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Dynamic crop growth modelling with AGROSIM Application on the Bad Lauchstädt site

Dynamic crop growth modelling with AGROSIM Application on the Bad Lauchstädt site. Wilfried Mirschel. Leibniz-Centre for Agricultural Landscape Research (ZALF) Müncheberg, Institute of Landscape Systems Analysis.

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Dynamic crop growth modelling with AGROSIM Application on the Bad Lauchstädt site

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  1. Dynamic crop growth modelling with AGROSIM Application on the Bad Lauchstädt site Wilfried Mirschel Leibniz-Centre for Agricultural Landscape Research (ZALF) Müncheberg, Institute of Landscape Systems Analysis Eberswalder Str.84, 15374 Müncheberg e-mail: wmirschel@zalf.de International Workshop „Modelling soil processes in different time scales“, Halle, 19th –20th September 2005

  2. Content • Motivation • 2. Agro-ecosystem model family AGROSIM • 2.1. AGROSIM model for winter cereals • 2.2. AGROSIM model for sugar beet • 2.3. AGROSIM model applications • 3. AGROSIM model workshop results for Bad Lauchstädt (short term experiment) • 3.1. Without parameter adaptation • 3.2 With parameter adaptation • 5.AGROSIM model transfer to other geographical sites • 6. Conclusions ZALF, Institut für Landschaftssystemanalyse

  3. Motivation ►Yield formation and biomass accumulation in agriculture play an essential role in water, energy and nutrient cycles in agro-ecosystems. ►While crop yield on farm level are mainly in the focus of interest because of economic considerations, the total biomass is in the focus of interest because of changed water, nutrient and carbon balances as consequence of land use and climate changes. ►In agro-ecosystems biomass formation and turnover is influenced by different factors. + climate and weather, + site conditions (incl. water and nutrients supply ), + crop properties (incl. cultivars, plant physiology and genetics), + management and + influences from other system components (pests and diseases). ► Simulation models are powerful tools to investigate the effects of different land use options and/or climate changes on water and matter cycles as well as to bridge the gap between different temporal and spatial scales. ZALF, Institut für Landschaftssystemanalyse

  4. Agro-ecosystem model family AGROSIM (1) The model family AGROSIM which consists plant physiological based agro-ecosystem models for agricultural crops was developed in the Institute of Landscape Systems Analysis of the Leibniz-Centre for Agriucultural Landscape Research Müncheberg (Germany) beginning in the 1990th. ZALF, Institut für Landschaftssystemanalyse

  5. Agro-ecosystem model family AGROSIM (2) The dynamic plant physiologically based AGROSIM models ►belong to the group of soil-plant-atmosphere-management models with the main focus on crop growth processes, ►were elaborated not for single plants, but for whole crop stands under field conditions, ►have a similar model structure on the basis of rate equations, ►describe the processes with a time step of 1 day, ►need only standard meteorological input values (minimum and maximum temperature, global radiation or sunshine duration, precipitation, CO2- content in the atmosphere) as driving forces and generally available inputs and parameters, ► are validated for weather and soil conditions of different locations in North- East Germany. ZALF, Institut für Landschaftssystemanalyse

  6. AGROSIM model for winter cereals - Model structure - ZALF, Institut für Landschaftssystemanalyse

  7. AGROSIM model for sugar beet - Model structure - ZALF, Institut für Landschaftssystemanalyse

  8. AGROSIM model validation results Model-experiment-comparison for winter barley, 1993/94, Müncheberg Model-experiment-comparison for above-ground biomass, 1991-1995, Müncheberg ZALF, Institut für Landschaftssystemanalyse

  9. AGROSIM model applications - Influence of water supply - Influence of water supply on yield and biomass for winter wheat (1991/92, N-fertilization: 125 kg N ha-1, Hohenfinow, cultivar: Alcedo) ZALF, Institut für Landschaftssystemanalyse

  10. AGROSIM model applications - Influence of nitrogen supply - Influence of nitrogen supply on yield and biomass for winter wheat (1991/92, with irrigation, Hohenfinow, cultivar: Alcedo) ZALF, Institut für Landschaftssystemanalyse

  11. AGROSIM model applications - Influence of increased CO2 in the atmosphere on biomass accumulation (1) - Basis: + influence ofCO2 on photosynthesis and respiration processes (not on stomata level) + Michaelis-Menten-equation for C3-plants, basis level: 350 ppm with: CO2 - CO2-content in the atmosphere GS - global radiation ZALF, Institut für Landschaftssystemanalyse

  12. AGROSIM model applications - Influence of increased CO2 in the atmosphere on biomass accumulation (2) - Sugar beet, 2001,N: 126 kg N ha-1, with irrigation, Simulation with AGROSIM-ZR Winter barley, 2002/03,N: 179 kg N ha-1, with irrigation, Simulation with AGROSIM-WG Data base: FACE – experiment ( 1999 – 2005) of the Federal Agricultural Research Centre Braunschweig, Germany ZALF, Institut für Landschaftssystemanalyse

  13. AGROSIM model applications - Influence of CO2 and temperature on biomass accumulation - Influence of temperature and CO2 increase on biomass accumulation of winter rye ZALF, Institut für Landschaftssystemanalyse

  14. AGROSIM model applications - Climate change effect assessment for winter rye biomass and yield: 1994 vs. 2034 - Basis:climate model ECHAM1/LSG of the Max- Planck-Institute for Meteorology Hamburg, Scenario: “business as usual“ ZALF, Institut für Landschaftssystemanalyse

