Showing results for: [ Agricultural and Veterinary Sciences ]
Three experimental data sets (WNRA0103, WNRA0305 and WNRA0506) involving three grapevine varieties and a range of deficit irrigation and pruning treatments are described.
The purpose for obtaining th... moree data sets was two-fold, (1) to meet 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. Key references relating to the model and data sets are provided under Related Links. A description of selected terms and outcomes of regression analysis between values predicted by the model and observed values are provided under Supporting Files.
Version 3 includes the following amendments: (1) to WNRA0103 – alignment of settings for irrigation simulation control and initial soil water contents for soil layers with those in WNRA0305 and WNRA0506, and addition of missing berry anthocyanin data for season 2002-03; (2) to WNRA0305 - minor corrections to values for berry and bunch number and weight, and correction of target Brix value for harvest to 24.5 Brix; (3) minor corrections to some measured berry anthocyanin concentrations as mg/g fresh weight; minor amendments to treatment names for consistency across data sets, and to the name for irrigation type to improve clarity; and (4) update of regression analysis between VineLOGIC-predicted versus observed values for key variables.less
Legacy data - - Published 14 Jan 2021
VineLOGIC View is an interactive web application developed to assist visualisation of outputs from the 'VineLOGIC Grapevine Growth and Development Model' (see Related Links). It enables comparisons o... moref model predicted outputs with measured outputs from 'VineLOGIC Experimental Data Sets' (see Related Links), a group of three data sets involving three grapevine varieties and a range of deficit irrigation and pruning treatments obtained during 2000-2006 at a commercial vineyard near Mildura, Victoria, Australia. Outputs are shown in table and graph formats, including time series graphs for selected key parameters. Supporting information, including an introduction to VineLOGIC View and commentary on the key outputs are provided. less
AusFarm modelling tool built using the Common Modelling Protocol.
SIP P1 APSIM - Software Development - Published 06 Jan 2021
These data were collected to trial passive eDNA collection methods in tropical and temperate waters for the detection of fishes. The data were generated as part of a feasibility study within the Envi... moreronomics Future Science Platform. The goal of the project was to develop low-cost, low-tech, easy to deploy methods for biomonitoring studies.less
Environomics FSP Phase 2 - Passive eDNA Collection Enabling Species Detection and Quantification - - Published 04 Jan 2021
This project sought to develop a database of fuel and energy characteristics for a range of Australian biomass and wastes to support their use in a range of thermochemical technologies. The database i... morencludes biomass sourced from agriculture (wheat straw, cotton trash, sugar cane trash, chicken manure, etc), forestry (mostly woody wastes from different forestry operations including some irrigated by effluent), industrial processing (timber residues, saw dust from timber mills, bagasse from sugar industry, rice hull from rice industry, paper sludge from paper industry, etc) and urban waste (municipal solid waste, green waste and biosolids and sewage from waste water treatment facilities). The database contains relatively simple-to-use information such as proximate analysis, ultimate analysis, calorific value as well as chemical composition of mineral matter to enable project proponents to draw conclusions as to likely plant capacity and annual output. It will be updated and expanded over time as more data are available.less
Gasification of priority feedstocks - - Published 16 Dec 2020
GrazFeed is a decision support tool developed by CSIRO to help graziers improve the profitability of livestock production, through more efficient use of pastures and supplementary feeds.
GrazFeed is r... moreegarded as the industry benchmark for the nutrition of grazing animals in temperate Australia. It is based on the Australian Feeding Standards (1990).less
SIP 60 APSIM Development - Software Development - Published 09 Dec 2020
These are VCF files that represent genomic variation of 60 Puccinia coronata f. sp. avenae isolates against two genome references. Raw Illumina sequencing reads used in this study are available in the... more NCBI BioProject PRJNA398546. less
Legacy data - Rust Fungal Collections - Published 16 Nov 2020
pyprecag is a Python package containing processing functions used for analysis of precision agricultural data collected by hand or using on-the-go sensors like yield monitors.
pyprecag functions und... moreerpin the tools found in PAT - Precision Agriculture Tools plugin for QGIS.less
CSA1603 Simple tools for spatial analysis - key enabling technologies for Precision and Digital Viticulture (GIS Freeware RRDFP) - - Published 29 Sep 2020
PAT - Precision Agriculture Tools plugin for QGIS is a suite of tools for Precision Agriculture (PA) and Precision Viticulture (PV) data analysis.
The tools run within Quantum Geographic Information ... moreSystem (QGIS). PAT aims to provide an easy-to-use interface for processing PA/PV data through an established workflow developed for constructing maps using on-the-go data and for the analysis of PA/PV data analysis.less
This data collection contains data produced for the Lancet Planetary Health publication Herrero et al. 2017: Farming and the geography of nutrient production for human use: a transdisciplinary analysi... mores. Volume 1, Issue 1, April 2017, Pages e33-e42.
