title: Methane emission hotspots from a drained peat soil under dairy grazing

authors: Hamill, Jacob

subjects: Methane

abstract: Methane (CH4) is a greenhouse gas that is emitted from natural peatland ecosystems due to their high water tables. However, large areas of natural peatlands have been drained for agricultural purposes, resulting in a reduction in overall methane emissions. However, where soil is saturated, such as within or adjacent to drainage ditches, methane emission can remain high. The aim of this research was to determine the magnitude of soil- and drainage ditch-derived methane emissions from a drained Waikato peatland under dairy grazing and where and when these emissions occur.

Gamma Farm is a pastoral dairy farm located on the remnants of the Moanatuatua peatland and drained by shallow surface “spinner” drains that discharge into deeper field-border drains. The paddocks were classified into four different landforms based on the location and hydrology of the drainage features. These landforms are crown, slope, ditch edge and drainage ditch. To adequately determine the spatial and temporal variation in methane fluxes chamber measurements of methane fluxes were undertaken along a transect across the width of the study site, approximately perpendicular to the border drains. A campaign approach was used, with chamber measurements being undertaken from autumn through to winter to capture methane fluxes under different environmental conditions. To measure seasonal and annual-scale methane emissions an eddy covariance flux tower was used.
Based on chamber measurements drainage ditches were shown to have average methane emissions of 0.071 ± 0.005 mg CH4 m-2 h-1 . Conversely, the soil of the crown, slope and ditch edges were shown to be a net methane sink, with average net methane oxidation of ‒0.019 ± 0.006, ‒0.01 ± 0.008 and ‒0.023 ± 0.006 mg CH4 m-2 h-1 respectively. Weighting the chamber measurements by landform area it was concluded that the study site was primarily a minor net methane sink. However, eddy covariance measurements indicated that the study site was a net source of methane with annual emissions of 44.72 kg CH4 ha-1 yr-1 . This large discrepancy between the chamber and eddy covariance measurements is likely caused by the large spatial and temporal scale differences between the two measurement techniques.

In addition, it was found that there was no relationship between soil methane fluxes and the soil temperature, Olsen-P, and nitrate concentration. However, methane fluxes were shown to decrease as the ammonium concentration and depth to the water table increased. In addition, methane fluxes decreased as volumetric moisture content (VMC) decreased, but at low VMCs (<40%) methane fluxes tended towards zero. For the water-borne methane fluxes there was no relationship found between methane fluxes and any measured variable (pH, dissolved oxygen, electrical conductivity, water temperature, water depth, nitrate concentration or dissolved phosphorus). However, at longer time scales such as monthly averages of eddy covariance measurements, methane fluxes were positively correlated with soil temperature and air temperature. Additionally, both seasonal and diurnal cycles in the eddy covariance methane fluxes were observed.

text: Methane emission hotspots from a drained peat soil under dairy grazing Methane (CH4) is a greenhouse gas that is emitted from natural peatland ecosystems due to their high water tables. However, large areas of natural peatlands have been drained for agricultural purposes, resulting in a reduction in overall methane emissions. However, where soil is saturated, such as within or adjacent to drainage ditches, methane emission can remain high. The aim of this research was to determine the magnitude of soil- and drainage ditch-derived methane emissions from a drained Waikato peatland under dairy grazing and where and when these emissions occur.

Gamma Farm is a pastoral dairy farm located on the remnants of the Moanatuatua peatland and drained by shallow surface “spinner” drains that discharge into deeper field-border drains. The paddocks were classified into four different landforms based on the location and hydrology of the drainage features. These landforms are crown, slope, ditch edge and drainage ditch. To adequately determine the spatial and temporal variation in methane fluxes chamber measurements of methane fluxes were undertaken along a transect across the width of the study site, approximately perpendicular to the border drains. A campaign approach was used, with chamber measurements being undertaken from autumn through to winter to capture methane fluxes under different environmental conditions. To measure seasonal and annual-scale methane emissions an eddy covariance flux tower was used.
Based on chamber measurements drainage ditches were shown to have average methane emissions of 0.071 ± 0.005 mg CH4 m-2 h-1 . Conversely, the soil of the crown, slope and ditch edges were shown to be a net methane sink, with average net methane oxidation of ‒0.019 ± 0.006, ‒0.01 ± 0.008 and ‒0.023 ± 0.006 mg CH4 m-2 h-1 respectively. Weighting the chamber measurements by landform area it was concluded that the study site was primarily a minor net methane sink. However, eddy covariance measurements indicated that the study site was a net source of methane with annual emissions of 44.72 kg CH4 ha-1 yr-1 . This large discrepancy between the chamber and eddy covariance measurements is likely caused by the large spatial and temporal scale differences between the two measurement techniques.

