J.N.G. Hargreaves

An overview of APSIM, a model designed for farming systems simulation

The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIMs structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided.

APSIM: a novel software system for model development, model testing and simulation in agricultural systems research

Abstract APSIM (Agricultural Production Systems Simulator) is a software system which allows (a) models of crop and pasture production, residue decomposition, soil water and nutrient flow, and erosion to be readily re-configured to simulate various production systems and (b) soil and crop management to be dynamically simulated using conditional rules. A key innovation is change from a core concept of a crop responding to resource supplies to that of a soil responding to weather, management and crops. While this achieves a sound logical structure for improved simulation of soil management and long-term change in the soil resource, it does so without loss of sensitivity in simulating crop yields. This concept is implemented using a program structure in which all modules (e.g. growth of specific crops, soil water, soil N, erosion) communicate with each other only by messages passed via a central ‘engine’. Using a standard interface design, this design enables easy removal, replacement, or exchange of modules without disruption to the operation of the system. Simulation of crop sequences and multiple crops are achieved by managing connection of crop growth modules to the engine. A shell of software tools has been developed within a WINDOWS environment which includes user-installed editor, linker, compiler, testbed generator, graphics, database and version control software. While the engine and modules are coded in FORTRAN, the Shell is in C ++ . The resulting product is one in which the functions are coded in the language most familiar to the developers of scientific modules but provides many of the features of object oriented programming. The Shell is written to be aware of UNIX operating systems and be capable of using the processor on UNIX workstations.

Development of a generic crop model template in the cropping system model APSIM

The Agricultural Production Systems slMulator, APSIM, is a cropping system modelling environment that simulates the dynamics of soil-plant-management interactions within a single crop or a cropping system. Adaptation of previously developed crop models has resulted in multiple crop modules in APSIM, which have low scientific transparency and code efficiency. A generic crop model template (GCROP) has been developed to capture unifying physiological principles across crops (plant types) and to provide modular and efficient code for crop modelling. It comprises a standard crop interface to the APSIM engine, a generic crop model structure, a crop process library, and well-structured crop parameter files. The process library contains the major science underpinning the crop models and incorporates generic routines based on physiological principles for growth and development processes that are common across crops. It allows APSIM to simulate different crops using the same set of computer code. The generic model structure and parameter files provide an easy way to test, modify, exchange and compare modelling approaches at process level without necessitating changes in the code. The standard interface generalises the model inputs and outputs, and utilises a standard protocol to communicate with other APSIM modules through the APSIM engine. The crop template serves as a convenient means to test new insights and compare approaches to component modelling, while maintaining a focus on predictive capability. This paper describes and discusses the scientific basis, the design, implementation and future development of the crop template in APSIM. On this basis, we argue that the combination of good software engineering with sound crop science can enhance the rate of advance in crop modelling. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Development and evaluation of a sorghum model based on CERES-Maize in a semi-arid tropical environment.

Birch, C.J., Carberry, P.S., Muchow, R.C., McCown, R.L. and Hargreaves, J.N.G., 1990. Development and evaluation of a sorghum model based on CERES-Maize in a semi-arid tropical environment. Field Crops Res., 24: 87-104. This paper reports on the development and evaluation of a grain sorghum model (CERESSorghum ( SAT ) ) for use in the semi-arid tropics. The model was developed from a version of CERESMaize, previously adapted for use in this climatic zone. Functions for phenology, leaf growth, leaf senescence, assimilate accumulation and grain growth were modified using a small subset of sorghum data and validated against a much larger field-data set. When tested with cultivar De Kalb DK55 at Katherine, Northern Territory, the model successfully predicted grain-yield with a root mean square deviation of 0.972 to ha- ~ over a range of sowing dates and water regimes resulting in observed yields ranging from 1.56 to 6.28 t ha -~. Deviations of predicted from observed yields were no greater than those of maize predictions by the parent model. Prediction of components of yield and biomass were also satisfactory. Calibration required 28 changes to the CERES-Maize(SAT) model, of which 15 were changes to coefficients in equations rather than substantial changes to the model. Because of the ease of conversion and the time-use efficiency found in these analyses, the techniques used in this paper could have application where locally calibrated models are required.

