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Transport infrastructure is essential to moving over 85 million tonnes of agricultural products and 30 million cubic metres of timber from farms and production areas to domestic and international markets each year in Australia. Agriculture supply chains in Australia are characterised by long distances with transport costs accounting for up to 40 percent of the market price. Targeted infrastructure investment and/or regulatory changes can substantially reduce transport-related logistics costs. To provide a comprehensive view of transport logistics costs and benefits due to infrastructure investments and regulatory changes in agriculture and forestry supply chains, CSIRO developed a computer-based tool – the Transport Network Strategic Investment Tool (TraNSIT). TraNSIT optimises transport routes for upto hundreds of thousands of enterprises and millions of vehicle trips between farms and their markets, providing information on routing to maximise cost efficiencies. Through an Australian Government initiative, TraNSIT was applied to over 30 commodities representing 98% of Australian agricultural and plantation forestry volume transported by road and rail. TraNSIT is now a comprehensive logistics tool that has been applied to the largest agricultural supply chain dataset ever assembled in Australia. This paper provides an overview of TraNSIT, its adaptation to agriculture and forestry logistics as part of the project, and its application to several case studies in Australia including new road links, rail versus road and flooding.

Stormwater contamination can threaten the health of aquatic ecosystems and human exposed to runoff via nutrient and pathogen influxes. In this study, the concentrations of 11 bacterial pathogens and 47 antibiotic resistance genes (ARGs) were determined by using high-throughput microfluidic qPCR (MFQPCR) in several storm drain outfalls (SDOs) during dry and wet weather in Tampa Bay, Florida, USA. Data generated in this study were also compared with the levels of fecal indicator bacteria (FIB) and sewage-associated molecular markers (i.e., Bacteroides HF183 and crAssphage markers) in same SDOs collected in a recent study (Ahmed et al., 2018). Concentration of FIB, sewage-associated markers, bacterial pathogens and many ARGs in water samples were relatively high and SDOs may be potentially hotspots for microbial contamination in Tampa Bay. Mean concentrations of culturable E. coli and Enterococcus spp. were tenfold higher in wet compared to dry weather. The majority of microbiological contaminants followed this trend. E. coli eaeA, encoding the virulence factor intimin, was correlated with levels of 20 ARGs, and was more frequently detected in wet weather than dry weather samples. The blaKPC gene associated with carbapenem resistant Enterobacteriaceae and the beta-lactam resistant gene (blaNPS) were only detected in wet weather samples. Frequency of integron genes Intl2 and Intl3 detection increased by 42% in wet weather samples. Culturable E. coli and Enterococcus spp. significantly correlated with 19 of 47 (40%) ARG tested. Sewage-associated markers crAssphage and HF183 significantly correlated (p

The Coal Seam Gas (CSG) extraction industry is developing rapidly within the Surat Basin in southern Queensland, Australia, with licenses already approved for tenements covering more than 24,000 km2. Much of this land is used for a broad range of agricultural purposes and the need for coexistence between the farm and gas industries has been the source of much conflict. Whilst much research has been undertaken into the environmental and economic impacts of CSG, little research has looked into the issues of coexistence between farmers and the CSG industry in the shared space that is a farm business, a home and a resource extraction network. We conducted three workshops with farmers from across a broad region undergoing CSG development to explore farmers’ perceptions of some of the issues arising from large scale land use change. Workshops explored the importance of place identity and landscape aesthetics for farmers, farmers’ acceptance and coping with change, and possible benefits from off-farm income. We found that farmers believed that place identity was not well understood by CSG staff from non-rural backgrounds and that farmers struggled to explain some concerns because of the different way they interpreted their landscape. Furthermore, high staff turnover, and the extensive use of contractors also impacted on communications. These factors were the cause of much frustration and farmers felt that this has led to severe impacts on mental health and wellbeing. Farmers felt that a change in culture within the CSG companies will be required if engagement with farmers is to improve and that efforts to employ local people in these communications was helping this. The workshops also identified a range of issues perceived by farmers arising from increased traffic volumes, impacts to mental health and wellbeing, place identity and loss of water resources for farmers. Finally, it was suggested that scientists and agricultural industry groups will need to work closely with farmers to develop understanding of these emerging issues and to develop solutions that are timely and relevant.

