[Day 2] Center Presentation: Bioversity and CIAT

Gap analysis of genetic

resources in the CG and beyond

Bioversity International and CIAT

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

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!

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

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

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)

HERBARIUM GENEBANK

Geographic Gaps

SITES WITH NO

GERMPLASM

SITES WITH

DEFICIENT

GERMPLASM

Geographic Gaps

0

100

200

300

400

500

600

700

800

900

1000

0 200 400 600 800 1000

Number of samples

(all collections)

Nu

mb

er

of

sam

ple

s

(germ

pla

sm

)P. vulgaris

P. coccineus

P. acutifolius

P. lunatus

P. filiformis

TAXONOMIC GAPS

Extent to which each

species has been

adequately sampled

• Compared with

herbarium

•Related to its

distributional range

0

100

200

300

400

500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

PC1 classes

Fre

quency

Herbarium distribution

Germplasm distribution

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

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

PC1 classes

Fre

quency

Real distribution

Theoretical distribution

At species level, identification of

species which are poorly represented

across the environmental gradient

For herbarium rich groups

For herbarium poor groups

Synthesis

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

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

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

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

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

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

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

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

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

esperanzae 8.8 N/A N/A N/A N/A 0.00

perplexus 1.3 N/A N/A N/A N/A 0.00

polystachios 0.1 0.1 0.0 0.0 8.3 0.45

amblyosepalus 0.0 0.0 0.0 N/A 10.0 1.00

nelsonii 0.0 0.0 0.0 N/A 10.0 1.00

pluriflorus 1.4 1.3 2.5 N/A 10.0 2.56

pedicellatus 0.9 2.7 3.3 0.0 9.5 2.56

angustissimus 0.5 1.2 6.7 0.6 6.1 2.83

grayanus 5.2 2.0 4.0 0.0 7.5 3.72

parvulus 1.3 5.0 5.0 0.0 8.6 3.82

tuerckheimii 1.5 10.0 0.0 0.0 8.2 3.86

pauciflorus 0.2 4.0 6.7 N/A 10.0 4.27

lunatus 3.9 3.3 5.6 3.9 8.9 4.47

parvifolius 4.5 2.2 5.0 N/A 10.0 4.50

filiformis 1.6 5.6 6.7 0.0 9.9 4.66

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

leptostachyus 2.9 6.5 6.7 0.0 9.9 5.32

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

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

oligospermus 3.4 10.0 5.0 10.0 9.5 6.51

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

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

neglectus 5.3 10.0 6.7 N/A 10.0 7.60

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

xanthotrichus 8.4 10.0 5.7 10.0 9.0 8.19

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

Simple scoring system:

If species not in genebank, highest

priority

If underrepresented in genebank,

with gaps, medium priority

If well represented with few gaps,

low priority

Priority

setting for a

species

• Predicted

distribution

• Limiting it to its

true range

• Eliminating

already sampled

sites

• High probability

of finding

Worse than pulling teeth


Barley Hordeum 27 1419 10965 12384 459

Bean Phaseolus 72 2435 2952 5387 75


Cowpea Vigna 64 2509 6306 8815 138

Faba bean Vicia 9 511 949 1460 162


Maize Zea 4 228 143 371 93



Sorghum Sorghum 31 320 4138 4458 144

Wheat Aegilops 23 4016 2231 6247 272


Aegilops and Triticum

Sorghum

Pennisetum

Hordeum

Cajanus

Phaseolus


Barley Hordeum 27 1419 10965 12384 459

Bean Phaseolus 72 2435 2952 5387 75


Cowpea Vigna 64 2509 6306 8815 138

Faba bean Vicia 9 511 949 1460 162


Maize Zea 4 228 143 371 93



Sorghum Sorghum 31 320 4138 4458 144

Wheat Aegilops 23 4016 2231 6247 272


Total number of herbarium

specimens and germplasm

accessions available for each major

crop wild relative genepool through

the GBIF portal

Current geographic distribution

of diversity for the 343 crop

wild relative species studied

Predicted future distribution of

diversity based on 18 GCM

models under the A2a scenario

Predicted change in

species richness to

2050.

