minimize. A preliminary
coefficients set were got
for SAB686 and some
sensitivity analysis,
calibration and evaluation
have been performed. Results
so far suggest that
phenology variables have a
better fit than the growth
variables. Moreover,
well-watered situations are
better simulated than
drought-stressed situations.
Extreme heat conditions are
generally simulated poorly.
Ramirez and collaborators
are currently reviewing
inputs for the model in
order to improve some
variables (weather, soil,
and experimental
information) that could
contribute a better fit for
the model and improve the
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Project
description: This
project, led by Timothy J.
Krupnik from CIMMYT
Bangladesh in partnership
with Jose Mauricio
Fernandes, from the
University of Passo Fundo in
Brazil, aims to develop an
early warning system (EWS)
for wheat blast outbreaks
applicable to Bangladesh,
Brazil, and beyond using
linked crop and disease
models and numerical weather
forecasting. Wheat blast,
caused by the fungus Magnaporthe
oryzae pathotype
Triticum (MoT), is one of
the most problematic wheat
diseases globally, and has
spread across South America
and is now found in South
Asia, resulting in
significant yield losses
undermining food security.
Because wheat is most
susceptible to MoT near
flowering, accurate
phenological prediction is
crucial. EWSs should
therefore support estimates
for flowering dates in order
to increase the
risk-prediction accuracy of
the model. This project will
increase flowering
predictability by
integrating the Decision
Support System for
Technology Transfer (DSSAT)
model to the MoT EWS
framework for Bangladesh and
Brazil, thereby allowing
improved risk assessments.
This work supplements the
CIMMYT-led ‘Climate Services
for Resilient Development
(CSRD)’ and ‘Mitigate Wheat
Blast’ projects (both USAID
funded) to adapt an existing
wheat blast forecasting
model to South Asian
climatic conditions, and to
train extension services in
its use. This activity also
provides the basis for
further regional modeling of
wheat blast risks using
historical climate data and
climate change models
combined with scenario
analysis. These activities
will result in both decision
support tools and advisories
to assist in rational and
integrated disease
management in South Asia.
The project started in July
2018 and will finish by the
end of 2019.
Results:
Krupnik and collaborators
are now actively assembling
data from experiments and
collecting all genetic
coefficients of wheat
varieties to feed into
modeling efforts. That
activity will be done within
the next 1-2 months, and
they will then move into
virtual experimentation.
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description: Agricultural
modelling of crops,
livestock and households is
widely carried out in CGIAR,
for a wide variety of
purposes, including
evaluating different options
in time and space in a way
that can add massive value
to the field-based
activities that CGAIR
centres undertake. WorldClim
v1 has been used very widely
in CGIAR’s (and other
organisations’) modelling
efforts, as has MarkSim,
software that basically
allows the user to move from
climate data for any
location to weather data for
the same location that are
characteristic of the
particular climate,
including future climates as
generated by successive
generations of IPCC climate
modelling activities. Now
that WorldClim has been
updated with new data, this
is an excellent time to
update the various
MarkSimGCM tools that can be
used by the general user,
and to provide a set of
climate grids that can be
used directly by modellers
who do their own
programming. The proposal
will build on the
recently-released version 2
of WorldClim, which updates
the previous version of the
dataset and corrects several
errors. WorldClim2 provides
gridded climate data by
month for the period
1970-2000 for several
variables (max and min temp,
average temp, precipitation,
solar radiation, wind speed
and water vapor pressure) at
four resolutions (10, 5 and
2.5 minutes and 30 seconds).
