Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS)
Rice is a critical staple crop that feeds more than half of the world’s population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by Xanthomonas oryzae . Understanding the possible effects of climate change on the geographic distribution o...
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| creator | Khalaf, Sameh M. H. Alqahtani, Monerah S. M. Ali, Mohamed R. M. Abdelalim, Ibrahim T. I. Hodhod, Mohamed S. |
| description | Rice is a critical staple crop that feeds more than half of the world’s population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by
Xanthomonas oryzae
. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of
X. oryzae
under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of
X. oryzae
and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that
X. oryzae
’s potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter’s mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.
Graphical Abstract |
| doi_str_mv | 10.1007/s10661-024-13215-8 |
| format | Article |
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Xanthomonas oryzae
. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of
X. oryzae
under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of
X. oryzae
and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that
X. oryzae
’s potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter’s mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.
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Xanthomonas oryzae
. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of
X. oryzae
under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of
X. oryzae
and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that
X. oryzae
’s potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter’s mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.
Graphical Abstract</description><subject>Agricultural practices</subject><subject>Algorithms</subject><subject>atmospheric precipitation</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Bacteria</subject><subject>Bioclimatology</subject><subject>Blight</subject><subject>Climate</subject><subject>Climate Change</subject><subject>Climate change scenarios</subject><subject>Climate effects</subject><subject>Climate models</subject><subject>Climate prediction</subject><subject>Crop production</subject><subject>Earth and Environmental Science</subject><subject>Ecological distribution</subject><subject>Ecological effects</subject><subject>Ecological niches</subject><subject>Ecology</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Environmental Monitoring</subject><subject>Food security</subject><subject>Geographic Information Systems</subject><subject>Geographical distribution</subject><subject>Geographical information systems</subject><subject>Host plants</subject><subject>Information systems</subject><subject>Management methods</subject><subject>Mean temperatures</subject><subject>Modelling</subject><subject>monitoring</subject><subject>Monitoring/Environmental Analysis</subject><subject>Niches</subject><subject>Oryza - microbiology</subject><subject>Performance prediction</subject><subject>Plant diseases</subject><subject>Plant Diseases - microbiology</subject><subject>Plant Diseases - statistics & numerical data</subject><subject>Plant layout</subject><subject>Precipitation</subject><subject>Rice</subject><subject>risk</subject><subject>staple crops</subject><subject>temperature</subject><subject>viruses</subject><subject>Xanthomonas</subject><subject>Xanthomonas oryzae</subject><subject>Xanthomonas oryzae pv. oryzae</subject><issn>0167-6369</issn><issn>1573-2959</issn><issn>1573-2959</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc1u1DAUhS0EokPhBVggS2ymC4N_4r8lqkoZqYgFILGLbhwn45LEg50swqv0ZXE7BaQuEKsr637nnGsdhF4y-oZRqt9mRpVihPKKMMGZJOYR2jCpBeFW2sdoQ5nSRAllT9CznK8ppVZX9ik6EbZi2lizQTcfY-uHMPXYDWGE2ZPkhzJa3A-xgQGnkL_jEQ4Zx648nMcNuNmnUHbNEPr9jB0suQiaFX-Dad7HMU5Q8LT-BI-3u7wPK4yAmeX8DC_5Nqz3sU9w2AdXbMLUxVSyQ5xwXvPsR7y93H0-e46edDBk_-J-nqKv7y--nH8gV58ud-fvrojjxs4EwHNNQXPHWLmtqpgEBo0H2lpDmZWt9aIBRbtOK-CtaW2npHSVbaySTSVO0fboe0jxx-LzXI8hOz8MMPm45FowKUyljeD_gTKtWGWEKOjrB-h1XNJUPnJHaS2FkoXiR8qlmHPyXX1IpYe01ozWty3Xx5br0nJ913JtiujVvfXSjL79I_ldawHEEchlNfU-_c3-h-0vKlmzMQ</recordid><startdate>20241101</startdate><enddate>20241101</enddate><creator>Khalaf, Sameh M. 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H.</au><au>Alqahtani, Monerah S. M.</au><au>Ali, Mohamed R. M.</au><au>Abdelalim, Ibrahim T. I.</au><au>Hodhod, Mohamed S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS)</atitle><jtitle>Environmental monitoring and assessment</jtitle><stitle>Environ Monit Assess</stitle><addtitle>Environ Monit Assess</addtitle><date>2024-11-01</date><risdate>2024</risdate><volume>196</volume><issue>11</issue><spage>1064</spage><epage>1064</epage><pages>1064-1064</pages><artnum>1064</artnum><issn>0167-6369</issn><issn>1573-2959</issn><eissn>1573-2959</eissn><abstract>Rice is a critical staple crop that feeds more than half of the world’s population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by
Xanthomonas oryzae
. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of
X. oryzae
under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of
X. oryzae
and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that
X. oryzae
’s potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter’s mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.
Graphical Abstract</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>39417898</pmid><doi>10.1007/s10661-024-13215-8</doi><tpages>1</tpages></addata></record> |
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| identifier | ISSN: 0167-6369 |
| ispartof | Environmental monitoring and assessment, 2024-11, Vol.196 (11), p.1064-1064, Article 1064 |
| issn | 0167-6369 1573-2959 1573-2959 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3153847832 |
| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Agricultural practices Algorithms atmospheric precipitation Atmospheric Protection/Air Quality Control/Air Pollution Bacteria Bioclimatology Blight Climate Climate Change Climate change scenarios Climate effects Climate models Climate prediction Crop production Earth and Environmental Science Ecological distribution Ecological effects Ecological niches Ecology Ecotoxicology Environment Environmental Management Environmental Monitoring Food security Geographic Information Systems Geographical distribution Geographical information systems Host plants Information systems Management methods Mean temperatures Modelling monitoring Monitoring/Environmental Analysis Niches Oryza - microbiology Performance prediction Plant diseases Plant Diseases - microbiology Plant Diseases - statistics & numerical data Plant layout Precipitation Rice risk staple crops temperature viruses Xanthomonas Xanthomonas oryzae Xanthomonas oryzae pv. oryzae |
| title | Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS) |
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