Modeling Seasonal Dynamics of Canopy and Fruit Growth in Grapevine for Application in Trellis Tension Monitoring

Abstract

Estimates of canopy and fruit fresh mass are useful for more accurate interpretation of data from the Trellis Tension Monitor, a tool for real-time monitoring of plant growth and predicting yield in trellised crops. In grapevines (Vitis labruscana Bailey), measurements of shoot and fruit fresh mass were collected at frequent intervals (14 to 21 days) over 5 years, and these data were correlated with variables that could be obtained nondestructively: shoot length, number of leaves per shoot, and number of clusters per shoot. Shoot length provided a good estimator of shoot fresh mass in all years. Nonlinear logistic regression models described the dynamics of canopy growth from bloom to the early stages of ripening, which often is poorly represented by simple linear regression approaches to seasonal data. A generalized function indicated a lower bound of ' '600 degree-days, after which an increase in shoot fresh mass could be considered on average to contribute only slightly to further increases in trellis wire tension. The dynamics of fruit mass were captured adequately by a nonlinear function, but not as well as vegetative mass because of larger variances in fruit mass. The number of clusters per shoot was associated with fruit mass only after the accumulation of ' '550 degree-days or, equivalently, the time at which fruit mass exceeded ' '25 g per shoot. Seasonal dynamics of the ratio of fruit to vegetative fresh mass were not sufficiently discernable by the logistic models because of the dominance of fruit mass and its large interannual variation.

Growing degree hours - a simple, accurate, and precise protocol to approximate growing heat summation for grapevines

“…Starting dates other than 1 April, such as 1 January (Tarara and Blom 2009), 20 February (Williams et al 1985), or 15 March (Nendel 2010) were also used for different regions in previous studies. It is obvious that the length of a growing season does impact relative accuracy of cGHS and change cGHS value to potentially alter the regional classification and phenological prediction.…”

“…The biologically effective degree-day model utilizes an upper threshold, a latitude factor for day length, and an adjustment for diurnal temperature range (Gladstones 1992). The length of the growing season and the base temperature were also modified to ensure the inclusion of budbreak regardless of seasonal variation (Huglin 1978;Tarara and Blom 2009) or for different requirements of various growth and development processes (Moncur et al 1989;Nendel 2010).…”

“…For example, temperatures measured at every 15 or 20 min provided partial correction of errors due to uneven distribution of diurnal temperatures (McIntyre et al 1987;Tarara and Blom 2009). More importantly, GDD based on hourly temperatures was assumed to be the most accurate (Cesaraccio et al 2001;Snyder et al 1999) and was used as the best to compare various approximation protocols (Purcell 2003;Rawworth 1994;Roltsch et al 1999;Worner 1998).…”

Nonlinear models to describe production of fruit in Cucurbita pepo and Capiscum annuum

“…In the agricultural sciences, the studies in this area evaluate the entire cycle of a specified species or growth model according to the application of different crop management techniques or comparison between genotypes, as can be seen in Hernández et al (2007), Barrera et al (2008), Tarara et al (2009), Akpo et al (2014 and Carson et al (2014). However, no studies have been published that describe the nonlinear relationships between fruit production in vegetable species undergoing multiple harvesting progresses and the progression of the production cycle.…”

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

“…From DT between budbreak and harvest one can infer the annual pattern of growth, or increase in mass, of the canopy and fruit. At the beginning of the season this increase represents rapid vegetative growth, whereas from soon after berry set, DT is progressively dominated by the increase in fruit mass, even under well-watered conditions (see review, Tarara et al, 2009;Williams et al, 2010;Williams and Matthews, 1990). Under naturally drying soils (e.g., Lebon et al, 2006) and under deficit irrigation (Williams et al, 2010), which is practiced worldwide in irrigated wine grape production, the dominance of DT by fruit growth can be inferred earlier because early water deficits are imposed in part to limit vegetative growth.…”