The collection includes data for production levels of 41 major crops, seven livestock, and 14 aquaculture and fish products and vitamin A, vitamin B₁₂, folate, iron, zinc, calcium, calories, and protein as well as food production diversity indices; the Shannon diversity index [H], the Modified Functional Attribute Diversity (MFAD), and species richness [S].less
OCE Science Leader - Herrero - - Published 07 Sep 2020
Using the Land-Use Trade-Offs (LUTO) model, this data collection was produced via a comprehensive, detailed, integrated, and quantitative scenario analysis of land-use and sustainability for Australia... more’s intensive-use agricultural land to 2050, under intersecting global change and domestic policies, and considering key uncertainties. We assessed land use competition between multiple land uses and assessed sustainability of economic returns and multiple ecosystem services at high spatial (1.1 km grid cell) and temporal (annual) resolution.
Results available are for 648 scenarios covering combinations of four global outlooks, three general circulation climate models, three domestic land-use policies, three productivity growth rates, three land-use change adoption hurdle rates, and two capacity constraint settings.
Outputs included for each scenario are:
- annual land-use layers
- summary data table
- graphical dashboard summary
- animation of potential land-use change, drivers, and impacts.
This analysis was conducted in conjunction with CSIRO’s Australian National Outlook 2015 initiative to assess future potential land-use change and the impacts for the sustainability of ecosystem services. A full description of the methods and synthesis of the results can be found in the papers listed in the Related Information below and freely available via email from the author.
The data is provided to support a national conversation on the future for Australian land systems, public decision-making and policy design, and further scientific research.
SIP 59 LUTO land use modelling science p - Modelling - Published 31 Jul 2020
A global gridded data set of physical cropland of different single and multiple cropping systems composed of 25 different rainfed and irrigated annual crops. The collection includes different levels o... moref aggregation, from individual crops to crop groups and overall cropping intensity.
This is the accompanying data publication for: Waha K et al. (2020): Multiple cropping systems of the world and the potential for increasing cropping intensity. Global Environmental Change.less
OCE Science Leader - Herrero - - Published 28 Jul 2020
X chromosome variants are associated with male fertility traits in two bovine populations. Fortes, Porto-Neto et al. 2020.
Twenty-five phenotypes were measured as indicators of bull fertility (1099 Br... moreahman and 1719 Tropical Composite bulls). Measurements included sperm morphology, scrotal circumference, and sperm chromatin phenotypes such as DNA fragmentation and protamine deficiency. We estimated the heritability of these phenotypes and carried genome-wide association studies (GWAS) within breed, using the bovine high-density chip, to detect quantitative trait loci (QTL). Our analyses suggested that both sperm DNA fragmentation and sperm protamine deficiency are heritable (h2 ranging from 0.10 to 0.22). To confirm these first estimates of heritability further studies on sperm chromatin traits, with larger datasets are desirable. Our GWAS identified QTL for bull fertility traits, based on at least five polymorphisms (P < 10-8) for each QTL. Most of the significant polymorphisms detected in both breeds were on the X chromosome. The QTL were breed-specific, but for some traits, a closer inspection of GWAS results revealed suggestive SNP associations (P < 10-7) in both breeds. For example, the QTL for inhibin in Braham could be relevant to Tropical Composites too (many polymorphisms reached P < 10-7 in the same region). The QTL for sperm midpiece morphological abnormalities on X (QTL peak at 4.92 Mb, P < 10-17) is an example of a breed-specific QTL, supported by several significant SNPs (P < 10-8) in Brahman, but absent in Tropical Composites. The reported GWAS results add evidence to the mammalian specialization of the X chromosome, which during evolution has accumulated genes linked to spermatogenesis. Some of the polymorphisms on X were associated to more than one genetically correlated trait (correlations ranged from 0.33 to 0.51). Correlations and shared polymorphism associations support the hypothesis that these phenotypes have the same underlying cause, i.e. defective spermatogenesis. Genetic improvement for bull fertility is possible through genomic selection, which is likely more accurate if the QTL on X are considered in the predictions. Polymorphisms associated with male fertility accumulate on X in cattle, as in humans and mice, suggesting specialization of this chromosome.less
MLA: L.GEN.1818 Bull fertility update: historical data, new cohort and advanced genomics - Livestock genetics and genomics - Published 20 Jul 2020
The regional-scale MAR opportunity assessment represents a pre-feasibility assessment and consists of two components: (i) regional-scale opportunity mapping and (ii) MAR guidelines for entry level ass... moreessment in the most promising area for MAR. This pre-feasibility assessment identifies promising areas for MAR and describes the nature of investigations required to provide data that would support a feasibility assessment of a specific MAR scheme. The key output of the MAR regional-scale opportunity assessment is a series of regional-scale MAR opportunity maps for each of the three study areas to identify the most promising locations for MAR.less
Northern Australia Basin assessments - - Published 15 Jul 2020
This collection contains nitrogen runoff data for an alternate furrow flood irrigation system. Data contains concentration and flow values, disaggregated by furrow type for irrigations 1, 2 and 4 of a... more single season. Chemical data includes pH, electrical conductivity (EC) and water temperature measured in situ, and nitrate—N (NO3—N), ammonium—N (NH4—N), urea—N and total dissolved N (TDN) measured from collected samples. Chemical sampling occurred at 30 minute intervals within irrigations. Flow data was measured at 30 second intervals. Data was performed at the Australian Cotton Research Institute (ACRI), Narrabri Australia.less
Legacy data - Irrigation water chemistry - Published 13 Jul 2020
The APSIM Classic crop modelling system.
