Climate change and agriculture: How models can guide our adaptation strategies

Andy Jarvis, Julian Ramirez, Edward Guevara, Peter Laderach and Emmanuel Zapata

Program Leader, Decision and Policy Analysis, CIAT

Contents

• About climate change and predictive models

• Global level changes (agriculture and biodiversity)…..

• …to regional crop specific changes….

• …to local adaptation options….

• Defining adaptation roadmaps

Sources of Agricultural Greenhouse Gasesexcluding land use change Mt CO2-eq

Source: Cool farming: Climate impacts of agriculture and mitigation potential, Greenpeace, 07 January 2008

How can we be sure that it is changing?

Arctic Ice is Melting

In order to prepare, we need to know what to prepare for….

….but how?

Global Climate Models (GCMs)

• 21 global climate models in the world, based on atmospheric sciences, chemistry, biology, and a touch of astrology

• Run from the past to present to calibrate, then into the future

• Run using different emissions scenarios

So, what do they say?

Changes in rainfall…

CIAT’s Data

• 18 GCM models to 2050, 9 to 2020

• Different scenarios, A1b, B1, commit

• Downscaled using empirical methods

http://gisweb.ciat.cgiar.org/GCMPage/

BCCR-BCM2.0 CCCMA-CGCM2CCCMA-CGCM3.1

T47 CCCMA-CGCM3.1-T63 CNRM-CM3 IAP-FGOALS-1.0G

GISS-AOM GFDL-CM2.1 GFDL-CM2.0 CSIRO-MK3.0 IPSL-CM4 MIROC3.2-HIRES

MIROC3.2-MEDRES MIUB-ECHO-G MPI-ECHAM5 MRI-CGCM2.3.2A NCAR-PCM1 UKMO-HADCM3

BCCR-BCM2.0 CCCMA-CGCM2CCCMA-CGCM3.1

T47 CCCMA-CGCM3.1-T63 CNRM-CM3 IAP-FGOALS-1.0G

GISS-AOM GFDL-CM2.1 GFDL-CM2.0 CSIRO-MK3.0 IPSL-CM4 MIROC3.2-HIRES

MIROC3.2-MEDRES MIUB-ECHO-G MPI-ECHAM5 MRI-CGCM2.3.2A NCAR-PCM1 UKMO-HADCM3

Climate characteristic

Climate Seasonality

The coefficient of variation of temperature predictions between models is 9.13%

The maximum number of cumulative dry months decreases from 8 months to 7 months

These results are based on the 2050 climate compared with the 1960-2000 climate. Future climate data is derived from 18 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 16.03%

General climate

characteristics

Extreme conditions

Variability between models

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

The driest month gets drier with 9.64 millimeters instead of 11.32 millimeters while the driest quarter gets wetter by 19.28 mm in 2050

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

The mean daily temperature range increases from 14.28 ºC to 14.74 ºC in 2050

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

Average Climate Change Trends of Junin (Peru)

General climate change description

The maximum temperature of the year increases from 14.68 ºC to 18.2 ºC while the warmest quarter gets hotter by 2.55 ºC in 2050The minimum temperature of the year increases from -3.51 ºC to -1.06 ºC while the coldest quarter gets hotter by 2.78 ºC in 2050The wettest month gets wetter with 152.07 millimeters instead of 141.48 millimeters, while the wettest quarter gets wetter by 20.25 mm in

The rainfall increases from 853.51 millimeters to 942.96 millimeters in 2050 passing through 829.18 in 2020Temperatures increase and the average increase is 2.57 ºC passing through an increment of 0.91 ºC in 2020

-5

0

5

10

15

20

0

20

40

60

80

100

120

140

160

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

Tem

per

atu

re (

ºC)

Pre

cip

itat

ion

(m

m)

Month

Current precipitation

Precipitation 2020

Precipitation 2050

Mean temperature 2020

Mean temperature 2050

Current mean temperature

Maximum temperature 2020

Maximum temperature 2050

Current maximum temperature

Minimum temperature 2020

Minimum temperature 2050

Current minimum temperature

The Impacts on Crop Suitability

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?

