APSIM – Evolution towards a new generation of agricultural systems simulation

Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. Keating et al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. This paper updates the earlier work by Keating et al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics.

Modelling the manager: Representing rule-based management in farming systems simulation models

We trace the evolution of the representation of management in cropping and grazing systems models, from fixed annual schedules of identical actions in single paddocks toward flexible scripts of rules. Attempts to define higher-level organizing concepts in management policies, and to analyse them to identify optimal plans, have focussed on questions relating to grazing management owing to its inherent complexity. “Rule templates” assist the re-use of complex management scripts by bundling commonly-used collections of rules with an interface through which key parameters can be input by a simulation builder. Standard issues relating to parameter estimation and uncertainty apply to management sub-models and need to be addressed. Techniques for embodying farmers' expectations and plans for the future within modelling analyses need to be further developed, especially better linking planning- and rule-based approaches to farm management and analysing the ways that managers can learn.

Exploring profit - Sustainability trade-offs in cropping systems using evolutionary algorithms

Models that implement the bio-physical components of agro-ecosystems are ideally suited for exploring sustainability issues in cropping systems. Sustainability may be represented as a number of objectives to be maximised or minimised. However, the full decision space of these objectives is usually very large and simplifications are necessary to safeguard computational feasibility. Different optimisation approaches have been proposed in the literature, usually based on mathematical programming techniques. Here, we present a search approach based on a multiobjective evaluation technique within an evolutionary algorithm (EA), linked to the APSIM cropping systems model. A simple case study addressing crop choice and sowing rules in North-East Australian cropping systems is used to illustrate the methodology. Sustainability of these systems is evaluated in terms of economic performance and resource use. Due to the limited size of this sample problem, the quality of the EA optimisation can be assessed by comparison to the full problem domain. Results demonstrate that the EA procedure, parameterised with generic parameters from the literature, converges to a useable solution set within a reasonable amount of time. Frontier ‘‘peels’’ or Pareto-optimal solutions as described by the multiobjective evaluation procedure provide useful information for discussion on trade-offs between conflicting objectives.

Validating economic and environmental sustainability of a short-term summer forage legume in dryland wheat cropping systems in south-west Queensland.

The present study set out to test the hypothesis through field and simulation studies that the incorporation of short-term summer legumes, particularly annual legume lablab (Lablab purpureus cv. Highworth), in a fallow-wheat cropping system will improve the overall economic and environmental benefits in south-west Queensland. Replicated, large plot experiments were established at five commercial properties by using their machineries, and two smaller plot experiments were established at two intensively researched sites (Roma and St George). A detailed study on various other biennial and perennial summer forage legumes in rotation with wheat and influenced by phosphorus (P) supply (10 and 40 kg P/ha) was also carried out at the two research sites. The other legumes were lucerne (Medicago sativa), butterfly pea (Clitoria ternatea) and burgundy bean (Macroptilium bracteatum). After legumes, spring wheat (Triticum aestivum) was sown into the legume stubble. The annual lablab produced the highest forage yield, whereas germination, establishment and production of other biennial and perennial legumes were poor, particularly in the red soil at St George. At the commercial sites, only lablab-wheat rotations were experimented, with an increased supply of P in subsurface soil (20 kg P/ha). The lablab grown at the commercial sites yielded between 3 and 6 t/ha forage yield over 2-3 month periods, whereas the following wheat crop with no applied fertiliser yielded between 0.5 to 2.5 t/ha. The wheat following lablab yielded 30% less, on average, than the wheat in a fallow plot, and the profitability of wheat following lablab was slightly higher than that of the wheat following fallow because of greater costs associated with fallow management. The profitability of the lablab-wheat phase was determined after accounting for the input costs and additional costs associated with the management of fallow and in-crop herbicide applications for a fallow-wheat system. The economic and environmental benefits of forage lablab and wheat cropping were also assessed through simulations over a long-term climatic pattern by using economic (PreCAPS) and biophysical (Agricultural Production Systems Simulation, APSIM) decision support models. Analysis of the long-term rainfall pattern (70% in summer and 30% in winter) and simulation studies indicated that ~50% time a wheat crop would not be planted or would fail to produce a profitable crop (grain yield less than 1 t/ha) because of less and unreliable rainfall in winter. Whereas forage lablab in summer would produce a profitable crop, with a forage yield of more than 3 t/ha, ~90% times. Only 14 wheat crops (of 26 growing seasons, i.e. 54%) were profitable, compared with 22 forage lablab (of 25 seasons, i.e. 90%). An opportunistic double-cropping of lablab in summer and wheat in winter is also viable and profitable in 50% of the years. Simulation studies also indicated that an opportunistic lablab-wheat cropping can reduce the potential runoff+drainage by more than 40% in the Roma region, leading to improved economic and environmental benefits.

