Factors influencing intention to apply spatial approaches to on-farm experimentation: insights from the Australian winegrape sector.

Grape growers are often constrained by available time and labor to conduct trials that deliver informative results. Spatially distributed trial designs coupled with data collection using sensing technologies can introduce efficiencies and also account for the impact of land variability on trial results. Various spatial approaches have been proposed, yet how farmers perceive them is largely unknown. We collaborated with four wine businesses in Australia to explore how grape growers and viticultural consultants perceive a simplified spatial approach to experimentation involving one or more vineyard rows or "strips." In each case, the simplified strip approach was applied alongside growers' or consultants' own methods to compare the perceived value of different methods. The Theory of Planned Behavior was used as an analytical framework to identify factors influencing participants' intentions towards adopting the strip approach. Our findings show that growers and consultants perceived several advantages of the strip approach over their own methods. Key factors impeding uptake were resource constraints for collecting trial data and lack of skills and knowledge to use and analyze spatial data to position the trial and interpret results. These constraints highlight the need to support growers and consultants who see value in this approach by developing automated and affordable measurements for viticultural variables beyond yield, and by providing training on how to analyze and interpret spatial and response data. This study provides novel insights for private and public sectors on where to focus efforts to facilitate adoption of spatial approaches to On-Farm Experimentation by specific target audiences.

Pathogen Incursions - Integrating Technical Expertise in a Socio-Political Context.

The incursion of a plant pathogen into a new geographic area initiates a series of decisions about appropriate control or eradication efforts. Incomplete, erroneous, and/or selective information may be used by diverse stakeholders to support individual goals and positions on how an incursion should be managed. We discuss the complex social, political, and technical factors that shape a biosecurity response prior to reviewing information needs and common stakeholder misunderstandings. Selected examples focus on the rust fungi (order Pucciniales). We then explore how plant pathologists, as technical experts, can interact with biosecurity stakeholders to build empathy and understanding that in turn allows a shift from being a distant subject matter expert to an active participant helping to structure problems and shape knowledge flows for better outcomes.

Automated Analysis of Aggregated Datasets to Identify Climatic Predictors of Botrytis Bunch Rot in Wine Grapes.

Botrytis bunch rot (BBR), caused by Botrytis cinerea, results in serious losses to wine-grape production in some seasons during the preharvest period. In order to predict seasons that are at risk from BBR, datasets consisting of 25 disease, weather and vine phenology variables were aggregated from 101 SiteYears across seven regions and nine growing seasons. Automated analyses were used to compare a range of statistical methods for their ability to predict BBR epidemics, including the Kruskal-Wallis test, logistic regression, receiver operating characteristic analysis, and skill-scores. Variables based on relative humidity and surface-wetness duration were significant and consistent predictors of BBR epidemics across the range of analyses applied. Variables integrating temperature and wetness duration, including the Bacchus and Broome models, also demonstrated high predictive ability; however, they did not outperform their constituent components in all analyses. Automation of data analyses was an effective way to compare a wide range of statistical methods and a large number of variables with minimal user input, following initial code development. Significant time was needed to check input data and software code, but a greater return on investment would occur should the analytical process be applied to new datasets, including those from other pathosystems.

Grape Sour Rot: A Four-Way Interaction Involving the Host, Yeast, Acetic Acid Bacteria, and Insects.

Sour rot, a disease affecting berries of cultivated Vitis spp. worldwide, has not been clearly defined. Reported symptoms of the disease include browning of the berry skin, oozing of disintegrated berry pulp, and the smell of acetic acid, all in the presence of fruit flies (Drosophila spp.). We determined acetic acid concentrations in multiple collections of symptomatic berries, isolated and identified microbes from them, and inoculated commonly isolated organisms into healthy berries with and without concurrent exposure to wild-type or axenic Drosophila melanogaster. Coinoculations combining one of several yeasts (Metschnikowia spp., Pichia spp., and a Saccharomyces sp.) plus an acetic acid bacterium (an Acetobacter sp. and Gluconobacter spp.) reproduced sour rot symptoms, defined here as decaying berries with a loss of turgor and containing acetic acid at a minimum of 0.83 g/liter, based on observed field levels. Symptoms developed only in the presence of D. melanogaster, either wild type or axenic, indicating a nonmicrobial contribution of these insects in addition to a previously suggested microbial role. We conclude that sour rot is the culmination of coinfection by various yeasts, which convert grape sugars to ethanol, and bacteria that oxidize the ethanol to acetic acid, and that this process is mediated by Drosophila spp.

