Agribusiness supply chain risk management: A review of quantitative decision models

Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Biochar in Co-Contaminated Soil Manipulates Arsenic Solubility and Microbiological Community Structure, and Promotes Organochlorine Degradation

<div><p>We examined the effect of biochar on the water-soluble arsenic (As) concentration and the extent of organochlorine degradation in a co-contaminated historic sheep-dip soil during a 180-d glasshouse incubation experiment. Soil microbial activity, bacterial community and structure diversity were also investigated. Biochar made from willow feedstock (<i>Salix sp</i>) was pyrolysed at 350 or 550°C and added to soil at rates of 10 g kg^-1 and 20 g kg^-1 (representing 30 t ha^-1 and 60 t ha^-1). The isomers of hexachlorocyclohexane (HCH) alpha-HCH and gamma-HCH (lindane), underwent 10-fold and 4-fold reductions in concentration as a function of biochar treatment. Biochar also resulted in a significant reduction in soil DDT levels (P < 0.01), and increased the DDE:DDT ratio. Soil microbial activity was significantly increased (P < 0.01) under all biochar treatments after 60 days of treatment compared to the control. 16S amplicon sequencing revealed that biochar-amended soil contained more members of the <i>Chryseobacterium</i>, <i>Flavobacterium</i>, <i>Dyadobacter</i> and <i>Pseudomonadaceae</i> which are known bioremediators of hydrocarbons. We hypothesise that a recorded short-term reduction in the soluble As concentration due to biochar amendment allowed native soil microbial communities to overcome As-related stress. We propose that increased microbiological activity (dehydrogenase activity) due to biochar amendment was responsible for enhanced degradation of organochlorines in the soil. Biochar therefore partially overcame the co-contaminant effect of As, allowing for enhanced natural attenuation of organochlorines in soil.</p></div

Soil concentrations (mg kg^-1) for A) Alpha-HCH B) Lindane C) DDT and D) Delta-HCH at the termination of the glasshouse trial under different treatments.

<p>Significant differences are observed between all biochar types and the control (P<0.01) (mean <i>n</i> = 3; ± s.e.).</p

Primer sequences used in this study.

<p>MID tag numbers with associated treatment are in italics. Primers had 5’ extensions with unique tags to identify correct DNA sequence.</p

Water extractable As (mg L^-1) in soil under biochar and control treatments at time (T) in days.

<p>Significant differences are denoted by different letters at P<0.05 (mean <i>n</i> = 3; ± s.e.). Soil pH values are listed in italics and in brackets for each treatment.</p><p>Water extractable As (mg L^-1) in soil under biochar and control treatments at time (T) in days.</p

Trial site locality map showing the North Island of New Zealand (left: courtesy of Geographix) and a blow-up view of the site in an area of Te Mahia peninsula on the East Coast (right: courtesy of Land Information New Zealand).

<p>Trial site locality map showing the North Island of New Zealand (left: courtesy of Geographix) and a blow-up view of the site in an area of Te Mahia peninsula on the East Coast (right: courtesy of Land Information New Zealand).</p

4,4'-DDT and its breakdown derivative 4,4'-DDE (mg kg^-1) under biochar treatments in co-contaminated soil after T = 180 d.

<p>A ratio between DDE:DDT in soil after treatment is also included. Significant errors are listed after the mean with all samples (mean <i>n</i> = 3; ± s.e.). Significant differences are noted by different letters.</p><p>4,4'-DDT and its breakdown derivative 4,4'-DDE (mg kg^-1) under biochar treatments in co-contaminated soil after T = 180 d.</p

Taxonomic structure of the soil bacterial community (%) highlighting genera, families, and orders that showed significant changes between the biochar treatments.

<p>Values are % of population +/- S.E. derived from the taxonomic summaries of the three different biological replicates. Selected taxonomy is based on significant changes within phyla. Biochar amendment and no biochar amendment (control). Significant differences are noted by different letters (mean <i>n</i> = 3; ± s.e.).</p><p>Taxonomic structure of the soil bacterial community (%) highlighting genera, families, and orders that showed significant changes between the biochar treatments.</p