Decadal climate

change 2000 – 2100,

one GCM

Trajectories of wild

populations to

“follow” their climate

Two parameters:

Max. migration rate

Plasticity

Wild relatives

• Phaseolus

• Vigna

• Zea

• Vicia

• Sorghum

• Cajanus

• Cicer

• Hordeum

• Pennisetum

• Triticum/Aegilops

• Eleusine

• Lentil

TOP 30 FAO PRODUCED CROPS WITH:

• Rice

• Cotton

• Soy

• Sugar cane

• Rapeseed

• Cassava

• Oil Palm

• Potato

• Coconut

• Coffee

• Sweet Potato

• Groundnut

• Sunflower

Climate change data and

analyses

CIAT

Climate change data

• Statistically downscaled from 18 GCM

models Originating Group(s) Country MODEL ID OUR ID GRID Year

Bjerknes Centre for Climate Research Norway BCCR-BCM2.0 BCCR_BCM2 128x64 2050

Canadian Centre for Climate Modelling & Analysis Canada CGCM2.0 CCCMA_CGCM2 96x48 2020-2050

Canadian Centre for Climate Modelling & Analysis Canada CGCM3.1(T47) CCCMA_CGCM3_1 96x48 2050

Canadian Centre for Climate Modelling & Analysis Canada CGCM3.1(T63) CCCMA_CGCM3_1_T63 128x64 2050

Météo-France

Centre National de Recherches MétéorologiquesFrance CNRM-CM3 CNRM_CM3 128x64

2050

CSIRO Atmospheric Research Australia CSIRO-MK2.0 CSIRO_MK2 64x32 2020

CSIRO Atmospheric Research Australia CSIRO-Mk3.0 CSIRO_MK3 192x96 2050

Max Planck Institute for Meteorology Germany ECHAM5/MPI-OM MPI_ECHAM5 N/A 2050

Meteorological Institute of the University of Bonn

Meteorological Research Institute of KMA

Germany

KoreaECHO-G MIUB_ECHO_G 96x48

2050

LASG / Institute of Atmospheric Physics China FGOALS-g1.0 IAP_FGOALS_1_0_G 128x60 2050

US Dept. of Commerce

NOAA

Geophysical Fluid Dynamics Laboratory

USA GFDL-CM2.0 GFDL_CM2_0 144x90

2050

US Dept. of Commerce

NOAA

Geophysical Fluid Dynamics Laboratory

USA GFDL-CM2.0 GFDL_CM2_1 144x90

2050

NASA / Goddard Institute for Space Studies USA GISS-AOM GISS_AOM 90x60 2050

Institut Pierre Simon Laplace France IPSL-CM4 IPSL_CM4 96x72 2050

Center for Climate System Research

National Institute for Environmental Studies

Frontier Research Center for Global Change (JAMSTEC)

Japan MIROC3.2(hires) MIROC3_2_HIRES 320x160

2050

Center for Climate System Research

National Institute for Environmental Studies

Frontier Research Center for Global Change (JAMSTEC)

Japan MIROC3.2(medres) MIROC3_2_MEDRES 128x64

2050

Meteorological Research Institute Japan MRI-CGCM2.3.2 MRI_CGCM2_3_2a N/A 2050

National Center for Atmospheric Research USA PCM NCAR_PCM1 128x64 2050

Hadley Centre for Climate Prediction and Research

Met OfficeUK UKMO-HadCM3 HCCPR_HADCM3 96x73

2020-2050Center for Climate System Research (CCSR)

National Institute for Environmental Studies (NIES) Japan NIES-99 NIES-99 64x322020

Climate

characteristic

Climate

Seasonality

The mean daily temperature range increases from 9.57 ºC to 9.85 ºC

The driest month gets wetter with 94.2 millimeters instead of 83.6 millimeters while the driest quarter gets wetter by 40.25 mm

Temperature predictions were uniform between models and thus no outliers were detected

