Andy Jarvis - Climate change scenarios for agricultural production and crop diseases in Colombia...
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Transcript of Andy Jarvis - Climate change scenarios for agricultural production and crop diseases in Colombia...
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