AgriHub Project for integrated pest management in Malta

Context

Over the past decade, Malta has consistently reported one of the highest rates of exceedance of maximum residue levels (MRLs) for pesticides in food out of all EU Member States. This has generated increased public and media scrutiny towards pesticide use in Malta (Martin, 2018). In 2020, around 6% of the fruit and vegetables sampled for pesticides exceeded the MRL (EFSA, 2022).

Measures

In response to growing public concern about pesticide use in Malta, the Ministry for Agriculture, Fisheries and Animal Rights of Malta (MAFA) launched the AgriHub project in 2020. The overall goal is to reduce chemical pesticide use in Maltese agriculture by promoting integrated pest management. The project aims to understand and predict the distribution and density of five key pest species affecting crops in Malta, including three species of moths and two species of flies, with the goal of improving their management.

A first step entails collecting data on pest abundance, as well as meteorological and soil conditions, using artificial intelligence (AI) traps (see Figure 1). The traps are powered by solar energy and use pheromones that are specific to each pest to attract and trap them. The traps also measure local parameters, including temperature, humidity and soil condition. The measurements are compiled into a single dataset, to be reviewed by Malta’s Ministry of Agriculture, and used to predict when and where a type of pest is likely to be most abundant based on specific conditions. For example, higher temperatures and humidity could indicate that a particular pest species is likely to be present in numbers that threaten crop production. The data are also meant to feed into modelling studies aiming to predict pest hotspots.

Figure 1. AI trap used as part of Malta’s AgriHub Project

 Source: MAFA

The project is currently in its second step: the data collection phase. Once sufficient data have been collected, MAFA will launch an online platform allowing farmers to access information about the conditions favoured by the five pests. This will function as an early warning system for potential pest outbreaks. Based on this information, farmers can use pesticides only when necessary to control pest outbreaks, thus increasing effectiveness and reducing overall use.

The third step will be developing guidelines for integrated pest management, covering the full range of crops.

Evidence of impact

To date, 30 farmers have been involved in data collection using AI traps. The AI traps have been deployed across Malta on land used to produce five main crops: tomatoes, potatoes, strawberries, olives and vines. Participating farmers receive a red, yellow or green signal to indicate whether the threshold for pest abundance has been exceeded, which allows them to determine when to apply pesticides. The aim is to replace unnecessary and excessive pesticide application with a more targeted approach.

Thus far, the project has seen effective collaboration between the Maltese government and local farmers. It relies on the farmers’ participation in the monitoring phase, because they need to permit monitoring trap installation on their agricultural land and allow researchers access.

Under the next phase of the project, farmers will test guidelines for integrated pest management on the five major crops. The farmers will be asked to follow the guidelines and only implement the agricultural practices recommended by researchers leading the project. MAFA will compensate farmers for their participation in this phase of the project to minimise their economic risk in case of crop failure.

Lessons learned

• Once optimal conditions for specific pests are discovered, monitoring data can be used to predict pest outbreaks. Farmers can use this information and apply pesticides only during periods when the pests are abundant.
• The project helped educate farmers on better approaches to pest management, involving more targeted pesticide application and an overall reduction in the volumes applied.