Precipitation predictions were uniform between models and thus no outliers were detected

General climate change description

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

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

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 rainfall increases from 2645.89 millimeters to 2702.41 millimeters

Temperatures increase and the average increase is 2.66 ºC

The coefficient of variation of temperature predictions between models is 3.7%

The maximum number of cumulative dry months keeps constant in 2 months

Average Climate Change Trends of Colombia

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

The coefficient of variation of precipitation predictions between models is 5.72%

General

climate

characteristics

Extreme

conditions

Variability

between

models

Overall this climate becomes more seasonal in terms of variability through the year in temperature and less seasonal in precipitation

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10 11 12

Month

Pre

cip

itati

on

(m

m)

0

5

10

15

20

25

30

35

40

Tem

pera

ture

(ºC

)

Current precipitation

Future precipitation

Future mean temperature

Current mean temperature

Future maximum temperature

Current maximum temperature

Future minimum temperature

Current minimum temperature

Incertidumbre

0

500

1000

1500

2000

2500

3000

3500B

CC

R B

CM

2 0

CC

CM

A C

GC

M3

1

CC

CM

A C

GC

M3

1 T

63

CN

RM

CM

3

CS

IRO

MK

3 0

GF

DL

CM

2

GF

DL

CM

2 1

MIR

OC

3 2

HIR

ES

MIR

OC

3 2

ME

DR

ES

MIU

B E

CH

O G

MP

I E

CH

AM

5

NC

AR

PC

M 1

CC

CM

A C

GC

M2

HC

CP

R H

AD

CM

3

Pre

cip

ita

tio

n (

mm

)

0

5

10

15

20

25

30

35

40

45

50

Te

mp

era

ture

(ºC

)

Total annual precipitation (bio 12) Annual mean temperature (bio 1)

Annual maximum temperature (bio 5) Annual minimum temperature (bio 6)

Site-specific predicted values of each GCM model (IPCC, 2007) for principal bioclimatic variables

Incertidumbre

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12

Month

Pre

cip

ita

tio

n c

oe

ffic

ien

t o

f v

ari

ati

on

(%

)

0

5

10

15

20

25

30

35

Te

mp

era

ture

co

eff

icie

nt

of

va

ria

tio

n (

%)

Precipitation Mean temperature Maximum temperature Minimum temperature

Site-specific monthly coefficient of variation using 14 GCM models (IPCC, 2007) for precipitation and

temperature

Yearly data too…

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

-200.0 -100.0 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0

Precipitation

Tem

pera

ture

India Myanmar Burma Mexico Dominican Republic Rwanda Brazil Uganda Korea Guatemala United States Colombia

1870

Baseline

Ecocrop approach

0

200

400

600

800

1000

1200

1400

1600

-5 0 5 10 15 20 25 30 35 40

Temperature (ºC)

Pre

cip

itati

on

(m

m)

Optimum

conditions

Marginal

conditions

Death

Not

suitable

conditions

Pros and cons of the approach

• Simple to use and apply

• Available for “minor” crops which are important components of food and nutritional security

• Captures the broad niche of the crop, including within crop genetic diversity

• Fails to capture complex physiological responses of within season climate

• Only provides index of suitability – not productivity

• Inferior model to those available for the “big” crops

PR

OS

CO

NS

The geography of crop suitability

Crop Species

Area

Harvested

(k Ha)

