Effect of seed size and sub-zero imbibition-temperature on the thermal time model of winterfat (Eurotia lanata (Pursh) Moq.)

Wang, R; Bai, Yuguang; Tanino, Karen

doi:10.1016/j.envexpbot.2003.10.001

Cited by 37 publications

(35 citation statements)

References 37 publications

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“…On the other hand, the base temperature was significantly lower in large seeded cultivars (Shendi) than that of small seeded cultivars (Jabel Marra), allowing large seeds to accumulate more thermal time units than small seeds at the same temperature and subsequently to germinate faster. Our results were in accord with [18] but did not agree with the assumption that base temperature had little variation among genotypes of the same species [12] [27] [28]. The reason that T b was often considered as a constant may be for the ease of modeling [29].…”

Section: Discussionsupporting

confidence: 78%

“…Clear plastic bags were used to seal Petri dishes to reduce water evaporation. Seeds were considered germinated when 2 mm of radicle was visible [18].…”

Section: Methodsmentioning

confidence: 99%

“…Probit transformation linearizes the cumulative normal distribution, which facilitates modeling efforts [18] [20]. For modeling purposes a seed population was considered to be composed of subpopulations based on relative germination rate [18] [21].…”

Section: Methodsmentioning

confidence: 99%

“…Final (cumulative) germination percentages were transformed using log 10 Probit [18] [19]. Probit transformation linearizes the cumulative normal distribution, which facilitates modeling efforts [18] [20].…”

Section: Methodsmentioning

confidence: 99%

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Variation in Thermal time model Parameters Between Two Contrasting Chickpea (<i>Cicer arietinum</i>) cultivars

Naim¹,

Ahmed²

2015

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A laboratory experiment was carried out to determine the effect of temperature on germination and early seedling establishment and to study the variation among thermal time model parameters for two contrasting chickpea cultivars. Seeds were subjected to six constant temperatures from 10˚C to 35˚C. A complete randomized design was used with four replications. Analysis of variance showed significant differences (p < 0.05) among all characters studied. The final germination percentage of both cultivars significantly (p < 0.05) increased with increasing temperature up to 25˚C, and thereafter there was a sharp decrease in final germination at super optimal temperatures (30˚C and 35˚C). Desi type cultivar (small seeded) locally called "Jabel Marra" significantly (p < 0.05) exhibited higher final germination percentage and germination rate compared with the smaller seeded kabui type cultivar "Shendi" at all temperatures. The median (θT(50)) of the thermal time was significantly (p < 0.05) different between the two chickpea cultivars. The large seeded cultivars (Shendi) recorded significantly (p < 0.01) higher median thermal time than the small seeded cultivars (Jabel Marra). The results also revealed significant (p < 0.01) differences between the two cultivars on the other parameters of thermal time model. On the other hand, the small seeded cultivar (Jabel Marra) scored lower total dry matter and temperature tolerance index (TTI) compared with the large seeded cultivar (Shendi) at all temperatures studied.

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Section: Discussionsupporting

confidence: 78%

“…Clear plastic bags were used to seal Petri dishes to reduce water evaporation. Seeds were considered germinated when 2 mm of radicle was visible [18].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Variation in Thermal time model Parameters Between Two Contrasting Chickpea (<i>Cicer arietinum</i>) cultivars

Naim¹,

Ahmed²

2015

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“…But after-ripening at a reduced rate may also occur at colder temperatures (e.g. 4°C) for some species (Chantre et al 2009;Steadman, Crawford, and Gallagher 2003;Widrlechner 2007), sometimes even at temperatures slightly below the freezing point (Sivakumar et al 2006;Wang, Bai, and Tanino 2004). In other words, a slow after-ripening process in cold storage may lead to a gradual increase in normal germination percentage, which is eventually counteracted by a long-term decline in overall viability.…”

Section: Data Descriptionmentioning

confidence: 99%

Scheduling Viability Tests for Seeds in Long-Term Storage Based on a Bayesian Multi-Level Model

