Record Details

Data-driven similar response units for agricultural technology targeting: An example from Ethiopia

CGSpace

View Archive Info
 
 
Field Value
 
Title Data-driven similar response units for agricultural technology targeting: An example from Ethiopia
 
Creator Tamene, Lulseged D.
Abera, Wuletawu
Bendito, Eduardo
Erkossa, Teklu
Tariku, Meklit
Sewnet, Habtamu
Tibebe, Degefie
Sied, Jema
Feyisa, Gudina
Wondie, Menale
Tesfaye, Kindie
 
Subject appropriate technology
machine learning
policies
agriculture
farming systems
tecnología apropiada
aprendizaje electrónico
políticas
 
Description Ethiopia has heterogeneous topographic, climatic and socio-ecological systems. Recommendations of agricultural inputs and management practices based on coarse domains such as agro-ecological zones (AEZ) may not lead to accurate targeting, mainly due to large intra-zone variations. The lack of well-targeted recommendations may contribute to the underperformance of promising technologies. Therefore, there is a need to define units where similar environmental and biophysical features prevail, based on which specific recommendations can be made for similar response units (SRUs). We used unsupervised machine learning algorithms to identify areas of high similarity or homogeneous zones called ‘SRUs’ that can guide the targeting of agricultural technologies. SRUs are landscape entities defined by integrating relevant environmental covariates with the intention to identify areas of similar responses. Using environmental spatial data layers such as edaphic and ecological variables for delineation of the SRUs, we applied K- and X-means clustering techniques to generate various granular levels of zonation and define areas of high similarity. The results of the clustering were validated through expert consultation and by comparison with an existing operational AEZ map of Ethiopia. We also augmented validation of the heterogeneity of the SRUs by using field-based crop response to fertiliser application experimental data. The expert consultation highlighted that the SRUs can provide improved clustering of areas of high similarity for targeting interventions. Comparison with the AEZ map indicated that SRUs with the same number of AEZ units captured heterogeneity better with less within-cluster variability of the former. In addition, SRUs show lower within-cluster variability to optimal crop response to fertiliser application compared with AEZs with the same number of classes. This implies that the SRUs can be used for refined agricultural input and technology targeting. The work in this study also developed an operational framework that users can deploy to fetch data from the cloud and generate SRUs for their areas of interest.
 
Date 2022
2022-08-24T13:43:38Z
2022-08-24T13:43:38Z
 
Type Journal Article
 
Identifier Tamene, L.; Abera, W.; Bendito, E.; Erkossa, T.; Tariku, M.; Sewnet, H.; Tibebe, D.; Sied, J.; Feyisa, G.; Wondie, M.; Tesfaye, K. (2022) Data-driven similar response units for agricultural technology targeting: An example from Ethiopia. Experimental Agriculture 58: e27 17 p. ISSN: 0014-4797
0014-4797
https://hdl.handle.net/10568/120934
https://doi.org/10.1017/S0014479722000126
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format 17 p.
application/pdf
 
Publisher Cambridge University Press
 
Source Experimental Agriculture