How the application of insecticides has created problems where none existed
Futurcrop - 16-05-2019
Several factors (monoculture, climate change, etc.), have influenced the uncontrolled development of pest populations in crops, but mainly the inadequate use of chemical insecticides has affected the natural balance between pests and their predators and parasitoids. General spectrum insecticides indiscriminately kill pests and their predators, both kill the pests that cause damage to crops and their natural enemies.
Red spiders mites (Tetranychus urticae), now a widespread pest in crops around the world, were never a serious pest in agriculture. The populations of the red spider mites were regulated by their natural predators, mainly by the predatory mites of the phytoseid family (although also other families such as some dipterans and coleoptera). But it has been human intervention that has broken that natural balance, through the indiscriminate and systematic use of synthetic organic pesticides since the second half of the 20th century. Twenty years later, populations of mites of the family Tetranychidae, such as the spider mite, are pests that can be very destructive in the agricultural sector.
The obscure mealybug (Pseudococcus viburni) usually do not constitute great damage in crops due to the control exercised over their population by their natural enemies, parasitoids and predators (the families of chrysopids, hemeropods, diptera and coccinellids). However, when the natural enemies of the insect are destroyed by the application of insecticides, the mealybug can be transformed into a harmful pest.
Therefore, the agricultural producer must assess the effect of the chemical treatments on the natural enemies of the pest that is to be controlled. Specific insecticides should be used to control specific pests, and it should be reduced the amount and spectrum of pesticides, in order to facilitate natural biological control.
Potential effects of climate change of insect pest dynamics
Real time pest and vegetable diseases prediction models