Using climate predictions for impact studies

Flora Mer

Nairobi, August 2012

Population growthIndustrial revolutionNon-environmentally friendly technologies/practices

LEAD TO GREENHOUSEGASES EMISSIONS INCREASING

The World is changing….

Temperature is increasing…

Rainfall is changing…

Changes in climates affect crops we grow...

Number of crops with more than 5% loss

Number of crops with more than 5% gain

There will be winners…

…But much more losers in developing countries

We need models to quantify impacts and design effective

adaptation options

GCMs

Effective adaptation options

MarkSim

DSSAT

Statistical Downscaling

Dynamical downscaling:Regional Climate Model

GLAM

EcoCrop

Statistical Downscaling

MaxEnt

Bias correction Any model

Crop Models

Models based on processes

Models based on crop niches

GLAM

EcoCrop

MaxEnt

DSSAT

Prob

abili

ty

Environmental gradient

– A simple algorithm to look at the broad niche of each species only based on climate data

– Ten growing parameters to set up the model• Absolute rainfall interval• Absolute temperature interval• Optimum rainfall interval• Optimum temperature interval• Length of the growing season• Crop freezing temperature

– Use of climate data• Statistical downscaling of GCMs (IPCC4) • Present-day climates from WorldClim

= Interpolations of observed data, representative of 1950-2000• 24 different climate models (GCMs) to sample uncertainties

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?

The Model: EcoCrop

Common Bean Current Suitability

Kiling temperature (°C) 0

Minimum absolute temperature (°C) 13.55

Minimum optimum temperature (°C) 17.45

Maximum optimum temperature (°C) 23.05

Maximum absolute temperature (°C) 25.63

Growing season (days) 90

Minimum absolute rainfall (mm) 200.0

Minimum optimum rainfall (mm) 362.5

Maximum optimum rainfall (mm) 449.5

Maximum absolute rainfall (mm) 710.0

Common Bean Future Suitability and Change

2030s SRES-A1B

2030s SRES-A1B

Bean regional impacts

Average change in suitability for 50 food crops in 2050s

Suitability changes Crop Comparison in Africa

MaxEntMaximum Entropy Modelling

• Model predicting the potential distribution of a species

• Statistical dowscaling method apply to climate data.

• Many modellers use the set of the bioclimatic variables Maxent use the principle of the maximum entropy Maxent use only presence point of specific species and

environmental variables• One of the most accurate model for the prediction of

shifts in suitable growth ranges of species

MaxEnt Application on Kenyan coffeeMain coffee-producing areas in Kenya are located in two areas:

- the central region around Mount Kanya - in the Rift Valley in the west

- The most suitable areas: in the higher areas of Bungoma, Embu, Kericho, Kiambu, Kirinyaga, Kisii, Machakos, Meru, Muranga, Nithi, Nyamira, Nyeri and Trans-Nzoia

Alternatives to tea

New markets

Management

DSSAT Decision Support System for Agrotechnology Transfer

• Based on crop processes• Integrate the interaction of weather, soil,

management and genetic factors• Prediction of yields, plant phenologic stages, plant

weight,harverst date, water soil quantity, N quantity…

• Current & Future predictions• Need precise and daily data

DSSAT predicts yields

Jones and Thornton, 2003

Maize Yield negatively impacted by CC in most areas in AfricaNeed effective adaptation options

Jones and Thornton, 2003

DSSAT predicts yields

In 2055: Maize Yield would be negatively impacted by CC in most areas in EthiopiaNeed to develop adaptation strategies

GLAM - General Large Area Model Challinor et al. (2004)

• Designed at climate model scale to capitalize on known large-scale relationships between climate and crop yield, thus avoiding over-parameterization.

Uses grid-scaled agricultural statistics to simulate yields

To simulate yields at climate model scale

Large-area models are able to reproduce large-scale historical yield responses to climate and inter-annual variability

Observed peanut yields (kg/ha) Rate of simulated to observed yields

Conclusions• Different models exist for evaluating the impact of CC on crops• The application of each model depends on what information we have and

what we want to know:• Daily/Monthly data• Crop suitability• Yield• Agricultural management practices

• Impact studies at agricultural level benefit from having climate data of higher resolution

• Inform adaptation to stakeholders: policy makers, donors, other researchers, but also farmers.

• Gaps: Need more research to understand better crop responses to climate change To decrease uncertainties

Future research plans

• Input climate data quality and its effects on impact predictions• Analysis of trial data to better understand crop responses to

environment (soil, nutrients, CO2, heat stress, drought stress, and their interactions)

• Expansion of crop model parameterisation, including multi-Ag-Model ensembles

• Impacts of future climate change and future variability on crop yields

• Uncertainty quantification (crop and climate)• Design of crop genotypic adaptation strategies (“ideotypes”) and

link with analogues to find useful germplasm• Scale up adaptation strategies to national/ international level

(policy-making)

Thank you !

Any questions?