Abstract

Postharvest soft rots of sweetpotato caused by Rhizopus stolonifer (Rhizopus soft rot) and Dickeya dadantii (bacterial root rot) occur sporadically and can result in significant losses. A 3-year field study related preharvest conditions, including soil texture, chemistry, and fertility; air temperature; soil temperature and moisture; and various cultural practices (153 total variables), to postharvest susceptibility to both diseases in 75 sweetpotato fields in North Carolina and 63 sweetpotato fields in Louisiana. Storage roots were sampled from each field, cured, stored, and inoculated with each pathogen after 100 to 120 days in storage. Disease susceptibility was measured as incidence of diseased storage roots 10 days following inoculation. There was wide variation from field to field in incidence of both diseases (0 to 100% for Rhizopus soft rot and 5 to 95% for bacterial root rot) in both states in each year. Correlations between disease incidence and each of the preharvest variables revealed numerous significant correlations but the variables that correlated with disease incidence were different between North Carolina and Louisiana. Models for both diseases were built by first using forward stepwise regression to identify variables of interest, followed by a mixed-model analysis to produce a final reduced model. For North Carolina fields, postharvest Rhizopus soft rot susceptibility was described by the percentage of the soil cation exchange capacity occupied by calcium, amount of plant-available soil phosphorus, percent soil humic matter, mean air temperature, mean volumetric soil moisture at 40 cm in depth, and mean soil temperature at 2 cm in depth. Postharvest bacterial soft rot susceptibility was described by soil pH and the number of days of high soil temperature late in the season. For Louisiana fields, Rhizopus soft rot susceptibility was described by a complex of variables, including late-season air and soil temperature and late-season days of extreme soil moisture. For bacterial root rot, days of low air temperature and days of high soil temperature late in the season as well as days of low soil moisture best described variation. Although the influence of preharvest variables on postharvest susceptibility was profound for each disease, the complexity of factors involved and differences between the data for the two states makes development of a predictive system extremely difficult.

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