4.2 Article

Emergence of Chenopodium album and Stellaria media of different origins under different climatic conditions

Journal

WEED RESEARCH
Volume 43, Issue 3, Pages 163-176

Publisher

BLACKWELL PUBLISHING LTD
DOI: 10.1046/j.1365-3180.2003.00330.x

Keywords

predictive modelling; weed populations; winter chilling; weed seed origins; dormancy; emergence; Chenopodium album; Stellaria media; climate

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The emergence behaviour of weed species in relation to cultural and meteorological events was studied. Dissimilarities between populations in dormancy and germination ecology, between-year maturation conditions and seed quality and burial site climate all contribute to potentially unpredictable variability. Therefore, a weed emergence data set was produced for weed seeds of Stellaria media and Chenopodium album matured and collected from three populations (Italy, Sweden and UK). The seeds were collected in two consecutive seasons (1999 and 2000) and subsequently buried in the autumn of the same year of maturation in eight contrasting climatic locations throughout Europe and the USA. The experiment sought to explore and explain differences between the three populations in their emergence behaviour. Evidence was demonstrated of synchrony in the timing of the emergence of different populations of a species at a given burial site. The relative magnitudes of emergence from the three populations at a given burial site in a given year were generally similar across all the burial sites in the study. The resulting data set was also used to construct a simple weed emergence model, which was tested for its application to the range of different burial environments and populations. The study demonstrated the possibility of using a simple thermal time-based model to describe part of the emergence behaviour across different burial sites, seed populations and seasons, and a simple winter chilling relationship to adjust for the magnitude of the flush of emergence at a given burial site. This study demonstrates the possibility of developing robust generic models for simple predictions of emergence timing across populations.

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