Presented by Andy Jarvis (Bioversity), Andy Farrow (CIAT), and Glenn Hyman (CIAT) at the CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya

1. Gap analysis of genetic
resources in the CG and beyond
Bioversity International and CIAT

2. A renewed effort in identifying gaps
• CG collections suffer from both over- and
under- collecting
• Duplications increase costs
• But also signficiant gaps in the collections
• GPG2 and GCDT project:
– Crop wild relatives
– Major cultivated crops

3. Gap analysis of what?
• What is a gap? Fundamental question
goes at the root objective of a
genebank!
• “95% of all the alleles at a random locus
occurring in the target population with a
frequency greater than 0.05” (Marshall
and Brown, 1975)
• Trait-focused vs. Neutral diversity focus
• Also function of use…breeders…
• ….and time
• The Jarvis-take on things: Today trait-
focused, 2020 neutral diversity focused,
2050 Arabidopsis. Chao genebank!

4. The Gap Analysis Protocol
• What and where: taxonomic and geographic
priorities
• Gaps are GREaT:
– Taxonomic underrepresentation in collections
– Geographic holes in collections (geography is a
decent indicator for all things, biotic, abiotic, quality
traits)
– Environmental underrepresentation in collections
– Rare environmental conditions at the edges of
collections (especially relevant for breeding for abiotic
stress)
• Final result: map and table of geographic and
taxonomic priorities for completing the collection

5. Worse than pulling teeth
Crop Genus # species G H Total Avg. Records/species
Barley Hordeum 27 1419 10965 12384 459
Bean Phaseolus 72 2435 2952 5387 75
Chickpea Cicer 23 314 19 333 14
Cowpea Vigna 64 2509 6306 8815 138
Faba bean Vicia 9 511 949 1460 162
Finger millet Eleusine 7 3 68 71 10
Maize Zea 4 228 143 371 93
Pearl millet Pennisetum 54 963 3409 4372 81
Pigeon pea Cajanus 26 197 601 798 31
Sorghum Sorghum 31 320 4138 4458 144
Wheat Aegilops 23 4016 2231 6247 272
Wheat Triticum 3 1374 1 1375 458

6. Herbarium versus Germplasm
• Herbarium samples essentially a reference
set of data for comparison
• Also used by collectors to re-locate
populations to complete germplasm
collections
• Our point of entry for the development of
the methodology, thanks to success in
Vigna (Maxted et al. 2008)

7. Geographic Gaps
GENEBANK
HERBARIUM

8. Geographic Gaps
SITES WITH NO SITES WITH
GERMPLASM DEFICIENT
GERMPLASM

9. 1000
900
P. vulgaris
800
Number of samples
700
(germplasm)
600
500
400
300 P. acutifolius
P. coccineus
200
100 P. lunatus
0 P. filiformis
0 200 400 600 800 1000
Num ber of sam ples
TAXONOMIC GAPS
(all collections)
Extent to which each
species has been
adequately sampled
• Compared with
herbarium
•Related to its
distributional range

10. ENVIRONMENTAL
GAPS
Sites with environmental
conditions not yet captured in
the germplasm collections
•19 bioclimatic indices reduced
to principal components
•Holes or under-representation
in PC classes mapped out
1000
Herbarium distribution
900
Germplasm distribution
800
700
Frequency
600
500
400
300
200
100
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
PC1 classes

11. At species level, identification of
species which are poorly represented
across the environmental gradient
For herbarium rich groups
For herbarium poor groups
700
600 Real distribution
Theoretical distribution
500
Frequency
400
300
200
100
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
PC1 classes

