Climate change and its impact on agriculture

Jorge Rubiano and Mark Mulligan, King’s College London

Outline

•Brief ANDES BFP Introduction•Climate Change Modeling•Impacts on the Agricultural sector with focus on Oil Palm in Colombia

BFPANDES : AimThe aim of the BFPANDES is “to provide local institutions with the best available (social) science for the formulation and testing of land and water policies for improved water productivity and better livelihoods in the Andes”.

AVAILABILITY

PRODUCTIVITY

INTERVENTIONS

POVERTY

INSTITUTIONS

How much water?Where?When?

BFPANDES : Focus on producing useful outputs

(a) capacity built in local students, institutions/stakeholders through training, workshops, tools, dissemination

(b) freely available report, maps and baseline data diagnosing current status of water poverty, water productivity, environmental security and their social and institutional context along with likely future impacts (http://www.bfpandes.org) . Released at upcoming conf.

(c) The AguAAndes Policy Support System – a simple, accessible web based tool for understanding the likely impact of particular scenarios of change and policy options on water and water poverty in detail in any Andean catchment . Batteries included! -all data supplied. (http://www.policysupport.org/links/aguaandes).

The AGUAANDES POLICY SUPPORT SYSTEM- Online (web service)- All data supplied (1km or 1 Ha.)- Detailed and easy to use IAM- Bilingual- Testable climate and land use scenarios

and policy options e.g. dam building

SimTerra : the most detailed

global databases, tiled

Detailed grid –based process

models

Tools to test scenarios and policy options

+

+

http://www.policysupport.org/links/aguaandes

The Andes ‘basin’ (all basins above 500 masl) and the 13 key CPWF sub-basins

Context:1. Transnational, globally important2. Heterogeneous (hyper humid to

hyper arid)3. Steep slopes, competing demands

on land use4. Environmentally sensitive5. Population of 108 million (Col,

Ecu, Peru, Bol ,2008)6. Pop growth 2.5% p.a. (1980-2005)7. Highly urbanised (<20% of

population is rural)8. 49 million considered poor

(income<essential needs)9. Agricultural area and fertiliser use

increasing since the 1960s, 40-80% national water use is agricultural

10. Hydropower is important11. Complex water legislation12. Climate change

Don’t we have enough to deal with : why also worry about climate change?

...because climate change changes everything and policy support based on current climate can be rendered irrelevant if it does not take climate change into account

So what will happen?

Who knows?

What can we do?

...use our best guess. A general circulation model (GCM)

How a general circulation modelsees the Earth.(a) spatially crude - the Hadley centre model is one of the best and has only 7 cells for the whole of Colombia(b) averaged - earth surface properties such as altitude, vegetation are averaged over hundreds of square kilometres(c) simplified - the complexity ofatmospheric, terrestrial, biospheric and oceanic processes is highly simplified

Even at this level of abstraction, resolving complex atmospheric dynamics over the whole earth with a GCM requires the most powerful computers available and is still computationally limited.

So how can we reduce uncertainty?

Use many models and see what they agree anddisagree on:

Method:

Anomalies (1950-2000 to 2040-2069) from 17 GCMs interpolated and applied to WorldClim 1km climate surfaces (Ramirez and Jarvis). A2 scenario used (Business as usual), second worst case.

• Temperature and precipitation anomalies compared for all 17 GCMs

• Mean, worst case, best case scenarios analysed

• Model disagreement and uncertainty analysed

• Seasonality of change compared

• Water balance model run for mean scenario

• Implications for water productivity analysed

A wide and deep analysis of climate change in Latin America

bccr_bcm2_0 cccma_cgcm2 cccma_cgcm3_1 cccma_cgcm3_t_t63 cnrm_cm3

csiro_mk3_0 gfdl_cm2_0 giss_aom hccpr_hadcm3

Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs

gfdl_cm2_1

Climate data source : Ramirez, J.; Jarvis, A. 2008. High Resolution Statistically Downscaled Future Climate Surfaces. International Centre for Tropical Agriculture, CIAT. Available at: http://gisweb.ciat.cgiar.org/GCMPage/home.html

All GCMS agree warming.There is some consistency in the pattern of warming for the Andes but all GCMs disagree elsewhere....