The effect of row configuration on yield reliability in grain sorghum: II. Modelling the effects of row configuration

In recent years many sorghum producers in the more marginal (<600 mm annual rainfall) cropping areas of Qld and northern NSW have utilised skip row configurations in an attempt to improve yield reliability and reduce sorghum production risk. But will this work in the long run? What are the trade-offs between productivity and risk of crop failure? This paper describes a modelling and simulation approach to study the long-term effects of skip row configurations. Detailed measurements of light interception and water extraction from sorghum crops grown in solid, single and double skip row configurations were collected from three on-farm participatory research trials established in southern Qld and northern NSW. These measurements resulted in changes to the model that accounted for the elliptical water uptake pattern below the crop row and reduced total light interception associated with the leaf area reduction of the skip configuration. Following validation of the model, long-term simulation runs using historical weather data were used to determine the value of skip row sorghum production as a means of maintaining yield reliability in the dryland cropping regions of southern Qld and northern NSW.

Graze to grain - measuring and modelling the effects of grazed pasture leys on soil nitrogen and sorghum yield on a Vertosol soil in the Australian subtropics

Highly productive sown pasture systems can result in high growth rates of beef cattle and lead to increases in soil nitrogen and the production of subsequent crops. The nitrogen dynamics and growth of grain sorghum following grazed annual legume leys or a grass pasture were investigated in a no-till system in the South Burnett district of Queensland. Two years of the tropical legumes Macrotyloma daltonii and Vigna trilobata (both self regenerating annual legumes) and Lablab purpureus (a resown annual legume) resulted in soil nitrate N (0-0.9 m depth), at sorghum sowing, ranging from 35 to 86 kg/ha compared with 4 kg/ha after pure grass pastures. Average grain sorghum production in the 4 cropping seasons following the grazed legume leys ranged from 2651 to 4012 kg/ha. Following the grass pasture, grain sorghum production in the first and second year was < 1900 kg/ha and by the third year grain yield was comparable to the legume systems. Simulation studies utilising the farming systems model APSIM indicated that the soil N and water dynamics following 2-year ley phases could be closely represented over 4 years and the prediction of sorghum growth during this time was reasonable. In simulated unfertilised sorghum crops grown from 1954 to 2004, grain yield did not exceed 1500 kg/ha in 50% of seasons following a grass pasture, while following 2-year legume leys, grain exceeded 3000 kg/ha in 80% of seasons. It was concluded that mixed farming systems that utilise short term legume-based pastures for beef production in rotation with crop production enterprises can be highly productive.