12. Species Sampling (%) Coverage (%) Distribution (%) Outlier (%) Rarity Score
albiviolaceus 0.0 N/A N/A N/A N/A 0.00
amabilis 0.0 N/A N/A N/A N/A 0.00
chacoensis 0.0 N/A N/A N/A N/A 0.00
Synthesis
diversifolius 0.0 N/A N/A N/A N/A 0.00
elongatus 0.0 N/A N/A N/A N/A 0.00
fraternus 0.0 N/A N/A N/A N/A 0.00
laxiflorus 0.0 N/A N/A N/A N/A 0.00
micranthus 10.0 N/A N/A N/A N/A 0.00
mollis 0.0 N/A N/A N/A N/A 0.00
nitensis 0.0 N/A N/A N/A N/A 0.00
opacus 0.0 N/A N/A N/A N/A 0.00
pachycarpus 0.0 N/A N/A N/A N/A 0.00
Simple scoring system: texensis 10.0 N/A N/A N/A N/A 0.00
trifidus 0.0 N/A N/A N/A N/A 0.00
xolocotzii 0.0 N/A N/A N/A N/A 0.00
If species not in genebank, highest anisophyllus 0.0 N/A N/A N/A N/A 0.00
oaxacanus 0.0 N/A N/A N/A N/A 0.00
pauper 0.0 N/A N/A N/A N/A 0.00
priority plagiocylix 0.0 N/A N/A N/A N/A 0.00
rosei 0.0 N/A N/A N/A N/A 0.00
sonorensis 0.0 N/A N/A N/A N/A 0.00
If underrepresented in genebank, falciformis 0.0 N/A N/A N/A N/A 0.00
marechalii 6.7 N/A N/A N/A N/A 0.00
rotundatus 6.7 N/A N/A N/A N/A 0.00
with gaps, medium priority salicifolius 3.3 N/A N/A N/A N/A 0.00
altimontanus 7.5 N/A N/A N/A N/A 0.00
esquincensis 0.0 N/A N/A N/A N/A 0.00
If well represented with few gaps, novoleonensis 5.0 N/A N/A N/A N/A 0.00
tenellus 0.0 N/A N/A N/A N/A 0.00
albiflorus 10.0 N/A N/A N/A N/A 0.00
low priority macrolepis 8.0 N/A N/A N/A N/A 0.00
reticulatus 2.0 N/A N/A N/A N/A 0.00
jaliscanus 1.7 N/A N/A N/A N/A 0.00
macvaughii 3.3 N/A N/A N/A N/A 0.00
magnilobatus 3.3 N/A N/A N/A N/A 0.00
venosus 0.0 N/A N/A N/A N/A 0.00
carteri 7.1 N/A N/A N/A N/A 0.00
formosus 0.0 N/A N/A N/A N/A 0.00
polymorphus 2.9 N/A N/A N/A N/A 0.00
parvifolius 4.5 2.2 5.0 N/A 10.0 4.50
esperanzae 8.8 N/A N/A N/A N/A 0.00
filiformis 1.6 5.6 6.7 0.0 9.9 4.66
perplexus 1.3 N/A N/A N/A N/A 0.00
maculatus 2.2 4.4 8.0 1.0 9.1 4.89
talamancensis 1.1 10.0 4.0 2.0 7.1 4.97
polystachios 0.1 0.1 0.0 0.0 8.3 0.45
leptostachyus 2.9 6.5 6.7 0.0 9.9 5.32
amblyosepalus 0.0 0.0 0.0 N/A 10.0 1.00
glabellus 5.3 6.0 4.0 N/A 10.0 5.60
pachyrrhizoides 8.8 6.5 2.9 0.0 6.7 5.77
nelsonii 0.0 0.0 0.0 N/A 10.0 1.00
costaricensis 2.3 10.0 6.0 1.1 8.0 5.96
coccineus 4.8 8.1 5.7 0.0 9.7 6.06
pluriflorus 1.4 1.3 2.5 N/A 10.0 2.56
oligospermus 3.4 10.0 5.0 10.0 9.5 6.51
pedicellatus 0.9 2.7 3.3 0.0 9.5 2.56
hintonii 7.7 4.3 7.5 N/A 10.0 6.86
microcarpus 5.9 8.6 6.7 0.0 9.8 6.86
angustissimus 0.5 1.2 6.7 0.6 6.1 2.83
acutifolius 6.4 8.3 8.0 0.0 9.9 7.30
augusti 7.4 10.0 4.3 10.0 9.5 7.49
grayanus 5.2 2.0 4.0 0.0 7.5 3.72
neglectus 5.3 10.0 6.7 N/A 10.0 7.60
parvulus 1.3 5.0 5.0 0.0 8.6 3.82
vulgaris 8.7 9.9 5.4 3.8 7.4 7.76
dumosus 5.3 10.0 8.6 6.0 8.8 7.89
tuerckheimii 1.5 10.0 0.0 0.0 8.2 3.86
xanthotrichus 8.4 10.0 5.7 10.0 9.0 8.19
pauciflorus 0.2 4.0 6.7 N/A 10.0 4.27
chiapasanus 9.0 9.5 7.5 N/A 10.0 8.80
zimapanensis 8.8 10.0 10.0 N/A 10.0 9.63
lunatus 3.9 3.3 5.6 3.9 8.9 4.47

