Escenarios de Cambio climático en Colombia y la agricultura: Impactos sobre productividad

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

Program Leader, Decision and Policy Analysis, CIAT

Contenido

• La importancia de tener buenos predicciones de clima para poder estimar impactos

• La demanda de informacion para la agricultura

• Un breve resumen de los modelos

• Impactos en productividad• Impactos en pestes y

enfermedades• Perspectivas para el futuro

La demanda - resolucion

• Agricultura es una industria de nicho

• Entonces necesitamos datos de clima relevantes para caracterizar el nicho

• Escala: 1km, 90m?

La demanda - variables

• Necesitamos multiples variables

–Temperatura• Max, min, media

–Precipitacion– Humedad relativa– Radiacion solar– Vientos– …….

Men

os im

port

ante

s

Mas

cer

tidum

bre

La demanda - tiempos

• Necesitamos como minimo datos mensuales

• Para algunas aplicaciones detallados (ej. modelos mechanisticos) necesitamos datos diarios

• 2050 y 2080 son irrelevantes para la toma de decision en agricultura

• Estamos buscando pronosticos para variabilidad climatica (within season, seasonal, annual, Nino/Nina)

• Y para cambio en linea base: 2020-2030

La demanda - certidumbre

• Los cultivos son suprememente sensibles a sus condiciones climaticos

• Para adaptaciones especificos, necesitamos alta certidumbre

• Faltando certidumbre, trabajamos en resiliencia (pero es mas dificil)

Los modelos de pronostico de clima

Los modelos

• Empezo con los GCMs– Grillas grandes, muy complejos

• Vamos hacia los RCMs– Grillas mas pequenhas, igualmente complejos

Modelos GCM : “Global Climate Models”

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

• Se corre desde el pasado hasta el futuro• Hay diferentes escenarios de emisiones de gases

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

En la agricultura, las diferentes

escenarios de emisiones no son

importantes: de aqui a 2030 la diferencia entre escenarios es

minima

Mensaje 1

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

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

La incertidumbre cientifico SI es relevante para la agricultura: tenemos

que tomar decisiones dentro de un contexto de incertidumbre

YDepender de un limitado numero de

GCM es peligroso

Mensaje 2

Bases de Datos

• Bases de datos de CIAT para 2030 y 2050• Para elaboración de senderos de adaptacion

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

Region DepartamentoCambio en

Precipitacion

Cambio en Temperatura

media

Cambio en estacionalidad de

precipitacion

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

La demanda vs. la ofertaDemanda GCMs RCMs GCMs con

downscaling empirico

Alta resolucion No Moderado Si

Variables Si Si No

Frecuencia Si Si No

Certidumbre Moderado Baja Moderado

Entonces que hacemos frente todo esto?

Entonces que hacemos frente todo esto?

• No hay una sola estrategia gana-gana• Necesitamos multiples acercamientos para mejorar

la base de informacion acerca de escenarios de cambio climatico– Desarollo de RCMs (multiples: PRECIS NO ES SUFICIENTE)– Downscaling empirico, metodos hybridos– Probamos diferentes metodologias

• Se requiere flujo de informacion (CCC): compartimos, comparemos, charlamos (chismoseamos)

Un análisis sectorial para Colombia

Un sector con mucho cultivo permanente

Maíz Café

Arroz t

otal

Plátan

o no exporta

ble

Caña d

e azú

car

Caña p

anela Yu

caPap

a

Palma a

frican

a

Frutal

esFrí

jolCaca

o

Algodón

Sorgo

Banan

o exporta

ción

Ñame

Soya

Hortaliza

sFiq

ue

Plátan

o exporta

ción

Trigo

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000Distribucion de cultivo Área (ha)

Distribucion de cultivo Pdn (Ton)

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

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?

Impactos en Colombia: cambio (%) en productividad a nivel Nacional

Plátano Café Algodón Caña Sorgo Fríjol Trigo Cebada Yuca Papa Ajonjolí Arroz Coco Ñame Maíz Tabaco Cacao PalmaBanano

