Callinikos: the new white table grapeseedless variety for biological produce

Contamination problems arising from the use of pesticides in viticulture have raised concerns. One of the alternatives to reduce contamination is the use of elicitors, molecules capable of stimulating the natural defences of plants, promoting the production of phenolic compounds (PC) that offer protection against biotic and abiotic stress. Previous studies on Cabernet-Sauvignon seedlings demonstrated that foliar application of elicitors methyl jasmonate (MeJ) and benzothiadiazole (BTH) increased proteins and PC involved in grapevine defence mechanisms. However, no trials had been conducted on Monastrell seedlings, a major winegrape variety in Spain.

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.

“Sangiovese” (Vitis vinifera L. sativa cv. Sangiovese) is the main grape variety to be established in Italy, being the only country in Europe where this grape is commonly found.

The implementation of food safety assurance systems in wineries involves ensuring that the wines produced do not pose a risk to consumer health and are therefore free from harmful substances, such as those that may be incorporated during the production process (pesticides, additives, etc.), allergens or mycotoxins.

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.