Dean P. Holzworth

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.

The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability

In Australia, and particularly in the northern part of the grain belt, wheat is grown in an extremely variable climate. The wheat crop manager in this region is faced with complex decisions on choice of planting time, varietal development pattern, and fertiliser strategy. A skilful seasonal forecast would provide an opportunity for the manager to tailor crop management decisions more appropriately to the season. Recent developments in climate research have led to the development of a number of seasonal climate forecasting systems. The objectives of this study were to determine the value of the capability in seasonal forecasting to wheat crop management, to compare the value of the existing forecast methodologies, and to consider the potential value of improved forecast quality. We examined decisions on nitrogen (N) fertiliser and cultivar maturity using simulation analyses of specific production scenarios at a representative location (Goondiwindi) using long-term daily weather data (1894-1989). The average profit and risk of making a loss were calculated for the possible range of fixed (i.e. the same every year) and tactical (i.e. varying depending on seasonal forecast) strategies. Significant increase in profit (up to 20%) and/or reduction in risk (up to 35%) were associated with tactical adjustment of crop management of N fertiliser or cultivar maturity. The forecasting system giving greatest value was the Southern Oscillation Index (SOI) phase system of Stone and Auliciems (1992), which classifies seasons into 5 phases depending on the value and rate of change in the SOI. The significant skill in this system for forecasting both seasonal rainfall and frost timing generated the value found in tactical management of N fertiliser and cultivar maturity. Possible impediments to adoption of tactical management, associated with uncertainties in forecasting individual years, are discussed. The scope for improving forecast quality and the means to achieve it are considered by comparing the value of tactical management based on SO1 phases with the outcome given perfect prior knowledge of the season. While the analyses presented considered only one decision at a time, used specific scenarios, and made a number of simplifying assumptions, they have demonstrated that the current skill in seasonal forecasting is sufficient to justify use in tactical management of crops. More comprehensive studies to examine sensitivities to location, antecedent conditions, and price structure, and to assumptions made in this analysis, are now warranted. We have examined decisions related only to management of wheat. It would be appropriate to pursue similar analyses in relation to management decisions for other crops, cropping sequences, and the whole farm enterprise mix.

Infusing the use of seasonal climate forecasting into crop management practice in North East Australia using discussion support software

Seasonal climate forecasting offers potential for improving management of crop production risks in the cropping systems of NE Australia. But how is this capability best connected to management practice? Over the past decade, we have pursued participative systems approaches involving simulation-aided discussion with advisers and decision-makers. This has led to the development of discussion support software as a key vehicle for facilitating infusion of forecasting capability into practice. In this paper, we set out the basis of our approach, its implementation and preliminary evaluation. We outline the development of the discussion support software Whopper Cropper, which was designed for, and in close consultation with, public and private advisers. Whopper Cropper consists of a database of simulation output and a graphical user interface to generate analyses of risks associated with crop management options. The charts produced provide conversation pieces for advisers to use with their farmer clients in relation to the significant decisions they face. An example application, detail of the software development process and an initial survey of user needs are presented. We suggest that discussion support software is about moving beyond traditional notions of supply-driven decision support systems. Discussion support software is largely demand-driven and can compliment participatory action research programs by providing cost-effective general delivery of simulation-aided discussions about relevant management actions. The critical role of farm management advisers and dialogue among key players is highlighted. We argue that the

Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate.

In Australia, a land subject to high annual variation in grain yields, farmers find it challenging to adjust crop production inputs to yield prospects. Scientists have responded to this problem by developing Decision Support Systems, yet the scientists’ enthusiasm for developing these tools has not been reciprocated by farm managers or their advisers, who mostly continue to avoid their use. Preceding papers in this series described the FARMSCAPE intervention: a new paradigm for decision support that had significant effects on farmers and their advisers. These effects were achieved in large measure because of the intensive effort which scientists invested in engaging with their clients. However, such intensive effort is time consuming and economically unsustainable and there remained a need for a more cost-effective tool. In this paper, we report on the evolution, structure, and performance of Yield Prophet®: an internet service designed to move on from the FARMSCAPE model to a less intensive, yet high quality, service to reduce farmer uncertainty about yield prospects and the potential effects of alternative management practices on crop production and income. Compared with conventional Decision Support Systems, Yield Prophet offers flexibility in problem definition and allows farmers to more realistically specify the problems in their fields. Yield Prophet also uniquely provides a means for virtual monitoring of the progress of a crop throughout the season. This is particularly important for in-season decision support and for frequent reviewing, in real time, of the consequences of past decisions and past events on likely future outcomes. The Yield Prophet approach to decision support is consistent with two important, but often ignored, lessons from decision science: that managers make their decisions by satisficing rather than optimising and that managers’ fluid approach to decision making requires ongoing monitoring of the consequences of past decisions.

