climate change scenarios_lac_rice

Escenarios de Cambio climático en Colombia y la agricultura: con una mirada hacia el arrozg

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

Program Leader, Decision and Policy Analysis, CIAT

Contenido

• Acerca de cambio climatico y los modelos GCM

• El futuro de America Latina

• Analisis de adaptabilidad global, y un ejemplo en Colombia

• Lo que se debe hacer

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

Arctic Ice is MeltingArctic Ice is Melting

Los modelos de pronostico de clima

Usando el pasado para aprender del futuroUsando el pasado para aprender del futuro

Modelos GCM : “Global Climate Models”

• 21 “global climate models” (GCMs) basados en ciencias• 21 global climate models (GCMs) basados en cienciasatmosféricas, química, física, biología

• Se corre desde el pasado hasta el futuroSe corre desde el pasado hasta el futuro

• Hay diferentes escenarios de emisiones de gases

INCERTIDUMBRE POLITICO (EMISIONES) YINCERTIDUMBRE POLITICO (EMISIONES), Y INCERTIDUMBRE CIENTIFICO (MODELOS)

Entonces, ¿qué es lo que dicen?Entonces, ¿qué es lo que dicen?Variaciones en la temperatura de la superficie de la tierra: de 1000 a 2100

Variabilidad y linea baseVariabilidad y linea base

+

mat

eC

li

_

TimescaleShort (change in baseline and variability) LongShort (change in baseline and variability) Long

Bases de DatosBases de Datos

• Bases de datos de CIAT para 2050 y 2020

P l b ió d d d d i• Para elaboración de senderos de adaptacion

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

Region Departamento Cambio en Precipitacion

Cambio en Temperatura

media

Amazonas Amazonas 12 2.9Amazonas Amazonas 12 2.9Amazonas Caqueta 138 2.7Amazonas Guania 55 2.9Amazonas Guaviare 72 2.8Amazonas Putumayo 117 2.6Andina Antioquia 18 2.1qAndina Boyaca 50 2.7Andina Cundinamarca 152 2.6Andina Huila 51 2.4Andina Norte de santander 73 2.8Andina Santander 51 2.7Andina Tolima 86 2.4Caribe Atlantico -74 2.2Caribe Bolivar 90 2.5Caribe Cesar -119 2.6Caribe Cordoba -11 2.3Caribe Guajira -69 2.2Caribe Magdalena -158 2.4Caribe Sucre 10 2.4Eje Cafetero Caldas 252 2.4Eje Cafetero Quindio 153 2.3Ej C f t Ri ld 158 2 4Eje Cafetero Risaralda 158 2.4Llanos Arauca -13 2.9Llanos Casanare 163 2.8Llanos Meta 10 2.7Llanos Vaupes 46 2.8Llanos Vichada 59 2 6Llanos Vichada 59 2.6Pacifico Choco -157 2.2Sur Occidente Cauca 172 2.3Sur Occidente Narino 155 2.2Sur Occidente Valle del Cauca 275 2.3

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

CCCMA‐CGCM3.1 CSIRO‐MK3.0 IPSL‐CM4 MPI‐ECHAM5

NCAR‐CCSM3.0 UKMO‐HADCM3 UKMO‐HADGEM1

20501A1B

Distribución del arrozDistribución del arroz en Colombia por

sistemas de producción

Climate characteristic

Average Climate Change Trends of

General climate

h t i ti The mean daily temperature range decreases from 11.3 ºC to 11.28 ºC

General climate change description

The rainfall decreases from 1444 millimeters to 1411.75 millimetersTemperatures increase and the average increase is 0.8 ºC

Climate

characteristics

Extreme conditions

The driest month gets wetter with 41 millimeters instead of 39 millimeters while the driest quarter gets wetter by 20.75 mm

The maximum number of cumulative dry months keeps constant in 4 monthsy p g

The maximum temperature of the year increases from 32.7 ºC to 33.48 ºC while the warmest quarter gets hotter by 0.85 ºC The minimum temperature of the year increases from 19.9 ºC to 20.9 ºC while the coldest quarter gets hotter by 0.8 ºC The wettest month gets wetter with 253.5 millimeters instead of 252 millimeters, while the wettest quarter gets drier by 6.75 mm

