paper
2020
Applications & impacts
Sub-seasonal to seasonal Application study
Classical ML (trees, GP, linear, kernels)Seasonal to annual Journal
2020-06-05
008
Jun 2020
Bin Wang, Puyu Feng, Cathy Waters et al.
Agricultural and Forest Meteorology
Uses machine learning to quantify how pre-occurring ENSO signals drive Australian wheat yield variation, linking seasonal climate drivers to agricultural impacts; abstract unavailable.
Quantifying the impacts of pre-occurred ENSO signals on wheat yield variation using machine learning in Australia
In one sentence. Uses machine learning to quantify how pre-occurring ENSO signals drive Australian wheat yield variation, linking seasonal climate drivers to agricultural impacts; abstract unavailable.
Labels
- Topic
- Applications & impacts, Sub-seasonal to seasonal
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1016/j.agrformet.2020.108043 OpenAlex:W3033674502
- Added
- 2026-10-22
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