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Sanai LIEmail: lisanai@apcc21.org
An introduction to the crop model GLAM
APEC Climate Center12 Centum 7-ro, Haeundae-gu, Busan, 612-020, Republic
of Korea
Introduction
Crop modelling methods
Empirical and semi-empirical methods+ Low input data requirement+ Can be valid over large areas May not be valid as climate, crop or management
change
Process-based+ Simulates nonlinearities and interactions Extensive calibration is often needed skill is highest at plot-level
Þ What is the appropriate level of complexity? Depend on the yield-determining process on the spatial
scale of interest (Sinclair and Seligman, 2000)
4
General Large Area Model for Annual Crops (GLAM)
Aims to combine: 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)
Yield Gap Parameter to account for the impact of differing nutrient levels, pests, diseases, non-optimal management etc.
Challinor et. al. (2004)
GLAM (General Large-Area Model for annual crops)
• Process based crop model
• Specifically designed for use on large spatial scales- simulates climatic influences on crop growth and development- low input data requirements
Typical climate model grid cell – GLAM can be run on this spatial scale
Daily weather data:
- Rainfall- Solar radiation- Min temperature- Max temperature
GLAM – Inputs and outputs
Soil type
Planting date
INPUTS
OUTPUTS
\
GLAM
Soil water
balance
Leaf canopy
Root growth
Biomass
Crop Yield
General Large Area Model for Annual Crops (GLAM)
d(HI)/dt
Pod yield Biomass
transpiration
efficiency
Root systemDevelopment Transpiration radiation
stage temperaturerainfall
Leaf canopy RH
water Soil water
stressCYG
the flowchart of the GLAM model structure and the processes of yield and biomass formation
General Large Area Model for Annual Crops (GLAM): some parameters
• Thermal duration: Determines development rateo Predict weather extremes at sensitive stages (e.g. flowering)
• Transpiration efficiency to calculate biomasso Yield changes under elevated CO2
• Maximum rate of change of LAI: determines growth of leaveso Check model consistency by looking at Specific Leaf Area
• Yield gap parameter: time-independent site-specific parameter to account for the impact of differing nutrient levels, pests, diseases, non-optimal management etc.o Process-based: acts on LAI to determine an effective LAI o In practice, YGP can bias correct input weather datao It is not the sole determinant of mean yield, however
Simulation of developmental stages
dtTTt b
t
t effTT
i
i
)(1
thermal time (oCd)
Time (days)
base temperature (oC)i development stage
daily effective temperature (oC)
Once the thermal time accumulation tTT reaches the specified thermal time for a given stage, next stage begins
In GLAM, the simulation of developmental stages is controlled by accumulated thermal time.
10
Modelling crop growth: biomass in GLAM
H2O CO2
Stomatal conductance
max,,min TNT
T EV
ET
t
W
above ground biomass
daily actual transpiration
transpiration efficiency
maximum transpiration efficiency
Vapor presser deficit
Maximum normalized transpiration efficiency
During the summer, cumulative photosynthesis increases linearly with cumulative transpiration
photosynthesis is not modeled directly, but it is represented by transpiration efficiency
11
Modelling canopy in GLAM Leaf Area Index
t
L
1,minmax cr
YG S
SC
t
L
Topt
T
T
TS
effective LAI
maximum LAI expansion rate
yield gap parameter
the soil water stress factor
• Water stress factor reduces leaf expansion
• decrease in leaf area index affect radiation interception and transpiration, and hence crop yield
12
Modelling crop growth: Yield in GLAM
Yield=biomass*harvest index (HI)
Harvest index =yield/biomass
Biomass is allocated to yield by harvest indexfrom the beginning of gain-flilling, if there is no any stress, harvest index linearly increase with time
13
Water Balance in GLAM model
RainEvaporation + Transpiration
net soil water input Sw=Trainfall-Ttranspiration-Tevaporation-Trunoff-Tdrainage
All of the non-runoff rain goes through the first soil layer first, then the total water infiltration into the soil is distributed into NSL (No. of soil layers) vertical soil layers
14
Water Balance in GLAM Runoff-US Soil Conservation Service method (USDA-SCS, 1964)
R is the runoffP is the precipitationS is the amount of water that can soak into the soil
S = ksat
ksat is saturated hydraulic conductivity of the soilKks is emperiacal constant
All of the on-runoff rainfall goes through the first soil layer firstly. When the soil water content is greater than the drainage limit, then the excess soil water is infiltrated into the next soil layers and the soil water content in each layer is simulated..
