Metadata-Version: 2.4
Name: aris_lite
Version: 0.3.0
Summary: Implementation of the ARIS-lite model package.
Author-email: Jan Haacker <jan.haacker@boku.ac.at>
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-Expression: MIT
License-File: LICENSE.txt
Requires-Dist: dask[complete]
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: snowmaus
Requires-Dist: xarray[accel, io, parallel]
Requires-Dist: zarr>=3
Requires-Dist: myst-parser ; extra == "docs"
Requires-Dist: sphinx ; extra == "docs"
Requires-Dist: sphinx-immaterial ; extra == "docs"
Requires-Dist: sphinxcontrib-bibtex ; extra == "docs"
Provides-Extra: docs

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# ARIS-lite

ARIS models plant growth based on environmental parameters. The model
draws on the references at the bottom.

## 🌱 state

The model has been validated against the original ARIS model. This is not
a stable software - future changes may break your work, but I will try
not to.
Canonical CLI namespace is now rooted at `aris`.
Legacy flat commands and legacy module-level `main*`/`cli` functions are
deprecated and will be removed in `0.4.0`.

## 🪛 usage

Small datasets (in-memory):
1. `aris 1go "winter wheat" "maize" input.zarr output.zarr`

Yearly staged processing:
1. `aris calc waterbudget -m snow 2019 2020 2021 2022 2023`
2. `aris calc pheno 2019 2020 2021 2022 2023`
3. `aris calc waterbudget -m soil 2019 2020 2021 2022 2023`
4. `aris calc yield -m both 2019 2020 2021 2022 2023 --yield-max <PATH> --yield-intercept <PATH> --yield-params <PATH>`

Notes:
- yearly path conventions default to `../data` and can be changed via `--base-dir`
- yield mode requires explicit parameter inputs (`--yield-max`, `--yield-intercept`, `--yield-params`)

Optional compatibility (deprecated until `0.4.0`): `aris-1go`,
`aris-calc-waterbudget`, `aris-calc-pheno`, `aris-calc-yield`.

## ✨ features

- calculate water up-take coefficients ("Kc factors") for winter wheat,
    spring barley, maize, soybean, norm potato, and grassland based on
    daily air surface temperature
- calculate soil water content and evapotranspiration
- compute daily crop-specific stress index based on maximum surface air
    temperature and soil water saturation
- estimate crop-specific yield depression (%) from stress indicators

## 📑 API documentation

<https://aris-lite.readthedocs.io>

## 🔗 dependencies

- dask, numpy, pandas, snowmaus, xarray, zarr
- meteorological data
- soil water capacity data

## ⚠️ limitations

- hard-coded observable names, e.g. "max_air_temp"
- stressor-yield depression relation needs to be provided

## 💸 funding

The implementation of ARIS-lite in Python, this repository, is funded by
the Austrian Research Promotion Agency (FFG, www.ffg.at) as part of
CropShift.

## 📚 references

\[1\] Allen, R. G. (Ed.). (2000). Crop evapotranspiration: Guidelines
    for computing crop water requirements (repr). Food and
    Agriculture Organization of the United Nations.  
\[2\] Eitzinger, J., Daneu, V., Kubu, G., Thaler, S., Trnka, M.,
    Schaumberger, A., Schneider, S., & Tran, T. M. A. (2024). Grid based
    monitoring and forecasting system of cropping conditions and risks
    by agrometeorological indicators in Austria – Agricultural Risk
    Information System ARIS. Climate Services, 34, 1.
    <https://doi.org/10.1016/j.cliser.2024.100478>.  
\[3\] Schaumberger, A. (2011). Räumliche Modelle zur Vegetations- und
    Ertragsdynamik im Wirtschaftsgrünland [Dissertation, Graz University
    of Technology].
    <https://repository.tugraz.at/publications/npc97-y3058>.  

