Applications of hydrothermal time to quantifying and modeling seed germination and dormancy

By K. Bradford. Weed science, 2002

547 citations recorded by the source corpus.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/4e748d3ea7e02992620fb6a13daaec44cd79ab34

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

1. J. Funk
2. J. Larson
3. L. Borrás
4. Paolo Benincasa
5. Christian Ritz
6. M. Mesgaran
7. Andrea Onofri
8. K. Bradford

Citations in the corpus

• Seed priming: state of the art and new perspectives (2015, background): “Experimental analyses have shown that w b (g) values change within a seed population according to normal or Gaussian distribution (Bradford 2002).”
• Regeneration: an overlooked aspect of trait‐based plant community assembly models (2016, methodology): “high moisture requirements) to be excluded from the seedling community (Bradford 2002).”
• The functional role of soil seed banks in natural communities. (2014, background): “When disturbances expose non-dormant seeds from the soil bank to light and when the progress to germination depending on temperature and moisture is sufficient (Bradford, 2002; Allen et al., 2007), seeds germinate and leave the soil seed bank.”
• Applications of hydrotime analysis in seed testing (2004, background): “There is considerable evidence now that this is essentially what happens in dormant seeds (Bradford, 1997; 2002; Meyer et al., 2000; Alvarado and Bradford, 2002).”
• Hydrothermal-time-to-event models for seed germination (2018, methodology): “…and threshold models are re-parameterised based on the assumption that one or several threshold parameters (e.g., base water potential) vary between individuals within the population, following a specific probability distribution (e.g., Bradford, 2002; Mesgaran et al., 2013 and Watt et al., 2010).”
• Coupling time to silking with plant growth rate in maize (2007, background): “An example is the prediction of seed lot performance across contrasting environments from quantitative information of germination at the population level and a qualitative assessment of individual seed germination (Ni and Bradford, 1992; Bradford, 2002).”
• A hydrothermal time model explains the cardinal temperatures for seed germination (2002, background): “The hydrotime and hydrothermal time models provide insight into how physiological and environmental factors interact to regulate the germination behavior of seed populations (Bradford 1995, 2002).”
• A hydrothermal after-ripening time model for seed dormancy loss in Bromus tectorum L. (2006, background): “Modelling efforts based on hydrothermal time have provided many insights into germination-related phenomena, and serve as a useful tool in predicting germination outcomes (Bradford, 2002).”