There is an ever growing demand for energy worldwide and the demand for gas alone is predicted to double between 2010 and 2035. This demand together with concurrent advances in drilling technologies caused the production of unconventional natural gas such as shale gas and coal seam gas (CSG), which is in the focus of this paper, to grow rapidly in the last decades. With the gas bearing coal seams extending across vast areas within their respective basins and with CSG production having to follow these seams through a network of production wells, pipelines and access roads, CSG activity affects large areas and therefore interferes with existing land uses, predominantly agriculture. For the eastern Australian Surat Basin and the southern Bowen Basin alone there are projected well numbers in excess of 15,000 to 20,000 between the years 2020 and 2030. The interference of CSG with agriculture on a large scale has raised concerns about the impact of CSG on farmland, food security, water resources and the socio-economic environment within the affected regions and beyond. This paper presents a newly developed spatial model which provides order of magnitude figures of the impact of CSG activity on gross economic returns of current agricultural land uses in a given region over the time of CSG production. The estimated gross figures do not account for any compensation payments received by farmers. The model is capable of accounting for a variation in a variety of parameters including impact frequency of distinct infrastructure elements, differences in soil types and associated varying responses of soil productivity, varying length of the CSG production phase and more. The model is flexible in that it can be transferred and applied in other regions as well. Based upon a literature review and given that CSG is an industry that started operating at larger scales relatively recently, we claim that the presented model is the first of its kind to provide these important agro-economic indicators.

The production of coal seam gas (CSG) in Australia is set to increase, driven by increasing global demand for energy and in response to the transition to a lower carbon economy through greater use of gas for electricity generation. Despite the many economic benefits delivered by the CSG industry, concerns have been raised over the potential environmental impacts associated with CSG production, particularly the long-term effects on the soil resource. Therefore, this work was conducted to: (1) assess the extent of damage to agricultural soil caused by the various elements of CSG development and (2) estimate the likely impact of soil compaction, caused during establishment of CSG infrastructure, on crop productivity. The study was undertaken using a paired-sites approach by comparing measurements conducted on selected soil properties in areas around and including well-head sites with measurements in neighboring agricultural fields. These spatial areas are referred to as “lease” and “field” areas, respectively. Measurements were used to guide parameterization and application of the Agricultural Production Systems Simulator (APSIM) model to assess the likely effects of changed soil conditions on crop productivity. To achieve this, the APSIM model was used to simulate wheat ( L.) yields for 115 years on Grey Vertosols in the Darling Downs region of Queensland. Simulations were conducted with soil properties representing: (1) field area conditions not affected by CSG activities, (2) lease area conditions in which soil had been impacted by CSG activities during the development phase, and (3) lease area conditions where soils had been rehabilitated. Results showed that soil compaction within lease areas in the top 300 mm of the profile was approximately 15% higher compared with field areas (p -3 for the 0 to 350 mm and 350 to 700 mm depth intervals, respectively, are suggested as references for CSG-rehabilitated soil. These critical values may be used as guidance until further studies are undertaken. The assessment of soil chemical properties indicated that these were affected to a lesser extent by the establishment of CSG infrastructure. However, a general requirement is careful manipulation of sodium-rich subsoil and avoidance of soil blending during reinstatement operations. The dataset acquired and the simulation approach employed in this study can be used to further develop soil management guidelines relevant to the Australian CSG industry. Cost-benefit analyses of soil management practices for reinstatement, development of soil quality standards, and industry best management practices are required.

