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Using climatic and microbiome data to predict apple fruit quality

  • Full or part time
    Prof X Xu
    Prof X Hong
  • Application Deadline
    Friday, February 28, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

REFERENCE: CTP_FCR_2020_1
Supervisors: Prof Xiangming Xu (NIAB EMR), Prof Xia Hong (University of Reading)

This student will be registered with the University of Reading. Beginning in October 2020, the successful candidate should have (or expect to have) an Honours Degree (or equivalent) with a minimum of 2.1, in Plant Science, Applied Statistics or other related science subjects.

Background

Apple fruit are usually stored for minim six months in controlled atmosphere before marketing in the UK. It is not unusual for the stored fruit to suffer from 10-15% post-storage losses due to various causes, including physiological disorders and fungal rotting. This leads to not only yield losses but also increased cost in sorting fruit post-storage. Fruit storability (i.e. post-storage fruit quality) can be affected by many factors, including flowering time, fruit ripeness at picking, fruit surface microflora, and climatic factors. Furthermore, the relationships of fruit quality with these factors are usually non-linear and the precise causal-relationships have yet to be elucidated.

Objectives and approaches

There are three specific research questions/objectives: (1) predicting flowering time, (2) predicting fruit ripeness, and (3) studying the relationship of epiphytes with latent fungal fruit infection. Predicting the degree of fruit ripeness is critically important since it has been well established that fruit ripeness at picking could significantly affect fruit storage potential.

WP1: Predicting flowering time. Historic data collected at NAB EMR over the last 80 years will be used to study the temporal flowering pattern of several specific cultivars in relation to winter and spring climatic data. Statistical modelling will be carried out to study whether the temporal flowering pattern could be predicated from the winter and spring climatic data alone.

WP2: Predicting fruit ripeness. One key research activity is to work with crop physiologist in order to define physiologically/biochemically what is “perfect fruit ripeness” for harvesting. Previous research on predicting fruit ripeness is based on batches of fruit. To understand the variability of fruit ripeness among individual fruit, we propose to follow the development of individual fruit on several popular cultivars to investigate whether ripeness is dependent on post-blossom temperatures as well as the actual flowering time.

WP3: Relating microbial epiphytes to latent fungal infection. Studies from other fruit crops indicate that epiphyte microbiomes on fruit surfaces could significantly affect the establishment of latent infection in fruit by fungal pathogens. We aim to determine whether post-harvest rot development of individual fruit is related to epiphyte microbiomes on the fruit surface. Neonectria ditissima will be used as a model pathogen system. Amplicon-based meta-barcoding technology will be used to characterise microbiomes on individual fruit surfaces.

Training

The successful candidate will gain a wide range of experience in plant physiology, plant pathology, statistical data analysis and modelling, modern sequence technology, and sequence data processing and subsequent statistical analysis.

Application

Anyone interested should contact for the application form and return the form to citing the reference before the deadline of 28th February 2020.

Contact Prof Xiangming Xu () for an informal discussion.

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