Showing results for: [ Agricultural and Veterinary Sciences ]
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
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
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
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
AusFarm modelling tool built using the Common Modelling Protocol.
One CSIRO Rural Decision Support - Software development - Published 20 Mar 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
These collection is comprised of mass spectral datasets obtained from proteomics analysis of shrimp haemolymph used to resolve the complexity of hemocyanin isoforms
Ridley Alliance - - Published 06 Jan 2020
simpleSA provides facilitates running fishery assessments for Data-Poor to Data-Moderate fisheries. Includes functions for conducting a modified form of the Catch-MSY analysis, catch-curve analyses, s... moreurplus production modelling, age-structured surplus production modelling and age-structured stock reduction analyses. Bootstrap functions are included to allow the characterization of uncertainty in those analyses. The software permits the construction of fisheries management advice as well as providing phase plot diagrams of stock status where both biomass and fishing mortality are estimated.
cede provides facilitates the exploration of variation and patterns within
fisheries data (Year, Catch, Effort, depth, vessel, etc), especially yearly
patterns. It also includes functions to produce sketch maps of fisheries
data where Lat and Long data, or other spatially explicit data, are
available. Finally, there are functions for conducting different types of
CPUE standardization that are there to act simply as a guide to what is
Reducing the Number of Undefined Species in Future Status of Australian Fish - - Published 13 Dec 2019
Accurate detection of inflorescences can lead to an inflorescence count to provide an early yield estimation in viticulture. The task here is to detect inflorescences in RGB images using image process... moreing, computer vision, and machine learning techniques. The purpose of this dataset is to provide a common platform for researchers to develop methods for inflorescence detection and compare their research.less
CSA1602 New non-destructive technologies for simultaneous yield, crop condition and quality estimation - - Published 02 Dec 2019
Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fraction... mores is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution.
A suite of derivative products are also produced including monthly fractional cover, total vegetation cover (PV+NPV), and anomaly of total cover against the time series.
Monthly: The monthly product is aggregated from the 8-day composites using the medoid method.
Anomaly: represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%.
Decile: represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series.
MODIS fractional cover has been validated for Australia. less
DoAWR - Improving the RaPP Map monitoring tool for Australia - - Published 11 Nov 2019
These are behavioural data from tests conducted on individual hens that differed in their range use. The tests applied were an Open Field Test, Tonic Immobility Test, and an Attention Bias test.
UNE: A17/2342 Effects of rearing environments on ranging, adaptation to stressors and health in free-range laying hens - - Published 07 Nov 2019
Pre-mortem welfare scores, post-mortem health assessment and body composition from CT scanning from free-range hens that differed in range usage patterns as well as different rearing enrichment treatm... moreents. less
Long-term modelling scenarios under historical (1970-2017) and future climate (2018-2050) using UNSATCHEM module in HYDRUS-1D to assess the longevity of irrigation, using a range of available water re... moresources, in four main soil groups.
File extensions: .in = input file, .out = output file, .dat and .h1d are system files that can't be opened.less
Sustainable Expansion of Recycled Water-Irrigated Agriculture/Horticulture in North Adelaide Plains - HYDRUS modelling - Published 30 Oct 2019
SWATH-MS acquisition was used to analyse the hordein-null barley lines. This dataset was investigated in order to understand the global proteome changes upon deletion of each hordein-class.
Legacy data - - Published 21 Aug 2019
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 09 Jul 2019