Despite great advancements in recent years, the availability of detailed and regionalised farm practice data at national scale remains an obstacle for spatially-detailed research on sustainable intensification. Parsing and information retrieval techniques were applied to 385 farm management handbooks to estimate farm practices (use of fertilisers, pesticides, water, fuel) of 72 commodities grown in 42 regions. Life-cycle inventories were used to derive GHG emissions and energy use from farm practice data. Practices and impacts were mapped at 1.1?km2 resolution using agricultural census data and a remote-sensing-based land use map. Existing data was linearly extrapolated using a rule-based approach to fill spatial gaps. Estimates were, in aggregate, comparable to the best available data at national and local scales. Our method contributes to the push to create more spatially-detailed assessments of agricultural impacts at a national scale by focusing on the production of basic data at the farm level. Our method estimates farm practices from a large number of farm management handbooks.It extracts handbook knowledge with natural language processing and query expansion.Practices are mapped to 1.1?km2 resolution and linearly extrapolated to fill gaps.The practices mapped are use of fertilisers, pesticides, water and fuel.GHG emissions and energy use are derived from practices with life-cycle inventories.

This study aimed to determine the prevalence and abundance of sewage and animal fecal contamination of sediment at seven estuarine locations in Sydney, NSW, Australia. Sediment samples were tested for the occurrence of microbial targets including molecular marker genes of enterococci (ENT), Bacteroides HF183 (HF183), Methanobrevibacter smithii (nifH), human adenovirus (HAdV) and emerging sewage-associated marker genes crAssphage (CPQ_056) and Lachnospiraceae (Lachno3) and animal feces-associated marker genes, including avian feces-associated Helicobacter spp. (GFD), canine-feces associated Bacteroides (DogBact), cattle-feces associated (cowM2) and horse feces-associated Bacteroides (HoF597). Results from this study showed that urban estuarine sediment can act as a reservoir of fecal indicator bacteria (FIB) and several microbial source tracking (MST) marker genes, including previously unreported Lachno3. The sewage-associated marker gene CPQ_056 was most prevalent, in 63.8% of sediment samples, while the avian associated marker gene GFD had the highest mean abundance. The GFD marker gene was highly abundant and widely detected in sediment samples from all seven locations compared to the other animal feces-associated marker genes. In all, 31 (44.9%) sediment samples were positive for at least two sewage-associated marker genes. However, the non-quantifiable detection of the HAdV marker gene did not always align with the detection of two or more sewage-associated marker genes. In addition, the most frequent wet weather overflow exposure occurred at locations that did not have a consistent pattern of detection of the sewage-associated marker genes, suggesting sediments may not be a suitable measure of recent sewage contamination. To assist water quality and public health managers better understand past microbial contamination of estuarine sediment, further studies seem justified to explore the role of decay of MST marker genes in sediment. Further work is also needed on the role of resuspension of MST marker genes from sediment during storm events to the water column as a source of contamination for both the GFD and sewage-associated marker genes.

Technical efficiency is the effectiveness with which a given set of inputs is used to produce one or more outputs. As such it is an important indicator that helps to differentiate between production entities that represent current best practice (or operate close to it) from those that do not. State of the art methods to estimate technical efficiency of a production entity are econometric frontier techniques such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) where observational data on inputs and outputs are analysed. While efficiency scores are then known for the observational units, efficiency scores cannot simply be interpolated to estimate efficiency scores for production units that have not been part of a survey. This paper focuses the development of a proxy measure of technical efficiency for specialised grain farms where no observational data are available for. The proxy measure is based upon crop simulation results and technical efficiencies that are estimated from available survey data. A moderate to strong correlation (0.5-0.7) is found between metrics inferred from crop simulation and technical efficiency scores. Fitted linear models with technical efficiency as the predictor variable returned an R^2 lower equal to 0.42 (linear models have zero intercept). To apply the linear model beyond observed locations, the integration of a remote sensing component is discussed and put into a conceptual framework. The suggested method would be suitable to be applied on a regional level and will help improving the regionalisation of technical efficiency. The findings are based on the analysis of a 4year plot level survey dataset from Australian grain specialists. Findings are to be considered preliminary at this stage and more research that involves a remote sensing component is needed to confirm the applicability of the suggested framework.

