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  1. An introduction to the crop model GLAM SanaiLI Email: APEC Climate Center12 Centum 7-ro, Haeundae-gu, Busan, 612-020, 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 – 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 Transpiration radiation Development stage temperature rainfall Leaf canopy RH water Soil water CYG 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. CO2 H2O Stomatal conductance Modelling crop growth: biomass in GLAM 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 ksatis 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 Qiis the incoming water flux from the layer above θdul is drained upper limit FD is the drainage rate θ is soil water content θsis 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 kDIFis the uptake diffusion coefficient zmaxis 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 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

  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 1.00 Cropyield 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 Soil Properties YGP = 0.2 YGP=1.0 Roots Uptake of water YGP = 0.2 YGP = 1.0 Leaf Area Index Potential rate of transpiration Rate of transpiration Biomass Crop Yield YGP = 1.0 YGP = 0.2 Soil Water Stress Factor Harvest Index

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

  25. 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 reductionin 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 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 flood Red line: infiltration is simulated to by the modified infiltration with flood. the original model with waterlogging stress is better thenwithout waterlogging stress. The modified GLAM model was better than the originalmodel

  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; 120.25◦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: 1.34 g MJ-1 Measured RUE of 1.81 g MJ-1 in semi-arid environmentby 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 0.5o 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 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

  41. Impacts of extremes –temperatureshort 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

  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 1981-2012, 2041-2050

  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. Cropyield Yield Gap Parameter = 1.00 Yield Gap Parameter = 0.80 Yield Gap Parameter = 0.05