A 3D model for simulating spatial and temporal fluctuations in grape berry temperature

Highlights

• A new 3D structure-resolving model was developed to predict grape berry temperature

• Model validation used a new data set measuring berry temperature in four vineyards

• Intermittent temperature increases from sun exposure were accurately reproduced

• Modeled berry heat storage reduced errors by damping excessive fluctuations

Abstract

Recent shifts in climatic patterns have influenced the frequency, timing, and severity of heat waves in many wine grape growing regions, which has introduced challenges for viticulturists. Growing the same varieties under these altered climatic conditions often requires mitigation strategies, but quantitative, generalized understanding of the impacts of such strategies can be difficult or time consuming to determine through field trials. This work developed and validated a detailed three-dimensional (3D) model of grape berry temperature that could fully resolve spatial and temporal heterogeneity in berry temperature, and ultimately predict the impacts of potential high berry temperature mitigation strategies such as the use of alternative trellis systems. A novel experimental data set was generated in which the temperature of exposed grape berry clusters was measured with thermocouples at four field sites with different trellis systems, topography, and climate. Experimental measurements indicated that the temperature of shaded berries closely followed the ambient air temperature, but intermittent periods of direct solar radiation could generate berry temperatures in excess of 10${}^{\circ }\mathrm{C}$ above ambient. Validation results indicated that by accurately representing the 3D vine structure, the model was able to closely replicate rapid spatial and temporal fluctuations in berry temperature. Including berry heat storage in the model reduced the errors by dampening extreme temporal swings in berry temperature.

Introduction

Increasing temperatures and temperature variability associated with a changing climate have become a major concern for grape producers due to the sensitivity of grape quality to climate, particularly in wine grape production (Jones, 2007, Mira de Ordua, 2010, Pathak, Maskey, Dahlberg, Kearns, Bali, Zaccaria, 2018, Van Leeuwen, Darriet, 2016, White, Diffenbaugh, Jones, Pal, Giorgi, 2006). Short-term temperature extremes associated with heat waves, along with longer-term shifts in seasonal temperature patterns are known to create significant challenges in managing grape quality. Diurnal fluctuations in solar irradiance and air temperature have been shown to affect amino acid and phenylpropanoid berry metabolism at hourly time scales (Reshef et al., 2019). Elevated temperatures during daily or weekly time periods have been shown to decrease anthocyanin concentration around véraison (Gouot et al., 2018). Furthermore, the duration of the elevated temperatures not only has an effect on berry composition but also on berry skin appearance. Exposed berries can be damaged by sunburn, and even a few minutes of high temperature exposure can result in cellular damage (Hulands, Greer, Harper, et al., 2014, Krasnow, Matthews, Smith, Benz, Weber, Shackel, 2010, Wahid, Gelani, Ashraf, Foolad, 2007). Moderate temperatures can also result in berry injury or death after long-term exposure (Wahid et al., 2007).

Grape producers have begun to implement a number of canopy design and management strategies in an attempt to mitigate the negative effects of elevated berry temperatures, including the use of shade cloth (Greer, Weston, Weedon, 2010, Martínez-Lüscher, Chen, Brillante, Kurtural, 2017), trellis design (Kliewer, Wolpert, Benz, 2000, Nicholas, Durham, 2012), and cluster height (Reynolds, Heuvel, 2009, Reynolds, Wardle, Naylor, 1996). However, grape berry microclimate is complex and highly heterogeneous due to interactions between the vine architecture and the environment, making it difficult to understand and predict the integrated effects of mitigation efforts. Experimental field trials are complicated by the fact that measurement of light and temperature at the berry level is labor-intensive and expensive (Zorer et al., 2013). Furthermore, the relatively slow development of grapevine systems means that field trials are costly and may require many years of data collection.

Because it is not feasible to independently vary every parameter that determines berry temperature in field experiments (e.g., radiation load, bunch exposure, climate, topography, latitude, trellis system), crop models provide a means for understanding, and ultimately optimizing, how grapevine design and management practices can be used to mitigate elevated berry temperatures. Previous process-based models have been developed to predict berry radiative fluxes (Pieri, 2010, Zorer, Moffat, Strever, Hunter, 2013) and berry temperatures from environmental parameters (Cola et al., 2009). However, in these models the calculation of absorbed radiation and the parameters to represent specific geometrical canopy structure are often simplified. Therefore, the models cannot account for the vertical and horizontal variability within the cluster or canopy, making it difficult to represent different design or management choices such as using altered trellis designs or pruning practices. Previous work has developed models for individual grape (Smart and Sinclair, 1976) and apple fruits (Saudreau, Sinoquet, Santin, Marquier, Adam, Longuenesse, Guilioni, Chelle, 2007, Thorpe, 1974), and the work of Saudreau et al. (2011) successfully developed a 3D model of apple fruit temperature. However, to the authors’ knowledge, previously developed 3D grapevine structural models (e.g., Iandolino, Pearcy, Williams, 2013, Louarn, Lecoeur, Lebon, 2008) have yet to be coupled with a physically-based berry temperature model.

This work develops and tests a new 3D model for grape berry temperature based on the Helios modeling framework (Bailey, 2019). The berry temperature model was validated using a unique data set that spans four different canopy geometries. The spatially-explicit nature of the model allows for robust representation of varying canopy architectures and their effect on berry temperature. The objective of this study was to accurately simulate the spatial and temporal grape berry temperature fluctuations from different vineyard designs, such that model predictions are robust to changes in vineyard configuration such as row spacing, trellis system, and row orientation.