The clones of the GiSelA® series are the most widely used rootstocks in cherry cultivation in temperate climates. GiSelA® were obtained from a genetic improvement programme carried out at the University of Giessen, Germany. The aim of this project was to obtain a series of rootstocks with gradual control of tree vigour: dwarfing, semi-dwarfing and medium vigour rootstocks.
Among the major advantages of using GiSelA® are excellent earliness of fruiting and high productivity, good graft compatibility with both sweet and sour cherry varieties, regular branching with a wide growth habitus, good winter frost resistance and virus tolerance.
Obviously, the cultivation management of cherry trees must be adapted to the needs of GiSelA® clones: as they are very fertile rootstocks, they require early and regular pruning in order to ensure sufficient vegetative renewal. In addition, they require high and constant water and nutrient inputs in order to ensure good fruit quality combined with high productivity.
Below is the report of Johannes Feldmann, Director of the Consortium Deutscher Baumschulen - CDB, presented at the workshop on cherry varietal innovation at Macfrut 2023.
06 May 2024
‘In production we find traditional and mechanised processes, but logistics are still mainly manual,’ says Mónica Tierno, general manager of the Valle del Jerte Cooperative Group, ‘although this does not mean that it is slower. Time is against this product’.
02 Oct 2024
Calcium is not very mobile throughout the plant. The lack of transport capacity makes it difficult to reach the growing fruits. Another issue is that Calcium follows the water flow through the xylem, which means that its absorption is closely tied to transpiration capacity.
12 Aug 2025
US cherry exports to China have plunged due to trade tensions, redirecting supply to markets like Vietnam and Hong Kong. Prices have hit record lows, sales are soaring, and new trade deals are in progress, signaling a significant shift in the global cherry market landscape.
12 Aug 2025
An innovative hybrid phenological model combines biological knowledge and machine learning to predict cherry blossom. Tested in Japan, South Korea and Switzerland, it delivers higher accuracy and adaptability than traditional models, even with limited data.
