A decision-support tool for autumn management in a spring-calving pasture-based dairy system

Success in a pasture-based dairy system relies on matching feed supply and feed demand in a profitable manner. Autumn is an important period for decision making to maximise current-season profitability, while ensuring key targets such as cow body condition score and the level of average pasture cover are met for the upcoming season. There are many tactical management strategies for farmers to consider during autumn to ensure that profitability is maximised in the current and next season (e.g. feeding crops, purchasing or using available supplementary feeds, reducing milking frequency, grazing off young stock, culling, or drying off cows). The complexity of trade-offs among these factors from January to calving, and the need to assess the impact of each of these on seasonal profitability led to the development of the ‘DairyNZ Autumn Management Resource’. This resource is an energy-based model that calculates the profit from different management strategies in pasture-based spring-calving systems. Feed demand is initially set to ensure that target body condition is achieved for the next season, and can then be altered using variables such as milking frequency, number of cows in milk and stock grazing on-farm. The assumption is made that energy supply comes from grazed pasture and crop first, followed by conserved forages, with the opportunity to fill remaining gaps with purchased feed. The model is a decision-support resource for farmers during the autumn that compares the economics of different management strategies in the current season, while ensuring that the performance in the next season is not compromised.

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A Decision Support Tool for Wastewater Catchment Management

Water Practice & Technology ◽

10.2166/wpt.2007.029 ◽

2007 ◽

Vol 2 (2) ◽

Author(s):

S.E. Walters ◽

D. Savic ◽

R.J. Hocking

Keyword(s):

Decision Support ◽

Asset Management ◽

Capital Investment ◽

Management Strategies ◽

Sewage Treatment ◽

Decision Support Tool ◽

Catchment Management ◽

Clean Water ◽

Support Tool ◽

South West

The water industry over the years has primarily focussed on upgrading and investing in clean water provision. However, as research into the science and management of clean water services has progressed rapidly, wastewater provision and services has been slower. Focus, though, is now shifting within Industry and Research into wastewater services. The water regulator, Ofwat, for England and Wales demands the Sewerage Undertakers demonstrate efficient management of wastewater systems in order to obtain funding for Capital Investment projects. South West Water, a Water Service Provider and Sewerage Undertaker located in the South West of England, identified a need gap in their asset management strategies for wastewater catchments. This paper will introduce the production of a Decision Support Tool, DST, to help SWW proactively manage their Wastewater Catchments, examining Sewage Treatment Works, Pumping Stations and Networks. The paper will discuss some concepts within the DST, its production, testing and a brief case study. The DST provides a framework for prioritising catchments to optimise investment choices and actions. The Tool ranks catchments utilising Compromise Programming, CP, as well as AHP Pair-wise comparisons for preference weights. The DST incorporates Asset models, a Whole life Costing Module, as well as a Decay and Intervention Module.

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The Impact of a Big Data Decision Support Tool on Military Logistics: Medical Analytics Meets the Mission

Defense Acquisition Research Journal ◽

10.22594/dau.16.769.24.03 ◽

2017 ◽

Vol 24 (3) ◽

pp. 468-487

Author(s):

Felix Chang ◽

Christopher Dente ◽

Eric Elster

Keyword(s):

Big Data ◽

Decision Support ◽

Decision Support Tool ◽

Support Tool ◽

Military Logistics ◽

The Impact

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Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

Energies ◽

10.3390/en11092190 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2190 ◽

Cited By ~ 3

Author(s):

Rafael Dawid ◽

David McMillan ◽

Matthew Revie

Keyword(s):

Wind Farm ◽

Real Life ◽

Synthetic Data ◽

Optimal Solution ◽

Decision Support Tool ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Support Tool ◽

Trade Offs ◽

The Impact

This paper for the first time captures the impact of uncertain maintenance action times on vessel routing for realistic offshore wind farm problems. A novel methodology is presented to incorporate uncertainties, e.g., on the expected maintenance duration, into the decision-making process. Users specify the extent to which these unknown elements impact the suggested vessel routing strategy. If uncertainties are present, the tool outputs multiple vessel routing policies with varying likelihoods of success. To demonstrate the tool’s capabilities, two case studies were presented. Firstly, simulations based on synthetic data illustrate that in a scenario with uncertainties, the cost-optimal solution is not necessarily the best choice for operators. Including uncertainties when calculating the vessel routing policy led to a 14% increase in the number of wind turbines maintained at the end of the day. Secondly, the tool was applied to a real-life scenario based on an offshore wind farm in collaboration with a United Kingdom (UK) operator. The results showed that the assignment of vessels to turbines generated by the tool matched the policy chosen by wind farm operators. By producing a range of policies for consideration, this tool provided operators with a structured and transparent method to assess trade-offs and justify decisions.

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Using a decision support tool to screen for pro-poor policies: Application of Extrapolate to smallholder dairy systems in East Africa

Proceedings of the British Society of Animal Science ◽

10.1017/s175275620000939x ◽

2005 ◽

Vol 2005 ◽

pp. 28-28

Author(s):

P. K. Thornton ◽

P. J. Thorne ◽

C. Quiros ◽

D. Sheikh ◽

R. L. Kruska ◽

...

