Sunburn in grapevine berries is known as a recurring disorder causing severe yield losses and a decline in berry quality. The transition from healthy to sunburnt along a temporal trajectory is not fully understood. It is driven by light-boosted local heat impact and modulated by, e.g., past environments of the berry and its developmental state. Events of berry sunburn are often associated with heatwaves, indicating a link to climate change. In addition, the sensitivity of grapevine architecture to changing environmental condition indicates an urgent need to investigate and adapt mitigation strategies of berry sunburn in future vineyards. In this perspective, we want to identify missing links in predicting berry sunburn in vineyards and propose a modeling framework that may help us to investigate berry sunburn in future vineyards. For this, we propose to address open issues in both developing a model of berry sunburn and considering dynamic canopy growth, and canopy interaction with the environment and plant management such as shoot positioning or leaf removal. Because local environmental conditions drive sunburn, we aim at showing that identifying sunburn-reducing strategies in a vineyard under future environmental conditions can be supported by a modeling approach that integrates effects of management practices over time and takes grapevine architecture explicitly into account. We argue that functional-structural plant models may address such complex tasks. Once open issues are solved, they might be a promising tool to advance our knowledge on reducing risks of berry sunburnin silico.

Future climatic conditions might have severe effects on grapevine architecture, which will be highly relevant for vineyard management decisions on shoot positioning, pruning or cutting. This study was designed to help gaining insight into how, in particular, increasing temperatures might affect grapevine canopies. We developed a functional-structural model for Riesling, Virtual Riesling, based on digitised data of real plants and a comprehensive state-of-the-art data analysis. The model accounts for the variability in temperature-sensitive morphological processes, such as bud break and appearance rates. Our simulation study using historical weather data revealed significant effects of the thermal time course over the year on bud burst of the cane and on primary shoots. High variabilities in these events affect canopy growth and leaf area distribution. This report shows that Virtual Riesling can be useful in assessing the significance of changing temperatures for grapevine architecture and thereby considering management techniques such as vertical shoot positioning. Further developments of Virtual Riesling might support the knowledge gain for developing necessary adaptations in future vineyard management and, thus, facilitate future work on climate change research using functional-structural model approaches.

Leaf removal is a standard vineyard management technique to influence grape composition or to reduce disease pressure; however, the timing and intensity of leaf removal is a widely discussed issue. The interplay of different goals and effects over time does not make experimental studies any easier. To gain insight into positive and negative consequences of leaf removal on grapevine development, a first step can be to study how leaf removal affects the canopy’s light absorption using a dynamic model approach. Functional–structural plant models combine canopy architecture with physiological processes and allow analysing canopy interaction with the environment with great topological detail. The functional–structural plant model Virtual Riesling simulates Riesling vines in a vineyard set-up depending on temperature and plant management. We implemented leaf removal and applied this method in or above the bunch zone to compare the light absorption in canopies. Leaf removal in the bunch zone led to greater loss of absorbed light, but canopies of both scenarios could compensate for most of the loss during the simulation time frame. Compensation was mainly driven by lateral leaves closing the gaps induced by leaf removal and by leaves in the proximity of the leaf removal zones, re-exposed to light. Results showed similar effects as observed in in vivo studies; hence, we suggest extending these simulations to investigate other effects linked to light distribution such as berry sunburn. Simple modifications of implemented leaf removal techniques also allow for testing different application scopes and their impact on canopy light absorption.

Modeling plant growth, in particular with functional-structural plant models, can provide tools to study impacts of changing environments in silico. Simulation studies can be used as pilot studies for reducing the on-field experimental effort when predictive capabilities are given. Robust model calibration leads to less fragile predictions, while introducing uncertainties in predictions allows accounting for natural variability, resulting in stochastic plant growth models. In this study, stochastic model components that can be implemented into the functional-structural plant model Virtual Riesling are developed relying on Bayesian model calibration with the goal to enhance the model towards a fully stochastic model. In this first step, model development targeting phenology, in particular budburst variability, phytomer development rate and internode growth are presented in detail. Multi-objective optimization is applied to estimate a single set of cardinal temperatures, which is used in phenology and growth modeling based on a development days approach. Measurements from two seasons of grapevines grown in a vineyard with free-air carbon dioxide enrichment (FACE) are used; thus, model building and selection are coupled with an investigation as to whether including effects of elevated CO${}_2$ conditions to be expected in 2050 would improve the models. The results show how natural variability complicates the detection of possible treatment effects, but demonstrate that Bayesian calibration in combination with mixed models can realistically recover natural shoot growth variability in predictions. We expect these and further stochastic model extensions to result in more realistic virtual plant simulations to study effects, which are used to conduct in silico studies of canopy microclimate and its effects on grape health and quality.