Assessing the Impact of Ramularia Leaf Spot on Barley: Prospects for Fungicide Protection Strategies and Weather-Based Prediction Models in Argentina
Ramularia leaf spot (RLS), caused by the fungus Ramularia collo-cygni (Rcc), has become a threat to barley production in Argentina and the world, causing grain yield and quality losses. Characteristics of the pathogen such as a long latency period, high evolutionary potential, numerous transposonic...
| Autores principales: | , , , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2025
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/21948 https://bsppjournals.onlinelibrary.wiley.com/doi/10.1111/ppa.14056 https://doi.org/10.1111/ppa.14056 |
| Sumario: | Ramularia leaf spot (RLS), caused by the fungus Ramularia collo-cygni (Rcc), has become a threat to barley production in Argentina and the world, causing grain yield and quality losses. Characteristics of the pathogen such as a long latency period, high evolutionary potential, numerous transposonic regions and the ability to infect alternative hosts facilitate Rcc adaption to environmental changes and/or control measures such as fungicides. RLS is considered a sporadic disease in Argentina and its occurrence is highly dependent on weather conditions. The objectives of this work were to quantify the impact of RLS on grain yield and its commercial quality, to establish an optimal protection period (PP) for barley with fungicides, and to describe the association between environmental variables and levels of RLS with the purpose of designing prediction models and more efficient protection strategies. Based on the results from field trials (n = 8), we estimated grain yield losses up to 16%, and these occurred due to reduced grain weight (8.7%) and size (20%). We also determined that the PP begins from the third detectable node (GS33) to first visible awns (GS49) and concluded that the flag leaf fully emerged stage (GS39) was the most efficient fungicide spraying time. Four possible forecast models were proposed based on the daily average temperature accumulated from early tillering (GS21) to GS39 stage, in combination with the number of days of soil water availability, number of days with water excess and duration of leaf wetness or accumulated rainfall from tillering to GS39 (n = 10). |
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