Plant Modelling Framework

The Plant Modelling Framework (PMF) is a software framework for creating models that represent the plant components of farm system models in the agricultural production system simulator (APSIM). It is the next step in the evolution of generic crop templates for APSIM, building on software and science lessons from past versions and capitalising on new software approaches. The PMF contains a top-level Plant class that provides an interface with the APSIM model environment and controls the other classes in the plant model. Other classes include mid-level Organ, Phenology, Structure and Arbitrator classes that represent specific elements or processes of the crop and sub-classes that the mid-level classes use to represent repeated data structures. It also contains low-level Function classes which represent generic mathematical, logical, procedural or reference code and provide values to the processes carried out by mid-level classes. A plant configuration file specifies which mid-level and Function classes are to be included and how they are to be arranged and parameterised to represent a particular crop model. The PMF has an integrated design environment to allow plant models to be created visually. The aims of the PMF are to maximise code reuse and allow flexibility in the structure of models. Four examples are included to demonstrate the flexibility of application of the PMF; 1. Slurp, a simple model of the water use of a static crop, 2. Oat, an annual grain crop model with detailed growth, development and resource use processes, 3. Lucerne, perennial forage model with detailed growth, development and resource use processes, 4. Wheat, another detailed annual crop model constructed using an alternative set of organ and process classes. These examples show the PMF can be used to develop models of different complexities and allows flexibility in the approach for implementing crop physiology concepts into model set up. Next step in the evolution of crop modelling software in APSIM.Designed to allow flexibility in the approach to construct different crop models without compiling source code.Achieves extensive code re-use through generic organ and process classes and devolving calculations into function classes.A set of 4 examples are given of crop models developed in this framework demonstrating its flexibility.

Simulating growth, development, and yield of tillering pearl millet: II. Simulation of canopy development

Tillering is an important adaptive feature of pearl millet (Pennisetum americanum [Pennisetum glaucum]) to the unpredictable growing conditions of dry areas of the semiarid tropics. Yet, this feature has largely been ignored in the development of simulation models for pearl millet. The objective of this paper is to parameterize and validate a leaf area module for pearl millet, which dynamically simulates crop leaf area from the leaf area of individual axes through simulating inter-axis competition for light. To derive parameters for the model, four cultivars (BJ 104, WRajPop, HHB 67 and RCB-IC 911), contrasting in phenology and tillering habit, were grown under well-watered and well-fertilized conditions across a range of plant densities in three experiments at two locations in India (Patancheru, Andhra Pradesh and Jodhpur, Rajasthan) during 1996 and 1997. For selected plants, observations on the number of primary basal tillers and on the number of visible, fully expanded, and senesced leaves on each axis were made twice a week throughout the growing season. Occurrence of panicle initiation (PI) was observed in two experiments only, but data were complemented by published and unpublished data, obtained for comparable cultivars. Parameters were obtained for the time from emergence to PI as a function of daylength, leaf initiation rate, ate of leaf and tiller appearance, and the leaf senescence rate. Parameters for leaf size were determined in a previous paper. Our parameter estimates compared well with published data and were, with the exception of time to PI and leaf size, mostly independent of cultivar, axis and density. Genotypic effects on productive tiller number could be attributed to differences in main shoot leaf size. Validation of the leaf are module showed that the module adequately reproduced the effects of density, photoperiod and genotype on the leaf area of individual axes and on productive tiller number. This was despite the fact that the reduction in leaf area of non-productive tillers was achieved in the module through a reduction in leaf size, whereas the crop reduced leaf area through a reduction in leaf number. Our results indicate that leaf area index (LAI) of a tillering crop can be simulated adequately by simulating LAI from individual leaf area and incorporating the effects of competition for light

Sacrificial grazing of wheat crops: identifying tactics and opportunities in Western Australia’s grainbelt using simulation approaches

