|Clark, Nicholas - University Of California|
|Friqulti, Tarilee - University Of California|
|Hutmacher, Robert - University Of California|
|Wright, Steven - University Of California|
|Keeley, Mark - University Of California|
Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/7/2016
Publication Date: 5/15/2016
Citation: Clark, N.E., Friqulti, T., Hutmacher, R.B., Wright, S.D., Keeley, M., Ulloa, M. 2016. The use of disease severity variables in predicting efficacy of FOV4 resistance selection. Beltwide Cotton Conference, January 5-6, 2016, New Orleans, Louisiana. p. 458-463.
Technical Abstract: In 2015, 85 Upland (Gossypium hirsutum L.) accessions from the USDA-ARS Cotton Collection and 126 F6 Pima-S6 x Pima-S7 (G. barbadense L.) recombinant inbred lines were evaluated for disease performance under pressure of Fusarium oxysporum f. sp. vasinfectum race 4 (FOV4) in a replicated field trial in the central San Joaquin Valley of California. Statistical analyses were performed to test the efficacy and biological importance of several variables with respect to each one’s ability to aid the selection of FOV4 resistant lines. Disease severity was measured by foliar symptoms (FS), vascular root staining (VRS), and plant survival (PS). Plant vegetative growth was measured as emergence, plant height, and number of main-stem nodes. We analyzed the relationships of these variables to evaluate the predictability of one variable response from the other variable observation. Selected results from this research demonstrated that FOV4 FS was a reliable predictor of VRS in Pima cotton (r = 0.80), but less so within the G. hirsutum collection (r = 0.54). In Pima, VRS was highly negatively correlated with 8 weeks PS (r = -0.73), a relationship not as dramatic in Upland cotton. Number of nodes and plant height were always well correlated within the two groups (r = 0.84 and 0.73 for Pima and Upland, respectively). Finally, analyses showed a statistically significant tendency toward increased variability with decreased symptom severity. In other words, the worst observed symptoms were proven to be most reliable for ejecting poor performing lines, providing a better resistance selection efficacy.