Department of Agriculture and Food Western Australia
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Managing herring in Western Australia
No full textFollowing the recovery of the Australian herring stock in 2021, DPIRD formed the Australian Herring Future Management Working Group, made up of recreational and commercial representatives, to help us determine the best way forward for management of the species in WA
Western Australia’s Primary Industries: 2021-22 Economic Overview
No full textThe Western Australia’s Primary Industries: 2021-22 Economic Overview (WAPIEO) is developed by DPIRD in partnership with the Forest Products Commission.
The WAPIEO has a trade focus and provides a single source for consistent statistics and insights on observed industry trends.
The WAPIEO is based primarily on 2021-22 economic indicators from the Department of Treasury, DPIRD data, and statistics from the Australian Bureau of Statistics (ABS), in line with the release cycle dates of final ABS data
Esperance, Shire of - Esperance Town, BEN sign map – 2 of 3
No full textBeach Emergency Number (BEN) Signage Installation Map – Shire of Esperance - Esperance Townhttps://library.dpird.wa.gov.au/gis_bens/1027/thumbnail.jp
Esperance, Shire of - West BEN sign map – 1 of 3
No full textBEN Signage Installation Map – Shire of Esperance (west)https://library.dpird.wa.gov.au/gis_bens/1029/thumbnail.jp
Mosman Park, Town of - BEN sign map – 1 of 1
No full textBeach Emergency Number (BEN) Signage Installation Map – Town of Mosman Parkhttps://library.dpird.wa.gov.au/gis_bens/1043/thumbnail.jp
Shark Bay, Shire of - BEN sign map - 1 of 1
No full textBEN Signage Installation Map - Shire of Shark Bayhttps://library.dpird.wa.gov.au/gis_bens/1058/thumbnail.jp
Clearing cloudy or coloured water on farms in Western Australia
No full textCloudy or coloured water can be a nuisance when used for household purposes, and sometimes be unsuitable for livestock, irrigation, or crop spraying. The following is for general information only, it is recommended that people seek expert advice for treating cloudy or coloured water
Adaptive water body detection: Integrating deep learning, normalised difference water index, and vector data for farm dam water monitoring with OmniWaterMask
No full textFarm dams are important water security features supporting both agricultural production and the natural environment. In Australia alone, over two million farm dams provide the water resources underpinning rural and regional primary industries with an annual export value of $80 billion. However, monitoring these water bodies to understand water security and vulnerability is challenging, primarily because of their large quantity, size and highly variable spectral signatures. These characteristics result in difficulty determining thresholds for index-based water detection methods and add to the difficulty of creating adequate training datasets for deep learning methods. We present an adaptive approach named OmniWaterMask (OWM) that uses existing mapped water features to optimise the combination of deep learning outputs and a common water index (Normalised Difference Water Index, NDWI) to achieve robust water detection, for both agricultural and other water resources. OWM demonstrates strong performance across multiple datasets and spatial scales, achieving Intersection over Union (IoU) scores of 96.9 % (Sentinel-2), 73.8 % (Landsat) and 90.9 % (National Agriculture Imagery Program, NAIP). When applied to farm dam monitoring in Western Australia using Sentinel-2 imagery, the approach successfully tracks water extent across a range of dam sizes, with Mean Absolute Error (MAE) of 587 m2 when using Sentinel-2 and 785 m2 when using PlanetScope. Our two case studies demonstrate the practicality and scalability of this approach by monitoring water levels in both a single dam and across 7,172 farm dams at monthly intervals over an 8-year period. This methodology enables reliable monitoring of small water bodies at scale, supporting rural water security assessment in increasingly uncertain climatic conditions. The open source OWM library is made available as a Python package on PyPI
Effect of rainfall reduction and competition on the phenology of the Mediterranean forage perennial legume Bituminaria bituminosa var. albomarginata cv. Lanza
No full textTedera (Bituminaria bituminosa (L.) C.H. Stirt.) is a Mediterranean drought-tolerant species that shows potential as a forage perennial legume for Mediterranean livestock systems. This three-year study investigated the phenology of the newly developed variety of tedera (Lanza) in response to an annual 24% rainfall reduction and competition compared to alfalfa (Medicago sativa L.) in a typical Mediterranean environment of the Iberian Peninsula. Tedera showed early phenology for the reproductive stages from inflorescence emergence to ripening compared to alfalfa, with a long flowering period from early April to mid-May, overlapping with mature fruits. In general, tedera responded to a 24% reduction in rainfall with earlier inflorescence emergence and flowering, demonstrating plasticity to drier conditions. Competition affected the phenology through delayed start of inflorescence emergence and flowering, and especially by reducing the probability of plants reaching reproductive stages. Tedera exhibited overall later leaf shedding than alfalfa and even retained green leaves throughout the summer of the establishment year, whereas alfalfa shed its leaves in July. Low cold tolerance and competition from weeds affect the phenology of tedera and may limit its persistence. Further research and breeding efforts are needed to define suitable management strategies and ensure the provision of green forage during the summer season by this species, which may play a crucial strategic role in facing future, more arid scenarios in Mediterranean livestock systems
Fluke egg sedimentation test procedure
No full textThe department’s approved procedure for detecting trematode eggs and the Eimeria leuckarti sedimentation method (FEST) on faecal samples