Kunstmann, H.

Laux, P.

Waongo, M.

KITopen

Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 PICREVAT Workshop: “Predictability of climatic information to                                              reduce vulnerability of Tropical Agriculture”    20th -22nd February 2012 - Izaak Walton Inn, Embu (Kenya) Calibration and application of a spatially distributed process-based crop model: A case study for maize cropping in Burkina Faso  Moussa Waongo, Patrick Laux, Harald Kunstmann Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Plan Ø  Context and challenges  Ø  Study area Ø  General Large Area Crop Model (GLAM)   Ø  Data and calibration methodology   Ø  Calibration performance Ø  Planting date (PD) as agricultural decision rule   Ø  Investigation of PD impact on crop maize productivity  Ø  Conclusion   Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Context and challenges As result, the issue of sustainability of rainfed agriculture and food security remain a challenge for stakeholders.  Developing countries Increase in predicted population growth Agriculture activities are the main source of income            (vulnerable to CV and CC) CV and CC  major environmental challenge to the world today, with significant  threats to ecosystems, food security, water resources and economic stability overall.   African agriculture  heavy reliance on low- input rainfed  agriculture  low adaptive capacity. (more affected by CV and CC) West Africa rainfed agriculture is by far dominant Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Context and challenges  Focus on  ü  GLAM calibration (maize yield in BF) ü  Relevant rainfed agriculture management strategy (planting date)  ü  Impact of planting dates on Maize yield To tackle this issue, climate research applied to agriculture can play  a major role in focusing on how  to minimize the effects of adverse climate conditions and how to maximize the benefit of favorable climate conditions.  Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 AFRICA Burkina Faso Area = 274 200 km2, 45 provinces Population ≈ 13 million Capital city: Ouagadougou Population main activity: agriculture (80%) Main cereal crops: millet, sorghum, maize Main cash crop: cotton (30% of GDP) Climate RS=Mai-October RR=300-1100 mm Tm=28°C-35°C  Study area Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 General Large Area Crop Model (GLAM) Crop models are mathematical models which describe the growth and development of a crop interacting with soil. There are two groups of crop models:     Statistical crop models  ü  use statistical regressions ü  low input data ü  less adaptable to different spatio-temporal conditions  Process-based crop models  ü  quantify the relationships (crop growth, weather, crop management) ü  Often perform well at the field scale ü  high input data requirement  GLAM is a process based crop model designed to operate on spatial scales corresponding in size to those of global and regional climate models. It aims to simulate the impact of climate on crop yield. Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 General Large Area Crop Model (GLAM)   crop Climate soil Observed yield Sowing date  Yield, Biomass, LAI Fortran code ( *.f90) Open source code! Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Data and calibration methodology  Meteorological data  ECMWF : ERA interim data (daily Tx, Tn, Rad) at 1°x1° from 2000-2010  BF National Meteorological Services : Daily precipitation (2000-2010) Data   Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Data and calibration methodology  Soil data  FAO : Harmonized World Soil Database (HWSD), 1km of resolution  Data   Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Data and calibration methodology  Observed crop yield  BF Agricultural Production and Statistics Services : district (province level) Maize yield from 2000-2010 via Regional Center AGRHYMET Data   Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Data and calibration methodology     Simulated crop yields  Set of crop parameters Reduce range of each parameter X times   Crop yield Statistic (rmse,corr) Maize Y times Variation of time period   Robust set of parameters 10% Monte Carlo simulation Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 GLAM simulation Two type of simulation:  SET simulation: one run. It is often use after calibration of the model. YGP should be set to : 1 : potential simulation, no reduction  of yield, biomass or LAI 0<YGP<1:  for non optimal condition (non-optimize management, pest and crop diseases) YGP>1: non optimal condition for only yield option -99: a path of  an  ASCII file of YGP by location is given  HYP simulation: loop run (iteration of SET run) for sensitivity analysis or for model calibration. A YGP file can be derive from the HYP run and then use it for the SET run.    Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 200300400500600Root Mean Square Deviation (RMSD)1GLOBALValues(Kg/ha)Calibration performance 00.10.20.30.40.50.61GLOBALValuesGlobal Correlation coefficientMaize simulated yield vs Observed yield (2000-2007) Simulation area Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 00.20.40.60.81NORTH CENTRE SOUTHValuesRegionalized correlation coefficient0100200300400500NORTH CENTRE SOUTHValues (Kg/ha)Regionalized root mean square deviationCalibration performance North Centre South Maize simulated yield vs Observed yield (2000-2007) 3 simulation areas Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Calibration performance Maize simulated yield vs Observed yield (2000-2007) ï6 ï5 ï4 ï3 ï2 ï1 0 1 2 3910111213141516LongitudeLatitudeCorrelation coefficient  0.30.350.40.450.50.550.60.650.7ï6 ï4 ï2 0 2910111213141516LongitudeLatitudeMaize Predicted  yield (Normalized RMSD)   20304050607080(%)ï6 ï5 ï4 ï3 ï2 ï1 0 1 2 3910111213141516LongitudeLatitudeMaize gridded mean yield from 2000ï2010 (Kg/ha) in Burkina Faso  70075080085090095010001050ï6 ï5 ï4 ï3 ï2 ï1 0 1 2 3910111213141516LongitudeLatitudeGridded annual precipitation ( average of 2000ï2010) in mm  300400500600700800Maize simulated yield (Nor alized SD) Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 ï6 ï4 ï2 0 2910111213141516LongitudeLatitudeMaize Predicted yield index for 2008 (%)  ï40ï20020406080ï6 ï4 ï2 0 2910111213141516LongitudeLatitudeMaize Predicted yield index for 2009 (%)  ï50ï40ï30ï20ï100102030ï6 ï4 ï2 0 2910111213141516LongitudeLatitudeMaize Predicted yield index for 2010 (%)  ï40ï20020406080ize simulated yield index for 2010 (%)Maize simulated i l  i ) Maize simulated yield index for 20 9 (%) Maize simulated yield for evaluation period  Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 Different approaches to estimate PD dates exist.   Methods based on different threshold combinations of rainfall amount, dry spell length and number of rainy events. Determination of potential planting dates   PD definitions:   •  Diallo (2001): The date after 1st May, when rainfall accumulated over 3 consecutive days is at least 20 mm and when no dry spell more than 10 days within the next 30 days. •  Dodd & Jolliffe (2001): The first day of a spell of 5 days in which at least 25 mm of rain falls, on condition that no dry period of more than 7days occurs in the following 30 days.  Planting date (PD) as agricultural decision rule  Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 •  Fuzzy logic approach of Laux et al. (2008) : The day with a mm of cumulative rainfall amount over b rainy days within 5 days spell, with no dry spell more than c days in the next 30 days. For computation a1, a2, b1, b2, c1, c2 and k are respectively set to 18 mm, 25 mm, 1 day,3 days, 7days, 10 days and 0.5  a2 a1 μ aμ1 1b2 b1 μ bμ2 1c2 c1 μ cμ3 Planting date (PD) as agricultural decision rule  µ1 x µ2x µ3 = k, k ε ]0,1] Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 ï6 ï5 ï4 ï3 ï2 ï1 0 1 2 3910111213141516LongitudeLatitude  1 2 34 5 6 7 8 915 1610 11 12 13 1417 18 19 20 2122 23 24Potential planting dates (period 2000-2010)  Grid number location June, 1st July, 1st Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 ï6 ï5 ï4 ï3 ï2 ï1 0 1 2 3910111213141516LongitudeLatitude  1 2 34 5 6 7 8 915 1610 11 12 13 1417 18 19 20 2122 23 24Potential planting dates (period 2000-2010)  Grid number location June, 1st July, 1st Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 1201401601802001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Grid numberDay of yearFuzzy logic approach for planting (k=0.4)1201401601802001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Grid numberDay of yearFuzzy logic approach for planting (k=0.5)1201401601802001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Grid numberDay of yearFuzzy logic approach for planting (k=0.6)1201401601802001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Grid numberDay of yearFuzzy logic approach for planting (k=0.7)Potential planting dates (period 2000-2010)  June, 1st July, 1st June, 1st July, 1st Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 40060080010001200140016001800YearsMaize yield(Kg/ha)Water Balance approach2008 2009 201040060080010001200140016001800YearsMaize yield(Kg/ha)Approach of Diallo et al (2001). 2008 2009 201040060080010001200140016001800YearsMaize yield(Kg/ha)Approach of Dodd et Jolliffe (2001).2008 2009 201040060080010001200140016001800YearsMaize yield(Kg/ha)Fuzzy logic approach of Laux et al. (2008), k=0.52008 2009 2010Yield ObsYield PredYield ObsYield PredYield ObsYield PredYield ObsYield PredMaize simulated yield variability for different planting dates  Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 CONCLUSION The impacts of climate change on agriculture are expected to be widespread across the globe.  Ø  The expected crop yield reduction for the future will certainly result in increase in prices of agricultural goods and this impact will be greater for food insecure regions as Africa particularly in West Africa. Ø  This study figure out that GLAM can be a good candidate to explore the climate impact on agriculture productivity in a regional or sub-regional scale where field scale crop models are enable to deal with. Ø  Finding the best date for sowing (avoid false starts) is still a challenge in West Africa. In light of CC, investigation on this field using crop models outputs can add a value on decision making processes in agriculture.   Institute for Meteorology and Climate Research  Atmospheric Environmental Research (IMK-IFU) February 2012 THANK YOU FOR YOUR  ATTENTION ! 

Calibration and application of a spatially distributed process-based crop model: A case study for maize cropping in Burkina Faso

https://publikationen.bibliothek.kit.edu/230087661/3816105

Calibration and application of a spatially distributed process-based crop model: A case study for maize cropping in Burkina Faso

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