A participatory systems approach to understanding climate adaptation needs

Emerging literature on climate adaptation suggests the need for effective ways of engaging or activating communities and supporting community roles, coupled with whole-of-system approaches to understanding climate change and adaptation needs. We have developed and evaluated a participatory approach to elicit community and stakeholder understanding of climate change adaptation needs, and connect diverse community members and local office bearers towards potential action. The approach was trialed in a series of connected social-ecological systems along a transect from a rural area to the coast and islands of ecologically sensitive Moreton Bay in Queensland, Australia. We conducted ‘climate roundtables’ in each of three areas along the transect, then a fourth roundtable reviewed and extended the results to the region as a whole. Influence diagrams produced through the process show how each climate variable forecast to affect this region (heat, storm, flood, sea-level rise, fire, drought) affects the natural environment, infrastructure, economic and social behaviour patterns, and psychosocial responses, and how sets of people, species and ecosystems are affected, and act, differentially. The participatory process proved effective as a way of building local empathy, a local knowledge base and empowering participants to join towards future climate adaptation action. Key principles are highlighted to assist in adapting the process for use elsewhere.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

Can teacher collaboration overcome barriers to interdisciplinary learning in a disciplinary university? A case study using climate change

A teacher network was formed at an Australian university in order to better promote interdisciplinary student learning on the complex social-environmental problem of climate change. Rather than leaving it to students to piece together disciplinary responses, eight teaching academics collaborated on the task of exposing students to different types of knowledge in a way that was more than the summing of disciplinary parts. With a part-time network facilitator providing cohesion, network members were able to teach into each other’s classes, and share material and student activities across a range of units that included business, zoology, marine science, geography and education. Participants reported that the most positive aspects of the project were the collegiality and support for teaching innovation provided by peers. However, participants also reported being time-poor and overworked. Maintaining the collaboration beyond the initial one year project proved difficult because without funding for the network facilitator, participants were unable to dedicate the time required to meet and collaborate on shared activities. In order to strengthen teacher collaboration in a university whose administrative structures are predominantly discipline-based, there is need for recognition of the benefits of interdisciplinary learning to be matched by recognition of the need for financial and other resources to support collaborative teaching initiatives.

Reconfiguring agriculture through the relocation of production systems for water, environment and food security under climate change

The prospect of climate change has revived both fears of food insecurity and its corollary, market opportunities for agricultural production. In Australia, with its long history of state-sponsored agricultural development, there is renewed interest in the agricultural development of tropical and sub-tropical northern regions. Climate projections suggest that there will be less water available to the main irrigation systems of the eastern central and southern regions of Australia, while net rainfall could be sustained or even increase in the northern areas. Hence, there could be more intensive use of northern agricultural areas, with the relocation of some production of economically important commodities such as vegetables, rice and cotton. The problem is that the expansion of cropping in northern Australia has been constrained by agronomic and economic considerations. The present paper examines the economics, at both farm and regional level, of relocating some cotton production from the east-central irrigation areas to the north where there is an existing irrigation scheme together with some industry and individual interest in such relocation. Integrated modelling and expert knowledge are used to examine this example of prospective climate change adaptation. Farm-level simulations show that without adaptation, overall gross margins will decrease under a combination of climate change and reduction in water availability. A dynamic regional Computable General Equilibrium model is used to explore two scenarios of relocating cotton production from south east Queensland, to sugar-dominated areas in northern Queensland. Overall, an increase in real economic output and real income was realized when some cotton production was relocated to sugar cane fallow land/new land. There were, however, large negative effects on regional economies where cotton production displaced sugar cane. It is concluded that even excluding the agronomic uncertainties, which are not examined here, there is unlikely to be significant market-driven relocation of cotton production.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

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.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Shared perceptions of supervisor conflict management style

- Purpose This study aims to investigate the extent to which employee outcomes (anxiety/depression, bullying and workers’ compensation claims thoughts) are affected by shared perceptions of supervisor conflict management style (CMS). Further, this study aims to assess cross-level moderating effects of supervisor CMS climate on the positive association between relationship conflict and these outcomes. - Design/methodology/approach Multilevel modeling was conducted using a sample of 401 employees nested in 69 workgroups. - Findings High collaborating, low yielding and low forcing climates (positive supervisor climates) were associated with lower anxiety/depression, bullying and claim thoughts. Unexpectedly, the direction of moderation showed that the positive association between relationship conflict and anxiety/depression and bullying was stronger for positive supervisor CMS climates than for negative supervisor CMS climates (low collaborating, high yielding and high forcing). Nevertheless, these interactions revealed that positive supervisor climates were the most effective at reducing anxiety/depression and bullying when relationship conflict was low. For claim thoughts, positive supervisor CMS climates had the predicted stress-buffering effects. - Research limitations/implications Employees benefit from supervisors creating positive CMS climates when dealing with conflict as a third party, and intervening when conflict is low, when their intervention is more likely to minimize anxiety/depression and bullying. - Originality/value By considering the unique perspective of employees’ shared perceptions of supervisor CMS, important implications for the span of influence of supervisor behavior on employee well-being have been indicated.

Modeling Efficient Classification as a Process of Confidence Assessment and Delegation

In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort. We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard. The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.