Simulation of parthenium weed canopy under changing climate using L-systems

Parthenium weed (Parthenium hysterophorus L.) is an erect, branched, annual plant of the family Asteraceae. It is native to the tropical Americas, while now widely distributed throughout Africa, Asia, Oceania, and Australasia. Due to its allelopathic and toxic characteristics, parthenium weed has been considered to be a weed of global significance. These effects occur across agriculture (crops and pastures), within natural ecosystems, and has impacts upon health (human and animals). Although integrated weed management (IWM) for parthenium weed has had some success, due to its tolerance and good adaptability to temperature, precipitation, and CO2, this weed has been predicted to become more vigorous under a changing climate resulting in an altered canopy architecture. From the viewpoint of IWM, the altered canopy architecture may be associated with not only improved competitive ability and replacement but also may alter the effectiveness of biocontrol agents and other management strategies. This paper reports on a preliminary study on parthenium weed canopy architecture at three temperature regimes (day/night 22/15 °C, 27/20 °C, and 32/25 °C in thermal time 12/12 hours) and establishes a threedimensional (3D) canopy model using Lindenmayer-systems (L-systems). This experiment was conducted in a series of controlled environment rooms with parthenium weed plants being grown in a heavy clay soil. A sonic digitizer system was used to record the morphology, topology, and geometry of the plants for model construction. The main findings include the determination of the phyllochron which enables the prediction of parthenium weed growth under different temperature regimes and that increased temperature enhances growth and enlarges the plants canopy size and structure. The developed 3D canopy model provides a tool to simulate and predict the weed growth in response to temperature, and can be adjusted for studies of other climatic variables such as precipitation and CO2. Further studies are planned to investigate the effects of other climatic variables, and the predicted changes in the pathogenic biocontrol agent effectiveness.

Assessing the sustainability of wheat-based cropping systems using simulation modelling: sustainability

Concepts of agricultural sustainability and possible roles of simulation modelling for characterising sustainability were explored by conducting, and reflecting on, a sustainability assessment of rain-fed wheat-based systems in the Middle East and North Africa region. We designed a goal-oriented, model-based framework using the cropping systems model Agricultural Production Systems sIMulator (APSIM). For the assessment, valid (rather than true or false) sustainability goals and indicators were identified for the target system. System-specific vagueness was depicted in sustainability polygons-a system property derived from highly quantitative data-and denoted using descriptive quantifiers. Diagnostic evaluations of alternative tillage practices demonstrated the utility of the framework to quantify key bio-physical and chemical constraints to sustainability. Here, we argue that sustainability is a vague, emergent system property of often wicked complexity that arises out of more fundamental elements and processes. A 'wicked concept of sustainability' acknowledges the breadth of the human experience of sustainability, which cannot be internalised in a model. To achieve socially desirable sustainability goals, our model-based approach can inform reflective evaluation processes that connect with the needs and values of agricultural decision-makers. Hence, it can help to frame meaningful discussions, from which actions might emerge.

Boosting the productivity and profitability of northern Australian beef enterprises: Exploring innovation options using simulation modelling and systems analysis

The financial health of beef cattle enterprises in northern Australia has declined markedly over the last decade due to an escalation in production and marketing costs and a real decline in beef prices. Historically, gains in animal productivity have offset the effect of declining terms of trade on farm incomes. This raises the question of whether future productivity improvements can remain a key path for lifting enterprise profitability sufficient to ensure that the industry remains economically viable over the longer term. The key objective of this study was to assess the production and financial implications for north Australian beef enterprises of a range of technology interventions (development scenarios), including genetic gain in cattle, nutrient supplementation, and alteration of the feed base through introduced pastures and forage crops, across a variety of natural environments. To achieve this objective a beef systems model was developed that is capable of simulating livestock production at the enterprise level, including reproduction, growth and mortality, based on energy and protein supply from natural C4 pastures that are subject to high inter-annual climate variability. Comparisons between simulation outputs and enterprise performance data in three case study regions suggested that the simulation model (the Northern Australia Beef Systems Analyser) can adequately represent the performance beef cattle enterprises in northern Australia. Testing of a range of development scenarios suggested that the application of individual technologies can substantially lift productivity and profitability, especially where the entire feedbase was altered through legume augmentation. The simultaneous implementation of multiple technologies that provide benefits to different aspects of animal productivity resulted in the greatest increases in cattle productivity and enterprise profitability, with projected weaning rates increasing by 25%, liveweight gain by 40% and net profit by 150% above current baseline levels, although gains of this magnitude might not necessarily be realised in practice. While there were slight increases in total methane output from these development scenarios, the methane emissions per kg of beef produced were reduced by 20% in scenarios with higher productivity gain. Combinations of technologies or innovative practices applied in a systematic and integrated fashion thus offer scope for providing the productivity and profitability gains necessary to maintain viable beef enterprises in northern Australia into the future.

