Using the EcoCrop niche model to forecast impacts of climate

change on global crop productionJulián Ramírez and Andy Jarvis

International Centre for Tropical Agriculture (CIAT)

Bioversity International

Cali, Colombia

Impact assessment: methods and data

• The model: EcoCrop– Based on expert knowledge– A simple algorithm to look at the broad niche of

each species– Ten growing parameters to set up the model

• Absolute rainfall interval• Absolute temperature interval• Optimum rainfall interval• Optimum temperature interval• Lenght of the growing season• Crop freezing temperature

The Model: EcoCrop

It evaluates on monthly basis if there are adequate climatic conditions within a growing season for temperature and precipitation…

…and calculates the climatic suitability of the resulting interaction between rainfall and temperature…

• So, how does it work?

Choosing target crops• 50 target crops selected based on area

harvested in FAOSTATN FAO name Scientific name

Area harvested

(kha)26 African oil palm Elaeis guineensis Jacq. 1327727 Olive, Europaen Olea europaea L. 889428 Onion Allium cepa L. v cepa 334129 Sweet orange Citrus sinensis (L.) Osbeck 361830 Pea Pisum sativum L. 673031 Pigeon pea Cajanus cajan (L.) Mill ssp 468332 Plantain bananas Musa balbisiana Colla 543933 Potato Solanum tuberosum L. 1883034 Swede rap Brassica napus L. 2779635 Rice paddy (Japonica) Oryza sativa L. s. japonica 15432436 Rye Secale cereale L. 599437 Perennial reygrass Lolium perenne L. 551638 Sesame seed Sesamum indicum L. 753939 Sorghum (low altitude) Sorghum bicolor (L.) Moench 4150040 Perennial soybean Glycine wightii Arn. 9298941 Sugar beet Beta vulgaris L. v vulgaris 544742 Sugarcane Saccharum robustum Brandes 2039943 Sunflower Helianthus annuus L v macro 2370044 Sweet potato Ipomoea batatas (L.) Lam. 899645 Tea Camellia sinensis (L) O.K. 271746 Tobacco Nicotiana tabacum L. 389747 Tomato Lycopersicon esculentum M. 459748 Watermelon Citrullus lanatus (T) Mansf 378549 Wheat, common Triticum aestivum L. 21610050 White yam Dioscorea rotundata Poir. 4591

N FAO name Scientific nameArea

harvested (kha)

1 Alfalfa Medicago sativa L. 152142 Apple Malus sylvestris Mill. 47863 Banana Musa acuminata Colla 41804 Barley Hordeum vulgare L. 555175 Bean, Common Phaseolus vulgaris L. 265406 Common buckwheat* Fagopyrum esculentum Moench 27437 Cabbage Brassica oleracea L.v capi. 31388 Cashew Anacardium occidentale L. 33879 Cassava Manihot esculenta Crantz. 18608

10 Chick pea Cicer arietinum L. 1067211 White clover Trifolium repens L. 262912 Cacao Theobroma cacao L. 756713 Coconut Cocos nucifera L. 1061614 Coffee arabica Coffea arabica L. 1020315 Cotton, American upland Gossypium hirsutum L. 3473316 Cowpea Vigna unguiculata unguic. L 1017617 European wine grape Vitis vinifera L. 740018 Groundnut Arachis hypogaea L. 2223219 Lentil Lens culinaris Medikus 384820 Linseed Linum usitatissimum L. 301721 Maize Zea mays L. s. mays 14437622 mango Mangifera indica L. 415523 Millet, common Panicum miliaceum L. 3284624 Rubber * Hevea brasiliensis (Willd.) 825925 Oats Avena sativa L. 11284

Playing around with all this…• Changes in suitability and confidence for

all crops… most impacted regions…

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Percent of models with accordance (%)

Su

ita

bili

ty C

ha

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%)

Global AsiaNorth America AustraliaLatin America CaribbeanEurope Sub-Saharan AfricaNorth Africa Pacif ic

More regional level impacts

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10

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WLD ASI PCF EUR NAF SSA CAR LAM AUS NAM

