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Machine learning for predicting climate change impacts on Pseudopithomyces chartarum spore counts: a risk indicator of facial eczema
(Taylor and Francis Group, 2025-11-09) Wada M; Sagarasaeranee O; Cogger N; Marshall J; Cuttance E; Macara G; Sood A; Vallee E
Aims
To determine the importance of 11 climate variables on pasture spore count of Pseudopithomyces chartarum, a risk indicator of facial eczema (FE), and to forecast spore counts in New Zealand until 2100, using longitudinal P. chartarum pasture spore count data.
Methods
Between 2010 and 2017, spore counts (n = 6,975) were collected from 862 paddocks spread over 102 farms in the North Island of New Zealand. Historical and projected climate data were obtained from the National Institute of Water and Atmospheric Research. The spore count dataset was merged with climate data from corresponding locations, incorporating time lags of 1–53 weeks. Linear regression models were fitted for describing crude associations, while random forest models were fitted for determining variable importance and predicting future spore counts.
Results
Mixed-effect linear regression models explained up to 11% of the variance of log-transformed spore counts by a single lagged climate covariate. The best-fit random forest model had a testing accuracy of 80% in classifying low or high FE risk (> 20,000 spores) with an R2 value of 43%. The random forest models suggested time-dependent importance of soil temperature at 10 cm depth, solar radiation, potential evapotranspiration, vapour pressure, soil moisture and minimum temperature, while no or weak evidence of variable importance was found for maximum temperature, rainfall, mean sea level atmospheric pressure, relative humidity and wind speed. Over the next 80 years, our model predicted an increase in the seasonal mean spore counts in the study farms by a mean of 17% (min 6, max 30%) under the high-end greenhouse gas emission scenario (representative concentration pathways (RCP) 8.5). Every decade was associated with an increase in the probability of high-risk spore counts (> 20,000) by 14–22% for the moderate to high emission scenarios (RCP 4.5–8.5). The model indicated increased peak spore counts across most regions over the next 80 years. Specifically, the entire North Island and three districts in the South Island were projected to have high mean peak spore counts by 2100.
Conclusions and clinical relevance
These findings could be used to target high-risk areas to implement mitigation or adaptation measures for FE. In addition, the study highlights the value of ecological data for forecasting environmental disease risks to enhance preparedness for climate change.
Using the Adapted Levenberg-Marquardt method to determine the validity of ignoring insulin and glucose data that is affected by mixing
(Elsevier B.V., 2021-04-14) Lam N; Docherty PD; Murray R; Chase JG; Morenga LT
Most parameter ID methods use least squares criterion to fit parameter values to observed behavior. However, the least squares criterion can be heavily influenced by outlying data or un-modelled effects. In such cases, least squares estimation can yield poor results. Outlying data is often manually removed to avoid inaccurate outcomes, but this process is complex, tedious and operator dependent.
This research presents an adaptation of the Levenberg-Marquardt (L-M) parameter identification method that effectively ignores least-square contributions from outlying data. The adapted method (aL-M) is capable of ignoring outlier data in accordance with the coefficient of variation of the residuals and was thus, capable of operator independent omission of outlier data using the 3 standard deviation rule. The aL-M was compared to the original Levenberg-Marquardt (L-M) method in C-peptide, insulin and glucose data. In total three cases were tested: L-M in the full dataset, L-M in the same data where the points that were suspected to be affected by incomplete mixing at the depot site were removed, and the aL-M in the full data set.
There were strong correlations between the aL-M and the reduced dataset from [0.85, 0.71] for the clinically valuable glucose parameters. In contrast, the unreduced data yielded poor residuals and poor correlations with the aL-M [0.44, 0.33]. The aL-M approach provided strong justification for consistent removal of data that was deemed to be affected by mixing.
