Z. Adimassu

Z. Adimassu


Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
24 July 2012 in Land Degradation & Development
Verified
Journal Article
Closed access
PDF file
ZAZ. Adimassu
Z. Adimassu
KMK. Mekonnen
K. Mekonnen
CYC. Yirga
C. Yirga
AKA. Kessler
A. Kessler

13 October 2016 in Environmental Management
Verified
Journal Article
Closed access
PDF file
ZAZ. Adimassu
Z. Adimassu
SLSimon Langan
Simon Langan
RJRobyn Johnston
Robyn Johnston
WMWolde Mekuria
Wolde Mekuria
TATilahun Amede
Tilahun Amede

8 August 2019 in Land Degradation & Development
Verified
Journal Article
Closed access
PDF file
WAWuletawu Abera
Wuletawu Abera
LTLulseged Tamene
Lulseged Tamene
DTDegefie Tibebe
Degefie Tibebe
ZAZ. Adimassu
Z. Adimassu
HKHabtemariam Kassa
Habtemariam Kassa
LVLouis Verchot
Louis Verchot

1 September 2017 in International Soil and Water Conservation Research
Verified
Journal Article
Open access
LTLulseged Tamene
Lulseged Tamene
ZAZ. Adimassu
Z. Adimassu
EAErmias Aynekulu
Ermias Aynekulu
TYTesfaye Yaekob
Tesfaye Yaekob
Soil erosion is a very critical form of land degradation resulting in the loss of soil nutrients and downstream sedimentation of water storages in the highlands of Ethiopia. As it is technically and financially impossible to conserve all landscapes affected by erosion, identification of priority areas of intervention is necessary. Spatially distributed erosion models can help map landscape susceptibility to erosion and identify high erosion risk areas. Integration of erosion models with geographic information systems (GIS) enables assessing evaluate the spatial variability of soil erosion and plan implementing conservation measures at landscape levels. In this study, the Revised Universal Soil Loss Equation adjusted for sediment delivery ratio was used in a GIS system to assess landscape sensitivity to erosion and identify hotspots. The approach was applied in three catchments with size being 10–20 km2 and results were compared against quantitative and semi-quantitative data. The model estimated mean soil loss rates of about 45 t ha−1 y−1 with an average variability of 30% between catchments. The estimated soil loss rate is above the tolerable limit of 10 t ha−1 y−1. The model predicted high soil loss rates at steep slopes and shoulder positions as well as along gullies. The results of the study demonstrate that knowledge of spatial patterns of high soil loss risk areas can help deploy site-specific conservation measures

21 June 2015 in Environment, Development and Sustainability
Verified
Journal Article
Closed access
PDF file
ZAZ. Adimassu
Z. Adimassu
SLSimon Langan
Simon Langan
RJRobyn Johnston
Robyn Johnston

1 January 2019 in Agricultural Systems
Verified
Journal Article
Closed access
ZAZ. Adimassu
Z. Adimassu
GAGetachew Alemu
Getachew Alemu
LTLulseged Tamene
Lulseged Tamene