andy j climate change models can guide our adaptation strategies supagro nov 2009
DESCRIPTION
How models can guide our adaptation actions, presented in Supagro on 13th november, 2009 in Montpellier, by Andy Jarvis.TRANSCRIPT
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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
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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
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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
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How can we be sure that it is changing?
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Arctic Ice is Melting
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In order to prepare, we need to know what to prepare for….
….but how?
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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
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So, what do they say?
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Changes in rainfall…
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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/
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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
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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
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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
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The Impacts on Crop Suitability
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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?
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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
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Average change in suitability for all crops in 2050s
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Winners and losers
Number of crops with more than 5% loss
Number of crops with more than 5% gain
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Message 1
Global suitability for agriculture reduces moderately, but problems
of food distribution are exacerbated
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But what about land-use and biodiversity
distribution in 2050?
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The current situation
• Covering 13.8% of the total global surface (3.8% international, 10% national)
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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
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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
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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
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Results: changes in species richness
• Null migration: losses everywhere
• Unlimited migration: mostly displacement
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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
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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
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Minimising impacts: Breeding for beans (Phaseolus vulgaris L.) towards 2020
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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.
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What will likely happen?
2020 – A2
2020 – A2 - changes
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GCM Uncertainties
COEFFICIENT OF VARIATION
PERCENT OF MODELS WITH AGREED DIRECTION
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What are the major climatic constraints for bean production?
• Most of the suitable environments are likely to be limited by temperatures (orange)
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0
5
10
15
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
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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
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Impacts on production of cassava
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Worldwide cassava production climatic constraints
Grey areas are the crop’s main niche.
Blue areas constrained by precipitation
Yellow-orange constrained by temperature
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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
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…….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
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…….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
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Pest and Disease Impacts
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Impacts on whitefly to
2020
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Message 3
Global impacts can be addressed in many cases through existing
diversity, or through crop improvement, but we must start
planning now
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Moving more local…
Coffee in Colombia and Central America
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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
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Adaptation Options
Management
New markets
Alternatives to coffee
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Message 4
Locally, some significant upheavals could occur in terms of economies, cultures, and land-use
patterns
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But it is worse in Central America
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So what do we do?
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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