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8 results for “APSIM”
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE soybean simulations
This data set contains output data from simulations with the model APSIM-UGOE for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE maize simulations
This data set contains output data from simulations with the model APSIM-UGOE for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE winter wheat simulations
This data set contains output data from simulations with the model APSIM-UGOE for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE spring wheat simulations
This data set contains output data from simulations with the model APSIM-UGOE for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE rice simulations
This data set contains output data from simulations with the model APSIM-UGOE for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
Data extracted from APSIM simulations for Sorghum
<p>The data are supplemental material for paper entitled APSIM-powered framework for effective post-rainy sorghum agri-system design in India.</p> <p>The APSIM sorghum model was run using environmental data spanning 30 years with a total of 13,824 Genotype × Management combinations per grid. To cover the Indian rabi sorghum production tract 311 grid items were used resulting in a total of 4299264 simulations. The computation took approximately 14 days including several downtime periods and generated 14.6 TB of output data. APSIM natively generates the data in raw text format, therefore for follow-up processing it was necessary to conduct extraction and parsing of relevant pieces of information. For the purposes of extraction and transformation into .csv file format a program was written in C# language to select only the relevant data.</p>
Data from: Evaluating the Agricultural Production Systems sIMulator (APSIM) wheat module for California
<p>Context: Computer-based crop simulation models are important tools for agricultural research and management. The Agricultural Production Systems sIMulator (APSIM) is commonly used around the world, but has not been widely validated in North America.</p> <p>Aims: The objective of this work was to evaluate the reliability of APSIM for simulating wheat production in California, and to identify future research needs, by using pre-existing data from state-wide variety trials.</p> <p>Methods: Environmental and management data from three seasons of state-wide wheat variety trials, were used to parameterize the APSIM-Wheat module (version 7.10 r4220). Simulated yield and protein data were compared to and actual field data to test the reliability of the APSIM simulations.</p> <p>Key results: The most reliable simulation of grain yield had a root mean square error of 1040 kg/ha and normalised root mean square error of 16 % relative to actual field data.</p> <p>Conclusions: The accuracy of the simulations was comparable to other tests of the APSIM-Wheat module in environments where it has not been previously calibrated, but was too low to be considered reliable. The lack of reliability was due to the poor representation of local Californian wheat genotypes, as well as inaccuracy of management and environmental data.</p> <p>Implications: APSIM could be a valuable tool for wheat research and management in California, but our work shows the current model is unreliable. Further research is needed to generate field data needed for model calibration.</p>
Data from: Evaluating the Agricultural Production Systems sIMulator (APSIM) wheat module for California
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