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Lund University (2021)

Remote Sensing Based Pre-Season Yellow Rust Early Warning in Oromia, Ethiopia

Endo, Chinatsu

Titre : Remote Sensing Based Pre-Season Yellow Rust Early Warning in Oromia, Ethiopia

Auteur : Endo, Chinatsu

Université de soutenance : Lund University

Grade : Master’s Degree (Two Years) 2021

Résumé
Yellow rust (Puccinia striiformis f. sp. Tritici) is a crop disease caused by a fungus that regularly infects wheat and causes yield loss in Ethiopia. The disease has a significant impact on the country’s crop production, food security, health, and socioeconomic well-being. Anticipating yellow rust epidemics can help to better manage them and mitigate their adverse impacts.

This study explores the potential of remote sensing-based early prediction of yellow rust in the Oromia region in Ethiopia. The study focuses on modeling the incidence of yellow rust among young wheat in the region by looking at unique environmental conditions that enable off-season survival of the yellow rust pathogen. Off-season rust survival can be influenced by climate conditions and geography of particular wheat fields. The ground yellow rust observation data was analyzed together with the environmental variables generated through AgERA5, CHIRPS, ProbaV-NDVI, and SRTM-DEM by applying the knowledge of Geographical Information Systems (GIS), remote sensing, statistical modeling, and rust epidemiology from past years.

The study demonstrated the potential of yellow rust early warning solely based on remote sensing. When the models are calibrated with the dataset from the same climate zones or the observations limited to only very early stage of wheat growth (tiller-stage), they were found to perform with a higher accuracy level. In order to make the models more reliable and practical, it is recommended that the models are further tested with a larger volume of data to confirm the strength. Consideration of the probability of varying rust severity (low, moderate, high) and types of wheat cultivars would further add value. Lastly, additional field and laboratory-based knowledge of the off-season rust survival would be a vital step towards a more accurate configuration of early warning models.

Mots Clés  : yellow rust, modeling, prediction, early warning, remote sensing, geography, geographical information systems, GIS

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Page publiée le 21 mars 2022