Extraction-modelling approach demonstrates grapevine rooting patterns varies significantly as a result of contrasting ground management and growing environment in cover cropped vineyards

Abstract

The use of cover crops in viticulture has increased in recent decades as growers seek to reduce herbicide use, improve soil organic matter and biodiversity, and minimize soil-related agronomic issues such as compaction and erosion. However, despite the well-documented benefits of cover cropping, soil resource competition from these plant species growing together with grapevines remains a concern among growers. Competition between grapevines and cover crops for water and nutrients is determined by the interactions between their root systems, and the demand for these resources driven by transpiration, growth and maintenance requirements of the whole plant. Recent research also suggests that grapevine root distribution relative to the distribution of cover crop roots is also an important factor, with interactive effects of rainfall, irrigation and vineyard floor management appearing to modify the relative grapevine share of the soil volume. An improved understanding of the functional implications of varying grapevine rooting patterns would provide growers with more informed strategies to manage grape production in cover-cropped vineyards, where for example such information would allow for better-optimized vineyard establishment where the grapevine root system is allowed to establish its territory before cover crop species are introduced. This current work proposes to address this question with a new approach to characterizing root system architecture (RSA) through a novel combination of in-field measurement and modelling, providing a root system structure that can later be used to model water and nutrient uptake. For the first validation of this method, soil core samples were collected from vineyards with contrasting ground management systems, the roots washed and for scanned image analysis in WinRhizo, and distribution data inputted into a newly developed deep-learning 3D RSA generator to create root system architecture snapshots representative of its growing environment. Some early generative results based on field-collected root parameters collected from commercial vineyards in Orange, NSW, Australia and Marlborough, New Zealand wine regions, from the 3D RSA generator, will be demonstrated in this presentation to showcase some of the capabilities of this RSA generator and the contrasting RSA of grapevines growing in different growing environments.