Climate change
Knowledge Sharing Week
May 2009

Contents
• Climate change in 3 short minutes
• The data we have in CIAT
• Analysing impacts in agriculture
– Our commodities
– Others
• Adaptation, adaptation, adaptation but what
does it really mean?

El clima esta
cambiando,
no me diga
que no

S o u r c e s o f A g r ic u lt u r a l G r e e n h o u s e
Ga s e s
excluding land use change Mt CO2-eq
Source: Cool farming: Climate impacts of agriculture and mitigation potential, Greenpeace, 07 January 2008

El pasado y el presente – El “hockey
stick”

El arctico esta descongelando

Modelos GCM : “Global Climate
Models”
• 21 “global climate models” (GCMs) basados en ciencias
atmosféricas, química, física, biología, y, dependiendo
de las creencias, algo de astrología
• Se corre desde el pasado hasta el futuro
• Hay diferentes escenarios de emisiones de gases

Y que dicen los modelos?

What do the 21 models say?
The world gets warmer

….and wetter, but not everywhere

The Data We have in CIAT
• First, data from Stanford (Lobell)
• Second, data downloaded from IPCC
• Now….strategic partnership with the
Tyndell Centre in UK who will provide us
with the latest projections (7 GCM models,
4 emissions scenarios) through their
AVOID project

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
France CNRM-CM3 CNRM_CM3 128x64
Centre National de Recherches Météorologiques 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 Germany
ECHO-G MIUB_ECHO_G 96x48
Meteorological Research Institute of KMA Korea 2050
LASG / Institute of Atmospheric Physics China FGOALS-g1.0 IAP_FGOALS_1_0_G 128x60 2050
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 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 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 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
UK UKMO-HadCM3 HCCPR_HADCM3 96x73
Met Office 2020-2050
Center for Climate System Research (CCSR)
National Institute for Environmental Studies (NIES) Japan NIES-99 NIES-99 64x32
2020

General climate change description
Average Climate Change Trends of Colombia
The rainfall increases from 2672.92 millimeters to 2739.29 millimeters in 2050 passing through 2613.89 in 2020
General
Temperatures increase and the average increase is 2.45 ºC passing through an increment of 0.94 ºC in 2020
climate
The mean daily temperature range increases from 9.52 ºC to 9.69 ºC in 2050
characteristics
The maximum number of cumulative dry months keeps constant in 2 months
The maximum temperature of the year increases from 30.81 ºC to 33.97 ºC while the warmest quarter gets hotter by 2.58 ºC in 2050
Extreme The minimum temperature of the year increases from 19.08 ºC to 21.16 ºC while the coldest quarter gets hotter by 2.4 ºC in 2050
conditions The wettest month gets wetter with 358.48 millimeters instead of 353.03 millimeters, while the wettest quarter gets wetter by 5.17 mm in 2050
The driest month gets wetter with 96.32 millimeters instead of 84.75 millimeters while the driest quarter gets wetter by 45.47 mm in 2050
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 3.62%
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
350 40 Current precipitation
Precipitation 2050
Precipitation 2020
35 Mean temperature 2020
300
Mean temperature 2050
Current mean temperature
30
250 Maximum temperature 2020
Maximum temperature 2050
Precipitation (mm)
25 Current maximum temperature
Temperature (ºC)
200 Minimum temperature 2020
Minimum temperature 2050
20 Current minimum temperature
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 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

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
1000 15
10
500
5
0 MIROC3 2 HIRES 0
MPI ECHAM 5
MIROC3 2
MIUB ECHO G
CSIRO MK3 0
CCCMA CGCM3
CCCMA CGCM3
CNRM CM3
CCCMA CGCM2
BCCR BCM2 0
GFDL CM2 1
HCCPR HADCM3
GFDL CM2
NCAR PCM 1
MEDRES
1 T63
1
Total annual precipitation (bio 12) Annual mean temperature (bio 1)
Annual maximum temperature (bio 5) Annual minimum temperature (bio 6)

