COBISS Kooperativni online bibliografski sistem in servisi COBISS
Steffen Ehrmann
Osebna bibliografija za obdobje 2024-2026
2024
1.
PARENTE, Leandro, EHRMANN, Steffen, FRITZ, Steffen, CINARDI, Giuseppina, WISSER, Dominik,
MALEK, Žiga, et al. Global Pasture Watch - Livestock reference samples based on multi-source sub-national
census data (2000—2024). [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/17665040, DOI: 10.5281/zenodo.14926055. [COBISS.SI-ID 287038723]
2025
2.
PARENTE, Leandro, EHRMANN, Steffen, HENGL, Tomislav, FRITZ, Steffen, MALEK, Žiga,
GONZALEZ FISCHER, Carlos, STANIMIROVA, Radost, WISSER, Dominik, CINARDI, Giuseppina,
SLOAT, Lindsey, et al. Global Pasture Watch - Annual buffalo density layers at 1-km for 2000–2022 (including
95% prediction interval). [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/17485929, DOI: 10.5281/zenodo.17485928. [COBISS.SI-ID 287045635]
3.
PARENTE, Leandro, EHRMANN, Steffen, HENGL, Tomislav, FRITZ, Steffen, MALEK, Žiga,
GONZALEZ FISCHER, Carlos, STANIMIROVA, Radost, WISSER, Dominik, CINARDI, Giuseppina,
SLOAT, Lindsey, et al. Global Pasture Watch - Annual cattle density layers at 1-km for 2000–2022 (including
95% prediction interval). [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/17486471, DOI: 10.5281/zenodo.14933659. [COBISS.SI-ID 287042819]
4.
PARENTE, Leandro, EHRMANN, Steffen, HENGL, Tomislav, FRITZ, Steffen, MALEK, Žiga,
GONZALEZ FISCHER, Carlos, STANIMIROVA, Radost, WISSER, Dominik, CINARDI, Giuseppina,
SLOAT, Lindsey, et al. Global Pasture Watch - Annual goat density layers at 1-km for 2000–2022 (including
95% prediction interval). [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/17490112, DOI: 10.5281/zenodo.14933652. [COBISS.SI-ID 287043587]
5.
PARENTE, Leandro, EHRMANN, Steffen, HENGL, Tomislav, FRITZ, Steffen, MALEK, Žiga,
GONZALEZ FISCHER, Carlos, STANIMIROVA, Radost, WISSER, Dominik, CINARDI, Giuseppina,
SLOAT, Lindsey, et al. Global Pasture Watch - Annual horse density layers at 1-km for 2000–2022 (including
95% prediction interval). [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/17490457, DOI: 10.5281/zenodo.14933646. [COBISS.SI-ID 287045123]
6.
PARENTE, Leandro, EHRMANN, Steffen, HENGL, Tomislav, FRITZ, Steffen, MALEK, Žiga,
GONZALEZ FISCHER, Carlos, STANIMIROVA, Radost, WISSER, Dominik, CINARDI, Giuseppina,
SLOAT, Lindsey, et al. Global Pasture Watch - Annual livestock headcount layers for cattle, goats, sheep,
horses, and buffaloes at 1-km 2000–2022 (FAOSTAT-adjusted) (Part-1). [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/17491242, DOI: 10.5281/zenodo.14933635. [COBISS.SI-ID 287046147]
7.
PARENTE, Leandro, MALEK, Žiga, EHRMANN, Steffen, HENGL, Tomislav, GONZALEZ FISCHER,
Carlos. Global Pasture Watch - Annual maps of potential land for livestock production at 1-km
for 2000–2022 (including production systems). [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/14933679, DOI: 10.5281/zenodo.14933678. [COBISS.SI-ID 287041539]
8.
PARENTE, Leandro, EHRMANN, Steffen, HENGL, Tomislav, FRITZ, Steffen, MALEK, Žiga,
GONZALEZ FISCHER, Carlos, STANIMIROVA, Radost, WISSER, Dominik, CINARDI, Giuseppina,
SLOAT, Lindsey, et al. Global Pasture Watch - Annual sheep density layers at 1-km for 2000–2022 (including
95% prediction interval). [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/17490692, DOI: 10.5281/zenodo.14933640. [COBISS.SI-ID 287044355]
9.
PARENTE, Leandro, EHRMANN, Steffen, BONANNELLA, Carmelo, MALEK, Žiga, STANIMIROVA,
Radost, WISSER, Dominik, CINARDI, Giuseppina, SLOAT, Lindsey, et al. Global Pasture Watch - Global machine learning models for livestock density predictions. [Genève]: Zenodo. 1 spletni vir. https://zenodo.org/records/17665388, DOI: 10.5281/zenodo.17665387. [COBISS.SI-ID 287040003]
2026
10.
PARENTE, Leandro, EHRMANN, Steffen, HENGL, Tomislav, FRITZ, Steffen, BONANNELLA, Carmelo,
MALEK, Žiga, WISSER, Dominik, CINARDI, Giuseppina, SLOAT, Lindsey, et al. Global distribution
of cattle, horses, goats, sheep and buffaloes at 1 km resolution for 2000–2022 based
on subnational census data and spatiotemporal machine learning. PeerJ. 2026, vol. 14, art. no. e21494, 43 str., ilustr. ISSN 2167-8359. https://peerj.com/articles/21494, Repozitorij Univerze v Ljubljani – RUL, DOI: 10.7717/peerj.21494. [COBISS.SI-ID 285294339]