ArticleNew Zealand veterinary journal2026
Machine learning for predicting climate change impacts on
Article in New Zealand veterinary journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
aimsTo determine the importance of 11 climate variables on pasture spore count of
methodsBetween 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.
resultsMixed-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 R 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.
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