Predicting carbon stocks following reforestation of pastures: a sampling scenario-based approach for testing the utility of field-measured and remotely derived variables

Published online 23 August 2016Reforestation of agricultural lands is an important means of restoring land and sequestering carbon (C). At large scales, the labour and costs of direct measurement of ecosystem responses can be prohibitive, making the development of models valuable. Here, we develop a new sampling scenario-based modelling approach coupled with Bayesian model averaging to build predictive models for absolute values in mixed-species woody plantings and differences from their adjacent pasture, for litter stocks, soil C stocks and soil C:N ratios. Modelling scenarios of increasing data availability and effort were tested. These included variables that could be derived without a site visit (e.g. location, climate and management) that were sampled in the adjacent pasture (e.g. soil C and nutrients) or were sampled in the environmental planting (e.g. vegetation, litter properties, soil C and nutrients). The predictive power of models varied considerably among C variables (litter stocks, soil C stocks and soil C:N ratios in tree plantings and their differences to their adjacent pastures) and the model scenarios used. The use of a sampling scenario-based approach to building predictive models shows promise for monitoring changes in tree plantings, following reforestation. The approach could also be readily adapted to other contexts where sampling effort for predictor variables in models is a major potential limitation to model utilization. This study demonstrates the benefit of exploring scenarios of data availability during modelling and will be especially valuable where the sampling effort differs greatly among variables. Copyright © 2016 John Wiley & Sons, Ltd.Timothy R. Cavagnaro, Shaun C. Cunningha

Exploring Point of Sale Strategies for Improving Seafood Retailing: The Case of the Australian Oyster Industry

The commodification of many food products, combined with increasing market share of supermarkets, has increased the importance of point of sale (POS) strategies in speciality food retailers such as fishmongers. The purpose of this study is to develop strategies to improve the retailing of seafood in fishmongers, specifically oysters, a species which is currently underutilised; as although they are eaten by many consumers, purchase frequency is low. A literature review identifies the key drivers and barriers to oyster consumption and the information consumers want at the POS. Based on these findings, a retailing strategy for oysters is developed and tested in two consumer focus groups. Based on focus group results, revisions are recommended to the retail strategy, importantly including a change in collateral from a production focus to a consumption focus. This study makes a clear contribution to theory and practice by bringing together the existing literature on drivers and barriers and consumer information requirements about oysters to develop and test practical retail strategy concepts

Implementing natural capital credit risk assessment in agricultural lending

Agriculture has critical impacts and dependencies on natural capital, and agriculturallenders are therefore exposed to natural capital credit risk through their loans tofarmers. Currently, however, lenders lack any detailed guidance for assessing naturalcapital credit risk in agriculture and are challenged by the fact that the relevant material risks vary considerably by agricultural sector and geography. This paper developsa natural capital credit risk assessment framework based on a bottom‐up review ofthe material risks associated with natural capital impacts and dependencies forAustralian beef production. It demonstrates that implementing natural capital creditrisk assessment is feasible in agricultural lending, using a combination of quantitativeand qualitative inputs. Implementation challenges include the complexity and interconnectedness of natural capital processes, data availability and cost, spatial data analytical capacity, and the need for transformational change, both within lendingorganisations and across the banking sector

Red Imported Fire Ant in Australia: What if we lose the war?

In Australia, a national eradication programme for the Red Imported Fire Ant (Solenopsis invicta Buren), one of the world's most invasive species, has been in operation since 2001 when the pest was first detected in Brisbane, Queensland. Since that time, four separate incursions of this ant have been successfully eradicated from this country, but the main Brisbane population remains. Cost-benefit analyses already conducted put the likely impact of Red Imported Fire Ant in Australia, if not eradicated, at between A$8.5 and A$45 billion. Despite this, ongoing funding for the eradication programme is not assured. A recent external review has concluded that it remains technically feasible, cost beneficial and in the national interest to eradicate. In support of previous analyses, our study highlights some of the potential impacts of Red Imported Fire Ant in Australia in more detail and provides case examples. Results show that adverse impacts are likely in most sectors of the economy, and will be felt not only by agricultural industries, but also the building and construction, tourism, electrical and communications industries. In addition to industry effects, there will also be negative impacts on public health and lifestyle, the environment and infrastructure such as main roads, airports and schools. Our estimates of potential cost impacts in the case examples where extrapolation was possible exceed A$1.65 billion/year and support previous predictions. We conclude that increased spending is justified to avert ‘invasion debt’ – the future cost of battling pests that escape today. This is a war that Australia cannot afford to lose

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.htm

Can bacterial indicators of a grassy woodland restoration inform ecosystem assessment and microbiota-mediated human health?

Understanding how microbial communities change with environmental degradation and restoration may offer new insights into the understudied ecology that connects humans, microbiota, and the natural world. Immunomodulatory microbial diversity and ‘Old Friends’ are thought to be supplemented from biodiverse natural environments, yet deficient in anthropogenically disturbed or degraded environments. However, few studies have compared the microbiomes of natural vs. human-altered environments and there is little knowledge of which microbial taxa are representative of ecological restoration—i.e. the assisted recovery of degraded ecosystems typically towards a more natural, biodiverse state. Here we use novel bootstrap-style resampling of site-level soil bacterial 16S rRNA gene environmental DNA data to identify genus-level indicators of restoration from a 10-year grassy eucalypt woodland restoration chronosequence at Mt Bold, South Australia. We found two key indicator groups emerged: ‘opportunistic taxa’ that decreased in relative abundance with restoration and more stable and specialist, ‘niche-adapted taxa’ that increased. We validated these results, finding seven of the top ten opportunists and eight of the top ten niche-adapted taxa displayed consistent differential abundance patterns between human-altered vs. natural samples elsewhere across Australia. Extending this, we propose a two-dimensional mapping for ecosystem condition based on the proportions of these divergent indicator groups. We also show that restoring a more biodiverse ecosystem at Mt Bold has increased the potentially immune-boosting environmental microbial diversity. Furthermore, environmental opportunists including the pathogen-containing genera Bacillus, Clostridium, Enterobacter, Legionella and Pseudomonas associated with disturbed ecosystems. Our approach is generalizable with potential to inform DNA-based methods for ecosystem assessment and help target environmental interventions that may promote microbiota-mediated human health gains.Craig Liddicoat, Philip Weinstein, Andrew Bissett, Nicholas J.C.Gelliea, Jacob G.Mills, Michelle Waycotta, Martin F.Bree