Agricultural systems analysis• 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

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

Message 1

Global suitability for agriculture reduces moderately, but problems

of food distribution are exacerbated

But what about land-use and biodiversity

distribution in 2050?

The current situation

• Covering 13.8% of the total global surface (3.8% international, 10% national)

Results: protected areas per region

0

1000

2000

3000

4000

5000

6000

0 1000 2000 3000 4000 5000

Maximum hotspot overall

Ma

xim

um

ho

tsp

ot

wit

hin

PA

s Complete representativeness

Average representativeness

UK

World

Mexico

US

South AfricaNorth Africa

Middle eastSaudi Arabia

West Africa

Brazil

Current extent of in situ conservation

Global biodiversity currently well conserved

Modeling approach

• Aplying the maximum entropy algorithm– Macoubea guianensis Aubl.: food for rural indigenous

communities in the Amazon

Data harvesting from GBIF Building the presence model Projecting on future climates

NULL MIGRATION

UNLIMITEDMIGRATION

Potential habitatexpansion

NULL MIGRATION UNLIMITED MIGRATION

CURRENT

Current and future predicted species richness

• Important hotspots in Latin America, Europe, Australasia and Central Africa

• Displacement and loss of niches

NULL MIGRATIONUNLIMITED MIGRATION

Results: changes in species richness

• Null migration: losses everywhere

• Unlimited migration: mostly displacement

Results: in situ conservation under the context of CC

• No matter if the best ‘adaptation’ scenario (unlimited dispersal) is chosen, negatives are expected in most regions

-800

-600

-400

-200

0

200

0 20 40 60 80 100 120

Percent of area with loss within PAs [UM]

Ch

an

ge

in s

pe

cie

s r

ich

ne

ss

wit

hin

P

As

[U

M]

Caribbean

Central America

France

Germany

Australia

ItalyMexico

South AmericaEurope West Africa

South KoreaBrazilMiddle EastUS

Message 2

There will be greater pressure on land resources for multiple uses,

as currently non-arable land becomes arable, and as we face

massive biodiversity loss

Minimising impacts: Breeding for beans (Phaseolus vulgaris L.) towards 2020

How are beans standing up currently?

Growing season (days) 90

13.6

17.5

23.1

25.6

Minimum absolute rainfall (mm)

200

Minimum optimum rainfall (mm)

363

Maximum optimum rainfall (mm)

450

Maximum absolute rainfall (mm)

710

Killing temperature (°C) 0

Minimum absolute temperature (°C)

13.6

Minimum optimum temperature (°C)

17.5

Maximum optimum temperature (°C)

23.1

Maximum absolute temperature (°C)

25.6

Parameters determined based on statistical analysis of current bean growing environments from the Africa and LAC Bean Atlases.

What will likely happen?

2020 – A2

2020 – A2 - changes

GCM Uncertainties

COEFFICIENT OF VARIATION

PERCENT OF MODELS WITH AGREED DIRECTION

What are the major climatic constraints for bean production?

• Most of the suitable environments are likely to be limited by temperatures (orange)

0

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10

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20

25

30

35

40

-25% -20% -15% -10% -5% None +5% +10% +15% +20% +25%

Crop resilience improvement

Ch

ang

e in

su

itab

le a

reas

[>

80%

] (%

)

Cropped lands

Non-cropped lands

Global suitable areas

Technology options: breeding for drought and waterlogging tolerance

0

2

4

6

8

10

12

14

Ropmin Ropmax Not benefited

Ben

efit

ed a

reas

(m

illi

on

hec

tare

s) Currently cropped lands

Not currently cropped landsSome 22.8% (3.8 million ha) would benefit from drought tolerance improvement to 2020s

Drought tolerance

Waterlogging tolerance

Technology options: breeding for heat and cold tolerance

0

10

20

30

40

50

60

70

-2.5ºC -2ºC -1.5ºC -1ºC -0.5ºC None +0.5ºC +1ºC +1.5ºC +2ºC +2.5ºC

Crop resilience improvement

Ch

ang

e in

su

itab

le a

reas

[>

80%

] (%

)

Cropped lands

Non-cropped lands

Global suitable areas

0

2

4

6

8

10

12

14

Topmin Topmax Not benefited

Ben

efit

ed a

reas

(m

illi

on

hec

tare

s)

Currently cropped lands

Not currently cropped lands

Cold tolerance

Heat tolerance

Some 42.7% (7.2 million ha) would benefit from heat tolerance improvement to 2020s

Impacts on production of cassava

Worldwide cassava production climatic constraints

Grey areas are the crop’s main niche.