Infiltration rate=precipitation -runoff
15
Water Balance in GLAM -drainage
Cd1, Cd2, Cd3 are empirical constants
FD is the drainage rate
θs is the initial value of θ
θ is soil water content
θdul is drained upper limit
F accounting for simultaneous inflow from the layer aboveQi is the incoming water flux from the layer above
If the soil moisture is greater than dul at the start of a timestep, the incoming water from above is percolated to the lower layers
16
potential evapotranspiration rate-Priestley Taylor
Δ = ∂esat/∂T - slope of the saturation-vapour pressure versus the temperature curve
RN - net all-wave radiation
G - soil heat flux,
λ - the latent heat of vaporisation of water
γ- ratio of the specific heat of air at constant pressure to the latent heat of vaporisation of water
α -PriestleyTaylor coefficient, is a function of VPD(vapour pressure deficit)
G = CGRNe−kL , CG constant, k-constant
The energy-limited evaporation and transpiration rates (Ee and e , respectively) areTT described using the simpler Priestley–Taylor equation (Priestly and Taylor, 1972)
17
Transpiration and evaporation potential evapotranspiration rate
maximum possible energy-limited evapotranspirationIs given by G=0
energy-limited evaporation
energy-limited transpiration
evaporation ratetranspiration rate
potentially extractable soil waterte(z) is the time of first root uptake in layer z kDIF is the uptake diffusion coefficientzmax is depth of soil profile
The available soil water is partitioned into transpiration and evaporation according to water demand where necessary
Model calibration
19
GLAM Calibration-Yield gap parameter (YGP)
GLAM run with YGP, varying from 0.05-1 in step of 0.05. The optimal value is chosen by minimizing the Root Mean Square Error (RMSR) between observed yields and simulated yields
GLAM – Calibration GLAM simulates the impact of weather on crop yields.
It does not directly simulate the impact of other factors such as nutrient deficiencies, pests, diseases, weeds
The yield gap parameter is a time-independent site-specific parameter that accounts for these factors.
It also acts to bias correct weather
1.00
Crop
yie
ld
0.05
Yield Gap Parameter = 0.80
Uptake ofwater
Leaf AreaIndex
Potentialrate of
transpiration
Rate oftranspiratio
nBiomass Crop
Yield
Roots
HarvestIndex
Soil Properties
Soil WaterStress Factor
GLAM – The Yield Gap Parameter (YGP) methodsYou can choose how the yield gap parameter reduces simulated yields.