13. Priority
setting for a
species
• Predicted
distribution
• Limiting it to its
true range
• Eliminating
already sampled
sites
• High probability
of finding

14. Worse than pulling teeth
Crop Genus # species G H Total Avg. Records/species
Barley Hordeum 27 1419 10965 12384 459
Bean Phaseolus 72 2435 2952 5387 75
Chickpea Cicer 23 314 19 333 14
Cowpea Vigna 64 2509 6306 8815 138
Faba bean Vicia 9 511 949 1460 162
Finger millet Eleusine 7 3 68 71 10
Maize Zea 4 228 143 371 93
Pearl millet Pennisetum 54 963 3409 4372 81
Pigeon pea Cajanus 26 197 601 798 31
Sorghum Sorghum 31 320 4138 4458 144
Wheat Aegilops 23 4016 2231 6247 272
Wheat Triticum 3 1374 1 1375 458

15. Zea

16. Vigna

17. Vicia

18. Aegilops and Triticum

19. Sorghum

20. Pennisetum

21. Hordeum

22. Cicer

23. Cajanus

24. Phaseolus

27. Current geographic distribution Predicted future distribution of
of diversity for the 343 crop diversity based on 18 GCM
wild relative species studied models under the A2a scenario
Total number of herbarium
specimens and germplasm
accessions available for each major
crop wild relative genepool through
the GBIF portal
Crop Genus # species G H Total Avg. Records/species
Barley Hordeum 27 1419 10965 12384 459
Bean Phaseolus 72 2435 2952 5387 75
Chickpea Cicer 23 314 19 333 14
Cowpea Vigna 64 2509 6306 8815 138
Faba bean Vicia 9 511 949 1460 162
Finger millet Eleusine 7 3 68 71 10
Maize Zea 4 228 143 371 93
Pearl millet Pennisetum 54 963 3409 4372 81
Pigeon pea Cajanus 26 197 601 798 31
Sorghum Sorghum 31 320 4138 4458 144
Predicted change in
Wheat Aegilops 23 4016 2231 6247 272
Wheat Triticum 3 1374 1 1375 458
species richness to
2050.

28. Decadal climate
change 2000 – 2100,
one GCM
Trajectories of wild
populations to
“follow” their climate
Two parameters:
Max. migration rate
Plasticity

31. Wild relatives
TOP 30 FAO PRODUCED CROPS WITH:
• Phaseolus
• Rice
• Vigna • Cotton
• Zea • Soy
• • Sugar cane
Vicia
• Rapeseed
• Sorghum
• Cassava
• Cajanus
• Oil Palm
• Cicer • Potato
• Hordeum • Coconut
• • Coffee
Pennisetum
• Sweet Potato
• Triticum/Aegilops
• Groundnut
• Eleusine
• Sunflower
• Lentil