Modeling multi-stakeholder multi-objective decisions during public participation in major infrastructure and construction projects: A decision rule approach

Major infrastructure and construction (MIC) projects are those with significant traffic or environmental impact, of strategic and regional significance and high sensitivity. The decision making process of schemes of this type is becoming ever more complicated, especially with the increasing number of stakeholders involved and their growing tendency to defend their own varied interests. Failing to address and meet the concerns and expectations of stakeholders may result in project failures. To avoid this necessitates a systematic participatory approach to facilitate decision-making. Though numerous decision models have been established in previous studies (e.g. ELECTRE methods, the analytic hierarchy process and analytic network process) their applicability in the decision process during stakeholder participation in contemporary MIC projects is still uncertain. To resolve this, the decision rule approach is employed for modeling multi-stakeholder multi-objective project decisions. Through this, the result is obtained naturally according to the “rules” accepted by any stakeholder involved. In this sense, consensus is more likely to be achieved since the process is more convincing and the result is easier to be accepted by all concerned. Appropriate “rules”, comprehensive enough to address multiple objectives while straightforward enough to be understood by multiple stakeholders, are set for resolving conflict and facilitating consensus during the project decision process. The West Kowloon Cultural District (WKCD) project is used as a demonstration case and a focus group meeting is conducted in order to confirm the validity of the model established. The results indicate that the model is objective, reliable and practical enough to cope with real world problems. Finally, a suggested future research agenda is provided.

Agronomic benefits and risks associated with the irrigated peanut–maize production system under a changing climate in northern Australia

With the aim of increasing peanut production in Australia, the Australian peanut industry has recently considered growing peanuts in rotation with maize at Katherine in the Northern Territory—a location with a semi-arid tropical climate and surplus irrigation capacity. We used the well-validated APSIM model to examine potential agronomic benefits and long-term risks of this strategy under the current and warmer climates of the new region. Yield of the two crops, irrigation requirement, total soil organic carbon (SOC), nitrogen (N) losses and greenhouse gas (GHG) emissions were simulated. Sixteen climate stressors were used; these were generated by using global climate models ECHAM5, GFDL2.1, GFDL2.0 and MRIGCM232 with a median sensitivity under two Special Report of Emissions Scenarios over the 2030 and 2050 timeframes plus current climate (baseline) for Katherine. Effects were compared at three levels of irrigation and three levels of N fertiliser applied to maize grown in rotations of wet-season peanut and dry-season maize (WPDM), and wet-season maize and dry-season peanut (WMDP). The climate stressors projected average temperature increases of 1°C to 2.8°C in the dry (baseline 24.4°C) and wet (baseline 29.5°C) seasons for the 2030 and 2050 timeframes, respectively. Increased temperature caused a reduction in yield of both crops in both rotations. However, the overall yield advantage of WPDM increased from 41% to up to 53% compared with the industry-preferred sequence of WMDP under the worst climate projection. Increased temperature increased the irrigation requirement by up to 11% in WPDM, but caused a smaller reduction in total SOC accumulation and smaller increases in N losses and GHG emission compared with WMDP. We conclude that although increased temperature will reduce productivity and total SOC accumulation, and increase N losses and GHG emissions in Katherine or similar northern Australian environments, the WPDM sequence should be preferable over the industry-preferred sequence because of its overall yield and sustainability advantages in warmer climates. Any limitations of irrigation resulting from climate change could, however, limit these advantages.

Climate savvy grazing (Developing improved grazing and related practices to assist beef production enterprises across northern Australia to adapt to a changing and more variable climate)

There is uncertainty over the potential changes to rainfall across northern Australia under climate change. Since rainfall is a key driver of pasture growth, cattle numbers and the resulting animal productivity and beef business profitability, the ability to anticipate possible management strategies within such uncertainty is crucial. The Climate Savvy Grazing project used existing research, expert knowledge and computer modelling to explore the best-bet management strategies within best, median and worse-case future climate scenarios. All three scenarios indicated changes to the environment and resources upon which the grazing industry of northern Australia depends. Well-adapted management strategies under a changing climate are very similar to best practice within current climatic conditions. Maintaining good land condition builds resource resilience, maximises opportunities under higher rainfall years and reduces the risk of degradation during drought and failed wet seasons. Matching stocking rate to the safe long-term carrying capacity of the land is essential; reducing stock numbers in response to poor seasons and conservatively increasing stock numbers in response to better seasons generally improves profitability and maintains land in good condition. Spelling over the summer growing season will improve land condition under a changing climate as it does under current conditions. Six regions were included within the project. Of these, the Victoria River District in the Northern Territory, Gulf country of Queensland and the Kimberley region of Western Australia had projections of similar or higher than current rainfall and the potential for carrying capacity to increase. The Alice Springs, Maranoa-Balonne and Fitzroy regions had projections of generally drying conditions and the greatest risk of reduced pasture growth and carrying capacity. Encouraging producers to consider and act on the risks, opportunities and management options inherent in climate change was a key goal of the project. More than 60,000 beef producers, advisors and stakeholders are now more aware of the management strategies which build resource resilience, and that resilience helps buffer against the effects of variable and changing climatic conditions. Over 700 producers have stated they have improved confidence, skills and knowledge to attempt new practices to build resilience. During the course of the project, more than 165 beef producers reported they have implemented changes to build resource and business resilience.