Ensuring Technical Readiness For Copilot in Microsoft 365
Cwr at eucarpia
1. Neil Palmer/CIAT
Wild relatives in prebreeding: Fishing the
genepool with dynamite
Hannes Dempewolf
Global Crop Diversity Trust
2. Human population growth
2.5 4.1 6.1 8.0 9.2
1950 1975 2000 2025 2050
Source: United Nations. 2007. World Population Prospects: The 2006 Revision.
3. Impact of humans on the planet
Crop and forage species now cover roughly half of the Earth's land surface (Kareiva et al. 2010)
4. Impact of humans on the planet
Crop and forage species now cover roughly half of the Earth's land surface (Kareiva et al. 2010)
5. Likelihood (in percent) that the summer average temperature in 2050 will
exceed the highest summer temperature ever observed (1900-2006).
Source: Battisti, D.S., and R.L. Naylor. 2009. Historical warnings of future food insecurity with unprecedented seasonal heat.
Science, 323, 240-244.
Impact of climate change on food security
6. Likelihood (in percent) that the summer average temperature in 2090 will
exceed the highest summer temperature ever observed (1900-2006).
Source: Battisti, D.S., and R.L. Naylor. 2009. Historical warnings of future food insecurity with unprecedented seasonal heat.
Science, 323, 240-244.
Impact of climate change on food security
7. Climate change and crop yields
Source: Schlenker W and Lobell D. B. 2010. Robust negative impacts of climate change on African agriculture. Environmental Research Letters 5, no. 1: 014010.
Predicted changes in total production (per cent) in SSA from climate change in 2046–2065
relative to 1961–2000. The median predicted impact is shown as solid line, while the box
shows the 25–75 percentile range. Whiskers extend to the 5 and 95 percentile
8. Climate change is threating CWR
populations in the wild
Jarvis et al 2008
10. But why CWR?
“When the crop you live by is
threatened you will turn to any
source of relief you can find. In
most cases, it is the wild
relatives that salvage the
situation, and we can point very
specifically to several examples
in which genes from wild
relatives stand between man
and starvation or economic
ruin.”
- Jack Harlan
12. Need to show impacts
Disease Resistance from CWR
• Rice Grassy Stunt Virus (RGSV) was a major disease problem in
Southeast Asia (especially in Indonesia) in the 1970s
• Resistance was found in India in several populations of a wild
relative of rice, O. nivarra.
• Resistance gene was bred in the 1980s into major varieties
released by IRRI, resulting in hundreds of released varieties across
many rice growing areas.
• Resistance is used in millions of hectares in South and Southeast
Asia and has reduced the occurrence of RGSV to minimal levels.
We need sound studies to measure the precise economic impact.
13. Numbers will likely increase with time, as value of crops
increases and technological advances allow us to use CWRs
more effectively.
Economic valuation of PGR
Study Parameters Figure
(US$)
Witt 1985 Annual benefits from disease
resistance introgressed from
wild wheat species
$50 million
Prescott-Allen
and Prescott-
Allen 1986
Annual contributions of CWR
to US economy from
domestic and imported
sources
$340 million
Iltis 1988 Annual contribution of genes
from Lycopersicon
chmielewskii
$8 million
Pimentel 1997 Annual contributions of CWR
to US economy
$20 billion
Annual contributions of CWR
to world economy
$115 billion
Hein and
Gatzweiler 2006
Net present value of wild
coffee genetic resources
$1.458
billion
14. Fishing with dynamite
Dynamite fishing
Pros:
• Large catch possible
Cons:
• Extremely destructive
• By-catch
Pre-breeding using CWRs
Pros:
• Major trait alterations possible
Cons:
• Can severely alter phenotype
• Linkage-drag
Or perhaps a better analogy:
“It's a bit like crossing a house cat with a
wildcat. You don't automatically get a big docile
pussycat. What you get is a lot of wildness that
you probably don't want lying on your sofa.”
15.