°C

miroc3_2_hires miroc3_2_medres miub_echo_g mpi_echam5

mri_cgcm2_3_2a ncar_pcm1

....the magnitude as well as the spatial pattern vary considerably (for the same scenario) between different models

Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs

°C

miroc3_2_hires miroc3_2_medres miub_echo_g mpi_echam5

mri_cgcm2_3_2a ncar_pcm1

ipsl_cm4

Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs

bccr_bcm2_0 cccma_cgcm2 cccma_cgcm3_1 cccma_cgcm3_t_t63 cnrm_cm3

csiro_mk3_0 gfdl_cm2_0 giss_aom hccpr_hadcm3gfdl_cm2_1

Data source : Ramirez, J.; Jarvis, A. 2008. High Resolution Statistically Downscaled Future Climate Surfaces. International Centre for Tropical Agriculture, CIAT. Available at: http://gisweb.ciat.cgiar.org/GCMPage/home.html

For precipitation there is disagreement on the direction of change as well as the magnitude. All models indicate wetting in the Andes...

mm/yr

miroc3_2_hires miroc3_2_medres miub_echo_g mpi_echam5

mri_cgcm2_3_2a ncar_pcm1

ipsl_cm4

...many models indicate considerable drying in parts of N Colombia, Venezuela and the Amazon

Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs

mm/yr

Change AR4 A2a (BasU) (1961-90) to 2050 – mean of 17 different GCMs

1. Temperature increases least on the coast (<2°C)2. Around 2 °C in the Andes3. Closer to 3 °C in the Amazon

1. Precipitation changes greatest in the Andes and West

2. A few hundred mm/yr more in the N Andes, W Amazon, Llanos

3. A few hundred mm/yr less in the S Andes, Guyana shield, E Brazil

mm/yr

°C

Pessimist AR4 A2a (1961-90) to 2050 – worst case of 17 different GCMs in each pixel

1. Worst case = highest temperature increase2. Worst case temperature 8 °C except coasts3. Guyana shield especially prone to high change

1. Worst case = greatest rainfall decrease2. Andes generally showing increase, rarely decrease3. Significant drying in forested NE Amazon and

north of Colombia

mm/yr

°C

Optimist AR4 A2a (1961-90) to 2050 – best case of 17 different GCMs in each pixel

1. Best case = lowest temperature increase2. For NW Amazon best case is liitle change,

slight cooling3. Central (dry) Andes even best case is 2 °C

warming

1. Best case = greatest increase in precip2. For most areas greatest increase 200 mm/yr3. A few areas in Andes/Amazon with

significant increases

mm/yr

°C

Uncertainty AR4 A2a (1961-90) to 2050 – SD of 17 different GCMs in each pixel

1. Standard deviation (SD) of results for 17 GCMs used as a measure of uncertainty

2. Low temperature uncertainty at high latitudes, coasts and mountains

3. Much greater certainty (low variability between model predictions) in the Andes

1. Low rainfall uncertainty in S and SE and parts of Andes

2. Much greater uncertainty in NE Brazil and Amazons

mm/yr

°C

So what will happen?

1. Who knows?2. It will be warmer and wetter but each GCM gives a different

spatial pattern3. Mean of 17 models warming is highest in the lowlands4. Mean of 17 models wetting is highest in the Andes5. Worst case is 6-8 °C warming throughout and wetting of 200-

600 mm/yr in the W and S, drying of 600-1000 mm/yr in the NE

6. Best case is 2 °C in the high Andes, less elsewhere and an increased rainfall of at least 200mm/yr throughout

7. Uncertainty in temperature change is low in the Andes (the model agree) and is greatest in the Amazon

8. Uncertainty in rainfall change follows a complex pattern but is much greater in the N than the S

9. And what about seasonality....

Seasonal change : temperature : mean of 17 models

J F M A M

J J A S O

N D

°CAt southern latitudes temperatureChange is much greater in N,D,J,FIn the equatorial regions change is much greater in J,J,A,S

Seasonal change : precipitation : mean of 17 models

J F M A M

J J A S O

N D

mm/yr Precipitation change (drying and wetting) is greatest and most spatially variable in J,J,A,S,O

So what are the implications for agriculture?

