Disease Incidence–Severity Relationships on Leaflets, Leaves, and Fruit in the Pecan–Venturia effusa Pathosystem

Abstract

The most destructive disease of pecan in the southeastern United States is scab, caused by Venturia effusa. Incidence (I)-severity (S) relationships have not previously been characterized in this pathosystem, but incidence measures can save time and should have higher accuracy compared with estimates of severity. Ten scab-susceptible cultivars and seedling trees were assessed for I and S of scab on fruit (1,972 trees) and foliage (compound leaves and leaflets, 1,129 trees) between 2010 and 2014. Samples were assessed on a tree basis, and sample size ranged from 10 to 100 specimens per tree. The range in mean I and S was different depending on the organ (fruit I = 0 to 100%, S = 0 to 100%; compound leaves and leaflets, I = 0 to 100%, S = 0–10.1%, respectively). However, mean I could be 100% at a mean S < 2.0% for fruit, compound leaves, and leaflets. Both I and S data were transformed by complementary log-log prior to linear regression analysis. A linear regression model described the relationship between transformed I and S per tree for fruit (P ≤ 0.0001, R2 = 0.61), compound leaves (P ≤ 0.0001, R2 = 0.82), and leaflets (P ≤ 0.0001, R2 = 0.91) for all cultivars. The regression analysis showed significant effects of cultivar and year on the relationship between I and S; therefore, separate analyses were performed for each cultivar and year. Back-transformed predicted severity values showed that the differences among cultivars and years were negligible at severity <80%, and were generally numerically small at severity >80%. The observation that low severity persists until a high incidence of scab is achieved may limit the ability of incidence data to clearly differentiate treatment effects, even when based on the CLL transformation. But if found to be effective, and if used, it would reduce the labor requirements and result in more accurate data being obtained, as incidence estimates do not tend to suffer from the same subjective biases as do visual estimates of severity.