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More on seed longevity phenotyping

Published online by Cambridge University Press:  29 March 2022

Fiona R. Hay*
Affiliation:
Department of Agroecology, Aarhus University, Forsøgsvej 1, Slagelse 4200, Denmark School of Agriculture, Policy and Development, University of Reading, Earley Gate, Whiteknights Road, PO Box 237, Reading RG6 6EU, UK
Rachael M. Davies
Affiliation:
Seed and Lab-Based Collections, Royal Botanic Gardens Kew, Ardingly, Haywards Heath, Sussex RH17 6TN, UK
John B. Dickie
Affiliation:
Seed and Lab-Based Collections, Royal Botanic Gardens Kew, Ardingly, Haywards Heath, Sussex RH17 6TN, UK
David J. Merritt
Affiliation:
Department of Biodiversity, Conservation and Attractions, Kings Park Science, Biodiversity and Conservation Science, Kings Park, WA, Australia School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
Dustin M. Wolkis
Affiliation:
Department of Science and Conservation, National Tropical Botanical Garden, Kalāheo, HI, USA Natural History Museum of Denmark, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
*
*Author for Correspondence: Fiona R. Hay, E-mail: fiona.hay@agro.au.dk
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Abstract

Understanding the relative longevity of different seed lots, perhaps of different species or genotypes, but also following production under different environments or using different cultivation methods, or following different post-harvest treatments, is relevant to anyone concerned with the retention of seed lot viability and vigour during storage. However, different scientists over the years have used different conditions to assess seed lot longevity, as well as different variables as the measure of ‘longevity.’ Here, we give some of the backgrounds to how two standard protocols, with an open and closed system respectively, were derived, and explain why we consider p50, defined as the time during storage when seed lot viability, as measured through a germination test, has declined to 50%, is a suitable longevity trait parameter.

Information

Type
Research Opinion
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press
Figure 0

Fig. 1. Outline of the standard seed storage experiment protocols used at (a) the MSB (Newton et al., 2009, 2014) and (b) at IRRI (Hay et al., 2019; Lee et al., 2019, 2020). The isotherms showing the relationship between seed moisture content and equilibrium relative humidity were estimated using Cromarty's equation (Cromarty et al., 1982). In (a) which is for illustration only, an oil content of 20% was used in the equation; in (b), the isotherm was determined for rice (2.2% oil content). The turquoise region of the isotherm is intended to reflect region II of the isotherm (Bewley et al., 2013), where there is a linear relationship (within limits) between seed moisture content and longevity (Roberts and Ellis, 1989). *Reference samples of a control species were included to make sure the ageing environment was consistent across ‘runs’ which typically involved a number of species. For species’ seeds that were found to be long-lived, with little or no viability loss over the 125 d, ageing was carried out at 60°C (Probert et al., 2009; Merritt et al., 2014). A number of species were then aged at both ageing temperatures to determine a correction factor that would allow comparison of the data across species aged at the different temperatures.

Figure 1

Fig. 2. Illustration of how different definitions of p50 would influence the estimate of seed longevity. In (a), the horizontal line is at 50% and p50 is estimated as the time when the respective survival curves cross this line. It is also possible to see in (a), how p50 would separate seed lots more than, for example, p85 (time for viability to fall to 85%). Note, the yellow survival curve is an example where there is a prolonged initial plateau phase before germination percentage declines. For data where there is a plateau phase (not necessarily as prolonged as this), probit analysis should be applied from the last observation with maximum germination (see also Hay et al., 2014). The length of the plateau phase is then indicated by the magnitude of Ki (equation 1). In (b) the survival curves are the same, but the respective dashed lines and arrows, show how the estimate of ‘p50’ would vary if defined as the time to half-initial viability. Since the initial viability is similar for the five curves, the p50 estimates are not very different from those estimated in (a). However, if there is greater variation in initial viability, or if the seeds show some dormancy which is released during storage (c), the estimates of ‘p50’ if defined as the time to half-initial viability would vary more. It can also be seen in (c) how, if there is a dormant seed lot and ‘p50’ is defined as the time to half-maximum viability, the time when a sample is removed from storage (circles) could influence the estimated value.