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Author: search.filters.author.Wada MSubject: search.filters.subject.livestock disease

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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.
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    Development of LIME-NZ: a generic tool for prompt estimation of economic impacts of disease for New Zealand livestock.
    (Taylor and Francis Group, 2024-02-04) Wada M; Compton C; Hickson R; Bingham P
    AIMS: To develop a simple and robust generic tool to measure the impacts of livestock diseases on New Zealand dairy, beef and sheep farms using enterprise gross margin models. METHODS: The most recent (2018-2020) livestock production benchmarking data was extracted from industry-led economic surveys. Gross margin models were built for each enterprise type, accounting for 11 dairy farm types and 16 farm types for beef and sheep. Disease parameters, including changes in mortality, reproduction performance, milk yield, price of animals and culling rate, as well as additional expenses for veterinary intervention, were applied to the infected compartment of the herd/flock using the assumed annual within-herd disease incidence. Farm-level disease impacts were estimated as the difference in annual profit between the baseline and infected farm. The baseline gross margin models were validated against the industry data. The disease impact models were validated using a recently published study on bovine viral diarrhoea (BVD). The impact assessment tool, LIME-NZ, was developed using the statistical software R and implemented in the web-based R package Shiny. The input parameters can be varied interactively to obtain a range of disease impacts for uncertain disease parameters. RESULTS: The baseline gross margin models demonstrated reasonable accuracy with a mean percentage error of <14% when compared with the industry reports. The estimated annual impacts of BVD were comparable to those reported in the BVD study, NZ$38.5-140.4 thousand and $0.9-32.6 thousand per farm per year for dairy and beef enterprises, respectively. CONCLUSIONS: LIME-NZ can be used to rapidly obtain the likely economic impacts of diseases that are endemic, recently introduced or at increased risk of introduction in the New Zealand context. This will aid communication and decision-making among government agencies and the livestock industry, including veterinarians and livestock producers, about the management of diseases, until refined information becomes available to improve decision-making.