Options include acting on:• EOS: end-of-season yield • LAI: Leaf area index• ASW: the available soil water
YGP = 0.2
YGP = 1.0
YGP = 0.2 YGP = 1.0
YGP = 0.2 YGP=1.0
Simulating the floods effect on
wheat
23
Flooding effect
There is increased risk of crop losses due to flooding and excess precipitation
crop damage from flooding and excess soil moisture is not included or not well simulated by some dynamic crop models
GLAM- New schedule of surface water storage, infiltration and waterlogging
24
Correlation coefficient between observed wheat yield and rainfall in Chinafrom 1985 to 2000
Simulating the impact of flood on wheat in China
PPTi ETi
Surface storage
Drainage
Runoff
Water Balance in GLAM -Surface water storage and runoff
More frequent heavy rainfall may increase surface water storage and cause crop loss due to excess soil moisture
26
Infiltration
method : the infiltration capacity of the soil is assumed to be affected the soil water content
INF is infiltration rate (cm/day) P is precipitation (cm/day) SURFSTORAGE is surface water storage SWsat is saturated volume soil water
SW is volume soil water content in the soil layer SWdul is drained upper limit
DZ is the depth of soil layer
The response of transpiration to waterlogging days for winter wheat
in China (Hu et al,2004)
•Waterlogging can result in the death of root cells, due to a reduction in oxygen availability. •Excessive soil moisture limits root growth and absorption of soil water, consequently decreasing crop transpiration
Parameterizing the flood effect
Method (Hu et al,2004): simulate flood effect by introducing a damage function that limited the plant's transpiration and roots growth roots when soil is greater than the field capacity
WSF is water stress factorWSFC0 is the sensitivity of different crops to waterlogging f(TW; PDT) is the response of transpiration to waterlogging days from empirical function of experimental data Kwl is the ratio of the lower limit of soil water content under waterlogging stress to field capacity SW is soil water content SWFC is field compacity SWSAT is saturated soil water content
Comparison of soil water content in the first soil layer with and without surface water storage in water balance model of GLAM
with surface water storage the simulated soil water content is slightly higher than that without surface storage
30
Probability distribution function of correlation coefficient between observedand simulated wheat yield in east China
Blue line : infiltration is calculated from original GLAM model without flood Green line: infiltration is calculated from original GLAM model with floodRed line: infiltration is simulated to by the modified infiltration with flood.
the original model with waterlogging stress is better then without waterlogging stress. The modified GLAM model was better than the original model
Simulating the flood effectComparison between observed yield and simulated yield with and without flooding effect from 1985 to 2000 at 0.5◦ grid cell (31.75◦N; 120.25◦E)
the modified model improved yield predictions in years with serious flooding damage year 1991 and 1998
Comparison of correlation coefficient between observed and simulated yield at the 0.5◦ scale in east China from 1985 to 2000
original modified models
With flooding effect, yield predictions showed a better agreement with observed yield compared with no flooding effect
Model performance
34
Evaluation of model consistency-RUE
Radiation Use Efficiency (RUE=biomass/radiation intercepted) of winter wheat :1.58 g MJ-1, spring wheat: 1.34 g MJ-1
Measured RUE of 1.81 g MJ-1 in semi-arid environment by O’Connell et al.(2004)
35
Correlation between observed and simulated yield at county(70-129km)/city(80-128km) level and field level in China
0
0. 1
0. 2
0. 3
0. 4
0. 5
0. 6
0. 7
0. 8
Guyuan Guyang Huma Zhengzhou Bei j i ng
Corr
elat
ion(
R) Rai nf ed-county(ci ty)
Rai nf ed- fi el d
Significant level
Spring wheat Winter wheat
36
Comparison of simulated and observed wheat yield (kg/ha) at 0.5o scale across China
(a) Observations (b) Simulations
37
Validation of GLAM-Wheat in China-Difference between observed and simulated mean wheat yield (%) in China (correlation r= 0.83,p<0.001)
38
Model skill for regional aggregated yieldSensitivity to spatial scale of weather data ( Li and Tompkins, 2012)
In order to find the appropriate spatial scale of climate data to run the regional crop model, the GLAM model is run with the 0.5◦×0.5◦ aggregated 1◦×1◦ and 2◦×2◦ scale weather data respectively
GLAM model driven by the 0.5 resolution of climate data gave a better fit of simulated yield in the rainfed agriculture dominant regions
39
Importance of subseasonal temporal variability in rainfall
Using 5 day pentad averages of rainfall, simulated yield is very similar to that using the original daily dataset for the selected sites in China.