Potential to improve on-farm wheat yield and WUE in Australia

Water-use efficiency (WUE) is defined here as the ratio of grain yield (kg/ha) to crop water use by evapotranspiration (mm). Much of the WUE literature has focussed on either the determination of the boundary of attainable WUE for any amount of available water, or on the practicalities of measurement of the WUE of a crop. While these are important issues for defining the gap between the attained and the potential WUE, little progress has been reported on clarifying the components that contribute to this gap or on how it can be bridged. To address these questions, we analysed 334 wheat fields for which we had the data necessary to both calculate WUE and to simulate crop growth and water use. Simulations were conducted through Yield Prophet®, an on-line version of the APSIM systems model. For this dataset, evapotranspiration accounted for 69% of observed yield variation, although the more commonly used growing-season (April–October) rainfall accounted for 50%. Considering that evapotranspiration efficiency does not account for a wide range of potentially yield-limiting factors including soil and fertiliser nitrogen supply, crop phenology, and sowing dates, or rainfall distribution, these results reinforce the importance of evapotranspiration efficiency as a yield determinant for well managed crops in water-limited environments. WUE attained over the whole dataset was 15.2 kg grain/ha.mm (x-intercept = 67 mm), although this value contained data subsets with important differences in WUE based on soil water-holding capacity and regional diversity. Yield Prophet® simulated commercial wheat yields with RMSDs of 0.80 t/ha (r2 = 0.71), with some systematic error between observed and simulated yields. Simulated crops achieved a higher WUE (16.9 kg grain/ha.mm; x-intercept = 72 mm) than the observed crops, probably because APSIM does not account for effects of factors such as weeds, pests and diseases and impacts of severe weather. Simulated ‘what-if’ analysis suggested that further improvement in WUE may be achieved with an early sowing strategy or a higher nitrogen input strategy. A ‘yield maximising’ strategy that included an optimal plant density, early sowing date, and higher nitrogen inputs resulted in an average WUE (21.4 kg grain/ha.mm; x-intercept = 80 mm) that is close to the previously reported (French-Schultz) boundary of WUE. This outcome suggests a great deal of scope for Australian wheat growers to adopt strategies that improve their WUE. Yield Prophet® farmers have already demonstrated significant improvement in on-farm WUE compared with previous studies. However, additional improvements will only be partially realised due to considerations of the cost: benefit ratio and risk in a highly variable climate, and the operational feasibility of these strategies with current technologies.

Scope for improved eco-efficiency varies among diverse cropping systems

Global food security requires eco-efficient agriculture to produce the required food and fiber products concomitant with ecologically efficient use of resources. This eco-efficiency concept is used to diagnose the state of agricultural production in China (irrigated wheat–maize double-cropping systems), Zimbabwe (rainfed maize systems), and Australia (rainfed wheat systems). More than 3,000 surveyed crop yields in these three countries were compared against simulated grain yields at farmer-specified levels of nitrogen (N) input. Many Australian commercial wheat farmers are both close to existing production frontiers and gain little prospective return from increasing their N input. Significant losses of N from their systems, either as nitrous oxide emissions or as nitrate leached from the soil profile, are infrequent and at low intensities relative to their level of grain production. These Australian farmers operate close to eco-efficient frontiers in regard to N, and so innovations in technologies and practices are essential to increasing their production without added economic or environmental risks. In contrast, many Chinese farmers can reduce N input without sacrificing production through more efficient use of their fertilizer input. In fact, there are real prospects for the double-cropping systems on the North China Plain to achieve both production increases and reduced environmental risks. Zimbabwean farmers have the opportunity for significant production increases by both improving their technical efficiency and increasing their level of input; however, doing so will require improved management expertise and greater access to institutional support for addressing the higher risks. This paper shows that pathways for achieving improved eco-efficiency will differ among diverse cropping systems.

Modelling the seasonal and geographical pattern of pasture production in New Zealand

The pasture growth module AgPasture was integrated into the APSIM (Agricultural Production System Simulator) simulation model, allowing pasture-based systems to be modelled in combination with other land uses at farm scale or within land use change studies. The models predictions of pasture growth were evaluated against 32 pasture growth datasets from a diverse range of soil types and climatic zones across New Zealand. The pasture herbage accumulation simulated by the model closely matched actual measurements over varying intervals. Both predicted and measured pasture growth rate demonstrated the same seasonal pattern, including mean growth rate and inter-annual variation across measurement years. Predicted and measured annual average net herbage accumulation (NHA) on a dryland pasture was similar over 37 observation years (mean, 6.83 and 7.27 t DM/ha respectively; coefficient of variation, 29% and 27% respectively) and highly correlated (R 2 = 0.838, P < 0.0001; relative root mean squared deviation (RMSD) = 16%). The models prediction of annual average NHA of all simulated pastures, spanning a wide range of pasture environments, also matched the measurement data well (R 2 = 0.777, P < 0.0001; relative RMSD = 21%). However, discrepancies between simulated and observed values occurred in some seasons and at some sites. Analysis of these discrepancies identified areas where the model could be improved by incorporating more accurate descriptions of the effects of plant development and grazing, soil temperature and the interactive effects of high temperature and soil moisture dynamics.

Landscapes Toolkit: an integrated modelling framework to assist stakeholders in exploring options for sustainable landscape development

At present, stakeholders wishing to develop land use and management change scenarios at the landscape scale and to assess their corresponding impacts on water quality, biodiversity and economic performance, must examine the output of a suite of separate models. The process is not simple and presents a considerable deterrent to making such comparisons and impedes the development of more sustainable, multifunctional landscapes. To remedy this problem, we developed the Landscapes Toolkit, an integrated modelling framework that assists natural resource managers, policy-makers, planners and local communities explore options for sustainable landscape development. The Landscapes Toolkit links spatially-explicit disciplinary models, to enable integrated assessment of the water quality, biodiversity and economic outcomes of stakeholder-defined land use and management change scenarios. We use the Tully–Murray catchment in the Great Barrier Reef region of Australia as a case study to illustrate the development and application of the Landscapes Toolkit. Results show that the Landscapes Toolkit strikes a satisfactory balance between the inclusion of component models that sufficiently capture the richness of some key aspects of social-ecological system processes and the need for stakeholders to understand and compare the results of the different models. The latter is a prerequisite to making more informed decisions about sustainable landscape development. The flexibility of being able to add additional models and to update existing models is a particular strength of the Landscapes Toolkit design. Hence, the Landscapes Toolkit offers a promising modelling framework for supporting social learning and adaptive management through participatory scenario development and evaluation as well as being a tool to guide planning and policy discussions at the landscape scale.