title: Consciousness-centered stewardship: An indigenous standpoint

authors: Spiller, Chellie

abstract: Dominion-driven stewardship, which stages human interventions with an anthropocentric lens, externalises problems. We present consciousness-centered stewardship, seen through the Māori ethic of kaitiakitanga, as the missing connective tissue that looks to fix ourselves. We advocate an approach that appreciates a collective self-intelligence in the world and being a steward with a “conscious mind” as part of a transformative way forward. Sustainable development from this perspective thus includes paying attention to personal growth.

text: Consciousness-centered stewardship: An indigenous standpoint Dominion-driven stewardship, which stages human interventions with an anthropocentric lens, externalises problems. We present consciousness-centered stewardship, seen through the Māori ethic of kaitiakitanga, as the missing connective tissue that looks to fix ourselves. We advocate an approach that appreciates a collective self-intelligence in the world and being a steward with a “conscious mind” as part of a transformative way forward. Sustainable development from this perspective thus includes paying attention to personal growth.

year: 2025

title: Protecting the heartwood: How can tikanga Māori and mātauranga Māori inform the construction of digital identities and relational responsibilities to data in Aotearoa New Zealand?

authors: Lucas, Danielle

abstract: A report prepared by Danielle Lucas as part of the Tikanga in Technology research project

text: Protecting the heartwood: How can tikanga Māori and mātauranga Māori inform the construction of digital identities and relational responsibilities to data in Aotearoa New Zealand? A report prepared by Danielle Lucas as part of the Tikanga in Technology research project

year: 2025-02-11

title: Assessment of Standing Herbage Dry Matter Using A Range Imaging Sytem

authors: Benseman, Mark

subjects: Herbage

abstract: It has been known for a long time that a device that could quickly and accurately ascertain dry matter content would be very useful to pastoral farmers. Despite many years of various products being developed there is still a lack of consistent and accurate measurements available. We present a proof of concept using a time of flight  imaging system to measure standing herbage dry matter. Scenes of herbage were captured using the SoftKinitec DS325 range imaging camera. Each scene included range and intensity images as well as colour images. Simple statistical analysis of the images was carried out and related to dry matter content. Twenty data points were gathered in late autumn growing conditions. The best correlation achieved was 0.9 with a standard deviation of 337 kgDM/ha. This was achieved used a multivariate linear regression. The predictors used were average depth, and standard deviations of both depth and intensity frames. The worst correlation achieved using a multivariate linear regression was 0.89 with a standard deviation of 365 kgDM/ha.  Thirteen data points were also gathered during severe drought conditions. The same statistical analysis resulted in a best fit of 0.52 and a standard deviation of 533 kgDM/ha. Range cameras show promise when compared to currently available methods of dry matter measurement.

text: Assessment of Standing Herbage Dry Matter Using A Range Imaging Sytem It has been known for a long time that a device that could quickly and accurately ascertain dry matter content would be very useful to pastoral farmers. Despite many years of various products being developed there is still a lack of consistent and accurate measurements available. We present a proof of concept using a time of flight  imaging system to measure standing herbage dry matter. Scenes of herbage were captured using the SoftKinitec DS325 range imaging camera. Each scene included range and intensity images as well as colour images. Simple statistical analysis of the images was carried out and related to dry matter content. Twenty data points were gathered in late autumn growing conditions. The best correlation achieved was 0.9 with a standard deviation of 337 kgDM/ha. This was achieved used a multivariate linear regression. The predictors used were average depth, and standard deviations of both depth and intensity frames. The worst correlation achieved using a multivariate linear regression was 0.89 with a standard deviation of 365 kgDM/ha.  Thirteen data points were also gathered during severe drought conditions. The same statistical analysis resulted in a best fit of 0.52 and a standard deviation of 533 kgDM/ha. Range cameras show promise when compared to currently available methods of dry matter measurement.

year: 2013