Surveillance protocols for management of invasive plants: modelling Chilean needle grass (Nassella neesiana) in Australia

Aim: To develop a surveillance support model that enables prediction of areas susceptible to invasion, comparative analysis of surveillance methods and intensity and assessment of eradication feasibility. To apply the model to identify surveillance protocols for generalized invasion scenarios and for evaluating surveillance and control for a context-specific plant invasion. Location: Australia. Methods: We integrate a spatially explicit simulation model, including plant demography and dispersal vectors, within a Geographical Information System. We use the model to identify effective surveillance protocols using simulations of generalized plant life-forms spreading via different dispersal mechanisms in real landscapes. We then parameterize the surveillance support model for Chilean needle grass [CNG; Nassella neesiana (Trin. & Rupr.) Barkworth], a highly invasive tussock grass, which is an eradication target in south-eastern Queensland, Australia. Results: General surveillance protocols that can guide rapid response surveillance were identified; suitable habitat that is susceptible to invasion through particular dispersal syndromes should be targeted for surveillance using an adaptive seek-and-destroy method. The search radius of the adaptive method should be based on maximum expected dispersal distances. Protocols were used to define a surveillance strategy for CNG, but simulations indicated that despite effective and targeted surveillance, eradication is implausible at current intensities. Main conclusions: Several important surveillance protocols emerged and simulations indicated that effectiveness can be increased if they are followed in rapid response surveillance. If sufficient data are available, the surveillance support model should be parameterized to target areas susceptible to invasion and determine whether surveillance is effective and eradication is feasible. We discovered that for CNG, regardless of a carefully designed surveillance strategy, eradication is implausible at current intensities of surveillance and control and these efforts should be doubled if they are to be successful. This is crucial information in the face of environmentally and economically damaging invasive species and large, expensive and potentially ineffective control programmes.

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.

Analysis and Modelling of the Effects of Water Stress on Maize Growth and Yield in Dryland Conditions.

It is essential to provide experimental evidence and reliable predictions of the effects of water stress on crop production in the drier, less predictable environments. A field experiment undertaken in southeast Queensland, Australia with three water regimes (fully irrigated, rainfed and irrigated until late canopy expansion followed by rainfed) was used to compare effects of water stress on crop production in two maize (Zea mays L.) cultivars (Pioneer 34N43 and Pioneer 31H50). Water stress affected growth and yield more in Pioneer 34N43 than in Pioneer 31H50. A crop model APSIM-Maize, after having been calibrated for the two cultivars, was used to simulate maize growth and development under water stress. The predictions on leaf area index (LAI) dynamics, biomass growth and grain yield under rain fed and irrigated followed by rain fed treatments was reasonable, indicating that stress indices used by APSIM-Maize produced appropriate adjustments to crop growth and development in response to water stress. This study shows that Pioneer 31H50 is less sensitive to water stress and thus a preferred cultivar in dryland conditions, and that it is feasible to provide sound predictions and risk assessment for crop production in drier, more variable conditions using the APSIM-Maize model.

Modelling the nitrogen-driven trade-off between nitrogen utilisation efficiency and water use efficiency of wheat in eastern Australia.

The nitrogen-driven trade-off between nitrogen utilisation efficiency (yield per unit nitrogen uptake) and water use efficiency (yield per unit evapotranspiration) is widespread and results from well established, multiple effects of nitrogen availability on the water, carbon and nitrogen economy of crops. Here we used a crop model (APSIM) to simulate the yield, evapotranspiration, soil evaporation and nitrogen uptake of wheat, and analysed yield responses to water, nitrogen and climate using a framework analogous to the rate-duration model of determinate growth. The relationship between modelled grain yield (Y) and evapotranspiration (ET) was fitted to a linear-plateau function to derive three parameters: maximum yield (Ymax), the ET break-point when yield reaches its maximum (ET#), and the rate of yield response in the linear phase ([Delta]Y/[Delta]ET). Against this framework, we tested the hypothesis that nitrogen deficit reduces maximum yield by reducing both the rate ([Delta]Y/[Delta]ET) and the range of yield response to evapotranspiration, i.e. ET# - Es, where Es is modelled median soil evaporation. Modelled data reproduced the nitrogen-driven trade-off between nitrogen utilisation efficiency and water use efficiency in a transect from Horsham (36°S) to Emerald (23°S) in eastern Australia. Increasing nitrogen supply from 50 to 250 kg N ha-1 reduced yield per unit nitrogen uptake from 29 to 12 kg grain kg-1 N and increased yield per unit evapotranspiration from 6 to 15 kg grain ha-1 mm-1 at Emerald. The same increment in nitrogen supply reduced yield per unit nitrogen uptake from 30 to 25 kg grain kg-1 N and increased yield per unit evapotranspiration from 6 to 25 kg grain ha-1 mm-1 at Horsham. Maximum yield ranged from 0.9 to 6.4 t ha-1. Consistent with our working hypothesis, reductions in maximum yield with nitrogen deficit were associated with both reduction in the rate of yield response to ET and compression of the range of yield response to ET. Against the notion of managing crops to maximise water use efficiency in low rainfall environments, we emphasise the trade-off between water use efficiency and nitrogen utilisation efficiency, particularly under conditions of high nitrogen-to-grain price ratio. The rate-range framework to characterise the relationship between yield and evapotranspiration is useful to capture this trade-off as the parameters were responsive to both nitrogen supply and climatic factors.

Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia.

Genotype-environment interactions (GEI) limit genetic gain for complex traits such as tolerance to drought. Characterization of the crop environment is an important step in understanding GEI. A modelling approach is proposed here to characterize broadly (large geographic area, long-term period) and locally (field experiment) drought-related environmental stresses, which enables breeders to analyse their experimental trials with regard to the broad population of environments that they target. Water-deficit patterns experienced by wheat crops were determined for drought-prone north-eastern Australia, using the APSIM crop model to account for the interactions of crops with their environment (e.g. feedback of plant growth on water depletion). Simulations based on more than 100 years of historical climate data were conducted for representative locations, soils, and management systems, for a check cultivar, Hartog. The three main environment types identified differed in their patterns of simulated water stress around flowering and during grain-filling. Over the entire region, the terminal drought-stress pattern was most common (50% of production environments) followed by a flowering stress (24%), although the frequencies of occurrence of the three types varied greatly across regions, years, and management. This environment classification was applied to 16 trials relevant to late stages testing of a breeding programme. The incorporation of the independently-determined environment types in a statistical analysis assisted interpretation of the GEI for yield among the 18 representative genotypes by reducing the relative effect of GEI compared with genotypic variance, and helped to identify opportunities to improve breeding and germplasm-testing strategies for this region.

Macadamia Modelling Scoping Study

Investigate the feasibility and utility of a macadamia physiological model.

Developing Queensland 3D Tropical Fruits and Nuts modelling capabilities

Development of 3D functional structural plant models for macadamias and other tropical fruit and nuts.

Modelling drought mortaility in spotted gum.

Increased climate variability and the need to establish production forests at more marginal sites requires an understanding of the mechanisms of drought death in production species so that predictions of growth and survival are robust and defendable.

Gene to Phenotype Integrative Science and Modelling Applied to Pioneer elite

This project aims to use simulatiion modelling to improve our understanding of the genetics and physiology of complex traits with a view to increasing the rate of genetic gain in plant breeding programs.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal

Hierarchical Bayesian models can assimilate surveillance and ecological information to estimate both invasion extent and model parameters for invading plant pests spread by people. A reliability analysis framework that can accommodate multiple dispersal modes is developed to estimate human-mediated dispersal parameters for an invasive species. Uncertainty in the observation process is modelled by accounting for local natural spread and population growth within spatial units. Broad scale incursion dynamics are based on a mechanistic gravity model with a Weibull distribution modification to incorporate a local pest build-up phase. The model uses Markov chain Monte Carlo simulations to infer the probability of colonisation times for discrete spatial units and to estimate connectivity parameters between these units. The hierarchical Bayesian model with observational and ecological components is applied to a surveillance dataset for a spiralling whitefly (Aleurodicus dispersus) invasion in Queensland, Australia. The model structure provides a useful application that draws on surveillance data and ecological knowledge that can be used to manage the risk of pest movement.

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.

Identifying risk-efficient strategies using stochastic frontier analysis and simulation: An application to irrigated cropping in Australia

In irrigated cropping, as with any other industry, profit and risk are inter-dependent. An increase in profit would normally coincide with an increase in risk, and this means that risk can be traded for profit. It is desirable to manage a farm so that it achieves the maximum possible profit for the desired level of risk. This paper identifies risk-efficient cropping strategies that allocate land and water between crop enterprises for a case study of an irrigated farm in Southern Queensland, Australia. This is achieved by applying stochastic frontier analysis to the output of a simulation experiment. The simulation experiment involved changes to the levels of business risk by systematically varying the crop sowing rules in a bioeconomic model of the case study farm. This model utilises the multi-field capability of the process based Agricultural Production System Simulator (APSIM) and is parameterised using data collected from interviews with a collaborating farmer. We found sowing rules that increased the farm area sown to cotton caused the greatest increase in risk-efficiency. Increasing maize area also improved risk-efficiency but to a lesser extent than cotton. Sowing rules that increased the areas sown to wheat reduced the risk-efficiency of the farm business. Sowing rules were identified that had the potential to improve the expected farm profit by ca. $50,000 Annually, without significantly increasing risk. The concept of the shadow price of risk is discussed and an expression is derived from the estimated frontier equation that quantifies the trade-off between profit and risk.

Herbicide resistance modelling: Past, present and future

Computer simulation modelling is an essential aid in building an integrated understanding of how different factors interact to affect the evolutionary and population dynamics of herbicide resistance, and thus in helping to predict and manage how agricultural systems will be affected. In this review, we first discuss why computer simulation modelling is such an important tool and framework for dealing with herbicide resistance. We then explain what questions related to herbicide resistance have been addressed to date using simulation modelling, and discuss the modelling approaches that have been used, focusing first on the earlier, more general approaches, and then on some newer, more innovative approaches. We then consider how these approaches could be further developed in the future, by drawing on modelling techniques that are already employed in other areas, such as individual-based and spatially explicit modelling approaches, as well as the possibility of better representing genetics, competition and economics, and finally the questions and issues of importance to herbicide resistance research and management that could be addressed using these new approaches are discussed. We conclude that it is necessary to proceed with caution when increasing the complexity of models by adding new details, but, with appropriate care, more detailed models will make it possible to integrate more current knowledge in order better to understand, predict and ultimately manage the evolution of herbicide resistance. © 2014 Society of Chemical Industry.

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.