cambio climático y agricultura en los andes€¦ · 34 swede rap brassica napus l. 27796 35 rice...

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

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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

http://en.wikipedia.org/wiki/Image:Global_Atmospheric_Model.jpg

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

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


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


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


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


Maximum optimum


Maximum absolute


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


Minimum optimum


Maximum optimum


Maximum absolute


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


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


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


Mejoramiento en la resiliencia del cultivo

Cam

bio

en

áre

as a

dap

tab

les

[>80%

] (%

)

Áreas cultivadas


Total áreas adaptables

Toleracia al calor

Toleracia al frío

0

2

4

6

8

10

12


Mejoramiento en la resiliencia del cultivo

Cam

bio

en

áre

as a

dap

tab

les

[>80%

] (%

)

Áreas 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


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

[email protected]

cambio climático y agricultura en los andes€¦ · 34 swede rap brassica napus l. 27796 35 rice...

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