Escenarios de Cambio climático en Colombia y la agricultura
Andy Jarvis, Julian Ramirez, Peter Laderach, Edward Guevara y Emmanuel Zapata
Program Leader, Decision and Policy Analysis, CIAT
Contenido
• Acerca de cambio climaticoy los modelos GCM
• El futuro de Colombia
• Analisis de adaptabilidadglobal, y la realidadColombiana
• Fitomejoramiento comouna opcion de adaptacion
• El susto de café
• Lo que se debe hacer
Idiota
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
DeforestacionColombia – Río Caquetá
• Size
– 480 * 300 [km2]
– 14400000 [ha]
• Vegetation type
– Tropical forest
Caqueta, Jan 2004 – May 2009Date
Colombia – Rio Caquetá
Cumulative detections in hectares
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
1/1
/2004
4/1
/2004
7/1
/2004
10/1
/2004
1/1
/2005
4/1
/2005
7/1
/2005
10/1
/2005
1/1
/2006
4/1
/2006
7/1
/2006
10/1
/2006
1/1
/2007
4/1
/2007
7/1
/2007
10/1
/2007
1/1
/2008
4/1
/2008
7/1
/2008
10/1
/2008
1/1
/2009
4/1
/2009
Time
Hecta
res
Porque tan seguros que el clima esta cambiando?
Arctic Ice is Melting
Los modelos de pronostico de clima
Usando el pasado para aprender del futuro
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
Entonces, ¿qué es lo que dicen?Variaciones en la temperatura de la superficie de la tierra: de 1000 a 2100
Los peligros de 4oC
Bases de Datos
• 18 modelos para 2050, 9 para 2020
• Diferentes escenarios, A1b, B1, commit
• Downscaled usando metodos estadisticos
http://gisweb.ciat.cgiar.org/GCMPage/home.html
BCCR-BCM2.0 CCCMA-CGCM2CCCMA-CGCM3.1
T47CCCMA-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
T47CCCMA-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
23.0
23.5
24.0
24.5
25.0
25.5
26.0
26.5
27.0
27.5
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090Año
Tem
pera
tura
med
ia a
nu
al (º
C)
Temperatura media anual (ºC)
Tendencia temporal
Intervalo de confianza (95%)
2500
2550
2600
2650
2700
2750
2800
2850
2900
2950
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090
Año
Pre
cip
itació
n t
ota
l an
ual (m
m)
Precipitación total anual (mm)
Tendencia temporal
Intervalo de confianza (95%)
Colombia y el mundo en cambio climático
Colombia
650
670
690
710
730
750
770
790
810
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090
Año
Pre
cip
itació
n t
ota
l an
ual (m
m)
Precipitación total anual (mm)
Tendencia temporal
Intervalo de confianza (95%)
6.0
7.0
8.0
9.0
10.0
11.0
12.0
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090Año
Tem
pera
tura
med
ia a
nu
al (º
C)
Temperatura media anual (ºC)
Tendencia temporal
Intervalo de confianza (95%)
Mundo +4.5ºC+14%
+3.1ºC+8.1%
Region DepartamentoCambio en
Precipitacion
Cambio en
Temperatura
media
Cambio en
estacionalidad de
precipitacion
Cambio en
meses
consecutivos
secos
Incertidumbre
entre modelos
(StDev prec)
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
Climate
characteristic
Climate
Seasonality
General climate change description
The maximum temperature of the year increases from 24.21 ºC to 27.37 ºC while the warmest quarter gets hotter by 2.45 ºC
The minimum temperature of the year increases from 13.31 ºC to 15.06 ºC while the coldest quarter gets hotter by 2.05 ºC
The wettest month gets wetter with 343.72 millimeters instead of 337.91 millimeters, while the wettest quarter gets wetter by 23.62 mm
The rainfall increases from 2753.76 millimeters to 2857.4 millimeters
Temperatures increase and the average increase is 2.21 ºC
Precipitation predictions were uniform between models and thus no outliers were detected
Average Climate Change Trends of Risaralda
The coefficient of variation of temperature predictions between models is 4.27%
The maximum number of cumulative dry months keeps constant in 0 months
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 5.09%
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
