Simplified modelling enhances biocontrol decision making in tomato greenhouses for three important pest species

Generalist and specialist predators are successfully used in biocontrol programs in greenhouse vegetable crops, like tomato. A greenhouse ecosystem is rather simple and provides an excellent opportunity for developing predator-prey decision models. Three systems were selected: (1) the generalist predatory bugMacrolophus pygmaeusand the greenhouse whiteflyTrialeurodes vaporariorum, (2) the generalist predatory bugNesidiocoris tenuisand the tobacco whitefly Bemisia tabaciand (3) the specialist predatory mitePhytoseiulus persimilisand the spider miteTetranychus urticae. The study is based on an extensive field dataset. No complex mathematical predator-prey models were developed. A binomial variable was given the value of 0 for the period when the pest was not under control. As soon as the population declined after the peak density, this variable was given a value of 1. The relationship between the densities of the prey and the predator was checked using a logistic regression model. The validated models do not calculate future pest densities but rather predict when pest control should be initiated, based on the number of pests and predators present at a certain time. Numerical simulation of the prey isoclines showed an L-shaped curve for the generalist predators and a linear curve for the specialist predators. Our simple, empirical modelling approach provides satisfactory models for biocontrol purposes. When combined with a standardized monitoring protocol, these models can be implemented in decision tools. In the future, more data will allow a machine learning approach, in which additional parameters like temperature, humidity, and time can be included.

Automated summary

The study examines the use of generalist and specialist predators in biocontrol programs for greenhouse vegetable crops, focusing on developing predator-prey decision models based on extensive field data to predict when pest control should be initiated. The study did not involve complex mathematical models but instead used a simple empirical approach, showing satisfactory biocontrol models which, when combined with standardized monitoring protocols, can be implemented in decision-making tools. In the future, additional data will allow for a machine learning approach incorporating parameters like temperature, humidity, and time.