... more .
APSIM LICENSING - Use of any of these files can only be by Licensed APSIM users as provided for on https://www.apsim.info/download-apsim/ . If you are not a Licenced APSIM user, please register for APSIM through the APSIM Registration System via https://www.apsim.info/download-apsim/downloads/. The Non-Commercial R&D APSIM licence agreement is found in the supplementary documents. Licensed users may access and modify all source code. All modifications to APSIM must be submitted to the AI and be subject to the Reference Panel evaluation process before inclusion into the official APSIM release. For non-commercial purposes, access to APSIM is free of charge to users. The APSIM Initiative allows commercial use of APSIM for an annual fee. Any questions, please email firstname.lastname@example.org.
The Agricultural Production Systems sIMulator (APSIM) is a comprehensive model developed to simulate biophysical processes in agricultural systems, particularly as it relates to the economic and ecological outcomes of management practices in the face of climate risk. It is also being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond.less
SIP P1 – APSIM - APSIM - Published 07 Jul 2020
This data collection contains behavioural and temperature data for 80 sheep during a modified attention bias test. Prior to testing sheep were treated with drugs to induce short-term anxiety-like, cal... morem-like and euphoric-like states. The work aimed to determine whether the modified test could be used to measure the induced states.less
SIP 326: Developing measures of positive affective states in livestock - - Published 16 Jun 2020
fuzzless-tufted upland cotton mutant (Gossypium hirsutum) and control at 4 time points (0DPA, 2DPA, 4DPA, 6DPA)
CBA04: DR - Marker Technology - - Published 11 Jun 2020
Physiological data (such as heart rate, shell gape) of sets of oysters deployed at two marine sites in the D'Entrecasteaux Channel, Tasmania, Australia: Coningham (approximate lat/lon -43.0915, 147.30... more15 ), and Redcliffs (approximate lat/lon -43.3120,147.083).
Water quality data (such as DO, Salinity, pH, Turbidity, Temp, Cond) for both Redcliffs and Coningham (also known as Tinderbox) site.
Sensors were deployed on 9th Nov 2018 at Coningham, and 18th Dec at Redcliffs. The dateset contains the measured water quality and oyster's physiological data up to 15 March 2019. At Redcliffs, there were four functional biosensors placed at the depth of 0.5, 1.5, 5, and 7 meters to measure the oyster's physiological data. On the other hand, there were five biosensors deployed at Coningham at the depth of 0.5, 1.5, 3.5, 5, and 10 meters.
The water quality at each site was measured using a YSI EXO2 multiparameter sonde at 5 m depth. less
Sentinel sensors: revolutionising our understanding and management of the estuarine environment - - Published 03 Jun 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
Behavioural, temperature and GPS data collected for project AEC 17/24 which looked at the effect of flock dynamics and determine what proportion of sheep in a flock would be required to be collared wi... morethout affecting the efficacy of the virtual fence. less
DAWR: Cost-effective weed management using targeted sheep grazing technology - - Published 07 May 2020
These data are historic weather records for Australia obtained from the Bureau of Meteorology, then processed for use with the GrazPlan suite of decision support tools (MetAccess, GrassGro and AusFarm... more). The raw data must not be redistributed without the consent of the Bureau of Meteorology.less
SIP 60 APSIM Development - - Published 11 Feb 2020
This collection contains the run and irrigation data for a 2 year study at the Australian Cotton Research Institute that investigate N run-off as a response to fertiliser placement. The dataset also c... moreontains nitrate, ammonium, urea, total nitrogen and dissolved organic nitrogen concentrations and EC and pH of the water.less
CRDC-DAFF FtRG2 Quantifying indir - N budgeting in irrigated cotton using N15 labelled urea - Published 11 Feb 2020
CSIRO aims to identify the differences in water uptake patterns by crops for constrained (acid and compacted) and ameliorated (limed, ripped, lime and ripped) treatments. To determine if the water pat... moreterns and yields in the trials can be estimated (using APSIM) through adjustments of the soil’s root growth (xf).less
GRDC/WAAA: DAW00242 Subsoil Constraints - Dandaragan subsoil constraints amelioration trial - Published 09 Jan 2020
GRDC/WAAA: DAW00242 Subsoil Constraints - Wubin subsoil constraints amelioration trial - Published 09 Jan 2020