In California, Drosophila suzukii, known as SWD, is developing increasing resistance to insecticides. This insect has become a problem for cherry, sugar cane and strawberry growers since its arrival in 2008, spreading to other states in the USA, Mexico and Europe.
SWD females, thanks to their serrated ovipositor, can even lay their eggs in hard-skinned fruits, damaging them. This leads to increased susceptibility to insects and diseases, as pointed out by the University of Minnesota Extension.
Entomologist Jhalindra Rijal conducted studies on SWD in cherries and revealed that resistance problems developed rapidly. Management practices based on spinosin, pyrethroids and malathion contributed to this resistance due to the limited use of active ingredients.
The inability to vary active ingredients has led to increasing resistance among SWD populations, not only in California but also in other regions of the US, such as Georgia and Michigan, which have experienced similar problems. Scientific studies have shown a significant decrease in the susceptibility of SWD adults to various insecticides, resulting in lower than expected mortality. Spinosad resistance has been widespread on the Central Coast.
Fonte: Farm Progress
Foto: Italian Berry
24 Apr 2024
From 2013 to 2022, world production increased from 2.3 to 2.75 million tonnes, while trade flows rose from 350,000 to 940,000 tonnes, an increase of 266%. The value of traded goods reached a record 4.3 billion euro.
06 Aug 2025
Consultant Jessica RodrÃguez explains how Chilean cherry growers can prevent decay and improve quality in 2025. Postharvest strategies, phytosanitary control and size selection ensure healthy fruit and profitability in competitive international markets.
07 May 2026
The 2026 cherry season in the Pacific Northwest starts with forecasts between 19.5 and 21.5 million boxes. Lower output than the 2025 record, an early harvest and closer coordination with California define a key phase for the USA cherry market trends.
07 May 2026
In Turkey, research from Firat University shows how computer vision, deep learning and machine learning can classify cherry ripening stages with accuracy above 95%, helping improve harvest timing, fruit quality and production chain management in orchards.
