Digital Challenges in Fruit Tree Breeding
"At InHort in Skierniewice, we conduct an apple breeding program. It takes between 15 and 20 years to obtain a new variety including seedling selection in tunnels, fruits evaluation and clones selection. The best clones are also evaluated before the registration to the Polish National List of Fruits Plant Varieties." -Mariusz Lewandowski, Apple breeder.
Like in conventional breeding, Fruit Tree breeders are looking for varieties that produce more with less. That is to say getting more yield, more resistance, more flavor, more colour… With less and less inputs.
But fruit trees can be tricky to breed: Trees take much room on the ground, and testing fields are limited so the first challenge is to optimize campaigns preparation, notably crossings decisions, materials choice and field setting up. It’s even more important because perennial crops give fruits over several years, thus researchers need to evaluate production over years generating voluminous data. Another challenge in Tree Breeding is the impact of cultural practices into variety performance which require advanced testing, by comparing a lot of heterogeneous data. Several generations are often tested at the same time and the same variety in different places, which makes the results hard to analyze.
Let’s discuss with 3 Fruit Tree breeders and see what can be done to solve their digital challenges in their breeding activities !
Biotechnologies to accelerate Fruit Tree Breeding
I’m a Prunus breeder and I search for nematode resistance¹ for the creation of Prunus rootstocks. For that I use MAB, marker assisted breeding. How to include molecular marker information to my selection program? - Marcus*
MAB is a set of biotech tools to study material’s genome and accelerate plant breeding. The aim is to use Molecular Markers to detect a trait from the first few leaves of a progeny. It avoids waiting several years before resistance detection.
Set up MAB is crucial to your Prunus breeding program, (as for any Tree in fact :) ) but it involves complex tasks and generates much data.
In order to avoid errors, spend less time on repetitive tasks and share more easily information between the lab and the breeding team it is recommended to integrate the MAB processes to your plant breeding software.
The objectives are:
• Optimize the harvest of samples based on Sequencer constraints;
• Get a full traceability of samples harvested to get prepared for sequencing, notably through label printing and barcode use
Roll out a strategy of MAB requires also to find a solution to store marker descriptions (genomic sequences, marking techniques, alleles) and build tailored data visualization tools to help you select lines with the targeted gene.
One last thing that you haven't thought about is data workflow. The lab results should be directly registered in a breeding database without needing to export any data from a spreadsheet. So they are reachable in real time for the researcher/breeder to possibly combine with other sources data (e.g. phenotyping, environment...), and to identify quickly in the field which elite progenies to select and promote to the next phase.
The CTIFL has implemented a greenhouse research protocol aimed at eliminating the plum cultivars most sensitive to ACL (Apricot Chlorotic Leafroll). © CTIFL
In vitro stage importance in tree breeding
As a lab manager, I do a lot of silt-micrografting, notably for the Grape breeding team. This technique avoids incompatibility problems between transplant and rootstock. But the protocol is very delicate, involves meticulous material identification and a rigorous exchange of information with the breeders. How can we automate and standardize processes and track each seedling? -Janet*
Tissue culture techniques like micropropagation are objects of great interest for the collection, multiplication and storage of plant germplasm². In vitro shoot tips, cut in a V-shape, are used as scions, while shoots induced for in vitro rooting are used as rootstocks.
Laboratory activities are a keystone of modern plant breeding programs.
Such in vitro processes involve much communication between biotechnicians and the breeding team sharing the same germplasm. In Vitro in Tree breeding enables to produce zero virus clones from selected progenies in field trials.
The technique generates new material which need to be inventoried to be then used by breeders. That is why it’s crucial for the best traceability and efficiency to build bridges between laboratory department and breeding department, withing common a germplasm management system that centralizes and standardizes all the data exchanges.
The lab and the breeders have much to gain in adopting a unique seedling codification system to track the seedling source and find IT solutions to generate comprehensive identifiers and labels. Such codification ensures the traceability of every material.
Micrografting of Grape with Phylloxera resistant rootstock cultivars © S.Mohan Jain, H. Häggman. (2007) Protocols for Micropropagation of Woody Trees and Fruits. Springer Science & Business Media.
Variety testing and analytical information
In my Cocoa tree research department, we test cocoa tree performance in the field through notably Cocoa beans quality, but another lab does it after the fermentation process on chocolate. How to combine this heterogeneous information into my breeding decision workflow? -Rodrigo*
We understand, Variety testing in fruit tree deals with a lot of information to take care for decision making:
- • Material description: phenotype observations and genetic identity
- • Harvest quality (and quantity of course :))
- • Processing criteria to valorize rough product (if necessary…)
- • Cultural practices and environmental conditions impacts
In terms of data management, the challenge is to build a comprehensive platform to centralize all this information and get the tools for valorize them notably through statistical tools.
Particularly dealing with multifactorial evaluations. Principal Component Analysis (PCA) for instance ³ can be of great help. The latter helps you deal with n individuals observed on p quantitative variables. It becomes a child’s play to analyze your results with interactive graphs and statistical reports.
These tools should comply with the specificities of tree testing:
- • Harvest quality evaluation over years with multiple observations to characterize yield performances, earliness and resistances.
- • Cross cultural practices (and environmental conditions) with variety performances as they can have a high impact on fruit tree productivity. It enables notably to evaluate impacts of Agroforestry or combination of cultures on farmers sustainability.
- • Land use optimization by the choice of experimental designs that allow (by reducing the effect of replication) to study more materials with less planting constraints but with more evaluation of GxE effect. (e.g. Incomplete block and fully randomized plans)
Research and Development all along the crop production and food processing leads to quality food products (Theobroma cacao)
Data collection and data management in Tree breeding
Along the breeding process, breeders have many files that come from different labs and with different data sources. Collecting and classifying all data before the analysis takes a lot of time.
A digital solution with a centralized platform optimizes field monitoring and the data gathering process notably between labs and field (for MAB or tissue culture for example) and helps taking efficient decisions from heterogeneous information.
Fruit Tree breeders have also much to gain from optimizing their land use for their trials. In that way, managing their data more efficiently shall address challenges of:
- • Decisions on Crosses to do and progenies to evaluate based on land resources;
- • Field setting up optimization with designs which can study a bigger population on more locations with acceptable level of repeatability
Doriane, software provider for vegetal R&D information management, support tree breeders to take up this digital challenges of their breeding programs with integrated information systems for process management and data valorization.
* Any resemblance to real and actual names is purely coincidental
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1. Lambert, E. Dirlewanger, F. Laurens. (2009) La sélection assistée par marqueurs (SAM) chez les arbres fruitiers: une approche prometteuse au service de l’innovation variétale - Innovations Agronomiques 7, 139-152.
2. Laurence Bourrain, Gérard Charlot. (2014) Microgreffage In Vitro Du Cerisier - Infos Ctifl N°303.
3. Leite, Paula Bacelar, Bispo, Eliete da Silva, & Santana, Ligia Regina Radomille de. (2013). Sensory profiles of chocolates produced from cocoa cultivars resistant to Moniliophtora Perniciosa. Revista Brasileira de Fruticultura, 35(2), 594-602.