Occurrence and distribution of important weed species in German winter oilseed rape fields

Abstract

Data on weed species currently found in winter oilseed rape, the extent of their occurrence and regional distinctions were collected in autumn 2005, 2006 and 2007 from a total of 1463 oilseed rape (OSR) fields throughout Germany. Investigations took place in untreated parts of OSR fields at the four to six leaf stage of rape. Weed frequencies and densities were determined by randomly placing a 0.1 m 2 square frame ten times in each plot, identifying and counting all plants within the frame. Results presented here mainly focus the distribution of important weed species and differences in their frequencies and densities between German Federal states.The majority of weed species occurring in winter oilseed rape is not evenly spread over Germany. There are weed species with a limited distribution which can be frequently found in some regions but are absent in others. Sisymbrium officinale, Descurainia sophia, Centaurea cyanus, Anchusa spp., Sonchus spp., Rumex spp. or Euphorbia spp. are some weed species with mainly regional importance. Several ubiquitous weed species show regional differences in their densities. Capsella bursa-pastoris, Chenopodium album, Galium aparine, Lamium spp., Matricaria spp., Stellaria media, Thlaspi arvense, Veronica spp. and Viola arvensis were among the most frequent weed species in OSR in all German federal states though sometimes differing significantly in regional densities. Recent field observations reporting an increased spreading of Geranium spp., Sisymbrium spp. or Anchusa arvensis in intense OSR cropping areas are verified by these results.

Impact of climate change on weeds in agriculture: a review

“…1) and Echinochloa crus-galli (Barrett and Wilson 1981;Otte et al 2006). Wetter and milder winters will increase the survival of some winter annuals such as S. media and Sisymbrium species that already possess strong traits related to these climate conditions (Walck et al 2011;Hanzlik and Gerowitt 2012).…”

“…With recent climate change, plant species are expected to track the climate favorable to their growth (Jump and Peñuelas 2005). This is well investigated and documented for Europe (Cimalova and Lososova 2009;Silc et al 2009;Walck et al 2011;Hanzlik and Gerowitt 2012). Rising temperatures can cause species range boundaries to be moved further toward the poles (Walther et al 2002).…”

“…Weeds are influenced by these altered abiotic conditions (Dukes et al 2009;Singer et al 2013). For example, wetter and milder winters are likely to increase the survival of some winter annual weeds, whereas warmer summers and longer growing seasons may permit thermophile summer annuals to grow in regions further north (Bloomfield et al 2006;Walck et al 2011;Hanzlik and Gerowitt 2012). Furthermore, climate change is predicted to result in a higher frequency of extreme weather events such as heavy storms, summer droughts, and extreme cold spells (Diaz et al 1999;Tubiello et al 2007;Jentsch et al 2009;Coumou and Rahmstorf 2012).…”

Methods to conduct and analyse weed surveys in arable farming: a review

“…To draw maps is an established method for processing weed survey data. We therefore include it, although it is not in the central focus of this review, because geographic maps mainly refer to single species (Kolářová et al 2013;Hanzlik and Gerowitt 2012;Novák et al 2009). …”

“…This information is not to be underestimated as it is known that there are numerous species which are common weeds in their areas of origin (mostly the Mediterranean) but towards the borders of their geographic ranges are first outcompeted to poorer arable sites and finally restricted to ruderal habitats (Holzner and Immonen 1982). Furthermore, maps from weed surveys can be supplemented with additional information, such as the species abundances (Mehrtens et al 2005;Goerke et al 2008;Novák et al 2009;Hanzlik and Gerowitt 2012), total weed infestation or species richness at the sampled sites, for a better illustration of regional differences or even be used to predict the probability of species occurrences by means of interpolation and spatial smoothing (Firbank et al 1998). …”