32. Climate change data and
analyses
CIAT

33. Climate change data
• Statistically downscaled from 18 GCM
models
Originating Group(s) GRID Year
Country MODEL ID OUR ID
Bjerknes Centre for Climate Research 2050
Norway BCCR-BCM2.0 BCCR_BCM2 128x64
Canadian Centre for Climate Modelling & Analysis 2020-2050
Canada CGCM2.0 CCCMA_CGCM2 96x48
Canadian Centre for Climate Modelling & Analysis 2050
Canada CGCM3.1(T47) CCCMA_CGCM3_1 96x48
Canadian Centre for Climate Modelling & Analysis 2050
Canada CGCM3.1(T63) CCCMA_CGCM3_1_T63 128x64
Météo-France
France CNRM-CM3 CNRM_CM3 128x64
Centre National de Recherches Météorologiques 2050
CSIRO Atmospheric Research 2020
Australia CSIRO-MK2.0 CSIRO_MK2 64x32
CSIRO Atmospheric Research 2050
Australia CSIRO-Mk3.0 CSIRO_MK3 192x96
Max Planck Institute for Meteorology 2050
Germany ECHAM5/MPI-OM MPI_ECHAM5 N/A
Meteorological Institute of the University of Bonn Germany
ECHO-G MIUB_ECHO_G 96x48
Meteorological Research Institute of KMA 2050
Korea
LASG / Institute of Atmospheric Physics 2050
China FGOALS-g1.0 IAP_FGOALS_1_0_G 128x60
US Dept. of Commerce
NOAA USA GFDL-CM2.0 GFDL_CM2_0 144x90
Geophysical Fluid Dynamics Laboratory 2050
US Dept. of Commerce
NOAA USA GFDL-CM2.0 GFDL_CM2_1 144x90
Geophysical Fluid Dynamics Laboratory 2050
NASA / Goddard Institute for Space Studies 2050
USA GISS-AOM GISS_AOM 90x60
Institut Pierre Simon Laplace 2050
France IPSL-CM4 IPSL_CM4 96x72
Center for Climate System Research
National Institute for Environmental Studies Japan MIROC3.2(hires) MIROC3_2_HIRES 320x160
Frontier Research Center for Global Change (JAMSTEC) 2050
Center for Climate System Research
National Institute for Environmental Studies Japan MIROC3.2(medres) MIROC3_2_MEDRES 128x64
Frontier Research Center for Global Change (JAMSTEC) 2050
Meteorological Research Institute 2050
Japan MRI-CGCM2.3.2 MRI_CGCM2_3_2a N/A
National Center for Atmospheric Research 2050
USA PCM NCAR_PCM1 128x64
Hadley Centre for Climate Prediction and Research
UK UKMO-HadCM3 HCCPR_HADCM3 96x73
Met Office 2020-2050
Center for Climate System Research (CCSR)
Japan NIES-99 NIES-99 64x32
National Institute for Environmental Studies (NIES) 2020

34. Climate
General climate change description
characteristic
Average Climate Change Trends of Colombia
The rainfall increases from 2645.89 millimeters to 2702.41 millimeters
General
Temperatures increase and the average increase is 2.66 ºC
climate
The mean daily temperature range increases from 9.57 ºC to 9.85 ºC
characteristics
The maximum number of cumulative dry months keeps constant in 2 months
The maximum temperature of the year increases from 30.84 ºC to 34.36 ºC while the warmest quarter gets hotter by 2.81 ºC
Extreme The minimum temperature of the year increases from 19.05 ºC to 21.23 ºC while the coldest quarter gets hotter by 2.6 ºC
conditions The wettest month gets wetter with 354.88 millimeters instead of 350.35 millimeters, while the wettest quarter gets wetter by 3.55 mm
The driest month gets wetter with 94.2 millimeters instead of 83.6 millimeters while the driest quarter gets wetter by 40.25 mm
Climate
Overall this climate becomes more seasonal in terms of variability through the year in temperature and less seasonal in precipitation
Seasonality
The coefficient of variation of temperature predictions between models is 3.7%
Variability
Temperature predictions were uniform between models and thus no outliers were detected
between
The coefficient of variation of precipitation predictions between models is 5.72%
models
Precipitation predictions were uniform between models and thus no outliers were detected
Current precipitation
350 40
Future precipitation
Future mean temperature
Current mean temperature
35
300 Future maximum temperature
Current maximum temperature
Future minimum temperature
30 Current minimum temperature
250
Precipitation (mm)
Temperature (ºC)
25
200
20
150
15
100
10
50 5
0 0
1 2 3 4 5 6 7 8 9 10 11 12
Month
These results are based on the 2050 climate compared with the 1960-2000 climate. Future climate data is derived from 14 GCM models from the 3th (2001)
and the 4th (2007) IPCC assessment, run under the A2a scenario (business as usual). Further information please check the website http://www.ipcc-data.org