Climate Seasonality

Precipitation predictions were uniform between models and thus no outliers were detectedThe coefficient of variation of precipitation predictions between models is 5.16%

Variability between models

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

Temperature predictions were uniform between models and thus no outliers were detectedThe coefficient of variation of temperature predictions between models is 0.3%

30

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300 Current precipitationFuture precipitationFuture mean temperatureCurrent mean temperatureFuture maximum temperatureCurrent maximum temperatureFuture minimum temperatureCurrent minimum temperature

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pera

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(ºC

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m)

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Campoalegre a 2020

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

001 2 3 4 5 6 7 8 9 10 11 12

Month

0 0


General climate

h t i ti The mean daily temperature range increases from 11.3 ºC to 11.82 ºC in 2050

Average Climate Change Trends of Campoalegre


The rainfall increases from 1444 millimeters to 1512.85 millimeters in 2050 passing through 1411.75 in 2020Temperatures increase and the average increase is 2.27 ºC passing through an increment of 0.8 ºC in 2020

Climate

The maximum number of cumulative dry months keeps constant in 4 monthscharacteristics

Extreme conditions

The driest month gets drier with 37.45 millimeters instead of 39 millimeters while the driest quarter gets wetter by 15.55 mm in 2050

The mean daily temperature range increases from 11.3 C to 11.82 C in 2050

The maximum temperature of the year increases from 32.7 ºC to 35.61 ºC while the warmest quarter gets hotter by 2.56 ºC in 2050The minimum temperature of the year increases from 19.9 ºC to 21.88 ºC while the coldest quarter gets hotter by 2.14 ºC in 2050The wettest month gets wetter with 252.2 millimeters instead of 252 millimeters, while the wettest quarter gets wetter by 14.6 mm in 2050

Climate Seasonality

The coefficient of variation of temperature predictions between models is 3%

The coefficient of variation of precipitation predictions between models is 12.03%



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

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

30

35

40

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300 Current precipitationPrecipitation 2050Precipitation 2020Mean temperature 2020Mean temperature 2050Current mean temperatureMaximum temperature 2020

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(ºC)

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(mm

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Maximum temperature 2020Maximum temperature 2050Current maximum temperatureMinimum temperature 2020Minimum temperature 2050Current minimum temperature

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10

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50

100

TP

Campoalegre a 2020 y 2050

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

001 2 3 4 5 6 7 8 9 10 11 12

Month


Average Climate Change Trends of Espinal

General climate


The rainfall increases from 1409 millimeters to 1476.2 millimeters in 2050 passing through 1364.5 in 2020Temperatures increase and the average increase is 2.24 ºC passing through an increment of 0.72 ºC in 2020The mean daily temperature range increases from 10 9 ºC to 11 38 ºC in 2050

The driest month gets wetter with 45.9 millimeters instead of 41 millimeters while the driest quarter gets wetter by 9.85 mm in 2050

characteristics

Extreme conditions

The maximum temperature of the year increases from 34.8 ºC to 37.77 ºC while the warmest quarter gets hotter by 2.5 ºC in 2050The minimum temperature of the year increases from 21.8 ºC to 23.78 ºC while the coldest quarter gets hotter by 2.17 ºC in 2050The wettest month gets wetter with 213.45 millimeters instead of 212 millimeters, while the wettest quarter gets wetter by 10.05 mm in

The maximum number of cumulative dry months keeps constant in 3 monthsThe mean daily temperature range increases from 10.9 ºC to 11.38 ºC in 2050

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




30

35

40

200


15

20

25

100

150

Temperature (ºC)

recipitation (mm)


E i l5

10

15

50

TPr Espinal2020 y

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

001 2 3 4 5 6 7 8 9 10 11 12

Month 2050

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


The mean daily temperature range increases from 13.71 ºC to 13.75 ºC in 2050

Average Climate Change Trends of Sikasso


The rainfall increases from 1061.65 millimeters to 1185.42 millimeters in 2050 passing through 1100.64 in 2020Temperatures increase and the average increase is 2.65 ºC passing through an increment of 1.05 ºC in 2020General climate

characteristics

Climate

The mean daily temperature range increases from 13.71 C to 13.75 C in 2050

The maximum temperature of the year increases from 37.41 ºC to 40.9 ºC while the warmest quarter gets hotter by 2.98 ºC in 2050The minimum temperature of the year increases from 14.74 ºC to 17.02 ºC while the coldest quarter gets hotter by 2.54 ºC in 2050The wettest month gets wetter with 300.47 millimeters instead of 282.08 millimeters, while the wettest quarter gets wetter by 14.07 mm in 2050

