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Modelling the emergence pattern of six summer annual weed grasses under no tillage systems in Argentina

Summary Field trials were established in various growing seasons in four experimental sites with soyabeans or maize grown under no tillage systems. Seeds of Digitaria sanguinalis, Eleusine indica, Sorghum halepense, Setaria geniculata, Echinochloa colonum and Urochloa platyphylla were incorporated i...

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Bibliographic Details
Published in:Weed research 2009-02, Vol.49 (1), p.98-106
Main Authors: LEGUIZAMÓN, E S, RODRIGUEZ, N, RAINERO, H, PEREZ, M, PEREZ, L, ZORZA, E, FERNANDEZ-QUINTANILLA, C
Format: Article
Language:English
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Summary:Summary Field trials were established in various growing seasons in four experimental sites with soyabeans or maize grown under no tillage systems. Seeds of Digitaria sanguinalis, Eleusine indica, Sorghum halepense, Setaria geniculata, Echinochloa colonum and Urochloa platyphylla were incorporated in surface soil, under the crop residues of the previous season, each autumn. Regular records of seedling emergence during the following spring and summer were used to model the pattern of each species as a function of the thermal time in the 2 cm upper layer of the soil profile. A Weibull function closely described (r2 = 0.73–0.93) the relationship between thermal time and seedling emergence for all species in each site and year. The coefficient of determination for the model verification ranged from 0.71 to 0.98. Species may be grouped into three emergence time‐span groups. Eleusine indica, U. platyphylla and E. colonum had a long‐lasting emergence, requiring 940–1660 d°C (growing day degrees) to complete this process. Digitaria sanguinalis and S. halepense had an intermediate emergence time‐span, completing this process in 540–1090 d°C. Setaria geniculata exhibited the shortest emergence time‐span (290–660 d°C required for full emergence). Different hypotheses were tested in order to explain species model parameter differences in different sites and to establish the basis for more refined models with improved prediction capabilities.
ISSN:0043-1737
1365-3180
DOI:10.1111/j.1365-3180.2008.00669.x