A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps
Rice brown spot (BS) is a chronic disease that affects millions of hectares of rice every growing season, grown by some of the most resource-poor farmers. Despite its widespread occurrence and impact, much still needs to be understood about BS. Reported yield losses in relative terms vary widely fro...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2013
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/165664 |
| _version_ | 1855539698595790848 |
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| author | Barnwal, M.K. Kotasthane, A. Magculia, N. Mukherjee, P.K. Savary, S. Sharma, A.K. Singh, H.B. Singh, U.S. Sparks, A.H. Variar, M. Zaidi, N. |
| author_browse | Barnwal, M.K. Kotasthane, A. Magculia, N. Mukherjee, P.K. Savary, S. Sharma, A.K. Singh, H.B. Singh, U.S. Sparks, A.H. Variar, M. Zaidi, N. |
| author_facet | Barnwal, M.K. Kotasthane, A. Magculia, N. Mukherjee, P.K. Savary, S. Sharma, A.K. Singh, H.B. Singh, U.S. Sparks, A.H. Variar, M. Zaidi, N. |
| author_sort | Barnwal, M.K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Rice brown spot (BS) is a chronic disease that affects millions of hectares of rice every growing season, grown by some of the most resource-poor farmers. Despite its widespread occurrence and impact, much still needs to be understood about BS. Reported yield losses in relative terms vary widely from 4 to 52 %. However, accurate, systematic estimates are lacking. BS is conventionally perceived as a secondary problem that reflects rice crops that experience physiological stresses, e.g. drought and poor soil fertility, rather than a true infectious disease. Much remains to be understood about the mechanisms leading to epidemics and crop losses. Quantitative and qualitative knowledge gaps exist in our understanding of the epidemiological processes, sources of resistance and biocontrol methods. In this review we identify several of these gaps, which if filled, could lead to a strong impact on the management of brown spot. We also use the architecture of a simulation model to position and prioritize these knowledge gaps, assess the epidemiological consequences of disease management options on BS monocyclic processes and explore the impact changing production situations on this important disease. |
| format | Journal Article |
| id | CGSpace165664 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1656642025-05-14T10:39:58Z A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps Barnwal, M.K. Kotasthane, A. Magculia, N. Mukherjee, P.K. Savary, S. Sharma, A.K. Singh, H.B. Singh, U.S. Sparks, A.H. Variar, M. Zaidi, N. brown spot fungal diseases fungi pathogens plant diseases plant pathogenic fungi simulation models Rice brown spot (BS) is a chronic disease that affects millions of hectares of rice every growing season, grown by some of the most resource-poor farmers. Despite its widespread occurrence and impact, much still needs to be understood about BS. Reported yield losses in relative terms vary widely from 4 to 52 %. However, accurate, systematic estimates are lacking. BS is conventionally perceived as a secondary problem that reflects rice crops that experience physiological stresses, e.g. drought and poor soil fertility, rather than a true infectious disease. Much remains to be understood about the mechanisms leading to epidemics and crop losses. Quantitative and qualitative knowledge gaps exist in our understanding of the epidemiological processes, sources of resistance and biocontrol methods. In this review we identify several of these gaps, which if filled, could lead to a strong impact on the management of brown spot. We also use the architecture of a simulation model to position and prioritize these knowledge gaps, assess the epidemiological consequences of disease management options on BS monocyclic processes and explore the impact changing production situations on this important disease. 2013-07 2024-12-19T12:55:19Z 2024-12-19T12:55:19Z Journal Article https://hdl.handle.net/10568/165664 en Open Access Springer Barnwal, M. K.; Kotasthane, A.; Magculia, N.; Mukherjee, P. K.; Savary, S.; Sharma, A. K.; Singh, H. B.; Singh, U. S.; Sparks, A. H.; Variar, M. and Zaidi, N. 2013. A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps. Eur J Plant Pathol, Volume 136 no. 3 p. 443-457 |
| spellingShingle | brown spot fungal diseases fungi pathogens plant diseases plant pathogenic fungi simulation models Barnwal, M.K. Kotasthane, A. Magculia, N. Mukherjee, P.K. Savary, S. Sharma, A.K. Singh, H.B. Singh, U.S. Sparks, A.H. Variar, M. Zaidi, N. A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps |
| title | A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps |
| title_full | A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps |
| title_fullStr | A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps |
| title_full_unstemmed | A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps |
| title_short | A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps |
| title_sort | review on crop losses epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps |
| topic | brown spot fungal diseases fungi pathogens plant diseases plant pathogenic fungi simulation models |
| url | https://hdl.handle.net/10568/165664 |
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