Download - Julian R - Assessing the Impacts of Climate Change on SSAn and SEAn Agriculture (PhD transfer)
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Assessing the impacts of climate change on Sub-Saharan Africa and South-East Asian
agricultureJulian Ramirez-VillegasClimate Impacts Group, ICAS
(c) Neil Palmer (CIAT)
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Contents
• Project title and supervision• Background and rationale• Objectives• Research topics• To-date results: aspects of
climate relevant to crop production modelling
• Other results
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Project title and supervision
• Title: Informing the adaptation of agricultural systems in Africa and Asia to progressive climate change over the coming decades
• Supervision– Andy Challinor (principal @SEE, Leeds)– Andy Jarvis (external @CIAT, Colombia)– Doug Parker (nominal @SEE, Leeds)
• Assessment– Peter Knippertz (RSG member @SEE, Leeds)
(c) Neil Palmer (CIAT)
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Background and rationale• Agriculture contribution to GDP is between 3 to 61% in
developed countries (World Bank, 2008)
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Background and rationale• By 2100, novel climates could happen in 10-48% of the earth
(Williams et al. 2007)• Climate change is predicted to decrease agricultural yields (many
authors), with major impacts in the DW (many authors)
Source: Lobell et al., 2008
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Background and rationale
• Hence, three challenges are expected– Adapting agriculture to future stresses– Meeting the future food demand– Mitigating GHG emissions
• Requiring some R&D to be done:– Generating base information for I.A.– Assessing impacts on agriculture at different scales– Development of adaptation strategies– Test and transfer of these strategies
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General objectives
• Assess the impact of climate change on agriculture in 12 countries of Africa and South-East Asia for a set of important crop production systems
• Produce a set of recommendations on how to adapt these systems to avoid crop yield drops in the future.
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Workflow
1
2
3
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Research overview
Research focus regions
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Research topics
• 1. Assessing relevant climate data for agricultural applications– Climate datasets screening– Assessment of available and relevant data• Quality• Uncertainties
– Analysis of GCM skill (CMIP3 and CMIP5) on crop-growth related variables
– Future climate projections uncertainty quantification
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Research topics
• 2. Assessing impacts of climate change on agricultural production– Selection of crops based on regional relevance– Development, calibration and evaluation of
EcoCrop for the target crops– Knowledge gap filling, calibration and evaluation
of GLAM for the target crops– Predicting current, future yields and suitability– Detecting vulnerable areas and systems
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Research topics: aspects of climate relevant to crop production modelling
• Photosynthesis is the fundamental process of interest
Storage in organs
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Research topics: aspects of climate relevant to crop production modelling
• And is affected by a number of factors
Source: Isdo et al., 1995 (sour orange trees) Source: Bates, 2002
Temperature and CO2 Avail. water and solar radiation
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EcoCropRamirez et al. (accepted for publication)
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…
• For assessing crop climatic suitability…
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GLAMChallinor et al. (2004)
• Designed at climate model scale to capitalise on known large-scale relationships between climate and crop yield, thus avoiding over-parameterisation.
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
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Research topics• 3. Adapting agriculture to climate
change through crop management and genetic adjustments– Genetic level modifications
• Drought and waterlogging tolerance• Heat and cold tolerance
– Management strategies• Tillage as an improvement of soil phys.
Characteristics• Shading as in albedo or extinction
coefficient
– Determining best combinations of Management-Genetic improvements
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Research topics: useful- and uniqueness
• Many studies focusing on the developing world• Many using process-based models• Few with broad coverage• Few with adaptation-focus• Few with proper uncertainty quantification (avg.
num. future scenarios is 3)• Few with solid background on climate science• Few using niche-based approaches• None using niche-based AND process-based
models at the same time…
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Results to date
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Results: aspects of climate relevant to crop production modelling
• So, accurate measurements are required
0.4
0.4
0.4
0.4
0.8
0.8
1.2
1.2
1.6
2.8
2.8
3.6
4.0
7.7
10.5
10.9
50.4
0.0 20.0 40.0 60.0
GHCN
GPCC
GPCP
PRISM
ATEAM
VEMAP
ARTES
GSOD
MARS
CRU-CL
WorldClim
Satellite
RCM
Other
GCM
CRU-TS
Weather station
Percent of studies
0.8
0.8
4.8
5.6
8.7
17.5
19.0
42.9
0.0 20.0 40.0 60.0
ARPEGE
Unclear
SC Variables
WG GCM
PS GCM
SD GCM
RCM
AI GCM
Percent of future-climate related studies
Common sources of present-day climate data
Common sources of future climate data
Field measurements
AND
Climatologically robust future
prediction methods
Source: Ramirez and Challinor, in prep.
*on the basis of 205 peer-reviewedpapers
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Results: aspects of climate relevant to crop production modelling
• Complexity of the systemHumid west Africa
Sahel
IGP
East African highlands
East Africa arid lands
+++Constraints to data and uncertainty quantification
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Results: aspects of climate relevant to crop production modelling
• Global climate model skill (IPCC 4AR)
Source: Ramirez and Challinor, in prep.
Mea
n te
mpe
ratu
reD
iurn
al te
mpe
ratu
re ra
nge
Rain
fall
Annual December-February June-July-August
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Results: aspects of climate relevant to crop production modelling
• Climate model skill (CMIP3)1961-1990 Rainfall 1961-1990 Temperature
Source: Ramirez and Challinor, in prep.
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GISS-MODEL-EH
NCAR-CCSM3.0
R-square (observed vs. climate m
odel) - RAINFALL
Range: 1-347Mean: 41 ± 63 points/gridcell
Range: 1-80Mean: 10 ± 12 points/gridcell
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Mean bias rate (observed vs. clim
ate model) - RAIN
FALLGISS-MODEL-EH
NCAR-CCSM3.0
Rain is mostly overestimated
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Other results
• Development and evaluation of the EcoCrop model and a case-study with sorghum (Crop-climate ensembles Special Issue in AFM)
• Crop data being organised in a database with stable design
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Future plans• Year 2:– Finalise data collection (agricultural statistics and crop-
presence)– Gather and asses CMIP5 climate model outputs– Physiology knowledge gap filling in GLAM– Crop model (EcoCrop) calibration and evaluation for other
crops– Crop model (GLAM) calibration and evaluation
• Year 3:– Queries to experts and literature on priority traits and
realistic ranges– Modifications to crop models to test realistic ranges of
genetic and/or management strategies– Assessing adaptation options
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Thanks