The provision of new housing for a growing population is a challenge that many cities and regions all over the world are confronted with. The design and management of new housing development is a complex task given it needs to be socially, environmentally and economically sustainable. This paper describes a new method that helps spatially direct new housing development to minimise impact on water quality. Currently, the model accommodates for the pollutants total nitrogen (TN), total phosphorus (TP) and suspended sediments (TSS), but can be extended. The method is based on mathematical programming linked with multi-criteria analysis, and kinematic wave equations. It utilises GIS to underpin multiple spatial constraints on where housing development can take place, and to provide an effective means of visually analysing the impacts of land development scenarios produced by the model. We demonstrate the method using a case study for Hornsby Shire Council, New South Wales, Australia. With water quality and waterway health being a major issue in any urban development the proposed methodology has wide applications beyond the current study area.

To feed a growing world population in the coming decades, agriculture must strive to reduce the gap between the yields that are currently achieved by farmers (Ya) and those potentially attainable in rainfed farming systems (Yw). The first step towards reducing yield gaps (Yg) is to obtain realistic estimates of their magnitude and their spatial and temporal variability. In this paper we describe a new yield gap assessment framework. The framework uses statistical yield and cropping area data, remotely sensed data, cropping system simulation and GIS mapping to calculate wheat yield gaps at scales from 1.1 km cells to regional. The framework includes ad hoc on-ground testing of the calculated yield gaps. This framework was applied to wheat in the Wimmera region of Victoria, Australia. Estimated Yg over the whole Wimmera region varied annually from 0.63 to 4.12 Mg ha −1 with an average of 2.00 Mg ha −1 . Expressed as a relative yield ( Y % ) the range was 26.3–77.9% with an average of 52.7%. Similarly large spatial variability was described in a Wimmera yield gap map. Such maps can be used to show where efforts to bridge the yield gap are likely to have the biggest impacts. Bridging the exploitable yield gap in the Wimmera region by increasing average Y % to 80% would increase average annual wheat production from 1.09 M tonnes to 1.65 M tonnes. Model estimates of Yw and Yg were compared with data from crop yield contests, experimental variety trials, and on-farm water use and yields. These alternative approaches agreed well with the modelling results, indicating that the proposed framework provided a robust and widely applicable method of determining yield gaps. Its successful implementation requires that: (1) Ya as well as the area and geospatial distribution of wheat cropping are well defined; (2) there is a crop model with proven performance in the local agro-ecological zone; (3) daily weather and soil data (such as PAWC) required by crop models are available throughout the area; and (4) local agronomic best practice is well defined.

Policy makers in the agricultural sector are confronted with challenges which might drive land use change and ultimately agricultural profitability to a substantial degree. The challenges include questions around climate variability, demographic changes, use of land for bio-fuel production and ensuring an increase in food production. As profitability triggers many agri-business decisions, knowledge about the existing socio-economic landscape and the economic profile of a region as well as potential impacts on profits provides useful contextual information when agricultural policies are designed. Given the upcoming challenges and their associated uncertainties, it is important to ensure that a map of agricultural profit can be reproduced in a scenario and simulation setting which will allow exploring uncertainties around the impacts on agricultural profits as well. There is however currently no flexible system in operation which allows for a consistent update of a map of agricultural profits in Australia or elsewhere. This paper describes a process that has been developed to produce a map of agricultural profit for Australia for the year 2005/2006. The process involves a complex data architecture that accounts for heterogeneous information that is collected by a variety of institutions across different scales. All information can be comfortably queried and query results can be forwarded for immediate processing and subsequent visualisation in a geographic information system (GIS). To facilitate the production of profit maps in the future, the system provides flexibility regarding an update of new economic information but it can also be linked to maps that show an updated distribution of land use. A map of agricultural profit on a large scale and regular updates thereof will help understand profit trends in time and across space. It will help identifying regions that have a lower economic profile and will inform decisions regarding the design of regulatory policies. As these maps are developed using national scale data, we do not recommend using the results at the farm level but we suggest using separate catchment scale profit assessments to calibrate the national scale profit map. The proposed system is well suited to be used in various land use management and economic scenarios and will represent a step forward regarding a scenario impact assessment on agricultural profits. It will also help understand the economic benefit of land use on a large scale.