-20

-15

-10

-5

0

5

Cambio adaptabilidad (%) 2050-A2

Cambio adaptabilidad (%) 2050-A2

Cambios promedios por departamento

Vichad

aSu

cre

Casanare

Bolívar

Magdale

na

Córdoba

Meta

Guaviar

eCesa

r

Guajira

Guanía

Arauca

Amazonas

Tolim

a

Vaupés

Antioquia

Atlántico

Choco

Caqueta

Santan

der

Valle d

el Cau

caHuila

QuindíoCau

ca

Putumay

oCald

as

Norte de S

antan

der

Cundina

Nariño

Risaral

da

Boyaca

-15

-10

-5

0

5

10

15

Cambio promedio en adaptabilidad

Cambio promedio en adaptabilidad

Dos casos diferentes: Bolivar vs. Cauca

Ajonjolí

Algodón

Arroz

Banan

oCaca

oCafé Cañ

a

Cebad

aCoco

Fríjol

MaízÑam

ePalm

aPap

a

Plátan

oSo

rgo

Tabaco Tri

go Yuca

-60.00

-50.00

-40.00

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

Bolivar

Cauca

Conclusiones preliminares

• Cultivos permanentes (66.4% del PIB de 2007) seriamente afectados: y son cultivos de inversiones de largo plazo

• Tema de seguridad alimentaria, y pobreza: muchas de los cultivos afectados son de agicultores pequenos

• Claras prioridades nacionales (por ejemplo. Costa Caribe, cultivos especificos)

• Prioridades locales: enfoque hacia seguridad alimentario

Pest and Disease Impacts

Impacts on green mite

to 2020

Impacts on whitefly to 2020

Mensaje 3

Hay retos y oportunidades: el pais deberia tener una estrategia para

enfrentar ambos

Un Ejemplo mas local

El susto de café en Cauca

Climas mueven hacia arriba

Rango Altitudinal

Tmedia anual actual

Tmedia anual futuro

Tmedia anual

cambio (ºC)

Ppt total anual actual

190-500 25.54 27.70 2.16 5891 6002 1.88501-1000 23.47 25.66 2.19 3490 3597 3.041000-1500 21.29 23.50 2.21 2537 2641 4.101500-2000 18.36 20.58 2.22 2519 2622 4.082000-2500 15.60 17.82 2.22 2555 2657 4.002500-3000 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

Mensaje 4

Localmente va a ver cambios drasticos con la geografia de los

cultivos cambiando

Minimising impacts: Breeding for beans (Phaseolus vulgaris L.) towards 2020

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.

What will likely happen?

2020 – A2

2020 – A2 - changes

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

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

Distribución del arroz en Colombia por

sistemas de producción

Climate characteristic

Climate Seasonality

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

Temperature predictions were uniform between models and thus no outliers were detectedThe coefficient of variation of temperature predictions between models is 3.03%

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

Average Climate Change Trends of Espinal

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 12.44%

General climate

characteristics

Extreme conditions

Variability between models

General climate change description

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 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 2020

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

0

5

10

15

20

25

30

35

40

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12

Temperature (ºC)

Precipitation (mm)

Month

Current precipitation

Precipitation 2050

Precipitation 2020

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

Espinal2020 y 2050

Climate characteristi

cGeneral climate change description

                           

  Average Climate Change Trends of Sikasso

   

General climate

characteristics

The rainfall increases from 1061.65 millimeters to 1185.42 millimeters in 2050 passing through 1100.64 in 2020

Temperatures increase and the average increase is 2.65 ºC passing through an increment of 1.05 ºC in 2020

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

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

                           

Extreme conditions

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 2050

The 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 2050

The 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 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

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

                           

Variability between models

The coefficient of variation of temperature predictions between models is 4.37%

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

The coefficient of variation of precipitation predictions between models is 11.68%

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

   

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

Climate characteristic

Climate Seasonality

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

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

Average Climate Change Trends of Sikasso

General climate change description

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 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 2020

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 11.68%

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

Temperature predictions were uniform between models and thus no outliers were detectedThe coefficient of variation of temperature predictions between models is 4.37%

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10 11 12Month

Pre

cip

itat

ion

(m

m)

0

5

10

15

20

25

30

35

40

45

Tem

pe

ratu

re (

ºC)

Current precipitationPrecipitation 2050Precipitation 2020Mean temperature 2020Mean temperature 2050Current mean temperatureMaximum temperature 2020Maximum temperature 2050Current maximum temperatureMinimum temperature 2020Minimum temperature 2050Current minimum temperature

Sikasso,Mali

Como adaptamos?

• Necesitamos saber que hacemos, como lo hacemos, cuando lo hacemos y donde?

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

adaptacion• Evaluar costo-beneficio para el sector• Implementar• HAZLO AHORA!

INVE

STIG

ACIO

N Y

DES

ARRO

LLO

TE

CNO

LOG

ICO

POLI

TICA

S PU

BLIC

OS

Y PR

IVAD

OS

BUEN AGRONOMIA

a.jarvis@cgiar.org