The driest month gets wetter with 154.32 millimeters instead of 150.3 millimeters while the driest quarter gets wetter by 33.43 mm
Temperature predictions were uniform between models and thus no outliers were detected
The mean daily temperature range increases from 9.91 ºC to 10.46 ºC
0
50
100
150
200
250
300
350
400
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
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
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 FAOSTAT
N FAO name Scientific name
Area
harvested
(kha)26 African oil palm Elaeis guineensis Jacq. 13277
27 Olive, Europaen Olea europaea L. 8894
28 Onion Allium cepa L. v cepa 3341
29 Sweet orange Citrus sinensis (L.) Osbeck 3618
30 Pea Pisum sativum L. 6730
31 Pigeon pea Cajanus cajan (L.) Mill ssp 4683
32 Plantain bananas Musa balbisiana Colla 5439
33 Potato Solanum tuberosum L. 18830
34 Swede rap Brassica napus L. 27796
35 Rice paddy (Japonica) Oryza sativa L. s. japonica 154324
36 Rye Secale cereale L. 5994
37 Perennial reygrass Lolium perenne L. 5516
38 Sesame seed Sesamum indicum L. 7539
39 Sorghum (low altitude) Sorghum bicolor (L.) Moench 41500
40 Perennial soybean Glycine wightii Arn. 92989
41 Sugar beet Beta vulgaris L. v vulgaris 5447
42 Sugarcane Saccharum robustum Brandes 20399
43 Sunflower Helianthus annuus L v macro 23700
44 Sweet potato Ipomoea batatas (L.) Lam. 8996
45 Tea Camellia sinensis (L) O.K. 2717
46 Tobacco Nicotiana tabacum L. 3897
47 Tomato Lycopersicon esculentum M. 4597
48 Watermelon Citrullus lanatus (T) Mansf 3785
49 Wheat, common Triticum aestivum L. 216100
50 White yam Dioscorea rotundata Poir. 4591
N FAO name Scientific name
Area
harvested
(kha)1 Alfalfa Medicago sativa L. 15214
2 Apple Malus sylvestris Mill. 4786
3 Banana Musa acuminata Colla 4180
4 Barley Hordeum vulgare L. 55517
5 Bean, Common Phaseolus vulgaris L. 26540
6 Common buckwheat* Fagopyrum esculentum Moench 2743
7 Cabbage Brassica oleracea L.v capi. 3138
8 Cashew Anacardium occidentale L. 3387
9 Cassava Manihot esculenta Crantz. 18608
10 Chick pea Cicer arietinum L. 10672
11 White clover Trifolium repens L. 2629
12 Cacao Theobroma cacao L. 7567
13 Coconut Cocos nucifera L. 10616
14 Coffee arabica Coffea arabica L. 10203
15 Cotton, American upland Gossypium hirsutum L. 34733
16 Cowpea Vigna unguiculata unguic. L 10176
17 European wine grape Vitis vinifera L. 7400
18 Groundnut Arachis hypogaea L. 22232
19 Lentil Lens culinaris Medikus 3848
20 Linseed Linum usitatissimum L. 3017
21 Maize Zea mays L. s. mays 144376
22 mango Mangifera indica L. 4155
23 Millet, common Panicum miliaceum L. 32846
24 Rubber * Hevea brasiliensis (Willd.) 8259
25 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
Un análisis sectorial para Colombia
Actual Temperatura (%) Precipitación (%) Cultivo Núm.
Deptos Área (ha) Pdn (Ton) 2-2.5ºC 2.5-3ºC -3-0% 0-3% 3-5%
Arroz total 26 460,767 2,496,118 64.6 35.4 15.7 23.6 60.7
Cebada 4 2,305 3,939 47.2 52.8 0.0 28.5 71.5
Maíz 31 626,616 1,370,456 80.5 19.5 27.7 37.1 35.2
Sorgo 14 44,528 137,362 97.0 3.0 33.8 3.8 62.4
Trigo 6 18,539 44,374 69.0 31.0 0.2 68.4 31.5
Ajonjolí 6 3,216 2,771 100.0 0.0 69.0 28.5 2.5
Fríjol 25 124,189 146,344 84.6 15.4 10.7 40.4 48.9
Soya 6 23,608 42,937 0.3 99.7 0.0 0.0 100.0
Maní 4 2,278 2,586 91.0 9.0 0.0 47.2 52.8
Algodón 15 55,914 126,555 98.0 2.0 14.6 55.7 29.7
Papa 13 163,505 2,883,354 71.5 28.5 2.6 27.1 70.4
Tabaco rubio 12 9,082 15,509 31.7 68.3 16.9 47.3 35.8
Hortalizas 14 20,265 270,230 84.9 15.1 16.1 28.7 55.2
Banano exportación 2 44,245 1,567,443 100.0 0.0 26.9 73.1 0.0
Cacao 27 113,921 60,218 40.2 59.8 17.3 53.2 29.5
Caña de azúcar 6 235,118 3,259,779 99.6 0.4 1.1 0.0 98.9
Tabaco negro 5 5,376 9,648 33.6 66.4 17.9 75.2 6.9
Flores 2 8,700 218,122 100.0 0.0 0.0 16.1 83.9
Palma africana 14 154,787 598,078 54.8 45.2 54.2 36.3 9.5
Caña panela 24 219,441 1,189,335 77.8 22.2 6.1 33.8 60.2
