Organic mulches improve vine vigour, yield and physiological response in a semi-arid region

The increase in temperature caused by climate change produces an earlier budbreak date that affects the vineyard, which generates a greater risk of damage by spring frosts.

The Intergovernmental Panel on Climate Change (IPCC) forecasts an increase in global temperature and a decrease in relative humidity (RH) in the coming decades, which may have implications for berry ripening and composition.

Grapevine is grown grafted in most of the world largely because of Phylloxera. Rootstocks not only provide tolerance to Phylloxera, but also ensure the supply of water and mineral nutrients to the scion. Rootstocks are an important means of adaptation to environmental conditions if we want to conserve the typical features of the currently used scion genotypes. To aid this adaptation, we can exploit the large diversity of rootstocks used worldwide. To fully explore this existing rootstock diversity, this work benefits from the unique GreffAdapt vineyard, in which four scion genotypes were studied onto 55 commercial rootstocks in three blocks. The aim of this study was to characterise rootstock regulation of scion mineral status and how it relates to scion development.

Aim: To investigate the impact of Geographical Indications (GI) of South Australia on the rootstock/scion-mediated methoxypyrazine accumulation within the rachis of Shiraz and Cabernet Sauvignon. 

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.