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

Extreme conditions

The driest month gets wetter with 2.86 millimeters instead of 0.81 millimeters while the driest quarter gets wetter by 30.71 mm in 2050

Climate Seasonality





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(mm

)

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(ºC

)


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Sikasso,Mali

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

01 2 3 4 5 6 7 8 9 10 11 12

Month

0


Average Climate Change Trends of Villahermosa, Mexico

General climate Th d il t t i f 11 3 ºC t 12 29 ºC i 2050


The rainfall decreases from 1925 millimeters to 1776.89 millimeters in 2050 passing through 1903.75 in 2020Temperatures increase and the average increase is 2.39 ºC passing through an increment of 0.98 ºC in 2020

characteristics

Extreme conditions

The driest month gets drier with 27.44 millimeters instead of 47 millimeters while the driest quarter gets drier by 47.39 mm in 2050

The maximum number of cumulative dry months keeps constant in 4 monthsThe mean daily temperature range increases from 11.3 ºC to 12.29 ºC in 2050

The maximum temperature of the year increases from 35.7 ºC to 39.06 ºC while the warmest quarter gets hotter by 2.69 ºC in 2050The minimum temperature of the year increases from 18.3 ºC to 19.67 ºC while the coldest quarter gets hotter by 1.99 ºC in 2050The wettest month gets wetter with 310.22 millimeters instead of 310 millimeters, while the wettest quarter gets drier by 28.5 mm in 2050

Climate Seasonality



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


p p

300

350

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40

45 Current precipitationPrecipitation 2050Precipitation 2020Mean temperature 2020Mean temperature 2050Current mean temperature

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n (m

m)

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(ºC)

Current mean temperatureMaximum temperature 2020Maximum temperature 2050Current maximum temperatureMinimum temperature 2020Minimum temperature 2050Current minimum temperature

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Villahermosa,

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The Impacts on Crop SuitabilityThe Impacts on Crop Suitability

Agricultural systems analysisAgricultural systems analysis• 50 target crops selected based on area harvested in FAOSTAT

N FAO name Scientific nameArea

harvested (kha)

N FAO name Scientific nameArea

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 Pl t i b M b lbi i C ll 5439

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 C bb B i l L i 3138 32 Plantain bananas Musa balbisiana Colla 5439

33 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 7539

7 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 10616 38 Sesame seed Sesamum indicum L. 7539

39 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. 8996

13 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 3848 S p p ( ) 8996

45 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

9 38 820 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

Adaptabilidad global para la agriculturad 2050 h breduce un poco a 2050, y habra

problemas de distribucion de alimentos: Opportunidades para arroz en America pp p

Latina

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 CCebada 4 2,305 3,939 47.2 52.8 0.0 28.5 71.5Maíz 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 70.4p , , ,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 9Caña de azúcar 6 235,118 3,259,779 99.6 0.4 1.1 0.0 98.9Tabaco 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 Plát t ió 1 19 187 209 647 100 0 0 0 0 0 100 0 0 0Plátano exportación 1 19,187 209,647 100.0 0.0 0.0 100.0 0.0Coco 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 56.6Frutales 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

Impactos en Colombia: cambio (%) en d d d l lproductividad a nivel Nacional

Cambio adaptabilidad (%) 2050‐A2

2

4

‐4

‐2

0

10

‐8

‐6

‐14

‐12

‐10

Cambio adaptabilidad (%) 2050‐A2

‐18

‐16Cambio adaptabilidad (%) 2050 A2

Hacia adaptacion: Un ejemplo de frijol (buenHacia adaptacion: Un ejemplo de frijol (buenacompanante al arroz)

How are beans standing up currently?How are beans standing up currently?

