Climatic drivers for assessment of false smut risk in rice ecosystems: A guide for planning effective management trials

Emergence of false smut (RFS) in rice agroecosystems is causing significant yield losses due to its direct impact on grains. To assess the factors associated with host-pathogen interaction, maximum entropy principle is used to identify bioclimatic variables behind the geo-spatial distribution patterns of RFS. Bioclimatic variables with ecological significance, derived from the monthly temperature and precipitation, such as precipitation in the wettest month, mean temperature of the warmest quarter and precipitation seasonality are linked to the occurrence of the disease. These three variables are identified as the strongest predictors of RFS distribution contributing 47.81 %, 26.63 % and 12.43 % respectively. Seasonal precipitation and its variability have played as a limiting factor for the disease. Temperature plays a crucial role in pathogen growth and development (mycelial growth, sclerotial germination and infection) and exerts influences on disease distribution. Spatial distribution pattern of the disease and its intensity, as well as the number of sclerotia/panicle development, germination and infection suitability, suggest that temperature response and daily precipitation during the booting stage have a significant impact on disease development. Number of favourable days composed of temperature response [<i>f(T)</i> > 20] and daily rainfall (>5 mm) is noted to be proportional to the RFS incidence (infected grains/panicle). RFS distribution pattern validated through ground truth data is noted to have a correspondence with rainfall pattern during the wettest month (June-Sep). The rainfall-induced monocyclic infection process at known stage of crop growth is implicated for evaluation of management options as effective fungicides are available.