35. Incertidumbre
Site-specific predicted values of each GCM model (IPCC, 2007) for principal bioclimatic variables
3500 50
45
3000
40
2500 35
Precipitation (mm)
Temperature (ºC)
30
2000
25
1500
20
15
1000
10
500
5
0 0
MPI ECHAM 5
MIROC3 2 HIRES
MIUB ECHO G
CNRM CM3
MIROC3 2
CCCMA CGCM3
CCCMA CGCM3
CSIRO MK3 0
CCCMA CGCM2
GFDL CM2
BCCR BCM2 0
GFDL CM2 1
NCAR PCM 1
HCCPR HADCM3
MEDRES
1 T63
1
Total annual precipitation (bio 12) Annual mean temperature (bio 1)
Annual maximum temperature (bio 5) Annual minimum temperature (bio 6)

36. Incertidumbre
Site-specific monthly coefficient of variation using 14 GCM models (IPCC, 2007) for precipitation and
temperature
16 35
14
30
Precipitation coefficient of variation (%)
Temperature coefficient of variation (%)
12
25
10
20
8
15
6
10
4
5
2
0 0
1 2 3 4 5 6 7 8 9 10 11 12
Month
Precipitation Mean temperature Maximum temperature Minimum temperature

37. Yearly data too…
5.0
4.0
3.0
Temperature
2.0
1.0
1870
0.0
Baseline
-200.0 -100.0 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0
-1.0
Precipitation
India Myanmar Burma Mexico Dominican Republic Rwanda Brazil Uganda Korea Guatemala United States Colombia

38. Ecocrop approach
1600
1400
Marginal
1200
Precipitation (mm)
conditions
1000
800 Death
Optimum
Not
600 conditions
suitable
conditions
400
200
0
-5 0 5 10 15 20 25 30 35 40
Temperature (ºC)

39. Pros and cons of the approach
• Simple to use and apply
• Available for “minor” crops which are
PROS
important components of food and nutritional
security
• Captures the broad niche of the crop,
including within crop genetic diversity
• Fails to capture complex physiological
CONS
responses of within season climate
• Only provides index of suitability – not
productivity
• Inferior model to those available for the “big”
crops

