Application of geovisual analytics to modelling the movements of ruminants in the rural landscape using satellite tracking data

Benke, Kurt K.; Sheth, F.; Betteridge, K.; Pettit, Christopher; Aurambout, Jean‐Philippe

doi:10.1080/17538947.2013.872703

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…The development of urine sensor technology to detect urination events (Betteridge, Hoogendoorn, et al., 2010) and the use of satellite tracking technology to locate the grazing animal (Benke et al., 2015) provide new opportunities to record and geo‐reference urine deposition across slope gradients. Three datasets were used to test the NT model (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Review and update of a Nutrient Transfer model used for estimating nitrous oxide emissions from complex grazed landscapes, and implications for nationwide accounting

et al. 2022

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In New Zealand, nitrous oxide emissions from grazed hill pastures are estimated using different emission factors for urine and dung deposited on different slope classes. Allocation of urine and dung to each slope class needs to consider the distribution of slope classes within a landscape and animal behavior. The Nutrient Transfer (NT) model has recently been incorporated into the New Zealand Agricultural GHG Inventory Model to account for the allocation of excretal nitrogen (N) to each slope class. In this study, the predictive ability of the transfer function within the NT model was explored using urine deposition datasets collected with urine sensor and GPS tracker technology. Data were collected from three paddocks that had areas in low (<12˚), medium (12-24˚), and high slopes (>24˚). The NT model showed a good overall predictive ability for two of the three datasets. However, if the urine emission factors (% of urine N emitted as N 2 O-N) were to be further disaggregated to assess emissions from all three slope classes or slope gradients, more precise data would be required to accurately represent the range of landscapes found on farms. We have identified the need for more geospatial data on urine deposition and animal location for farms that are topographically out of the range used to develop the model. These new datasets would provide livestock urine deposition on a more continuous basis across slopes (as opposed to broad ranges), a unique opportunity to improve the performance of the NT model. INTRODUCTIONIn grazed grasslands, livestock harvest herbage from pastures across the landscape and aggregate the excreta waste Abbreviations: AIM, Agricultural Greenhouse Gas Inventory Model; EF 3 , emission factor (N 2 O-N emitted as % of excreta N applied); GHG, greenhouse gas; HS, high slope; LS, low slope; MAE, mean absolute error; MS, medium slope; NT, Nutrient Transfer.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Methodsmentioning

confidence: 99%

Review and update of a Nutrient Transfer model used for estimating nitrous oxide emissions from complex grazed landscapes, and implications for nationwide accounting

et al. 2022

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“…The fourth and final paper in this special issue is by Benke et al (2013) and focuses on a geovisual analytics framework to enhance the understanding of the spatial-temporal movements of sheep through their grazing behaviour and in particular N emissions. This is done through the development of a multi-agent model in the context of sheep movements in a paddock taking into account data inputs such as weather, land use and animal location logged using GPS.…”

Section: Editorial Geovisual Analytics: Design and Implementationmentioning

confidence: 99%

“…The work by Benke et al (2013) provides a novel design and implementation of a geovisual analytics approach as a rural landscape design decision support framework. This research takes into account spatial-temporal datasets based on ruminant behaviour captured using GPS collars attached to the animals.…”

Section: Editorial Geovisual Analytics: Design and Implementationmentioning

confidence: 99%

Geovisual analytics: design and implementation

Pettit

Çöltekin

2015

International Journal of Digital Earth

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“…Recent work by cartographers in geovisual analytics has resulted in new approaches for understanding complex human mobility patterns (Andrienko & Andrienko, 2011;Benke, Sheth, Betteridge, Pettit, & Aurambout, 2015;Kveladze, Kraak, & Van Elzakker, 2015), characterizing the role of location in social media streams (MacEachren et al, 2011;Morstatter, Kumar, Liu, & Maciejewski, 2013;Pezanowski, Maceachren, Savelyev, & Robinson, 2017), and supporting spatio-temporal analysis of political and social change (Burns & Skupin, 2013;Nelson, Quinn, Swedberg, Chu, & MacEachren, 2015;Peuquet, Robinson, Stehle, Hardisty, & Luo, 2015). In addition to the development of new geovisual analytics approaches for problem solving in domains like these, a concurrent stream of GIScience research has focused on evaluating the utility and usability of such approaches, with the aim of developing new guidelines for designing effective systems (Coltekin, Pettit, & Wu, 2015;Griffin & Bell, 2009;Roth et al, 2017).…”

Section: Leveraging Learning Data With Geovisual Analyticsmentioning

confidence: 99%

Evaluating geovisualization for spatial learning analytics

Robinson

Anderson

Quinn

2020

International Journal of Cartography

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Contemporary systems for supporting digital learning are capable of collecting a wide range of data on learner behaviours. The emerging science and technology of learning analytics seeks to use this information to improve learning outcomes and support institutional assessment. In this work we explore the potential for the spatial dimension in learning analytics, and we evaluate a prototype geovisualization system designed to support what we call spatial learning analytics. A user evaluation with geographers and educators was conducted to characterize the usability and utility of our prototype spatial learning analytics system. By helping us understand what our prototype system does and does not do well, we are able to suggest a variety of new ways in which future spatial learning analytics systems can be developed.

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Application of geovisual analytics to modelling the movements of ruminants in the rural landscape using satellite tracking data

Cited by 12 publications

References 14 publications

Review and update of a Nutrient Transfer model used for estimating nitrous oxide emissions from complex grazed landscapes, and implications for nationwide accounting

Review and update of a Nutrient Transfer model used for estimating nitrous oxide emissions from complex grazed landscapes, and implications for nationwide accounting

Geovisual analytics: design and implementation

Evaluating geovisualization for spatial learning analytics

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