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Disease-warning systems are management tools that use information about the pathogen, host and/or environment to advise growers when to efficiently take management actions, such as fungicide sprays. This study examined usage of alternative weather information as inputs to disease-warning systems for control of sooty blotch and flyspeck (SBFS) of apple and watermelon anthracnose, as well as the morphology of isolates of the SBFS fungi that grouped with the Mycosphaerellaceae. The first objective was to evaluate the effectiveness of alternative sources of weather data as inputs to disease-warning systems for sooty blotch and flyspeck (SBFS) on apple and watermelon anthracnose caused by Colletotrichum orbiculare (Berk & Mont.) Arx (1957) on watermelon in replicated field trials conducted in Iowa during 2006 to 2008. Leaf wetness duration (LWD) data estimated by commercial site-specific technology, or by model-based corrections of these estimates, were compared with measurements made by on-site sensors. In both warning systems, using remotely estimated and model-corrected LWD data resulted in disease levels that were similar or equal to those observed when using on-site measurements. Our study provides evidence that remotely estimated LWD data and model-corrected versions of these data may be used successfully in implementation of warning systems for SBFS on apple and anthracnose on watermelon. The second objective was to examine the morphology and rDNA sequences of 124 isolates of SBFS fungi from Germany and the U.S. Parsimony analysis of the internal transcribed spacer (ITS) region of the rDNA grouped isolates into 14 clades. Molecular evidence coupled with morphology of representative isolates from each iv

Sooty blotch and flyspeck: Evaluation of remotely estimated weather inputs to disease-warning systems and delineation of fungal taxa closely grouping with Mycosphaerella madeirae