Mapping

of European Weeds Status quo of the

new

EWRS –

Working Group

2nd

EU Weed

Mapping

Meeting, July

12th, 2010

in Kaposvar

Hansjörg

Krähmer

Slide 2

Mission of our working group

We want to provide an overview on the occurrence and spreading of weeds in Europe.

The Working Group wants to exchange data, tools and methods for the assessment and spatial documentation of species and biotypes on arable and non-crop land, e.g. on amenity areas.

Our major tasks are to:

• compare and combine data from weed surveys in physical maps

• document population dynamics and regional weed changes

• derive predictions for weed problems in selected areas and on selected sites

•

communicate developments in defined segments and to compare them with developments outside the EU

•

find common and most efficient rules and tools for the assessment and documentation of data

Slide 3

Objectives for 2009/10

Unite results of existing national weed surveys in one physical map accessible to all EWRS members.

Create common or exchangeable databases.

Analyse rules for the assessment of weed populations

Find ostensive maps for the description of trends

4

What did we achieve so far?

Presentation of the project at AK Herbologie in Braunschweig January 2009

Presentation at EWRS Working Group „Resistence“

in Gent im May 2009

Meeting of a project group in Prague, May 13th

to15th, 2009

Proposal and acceptance of a new EWRS working group at EWRS SciCom in Volterra, November 2009

Website -

proposal in January 2010

Presentation of achievements at AK Herbologie in Frankfurt on February 17th, 2010

Completion of website on March 1st, 2010

5

What did we achieve so far? --

1 1 --

First Meeting in First Meeting in Prague in May 2009Prague in May 2009

Lvoncik, Kolářová, Dobrovodsky, Auskalnis, Zajac, Weis, Massa, Meseldzija, Dancza, Gerowitt, Zarodnik, Liska, Glemnitz, Rubin, Salonen, Hyvönen, Pinke, Suchanek, Hamouzowa, Holec, Hamouz

6

What did we achieve so far? --

2 2 --

Nomination of regional Coordinators

Scandinavia und Baltic States : T. Hyvönen and A. Auskalnis Spain, Portugal, France and Italy: J. Recasens, P. Barberi, C. Moonen

Poland, Belarus and Ukraine: M. Zajac and A. AuskalnisCzech Republic, Slovenia, Slovakia and Austria: S. Lvoncik and M. KolářováIsrael, Turkey, Jordan, Egypt and Greece: B. Rubin and A. UludagGermany, Benelux, Great Britain, Switzerland: Kristin HanzlikHungary, Romania and maybe Moldova: I. Dancza and N.N.Serbia, Croatia, Bosnia, Montenegro, Bulgaria: M. Meseldzija

Slide 7

Unite results of existing national weed surveys in one physical map accessible to all EWRS members

--

3 3 --

Weeds are prioritized according to frequency and prevention of biodiversity

For major crops, the 3 most frequent grass species and dicot species are mapped

For invasive weeds, the 3 major grass and dicot species

will also be mapped

Later,

the 6 most endangered weed species

will be mapped

Slide 8

Europe: Cereals / most frequent grasses (draft, medium certainty level)

Apera

Spica-venti

Alopecurus

myosuroides

Avena sterilis

Poa annua

Elytrigia

repens

Slide 9

Europe: Cereals / most frequent dicots (draft, low certainty level)

Papaver rhoeas

Galium aparine

Amaranthus retroflexus

Stellaria media

Cirsium arvense

Sinapis arvensis

Chenopodium

album

Matricaria spec

Viola arvensis

Slide 10

Europe: Oilseed rape / most frequent grasses (draft, medium certainty level)

Volunteer Cereals

Elytrigia

repens

No data,

or no major OSR areas

Slide 11

Europe: Oilseed rape / most frequent dicots (draft, medium certainty level)

Matricaria

spec.

