Location: Horticultural Crops ResearchTitle: Analytical determination of the lag phase in grapes by remote measurement of trellis tension) Author
Submitted to: HortScience
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/27/2013
Publication Date: 4/24/2013
Citation: Tarara, J.M., Chaves, B., Sanchez, L.A., Dokoozlian, N.K. 2013. Analytical determination of the lag phase in grapes by remote measurement of trellis tension. HortScience. 48:453-461. Interpretive Summary: Grape growers need to know in advance of harvest an estimate of crop yield so that they can schedule harvest, wine processing, and the potential for product supply. They make their estimations by hand-collecting fruit at a certain time of grape development that they refer to as "lag phase" when the berries are in a brief hiatus in growth. The days comprising "lag phase" allows them time to pick berries at fixed size and then multiply berry size to estimate final, or harvest size. The timing for the process is tricky because it is hard to tell in the vineyard when the berries are in "lag phase." We used a sensor attached to the vineyard trellis wire to gather daily data on berry growth and thus show the growers when the increase in berry size was minimal, meaning that it was time to collect samples for making yield estimations. The data from the sensor was more accurate for picking "lag phase" than were human assessments by experienced vineyard workers.
Technical Abstract: 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 ('T) 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 d 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 d earlier than was identified by trained observers who examined 'T curves. Remote sensing of 'T 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.