40. Cow peas Vigna unguiculata unguic. L 10176
Grapes Vitis vinifera L. 7400
Groundnut Arachis hypogaea L. 22232
Lentil Lens culinaris Medikus 3848
Linseed Linum usitatissimum L. 3017
The geography of crop suitability Maize Zea mays L. s. mays 144376
Mango Mangifera indica L. 4155
Millet Panicum miliaceum L. 32846
Natural rubber Hevea brasiliensis (Willd.) 8259
Area
Oats Avena sativa L. 11284
Crop Species Harvested
Oil palm Elaeis guineensis Jacq. 13277
(k Ha)
Olive Olea europaea L. 8894
Alfalfa Medicago sativa L. 15214
Onion Allium cepa L. v cepa 3341
Apple Malus sylvestris Mill. 4786
Oranges Citrus sinensis (L.) Osbeck 3618
Banana Musa acuminata Colla 4180
Pea Pisum sativum L. 6730
Barley Hordeum vulgare L. 55517
Pigeon pea Cajanus cajan (L.) Mill ssp 4683
Common Bean Phaseolus vulgaris L. 26540
Plantain bananas Musa balbisiana Colla 5439
Common buckwheat Fagopyrum esculentum Moench 2743
Potato Solanum tuberosum L. 18830
Cabbage Brassica oleracea L.v capi. 3138
Rapeseed Brassica napus L. 27796
Cashew nuts Anacardium occidentale L. 3387
Rice Oryza sativa L. s. japonica 154324
Cassava Manihot esculenta Crantz. 18608
Rye Secale cereale L. 5994
Chick pea Cicer arietinum L. 10672
Perennial reygrass Lolium perenne L. 5516
Clover Trifolium repens L. 2629
Sesame seed Sesamum indicum L. 7539
Cocoa bean Theobroma cacao L. 7567
Sorghum Sorghum bicolor (L.) Moench 41500
Coconut Cocos nucifera L. 10616
Perennial soybean Glycine wightii Arn. 92989
Coffee Coffea arabica L. 10203
Sugar beet Beta vulgaris L. v vulgaris 5447
Cotton Gossypium hirsutum L. 34733
Sugarcane Saccharum robustum Brandes 20399
Cow peas Vigna unguiculata unguic. L 10176
Sunflower Helianthus annuus L v macro 23700
Grapes Vitis vinifera L. 7400
Sweet potato Ipomoea batatas (L.) Lam. 8996
Groundnut Arachis hypogaea L. 22232
Tea Camellia sinensis (L) O.K. 2717
Lentil Lens culinaris Medikus 3848
Tobacco Nicotiana tabacum L. 3897
Linseed Linum usitatissimum L. 3017
Tomato Lycopersicon esculentum M. 4597
Maize Zea mays L. s. mays 144376
Watermelon Citrullus lanatus (T) Mansf 3785
Mango Mangifera indica L. 4155
Wheat Triticum aestivum L. 216100
Millet Panicum miliaceum L. 32846 Yams Dioscorea rotundata Poir. 4591
Natural rubber Hevea brasiliensis (Willd.) 8259
Oats Avena sativa L. 11284
Oil palm Elaeis guineensis Jacq. 13277
Olive Olea europaea L. 8894
Onion Allium cepa L. v cepa 3341
Oranges Citrus sinensis (L.) Osbeck 3618

41. Current suitability for agriculture

42. Future suitability for agriculture
18 GCM models, A2a scenario

43. Change in global suitability

44. Number of crops that lose out

45. Number of crops that gain

46. Current suitability for common
bean
Gmin: 60, Gmax: 100
Ttmp:0, Tmin: 7, TOPmn: 16, TOPmx: 27, Tmax: 32
Rmin: 220, ROPmn: 350, ROPmx: 900, Rmax: 1500

47. Future suitability for common
bean
Gmin: 60, Gmax: 100
Ttmp:0, Tmin: 7, TOPmn: 16, TOPmx: 27, Tmax: 32
Rmin: 220, ROPmn: 350, ROPmx: 900, Rmax: 1500

48. Cassava and maize in Africa and
India – not all bad news

49. Crop adaptability anomaly
-80
-60
-40
-20
20
40
60
80
0
Angola cass
Angola maiz
Congo cass
Congo maiz
Ghana cass
Ghana maiz
India cass
India maiz
Malawi cass
Malawi maiz
Mozambique cass
Mozambique maiz
Tanzania cass
Tanzania maiz
Nigeria cass
Nigeria maiz
Uganda cass
Uganda maiz
Differential response in maize

50. Change in bean suitability

51. Technological options
• Impact of a
100mm more
drought resistant
bean in Africa
• Change in the
change with Ropt
less 100mm
• Green areas show
regions that will
benefit from such
a technology

52. Context What is drought? Where is drought? Who is affected?
Will the weather cause the
crop to fail or significantly
reduce yields?
– What variety?
– When planted?
– What kind of soil and terrain?
– What management?