Dixon

Widrlechner

et al. 2012

JABES

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Genebank managers conduct viability tests on stored seeds so they can replace lots that have viability near a critical threshold, such as 50 or 85 % germination. Currently, these tests are typically scheduled at uniform intervals; testing every 5 years is common. A manager needs to balance the cost of an additional test against the possibility of losing a seed lot due to late retesting. We developed a data-informed method to schedule viability tests for a collection of 2,833 maize seed lots with 3 to 7 completed viability tests per lot. Given these historical data reporting on seed viability at arbitrary times, we fit a hierarchical Bayesian seed-viability model with random seed lot specific coefficients. The posterior distribution of the predicted time to cross below a critical threshold was estimated for each seed lot. We recommend a predicted quantile as a retest time, chosen to balance the importance of catching quickly decaying lots against the cost of premature tests. The method can be used with any seed-viability model; we focused on two, the Avrami viability curve and a quadratic curve that accounts for seed after-ripening. After fitting both models, we found that the quadratic curve gave more plausible predictions than did the Avrami curve. Also, a receiver operating characteristic (ROC) curve analysis and a follow-up test demonstrated that a 0.05 quantile yields reasonable predictions. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. Genebank managers conduct viability tests on stored seeds so they can replace lots that have viability near a critical threshold, such as 50 or 85 % germination. Currently, these tests are typically scheduled at uniform intervals; testing every 5 years is common. A manager needs to balance the cost of an additional test against the possibility of losing a seed lot due to late retesting. We developed a data-informed method to schedule viability tests for a collection of 2,833 maize seed lots with 3 to 7 completed viability tests per lot. Given these historical data reporting on seed viability at arbitrary times, we fit a hierarchical Bayesian seed-viability model with random seed lot specific coefficients. The posterior distribution of the predicted time to cross below a critical threshold was estimated for each seed lot. We recommend a predicted quantile as a retest time, chosen to balance the importance of catching quickly decaying lots against the cost of premature tests. The method can be used with any seed-viability model; we focused on two, the Avrami viability curve and a quadratic curve that accounts for seed after-ripening. After fitting both models, we found that the quadratic curve gave more plausible predictions than did the Avrami curve. Also, a receiver operating characteristic (ROC) curve analysis and a follow-up test demonstrated that a 0.05 quantile yields reasonable predictions.

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Seed composition in oil crops

et al. 2017

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Effect of seed size and sub-zero imbibition-temperature on the thermal time model of winterfat (Eurotia lanata (Pursh) Moq.)

Cited by 37 publications

References 37 publications

Variation in Thermal time model Parameters Between Two Contrasting Chickpea (<i>Cicer arietinum</i>) cultivars

Variation in Thermal time model Parameters Between Two Contrasting Chickpea (<i>Cicer arietinum</i>) cultivars

Scheduling Viability Tests for Seeds in Long-Term Storage Based on a Bayesian Multi-Level Model

Seed composition in oil crops

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Effect of seed size and sub-zero imbibition-temperature on the thermal time model of winterfat (Eurotia lanata (Pursh) Moq.)

Cited by 37 publications

References 37 publications

Variation in Thermal time model Parameters Between Two Contrasting Chickpea (&lt;i&gt;Cicer arietinum&lt;/i&gt;) cultivars

Variation in Thermal time model Parameters Between Two Contrasting Chickpea (&lt;i&gt;Cicer arietinum&lt;/i&gt;) cultivars

Scheduling Viability Tests for Seeds in Long-Term Storage Based on a Bayesian Multi-Level Model

Seed composition in oil crops

Contact Info

Product

Resources

About

Variation in Thermal time model Parameters Between Two Contrasting Chickpea (<i>Cicer arietinum</i>) cultivars

Variation in Thermal time model Parameters Between Two Contrasting Chickpea (<i>Cicer arietinum</i>) cultivars