Machine learning for predicting climate change impacts on <i>Pseudopithomyces chartarum</i> spore counts: a risk indicator of facial eczema
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.
Authors
- Naomi Cogger (ORCID: https://orcid.org/0000-0002-7940-5486)
- Masako Wada (ORCID: https://orcid.org/0000-0002-1186-9364)
- E.L. Cuttance (ORCID: https://orcid.org/0000-0003-0354-5295)
- Jonathan C. Marshall (ORCID: https://orcid.org/0000-0003-0758-9658)
- Onpawee Sagarasaeranee
- Émilie Vallée (ORCID: https://orcid.org/0000-0002-3145-5468)
- Abha Sood (ORCID: https://orcid.org/0000-0002-0231-5757)
- Macara Gregor (ORCID: https://orcid.org/0000-0002-0369-9190)
Institutions
- Equibreed (New Zealand) (NZ)
- Velammal Educational Trust (IN)
- Department of Livestock Development (TH)
- Electronic Product Services (Czechia) (CZ)
- National Institute of Water and Atmospheric Research (NZ)
- Massey University (NZ)
Publication Details
- Journal
- New Zealand Veterinary Journal
- Published
- 2025-11-09
- DOI
- https://doi.org/10.1080/00480169.2025.2579134
- Primary Topic
- Dermatology and Skin Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00