DAPA on World climate teach-in day

The implications of climate change on agriculture and small-farmers livelihoods
A. Eitzinger, P. Laderach, A. Jarvis, J.Ramirez
CIAT - (International Center of Tropical Agriculture)
Presentation in the context of the „World Climate Teach-In Day“

OUTLINE
Climate change: demanding information for agriculture
Estimate the impact using crop prediction models
Translate results on livelihoods
Upstream supply chain adaptation by participation
Conclusions
Some questions

Climate change: demanding information for agriculture
Agriculture is a niche industry - high resolution of IPCC prediction models are needed; Downscaling techniques for 1 - 5 km resolution
Climate baseline; www.worldclim.org database (Hijmans et al, 2005).
Timeseries of future climate data; monthly data until 2030 (2050) a relevant for making decisions now
Certainty of prediction; Measurement of agreement between models
Biologically meaningful variables for crop caracterization
Modelling geographic distribution; crop-niche modeling

Climate baseline WorldClim
Data from mayor climate db (more than 47.000)
SRTM elevation database as input
Interpolated by using thin plate smoothing splines
21 „global climate models“ GCMs
based on atmospheric science, chemistry, physics, biology
Run from the past through to the present and into the future
Use different scenarios for emissions
Downscaled by CIAT to 1 km resolution

Bioclimatic variables
19 variables derived from monthly temperature and rainfall
Represent annual trends
Seasonality and extreme or limiting environmental factors
Examples: Annual mean temperature, Annual Precipitation, Maximum temperature of warmest month, Precipitation of Driest Month, Mean Temperature of Driest Quarter, Precipitation of Wettest Quarter, …

Generation of future climate
Current climate from worldclim (1km resolution)
Prec, temp min/mean/max, 19 bioclims
Future climate
Calculate anomaly (future – current)
Downscale (spline interpolation)
Add to current climate (worldclim)
Calculate 19 bioclimatic variables for future climate
Current climate + Climate-Change = Future climate

Estimate the impact using crop prediction models
EcoCrop - developed by the FAO(http://ecocrop.fao.org/ecocrop/srv/en/home)
MaxEnt - Maximum Entropy modelling of species geographic distributionhttp://www.cs.princeton.edu/~schapire/maxent/
CaNaSTA – Crop Niche Selection for Tropical Agriculture http://csusap.csu.edu.au/~robrien/canasta/index.htm
AquaCrop - Crop Water Productivity Modelhttp://www.fao.org/nr/water/aquacrop.html

Suitability of crops: MaxEnt principle
Example: Coffea arabica in Nicaragua
Input: Crop evidence (5.000 GPS points)
19 bioclimatic variables of
current (worldclim) & future climate (18 GCM)
Output:
Crop suitability (%)

Relation between crop-suitability and altitude for current climates, and predicted for 2050

Importance of different climatic drivers (of 19 bioclims)by stepwise regression

Crop prediction models: Suitability of crops: Ecocrop model
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…

Example: department of Guatemala
Suitability change of 14 mayor crops
by the year 2050
Most crops are loosingsuitability between 20-40%
Few are gaining

Translate results on livelihoods
Impact on production, pest and desease of crops in supply chain
Definition Systemvulnerability to climate change
Exposition of system
Sensibility of system on climate
Capacity to adapt
Searching for socio-economic indicators depending on climate-change by participatory analysis (Focus groups)
Use identified indicators for socio-econometric models to transalte results to livelihoods

Data collection by surveys
Questions based on indicators ofThe Five Capitals Model of sustainable development
natural
human
social
financial
manufacture

Methodology
Current and future crop Suitability
Socio-economic indicators
Economtetric models
System Vulnerability
econometric
models

Upstream supply chain adaptation by participation
Workshops with case-studies
Include local expert-knowledge
Sharing experience
Best practise examples and adaptation strategies
Identify crop/production alternatives

Conclusions
changes in crop suitability a site-specific because of its own very specific environmental conditions.
Solution is site-specific management
Climate change will bring not only bad news but also a lot of new potential.
The winners will be those who are prepared for change and know how to adapt.

Some guiding questions
Where will crop grow in the future?
Where will crop not grow any more?
Where can crop still grow with adapted mgt?
What are crop prediction models?
What are the decisive climate factors to manage?
Which livelihoods are most affected?
How to translate to livelihoods?
How can we design value-chain up-streaming adaptation strategies?
What means participate analysis on climate change?

What can we do?
investigate
share
compare
discuss
…. existing information on climate-change impacts on agriculture
CIAT - DAPA Blog on climate change
http://gisweb.ciat.cgiar.org/dapablogs/

THANK YOU VERY MUCH
DAPA – “Decision and Policy Analysis” Program of CIAT
P. Laderach, A. Eitzinger, J.Ramirez, A. Jarvis

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