Method:

Use monthly anomalies (mean of 17 models) to force FIESTA hydrological model at Andes scale

Look into implications for evapotranspiration and water balance

Examine current distribution of productivity from 10 years of 10-daily remote sensing data

Associate current productivity to current climate

Draw implications for impacts of climate change scenario

Change question from what will the future be like? to how much change can the systems stand?

Mean annual evapo- transpiration change to

2050s (mm)

Mean annual temperature change to 2050s (°C)

Mean annual precipitation change to 2050s (mm)

Mean annual water balance change to 2050s (mm)

Regional scale hydrological impact

Temperature and rainfall will increase and this drives up evapo-transpiration . But, the balance between increased evapo-transpiration and increased rainfall tends towards more available water (water balance increases)

Based on Ramankutty (2008) we see the distribution of oil palm. For Latin America, especially in central America, Colombia and Ecuador.

The image to the left shows the mean productivity (Dg/ha/yr) measured over 10 years of SPOT-VGT data and indicates the highest productivity in the lowlands.

Distribution of oil palm

Dry Matter Productivity (Dg/ha/yr)

DMP (in Dg/ha/day)

Relationships between productivity and rainfall indicate a linear trend between 0 and 1000 mm/yr but little effect in wetter areas

Rainfall (mm/yr)

DMP (in Dg/ha/day)

Mean annual temperature (°C)Temeprature strongly increases productivity in the range 0-20 with a decline from 20-30°.

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Cambio en temperatura mayor a 2.5ºC

Cambio en ppt mayor 3%

• 50-60% of Colombian producers are small

• 28.6% of the agricultural area in Colombia is above 1200masl

• Permanent crops (66.4% of GDP in 2007) are severely affected

Fuente: CIAT, 2009

There are vulnerabilities throughout the agricultural sector

Slide by Andy Jarvis (CIAT)

Gmin: número mínimo de días en los que la planta crecerá*.Gmax: número máximo de días en los que la planta crecerá*.KTmp: temperatura absoluta que acabará con la vida de la planta.Tmin: temperatura mínima promedio (°C) con la que la planta crecerá.TOPmn: temperatura mínima promedio (°C) con la que la planta crecerá óptimamente .TOPmx: temperatura máxima absoluta promedio (°C) con la que la planta crecerá óptimamente.Tmax: temperatura máxima promedio (°C) con la que la planta dejará de crecer.Rmin: lluvia mínima (mm) durante la estación de crecimiento.ROPmn: lluvia mínima optima (mm) durante la estación de crecimiento.ROPmx: lluvia máxima optima (mm) durante la estación de crecimiento.Rmax: lluvia máxima (mm) durante la estación de crecimiento.

Definición de parámetros de adaptabilidad para la palma de aceite (Elaeis guineensis Jacq.)

Gmin: 300, Gmax: 365. KTmp: 0, Tmin:12, TOPmn:20, TOPmx: 35, Tmax: 38.

Rmin: 1000, ROPmn: 1500, ROPmx: 3000, Rmax: 8000.

Adaptabilidad actual de palma de aceite

Cambio en la adaptabilidad de palma de aceite

Gracias

Retroalimentación

EcoCrop es un modelo de nicho ecológico que expresa la adaptabilidad de un cultivo sobre la base de parámetros generales de adaptación (duración de la estación de crecimiento, temperatura y precipitación). En este análisis fue examinada la adaptación actual, futura y el cambio en adaptabilidad sobre la base de 18 modelos globales de cambio climático provenientes del tercer y cuarto reportes de evaluación del IPCC (Intergovernmental Panel on Climate Change).

Lo que se pretende es llegar a unos parámetros de adaptabilidad que se acerquen a la realidad del cultivo de la palma de aceite en Colombia, pues estos mapas han sido generados a partir de la base de datos proporcionada por la FAO, la cual ha determinado unos parámetros generales a nivel mundial. Es por eso que sería de gran ayuda recibir sugerencias sobre posibles modificaciones en los valores de duración de la estación de crecimiento, temperaturas y precipitaciones consideradas óptimas para el crecimiento del cultivo en nuestro país.

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