Bioscience for sustainable agriculture and food

Category: CASE Studentships

Predicting blackcurrant gallmite development in the spring

Project No. 2484


Primary Supervisor

Prof  Xiangming Xu- NIAB East Malling


Prof Alan Stewart – University of Sussex

Mr Adrain Harris – NIAB East Malling

Mr Rob Saunders – The Blackcurrent Grower Association (CASE partner)


Cecidophyopsis ribis is a serious pest of blackcurrant crops across Europe, infesting buds and transmitting blackcurrant reversion virus

Rationale: Mites emerge from infected buds in the spring, breed and lay their eggs inside new buds. Predicting mite emergence is important for the management of this pest. Over 20 years, mite emergence has been satisfactorily predicted by an empirical model developed at East Malling. However, this model has failed to give good predictions for most blackcurrant varieties in recent years. Since 2001 when the model was developed, the UK climate has undergone considerable change, affecting both blackcurrant and mite development. This project aims to improve the current model by understanding why it is failing.

Approach: The work is divided into three work packages.

WP1: modelling bud-breaking. Currently blackcurrant bud-breaking is predicted sequentially by a chilling model and then a forcing model. Recently, modelling framework has been developed to flexibly integrate chilling and forcing models, leading to better predictions than previous approaches. In this WP, we will use historical bud-breaking data to develop a flexible integrated model for predicting blackcurrant bud-breaking.

WP2: modelling gall mite development. This WP focuses on the effect of temperature on mite development under controlled conditions. The effects of blackcurrant genotypes on mite development will also be considered. This WP will build a mite phenology model, focusing on the spring emergence stage.

WP3: validating and refining models. With help from growers, blackcurrant bud-breaking and gall mite emergence will be monitored at ≥ 15 fields in several regions for three years. These data will be used to validate the models developed in WP1 and WP2; validation results will be fed back to WP1/2 for model refinement.

Impact: The models will assist growers in timing control measures in the spring, improving crop productivity, and contribute to our understanding on the effect of changing climates on pest-host interactions.