challenges for the simulation of crop yields in a changing climate tim wheeler crops and climate...
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Challenges for the simulation of crop yields in a changing climate
Tim Wheeler
Crops and Climate Group
What are effects of climate on crops?
Can we forecast the productivity of crops in a changing climate?
180
200
220
240
260
1760 1780 1800 1820 1840 1860 1880 1900 1920
Year
day
of
har
vest
DOY 200 is 19 July
DOY 240 is 28 Aug
Harvest records at Chilgrove, Sussex,
1769-1910
Data from, Russell, 1920
Harvest records at Chilgrove,Sussex, 1769-1910
y = -11.294x + 321.31
R2 = 0.5168
180
200
220
240
260
7 8 9 10 11Central England mean surface air temperature (°C)
Day
of h
arve
st
Climate and weather are vital for crops
but …
Climate change that is important for crops
By 2100 …
• Carbon dioxide, CO2 (emissions of 550 to 950 ppm)
• Temperature (+1.4 to +5.5 oC)
• Rainfall amount (huge regional range)
• Variability in weather (more intense storms,
increased drought risk; more frequent hot days)
from IPCC TAR (2001)
How are these effects on crops investigated?
Plant Environment Laboratory, University of Reading
How are these effects on crops investigated?
Free Air CO2 Enrichment, FACE
Courtesy of Steve Long, University of Illinois
Effects of elevated CO2
Elevated CO2 was 475-600 ppm from Ainsworth and Long (2005)
0
10
20
30
40
50
all no stress drought
Incre
ase y
ield
(%
)
+19%
+40%
+28%
Maize, millet, sorghum, sugar cane will not benefit
Effects of warmer temperature
from Lawlor & Mitchell (2000), Baker et al (1995), Daymond et al (1997)
-12
-10
-8
-6
-4
-2
0
Wheat Rice Onion 1 Onion 2
Incr
ease
in y
ield
(%
)
-8%
-10% -10%
-4% Some adaptation is possible through use of varieties
Rice
Warmer season... … or a few hot days
Groundnut From Vara Prasad et al (2001)
Flower bud temperature (oC)
24 28 32 36 40 44 48
Fru
it se
t (%
)
0
20
40
60
Warmer season... … or a few hot days
20
22
24
26
28
30
32
34
36
38
T m
ax
(o
C)
20
22
24
26
28
30
32
34
36
38
T m
ax
(o
C)
180 200 220 240 260 280 300 320 340
Day of the year
0
10
20
30
40
50
60
70
80
Ra
infa
ll (m
m)
sow flower harvest
Groundnut crop growing in Andhra Pradesh, India
Heat stress
20
22
24
26
28
30
32
34
36
38
T m
ax
(o
C)
+2oC
Variability in rainfall within a season
1975Total rainfall: 394mm
Yield = 1360 kg/ha
1981Total rainfall 389mm
Yield = 901 kg/ha
Groundnut crop growing in Andhra Pradesh, India
-40
-30
-20
-10
0
10
20
30
40
elev CO2 HT WD HTWD
% c
han
ge
fro
m c
on
tro
l Seed yield
Seed number
Soyabean - 8 days of up to 40oC / 40% water supply during early seed-filling at 360 / 700 ppm CO2 from Ferris et al, 1999
Variability in rainfall and temperaturewithin a season
-30%
+22%
-26%
+32%
What are effects of climate on crops?
Can we forecast the productivity of crops in a changing climate?