53. Context What is drought? Where is drought? Who is affected?
Types of drought
I. Terminal
drought
II. Intermittent
drought
III. Predictable
drought
IV. Semi-arid
Amede et al,
2004

54. Context What is drought? Where is drought? Who is affected?
Expert knowledge

55. Context What is drought? Where is drought? Who is affected?
Cons and Pros
• Expert knowledge
Only as good as the experts
x
Difficult to extrapolate
x
Some areas not considered
x
Consistency
x
Transparency
x
– Potentially quick
– Useful for defining indicators
– Validation of results

56. Context What is drought? Where is drought? Who is affected?
Homologue environments

57. Context What is drought? Where is drought? Who is affected?
Water Balance models
• Failed Seasons
– WATBAL model to
determine length of
season
– MarkSim to simulate
rainfall and temperature
– Viable growing seasons
>= 50 growing days
(defined as Ea/Et > 0.5)
with no more than 20
days in this period with
stress (where Ea/Et < 0.5)
(Thornton et al, 2006)

58. Seasonal Drought Index
Sorghum in Sub Saharan Africa

59. Seasonal Drought Index
Period 1, 0 to 20
days after sowing.
0 - 10 %
10 - 20 %
20 - 30 %
Sorghum 30 - 40 %
40 - 50 %
Proportion of 50 - 60 %
days in sorghum 60 - 70 %
regions with 70 - 80 %
Ea/Et < 0.35 80 - 90 %
90 - 100 %

60. Seasonal Drought Index
Period 2, 20 to 40
days after sowing.
0 - 10 %
10 - 20 %
20 - 30 %
Sorghum 30 - 40 %
40 - 50 %
Proportion of 50 - 60 %
days in sorghum 60 - 70 %
regions with 70 - 80 %
Ea/Et < 0.35 80 - 90 %
90 - 100 %

61. Seasonal Drought Index
Period 3, 40 to 60
days after sowing.
0 - 10 %
10 - 20 %
20 - 30 %
Sorghum 30 - 40 %
40 - 50 %
Proportion of 50 - 60 %
days in sorghum 60 - 70 %
regions with 70 - 80 %
Ea/Et < 0.35 80 - 90 %
90 - 100 %

62. Seasonal Drought Index
Period 4, 60 to 80
days after sowing.
0 - 10 %
10 - 20 %
20 - 30 %
Sorghum 30 - 40 %
40 - 50 %
Proportion of 50 - 60 %
days in sorghum 60 - 70 %
regions with 70 - 80 %
Ea/Et < 0.35 80 - 90 %
90 - 100 %

63. Seasonal Drought Index
Period 5, 80 to 100
days after sowing.
0 - 10 %
10 - 20 %
20 - 30 %
Sorghum 30 - 40 %
40 - 50 %
Proportion of 50 - 60 %
days in sorghum 60 - 70 %
regions with 70 - 80 %
Ea/Et < 0.35 80 - 90 %
90 - 100 %

64. Seasonal Drought Index
Period 6, 100 to 120
days after sowing.
0 - 10 %
10 - 20 %
20 - 30 %
Sorghum 30 - 40 %
40 - 50 %
Proportion of 50 - 60 %
days in sorghum 60 - 70 %
regions with 70 - 80 %
Ea/Et < 0.35 80 - 90 %
90 - 100 %

65. Seasonal Drought Index
Period 7, 120 to 140
days after sowing.
0 - 10 %
10 - 20 %
20 - 30 %
Sorghum 30 - 40 %
40 - 50 %
Proportion of 50 - 60 %
days in sorghum 60 - 70 %
regions with 70 - 80 %
Ea/Et < 0.35 80 - 90 %
90 - 100 %

66. Context What is drought? Where is drought? Who is affected?
Drought for common beans
• Stress due to water deficiency
• Susceptible period
– during flowering, pod set and early
grain-fill
– ranges from 30-60 DAP to 45-75 DAP in
the case of later maturing varieties
– varies according to elevation

67. Pest and Disease Mapping
• Over to Glenn…

68. Step 1: collect literature and
databases
• CIAT Green mite database
• Scientific literature on cassava mealybug
and CIAT database
• System-wide IPM data on whitefly and
cassava mosaic disease
• Scientific literature on cassava brown
streak disease

69. Step 2: geo-reference and
characterize

70. Step 3: Run models
CLIMEX - CBSD
Open Modeler

71. Step 4: validation

73. Potential for whitefly
Potential for CBSD