16. CWR initiative
• Identify, collect, conserve, document and use key crop wild
relative diversity for climate change adaptation (in developing
countries)
• $50 million over 10 years pledged by Norwegian government,
starting 2011
Species Common name
Avena sativa Oat
Cajanus cajan Pigeonpea
Cicer arietinum Chickpea
Daucus carota Carrot
Eleusine coracana Finger millet
Helianthus annuus Sunflower
Hordeum vulgare Barley
Ipomoea batatas Sweet potato
Lathyrus sativus Grass pea/Common chickling
Lens culinaris Lentil
Malus domestica Apple
Medicago sativa Alfalfa/Lucerne
Musa acuminata Cavendish banana
Musa balbisiana Guangdong plantain
Species Common name
Oryza glaberrima African rice
Oryza sativa Rice
Pennisetum glaucum Pearl millet
Phaseolus lunatus Butter bean/Lima bean
Phaseolus vulgaris Garden bean
Pisum sativum Garden pea
Secale cereale Rye
Solanum melongena Eggplant/Aubergine
Solanum tuberosum Potato
Sorghum bicolor Sorghum
Triticum aestivum Bread wheat
Vicia faba Faba bean
Vicia sativa Common vetch
Vigna subterranea Bambara groundnut
Vigna unguiculata Cowpea
19. Gap analysis
Determine gaps
in collections
Model
distributions
Gather
taxonomic data
Gather
occurrence
data
Make collecting
recommendations
Georeferencing
Source: concept and images from Jarvis et al. 2009. Value of a Coordinate: geographic analysis of agricultural biodiversity. Presentation for Biodiversity
Information Standards (TDWG), November 2009.
22. Expert consultations
• There is generally a lot of interest and excitement about the use of
wild relatives amongst many breeders
• There is a concern that in current funding schemes pre-breeding
falls through the cracks
• Each crop requires a specific approach, considering:
• Specific life-history traits of the crop
• Crop-specific breeding goals
• Capacity and „level of advancement‟ of community of breeders
• Often little knowledge about trait characteristics of CWRs
(challenge of phenotyping wild relatives (need to develop effective
screening methods))
• Interest of private sector in some crops
23. Pre-breeding options
• First evaluate CWRs, then pick most promising genotypes and use in
pre-breeding with cultivated lines, evaluate again
• Assess genetic diversity of accessions, pick set of diverse CWR
genotypes and cross with cultivars, create BCs and RILs and
evaluate
• QTL (and MAS) approaches
• Candidate gene approaches and allele mining in CWRs
24. Image by:Neil Palmer/CIAT
Meeting Participants/Signatories: Susan McCouch, Loren H. Rieseberg, Scott Jackson, Edward Buckler, Hannes Dempewolf,
Luigi Guarino, Gregory J. Baute, James Bradeen, Paula Bramel, Peter K. Bretting, John Burke, David Charest, Sylvie Cloutier,
Glenn Cole, Michael Dingkuhn, Catherine Feuillet, Paul Gepts, Dario Grattapaglia, Sandra Knapp, Peter Langridge, Amy
Lawton-Rauh, Qui Lijua, Charlotte Lusty, Jonathan P. Lynch, Todd Michael, Sean Myles, Ken Naito, Randall L. Nelson, Reno
Pontaroll, Christopher M. Richards, Jeffrey Ross-Ibarra, Steve Rounsley, Ruaraidh Sackville Hamilton, Ulrich Schurr, Ruth
Shaw, Nils Stein, Norihiko Tomooka, Esther van der Knaap, David van Tassel, Jane Toll, Jose Valls, Rajeev Varshney, Judson
Ward, Robbie Waugh, Peter Wenzl and Daniel Zamir
Outcomes of meeting on the use of ‘omics’ to unlock
the potential of plant biodiversity
11th to 13th of December 2012 at Asilomar, California
25. CWR genomics
• characterization of diversity and
structure of wild relative accessions
(population genomics)
• better understanding of the genetic
basis and genomic architecture of
selection and adaptation
• discovery of useful cryptic variation
hidden in wild ancestors by
developing and genotyping
interspecific backcross populations
with elite materials
• Showing the historic significance of
past wild relative introgression for
current cultivars
26. CWR genomics
Some core questions that genomics can help us answer:
Is it possible to discover useful cryptic variation hidden in wild
ancestors by developing interspecific backcross populations
with elite materials?
Does the breeding value of a given wild relative accession
depend on the genetic architecture of the trait(s) of interest?
Is it possible to pinpoint specific regions of the CWR genomes
that harbor valuable, cryptic variation when crossed with
cultivated lines and is it possible to predict these regions a
priori?
To what extent can „genomic selection‟ methods be applied to
breeding with crop wild relatives?
27. Big data and CWR
„Big data‟ has significant potential
for the exploration of genetic
diversity contained in CWR and
for more efficiently moving traits
(genes) into elite germplasm for
use by breeders.
A key challenge is to link up
passport, genomic and
phenotypic information on
genebank accessions, which are
typically recorded and managed
independently.
Image by:Neil
28. Advancing the use of CWRs
• Need for long-term funding
schemes
• Public/private partnerships
• Information sharing is key
• Programs that encourage the
systematic exploration of CWR
diversity are needed
• Policies that facilitate access to
crop wild relatives are important