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

Colombia y el mundo en cambio
climático
2950 27.5
Colombia
2900 27.0
+8.1% 26.5
+3.1ºC
Precipitación total anual (mm)
2850
Temperatura media anual (ºC)
2800 26.0
2750 25.5
2700 25.0
2650 24.5
2600 24.0 Temperatura media anual (ºC)
Precipitación total anual (mm)
Tendencia temporal
2550 Tendencia temporal
23.5 Intervalo de confianza (95%)
Intervalo de confianza (95%)
2500 23.0
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090
Año Año
810 12.0
Mundo
790
+14% 11.0
+4.5ºC
Precipitación total anual (mm)
Temperatura media anual (ºC)
770
10.0
750
730 9.0
710
8.0
690
Temperatura media anual (ºC)
Precipitación total anual (mm)
7.0 Tendencia temporal
670 Tendencia temporal
Intervalo de confianza (95%) Intervalo de confianza (95%)
650 6.0
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090
Año
Año

5.0
4.0
3.0
Temperature
2.0
1.0
1870
0.0
-200.0 -100.0 Baseline
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

Cambio en
Cambio en Cambio en Incertidumbre
Cambio en meses
Region Departamento Temperatura estacionalidad de entre modelos
Precipitacion consecutivos
media precipitacion (StDev prec)
secos
Amazonas Amazonas 12 2.9 1.4 0 135
Amazonas Caqueta 138 2.7 -1.3 0 193
Amazonas Guania 55 2.9 -3.2 0 271
Amazonas Guaviare 72 2.8 -2.9 -1 209
Amazonas Putumayo 117 2.6 0.6 0 170
Andina Antioquia 18 2.1 1.3 0 129
Andina Boyaca 50 2.7 -3.9 -1 144
Andina Cundinamarca 152 2.6 -2.6 0 170
Andina Huila 51 2.4 1.0 0 144
Andina Norte de santander 73 2.8 -0.4 0 216
Andina Santander 51 2.7 -2.4 0 158
Andina Tolima 86 2.4 -3.1 0 148
Caribe Atlantico -74 2.2 -2.9 2 135
Caribe Bolivar 90 2.5 -1.8 0 242
Caribe Cesar -119 2.6 -1.3 0 160
Caribe Cordoba -11 2.3 -3.8 0 160
Caribe Guajira -69 2.2 -1.8 0 86
Caribe Magdalena -158 2.4 -1.8 0 153
Caribe Sucre 10 2.4 -4.1 -1 207
Eje Cafetero Caldas 252 2.4 -4.2 -1 174
Eje Cafetero Quindio 153 2.3 -4.1 -1 145
Eje Cafetero Risaralda 158 2.4 -3.5 -1 141
Llanos Arauca -13 2.9 -6.4 -1 188
Llanos Casanare 163 2.8 -5.7 -1 229
Llanos Meta 10 2.7 -5.4 -1 180
Llanos Vaupes 46 2.8 -1.4 0 192
Llanos Vichada 59 2.6 -2.6 0 152
Pacifico Choco -157 2.2 -1.2 0 148
Sur Occidente Cauca 172 2.3 -1.6 0 168
Sur Occidente Narino 155 2.2 -1.4 0 126
Sur Occidente Valle del Cauca 275 2.3 -5.1 -1 166

Estimating Likely Impacts in
Agriculture
• Three modelling approaches:
– Niche-based
– Empirical
– Mechanistic model- based

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

Pros and cons of the approach
• Simple to use and apply
PROS
• 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
CONS
• 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

Current Cassava Suitability
Gmin: 150, Gmax: 365.
KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45.
Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.

Future Cassava Suitability (2020)
Gmin: 150, Gmax: 365.
KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45.
Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.

Change in Cassava Suitability
Gmin: 150, Gmax: 365.
KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45.
Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.

TECHNOLOGY OPTION:
+2-3oC HEAT TOLERANCE
Only India benefits from heat tolerance. This is a
national strategy for technology development. CIAT’s
strategy probably better placed in pests/diseases.

