Topic 8 - RTB 1. Banana data collection - van den Bergh.pdf

COLLECTION AND MANAGEMENT

OF BANANA GERMPLASM

EVALUATION DATA

Strategy doc

à

different types of evaluations

•In situ assessment of GR and compilation of traditional knowledge, to

guide early selection and acquisition of new accessions with relevant traits into collections

•Preliminary evaluation of accessions in ex situ collections, recording

relevant observations on general traits, without specific trial set-up

•Targeted screening of wide range of germplasm for specific traits of

interest through phenotyping and high-throughput mass-screening under controlled conditions

•Evaluation in early stages of selection and preliminary yield trials to

assess the performance of accessions for specific traits under field conditions

•Advanced yield trials in multiple locations to fully assess the influence

of the environment and growing conditions on the overall performance of promising accessions

•Farmers’ participatory trials in target end-user environments to select

• These stages are not always clearly delineated, nor do they always take place in a linear (sequential) way

• But the methods and tools needed will be different depending on the objective of your study

è Can we develop a method and accompanying tools that are flexible enough that they can be used

NEED FOR

Comparisons over space and time

•Individual studies can only give you a certain amount of

information, restricted to the experimental conditions of that specific trial

•For a fuller understanding of the characteristics of a cultivar across

different environments, including ones that have not yet been tested (e.g. future environments), multi-location trials can be set up that will allow GxE analyses

•In addition, methods of meta-analysis exist to combine the results

from multiple studies

•Regardless of whether we are dealing with multi-location trials in

one study, or with meta-analysis across a range of studies, certain minimum criteria need to be in place:

• at least some common cultivars • common variables

Variables - the ideal scenario

Variable Trial A Trial B Trial C Trial D

Plant height at flowering ü ü ü ü

Height of following sucker at flowering ü ü ü ü

Number of standing leaves at flowering ü ü ü ü

Number of functional leaves at flowering ü ü ü ü

Number of standing leaves at harvest ü ü ü ü

Number of functional leaves at harvest ü ü ü ü

Plant girth at harvest ü ü ü ü

Bunch weight ü ü ü ü

Number of hands ü ü ü ü

The more common scenario

Variable Trial A Trial B Trial C Trial D

Plant height at flowering ü ü ü ü

Plant girth at flowering1 _{ü ü ü û}

Height of following sucker at flowering2 _{ü ü û ü}

Number of standing leaves at flowering ü ü ü ü

Number of functional leaves at flowering2 _{ü ü û ü}

Number of standing leaves at harvest2 _{û ü ü û}

Number of functional leaves at harvest2 _{û ü û û}

Functional leaf length at harvest3 _{û ü û û}

Functional leaf width at harvest3 _{û ü û û}

Plant girth at harvest2 _{û ü û û}

Bunch weight2 _{ü ü ü û}

Number of hands2 _{û ü ü ü}

Number of fruits2 _{ü ü û ü}

Number of fruits on hand4 _{û û ü û}

Calculation of the number of fruits4 _{û û ü û}

1_{: Existing variable measured at a new time point} 2_{: Existing variable not measured}

3_{: New variable added}

• At least some shared understanding / common methodology and

terminology is needed to allow cultivar descriptions across environments

è Can we develop a method and accompanying tools

ADDITIONAL

Things can, and will, go wrong

•

During data collection

After data collection:

•Unreadable hand-written files

•Errors made during transfer from paper to excel •Lost paper files

•Lost electronic files

•Long delays before data are in electronic format and can be shared with project team

è Can we develop a method and accompanying tools that reduces chances for making errors and

facilitates data management and sharing?

NEW TOOLS AVAILABLE

TO THE COMMUNITY

Ontology -

http://www.cropontology.org/

•

Crop ontology = formal naming and

definition of:

• the anatomy, structure and phenotype of crops

• germplasm passport descriptors

• traits + variables and methods to measure

these traits

• the relationships between all these

•

Variable = trait + method + scale

http://www.cropontology.org/ontology/C

O_325/

Banana

Mobile data collection tool - FieldTask, SMAP

•

Application to create data collection protocols, to collect

evaluation data from planting to harveston a mobile device

(tablets) in the field, and to manage the data

•

Flexible:

•Variables can be selected from the ontology, according to the specific

needs of the trial

•Sequence of data collection can be adjusted to needs (branched

structure)

•Data formats can be numbers, text, photos, gps, etc

Mobile data collection tool

•

But also standardized:

•Within one project, multiple locations/sites can use exactly the same

protocols (customized but standardized form)

•Through the unique ontology IDs, we know exactly what is being

measured

•Possibility to build in data quality checks (e.g. min-max plant height,

format for recording dates, units of measurements, …)

•Additional notes can be built in to guide data collectors (pictures of

scorings, guidelines, …)

•Photos can be taken for general info, or to check data entry à harmonization of the data capture _•

Plant height in cms,

requiring three characters, no decimal

• The unique ID that links all the data collected from the plant across the different times and lifecycle stages

• Stored into the database

Mobile data collection tool

•

Also data management after collection

•

Collected data forms are saved on tablet

•

Can be sent off to central server:

MusaBase -

https://musabase.org/

• Trial info (meta-data) and collected data stored in MusaBase

• Shared access by partners

Conclusion - current tools

Standardization Flexibility