On Systems Thinking, Systems Biology, and the in Silico Plant

The recent summary report of a Department of Energy Workshop on Plant Systems Biology (P.V. Minorsky [2003] Plant Physiol 132: 404-409) offered a welcomed advocacy for systems analysis as essential in understanding plant development, growth, and production. The goal of the Workshop was to consider methods for relating the results of molecular research to real-world challenges in plant production for increased food supplies, alternative energy sources, and environmental improvement. The rather surprising feature of this report, however, was that the Workshop largely overlooked the rich history of plant systems analysis extending over nearly 40 years (Sinclair and Seligman, 1996) that has considered exactly those challenges targeted by the Workshop. Past systems research has explored and incorporated biochemical and physiological knowledge into plant simulation models from a number of perspectives. The research has resulted in considerable understanding and insight about how to simulate plant systems and the relative contribution of various factors in influencing plant production. These past activities have contributed directly to research focused on solving the problems of increasing biomass production and crop yields. These modeling approaches are also now providing an avenue to enhance integration of molecular genetic technologies in plant improvement (Hammer et al., 2002).

Managing the risk of glyphosate resistance in Australian glyphosate-resistant cotton production systems

BACKGROUND: Glyphosate-resistant cotton varieties are an important tool for weed control in Australian cotton production systems. To increase the sustainability of this technology and to minimise the likelihood of resistance evolving through its use, weed scientists, together with herbicide regulators, industry representatives and the technology owners, have developed a framework that guides the use of the technology. Central to this framework is a crop management plan (CMP) and grower accreditation course. A simulation model that takes into account the characteristics of the weed species, initial gene frequencies and any associated fitness penalties was developed to ensure that the CMP was sufficiently robust to minimise resistance risks. RESULTS: The simulations showed that, when a combination of weed control options was employed in addition to glyphosate, resistance did not evolve over the 30 year period of the simulation. CONCLUSION: These simulations underline the importance of maintaining an integrated system for weed management to prevent the evolution of glyphosate resistance, prolonging the use of glyphosate-resistant cotton.

Simulating pasture growth rates in Australian and New Zealand grazing systems

DairyMod, EcoMod, and the SGS Pasture Model are mechanistic biophysical models developed to explore scenarios in grazing systems. The aim of this manuscript was to test the ability of the models to simulate net herbage accumulation rates of ryegrass-based pastures across a range of environments and pasture management systems in Australia and New Zealand. Measured monthly net herbage accumulation rate and accumulated yield data were collated from ten grazing system experiments at eight sites ranging from cool temperate to subtropical environments. The local climate, soil, pasture species, and management (N fertiliser, irrigation, and grazing or cutting pattern) were described in the model for each site, and net herbage accumulation rates modelled. The model adequately simulated the monthly net herbage accumulation rates across the range of environments, based on the summary statistics and observed patterns of seasonal growth, particularly when the variability in measured herbage accumulation rates was taken into account. Agreement between modelled and observed growth rates was more accurate and precise in temperate than in subtropical environments, and in winter and summer than in autumn and spring. Similarly, agreement between predicted and observed accumulated yields was more accurate than monthly net herbage accumulation. Different temperature parameters were used to describe the growth of perennial ryegrass cultivars and annual ryegrass; these differences were in line with observed growth patterns and breeding objectives. Results are discussed in the context of the difficulties in measuring pasture growth rates and model limitations.