Changes in crop yieldfrom the present day to the 2080s
Unmitigated emissionsParry et al., University
of East Anglia
Potential change in cereal yields (%)
No data
10 – 5
0 – -2.5
-5 – -10-2.5 – -5
-10 – -20
2.5 – 05 – 2.5
Linking climate informationto crop models
general circulation model
crop model
400
500
600
700
800
900
1000
1100
1200
1965 1970 1975 1980 1985 1990
Year
Gro
un
dn
ut
yie
ld (
kg
ha
-1)
National YieldStatistics
Groundnut (peanut) production in India, 1965 - 1990
Patterns of seasonal rainfall and yield of groundnut in India
District level groundnut yields (kg ha-1)
Mean of 1966 - 1990
Data source: ICRISAT
Patterns of seasonal rainfall and yield of groundnut in India
Sub-divisional level seasonal rainfall (JJAS, cm)
Mean of 1966 - 1990
Data source: IITM
Correlation between patterns of seasonal rainfall and yield
First principal component of
rainfall
yield
Correlation between patterns of seasonal rainfall, yield and circulation
First principal component of
rainfall
yield
and PC3 of
850hPa
circulation
Sites/weather stations in the main maize producer region
Surrounding counties, to each weather station (micro-regions)
Correlation between seasonal rainfalland yield of maize
Rio Grande do Sul, Brasil
1990-2005
Homero
Bergamaschi,
et al. 2006
Annual yield of maize in 11 micro-regions of RS State, Brazil (1990-2004) as function of rainfall from 5 days before to 40 days after tasseling
y = 6,5858x + 1276,6
R2 = 0,56790
500
1000
1500
2000
2500
3000
3500
4000
4500
0 50 100 150 200 250 300 350 400
Average rainfall in the 5-tasseling+40 days (mm)
Ave
rage
gra
in y
ield
(kg
ha-1
)
Combines:
• the benefits of more empirical approaches (low input data requirements, validity over large spatial scales)
with
• the benefits of the process-based approach (e.g. the potential to capture intra-seasonal variability, and so cope with changing climates)
General Large Area Modelfor Annual Crops (GLAM)
Challinor et. al. (2004)
d(HI)/dt
Yield Biomass
transpiration
efficiency
Root systemDevelopment Transpiration radiation
stage temperatureRH
rainfall
water Soil water
stressCYG
Leaf canopy
General Large Area Modelfor Annual Crops (GLAM)
Hindcasts of groundnut yield for India
400
600
800
1000
1200
1965 1970 1975 1980 1985 1990
Gro
un
dn
ut
yie
ld (
kg
ha
-1) National Yield Statistics
GLAM prediction
from Challinor et al (2004)
Impact of extreme temperatures
Hadley Centre PRECIS model, A2 (high emission) scenario 2071-2100
Number of years when the total number of pods setting is below 50%.
Sensitive variety Tolerant variety
Challinor et al., 2005
1975Total rainfall: 394 mmModel: 1059 kg/haObs: 1360 kg/ha
1981Total rainfall 389 mmModel: 844 kg/haObs: 901 kg/ha
Impacts of variability in rainfall within a season
Groundnut yield in Gujarat
Modelling the impacts of climatechange on rice
Changes in rice production across Asia under 2 x CO2
from Matthews & Wassmann (2003)
Climate model
GFDL GISS UKMO
ORYZA
+6.5
-4.4
-5.6 Crop
model SIMRIW
+4.2
-10.4
-12.8
0
5
10
15
20
25
200 300 400 500 600 700 800 900 1000 1100 1200
Yield (kg ha-1)
Fre
qu
ency
Using probabilistic climate forecasts
Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998 from Challinor et al (2005)
Model average 63 ensemble members
Observed
775 kg ha-1
713 kg ha-1
Fully coupled crop-climate simulation to represent crop-climate feedbacks
Crops ‘growing’ in HadAM3
Osborne et al., (2006)
All-India groundnut yield (red) with simulated mean yield (black) and spatial standard deviation (grey shading).
Fully coupled crop-climate simulation
Osborne et al., (2006)
FAO statistics
Area mean
s.d (spatial variability)
Representation of feedbacks between
crops and atmosphere at an
early stage
A coupled crop-climate model run
Tom Osborne,
University of Reading
Summary. The effects of climate on crops
Crop growth and yield will be enhanced by elevated CO2
Warmer seasons will be shorter and yields less
… but, adaptation can counter this to some extent
… but, benefit could be less on farmer’s fields
A few days of hot temperature can severely reduce yields
Crops will be vulnerable to variability in rainfall.
Summary. Forecasting crop yields
Crop models summarise observations and allow predictions ahead of time
Most crop models simulate fields of crops,
… but, crop forecasts often needed over countries and regions, nearer to the scale of climate model predictions
New developments in crop and climate modelling should improve our forecasts of crops in a changing climate
Crop observations
– Magnitude of CO2 effect, effects of climate extremes and poor soil fertility
Climate models not ideal for crop prediction– Differences in spatial and temporal scale– Precipitation is key and is a difficult variable to predict
Combining crop and climate models– Cascade of uncertainties
Summary. Challenges for the simulation of crop yields in a changing climate
Many thanks to …
Andrew Challinor
Julia Slingo
Peter Craufurd
David Grimes
Tom Osborne
Laurence Hansen
Richard Betts