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.

Selecting replacements for invasive plants to support frugivores in highly modified sites: a case study focusing on Lantana camara

Invasive plants are regarded as a major threat to biodiversity worldwide. Yet, in some cases, invasive plants now perform important ecological functions. For example, fleshy-fruited invasive plants provide food that supports indigenous frugivore populations. How can the disparate goals of conservation versus invasive weed control be managed? We suggest using the fruit characteristics of the invasive plant to select replacement indigenous plants that are functionally similar from the perspective of frugivores. These could provide replacement food resources at sites where plants with these characteristics are part of the goal plant community and where such plants would not otherwise regenerate. Replacement plants could also redirect seed dispersal processes to favour indigenous, rather than invasive, plant species. We investigated the utility of this approach by ranking all indigenous fleshy-fruited plant species from a region using a simple model that scored species based upon measures of fruit phenology, morphology, conspicuousness and accessibility relative to a target invasive species, Lantana (Lantana camara). The model successfully produced high scores for indigenous plant species that were used by more of the frugivores of Lantana than a random selection of plants, suggesting that this approach warrants further investigation.

Application of a model to assess aflatoxin risk in peanuts.

When exposed to hot (22-35 degrees C) and dry climatic conditions in the field during the final 4-6 weeks of pod filling, peanuts (Arachis hypogaea L.) can accumulate highly carcinogenic and immuno-suppressing aflatoxins. Forecasting of the risk posed by these conditions can assist in minimizing pre-harvest contamination. A model was therefore developed as part of the Agricultural Production Systems Simulator (APSIM) peanut module, which calculated an aflatoxin risk index (ARI) using four temperature response functions when fractional available soil water was <0.20 and the crop was in the last 0.40 of the pod-filling phase. ARI explained 0.95 (P <= 0.05) of the variation in aflatoxin contamination, which varied from 0 to c. 800 mu g/kg in 17 large-scale sowings in tropical and four sowings in sub-tropical environments carried out in Australia between 13 November and 16 December 2007. ARI also explained 0.96 (P <= 0.01) of the variation in the proportion of aflatoxin-contaminated loads (>15 mu g/kg) of peanuts in the Kingaroy region of Australia during the period between the 1998/99 and 2007/08 seasons. Simulation of ARI using historical climatic data from 1890 to 2007 indicated a three-fold increase in its value since 1980 compared to the entire previous period. The increase was associated with increases in ambient temperature and decreases in rainfall. To facilitate routine monitoring of aflatoxin risk by growers in near real time, a web interface of the model was also developed. The ARI predicted using this interface for eight growers correlated significantly with the level of contamination in crops (r=095, P <= 0.01). These results suggest that ARI simulated by the model is a reliable indicator of aflatoxin contamination that can be used in aflatoxin research as well as a decision-support tool to monitor pre-harvest aflatoxin risk in peanuts.

A Practitioner-Oriented Decision Support Process for Forestry Pest Management

The article discusses a new decision support process for forestry pest management. Over the past few years, DSS have been introduced for forestry pest management, providing forest growers with advice in areas such as selecting the most suitable pesticide and relevant treatment. Most of the initiatives process knowledge from various domains for providing support for specific decision making problems. However, very few studies have identified the requirements of developing a combined process model in which all relevant practitioners can contribute and share knowledge for effective decision making; such an approach would need to include the decision makers’ perspective along with other relevant attributes such as the problem context and relevant policies. We outline a decision support process for forestry pest management, based on the design science research paradigm, in which a focus group technique has application to acquire both expert and practical knowledge in order to construct the DSS solution.

Assessment of functional forms of crop yield loss models of invasive plant species applied in decision support tools and bioeconomic modelling

AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.

Exploring the Potential Impacts of Climate Variability on Spring Wheat Yield with the APSIM Decision Support Tool

Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from -24 to -94 depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.

The Ethics of a Co-regulatory Model for Farm Animal Welfare Research

Standards for farm animal welfare are variously managed at a national level by government-led regulatory control, by consumer-led welfare economics and co-regulated control in a partnership between industry and government. In the latter case the control of research to support animal welfare standards by the relevant industry body may lead to a conflict of interest on the part of researchers, who are dependent on industry for continued research funding. We examine this dilemma by reviewing two case studies of research published under an Australian co-regulated control system. Evidence of unsupported conclusions that are favourable to industry is provided, suggesting that researchers do experience a conflict of interest that may influence the integrity of the research. Alternative models for the management of research are discussed, including the establishment of an independent research management body for animal welfare because of its public good status and the use of public money derived from taxation, with representation from government, industry, consumers, and advocacy groups.