Changes in
adaptability in
Green Mite 2020

Change in
adaptability of
Whitefly 2020

Arachis pintoi Krap.& Greg.
Gmin: 180, Gmax: 300
KTmp: 0, Tmin: 12, TOPmn: 22, TOPmx: 28,
Tmax: 30
Rmax: 1600, ROPmn: 1800, ROPmx:2000,
Rmax: 3000

Clitoria ternatea L.
Gmin: 50, Gmax: 365
KTmp: -2, Tmin: 15, TOPmn: 19, TOPmx: 28,
Tmax: 32
Rmax: 400, ROPmn: 1200, ROPmx:1800,
Rmax: 4300

Leucaena leucocephala (La.)
Gmin: 180, Gmax: 365
KTmp: 0, Tmin: 10, TOPmn: 20, TOPmx: 32,
Tmax: 42
Rmax: 250, ROPmn: 600, ROPmx: 3000,
Rmax: 5000

Brachearia x hybrid
Gmin: 120, Gmax: 365
KTmp: 0, Tmin: 20, TOPmn: 24, TOPmx: 30,
Tmax: 35
Rmax: 800, ROPmn: 1200, ROPmx:1800,
Rmax: 3000

Cow peas Vigna unguiculata unguic. L 10176
Grapes Vitis vinifera L. 7400
Groundnut Arachis hypogaea L. 22232
The geography of crop suitability
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
Area Natural rubber Hevea brasiliensis (Willd.) 8259
Crop Species Harvested Oats Avena sativa L. 11284
(k Ha) Oil palm Elaeis guineensis Jacq. 13277
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

Change in global suitability

Number of crops that lose out

Number of crops that gain

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

Crop adaptability anomaly
-80
-60
-40
-20
0
20
40
60
80
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

COFFEE SUITABILITY
COFFEE: 14.3% of GDP in
Nicaragua, 2006.

COFFEE SUITABILITY
0,6
0,5
0,4
Suitability
0,3
0,2
0,1
current
2050
0,0
0 500 1000 1500 2000
Altitude (m asl)

COFFEE ACIDITY SUITABILITY

COFFEE ACIDITY SUITABILITY
1,2
1,0
0,8
Suitability
0,6
0,4
0,2
current
2050
0,0
0 500 1000 1500 2000 2500
Altitude (m asl)

ADAPTATION STRATEGIES
1. Areas not anymore suitable for coffee
(alternatives)
2. Areas only suitable with adapted
management (varieties, irrigation, shade, etc)
3. New potential areas (expand where viable
and possible)

IMPLICATIONS ON SUPPLY
CHAIN
1. Shortage of commodity coffee and high value
coffee
2. Possible increase in prices and income for
supply chain actors
3. Change in sourcing areas and channels
4. Possible loss of product reputation
(Denomination of Origin in Veracruz)

Adaptation, adaptation, adaptation
• We see four types of agriculture within the
context of climate change:
– Traditional staple, short-cycle
– Short-cycle low investment cash crop
– Short-cycle high investment cash crop
– Perennial long-term high investment system

Different strategies
• Traditional staple, short-cycle
– Technology development, community-based adaptation
strategies (first practices)
• Short-cycle low investment cash crop
– Technology development, crop substitution
• Short-cycle high investment cash crop
– Careful planning, supply chain level adaptation
strategies
• Perennial long-term high investment system
– Technology development, entire supply-chain level
adaptation strategies (The Anchor)

Adaptive management across the supply
chain
MARKET
Short-cycle high
investment cash crops
Perennial long-term
high investment system
FARM

Conclusions
• In multiple CIAT crops no panaceae in breeding
strategies – we need to do more work on this
• Regional-level challenges at both all ends of
spectrum (heat, drought, excess water)
• We need to strengthen the analysis of economic
and social implications of climate change
• Technology development today for 2020
• Scientific gap in understanding of crop
substitution (how common, implications, nutrition
etc.)