Failing grain crops are sometimes sacrificed for grazing by mixed farmers, a decision involving a complex range of factors. This simulation study used two APSIM (Agricultural Production Systems Simulator)-based approaches to investigate the circumstances under which more revenue might be obtained by sacrificing a wheat crop for grazing rather than harvesting it for grain in Western Australia’s grainbelt. First, we developed a simple partial budget calculation to estimate and compare revenue from grain or grazing alternatives using data for grain yield and standing biomass at flowering. This was simulated for a factorial of soil types and locations varying in mean annual rainfall. We then simulated wheat quality and livestock production on spring wheat grazed at different stages of crop development and at a range of stocking rates. Dynamic simulations of grazing showed that livestock production increased as grazing was delayed; stocking rate had little impact at this time. Grazing earlier necessitated lighter stocking rates but surprisingly had little benefit for animal performance. Partial budgets showed that under average commodity prices, grazing a wheat crop could be more profitable 40–75% of the time on poorer soil types in lower rainfall environments. In these situations, by tactically grazing when grain yield is below a critical level economic returns could be increased by more than A

Modelling crop growth and yield under the environmental changes induced by windbreaks 1. Model development and validation

50/ha in 30–40% of years and over the long term average revenues could be increased by A

Modelling crop growth and yield under the environmental changes induced by windbreaks. 2. Simulation of potential benefits at selected sites in Australia

30/ha.year. This critical grain yield ranged from 1.3 to 1.7 t/ha on shallow gravel soil and 1.9 to 2.2 t/ha on a deep sand. In higher rainfall environments and on better soil types grazing was rarely a better option unless livestock prices were high relative to grain. This approach, combining crop simulation with partial budgets, was useful for developing simple management rules for a complex system. Overall, the findings of this study suggest that making tactical use of a wheat crop for forage in situations with low grain yield prospects is a major opportunity to increase profitability and help respond to climate variability in mixed farms in many areas of the Western Australian wheatbelt.

Calibration of the APSIM-Lucerne model for ‘Grasslands Kaituna’ lucerne crops grown in New Zealand

Yield advantages of crops grown behind windbreaks have often been reported, but underlying principles responsible for such changes and their long-term consequences on crop productivity and hence farm income have rarely been quantified. Physiologically and physically sound simulation models could help to achieve this quantification. Hence, the APSIM systems model, which is based on physiological principles such as transpiration efficiency and radiation use efficiency (termed here APSIMTE), and the Soil Canopy Atmosphere Model (SCAM), which is based on the Penman–Monteith equation but includes a full surface energy balance, were employed in developing an approach to quantify such windbreak effects. This resulted in a modified APSIM version (APSIMEO), containing the original Penman equation and a calibration factor to account for crop- and site-specific differences, which were tested against field data and simulations from both the standard APSIMTE and SCAM models. The APSIMEO approach was tested against field data for wheat and mungbean grown in artificial enclosures in south-east Queensland and in south-east Western Australia. For these sheltered conditions, daily transpiration demand estimates from APSIMEO compared closely to SCAM. As the APSIMEO approach needed to be calibrated for individual crops and environments, average transpiration demand for open field conditions predicted by APSIMEO for a given site was adjusted to equal that obtained using APSIMTE by modifying a calibration parameter β. For wheat, a β-value of 1.0 resulted in best fits for Queensland, while for Western Australia a value of 0.85 was necessary. For mungbean a value of 0.92 resulted in the best fit (Qld). Biomass and yields simulated by APSIMTE and the calibration APSIMEO for wheat and mungbean grown in artificial enclosures were generally distributed around the 1:1 line, with R2 values ranging from 0.92 to 0.97. Finally, APSIMEO was run at 2 sites using long-term climate data to assess the likely year-to-year variability of windbreak effects on crop yields. Assuming a 70% reduction in wind speed as representing the maximum potential windbreak effect, the average yield improvement for the Queensland site was 13% for wheat and 3% for mungbean. For wheat at the WA site the average yield improvement from reduced wind speed was 5%. In any year, however, effects varied from negative, neutral to positive, highlighting the highly variable nature of the expression of windbreak effects. This study has shown how physical and biological modelling approaches can be combined to aid our understanding of systems processes. Both the environmental physics perspective and the biological perspective have shortcomings when issues that sit at the interface of both approaches need to be addressed. While the physical approach has clear advantages when investigating changes in physical parameters such as wind speed, vapour pressure deficit (VPD), temperature or the energy balance of the soil–plant–atmosphere continuum, it cannot deal with complex, biological systems adequately. Conversely, the crop physiological approach can handle such biological interactions in a scientific and robust way while certain atmospheric processes are not considered. The challenge was not to try and capture all these effects in 1 model, but rather to structure a modelling approach in a way that allowed for inclusion of such processes where necessary.