Multi-purpose tree seed characterization year 2021

2021

The dataset contains seed data on seed; surface area, width, length, colour, texture, shape and 1000 seed weight.

The seed analysis was done using a Marvin seed analyzer.

Partner
Crop name
multipurpose-trees
agroforestry
Number of accessions
709
Number of traits
7
Start of evaluation
January 2, 2021
End of evaluation
December 31, 2021
Dataset metadata

Dataset creators

Data manager
Hendre Prasad World Agroforestry Centre
Data curator
Simon Kangethe World Agroforestry Centre
Data digitizer
Zakayo Kinyanjui World Agroforestry Centre
Data digitizer
Ludy Keino World Agroforestry Centre
Data digitizer
Geffrey Abuor World Agroforestry Centre (ICRAF)
Data digitizer
Agnes Were World Agroforestry Centre (ICRAF)
Data digitizer
Eliza King'ori World Agroforestry Centre (ICRAF)

Dataset use and licensing


Data and resources

The dataset contains quantitative and qualitative morphological traits of Tree Seeds Analysed using Marvin Seed Analyzer.

Multi-purpose_Tree_Seed_Characterization_year_2021.xlsx

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Dataset metadata

Metadata.xlsx

Preview

MCPD passport data

MCPD - bf0be474-0966-472d-af7b-1303439c72af.xlsx

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Data provider contact information

Address
Gigiri, Nairobi, Kenya

Other Metadata

Date of dataset
January 1, 2021
Metadata create date
December 10, 2021
Metadata updated date
December 16, 2021
Published in Genesys
December 16, 2021
Locations
Location
The analysis was conducted at the ICRAF Seed Lab, Kenya
Date of evaluation
January 2, 2021 — December 31, 2021
Description

Tree Seed analysis carried out using Marvin seed analyzer at the Seed Lab

Traits observed

Number of seeds analysed

Average surface area of individual seeds in square millimeters.

Average width of individual seeds in mm

Average Length of individual seeds in mm

Color of the seed using Royal Hortilcultural Society (RHS) colour chart

Date when the seed analysis was conducted

Weight of one thousand seed in kilograms.

Accessions

List of accessions included in the dataset

KEN023 • DOI: 10.18730/K6TK*
KEN023 • DOI: 10.18730/K6V3B
KEN023 • DOI: 10.18730/K6V08
KEN023 • DOI: 10.18730/K6V5D
KEN023 • DOI: 10.18730/K6TS1
KEN023 • DOI: 10.18730/K6V19
KEN023 • DOI: 10.18730/K6TEV
KEN023 • DOI: 10.18730/K6TT2
KEN023 • DOI: 10.18730/K6TN$
KEN023 • DOI: 10.18730/K6TW4
KEN023 • DOI: 10.18730/K6TR0
KEN023 • DOI: 10.18730/K6TX5
KEN023 • DOI: 10.18730/K69N8
KEN023 • DOI: 10.18730/K69TD
KEN023 • DOI: 10.18730/K69P9
KEN023 • DOI: 10.18730/K69WF
KEN023 • DOI: 10.18730/K69SC
KEN023 • DOI: 10.18730/K69QA
KEN023 • DOI: 10.18730/K69VE
KEN023 • DOI: 10.18730/K69XG
KEN023 • DOI: 10.18730/K69M7
KEN023 • DOI: 10.18730/K69J5
KEN023 • DOI: 10.18730/K69K6
KEN023 • DOI: 10.18730/K6A0K
KEN023 • DOI: 10.18730/K6A3P
KEN023 • DOI: 10.18730/K6A1M
KEN023 • DOI: 10.18730/K69RB
KEN023 • DOI: 10.18730/K6A2N
KEN023 • DOI: 10.18730/K69YH
KEN023 • DOI: 10.18730/K69ZJ
KEN023 • DOI: 10.18730/K68ND
KEN023 • DOI: 10.18730/K68PE
KEN023 • DOI: 10.18730/K68RG
KEN023 • DOI: 10.18730/K68G8
KEN023 • DOI: 10.18730/K687U
KEN023 • DOI: 10.18730/K6891
KEN023 • DOI: 10.18730/K68JA
KEN023 • DOI: 10.18730/K68A2
KEN023 • DOI: 10.18730/K68MC
KEN023 • DOI: 10.18730/K68TJ
KEN023 • DOI: 10.18730/K68E6