Practical Risk Prediction to Support Management and Variety Trials: Dealing with Variation in Fusarium oxysporum f. Sp. Vasinfectum Inoculum Density

This presentation occurs during the first year of work toward developing and implementing a predictive model of inoculum dynamics for cotton-Fusarium systems. I present the premise and expected benefits of the work, and preliminary data generated during Cotton Inc. supported research. The motivating issue is the re-emergence of FOV4 in Texas, on which this work focuses, but from which this work will generalize to apply to other soilborne pathosystems. Given the nature of FOV4's pathology, best current options for mitigation are 1) development of resistant varieties, and 2) preventing inoculum movement.  I demonstrate of how in-field inoculum density variation can be characterized to enhance germplasm screening, and describe planned implementation of a predictive epidemiological (risk) model.

The premise is that FOV4 inoculum density varies, and this variation has not only obvious consequences to commercial producers, but also consequences to breeders' and researchers' ability to study varieties for promise in being FOV4 resistant -- with long-term consequences again to commercial producers. Addressing related challenges requires either a degree of experimental control which is not feasible (experimentally uniform inoculum and environment, or prohibitive sample sizes to establish adequate randomization), or an analytical approach that is straightforward to take. I explain how the latter can be accomplished through methods already well-known to practitioners, and how we are measuring inoculum density to support this approach.