Assessing the sustainability of wheat-based cropping systems using APSIM: Model parameterisation and evaluation

Assessing the sustainability of crop and soil management practices in wheat-based rotations requires a well-tested model with the demonstrated ability to sensibly predict crop productivity and changes in the soil resource. The Agricultural Production Systems Simulator (APSIM) suite of models was parameterised and subsequently used to predict biomass production, yield, crop water and nitrogen (N) use, as well as long-term soil water and organic matter dynamics in wheat/chickpea systems at Tel Hadya, north-western Syria. The model satisfactorily simulated the productivity and water and N use of wheat and chickpea crops grown under different N and/or water supply levels in the 1998-99 and 1999-2000 experimental seasons. Analysis of soil-water dynamics showed that the 2-stage soil evaporation model in APSIM's cascading water-balance module did not sufficiently explain the actual soil drying following crop harvest under conditions where unused water remained in the soil profile. This might have been related to evaporation from soil cracks in the montmorillonitic clay soil, a process not explicitly simulated by APSIM. Soil-water dynamics in wheat-fallow and wheat-chickpea rotations (1987-98) were nevertheless well simulated when the soil water content in 0-0.45 m soil depth was set to 'air dry' at the end of the growing season each year. The model satisfactorily simulated the amounts of NO3-N in the soil, whereas it underestimated the amounts of NH 4-N. Ammonium fixation might be part of the soil mineral-N dynamics at the study site because montmorillonite is the major clay mineral. This process is not simulated by APSIM's nitrogen module. APSIM was capable of predicting long-term trends (1985-98) in soil organic matter in wheat-fallow and wheat-chickpea rotations at Tel Hadya as reported in literature. Overall, results showed that the model is generic and mature enough to be extended to this set of environmental conditions and can therefore be applied to assess the sustainability of wheat-chickpea rotations at Tel Hadya.

Predicting risk and benefit a priori in biological control of invasive plant species: a systems modelling approach

While the method using specialist herbivores in managing invasive plants (classical biological control) is regarded as relatively safe and cost-effective in comparison to other methods of management, the rarity of strict monophagy among insect herbivores illustrates that, like any management option, biological control is not risk-free. The challenge for classical biological control is therefore to predict risks and benefits a priori. In this study we develop a simulation model that may aid in this process. We use this model to predict the risks and benefits of introducing the chrysomelid beetle Charidotis auroguttata to manage the invasive liana Macfadyena unguis-cati in Australia. Preliminary host-specificity testing of this herbivore indicated that there was limited feeding on a non-target plant, although the non-target was only able to sustain some transitions of the life cycle of the herbivore. The model includes herbivore, target and non-target life history and incorporates spillover dynamics of populations of this herbivore from the target to the non-target under a variety of scenarios. Data from studies of this herbivore in the native range and under quarantine were used to parameterize the model and predict the relative risks and benefits of this herbivore when the target and non-target plants co-occur. Key model outputs include population dynamics on target (apparent benefit) and non-target (apparent risk) and fitness consequences to the target (actual benefit) and non-target plant (actual risk) of herbivore damage. The model predicted that risk to the non-target became unacceptable (i.e. significant negative effects on fitness) when the ratio of target to non-target in a given patch ranged from 1:1 to 3:2. By comparing the current known distribution of the non-target and the predicted distribution of the target we were able to identify regions in Australia where the agent may be pose an unacceptable risk. By considering risk and benefit simultaneously, we highlight how such a simulation modelling approach can assist scientists and regulators in making more objective decisions a priori, on the value of releasing specialist herbivores as biological control agents.

Issues of Digital Ecosystems Approach to Biological Ecosystem Simulation Model Fit Optimization

This study examines the application of digital ecosystems concepts to a biological ecosystem simulation problem. The problem involves the use of a digital ecosystem agent to optimize the accuracy of a second digital ecosystem agent, the biological ecosystem simulation. The study also incorporates social ecosystems, with a technological solution design subsystem communicating with a science subsystem and simulation software developer subsystem to determine key characteristics of the biological ecosystem simulation. The findings show similarities between the issues involved in digital ecosystem collaboration and those occurring when digital ecosystems interact with biological ecosystems. The results also suggest that even precise semantic descriptions and comprehensive ontologies may be insufficient to describe agents in enough detail for use within digital ecosystems, and a number of solutions to this problem are proposed.