With the development of digital agriculture and smart agriculture, process-based agricultural production system models play an important role in simulating crop response and adaptation to climate change, farmland management optimization, crop variety and plant type selection, farmland carbon sequestration and greenhouse gas emissions. increasingly important role.
APSIM (Agricultural Production Systems sIMulator) model [1] is one of the world-renowned crop growth simulation models. The APSIM model has two series of models, Classic and Next Generation, which can simulate the soil-plant-atmosphere process of dozens of crops, pastures and trees, and is widely used in precision agriculture, water and fertilizer management, climate change, food security, soil carbon turnover, Environmental impact, agricultural sustainability, agricultural ecology and many other fields related to agricultural production and scientific research.
The core algorithm of the APSIM model is developed based on Fortran language, and the software interface is developed based on C#, driven by components, and each module can be freely combined. Understanding and being familiar with the key algorithms and software operations of the APSIM model is the basis for learning the APSIM model.
In addition, in order to become an excellent crop model user and an indispensable talent for the scientific research team, in addition to mastering the knowledge of crop models, one must also master the fast simulation and efficient data analysis capabilities of the model. R language is a programming language with a wide range of application scenarios and is easy to learn. The APSIM model has developed many R language auxiliary packages, which are used in data preparation, automatic simulation, parameter optimization and result analysis of the APSIM model for climate, soil, and management measures. play an important role.
So how to use the R language to quickly use the APSIM model? This time, we have selected a large number of crop model application cases, and the whole process is dry. You can be fully familiar with APSIM, a comprehensive agricultural ecosystem model, and improve students' model application ability, data analysis and chart making skills.
APSIM model application and R language data cleaning
1) The concept of crop growth model
2) Development status of crop growth models
3) Development process of APSIM model
4) Modules and simulation process of APSIM model
5) APSIM model operation
APSIM installation
APSIM model operation interface explanation
R language programming and data cleaning
APSIM meteorological file preparation and R language fusion application
Preparation of meteorological data that comes with APSIM
1) Introduction of APSIM meteorological file .met
2) Sunshine to radiation algorithm
3) APSIM weather file conversion
4) APSIM model land-atmosphere exchange and energy balance process
Case 1: Using R language to generate meteorological files
Case 2: Use R language to mass-produce APSIM meteorological files from meteorological sharing network data/NC data
The phenological development and photosynthetic production modules of the APSIM model
APSIM phenology development and photosynthetic production
1) Growth period scale of APSIM model
2) Accumulated temperature calculation of APSIM model
3) Growth period algorithm of APSIM model
4) Factors and Algorithm of Growth Period of APSIM Model
APSIM Model Photosynthetic Production Algorithm
APSIM Material Allocation and Yield Simulation
Substance Allocation Algorithm of APSIM Model
2 APSIM model output simulation module
1) Simulation of grain number per panicle of APSIM model
2) Yield simulation of APSIM model
3) Yield-related parameters of the APSIM model
Case 1: Simulation of Crop Potential Biomass and Potential Yield
Case 2: Simulation of Crop Yield under Different Variety Parameters
APSIM Soil Water Balance Module
Soil Water Balance Algorithm of APSIM Model
1) Soil water evapotranspiration and plant transpiration algorithm
2) Soil water runoff and drainage algorithm
3) Testing of soil hydraulic parameters
Case 1 APSIM model input parameters and preparation of soil files
Case 2 Approximate estimation of soil parameters of APSIM model in case of missing data
APSIM Soil Carbon and Nitrogen Balance Module
APSIM model soil nutrient dynamic process simulation and greenhouse gas emission simulation
1) Mineralization and fixation process of nitrogen
2) Nitrification and denitrification of nitrogen
3) Simulation of soil N2O
APSIM model soil carbon pool model and simulation of soil organic carbon SOC
1) The development history of soil carbon pool model
2) The turnover model of soil carbon
3) Simulation of soil organic carbon
Case 1 APSIM model N2O emission simulation and soil organic carbon simulation
APSIM farmland management module and scenario simulation
Preparation of Farmland Management Measures for APSIM Model
1) APSIM model sowing date and sowing density settings
2) APSIM model fertilization settings (chemical fertilizer + organic fertilizer)
3) Irrigation settings of the APSIM model
4) APSIM model straw return setting
5) APSIM model multi-year simulation and crop rotation simulation
Case 1: APSIM model simulates the impact of climate change on crop growth
Case 2: APSIM model simulates the impact of multi-year crop rotation on soil organic carbon and greenhouse gas emissions
Case 3: APSIM simulation simulates the impact of different management scenarios on crop yield
APSIM model parameter optimization and result analysis and model evaluation
Parameter Optimization of APSIM Model
1) The main genetic parameters of the APSIM model
2) Parameter optimization method of APSIM model
Case 1: Using MCMC and other methods to optimize the parameters of the APSIM model
Case 2: Use R language to read simulation results in batches and evaluate the APSIM model
Case 3: Use R language to visualize simulation results (dynamic graphs and 1:1 graphs of simulation results, etc.)
More case simulations and troubleshooting
1) Instance review, training and consolidation
Q&A and discussion (everyone sorts out the questions in advance)
Fast batch operation of DSSAT model based on Python language and cross fusion and extended application
Assimilation of Remote Sensing Data and Crop Growth Model and Its Application in Crop Growth Monitoring and Yield Estimation
Implementation method of UAV remote sensing in agricultural and forestry information extraction and GIS fusion application