Sinapis

arvensis

Chenopodium

album

No data,

or no major OSR areas

Slide 12

Europe: Corn / most frequent dicots (draft, medium certainty level)

Chenopodium album

Ambrosia artemisiifolia

Convolvulus

arvensis

No data or no major corn areas

Slide 13

Europe: Corn / most frequent grasses (draft, medium certainty level)

Echinochloa

crus-galli

Elytrigia repens

Sorghum halepense

No data,

or no major corn areas

14

What did we achieve so far? --

4 4 --

Server at the University of Hohenheim: −

no resistence data

−

exchange of data in Excel-format−

maps will be prepared with local software.

−

data assessment according to Braun-Blanquet-principals

15

Data assessment Example of Michaela Kolářová, Prague, orchard

Releve number 2 9 12 13 14 Country code CZ CZ CZ CZ CZ Date (year/month/day) 20090805 20090909 20090909 20090604 20090805

Relevé area (m2) 1.00 1.00 1.00 1.00 1.00 AMARE* + + . + + ANGAR r . . . . CAPBP + . . . r CHEAL 1 + . 1 + CIRAR 1 . r + 1 CONAR 2 + r . . AGRRE 1 1 + 3 3 LAMPU + . . + + MEDSA 2 1 1 1 1 SILNO + . . . .

* r : rare , +: less than 1% coverage , 1 : ≤

5 % , 2 : > 5 to 25 % , 3 : > 25 to 50 %

Slide 16

Caveats (1)

Weeds don’t stop at country borders; the presented maps are the results of questionnaires and publications for single countries

Maps show average weed infestation

for a given country only

The basis

for presented maps is not equal in all countries; more data are required (e.g. number of counts, abundance, frequency..)

Weed identification

is sometimes complicated at early stages: Avena-, Lolium-, Matricaria-species

Data

were not assessed in the same year

The

frequency of single weeds can vary from year to year

Slide 17

In a typical field there is a rather great number of weeds competing with the crop

and contributing to yield losses

Frequency of weeds

is not necessarily

correlated with agronomic importance

The assessment timing

is important

when trying to get an overview: autumn or spring in case of winter cereals or oilseed rape, before or after herbicide treatments

Caveats (2)

Slide 18

Best

presentation of the frequency

of a weed I. Dancza: Experiences on prevention and control of ragweed

(Ambrosia artemisiifolia) in Hungary, 2005

Slide 19

Principle Considerations

The best times for mapping weeds are before weed control applications (pre and post) and before harvest

Weed densities should be clustered according to Braun-Blanquet

Every species/biotype should be documented separately

The most evident way to demonstrate weed densities appears to be

a colored depiction where a dark color represents high densities and light color low densities –

similar to temperature maps of weather services

Data exchange between institutes/scientist should be easy, data assessment also

The maps should be built up independently of a central input location

Several coordinators should guarantee the consistency of rules

Slide 20

First cautious conclusions on trends (1)

Weeds

might be grouped according to zones:

Scandinavia, partially UK: Poa annua, Stellaria media, Viola arvensis prevailing

Central Europe: Apera spica-venti, Galium aparine, Tripleurosporum inodorum, Veronica spec.

Mediterranean area: Avena sterilis, Lolium rigidum, Sorghum halepense, Setaria spec.

….

Some species

show a wide ecological valence

(temperature, soil, water): Chenopodium album, Echinochloa crus-galli

It appears as if the history of agriculture

often plays a greater role

than local environmental conditions (temperature, soil):

Spring crops were more important in Denmark 25 years ago, i.e. seed bank was dominated by spring weeds (P. Kudsk)

Some countries grow more spring than winter cereals and oilseed rape. There, typical “spring weeds”

prevail, e.g. Chenopodium in spring oilseed rape and Tripleurospermum inodurum in winter oilseed rape in the case of Lithuania (Albinas Auskalnis)

Large scale farming with monocultures, high fertilizer input and

low tillage favor the occurrence of characteristic weeds (Elytrigia, Cirsium, Galium e.g.)

Slide 21

First cautious conclusions on trends (2)

Global warming

may lead to a migration of Avena sterilis, Lolium rigidum and Papaver rhoeas

into more northern cereal growing areas.

