![]() ![]() Python is one of the few programming languages flexible and adaptable enough to combine all of this functionality into a streamlined, integrated, modeling framework. This one-way nested modeling framework is capable of creating APSIM XML files using the lxml package, running thousands of simulations in parallel by using Python’s built-in multiprocessing and subprocess capabilities, saving the output of each APSIM simulation to a SQLite database through the use of Python’s native sqlite3 support, conducting a statistical analysis on the output using StatsModels package, and generating maps, timeseries, and figures by using the NumPy, Basemap, matplotlib, and pandas packages. Apsim model simulator#By using Python, an automated pipeline was created to link gridded Regional Climate Model (RCM) output (rain, temperature, etc.) with the point-specific Agricultural Production Systems sIMulator (APSIM) crop model. With the growing availability of such datasets, and the desire to analyze spatial and temporal yield changes at the regional scale, the ApsimRegions modeling framework was developed to fill this need. Few previous studies have used these models over broader geographic regions, largely due to the lack of gridded high-resolution meteorological and soil datasets required as inputs for these data intensive process-based models. Traditionally, process-based crop models have been run at the individual farm level for yield optimization and management scenario testing. OVERSEER® N balance: APSIM vs.Over the past few decades, there have been many process-based crop models developed with the goal of better understanding the impacts of climate, soils, and management decisions on crop yields. Phase I Phase II Phase III APSIM evaluation Long –term simulations APSIM vs. Winter Wheat Winter Forage Barleyġ3 Using APSIM to test another model (OVERSEER®) Aim is to quantify the importance (% of total variation) of each cropping system component for different impact variables. ![]() ![]() Impact on Nitrogen Leaching (Lincoln – preliminary only) Kale Winter Forage Field peas 3 4 5 1 2 Preliminary results to illustrate only (work under way now). ![]() Total maize silage biomass Production system: - High WHC soil Irrigated Adaptation: Sowing dates Maize Hybrid kg/ha Running APSIM spatially using NIWA’s virtual weather for current climate and different climate scenarios Setting up different levels of autonomous adaptation “modeled” by APSIM (change sowing date and genotype in this case) Baseline ( ) Climate Change A2 ( )ġ2 Sensitivity analysis on crop rotations NOTE error bars are standard deviation, they are all present but some are too small to see NOTE value for DUL was determined experimentally for this soilġ1 Spatial assessment of climate change impact & adaptation Carbon mineralisation in the data is over 3 times that simulated by APSIM. Highlighting the 35 degrees data and model outputs (red lines) – at DUL or field capacity and at 35 degrees this is maximum mineralisation, so regardless of whether the temperature and moisture response functions are correct or not, the model should approximate the experimental data. APSIM greatly underestimates mineralisation at all temperatures. Here are the experimental and modelled results for carbon mineralisation at field capacity under the range of temperatures. Testing SoilWat Variable rate irrigation & spatial management Hydrophobicity Residues & mulchesġ0 Testing and improving soil C & N mineralisation Environmental Modelling and Simulationĭeveloping the model (new crop model in APSIM) Current focus on phenology Phenology: Dormancy Budding Shoot growth Flower development Berry Development Senescence Budburst Flowering Veraison Leaf fallīudburst Experimental and simulation approach to define Chill Units + Thermal time Define start of development clock Subsequent phenology based on thermal time Apsim model software#Plant Modelling Framework: Software for building and running crop models on the APSIM platform. Robert Zyskowski – Plant and Food Research Edmar Teixeira – Plant and Food Research John Hargreaves - CSIRO Derrick Moot – Lincoln University (New Zealand)Īpplied to a number of crops Broccoli, Carrot, Field Pea, French Bean, Fodder Beet, Grapes, Kale, Lucerne, Oat, Oil Palm, Potato Ongoing APSIM plant model development Plant model for APSIMX Existing crop models to be implemented into PMF New crop models to be build in PMF Upcoming publication Brown et al (in press). Programming skills needed to make changes Solution Next generation generic crop model Exploits modern software systems (.Net) Allows flexible approaches to model structure Without needing to write codeĤ Contributors Dean Holzworth - CSIRO Neil Huth - CSIRO 26 released models use generic crop template (cereals, legumes, vines, trees, weeds, vegetables, pastures)ģ History Generic Crop Model problems Inflexible model structure ![]()
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