Alfalfa Medicago sativa L. 15214

Apple Malus sylvestris Mill. 4786

Banana Musa acuminata Colla 4180

Barley Hordeum vulgare L. 55517

Common Bean Phaseolus vulgaris L. 26540

Common buckwheat Fagopyrum esculentum Moench 2743

Cabbage Brassica oleracea L.v capi. 3138

Cashew nuts Anacardium occidentale L. 3387

Cassava Manihot esculenta Crantz. 18608

Chick pea Cicer arietinum L. 10672

Clover Trifolium repens L. 2629

Cocoa bean Theobroma cacao L. 7567

Coconut Cocos nucifera L. 10616

Coffee Coffea arabica L. 10203

Cotton Gossypium hirsutum L. 34733

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

Maize Zea mays L. s. mays 144376

Mango Mangifera indica L. 4155

Millet Panicum miliaceum L. 32846

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

Pea Pisum sativum L. 6730

Pigeon pea Cajanus cajan (L.) Mill ssp 4683

Plantain bananas Musa balbisiana Colla 5439

Potato Solanum tuberosum L. 18830

Rapeseed Brassica napus L. 27796

Rice Oryza sativa L. s. japonica 154324

Rye Secale cereale L. 5994

Perennial reygrass Lolium perenne L. 5516

Sesame seed Sesamum indicum L. 7539

Sorghum Sorghum bicolor (L.) Moench 41500

Perennial soybean Glycine wightii Arn. 92989

Sugar beet Beta vulgaris L. v vulgaris 5447

Sugarcane Saccharum robustum Brandes 20399

Sunflower Helianthus annuus L v macro 23700

Sweet potato Ipomoea batatas (L.) Lam. 8996

Tea Camellia sinensis (L) O.K. 2717

Tobacco Nicotiana tabacum L. 3897

Tomato Lycopersicon esculentum M. 4597

Watermelon Citrullus lanatus (T) Mansf 3785

Wheat Triticum aestivum L. 216100Yams Dioscorea rotundata Poir. 4591

Crop Species

Area

Harvested

(k Ha)

Alfalfa Medicago sativa L. 15214

Apple Malus sylvestris Mill. 4786

Banana Musa acuminata Colla 4180

Barley Hordeum vulgare L. 55517

Common Bean Phaseolus vulgaris L. 26540

Common buckwheat Fagopyrum esculentum Moench 2743

Cabbage Brassica oleracea L.v capi. 3138

Cashew nuts Anacardium occidentale L. 3387

Cassava Manihot esculenta Crantz. 18608

Chick pea Cicer arietinum L. 10672

Clover Trifolium repens L. 2629

Cocoa bean Theobroma cacao L. 7567

Coconut Cocos nucifera L. 10616

Coffee Coffea arabica L. 10203

Cotton Gossypium hirsutum L. 34733

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

Maize Zea mays L. s. mays 144376

Mango Mangifera indica L. 4155

Millet Panicum miliaceum L. 32846

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

Pea Pisum sativum L. 6730

Pigeon pea Cajanus cajan (L.) Mill ssp 4683

Plantain bananas Musa balbisiana Colla 5439

Potato Solanum tuberosum L. 18830

Rapeseed Brassica napus L. 27796

Rice Oryza sativa L. s. japonica 154324

Rye Secale cereale L. 5994

Perennial reygrass Lolium perenne L. 5516

Sesame seed Sesamum indicum L. 7539

Sorghum Sorghum bicolor (L.) Moench 41500

Perennial soybean Glycine wightii Arn. 92989

Sugar beet Beta vulgaris L. v vulgaris 5447

Sugarcane Saccharum robustum Brandes 20399

Sunflower Helianthus annuus L v macro 23700

Sweet potato Ipomoea batatas (L.) Lam. 8996

Tea Camellia sinensis (L) O.K. 2717

Tobacco Nicotiana tabacum L. 3897

Tomato Lycopersicon esculentum M. 4597

Watermelon Citrullus lanatus (T) Mansf 3785

Wheat Triticum aestivum L. 216100Yams Dioscorea rotundata Poir. 4591

Current suitability for agriculture

Future suitability for agriculture

18 GCM models, A2a scenario

Change in global suitability

Number of crops that lose out

Number of crops that gain

Gmin: 60, Gmax: 100

Ttmp:0, Tmin: 7, TOPmn: 16, TOPmx: 27, Tmax: 32

Rmin: 220, ROPmn: 350, ROPmx: 900, Rmax: 1500

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

Future suitability for common

bean

Cassava and maize in Africa and

India – not all bad news

Differential response in maize

-80

-60

-40

-20

0

20

40

60

80

Angola

cass

Angola

maiz

Congo c

ass

Congo m

aiz

Ghana c

ass

Ghana m

aiz

India

cass

India

maiz

Mala

wi cass

Mala

wi m

aiz

Mozam

biq

ue c

ass

Mozam

biq

ue m

aiz

Tanzania

cass

Tanzania

maiz

Nig

eria c

ass

Nig

eria m

aiz

Uganda c

ass

Uganda m

aiz

Cro

p a

dap

tab

ilit

y a

no

maly

Change in bean suitability

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

– What variety?

