Internal seminar on the progress for the project Environmental characterization of crop wild relative pre-breeding environments, funded by The Crop Trust.

1. Characterising testing environments
for CWR pre-breeding
Brayan Mora
Harold Achicanoy
Julián Ramirez-Villegas
b.mora@cgiar.org
h.a.achicanoy@cgiar.org
j.r.villegas@cgiar.org
CIAT, 2018

2. Crop Wild Relatives: phases
Identify Collect
Use & improve
(Pre-breeding)

3. CWR – Pre-breeding
Cultivated material
+
Wild relatives
Improved material
Pre-breeding
5 – 8 years
Breeding
8 – 10 years
Sharma, S., et al. (2013). Pre-breeding for diversification of primary gene pool and genetic enhancement of grain legumes.
Frontiers in Plant Science, 4(August). https://doi.org/10.3389/fpls.2013.00309

4. CWR – Pre-breeding
Environmental characterization of target
environments for the pre-breeding of crop wild
relatives
Objective 1
• Expand work to
countries where
no pre-breeding
has been done
Objective 2
• Invite experts
Objective 3
• Determine
potential impact
areas of the
germplasm being
pre-bred

5. CWR – Pre-breeding
Current Future
Partners meetings
Define climate stresses or
hazards
Identify crop zones
Calculate climate indices as a
proxy
Calculate spatial similarity
index
Partners meetings
Define climate stresses or
hazards
Identify crop zones
Calculate climate indices as a
proxy
Calculate spatial similarity
index

6. Meetings with partners
Crop Stress to
consider
Trial
coordinates
General
information
Experimental
design
Climate
information
Soil conditions
Common bean
Waterlogging
Pathogen (Pythium),
Heat
Finger Millet Drought
Eggplant
Drought
Sunflower
Drought, Heat,
Lack of nutrients,
Sclerotin,
Verticillium
Potato
Drought, reduction of
the volume of watering
Carrot
Heat, Drought,
Salinity
Lentil
Drought

7. Methodology
- Daily climate data*
- Current (1981-2010)
- Future: 2030 (2020-2049)
- RCP 8.5
- 5 GCMs**
- Coordinates shared by partners
- Crop cycle
Inputs
Season 1
Season 2
* Geographically restricted by CHIRPS extent and crop areas using Monfreda and MapSPAM
** McSweeney CF, Jones RG (2016) How representative is the spread of climate projections from
the 5 CMIP5 GCMs used in ISI-MIP, Climate Services, 1, 24–29.

8. Methodology
Calculating
potential
impact areas
• Define a
percentile of 10%
or less for high
similarity areas
Identify areas
with high
similarity
• Dynamic time
warping (DTW)
algorithm
• Pixel/Crop/Stress
type
Calculating
similarity
index
• Index definition
• Using anual crop
cycle
• Using stress type
by crop
Agroclimatic
indices

9. Methodology: Agroclimatic indices
HEAT STRESS DROUGHT STRESS
𝑇𝑒𝑥𝑡𝑟𝑒𝑚𝑒 Number of days with extreme temperatures 𝑃𝑂𝑝𝑡𝑖𝑚𝑎𝑙 Number of days Number of days with :
𝐿𝑖𝑚𝐼𝑛𝑓 𝑃 𝐷𝑎𝑦 𝐿𝑖𝑚 𝑆𝑢𝑝
𝐷 𝑇𝑚𝑖𝑛 or 𝐷 𝑇𝑚𝑎𝑥 Number of days with higher temperatures a
𝐷 𝑇𝑚𝑖𝑛 or 𝐷 𝑇𝑚𝑎𝑥
𝑃𝐿𝑜𝑤 Number of consecutive days with:
𝑃 𝐷𝑎𝑦 < 𝑃𝐿𝑖𝑚 𝐼𝑛𝑓
𝐼𝑛𝑑𝑒𝑥 𝐻𝑒𝑎𝑡 (𝐷 𝑇𝑚𝑖𝑛 or 𝐷 𝑇𝑚𝑎𝑥)
(𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑦𝑠)
𝑃 𝐷𝑎𝑦 < 1 𝑚𝑚 Number of consecutive days with:
𝑃 𝐷𝑎𝑦 < 1 𝑚𝑚
𝑇𝑂𝑝𝑡𝑖𝑚𝑎𝑙 Number of days Number of days with :
𝐿𝑖𝑚𝐼𝑛𝑓 𝑇 𝑀𝑒𝑎𝑛 𝐿𝑖𝑚 𝑆𝑢𝑝
𝑃 𝐷𝑎𝑦 < 𝑃 𝐷𝑒𝑐 1 Number of consecutive days with:
𝑃 𝐷𝑎𝑦 < 𝑃 𝐷𝑒𝑐 1
*All of these indices depends on the specific crop parameters

10. Methodology: Similarity index

11. Results bean heat

12. Results carrot heat

13. Results eggplant drought

14. Results finger millet drought

15. Results potato drought

16. Results sunflower heat

17. Results highly similar areas

18. Next steps
• Finish to process future agroclimatic indices
• Calculate potential impact areas (using future and current most similarity areas)
• Calculate summary statistics of potential impact areas per crop/stress