DPIRD Digital Library
Not a member yet
10818 research outputs found
Sort by
Urochloa mosambicensis - environmental weed risk assessment 2022
No full textIntroducing new plants to an area may have both positive and negative effects on the environment, economy and community. To minimise the negative environmental impact of introducing new agricultural species, DPIRD conducts a risk assessment procedure based on widely accepted scientific standards. This report assesses Sabi grass (Urochloa mosambicensis). Sabi grass is a creeping, tropical, perennial grass of variable size and growth habit usually with short stolons or tufted and sometimes rooting and branching from the lower nodes. Sabi grass is native to central and southern Africa (i.e. Kenya, Tanzania, Uganda, Malawi, Mozambique, Zambia, Zimbabwe, Botswana, South Africa and Swaziland). It is now naturalised in the tropics and subtropics including USA (Hawaii, Texas), Australia (north), India, Sri Lanka, Myanmar, Thailand, Indonesia and Fiji (McIvor 1992; Cook et al. 2020)
Water use efficiency in Western Australian cropping systems
No full textRotations and associated management practices in rainfed farming systems of southwest Australia have shifted towards intensified cropping. Survey data from 184 fields spanning 14 Mha of southwest Australia were used to document water use efficiency (WUE) and water-limited yield potential (WLYP) of commercial crops and crop sequences and identify biophysical variables influencing WUE. WUE achieved in commercial wheat crops was 10.7 kg grain/ha.mm. Using a boundary function Ywl = 25 × (WU − 45), farmers achieved 54% of WLYP. Climate variables affected WUE more than management and biotic variates, the highest latitude region having WUE of 9.0 kg grain/ha.mm, compared to 11.8 kg grain/ha.mm for regions further south. Increased soil nitrogen and nitrogen fertiliser increased WUE, as did sowing earlier; in keeping with farmers in southern Australia sowing crops earlier and trebling fertiliser nitrogen usage since 1990. Wheat yield and WUE increased a small amount after break crop or pasture (12.5 kg grain/ha.mm) compared to wheat grown after wheat (11.2 kg grain/ha.mm), due to good weed and root pathogen control, and high fertiliser nitrogen application. However, WUE of wheat declined to 8.4 kg grain/ha.mm when more than three wheat crops were grown in succession. Farmers continue to improve WUE with increased inputs and new technologies replacing some traditional functions of break crops and pasture. However, break crops and pastures are still required within the rotation to maintain WUE and break effects need to be measured over several years
Automatic and fast classification of barley grains from images: A deep learning approach
No full textAustralia has a reputation for producing a reliable supply of high-quality barley in a contaminant-free climate. As a result, Australian barley is highly sought after by malting, brewing, distilling, and feed industries worldwide. Barley is traded as a variety-specific commodity on the international market for food, brewing and distilling end-use, as the intrinsic quality of the variety determines its market value. Manual identification of barley varieties by the naked eye is challenging and time-consuming for all stakeholders, including growers, grain handlers and traders. Current industrial methods for identifying barley varieties include molecular protein weights or DNA based technology, which are not only time-consuming and costly but need specific laboratory equipment. On grain receival, there is a need for efficient and low-cost solutions for barley classification to ensure accurate and effective variety segregation. This paper proposes an efficient deep learning-based technique that can classify barley varieties from RGB images. Our proposed technique takes only four milliseconds to classify an RGB image. The proposed technique outperforms the baseline method and achieves a barley classification accuracy of 94% across 14 commercial barley varieties (some highly genetically related)
PestFacts WA Issue 07 - June 2022
No full textPestFacts WA Issue 07 - June 2022 contents: Native budworm moths and caterpillars are being found early 2022 winter spring insecticide spray guide is now available Monitor barley crops for net blotches Powdery mildew in barley and wheat Sclerotinia apothecia Spartacus leaf tippinghttps://library.dpird.wa.gov.au/fc_pestfactswa/1037/thumbnail.jp
PestFacts WA Issue 09 - July 2022
No full textPestFacts WA Issue 09 - July 2022 contents: Diamondback mothhttps://library.dpird.wa.gov.au/fc_pestfactswa/1039/thumbnail.jp
PestFacts WA Issue 17 - September 2022
No full textPestFacts WA Issue 17 - September 2022 contents: Armyworm update Clean augers, field bins and silos to prevent insects contaminating grain Flag smut in wheat PestFacts WA Reporter app now available for android deviceshttps://library.dpird.wa.gov.au/fc_pestfactswa/1047/thumbnail.jp
PestFacts WA Issue 21 - November 2022
No full textPestFacts WA Issue 21 - November 2022 contents: Final PestFacts WA newsletter for 2022! Green bridge management over summer PestFacts WA continues successful collaboration with Agworld and Back Paddockhttps://library.dpird.wa.gov.au/fc_pestfactswa/1051/thumbnail.jp
Gouldian Finch Monitoring: Nesting Activity - Goomig Project 2021 to 2022
No full textThe Goomig Project (Weaber Plains Development Project) is an irrigated agricultural development located approximately 30 km north of Kununurra, which involved clearing approximately 9,260 ha of vegetation for agriculture. Approximately 11,470 ha of native vegetation surrounding, or remaining between, the cleared areas has been designated as a buffer area (the study area) to be managed to protect surrounding conservation reserves and watercourses (Strategen 2014). Gouldian Finch monitoring in the buffer area during ongoing operation is required as a condition of the approval for the project, and is outlined in the Gouldian Finch Conservation Plan (GFCP; Strategen 2014). Item 7 of the monitoring regime requires “annuaI monitoring of breeding populations, including timing and reproductive outputs (i.e. clutch size and fledging success), to be undertaken annually between February and July”.
Breeding surveys in the buffer area were conducted across three phases, at monthly intervals in March, April and May 2022, to coincide with the primary breeding period previously recorded within the study area. Artificial nest boxes previously deployed in the study area were checked for evidence of usage by Gouldian Finches using a burrow scope camera, and if active nests were detected, details on the status of the nest (e.g. clutch size, development of chicks) were recorded
DPIRD scientist research tshirt
No full texthttps://library.dpird.wa.gov.au/site_carousel/1055/thumbnail.jp
Soil Quality: 7 Soil Water Repellence
No full textThe Soil Quality ebook series is a resource for farmers, agricultural professionals and students. Experts from their fields share current knowledge and best practice techniques in layers of information, allowing readers to choose the level of detail they require.
Book 7 Soil Water Repellence explains the impact, expression, diagnosis and management of water repellence in agricultural soil, supported by evidence-based case studies and farmer experiences.https://library.dpird.wa.gov.au/sq_ebooks/1006/thumbnail.jp