In the San Joaquin Valley of California, leafhoppers (Erythroneura elegantula and Erythroneura variabilis) can severely damage the foliage of grapevines resulting in economic loss. Most Thompson Seedless raisin vineyards, however, don't require treatment for leafhoppers every year. To help make the correct treatment decision, monitoring guidelines and action levels are important. This study provides information on monitoring techniques and action levels for this leafhopper complex. A sustained population of 20 nymphs per leaf during summer broods results in 20% to 30% visible damage to the canopy by harvest in early September. Populations higher than this may require chemical intervention to prevent an economic loss. The photosynthetic activity was reduced in proportion to visible leaf damage. Methods of estimating damage to the canopy from leafhopper activity are presented.
William L. Peacock, Nick K. Dokoozlian, and Billie J. Shaver
Julie M. Tarara, Bernardo Chaves, Luis A. Sanchez, and Nick K. Dokoozlian
The lag phase (L) of grape berry growth is used to determine the timing of hand sampling for yield estimation. In commercial practice, growers apply scalars to measurements of berry of cluster masses under the assumption that fruit was assessed during L, which is the short period of slowest increase in fruit mass that occurs between the first and second sigmoid curves that describe growth in fleshy fruits. To estimate L, we used an automated remote system that indirectly detects increases in vegetative and fruit mass in grapevines by monitoring the tension (T) in the main load-bearing wire of the trellis. We fitted logistic curves to the change in T (ΔT) such that the parameters could be interpreted biologically, particularly the onset of L: the asymptotic deceleration of growth. Curves fit the data well [root mean square error (RMSE) 4.2 to 14.9] in three disparate years and two vineyards. The onset of L was most sensitive to the inflection point of the first logistic curve but relatively insensitive to its shape parameter. The analytical solution of the second derivative of the first logistic curve for its minimum predicted the apparent onset of L with a range of 3 to 5 days among replicates. The roots of the third derivative allowed analytical solutions for the onset of the first rapid growth phase and L, consistently predicting the onset of L 2 to 15 days earlier than was identified by trained observers who examined ΔT curves. Remote sensing of ΔT could better time field sampling and decrease current reliance on visual and tactile assessment to identify the onset of L, thus improving yield estimation in grapes.