  15. AGROSIM model workshop results for Bad Lauchstädt ► AGROSIM models run for the short time experiment (1999-2004). ► Because of not availability of AGROSIM models for potatoes and spring barley model runs for sugar beet in 1999 and 2003, and winter wheat in 2001/02 were realized only. 1. Without any parameter adaptation  original model parameter set for Müncheberg was used 2. With parameter adaptation  cultivar dependent model parameters were adapted only ZALF, Institut für Landschaftssystemanalyse

  16. AGROSIM model workshop results for Bad Lauchstädt – without parameter adaptation – (1) Sugar beet, 1999 ► root and leaf biomass estimation with a good accuracy (light underestimation at harvest time) ► soil water is overestimated, especially in 90 cm depth during summer and late summer ZALF, Institut für Landschaftssystemanalyse

  17. AGROSIM model workshop results for Bad Lauchstädt – without parameter adaptation – (1) Sugar beet, 2003 ► root and leaf biomasses are under- and overestimated, respectively ► soil water estimation with a good accuracy (light overestimation especially in 90 cm depth during summer and late summer) ZALF, Institut für Landschaftssystemanalyse

  18. AGROSIM model workshop results for Bad Lauchstädt – without parameter adaptation – (2) Winter wheat, 2001/02 ► significant overesti- mation in above- ground biomass and N- uptake during grain filling period ► soil water estimation in 45 cm and 90 cm depth is a little bit underesti- mated, especially in 45 cm depth during spring and summer ZALF, Institut für Landschaftssystemanalyse

  19. AGROSIM model workshop results for Bad Lauchstädt – with parameter adaptation – (1) Sugar beet, 1999 ► after adaptation of cultivarmodel parameters (distribution ratio between leaf and root) the biomasses can be estimated with a higher accuracy ► the cultivar parameter change does not influence the soil water course ZALF, Institut für Landschaftssystemanalyse

  20. AGROSIM model workshop results for Bad Lauchstädt – with parameter adaptation – (1) Sugar beet, 2003 ► here also the same parameter adaptation (distribution ratio function between leaf and root) ► adapted variant (dotted lines) has a better agreement with the measured biomasses over the time ZALF, Institut für Landschaftssystemanalyse

  21. AGROSIM model workshop results for Bad Lauchstädt – with parameter adaptation – (2) Winter wheat, 2001/02 ► adaptation of cultivar model parameters gives significant better results in biomass accumulation (dotted lines) ► ontogenesis and soil water are not changed significant ZALF, Institut für Landschaftssystemanalyse

  22. parameter maximum ontogenesis rate gross photosynthetic rate tillering shooting grain filling tillering shooting grain filling country France 0.10...0.11 0.37 0.07 0.95...1.10 0.25...0.31 0.03 Germany 0.17 0.40 0.035 0.90 0.245 0.055 Hungary 0.10 0.45 0.07 0.90 0.245 0.055 Italy 0.10 0.34 0.12 0.90 0.26 0.03 Netherlands 0.12 0.60 0.035 0.96 0.27 0.055 Poland 0.07 0.45 0.08 1.10 0.31 0.03 0.07...0.09 0.45 0.08 1.10 0.31...0.46 0.03...0.055 AGROSIM model transfer to other geographical sites (1) • ► To transfer crop growth and ecosystem models from one geographical site to another successfully it means to recalibrate model parameter in every case, more or less intensive! This is shown by • workshop results with the Bad Lauchstädt data set from the short time experiment • transfer investigations with the AGROSIM model for winter wheat to different European sites Russia ZALF, Institut für Landschaftssystemanalyse

  23. AGROSIM model transfer to other geographical sites (2) – AGROSIM-WW transfer to European sites - Latitude: 39.3° ... 55.0° Experimental sites: 24 Growing periods: 1957 ... 1997 different Cultivars: 29 Model-experiment-comparison for winter wheat grain yield (simulation with AGROSIM-WW) ZALF, Institut für Landschaftssystemanalyse

  24. Conclusions ► The AGROSIM models for sugar beet and winter wheat can describe the real situation on the Bad Lauchstädt experimental station for 1999, 2001/02 and 2003 with a sufficient accuracy only after a recalibration of cultivar model parameters. ►The workshop results show that a model transfer to other geographical and sits conditions model parameters representing crop, site and other properties must be re- estimated or newly derived.  A model transfer without any adaptation is not useful ! ► The better considered the influence of site, weather, agro-management and cultivar properties the more accurate the simulation results and the greater the possibilities to transfer a model from one geographical site to another and from one time period to another. ► The chances of a broad model application increase if model adaptation could be limeted to weather and soil information and only a few clearly defined parameters. For this coherent data series are needed. ZALF, Institut für Landschaftssystemanalyse

  25. Conclusions What is the parameter situation of crop growth models within long-term simulations ? ► In opposite to the soil processes with more or less constant laws of soil physics and more or less constant process parameters, the crop growth processes are adaptable processes controlled by genetic memory and genetic information, i.e with changeable process parameters (ontogenetic rates, shoot-root-ratio, straw-grain-ratio ...) over a long time. On the one hand there are anthropogenious reasons like plant breeding, and on the other hand there are natural reasons like the self adaptation of plants to changing environmental factors. ► Investigation results that the CO2-reaction of old winter wheat cultivars from the 1930th differ from that of modern winter wheat cultivars underlines this fact (R. Manderscheid, Federal Agricultural Research Centre Brunswick, Germany). ► Changing genetic plant-own reactions from plant generation to plant generation make it necessary to adapt parameters in crop growth models anew for different time periods. So it is necessary to adapt these parameters any times for long-term simulation runs, like for the about one hundred years experiment here in Bad Lauchstädt. ZALF, Institut für Landschaftssystemanalyse

  26. Thank you for your attention ! ZALF, Institut für Landschaftssystemanalyse

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