High nutrient loads are a widespread problem for many rivers and river catchments and cause damage to various ecological assets. Negative effects can be mitigated by changes in land management such as land use changes and/or implementation of intervention measures such as - amongst others - the construction of artificial wetlands and water treatment plants. Usually budget constraints limit the number of measures that can be implemented by catchment management authorities which creates an optimisation problem, namely maximising the total water quality benefits subject to a budget constraint. Here we present a case study from the Ellen Brook catchment in Perth, Western Australia, which suffers from serious waterway health problems. We evaluate a variety of suggested intervention measures and determine the benefits they return. Cost utility analysis (CUA) and subsequent combinatorial optimisation are employed to determine a portfolio of intervention sites that returns the maximum aggregated benefit subject to a budget constraint. Shortcomings in this approach are identified when future uncertainties are to be accounted for and we perform a comparative study using modern portfolio theory (MPT). It is demonstrated how MPT and CUA can be jointly used to consistently account for triple bottom line aspects and aspects of future uncertainties such as uncertainties in future climate variability.

Land-use decisions are usually made on the basis of a variety of criteria. While it is common practice to integrate economic, ecological and social (triple bottom line) criteria, explicit geoscientific factors are relatively rarely considered. If a planned land use involves an interaction with the geosphere, geoscientific aspects should be playing a more important role in the process. With the objective to facilitate a sustainable land-use decision-making a research project was initiated. The area around the city of Zaragoza, in the Ebro Basin of northern Spain, was chosen due to its high degree of industrialisation and urbanization. The area is exposed to several geohazards (e.g., sinkholes and erosion) that may have significant negative effects on current and future land uses. Geographical Information System (GIS) technologies are used to process the complex geoscientific information. Further GIS analysis comprised the creation of an erosion susceptibility map that follows the ITC (International Institute for Geo-Information Science and Earth Observation) system of terrain analysis. The agricultural capability of the soil was determined using the Microleis System. We identify geomorphologic units that show high susceptibility to erosion and high agricultural potential and suggest a method to implement this information in a land-use planning process. Degraded slopes developed upon Tertiary rocks show the highest susceptibility to erosion and low values of agricultural capability, whereas the flat valley bottoms and irrigated flood plains have the highest values of agricultural capability.

This article describes a decision support software system referred to as the multiple criteria analysis tool (MCAT). MCAT identifies a portfolio of decision options that return a maximum aggregated benefit under a constrained budget. Benefits scores of decision options - which we will refer to as projects - are computed using multiple criteria analysis whereas in a subsequent step, binary combinatorial optimisation is employed to identify the combination of projects that return a maximised aggregated benefit subject to a constraint. MCAT has primarily been developed to be used in natural resource management contexts. Though we illustrate MCAT through three Australian natural resource management case studies its use is explicitly not restricted to environmental decision problems. Wherever multi-criteria analysis (MCA) is regarded to be a suitable approach to evaluate decision options subject to a budget constraint, MCAT can be applied. We therefore believe that MCAT has potential for widespread application. It can help improve the transparency, analytic rigour and auditability of investment decisions.

This study borrows concepts from healthcare economics and uses cost utility analysis (CUA) to select an optimum portfolio of water quality enhancement projects in Perth, Western Australia. In CUA, costs are handled via standard discounted cash flow analysis, but the benefits, being intangible, are measured with a utility score. Our novel methodology combines CUA with a binary combinatorial optimisation solver, known as a 'knapsack algorithm', to identify the optimum portfolio of projects. We show how water quality projects can be selected to maximise an aggregate utility score while not exceeding a budget constraint. Our CUA model applies compromise programming (CP) to measure utility over multiple attributes in different units. CUA is shown to provide a transparent and analytically robust method to maximise benefits from water quality remediation investments under a constrained budget.

