Horticulture: predicting strawberry fruit infection by Mucor and Rhizopus using climatic conditions and pathogen inoculum levels

Over the past 25 years soft fruit production has grown by 600% in the UK and is now valued at > £1.5 billion. Postharvest strawberry losses in particular are largely caused by three fungal pathogens: Botrytis cinerea, Mucor spp. and Rhizopus spp. Economic losses due to fruit rotting do not result from the direct loss of marketable fruit but due primarily to the rejection of whole consignment of fruit if a single rotting fruit is detected in the consignment.

Epidemiology and management of B. cinerea on strawberry is well studied worldwide. Managing B. cinerea on strawberry grown in open field can be effectively achieved with the use of disease forecasting models as demonstrated in a number of countries, including UK. Under protected cropping systems, recent research in the UK has demonstrated that rapid removal of field heat after harvesting and subsequent cool-chain management are sufficient to manage grey mould without the need to use fungicides, similar to managing grey mould on protected raspberry crops. This is because nearly all crop losses due to B. cinerea under protected cropping result from post-harvest rot development of latent infections.

In contrast, as both Mucor and Rhizopus can infect fruit and rapidly lead to visual, pre-harvest fruit decay, pre-harvest management intervention may thus be necessary for the two pathogens. Currently there is, however, insufficient knowledge to predict and manage the infection of Mucor and Rhizopus on strawberry.

This project aims to fill the knowledge gap on the epidemiology of soft rot caused by Mucor and Rhizopus. Specifically, it has three specific objectives:

(1) To collect field data on the infection of fruit by Rhizopus and Mucor;
(2) To develop, validate and finalise models predicting the risk of infection of strawberry fruit by Rhizopus and Mucor;
(3) To develop and plan model implementation for use by the commercial project partner (Berry Garden Growers).

Once implemented, the model can be used by growers for targeted disease management.

Fruits become susceptible to infection by Mucor and Rhizopus spp. from 'pale yellow/white' onwards with the susceptibility increasing with increasing fruit ripeness and infection can complete within a few hours (Agyare 2016). To relate the incidence of fruit infection to the inoculum level and weather conditions, the shorter the period of fruit exposure to pathogen inoculum, the better it is.

We will collect samples of fruit from a commercial field to estimate percent infection by Mucor and Rhizopus within a 24 h period based on a protocol developed in the previous Innovate UK project. We will expose attached developing fruit to external inoculum for 24 h before surface sterilisation and incubation for assessment of fungal infection. Airborne inoculum will be estimated with a plate trapping method based on the MYA-K selective medium for Mucor and Rhizopus (developed at NIAB East Malling), and weather conditions (temperature and humidity) recorded at an interval of 15 min for the 24 h exposure period. The new data set (disease incidence, inoculum, weather) will be combined with the previous data set collected in the IUK project for development and validation of prediction models for the two diseases. Furthermore, Berry Garden Growers (BGG) IT engineers will be involved in discussing implementation of the models into their cloudy server in which several pest and disease models developed at NIAB East Malling had already been implemented and used by BGG growers.

The project will be divided into three work packages (WPs):
WP1. Field data collection. Two commercial tunnels will be used for sampling from early June to October to estimate percent infection by Mucor and Rhizopus within a 24 h exposure period.
WP2. Statistical modelling. Data will be analysed to develop/validate/finalise predictive models.
WP3. Implementation and project management. Develop a plan for implementing the models for practical use.