Blue areas constrained by precipitation

Yellow-orange constrained by temperature

Impact of climate change on cassava suitable environments

Global cassava suitability will increase 5.1% on average by 2050… but many areas of Latin America suffer negative impacts

…….and for Latin America?Drought or flooding tolerance

30% of current cassava fields would benefit from enhanced drought or flooding tolerance

1.6m Ha still suffering climatic constraint

2.23m Ha of current production

2.1m Ha of new land would become suitable for cassava

0

5

10

15

20

25

30

35

-2.5% -2% -1.5% -1% -0.5% None +0.5% +1% +1.5% +2% +2.5%

Mejora en la resiliencia de los cultivos

Cam

bio

en

áre

as a

dap

tab

les

[>80

%]

(%)

Áreas cultivadas

Áreas no-cultivadas

Total áreasadaptables

Toleracia a sequias

Toleracia a inundación

0

5

10

15

20

25

30

35

-2.5% -2% -1.5% -1% -0.5% None +0.5% +1% +1.5% +2% +2.5%

Mejora en la resiliencia de los cultivos

Cam

bio

en

áre

as a

dap

tab

les

[>80

%]

(%)

Áreas cultivadas

Áreas no-cultivadas

Total áreasadaptables

Toleracia a sequias

Toleracia a inundación

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Ropmin Ropmax Not benefited

Áre

as b

enef

icia

das

(m

illi

ón

de

hec

táre

as)

Áreas cultivadas actualmente

Áreas no-cultivadasactualmente

…….and for Latin America?Heat or cold tolerance

27% of current cassava fields would benefit from enhanced cold or heat tolerance

2.23m Ha of current production

2.2m Ha of new land would become suitable for cassava

0

2

4

6

8

10

12

-2.5ºC -2ºC -1.5ºC -1ºC -0.5ºC None +0.5ºC +1ºC +1.5ºC +2ºC +2.5ºC

Mejoramiento en la resiliencia del cultivo

Cam

bio

en

áre

as a

dap

tab

les

[>80

%]

(%)

Áreas cultivadas

Áreas no-cultivadas

Total áreas adaptables

Toleracia al calor

Toleracia al frío

0

2

4

6

8

10

12

-2.5ºC -2ºC -1.5ºC -1ºC -0.5ºC None +0.5ºC +1ºC +1.5ºC +2ºC +2.5ºC

Mejoramiento en la resiliencia del cultivo

Cam

bio

en

áre

as a

dap

tab

les

[>80

%]

(%)

Áreas cultivadas

Áreas no-cultivadas

Total áreas adaptables

Toleracia al calor

Toleracia al frío

0

1

1

2

2

3

Topmin Topmax Not benefited

Áre

as b

enef

icia

das

(m

illó

n d

e h

ectá

reas

) Áreas cultivadas actualmente

Áreas no-cultivadasactualmente

Pest and Disease Impacts

Impacts on whitefly to

2020

Message 3

Global impacts can be addressed in many cases through existing

diversity, or through crop improvement, but we must start

planning now

Moving more local…

Coffee in Colombia and Central America

Suitability in Cauca

• Significant changes to 2020, drastic changes to 2050

• The Cauca case: reduced coffeee growing area and changes in geographic distribution. Some new opportunities.

MECETA

Adaptation Options

Management

New markets

Alternatives to coffee

Message 4

Locally, some significant upheavals could occur in terms of economies, cultures, and land-use

patterns

But it is worse in Central America

So what do we do?

Models to support adaptation roadmaps

• What to do, how, where, and when?• Describe the problem• Ex ante analysis of potential benefits from

an action• Cost benefit analysis of adaptation options• Supporting actions on the ground, through

participatory, community based processes• Ensure a holistic view: adaptation of

agriculture and environment

a.jarvis@cgiar.org