Showing results for: [ Agricultural and Veterinary Sciences ]
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
X chromosome variants were associated to heritable fertility traits, in two bovine populations.
Fortes, Porto-Neto et al. 2020.
Background: Targeting phenotypes related to bull fertility, such as spe... morerm morphology and sperm DNA fragmentation can help identify mutations that limit reproduction capacity. Twenty-five fertility-related phenotypes were measured as indicators of bull fertility (1,099 Brahman bulls and 1,719 Tropical Composite bulls). Measurements included standard bull breeding soundness evaluation (e.g. sperm motility and morphology), scrotal circumference, and sperm DNA fragmentation and sperm protamine deficiency. These phenotypes were used in genome-wide association studies (GWAS) that aimed to estimate heritability and detect quantitative trait loci (QTL).
Results: Genomic analyses suggest that bull fertility traits have a heritable component, which makes selective breeding possible. The phenotype variation in sperm DNA fragmentation and sperm protamine deficiency traits have a heritable component (h2 ~ 0.05 – 0.22). These first estimates for heritability of sperm chromatin phenotypes require further studies, with larger datasets, to corroborate present results. Our GWAS identified eleven QTLs for bull fertility traits, based on five or more significant SNPs (P < 10-8). Six QTLs were identified in Brahman and five in Tropical Composite bulls. Most of the significant polymorphisms in both breeds and eight out of the eleven QTLs mapped to chromosome X. The autosomal QTLs were for sperm DNA fragmentation mapping to chromosome 11 (Brahman), for Inhibin mapping to chromosome 2 (Brahman), and for scrotal circumference mapping to chromosome 5 (Tropical Composites). The QTLs were breed specific, but for some traits, a closer inspection of GWAS reveals suggestive SNP associations (P < 10-7) in both breeds. For example, the Inhibin QTL observed in Braham might be relevant to Tropical Composites too, because we observed an association peak in that same region (many SNPs reaching P < 10-7). An example of breed specific results was the QTL for sperm midpiece morphological abnormalities, in the X chromosome (QTL peak at 4.92 Mb, P < 10-17). This QTL was supported by 487 significant polymorphisms (P < 10-8) in Brahman and yet it was absent in Tropical Composites. The reported GWAS add evidence to a mammalian specialization of X that evolved to harbor genes linked to spermatogenesis, as similarly observed in mice and humans. Some variants in X may affect more than one genetically correlated trait (r2 ~ 0.33 – 0.51), such as sperm morphology and sperm DNA fragmentation. Correlations and shared SNP associations support the hypothesis that these phenotypes have the same underlying cause: faulty spermatogenesis.
Conclusions: Genetic improvement for bull fertility is possible through selective breeding. Genomic selection for bull fertility might be more accurate if the X chromosome mutations that underlie the discovered QTL are included in the analyses. Polymorphisms associated with bull fertility accumulate in the X chromosome, as they do in humans and mice, thus, 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 10 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
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
SNP genotypes, collected from 11 populations and 184 Pacific oysters, as part of a published study on genetic diversity. Details of published work are:
"Assessment of genetic diversity and population ... morestructure in cultured Australian Pacific oysters". Animal Genetics (In Press).less
SIP 254 FY18 - Functional Annotation of Salmon Genome - - Published 02 Jul 2019
This data collection includes the data generated under the modelling component of a GRDC project. The APSIM model was optimized using data set collected from Termora, Horsham, and Karoonda to simulate... more observed soil mineral nitrogen dynamics, water dynamics, residue recovery and crop production. The key outputs are the optimized two model parameters: the decay rate constant of humic organic carbon and carbon use efficiency of microbial decomposition. less
GRDC CSP00186 Stubble Initiative Module1 - - Published 26 Jun 2019
Scientific reference collection of physical samples of Australian soils. There are over 100,000 soil specimens in the collection: 70,000 fully archived and 30,000 stored specimens (still to be archive... mored). The specimen collection reflects the changes in research focus over the last 70 years and expects to continue this into the future.
The starting collection, which was received at the founding of the Soil Archive in 2003, has been complemented by more recent submissions from other agencies and current CSIRO projects from a range of Business Units (predominantly Agriculture & Food, Land & Water). Researchers can submit their soil specimens to the Soil Archive. These soil specimens will need to be accompanied by the collected soil data. For more information, see our website.
The sub sample collection of the Soil Archive holds 43,000 specimens of fine earth (< 2 mm) (10 gram duplicates of the larger specimens) that are suitable to near-infrared spectroscopic analysis. less
Legacy data - Australian Soil Resource Information System - Published 25 Jun 2019