Applying population-based threshold models to quantify and improve seed quality attributes

Bradford, Kent J.; Bello, Pedro

doi:10.19103/as.2022.0105.05

Cited by 10 publications

(16 citation statements)

References 199 publications

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“…Thus, the response of a seed population is probabilistically distributed around a population norm. This explains why some grade of partial dormancy is typically observed even in seeds harvested from the same plant [21][22][23].…”

Section: Basic Features Of Seed Physiological Dormancymentioning

confidence: 99%

A Travel through Landscapes of Seed Dormancy

Gianinetti

2023

Plants

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Basic features of seed dormancy are illustrated. The seed overall regulatory network governs seed metabolism and development, and it is coordinated by plant hormones. A functional model focused on abscisic acid (ABA), the foremost plant hormone in dormancy, is used as a framework to critically discuss the literature. Gibberellins (GAs) have a main role in germination, and the ABA–GAs balance is a typical feature of the seed state: ABA dominates during dormancy and GAs prevail through germination. Thus, the literature converges toward envisaging the development switch between dormancy and germination as represented by the ABA/GAs ratio. The ABA–GAs antagonism is based on mutual inhibition, a feature of the regulatory network architecture that characterizes development trajectories based on a regulatory circuit with a bistable switch. Properties of such kind of regulatory architecture are introduced step by step, and it is shown that seed development—toward either dormancy or germination—is more properly represented by a tristable regulatory circuit, whose intermediate metastable states ultimately take one or the other development trajectory. Although the ABA/GAs ratio can conveniently represent the state of the seed overall regulatory network along the seed development trajectory, specific (unknown) dormancy factors are required to determine the development trajectory. The development landscape is shown to provide a well-suited representation of seed states travelling along developmental trajectories, particularly when the states are envisioned as regulatory circuits. Looking at seed dormancy in terms of regulatory circuits and development landscapes offers a valuable perspective to improve our understanding of this biological phenomenon.

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Section: Basic Features Of Seed Physiological Dormancymentioning

confidence: 99%

A Travel through Landscapes of Seed Dormancy

Gianinetti

2023

Plants

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“…1, to also have greater longevity in genebank or controlled environment/ temperate climate warehouse storage. Nonetheless, we will not be certain that the results from laboratory storage experiments are dependable until we do the experiments where samples of genebank seed lots are subjected to a storage experiment before the bulk is put into genebank storage (Bradford and Bello, 2022). The method for such pre-tests could be as described in Fig.…”

Section: More On P 50mentioning

confidence: 99%

More on seed longevity phenotyping

et al. 2022

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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.

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“…However, the construction of a predictive model necessitates preliminary studies to obtain species-specific biological parameters, such as base (T b ), optimal (T o ), and ceiling (T c ) temperatures, also referred to as cardinal temperature thresholds (Holt and Orcutt 1996;Masin et al 2010). Based on experimental results, threshold models have been employed to predict rhizome sprouting and seed germination responses to temperature (Bradford and Bello 2022;Holt and Orcutt 1996). Such models are being increasingly utilized to enhance weed management and crop protection strategies.…”

Section: Introductionmentioning

confidence: 99%

Modeling the effect of temperature on rhizome sprouting in the invasive weed silverleaf nightshade (Solanum elaeagnifolium)

Kapiluto,

Smirnov,

Achdary

et al. 2024

Weed Sci

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Silverleaf nightshade (Solanum elaeagnifolium Cav.), a noxious, highly invasive perennial weed, poses a significant threat to irrigated summer crops, vegetables, and orchards. This weed has the ability to reproduce both sexually, through seed production, and asexually, via an extensive underground rhizome network, the latter being major role in the weed’s invasion, establishment, and persistence. Our aims were thus to assess the impact of temperature on rhizome sprouting for fragments of different lengths and to model the sprouting dynamics. The influence of temperature on the sprouting of rhizome fragments (2.5, 5, 7.5, or 10 cm long) was investigated in growth chambers at eight temperatures ranging from 10–45 C. The highest sprouting proportions for 10-cm rhizome fragments were recorded at 30 and 35 C in complete darkness. The highest sprouting time for all fragment lengths was observed at 15 C in complete darkness. Modeling sprouting rates as a function of temperature gave the cardinal temperatures for the four different rhizome fragment lengths, with values of: T b (base temperature) 12.80, 9.34, 9.14 and 9.50 C, T o (optimal temperature) 38.90, 36.60, 35.16 and 34.86 C, Tc (ceiling temperature) 39.80, 40.08, 40.50 and 40.80 C for the rhizome length of 2.5, 5, 7.5 and 10 cm respectively. Based on these findings, the potential for S. elaeagnifolium to spread to new areas and possible new management strategies are discussed; these offer a novel approach for informed decision making regarding the control of this weed.

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Applying population-based threshold models to quantify and improve seed quality attributes

Cited by 10 publications

References 199 publications

A Travel through Landscapes of Seed Dormancy

A Travel through Landscapes of Seed Dormancy

More on seed longevity phenotyping

Modeling the effect of temperature on rhizome sprouting in the invasive weed silverleaf nightshade (Solanum elaeagnifolium)

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