Showing results for: [ Benn, David ]
Three experimental data sets involving three grapevine varieties and a range of deficit irrigation and pruning treatments are described.
The purpose for obtaining the data sets was two-fold, (1) to m... moreeet the research goals of the Cooperative Research Centre for Viticulture (CRCV) during its tenure 1999-2006, and (2) to test the capacity of the VineLOGIC grapevine growth and development model to predict timing of bud burst, flowering, veraison and harvest, yield and yield components, berry attributes and components of water balance. A test script included with the VineLOGIC source code publication (https://doi.org/10.25919/5eb3536b6a8a8) enables comparison between model predicted and measured values for key variables. Supporting information, including key references relating to the data sets, a description of selected terms, and outcomes of regression analysis between values predicted by the model and observed values, are provided as attachments in supporting documentation.
Legacy data - - Published 17 Jul 2020
VineLOGIC is a model of grapevine growth and development. It integrates the influence of climate, soil water and salt balance on growth and yield. It operates at a point scale and uses a daily time st... moreep, requiring historical daily weather data from the closest station as inputs. Source code released through this publication underpins separate software with capacity to vary inputs and run simulations. Inputs include the amount and salinity of irrigation water, the way in which the irrigation is applied, e.g. full or partial ground cover, and key components of a typical vineyard, e.g. wine grape variety and rootstock type, pruning and trellis system type, soil type, vine carbohydrate reserve and depth to watertable at the start of the season, groundwater salinity, root zone salinity at simulation start time and mid-row floor management, such as presence or absence of a cover crop. Outputs include key phenology dates, for example, budburst, flowering, veraison and harvest time, growth parameters such as leaf area index and weights of dormant pruning wood, bunch number and yield per vine, berry number per bunch, berry attributes at harvest plus all key parameters of soil water and salt balance, e.g. total evapotranspiration, vine water uptake, surface soil water evaporation, irrigation added, drainage and water stress indices if water deficits occurred at specific growth stages. While the model has access to a wide range of regional climate data and vineyard soil types, climate data for just one region (Mildura, Victoria) and one soil type are intended to be provided through experimental data sets to be published separately in Data Access Portal for running with a test script included in the current collection. Similarly, while the model has access to a range of wine grape varieties and rootstock types, only a limited number are intended to be provided for running with this test script.less
Legacy data - - Published 07 May 2020
The need to tag digital objects in various repositories such as datasets or scientific papers to allow for thematic classification is a well-known problem. In some cases a free tagging approach is use... mored where contributors can add any keywords they desire while in other cases, selection from a controlled vocabulary is mandated. To enable repository managers to allow both free tagging and the use of controlled vocabularies, we have created the Keyword Aggregator (KWA) system, key aspects of which are outlined next. A web service that: provides fast search access to vocabulary content; stores multiple, controlled, vocabularies of terms; permits the addition of new keywords. An example widget that makes use of the web service and can be embedded in a web page. Use of a relational database that captures keyword use statistics. A management methodology that: allows particular vocabularies to be selected for use on a per-widget instance basis; allows vocabulary updating through storage in versioned repositories; stores vocabularies with vocabulary-level metadata enabling vocabulary discovery; permits one vocabulary to link to a single term in another vocabulary (allows a 'vocabulary-of-vocabularies'). The focus of KWA is on the creation of a flexible keyword aggregation, management and search system for science keywords, however vocabularies of any kind could, in principle, be handled by this system. This release allows KWA to be run under Python 2 or 3 and more importantly, adds containerisation support via Docker to simplify deployment. In addition, graph loading scripts were added and various internal improvements and bug fixes were made. See the README.pdf supporting attachment for more detail.less
eReefs - Science of GBR - Semantic Tools - Published 19 Jan 2018
This collection contains a number of 'special' pulsar data sets. In 2015, these have been collated from Parkes pulsar observations for a range of semesters. The main goals of this project proposal is ... moreto provide a subset of pulsar data of special interest, to the Amazon Cloud together with software and tools to facilitate data processing. This is part of a larger project to develop and try out Amazon computing resources. less
Australia Telescope National Facility - P910 - Amazon Project: Special Data Sets - Published 05 Feb 2016