An ice sheet model validation framework for the Greenland ice sheet.

We propose a new ice sheet model validation framework - the Cryospheric Model Comparison Tool (CmCt) - that takes advantage of ice sheet altimetry and gravimetry observations collected over the past several decades and is applied here to modeling of the Greenland ice sheet. We use realistic simulations performed with the Community Ice Sheet Model (CISM) along with two idealized, non-dynamic models to demonstrate the framework and its use. Dynamic simulations with CISM are forced from 1991 to 2013 using combinations of reanalysis-based surface mass balance and observations of outlet glacier flux change. We propose and demonstrate qualitative and quantitative metrics for use in evaluating the different model simulations against the observations. We find that the altimetry observations used here are largely ambiguous in terms of their ability to distinguish one simulation from another. Based on basin- and whole-ice-sheet scale metrics, we find that simulations using both idealized conceptual models and dynamic, numerical models provide an equally reasonable representation of the ice sheet surface (mean elevation differences of <1 m). This is likely due to their short period of record, biases inherent to digital elevation models used for model initial conditions, and biases resulting from firn dynamics, which are not explicitly accounted for in the models or observations. On the other hand, we find that the gravimetry observations used here are able to unambiguously distinguish between simulations of varying complexity, and along with the CmCt, can provide a quantitative score for assessing a particular model and/or simulation. The new framework demonstrates that our proposed metrics can distinguish relatively better from relatively worse simulations and that dynamic ice sheet models, when appropriately initialized and forced with the right boundary conditions, demonstrate predictive skill with respect to observed dynamic changes occurring on Greenland over the past few decades. An extensible design will allow for continued use of the CmCt as future altimetry, gravimetry, and other remotely sensed data become available for use in ice sheet model validation.

Toward a science of tumor forecasting for clinical oncology.

We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.

Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission.

Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10-15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector-pathogen systems.

The influence of known-word-frequency on the acquisition of new neighbors in adults: evidence for exemplar representations in word-learning.

Previous studies showed that a new word that is similar to many known words will be learned better than a new word that is similar to few known words (Storkel et al., 2006). In the present study we created novel words that were phonological neighbors to lexical hermits-or known words that do not have any phonological neighbors-that varied in frequency of occurrence. After several exposures, participants learned a higher proportion of novel words that were neighbors of high frequency known-words than nonwords that were neighbors of low frequency known-words. The present results have implications for abstractionist versus exemplar models of the mental lexicon and language processing, as well as for accounts of word frequency in models of language processing.

Genetic diversity in the blackberry rust pathogen, Phragmidium violaceum, in Europe and Australasia as revealed by analysis of SAMPL.

Indigenous to Europe, the blackberry rust fungus Phragmidium violaceum was introduced to Australia and subsequently appeared in New Zealand, with the most recent authorised introductions to Australia specifically for the biological control of European blackberry. Markers for 'selective amplification of microsatellite polymorphic loci' (SAMPL) were developed for studying the population genetics of P. violaceum. Modification of one of the two SAMPL primers with a HaeIII adapter (H) revealed significantly greater levels of genetic variation than primers used to generate AFLPs, the latter revealing little or no variation among 25 Australasian and 19 European isolates of P. violaceum. SAMPL was used to describe genetic variation among these 44 isolates of P. violaceum from 51 loci generated using primer pairs (GACA)4 +H-G and R1+H-G. The European isolates were more diverse than Australasian isolates, with 37 and 22 % polymorphic loci, respectively. Cluster analysis revealed geographic clades, with Australasian isolates forming one cluster separated from two clusters comprising the European isolates. However, low bootstrap support at these clades suggested that Australian isolates had not differentiated significantly from European isolates since the first record of P. violaceum in Australia in 1984. In general, the results support two hypotheses. First, that the population of P. violaceum in Australia was founded from a subset of individuals originating from Europe. Second, that P. violaceum in New Zealand originated from the Australian population of P. violaceum, probably by wind dispersal of urediniospores across the Tasman Sea. The application of SAMPL markers to the current biological control programme for European blackberry is discussed.