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

Endo, Chinatsu LU (2021) In Master Thesis in Geographical Information Science GISM01 20202
Dept of Physical Geography and Ecosystem Science
Abstract
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... (More)
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. (Less)
Popular Abstract
Ethiopia is Africa’s leading wheat producer, but the country also suffers from regular yield losses from crop diseases like yellow wheat rust. Wheat rust is a fungal disease, endemic to Ethiopia that has been causing crop loss for a long time.

Scientific contributions to the body of knowledge of yellow rust over centuries have helped to better understand how the fungus infects wheat, how to identify its spread in crops, and aided the development of disease resistant seed varieties. Today, some yellow rust prediction models exist that can provide a warning to farmers so that they can invest in fungicide to potentially mitigate their losses from yellow rust. Many of such models rely on the rust data that comes from the middle of the... (More)
Ethiopia is Africa’s leading wheat producer, but the country also suffers from regular yield losses from crop diseases like yellow wheat rust. Wheat rust is a fungal disease, endemic to Ethiopia that has been causing crop loss for a long time.

Scientific contributions to the body of knowledge of yellow rust over centuries have helped to better understand how the fungus infects wheat, how to identify its spread in crops, and aided the development of disease resistant seed varieties. Today, some yellow rust prediction models exist that can provide a warning to farmers so that they can invest in fungicide to potentially mitigate their losses from yellow rust. Many of such models rely on the rust data that comes from the middle of the season or later. For small-scale farmers in Ethiopia, detection of already spreading rust is too late, as crops are already infected and dying, causing yield loss. Ideally, farmers would have an early warning system that could signal when there is a higher risk of an outbreak of yellow wheat rust before it happens. How can we do this?

This study examines this question by looking at the relationship between the yellow rust incidence among the young wheat and the unique characteristics of climate and geography of the wheat field before the planting season starts. Yellow rust pathogens often survive during the off-season, which causes its spread during the crop season. Using remote sensing and Geographical Information Systems (GIS) technology, environmental conditions such as temperature, precipitation, condition of vegetation, and terrain characteristics were analyzed together with the yellow rust data from the Oromia region in Ethiopia, and prediction models were built.

The models demonstrated the potential of using remote sensing-based earlier warning of yellow rust, especially when they were calibrated with the rust data that comes from the same climate zones or very early stage of wheat growth (tiller stage). The models should be improved with a larger volume of data and with added features of predicting rust probabilities for different severity levels (high, moderate, low) and different wheat varieties based on more rigorous empirical studies in the context of Ethiopia. (Less)
Please use this url to cite or link to this publication:
author
Endo, Chinatsu LU
supervisor
organization
course
GISM01 20202
year
type
H2 - Master's Degree (Two Years)
subject
keywords
yellow rust, modeling, prediction, early warning, remote sensing, geography, geographical information systems, GIS
publication/series
Master Thesis in Geographical Information Science
report number
126
language
English
additional info
External supervisor: Associate Professor Dr. Kees de Bie,
ITC, The University of Twente, The Netherlands
id
9035550
date added to LUP
2021-01-13 13:11:21
date last changed
2021-01-13 13:11:21
@misc{9035550,
  abstract     = {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.},
  author       = {Endo, Chinatsu},
  keyword      = {yellow rust,modeling,prediction,early warning,remote sensing,geography,geographical information systems,GIS},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master Thesis in Geographical Information Science},
  title        = {Remote Sensing Based Pre-Season Yellow Rust Early Warning in Oromia, Ethiopia},
  year         = {2021},
}