Per

cen

t o

f cr

op

s (%

)

With 5% or more gain

With 5% or more loss

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WLD ASI PCF EUR NAF SSA CAR LAM AUS NAM

Su

itab

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ang

e (%

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Maximum gainMaximum loss

Percent of crops with significant gains/losses in each region… bad news for North and Sub-Saharan Africa, Latin America, the Caribbean and the Pacific…

Maximum positive and negative changes per region… Europe: the big winner…

And a bit more…

ID Region Highest gain crop Highest loss cropNeither positively nor

negatively affected

WLD Global Common buckwheat* Wheat, common Sorghum (low altitude)ASI Asia Coconut Sorghum (low altitude) CabbagePCF Pacific Potato Cabbage Plantain bananasEUR Europe Millet, common Cacao Wheat, commonNAF North Africa Cotton, American upland Perennial reygrass European wine grapeSSA Sub-Saharan Africa Sugarcane White clover Sesame seedCAR Caribbean Sorghum (low altitude) Sugar beet CashewLAM Latin America White yam Sugar beet WatermelonAUS Australia Pigeon pea Perennial soybean TeaNAM North America Sugarcane Wheat, common Swede rap

• Globally… the most negatively impacted is wheat, specially in North America…

• Staples to be affected significantly…

Crop-based results

• Global suitability change of all crops. Bubble size is coefficient of variation

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Models with accordance (%)

Glo

ba

l su

ita

bili

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ng

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%)

White clover

Wheat

Sugarbeet

OatsCoffee

Perennial soybean

Sweet orange

Common buckwheat

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10

15

22 32 42 52 62 72 82

%Area with suitability loss

Su

ita

bili

ty c

ha

ng

e (

%)

Coffee

Perennial soybean

Sugar beetWheat

Rice

White clover

Maize

White yam

Bananas

Plantains

Common buckwheat

Sugarcane

Crop-based results• Relative importance versus percent of impacted

lands

Contrasting responses on the same industry

Regional and crop-based results: contrasts

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Models with accordance (%)

Glo

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%)

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Models with accordance (%)

Glo

bal

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e (%

)EUROPE NORTH AFRICA

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Models with accordance (%)

Glo

bal

su

itab

ilit

y ch

ang

e (%

) SUB-SAHARAN AFRICA

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Models with accordance (%)

Glo

bal

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LATIN AMERICA

Country based results• Change in suitability for all world countries

for all crops, versus percent of area with loss. Bubble size is the percent of rural population within the country.

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%Area with suitability loss

Su

ita

bili

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%)

Developing

Developed

Mongolia

China

Turkey

Pakistan

Australia

Chile

Spain

Morocco

US India

Algeria

Libya

Greece

ColombiaBrazil

SamoaEq. Guinea

Country based results

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Current suitability (%)

Su

ita

bili

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ng

e (

%)

AustralasiaSub-Saharan AfricaNorth AfricaLatin America and the CaribbeanEurope

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5

15

25

35

45

55

65

-1 4 9 14 19

Average number of crops losing suitability

Cu

rre

nt

cro

p s

uit

ab

ility

(%

)

Developing

Developed

Mali

Bulgaria

Togo

USChina

Turkey

India

Australia

Colombia

Current suitability and predicted changes to 2050s for geographic regions.

Note the high variability in SSA and Asia, while relatively low variability in changes within Latin America

Current suitability and number of crops with significant negative changes. Bubble size is Infant Mortality Rate

Average change in suitability for all crops in 2050s

Winners and losers

Number of crops with more than 5% loss

Number of crops with more than 5% gain

Next steps• Validate the model and adjust the

parameters• Downscale suitability predictions to yields

and… producers’ income• Evaluate the economic impacts of adaptation

strategies. Where, when, how to adapt?• Use the model to evaluate likelihood of crop

substitution• More regionally oriented results (incl. RCM)

Next steps: validate and adjust the parameters

• Expert based model… expert based validation and re-parameterisation

• GoogleEarth KMLs and web-based plugin

Suitability predictionVisual validation Survey to gather

validation data