– When planted?

– What kind of soil and terrain?

– What management?

Who is affected?What is drought? Where is drought?Context

Will the weather cause the

crop to fail or significantly

reduce yields?

Types of drought

I. Terminal drought

II. Intermittent drought

III. Predictable drought

IV. Semi-arid

Amede et al, 2004


Expert knowledge



Cons and Pros

• Expert knowledgex Only as good as the experts

x Difficult to extrapolate

x Some areas not considered

x Consistency

x Transparency

– Potentially quick

– Useful for defining indicators

– Validation of results

Homologue environments


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)


Seasonal Drought Index

Sorghum in Sub Saharan Africa

0 - 10 %

10 - 20 %

20 - 30 %

30 - 40 %

40 - 50 %

50 - 60 %

60 - 70 %

70 - 80 %

80 - 90 %

90 - 100 %


Period 1, 0 to 20

days after sowing.

Sorghum

Proportion of

days in sorghum

regions with

Ea/Et < 0.35

0 - 10 %

10 - 20 %

20 - 30 %

30 - 40 %

40 - 50 %

50 - 60 %

60 - 70 %

70 - 80 %

80 - 90 %

90 - 100 %


Period 2, 20 to 40

days after sowing.

Sorghum

Proportion of

days in sorghum

regions with

Ea/Et < 0.35

0 - 10 %

10 - 20 %

20 - 30 %

30 - 40 %

40 - 50 %

50 - 60 %

60 - 70 %

70 - 80 %

80 - 90 %

90 - 100 %


Period 3, 40 to 60

days after sowing.

Sorghum

Proportion of

days in sorghum

regions with

Ea/Et < 0.35

0 - 10 %

10 - 20 %

20 - 30 %

30 - 40 %

40 - 50 %

50 - 60 %

60 - 70 %

70 - 80 %

80 - 90 %

90 - 100 %


Period 4, 60 to 80

days after sowing.

Sorghum

Proportion of

days in sorghum

regions with

Ea/Et < 0.35

0 - 10 %

10 - 20 %

20 - 30 %

30 - 40 %

40 - 50 %

50 - 60 %

60 - 70 %

70 - 80 %

80 - 90 %

90 - 100 %


Period 5, 80 to 100

days after sowing.

Sorghum

Proportion of

days in sorghum

regions with

Ea/Et < 0.35

0 - 10 %

10 - 20 %

20 - 30 %

30 - 40 %

40 - 50 %

50 - 60 %

60 - 70 %

70 - 80 %

80 - 90 %

90 - 100 %


Period 6, 100 to 120

days after sowing.

Sorghum

Proportion of

days in sorghum

regions with

Ea/Et < 0.35

0 - 10 %

10 - 20 %

20 - 30 %

30 - 40 %

40 - 50 %

50 - 60 %

60 - 70 %

70 - 80 %

80 - 90 %

90 - 100 %


Period 7, 120 to 140

days after sowing.

Sorghum

Proportion of

days in sorghum

regions with

Ea/Et < 0.35

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

Where is drought? Who is affected?What is drought?Context

Pest and Disease Mapping

• Over to Glenn…

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

Step 2: geo-reference and

characterize

Step 3: Run modelsCLIMEX - CBSD

Open Modeler

Step 4: validation

Potential for whitefly

Potential for CBSD

[Day 2] Center Presentation: Bioversity and CIAT

Technology

Transcript of [Day 2] Center Presentation: Bioversity and CIAT