Plátano exportación 1 19,187 209,647 100.0 0.0 0.0 100.0 0.0
Coco 10 16,482 127,554 100.0 0.0 10.7 69.3 19.9
Fique 8 19,651 21,687 78.1 21.9 0.3 55.1 44.6
Ñame 9 25,105 261,188 100.0 0.0 46.7 53.3 0.0
Yuca 31 194,572 2,107,939 70.9 29.1 39.8 41.4 18.9
Plátano no exportable 31 375,232 3,080,718 79.8 20.2 7.2 36.1 56.6
Frutales 18 148,574 1,417,919 72.5 27.5 7.7 22.5 69.8
Café 17 613,373 708,214 84.7 15.3 8.2 28.8 63.1
0
10
20
30
40
50
60
70
80
90
100
Caña d
e
azúcar
Café
Maíz
Plá
tano n
o
export
able
Caña p
anela
Fru
tale
s
Papa
Yuca
Arr
oz t
ota
l
Palm
a
afr
icana
Cacao
Po
rcen
taje
de á
rea c
on
cam
bio
Cambio en temperatura mayor a 2.5ºC
Cambio en ppt mayor 3%
•50-60% de los productores de al menos el 70% de las actividades del pais son pequeños
•Agricultura aporta ~50% de las emisiones nacionales (Colombia aporta 0.37% de emisiones al nivel mundial)
•28.6% de los productos agrícolas arriba de 1200msnm (fraccion del area de Colombia)
•Cultivos permanentes (66.4% del PIB de 2007) seriamente afectados
Fuente: MADR, 2005 Fuente: CIAT, 2009
0
10
20
30
40
50
60
70
80
90
100
Palma Banano Café Caña Arroz Cacao
Po
rcen
taje
de f
incas <
10h
a
Vulnerabilidades del Sector
Minimising impacts: Breeding for beans (Phaseolus vulgaris L.) towards 2020
How are beans standing up currently?
Growing season (days) 90
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
Minimum absolute
rainfall (mm)200
Minimum optimum
rainfall (mm)363
Maximum optimum
rainfall (mm)450
Maximum absolute
rainfall (mm)710
Growing season (days) 90
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
5
10
15
20
25
30
35
40
-25% -20% -15% -10% -5% None +5% +10% +15% +20% +25%
Crop resilience improvement
Ch
an
ge i
n s
uit
ab
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
efi
ted
are
as (
mil
lio
n h
ecta
res) 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
an
ge i
n s
uit
ab
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
efi
ted
are
as (
mil
lio
n h
ecta
res) 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
en
efi
cia
das (
mil
lió
n d
e
hectá
reas)
Áreas cultivadas actualmente
Áreas no-cultivadas
actualmente
…….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
en
efi
cia
das (
mil
lón
de h
ectá
reas)
Áreas cultivadas actualmente
Áreas no-cultivadas
actualmente
Pest and Disease Impacts
Impacts on whitefly to 2020
Message 2
Global impacts can be addressed in many cases through existing diversity,
or through crop improvement, but we must start planning now
Un Ejemplo
El susto de café en Cauca
Pongámoslo en perspectiva
• Café prefiere 19 a 21.5oC y 1,800 a 2,800mm de lluvia
• Mes mas seco > 120mm
• Mucha lluvia durante floración resulta en poca productividad – ej. 2008/2009
• Aumento en broca y roya con mas calor (>21.5oC)
• La sombra reduzca temperatura del cafetal por unos 1-2oC, pero reduzca también la variabilidad de temperaturas día a noche
Climas mueven hacia arriba
Rango
Altitudinal
Tmedia
anual
actual
Tmedia
anual
futuro
Tmedia
anual
cambio
(ºC)
Ppt total
anual
actual
Ppt total
anual
futuro
Cambio
ppt total
(%)
190-500 25.54 27.70 2.16 5891 6002 1.88
501-1000 23.47 25.66 2.19 3490 3597 3.04
1000-1500 21.29 23.50 2.21 2537 2641 4.10
1500-2000 18.36 20.58 2.22 2519 2622 4.08
2000-2500 15.60 17.82 2.22 2555 2657 4.00
2500-3000 13.33 15.54 2.21 2471 2575 4.20
Temperatura media reduce por 0.51oC por cada 100m en la zona cafetero. Un cambio de 2.2oC equivale a una diferencia de 440m.
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
Instrumentos de Adaptación
Manejo
Nuevos mercados
Alternativas al cafe
Pero es peor en América Central
Message 3
Locally, some significant upheavals could occur in terms of economies,
cultures, and land-use patterns
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
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