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Manipulation of Seed Water Relations Via Osmotic Priming to Improve Germination Under Stress Conditions

Open access
Bradford, Kent J.·1986·HortScience

Abstract

For annual crops, the time from planting to seedling establishment is a crucial phase in the production cycle. Uniformity and percentage of emergence of direct-seeded crops can have a major impact on final yield and quality, particularly for vegetables (84). Techniques of precision planting to achieve desired plant populations depend on a high probability of successful establishment for each seed planted (26). Furthermore, increasing use of expensive hybrid seed has placed additional emphasis on the performance of each seed planted. The soil environment often, however, is not conducive to rapid germination and seedling growth. Physical stresses, such as extreme temperatures, excess or deficit of water, salinity, or soil crusting; and biological stresses, including pathogens and insects, can all adversely affect germination and seedling growth. These problems are compounded by the increased susceptibility of plants to many of these stresses during germination and emergence. It is not surprising, therefore, that there have been many attempts to devise presowing treatments to improve seed performance in the field. The wide range of such treatments has been thoroughly reviewed and categorized by Heydecker and Coolbear (38).

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Metadata

Authors
Bradford, K. J.
Venue
HortScience
Published
1986-10
Volume
21
Issue
5
Pages
1105-1112
ISSN
  • 0018-5345 (ISSN-L)
  • 2327-9834
OpenAlex
W2164916177
Cited by
799
References
0
FWCI
8.22
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Manipulation of Seed Water Relations Via Osmotic Priming to Improve Germination Under Stress Conditions. https://app.cochat.ai/papers/10.21273/hortsci.21.5.1105
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Bradford, K. J. (1986). Manipulation of Seed Water Relations Via Osmotic Priming to Improve Germination Under Stress Conditions. HortScience. https://doi.org/10.21273/hortsci.21.5.1105

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.