Crop density rather than ruderal plants explains the response of ancient segetal weeds View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

S. Nowak, K. Waindzoch, S. Świerszcz, M. Niemczyk, K. Spałek, Arkadiusz Nowak

ABSTRACT

The influence of ruderal species and crop density on ancient segetal weeds was examined. The experiment was carried out on experimental plots with three different sewing densities of winter triticale. Weeding of ruderal taxa was applied on half of the plots to explore the relation between segetal and ruderal weeds. Variation in species composition by environmental variables was analysed by running Redundancy Analysis (RDA) combined with performing forward selection and variation partitioning for “weeding” and “crop density” as explanatory variables. Additionally, the effect of crop density and weeding was tested separately for segetal and ruderal species along the seasons with the use of co-variance analysis (ANCOVA). The overall species composition changes due to crop density and weeding revealed by the redundancy analysis were significant, with the total explained variation of 15.7%. The authors found that crop density has a stronger influence on species composition than weeding (56.2% vs. 47.2%). Weeding increases mean segetal weed cover from ca. 19% to more than 35%. Along the vegetation period, weeding has an increasing explanatory power, with the highest scores in the autumn. The combined effect of both variables explains the highest share of variation for summer data (54%), then for autumn (40%), and spring (35%). Crop density is much more influential on segetal weeds, dropping inconsiderably from spring to summer and then abruptly in autumn. For several species we found the optimum crop density being a loose stand with ca. 20–40% cover (e.g. for Agrostemma githago). More... »

PAGES

351-359

Identifiers

URI

http://scigraph.springernature.com/pub.10.2478/s11756-018-00178-8

DOI

http://dx.doi.org/10.2478/s11756-018-00178-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110750301


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