Money talks : a critique of gender and class relations in the family : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Arts, Massey University
(Massey University, 1991) Morine, Rodney G
This study is about the relationship between women and men in the household. The impact of the non-domestic sphere on the domestic sphere, and vice-versa, is its focus. It explores control over financial resources and the allocation of domestic tasks. Despite a common belief between husbands and wives of more egalitarian ideals operating in both the non-domestic and domestic spheres, this study confirms that inequalities continue. Both gender and class condition the roles of women and men, and the distribution of resources in the household. Overall, men still hold more control over resources than women. However, women with tertiary qualifications, marketable skills and the material resources, had more control over money management and task allocation in their homes relative to women who were either full-time housewives and mothers, or were part of the secondary labour market.
Greenhouse gas mitigation in pasture-based dairy production systems in New Zealand: A review of mitigation options and their interactions
(Elsevier B.V., 2025-08) Kalehe Kankanamge E; Ramilan T; Tozer PR; de Klein C; Romera A; Pieralli S
Reducing greenhouse gas (GHG) emissions from dairy farming is crucial for mitigating climate change and enhancing the environmental credentials of New Zealand's dairy exports. This paper aims to explore potential GHG mitigation measures and their interactive effects when combined within New Zealand context, emphasising the practicality of these combinations, particularly focusing on recent studies of pasture-based dairy systems. The review assesses various mitigation options across animal, manure management, feed-based, soil-related, and system-related interventions and identifies immediately applicable mitigation options based on specific criteria. It also discusses the implementation costs, implications on emissions, and the combined effects of these options when applied as bundles in pasture-based systems using a combination matrix. It is indicated that mitigation options on New Zealand's dairy farms can yield diverse outcomes and costs based on farming characteristics. By analysing different combinations of short-listed, it was found that although most mitigation options are compatible, some may have a lower overall reduction potential because of interaction effects. Integrating lower N fertiliser use, low-emission feed, and reduced stocking rates with high-performing animals provides a practical approach for GHG reductions and potential cost savings. However, implementing compatible mitigation bundles requires better quantification of their interactions, economic viability, and compatibility with existing farming systems which need further research.
Household food insecurity, nutrient intakes and BMI in New Zealand infants
(Cambridge University Press on behalf of The Nutrition Society, 2025-11-03) Katiforis I; Smith C; Haszard JJ; Styles SE; Leong C; Fleming EA; Taylor RW; Conlon CA; Beck KL; Von Hurst PR; Te Morenga LA; Daniels L; Rowan M; Casale M; McLean NH; Cox AM; Jones EA; Brown KJ; Bruckner BR; Jupiterwala R; Wei A; Heath A-LM
Objective: The first year of life is a critical period when nutrient intakes can affect long-term health outcomes. Although household food insecurity may result in inadequate nutrient intakes or a higher risk of obesity, no studies have comprehensively assessed nutrient intakes of infants from food insecure households. This study aimed to investigate how infant nutrient intakes and body mass index (BMI) differ by household food security.
Design: Cross-sectional analysis of the First Foods New Zealand study of infants aged 7–10 months. Two 24-hour diet recalls assessed nutrient intakes. “Usual” intakes were calculated using the Multiple Source Method. BMI z-scores were calculated using World Health Organization Child Growth Standards.
Setting: Dunedin and Auckland, New Zealand.
Participants: Households with infants (n=604) classified as: severely food insecure, moderately food insecure, or food secure.
Results: Nutrient intakes of food insecure and food secure infants were similar, aside from slightly higher free and added sugars intakes in food insecure infants. Energy intakes were adequate, and intakes of most nutrients investigated were likely to be adequate. Severely food insecure infants had a higher mean BMI z-score than food secure infants, although no significant differences in weight categories (underweight; healthy weight; overweight) were observed between groups.
Conclusions: Household food insecurity, in the short term, does not appear to adversely impact the nutrient intakes and weight status of infants. However, mothers may be protecting their infants from potential nutritional impacts of food insecurity. Future research should investigate how food insecurity affects nutrient intakes of the entire household.