Driving GLAM with 10 and 30 -day average rainfall resulted in a larges difference as using the original daily dataset
40
Importance of subseasonal temporal variability in temperature
The use of averages temperature over 5 days and 10 days in GLAM has a minor impact compared with simulations with daily temperature at all sites. Only the use of a 30 day running mean had great impact on the yield
Impacts of extremes –temperatureshort periods of exposure to high daily maximum temperatures can also exert a dramatic impact on crop yield
Also observed for rice (above) and groundnut
Temperature at anthesis ~ time of flowering
0
0. 2
0. 4
0. 6
0. 8
1
1. 2
24 26 28 30 32 34 36 38Maxi mum temprature( oC)
Gra
in-s
et f
ract
ion
Observati ons GLAM-Spri ng GLAM-wi nter
,maxmin( , )TT TN
EWT E
t V
Increase in CO2 ppm
Increasee in yield (%)
Methods Source
330-660 37 Glasshouse or growth chambers
Kimball, 1983
350-700 31 Estimated by cubic equation from multiple experiments
Amthor, 2000
350-700 28 Linear extrapolation of FACE experiment
Easterling et al., 2005
370-550 7 -23 FACE experiment
Kimball,2002
330-660 25 CERES for C3 crops Boote,1994
350-700 16-30 GLAM model in China
Summary of observed and modeled increase in wheat yield in response to elevated CO2
Case studies for lab class
GLAM lab classFirst download GLAM
www.see.leeds.ac.uk/research/icas/climate_change/glam/glam.html
GLAM lab class
Then choose case study:• Ghana• China
For details seehttps://www.see.leeds.ac.uk/redmine/public/projects/glam/wiki(unique username and password needed)
And decide whether to use a text edit or the beta version GUI
See https://www.see.leeds.ac.uk/redmine/public/projects/glam/wiki/blabla
Step 1: Decide on the grid cells/regions GLAM will be run for.
Depends on:
• Available input data
• Scale of relationship between weather and yield
• Aim of the project
Thailand– GLAM will be run on 0.5°x 0.5° gridcells
Step 2: Collect and organise input data
Daily weather dataRainfall, min and max temperature, solar radiation.thailand- PRECIS output for 1981-2012, 2041-2050
Step 3: Collect and organise input data
Soil data Soil hydrological properties (can be found from soil texture):
R. Evans et al 1996
Saturation limit: maximum amount of
water in the soil.
Drained upper limit: water held after thorough
wetting and drainage
Lower limit: any remaining water can not be extracted.
Thailand– Soil texture information from FAO soil map of the world – Data averaged onto model grid
Step 4: Collect and organise input data
Planting date informationSpecify planting date or start of ‘intelligent sowing window’
Observed yield data
Crop yield (kg/ha) = Production (kg) Cultivated area (ha)
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
0
100
200
300
400
500
600
700
800 OriginalLinear (Original)
Year
Crop
yie
ld (k
g/ha
)
Thailand– provincial yield data is converted to grid cells by ArcGIS – Remove ‘technology trend’
Step 5: Check parameter values are appropriate for local cultivars.
Step 6: Run GLAM in ‘calibration mode’ to find the yield gap parameter (YGP) for each grid cell.
Step 7: Run GLAM using these YGPs – compare simulated yields to observed yields.
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 20070
100200300400500600700800900
1000
Observed Yields
Year
Yiel
d (k
g/ha
)Yield Gap Parameter = 1.00
Crop
yie
ld
Yield Gap Parameter = 0.05
Yield Gap Parameter = 0.80
51
Conclusions
The GLAM-wheat model was able to capture a large fraction of interannual variability in observed yields in the rain-fed agricultural regions
Spatially averaging the driving climate data show that the use of the highest spatial resolution maximized model skill at reproducing yield
5 day (pentad) averaged rainfall can be used to drive crop model, however 10 day or longer averaging rainfall strongly impacts crop yield
temperature, a smoother field, could be averaged for 10 days or even monthly for crop modeling
52
Conclusions
There is an increase in frequency of heavy precipitation events in many parts of the world. It often cause large agriculture losses and great economic costs
By including the waterlogging damage and surface water storage, the modified GLAM-wheat model was able to capture most serious flooding damage to wheat yield in China
It is critical to consider the yield loss due to waterlogging when crop model is used for crop forecasting and climate impacts studies in region with high flooding probability
53
Thank you for your
attentation
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