This paper presents a new approach to calculate flow accumulation with geographic information systems (GIS). It is based on the well-known D8 single-flow algorithm that is extended to consider the trap-efficiencies of dams and their specific operation time. This allows realistic calculations of flow accumulation for any time period. The new approach is not restricted to surface water runoff but can be applied to all kinds of mass fluxes like suspended or dissolved sediment load (weighted flow accumulation). To facilitate its use, two GIS extensions for ArcView and ArcGIS have been developed. This paper presents the principles of the new approach, the functionality of the extensions and gives some applications in the fields of hydrology and sedimentology.

In the surroundings of Zaragoza, karstification processes are especially intense in covered karst areas where fluvial terraces lie directly on Tertiary evaporites. Since the beginning of Quaternary, these processes have lead to the development of collapse and subsidence dolines with a wide range of sizes, which have significant economic impacts. To reduce economic impact and increase safety, a regional analysis of this phenomenon is needed for spatial management. Therefore, a probability map of dolines was developed using logistic regression and geographic information system (GIS) techniques. This paper covers the selection of input data, manipulation of data using the GIS technology, and the use of logistic regression to generate a doline probability map. The primary variable in the doline development in this area is geomorphology, represented by the location of endorheic areas and different terrace levels. Secondary variables are the presence of irrigation and the water table gradient.

The Quaternary deposits of the Ebro Basin in the surroundings of Zaragoza (Spain) form an unconfined alluvial aquifer with a high degree of permeability and low thickness of unsaturated materials. This fact causes a high degree of vulnerability to contamination, which implies consequences for land-use decisions and the risk management of existing industrial facilities. In addition, in the last decades the intense irrigation and the use of pesticides is threatening the quality of the groundwater and as a consequence the amount of usable groundwater at a low cost. Thus, the development of groundwater vulnerability maps has great importance in a regional planning process. Consequently, groundwater vulnerability maps have been developed following a method proposed by the German State Geological Surveys. In this paper the methodology developed within ArcGIS and Gocad, a two- and three-dimensional software, respectively, is presented. Although the results obtained within GIS (Geographical Information System) are acceptable it is to say that the 3D-model improves considerably the final product.

Decisions on landuse have become progressively more difficult in the last decades. The main reasons for this development lie in the increasing population combined with an increasing demand for new land and resources and in the growing consciousness for sustainable land and resource use. The steady reduction of valuable land leads to an increase of conflicts in land use decision-making processes since more interests are being affected and therefore more stakeholders with different land use interests and different valuation criteria are being involved in the decision-making process. In the course of such a decision process all identified criteria are weighted according to their relative importance. But assigning weights to the relevant criteria quickly becomes a difficult task when a greater number of criteria are being considered, especially with regard to land use decisions where decision makers expect some kind of mapped result it is therefore useful to use procedures that not only help to derive criteria weights but also accelerate the visualisation and mapping of land use assessment results. Both aspects can easily be facilitated in a GIS. This paper focuses the development of an ArcGIS VBA macro which enables the user to derive criteria weights with the analytical hierarchy process and which allows a mapping of the land use assessment results by a weighted summation of GIS raster data sets. A dynamic link library for the calculation of the eigenvalues and eigenvectors of a square matrix is provided.

Typical water management or natural resource management decision problems require decision makers to select a subset of available decision options that return a maximum benefit whilst satisfying limiting constraints, usually a budget constraint. This optimisation problem becomes increasingly difficult to solve if uncertainty aspects are being considered and/or as more decision options are included. To tackle this selection problem the multi-criteria analysis tool (MCAT) which contains both multi-criteria functionality and solution methods was developed. The benefit scores for the decision options are computed with the well known compromise programming technique. To optimise the selection of options subject to the constraints, two heuristics, namely Local Search and Tabu Search were coded. MCAT was primarily developed to optimise water management decision making in Australia. However it can also be used to solve a range of other decision problems in natural resources management as well.