The plant modelling framework (PMF) in the next generation of APSIM enables plant models to be constructed in a novel, more reliable and robust manner. It provides a library of small processes that can be aggregated into bigger processes which in turn can be combined into larger constructs, ultimately forming a plant model. The user interface supports a workflow that allows the model builder to collate and store experimental data, construct the model (by process aggregation), create and run simulations of the experimental data and graphically view the performance of the model. The model builder can then iteratively modify the model, re-run the simulations and examine model performance. When new process building blocks are required, they are coded in C# and added to the process library. Once the model builder is satisfied with the performance of the model, the model undergoes peer-review (akin to a journal paper review) and ultimately is included in the APSIM release. When the model is completed, documentation is auto-generated and uploaded to the web site. The documentation is created from the experimental data, source code (via code inspection/reflection) and simulation configuration. The resulting PDF contains a complete description of the model and graphs of its performance. APSIM Next Generation is an open source product, available on GitHub (https://github.com/APSIMInitiative/ApsimX) and freely available for non-commercial use. Binary installations can be downloaded from www.apsim.info.
Mungbean, a grain legume with high nutritional value, is grown widely throughout Asia and increasingly in Australia. Despite growing interest amongst farmers, mungbean remains an inconsistent and thus risky crop to plant in Australia. Cropping system models like the Agricultural Production Systems sIMulator (APSIM) are valuable tools for helping farmers to examine options for improving crop management and assess production risks across potential growing regions for mungbean. This paper outlines the simulation capacity of a new mungbean crop model parameterized using the Plant Modelling Framework in APSIM Next Generation, the newest version of the APSIM framework. The aim of the paper is to document the parameterization and validation processes of the model. The new mungbean model was built using data from 28 field experiments to simulate measured phenology, canopy development, biomass accumulation/partitioning, stress responses, N fixation, root growth, and yield across a wide range of environments. The root mean squared error (RMSE) in predictions for grain weight and aboveground weight were 25.4 g m−2 and 91.4 g m−2, respectively. The model successfully captured the dynamics of crop response to sowing dates, water/irrigation regimes, and climate. The new mungbean model is a robust and accurate tool for use in Australia and tropical/sub-tropical Asia. Researchers can use the new mungbean model to determine best management practices such as the optimal time to sow mungbeans in different environments. The output from model simulations can help farmers assess risks associated with sowing at different times and soil water conditions specific to their region. Such risk analysis can improve farmer decision-making confidence in mungbean, increasing its potential production for Australia. Overall, the new APSIM mungbean model can be used effectively to identify and close the mungbean yield gap, mitigate risk of crop failure, and increase profits for mungbean farmers in Australia and tropical/sub-tropical Asia; it has the capacity to assist with increasing mungbean production globally under changing climate conditions.
Process-based crop models are popular tools to analyze and simulate the response of agricultural systems to weather, agronomic, or genetic factors. They are often developed in modeling platforms to ensure their future extension and to couple different crop models with a soil model and a crop management event scheduler. The intercomparison and improvement of crop simulation models is difficult due to the lack of efficient methods for exchanging biophysical processes between modeling platforms. We developed Crop2ML, a modeling framework that enables the description and the assembly of crop model components independently of the formalism of modeling platforms and the exchange of components between platforms. Crop2ML is based on a declarative architecture of modular model representation to describe the biophysical processes and their transformation to model components that conform to crop modeling platforms. Here, we present Crop2ML framework and describe the mechanisms of import and export between Crop2ML and modeling platforms.
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