1

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)

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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.

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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

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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

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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

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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

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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

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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

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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)

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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

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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

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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

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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

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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

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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

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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)

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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

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Comparison of simulated and observed wheat yield (kg/ha) at 0.5o scale across China

(a) Observations (b) Simulations

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Validation of GLAM-Wheat in China-Difference between observed and simulated mean wheat yield (%) in China (correlation r= 0.83,p<0.001)

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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

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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

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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

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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

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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

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Thank you for your

attentation