ORCID Profile
0000-0003-3383-8902
Current Organisations
Bush Heritage Australia
,
Curtin University
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Publisher: MDPI AG
Date: 05-11-2020
Abstract: On-farm experimentation (OFE) is a farmer-centric process that can enhance the adoption of digital agriculture technologies and improve farm profitability and sustainability. Farmers work with consultants or researchers to design and implement experiments using their own machinery to test management practices at the field or farm scale. Analysis of data from OFE is challenging because of the large spatial variation influenced by spatial autocorrelation that is not due to the treatment being tested and is often much larger than treatment effects. In addition, the relationship between treatment and yield response may also vary spatially. We investigate the use of geographically weighted regression (GWR) for analysis of data from large on-farm experiments. GWR estimates local regressions, where data are weighted by distance from the site using a distance-decay kernel. It is a simple approach that can be easily explained to farmers and their agronomic advisors. We use simulated data to test the ability of GWR to separate yield variation due to treatment from any underlying spatial variation in yield that is not due to treatment show that GWR kernel bandwidth can be based on experimental design to accurately separate the underlying spatial variability from treatment effects and demonstrate a step-wise model selection approach to determine when the response to treatment is global across the experiment or locally varying. We demonstrate our recommended approach on two large-scale experiments conducted on farms in Western Australia to investigate grain yield response to potassium fertiliser. We discuss the implications of our results for routine practical application to OFE and conclude that GWR has potential for wide application in a semi-automated manner to analyse OFE data, improve farm decision-making, and enhance the adoption of digital technologies.
Publisher: Informa UK Limited
Date: 03-09-2016
Publisher: Acoustical Society of America (ASA)
Date: 17-04-2014
DOI: 10.1121/1.4871581
Abstract: Non-song vocalizations of migrating pygmy blue whales (Balaenoptera musculus brevicauda) in Western Australia are described. Simultaneous land-based visual observations and underwater acoustic recordings detected 27 groups in Geographe Bay, WA over 2011 to 2012. Six different vocalizations were recorded that were not repeated in a pattern or in association with song, and thus were identified as non-song vocalizations. Five of these were not previously described for this population. Their acoustic characteristics and context are presented. Given that 56% of groups vocalized, 86% of which produced non-song vocalizations and 14% song units, the inclusion of non-song vocalizations in passive-acoustic monitoring is proposed.
Publisher: Inter-Research Science Center
Date: 18-02-2015
DOI: 10.3354/ESR00655
Publisher: Wiley
Date: 08-11-2020
Abstract: 1. Despite aspirations for conservation impact, mismatches between research and implementation have limited progress towards this goal. There is, therefore, an urgent need to identify how we can more effectively navigate the spaces between research and practice. 2. In 2014, we ran a workshop with conservation researchers and practitioners to identify mismatches between research and implementation that needed to be overcome to deliver evidence‐informed conservation action. Five mismatches were highlighted: spatial, temporal, priority, communication, and institutional. 3. Since 2014, thinking around the ‘research–implementation gap’ has progressed. The term ‘gap’ has been replaced by language around the dynamic ‘spaces’ between research and action, representing a shift in thinking around what it takes to better align research and practice. 4. In 2019, we ran a follow‐up workshop reflecting on this shift, whether the five mismatches identified in the 2014 workshop were still present in conservation, and whether progress had been made to overcome these mismatches during the past 5 years. We found that while there has been progress, we still have some way to go across all dimensions. 5. Here, we report on the outcomes of the 2019 workshop, reflect on what has changed over the past 5 years, and offer 10 recommendations for strengthening the alignment of conservation research and practice.
Publisher: Wiley
Date: 03-03-2022
DOI: 10.1002/SAJ2.20385
Abstract: Raw soil core physical data used in machine learning algorithms with corresponding spatial remotely sensed data is an emerging science. Using data derived from soil core s les previously collected in Universal Transverse Mercator zone 50 (Western Australia) and remotely sensed data, a model that predicted ground movement (GM) was developed specific to Australian Standards manual AS 1726–2017. This is the first approach for Australian soils and first in the world for soils older than 200 million yr. The model developed reliably predicted GM with 91.1% accuracy. The error obtained from the prediction is within acceptable limits currently used by engineers in calculations concerning soil classification for engineering purposes. Concerning the remotely sensed data analyzed, accuracy of the Atterberg limits method might be improved if additional information about soil structure (layering and horizon) or other variables (seasonal data) are built into this model. This model can be used to save on construction material costs, reduce the potential for human error associated with data collection and s le manipulation, but also fast‐track (by up to 6 wk based on current wait times) building approvals while ensuring compliance to the relevant legislation. This platform also reduces the environmental effects of invasive drilling techniques. A requirement within principles of sustainable building practices, and associated with current standards commonly used by structural engineers who may seek better understanding of soil properties in Australia as a software service (with application potential in North America).
No related grants have been discovered for Angela Recalde-Salas.