This document summarizes efforts to use mathematical modeling and machine learning algorithms on large genetic and environmental datasets to identify plant traits that could help crops better tolerate future climate conditions. Researchers are analyzing over 7 million plant genetic resource records and climate data to search for relationships between environmental factors and adaptive traits. Successful examples are presented of identifying heat tolerant barley varieties, salt tolerant wheat, and drought tolerant faba bean. A global open-access platform has also been launched to facilitate broader assessment of genetic resources for climate change adaptation.

1. Capturing and understanding
patterns in plant genetic
resources data to help develop
“climate-proof” crops
R platform
The R User Conference 2016
June 27 - June 30 2016
Stanford University, Stanford, California

2. Source: Millennium Ecosystem Assessment (2005)
http://oceanworld.tamu.edu/resources/environment-
book/Images/drylandmap.jpg
Visited October 21, 2013
GCMs all converge with regard to
projections of:
Increased frequency of drought, and
high temperatures
In
 central North America,
 northern Africa,
 central Asia, and
 western Australia
(Girvetz et al. 2009, Elert & Lemonick 2011)
Climate Change
Global Climate Models’ projections

3. Climate Change - shift
GHG emissions -> heating up of low
atmosphere
(Mendelsohn & Dinar 2009)
Heat stress will increase vulnerability
of crops ..more than drought.
(Semenov & Shewry 2011)
sShift
This will require to aim for yields /environmental adaptation
in unprecedented/different circumstances!

4. 0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
NUMBER OF ACCESSIONS
Source: The CGIAR Genebanks - Seeds for Life (2006)
Background
Genetic Resources / Biodiversity
• More than 7 million genetic resources
accessions (seed plants)
• More than 1400 gene banks world wide
• Cost/search implications1,2
-----------------Koo B, Wright BD (2000) The optimal timing of evaluation of
genebank accessions and the effects of biotechnology.
Am J Agric Econ 82:797–811
Gollin D, Smale M, Skovmand B (2000) Searching an ex situ
collection of wheat genetic resources.
Am J Agric Econ 82:812–827
1
2

5. Presence of patterns -----> quantification and predictions
Dependency between Environment and the trait (Envt, Trait) ->
prediction (unknown)
Assessing genetic resources /Agro-Biodiversity for CC traits
Exploring patterns - Modelling/predictions
Bayes – Laplace approach (inverse probability)
Learning based approach (risk minimization)
Environment
(tmin, tmax, prec)
Trait -grain filling period (gfp)
(probability of occurrence)
Bari et al. (2016). In silico evaluation of plant genetic resources to search for traits for adaptation to climate change. Climatic Change 134(4) 667-680.
http://dx.doi.org/10.1007/s10584-015-1541-9
-----------------

6. Genetic
Resources
(data)
Platform
Conceptual
frameworks
∅(𝒙)
Mathematics
along with
farmers’ insights
factored in the
process
MitigationAdaptation
Tolerance to
heat, drought,
salinity and low
inputs
Merge and integrate data
for a more comprehensive
procedure
GHGs elimination, namely
carbon dioxide (CO2
sequestration and
methane (CH4)
R platform – practical CC solutions
Large data sets including
Canadian climate centre data
and UN FAO data
FAO

7. Geographical
Information System
(GIS)
Environmental data/layers
(surfaces)
R language
(Development of algorithms)
> Data transformation ()
> Model <- model(trait ~ climate)
> Measuring accuracy metrics
> ….
R Platform – data integration and analysis
Modeling purpose Generation of
environmental data
Algorithms :
to search for dependency, if it exists!
Climate data
to generate surface (CC)

8. R Platform – data integration and analysis
UN Food and Agriculture OrganisationCanadian Centre for Climate
Climatic data extracted from
current and future climate
scenarios
FAO Database (30 arc-
second raster database)
Searching for climate change related traits in plant genetic resources collections
http://om.ciheam.org/om/pdf/a110/00007061.pdf
Data extraction, integration and preparation (transformation) under R
Organisation, community or company
names or trademarks are referred to for
identification purpose!.

9. Support Vector Machines (SVM)
Random Forest (RF)
Neural Network (NN)
x1
x2
x
p

F(
x)
R Platform – data analysis and predictions
AUC curve
0.00
0.25
0.50
0.75
1.00
0.00 0.25 0.50 0.75 1.00
False Positive Rate
TruePositiveRate
A. Bari, A.B. Damania, M. Mackay and S. Dayanandan (Eds.). Applied Mathematics and Omics
to Assess Crop Genetic Resources for Climate Change Adaptive Traits. CRC Press, Taylor &
Francis Group, Boca Raton, FL, USA. ISBN 9781498730136. .
https://www.routledge.com/products/9781498730136

10. Modelling/predictions
Capturing the shift induced by climate - verification
0 100 200 300
020406080
x$x
x$ysmth
Data alignment to
growing season
Algorithms
Separate phase variation
from amplitude variation
0 100 200 300
50100150200
x$x
x$ysmth
Site (i) : Si(xi, yi) Site (j): Sj(xj, yj)
day
rainfall
day

11. The ROC curve and the resulting and trait distribution (trait states)
1
1 
1-

ROC curve trait distribution

Parameters that provide information on the specificity
(“trait agro-climate”) ..
High AUC (area) values indication of potential trait-environment relationship
Presence of patterns –
Accuracy metrics
0.0 0.2 0.4 0.6 0.8 1.0
01234
Probability predictions of
resistance to Stripe rust
in wheat
Predicted probability
Density

12. Barley plants grown to confirm math predictions vis a vis tolerance to heat (plant
canopy temperature lower than air temperature)
-8 -6 -4 -2 0 2 4 6
-8 -6 -4 -2 0 2 4 6
15
10
5
0
15
10
5
0
Plants predicted (in silico)
to sustain heat
Plants selected at random
(purposive sampling)
Temperature (TPlant – TAir)
Temperature (TPlant – TAir)
Numberofplants
Jilal/INRAMorocco
Modelling/predictions
Applied to assess barley genetic resources for heat traits traits)
Long-sought-for and different traits of tolerance to heat have
been found !

13. Salt-tolerant varieties/genotypes
are also sought-for as sea level
rises.
Screening durum wheat for salt tolerance using imaging techniques - Tunisia
overthepast30years
R based Imaging techniques have been used
to capture root architectural traits vis-à-vis
tolerance to salinity.
Durum wheat
Modelling/predictions
Applied to assess wheat genetic resources for salinity traits (root traits)

14. Faba bean is a valuable source of protein
grown mostly prone to climate change
effects.
Its diversity is limited as it has no wild or
close relatives to help broaden its genetic
base.
Recent assessment of accessions held in
gene banks by University of Helsinki
yielded promising results in terms of
tolerance to drought.
Screening for drought tolerance in faba bean
(earliness in right found among accessions) -
Helsinki
Stoddard/UniversityofHelsinki
pbs.org
Modelling/predictions
Applied to assess faba bean genetic resources for drought tolerance traits
(root traits)

15. Global Platform launched to assess genetic resources for
Climate Change related genes/traits
A. Bari, Y.P. Chaubey, M.J. Sillanpää, F.L. Stoddard, H. khazaei, S. Dayanandan, A.B. Damania, , S.B. Alaoui, H.
Ouabbou, A. Jilal, M. Maatougui, M. Nachit, R. Chaabane, Z. Kehel and M. Mackay
http://www.dataorigin.net//