1. An introduction to the crop model GLAM
Sanai LI
APEC Climate Center 12 Centum 7-ro, Haeundae-gu, Busan, , Republic of Korea

2. Introduction

3. 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
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)

5. 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

6. GLAM GLAM – Inputs and outputs \ INPUTS Daily weather data: - Rainfall
- Solar radiation
- Min temperature
- Max temperature
OUTPUTS \
Soil water balance
Leaf canopy
Root growth
Biomass
Crop Yield
Soil type
GLAM
Planting date

7. General Large Area Model for Annual Crops (GLAM)
d(HI)/dt
Pod yield
Biomass
transpiration
efficiency
Root system
Development
Transpiration
radiation
stage
temperature
rainfall
Leaf canopy RH
CYG
water
Soil water
stress
the flowchart of the GLAM model structure and the processes of yield and biomass formation

8. General Large Area Model for Annual Crops (GLAM): some parameters
Thermal duration: Determines development rate
Predict weather extremes at sensitive stages (e.g. flowering)
Transpiration efficiency to calculate biomass
Yield changes under elevated CO2
Maximum rate of change of LAI: determines growth of leaves
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.
Process-based: acts on LAI to determine an effective LAI
In practice, YGP can bias correct input weather data
It is not the sole determinant of mean yield, however

9. Simulation of developmental stages
daily effective temperature (oC)
Time (days)
i development stage
base temperature (oC)
thermal time (oCd)
In GLAM, the simulation of developmental stages is controlled by accumulated thermal time.
Once the thermal time accumulation tTT reaches the specified thermal time for a given stage, next stage begins

10. Modelling crop growth: biomass in GLAM
H2O
CO2
Stomatal conductance
During the summer, cumulative photosynthesis increases linearly with cumulative transpiration
above ground biomass
photosynthesis is not modeled directly, but it is represented by transpiration efficiency
transpiration efficiency
Maximum normalized transpiration efficiency
Vapor presser deficit
daily actual transpiration
maximum transpiration efficiency

11. Modelling canopy in GLAM Leaf Area Index
effective LAI
yield gap parameter
the soil water stress factor
maximum LAI expansion rate
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 index
from the beginning of gain-flilling, if there is no any stress, harvest index linearly increase with time

13. Water Balance in GLAM model
Evaporation + Transpiration
Rain
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 runoff
P is the precipitation
S is the amount of water that can soak into the soil
S = ksat
ksat is saturated hydraulic conductivity of the soil
Kks is emperiacal constant
Infiltration rate=precipitation -runoff
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..

15. Water Balance in GLAM -drainage
Cd1, Cd2, Cd3 are empirical constants
F accounting for simultaneous inflow from the layer above
Qi is the incoming water flux from the layer above
θdul is drained upper limit
FD is the drainage rate
θ is soil water content
θs is the initial value of θ
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
RN - net all-wave radiation
G - soil heat flux,
G = CGRNe−kL , CG constant, k-constant
λ - the latent heat of vaporisation of water
Δ = ∂esat/∂T - slope of the saturation-vapour pressure versus the temperature curve
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)
The energy-limited evaporation and transpiration rates (Ee and e , respectively) are
TT described using the simpler Priestley–Taylor equation (Priestly and Taylor, 1972)

17. Transpiration and evaporation
potential evapotranspiration rate
maximum possible energy-limited evapotranspiration
Is given by G=0
energy-limited evaporation
energy-limited transpiration
potentially extractable soil water
te(z) is the time of first root uptake in layer z
kDIF is the uptake diffusion coefficient
zmax is depth of soil profile
transpiration rate
evaporation rate
The available soil water is partitioned into transpiration and evaporation according to water demand where necessary

18. Model calibration

19. GLAM Calibration-Yield gap parameter (YGP)
GLAM run with YGP, varying from in step of The optimal value is chosen by minimizing the Root Mean Square Error (RMSR) between observed yields and simulated yields

20. 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
Crop yield
1.00
Yield Gap Parameter = 0.80
0.05

21. GLAM – The Yield Gap Parameter (YGP) methods
You 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 = YGP=1.0
Uptake of
water
Leaf Area
Index
Potential
rate of
transpiration
Rate of
Biomass
Crop
Yield
Roots
Harvest
Soil
Properties
Soil Water
Stress Factor
YGP = YGP = 1.0
YGP = 1.0
YGP = 0.2

22. 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. Simulating the impact of flood on wheat in China
Correlation coefficient between observed wheat yield and rainfall in China from 1985 to 2000

25. -Surface water storage and runoff
Water Balance in GLAM
-Surface water storage and runoff
PPTi
ETi
Surface storage
Runoff
More frequent heavy rainfall may increase surface water storage and cause crop loss due to excess soil moisture
Drainage

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

27. 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

28. 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 factor
WSFC0 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

29. 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 observed and 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 flood
Red 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

31. Simulating the flood effect
Comparison between observed yield and simulated yield with and without flooding effect from 1985 to 2000 at 0.5◦ grid cell (31.75◦N; ◦E)
the modified model improved yield predictions in years with serious flooding
damage year 1991 and 1998

32. Comparison of correlation coefficient between observed and simulated yield at the 0.5◦ scale in east China from 1985 to 2000
With flooding effect, yield predictions showed a better agreement with observed yield compared with no flooding effect
original
modified models

33. 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: 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
Significant level
Winter wheat
Spring wheat

36. Comparison of simulated and observed wheat yield (kg/ha) at 0
Comparison of simulated and observed wheat yield (kg/ha) at 0.5o scale across China
(b) Simulations
(a) Observations

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 yield Sensitivity 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

41. Impacts of extremes –temperature short periods of exposure to high daily maximum temperatures can also exert a dramatic impact on crop yield
Temperature at anthesis ~ time of flowering
Also observed for rice (above) and groundnut

42. Summary of observed and modeled increase
in wheat yield in response to elevated CO2
Increase in CO2 ppm
Increasee in yield (%)
Methods
Source 37
Glasshouse or growth chambers
Kimball, 1983 31
Estimated by cubic equation from multiple experiments
Amthor, 2000 28
Linear extrapolation of FACE experiment
Easterling et al., 2005
7 -23
FACE experiment
Kimball,2002 25
CERES for C3 crops
Boote,1994
16-30
GLAM model in China

43. Case studies for lab class

44. GLAM lab class First download GLAM

45. GLAM lab class Then choose case study: Ghana China For details see
(unique username and password needed)
And decide whether to use a text edit or the beta version GUI
See

46. 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

47. Step 2: Collect and organise input data
Daily weather data
Rainfall, min and max temperature, solar radiation.
thailand- PRECIS output for ,

48. 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

49. Step 4: Collect and organise input data
Planting date information
Specify planting date or start of ‘intelligent sowing window’
Observed yield data
Crop yield (kg/ha) = Production (kg)
Cultivated area (ha)
Thailand– provincial yield data is converted to
grid cells by ArcGIS
– Remove ‘technology trend’

50. 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.
Yield Gap Parameter = 1.00
Crop yield
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