This project will (1)
Increase the functionality
of WorldClim2 by developing
grid files that include
variables by location, as
well as providing the basis
for deriving a key variable
not currently included,
number of rain days per
month; (2) For one kind
of user, reformat WorldClim
data into a format that is
of direct use to crop and
other modellers; (3) For
users who do not do their
own programming, upgrade
MarkSimGCM to work from the
WorldClim2 data set,
providing weather files that
can be used directly in crop
modelling software such as
DSSAT and APSIM. The project
started in July 2018 and
will be finish in October
2018. |
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One
of the main objectives of
the CoP on Crop Modeling
was to conduct a CGIAR
wide gap analysis to
determine the strengths
and weaknesses of each
CGIAR center in terms of
modeling capacity, skills
and expertize, and to
determine how modeling can
support other research and
outreach activities at the
CGIAR centers. With this
analysis, we aim to
identify:
- Different
models, tools and
databases used for each
center.
- Main
modeling goals within
each area
- Main
modeling inputs in each
area
- Necessities
(tools, methods, access
to databases, funding,
needs for
infrastructural
support…) to achieve
desired goals
With
the results from this
analysis the CM CoP will
initiate a plan for
sharing the expertise
among CGIAR centers and
collaborators via
workshops or on-line
tools.
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The
CoP CM decided in
2017 to elaborate
four review articles
documenting modeling
activities and
potential impacts
from CGIAR centers
and partners under
each of the 4
drivers of GEMS:
- Breeding
(G)
- Environment
(E)
- Crop
Management (M)
- Policy/Socio-Economic
(S)
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The
reviews will showcase the
value and potential impact
of modeling/quantitative
approaches in crop
research at CG centers.
The reviews will also help
to identify:
- Strengths
and gaps across CGIAR
centers in terms of
quantitative methods,
tools and expertize.
- Modelling
skills, expertize, and
data bases that could be
shared among CG centers.
- Opportunities
to share best practices
through face-face
training, webinars and
other online tools.
- Precedents
in modeling on which
decision support tools
can be based.
- Near
wins in developing
quantitative approaches
for research and farmer
friendly decision
support tools.
- Improvement
in understanding big
picture crop challenges,
for example sensitivity
of crops to night
temperature and factors
that may interact with
it and through
comparative biology
where functional models
in one crop can be used
to identify and help
fill knowledge gaps in
other species, etc.
Over
50 people from different
CGIAR centers and
collaborators were
contacted via email during
2017 to ask for their
willingness to participate
in the reviews. The
response was very
positive, and we were able
to identify main leaders
from 7 different CGIAR
centers that agreed to
share their modeling
activities, to participate
in the reviews and to
collect the modeling
activities from their
centers and collaborators.
In the following table
there is a list of the
leaders from each center
and the main leader of
each of the reviews (in
bold):
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| The first draft of the Policy and
Socio-Economic modeling
activities is already
finished and has been shared
with the main leader, Gideon
Kruseman. We are currently
working in finishing a first
draft of the remaining 3
reviews, which will be
shared with the respective
main leaders in September
and October. These 4 reviews
will be published in a
special issue in Crop
Science during 2019.
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Platform for Big
Data in Agriculture
Convention 2018
3-5
Oct , Nairobi, Kenya
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During the convention,
the CoP on Crop
Modeling will lead two
discussion sessions about
the “Gaps, strengths and
ways of sharing expertise
among the Crop Modeling
Community”.
Based on different CoP Crop
Modeling activities, some
specific strengths and gaps
have been identified in in
the areas of
Breeding, Environment, Crop
Management and
Socio-Economics/Policy.
During the CoP sessions,
some of the strengths
identified in some of these
areas will be presented and
discussed in terms of how to
share the expertise among
the Crop Modeling
Community. Examples of
specific ongoing activities
will be presented.
If you have some suggestions
and/or questions about the
sessions, please send them
by pressing here.
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We
hope to see you in Nairobi
to have a productive CoP
session and being able to
move forward on our
objectives.
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One
of the CoP objectives is
sharing knowledge and
working together to
improve the global
coordination of crop
modeling efforts in
agricultural research. If
you have some suggestions,
needs, ideas, resources
you would like to share
with the community of
practice, please send them
here.
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Platform for Big data in
Agriculture, All rights
reserved.
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