Analytical Determination of the Lag Phase in Grapes by Remote Measurement of Trellis Tension

Abstract

<jats:p>The lag phase (<jats:italic>L</jats:italic>) 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 <jats:italic>L</jats:italic>, 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 <jats:italic>L</jats:italic>, we used an automated remote system that indirectly detects increases in vegetative and fruit mass in grapevines by monitoring the tension (<jats:italic>T</jats:italic>) in the main load-bearing wire of the trellis. We fitted logistic curves to the change in <jats:italic>T</jats:italic> (Δ<jats:italic>T</jats:italic>) such that the parameters could be interpreted biologically, particularly the onset of <jats:italic>L</jats:italic>: 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 <jats:italic>L</jats:italic> 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 <jats:italic>L</jats:italic> 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 <jats:italic>L</jats:italic>, consistently predicting the onset of <jats:italic>L</jats:italic> 2 to 15 days earlier than was identified by trained observers who examined Δ<jats:italic>T</jats:italic> curves. Remote sensing of Δ<jats:italic>T</jats:italic> could better time field sampling and decrease current reliance on visual and tactile assessment to identify the onset of <jats:italic>L</jats:italic>, thus improving yield estimation in grapes.</jats:p>

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

“…Other indirect methods include Bayesian growth models [70], weather-based models [71], models based on a combination of variables (meteorological, phenological and phytopathological) [6,72], dynamic crop models such as the "Simulateur mulTIdisciplinaire pour les Cultures Standard" (STICS) [73,74], crop biometric maps [75], and the continuous measurement of the tension in the horizontal (cordon) support wire of the trellis [37,76,77], also used to determine the best moment of hand sampling for yield estimation [78].…”

“…The linear regression found in the studies demonstrates good results and estimation, with better results than the traditional manual sampling. The trellis tension methodology can also be used to determine the timing for traditional hand sampling for yield estimation to determine the lag phase, thus eliminating the field scouting subjective visual and tactile assessments to assess whether berries are at lag phase [78].…”

Detecting Variability and Analyzing Vineyards Vegetation Characteristics Using Satellite Remote Sensing Data in Aswan, Egypt

“…One of the main problems facing farmers is variability in the growth of grape vines which affects production. The technique of remote sensing is used for the rapid, precise and early detection of these differences within the field (Clay & Shanahan, 2011;Tarara et al, 2013 andVanino et al, 2015). These studying enable farmers to intervene rapidly to solve the minute problem.…”