Keyword(s):

Decision Support ◽

Poverty Reduction ◽

Intervention Mapping ◽

Decision Support Tool ◽

Target Groups ◽

Support Tool ◽

Policy Alternatives ◽

Ex Ante ◽

Policy Measures ◽

The Impact

Extrapolate (EX-ante Tool for RAnking POLicy AlTErnatives) is a decision support tool to assess the impact of policy measures on different target groups. It is designed to serve as a “filter” that, given the broad characteristics of the population, allows the user to sift through different policy measures to assess ex ante the broad potential impacts of these before deciding to look at particular policy options in more detail. Extrapolate models, in a very simple way, the impact of changes on constraints facing potential beneficiary groups, and how these may affect outcomes and their livelihood status. Extrapolate now makes use of mapping facilities from another decision-support tool, PRIMAS (Poverty Reduction Intervention Mapping in Agricultural Systems), that allows the user to match characteristics of particular technological options and constraints with the spatial characteristics of particular target groups in the landscape.

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Clinical implementation of pharmacogenomics via a health system-wide research biobank: the University of Colorado experience

Pharmacogenomics ◽

10.2217/pgs-2020-0007 ◽

2020 ◽

Vol 21 (6) ◽

pp. 375-386 ◽

Cited By ~ 5

Author(s):

Christina L Aquilante ◽

David P Kao ◽

Katy E Trinkley ◽

Chen-Tan Lin ◽

Kristy R Crooks ◽

...

Keyword(s):

Decision Support ◽

Health System ◽

Clinical Decision Support ◽

Decision Support Tool ◽

Clinical Decision ◽

Clinical Implementation ◽

Support Tool ◽

University Of Colorado ◽

The University ◽

The Impact

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

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BEETSOIL: a decision support tool for forecasting the impact of soil conditions on sugar beet harvest

Soil and Tillage Research ◽

10.1016/j.still.2019.04.001 ◽

2019 ◽

Vol 191 ◽

pp. 131-141

Author(s):

Miguel A. Gabarron-Galeote ◽

Jacqueline A. Hannam ◽

Thomas Mayr ◽

Patrick J. Jarvis

Keyword(s):

Decision Support ◽

Sugar Beet ◽

Decision Support Tool ◽

Soil Conditions ◽

Support Tool ◽

The Impact

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Exploring the Impact of a Clinical Decision Support Tool from the Perspective of Service Users & Carers

Mental Health and Learning Disabilities Research and Practice ◽

10.5920/mhldrp.2008.51147 ◽

2008 ◽

Vol 5 (1) ◽

pp. 147-161

Author(s):

Nicola Roberts ◽

Virginia Minogue ◽

Mike Lucock ◽

Lesley Dale ◽

Rom Glen

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Decision Support Tool ◽

Clinical Decision ◽

Service Users ◽

Support Tool ◽

Clinical Decision Support Tool ◽

The Impact

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A decision-support tool to assist muscle-invasive bladder cancer patients choosing between radical cystectomy and chemoradiotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2018.36.6_suppl.505 ◽

2018 ◽

Vol 36 (6_suppl) ◽

pp. 505-505

Author(s):

Brian Christopher Baumann ◽

Wei-Ting Hwang ◽

Sharadha Srinivasan ◽

Xingmei Wang ◽

Ronac Mamtani ◽

...

Keyword(s):

Bladder Cancer ◽

Decision Support ◽

Decision Support Tool ◽

Short Term ◽

Muscle Invasive Bladder Cancer ◽

Support Tool ◽

Trade Offs ◽

Muscle Invasive ◽

Break Even Point

505 Background: Patients with high-risk muscle-invasive bladder cancer (MIBC) who are borderline medically operable for radical cystectomy (RC) face a difficult decision between RC which has higher short-term treatment-related morbidity/mortality & chemoradiotherapy (CRT) which is better tolerated in the short-term but may have worse long-term cancer control outcomes. There are no existing decision support tools to assist patients & providers in understanding these trade-offs. Herein, we developed a visualization tool to inform patients & providers how the relative risks & benefits of RC & CRT vary over time with respect to overall survival (OS). Methods: We identified cT2-3 N0 M0 urothelial bladder cancer patients ≥65 y/o treated with RC +/- chemo (n = 5981) or definitive-dose CRT after TURBT (n = 793) in the National Cancer Database, 2003-2011. The database was split into a development & validation cohort. Multivariate Cox regression with time-varying hazard ratio was performed to assess pre-treatment factors associated with OS. The inverse probability of treatment weighting method using the propensity score was employed to reduce selection bias. External validation was performed. Visualization tool showing adjusted survival curves based on pre-op patient features was generated with input from patients & a multidisciplinary expert panel. Tool calculates median OS & the “break-even point,” where the short-term OS disadvantage of RC equals the long-term advantage of RC (i.e. the point where the restricted mean survival for RC & CRT are equal). Results: On MVA, significant predictors of OS were age, Charlson Deyo comorbidity index, & cT stage (p < 0.001 for all). Using these results, we iteratively developed a web application that utilizes clinical inputs to generate patient-specific survival curves that display estimated OS differences over time. Median OS, the break-even point, & percent alive at the break-even point are provided. Conclusions: This is the first decision-support tool developed to assist high-risk borderline operable MIBC patients & their providers in understanding the short-term & long-term trade-offs between RC & CRT. Additional testing is underway.

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