Growing season (days) 90 Minimum absolute rainfall (mm) 200Killing temperature (°C) 0 rainfall (mm)Minimum optimum rainfall (mm) 363

Maximum optimum rainfall (mm) 450

Maximum absolute

Minimum absolute temperature (°C) 13.6

Minimum optimum 17 5

Parameters determined based on statistical analysis of current bean

Maximum absolute rainfall (mm) 710temperature (°C) 17.5

Maximum optimum temperature (°C) 23.1

Maximum absolute temperature (°C) 25.6

ygrowing environments from the Africa and LAC Bean Atlases.

What will likely happen?

2020 – A2

2020 – A2 ‐ changes

Technology options: breeding for drought d l i l

35

40

%] (

%)

Cropped lands

and waterlogging tolerance

12

14

res) Currently cropped lands

S 22 8% (3 8 illiDrought

15

20

25

30

n su

itabl

e ar

eas

[>80

% Non-cropped lands

Global suitable areas

4

6

8

10

are

as (m

illio

n he

ctar Not currently cropped landsSome 22.8% (3.8 million

ha) would benefit from drought tolerance improvement to 2020s

gtolerance

Waterlogging tolerance

0

5

10

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

Crop resilience improvement

Cha

nge

in

0

2

4

Ropmin Ropmax Not benefited

Bene

fited

p

Technology options: breeding for heat and ld lcold tolerance

60

70

0%] (

%)

Cropped lands

Non-cropped lands12

14

tare

s)

Currently cropped landsNot currently cropped lands

Some 42 7% (7 2

20

30

40

50n

suita

ble

area

s [>

80

Non cropped lands

Global suitable areas

4

6

8

10

d ar

eas

(mill

ion

hect

Cold tolerance

Some 42.7% (7.2 million ha) would benefit from heat tolerance

0

10

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

Crop resilience improvement

Chan

ge in

0

2

4

Topmin Topmax Not benefited

Bene

fitedHeat

toleranceimprovement to 2020s

Adaptacion ideal

CASE 1: Transition

Adaptacion ideal

Risk management Progressive

(win‐win)

g gadaptation

Mitigation

Potential examples: ecosystem service payments – risk manages by offering immediate financial capital/relief, mitigates by reducing emissions, and adapts by creating incentives/opportunities to diversity away from just agricultureby creating incentives/opportunities to diversity away from just agriculture

Risk management(coping)

?Clim

ate

CASE 2: Disjointed adaptation (win‐win)

(coping)?C e

Progressive adaptation (transformational

Example: subsidies that would lower emissions and give farmers extra financial capital to invest in higher production (risk management and mitigation, but not significant long‐term adaption

(transformational change)

higher production (risk management and mitigation, but not significant long term adaption strategy)

CASE 3: Disjointed adaptation (no win‐win)

Risk management(coping)

Progressive adaptation

?

(coping)(transformative change)Trade‐offs

e.g.) Taxing fertilizers and pesticides –mitigates at farmer’s cost

Trade‐offse.g.) Occupational change from agricultural to industrial work–

Mitigation

farmer “adapts” at potential cost to environment

La variabilidad genetic existe en arroz….La variabilidad genetic existe en arroz….

• Intercambiar materiales y practicas dentro del te ca b a ate a es y p act cas de t o depais….

• ….y por fuera del pais:y p p• N22 la mas tolerante• IR64 tiene cierta tolerancia• IR6 por muchos anos ha sido sembrada en Pakistan en donde se presentan temperaturasaltas en epoca de floracion de 45 gradoscentigrados

Y la heterogeneidadpermite transferencia

de practicas y tecnologias de untecnologias de un

sitio al otro

Message 3

Los impactos pueden ser enfrentadoscon la diversidad de materialescon la diversidad de materialesexistentes, o por medio de mejoramiento pero hay quemejoramiento, pero hay que

empezar ya

Como adaptamos?Como adaptamos?

• Necesitamos saber que hacemos como O

OS • Necesitamos saber que hacemos, como

lo hacemos, cuando lo hacemos y donde? RR

OLLO

RIVA

DO

• Primero paso es analisar el problema• Segundo analisar opciones de Y

DESA

OGICO

COS Y PR

Segundo, analisar opciones de adaptacion

• Evaluar costo‐beneficio para el sector ACION Y

ECNOLO

PUBLIC

Evaluar costo beneficio para el sector• Implementar

VESTIGA TE

ITICAS

INV

POLI

BUEN AGRONOMIA

[email protected]

climate change scenarios_lac_rice

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