In addition, it was found that there was no relationship between soil methane fluxes and the soil temperature, Olsen-P, and nitrate concentration. However, methane fluxes were shown to decrease as the ammonium concentration and depth to the water table increased. In addition, methane fluxes decreased as volumetric moisture content (VMC) decreased, but at low VMCs (<40%) methane fluxes tended towards zero. For the water-borne methane fluxes there was no relationship found between methane fluxes and any measured variable (pH, dissolved oxygen, electrical conductivity, water temperature, water depth, nitrate concentration or dissolved phosphorus). However, at longer time scales such as monthly averages of eddy covariance measurements, methane fluxes were positively correlated with soil temperature and air temperature. Additionally, both seasonal and diurnal cycles in the eddy covariance methane fluxes were observed.

year: 2019

title: Perspectives and Experiences of Off-field Problematic Behaviour Among Elite New Zealand Athletes

authors: Easter, Caitlin Davys

subjects: elite athletes

abstract: In recent years, the off-field problematic behaviour of elite athletes has received considerable media interest and attention. However, such behaviour has been investigated largely in college/university athlete populations and has focused predominantly on specific sports and on-field behaviours. This qualitative study examined perspectives and experiences of off-field problematic behaviour among elite New Zealand athletes. Semi-structured interviews with 10 athletes (five female and five male) from seven sporting codes were completed. Interview transcripts were analysed and interpreted using thematic analysis. Five key themes emerged from the data, which provided insight into the development of off-field problematic behaviour in the current context and the wider consequences such behaviour has for athletes and others. The findings suggest that a range of off-field problematic behaviour is present within the elite New Zealand sporting environment. Alcohol misuse was the most prevalent type of behaviour discussed. A number of other problematic behaviours were also reported, such as risk-taking, violence, inappropriate sexual behaviour, disordered eating, and psychological manipulation. Participants’ definitions of ‘problematic behaviour’ were broad and included a number of less severe behaviours that were considered to be problematic within the elite sporting context. The main factors that appeared to increase the likelihood of off-field problematic behaviour were the influence of the media, pressures associated with professionalism, and the impact of management-athlete relationships. Having an elite public profile and the influence of drinking culture were identified as added pressures for participants, specific to the New Zealand sporting context. Furthermore, being on tour or competing overseas appeared to exacerbate potential vulnerabilities and provided unique opportunities for athletes to engage in off-field problematic behaviour. Numerous stressor factors appeared to have dual functions and, at times, also had protective value. Having stable support networks and positive athlete-coach relationships were protective factors central to the current study. Barriers to seeking help in relation to off-field problematic behaviour were also identified as an interesting finding in this study. Mistrust of professionals and breaches of confidentiality greatly impacted athletes’ willingness to seek help. Participants also made a number of recommendations to address off-field problematic behaviour that are highly relevant to professionals working within elite sporting environments. Overall, this study confirmed that off-field problematic behaviour of elite athletes was present in New Zealand; however, there were perceived differences as to whether such behaviour was increasing or decreasing in the current climate. Further research is needed to validate these findings and to better understand the experiences of elite athletes within the New Zealand sporting context.

text: Perspectives and Experiences of Off-field Problematic Behaviour Among Elite New Zealand Athletes In recent years, the off-field problematic behaviour of elite athletes has received considerable media interest and attention. However, such behaviour has been investigated largely in college/university athlete populations and has focused predominantly on specific sports and on-field behaviours. This qualitative study examined perspectives and experiences of off-field problematic behaviour among elite New Zealand athletes. Semi-structured interviews with 10 athletes (five female and five male) from seven sporting codes were completed. Interview transcripts were analysed and interpreted using thematic analysis. Five key themes emerged from the data, which provided insight into the development of off-field problematic behaviour in the current context and the wider consequences such behaviour has for athletes and others. The findings suggest that a range of off-field problematic behaviour is present within the elite New Zealand sporting environment. Alcohol misuse was the most prevalent type of behaviour discussed. A number of other problematic behaviours were also reported, such as risk-taking, violence, inappropriate sexual behaviour, disordered eating, and psychological manipulation. Participants’ definitions of ‘problematic behaviour’ were broad and included a number of less severe behaviours that were considered to be problematic within the elite sporting context. The main factors that appeared to increase the likelihood of off-field problematic behaviour were the influence of the media, pressures associated with professionalism, and the impact of management-athlete relationships. Having an elite public profile and the influence of drinking culture were identified as added pressures for participants, specific to the New Zealand sporting context. Furthermore, being on tour or competing overseas appeared to exacerbate potential vulnerabilities and provided unique opportunities for athletes to engage in off-field problematic behaviour. Numerous stressor factors appeared to have dual functions and, at times, also had protective value. Having stable support networks and positive athlete-coach relationships were protective factors central to the current study. Barriers to seeking help in relation to off-field problematic behaviour were also identified as an interesting finding in this study. Mistrust of professionals and breaches of confidentiality greatly impacted athletes’ willingness to seek help. Participants also made a number of recommendations to address off-field problematic behaviour that are highly relevant to professionals working within elite sporting environments. Overall, this study confirmed that off-field problematic behaviour of elite athletes was present in New Zealand; however, there were perceived differences as to whether such behaviour was increasing or decreasing in the current climate. Further research is needed to validate these findings and to better understand the experiences of elite athletes within the New Zealand sporting context.

year: 2014

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22