Longitudinal analysis of input use farm management practices and climate adaptation on Ethiopian oilseed crop yields

This study examines the dynamic drivers of oilseed crop yields in Ethiopia, a subsector that plays a vital role in Ethiopia’s agricultural economy. It aims to identify the key determinants of oilseed productivity, focusing on agricultural inputs, farm management practices, climate change adaptation strategies, and institutional factors, using a dynamic System Generalised Method of Moments (GMM) model. This model is applied to pseudo-panel data from the Ethiopian Annual Agricultural Survey (2003–2021). The results indicate that yields from the previous season significantly affect current yields. This is confirmed by an elasticity of lagged yields of 0.992. Land used for cultivation is found to be the main input with an elasticity of 0.812, while urea fertiliser has a negative elasticity (-0.162), indicating inefficiency in its use. Productivity growth is also found to be driven by specific oilseed crop management practices such as soil conservation (32.91%) and crop protection (47.79%). Climate change adaptation practices also contribute to oilseed productivity growth, with specific emphasis on practices such as terracing (18.88%) and water catchments (21.89%). Access to credit also increases productivity by 14.67%. These results are useful in developing an integrated policy aimed at improving oilseed productivity in Ethiopia.

Authors

Institutions

Publication Details

Journal
Discover Agriculture
Published
2026-09-25
DOI
https://doi.org/10.1007/s44279-026-00781-3
Primary Topic
Climate change impacts on agriculture
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Longitudinal analysis of input use farm management practices and climate adaptation on Ethiopian oilseed crop yields

Daregot Berihun Tenessa
Discover Agriculture
Climate change impacts on agriculture
article

Longitudinal analysis of input use farm management practices and climate adaptation on Ethiopian oilseed crop yields

Daregot Berihun Tenessa
article en

Abstract

This study examines the dynamic drivers of oilseed crop yields in Ethiopia, a subsector that plays a vital role in Ethiopia’s agricultural economy. It aims to identify the key determinants of oilseed productivity, focusing on agricultural inputs, farm management practices, climate change adaptation strategies, and institutional factors, using a dynamic System Generalised Method of Moments (GMM) model. This model is applied to pseudo-panel data from the Ethiopian Annual Agricultural Survey (2003–2021). The results indicate that yields from the previous season significantly affect current yields. This is confirmed by an elasticity of lagged yields of 0.992. Land used for cultivation is found to be the main input with an elasticity of 0.812, while urea fertiliser has a negative elasticity (-0.162), indicating inefficiency in its use. Productivity growth is also found to be driven by specific oilseed crop management practices such as soil conservation (32.91%) and crop protection (47.79%). Climate change adaptation practices also contribute to oilseed productivity growth, with specific emphasis on practices such as terracing (18.88%) and water catchments (21.89%). Access to credit also increases productivity by 14.67%. These results are useful in developing an integrated policy aimed at improving oilseed productivity in Ethiopia.

Discover AgricultureVol. 4(1)
Bahir Dar University (ET)
Climate action
Openalex Percentile: Top 8%
Climate change impacts on agriculture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.