European corn weed spectra seem to resemble more and more the typical US American weed spectra,

i.e. similar resistance problems might

show up sooner or later (ALS resistance in Amaranthus).

Based on differences in weed spectra, herbicides used in mediterranean areas should differ from those used in more northern countries.

Slide 22

Contributors to this compilation

A. Aksoy, BCS Adana

A. Auškalnis, Kedainiai

G. Bonfig-Picard, BCS Frankfurt

J. Dobrovodský, BCS Prštice

T. Eggers, Braunschweig

D. Feucht, BCS Frankfurt

R. Gerhards, Hohenheim

M. Hess, BCS Frankfurt

K. Hurle, Hohenheim

G. Kazinczy, Kaposvar

P. Kudsk, Aarhus

B. Laber, BCS Frankfurt

J. Petersen, Bingen

J. Meyer, BCS MailandJ. Petersen, BingenB. Pallutt, JKI PotsdamJ. Recasens, LleydaJ. Salonen, JokioinenD. Schreiber, BCS FrankfurtJ. Soukup, PragL. Talgre, EstlandS. Uygur, AdanaI. Vanaga, RigaH. Walter, BASFJ.-M. Wolff, BCS MonheimM. Zajac, Krakau

Slide 23

OUTLOOKOUTLOOK

Slide 24

N.N. Luneva 2003-2009 Project «Interactive Agricultural Ecological

Atlas of Russia and Neighboring Countries. Economic Plants and their Diseases, Pests and Weeds»

Slide 25

Extension

of weed mapping project worldwide e.g. RUSSIA: Cereals / most frequent grasses

(draft)

Apera

Spica-venti

Alopecurus

spec

Avena spec

Poa annua

Elytrigia

repens

26

National Progress

27

Classification Across Crops

OD = Order of Dominance; C = Cover

28

Maize –

Assessment Early Summer

29

Maize –

Assessment Late Summer

30

Wheat

31

Conclusion from Hungarian Surveys

•

The rank for some weeds has stayed rather constant for almost 50

years: Chenopodium, Echinochloa in maize

•

Single weeds have gained ground over the years: Ambrosia across all crops from rank 21 to 1 or Tripleurospermum in wheat from rank 44 to 1

•

Apera has also become quite frequent : from rank 37 to 3•

Other weeds have lost ground: Convolvulus from rank 1 to 6 in wheat•

The position of some weeds can vary from year to year: Setaria pumila in maize between rank 5 and 16

•

The assessment date can be important: in early summer Echinochloa becomes number 1, in late summer Ambrosia

32

Outlook

•

Finland and Czech Republic are publishing their latest results this year

•

Paolo Barberi and Camilla Moonen will start with activities in Italy

•

Greece has published first data for cotton•

First maps are available for Brazil and for the USA

33

BRASIL

/ Corn: most frequent monocots

Digitaria

horizontalis

Commelina

benghalensis

Brachiaria

plantaginea

34

BRASIL / Corn: most frequent dicots

Bidens

pilosa

Euphorbia heterophylla

Tridax

procumbens

Ipomoea grandifolia

Slide 35

USA

/ Corn: most frequent grasses

Sorghum halepense

(Johnsongrass)

Brachiaria

platyphylla

(broadleaf

signalgrass)

Elytrigia

repens

(Quackgrass)

Digitaria

spec. (Crabgrass spec.)

Setaria

spec.

(Foxtail)

Slide 36

USA

/ Corn: most frequent dicots

Amaranthus

spec. (pigweeds) or Amaranthus

hybridus

(smooth pigweed) or Amaranthus

palmeri

(Palmer amaranth)

Ipomoea spec.

(Morningglories)

Xanthium L. spec. (Cocklebur)

Abutilon theophrasti

(Velvetleaf)

Raphanus

raphanistrum

(wild radish)

Chenopodium album

(Lambsquarters)

Kochia

scoparia

(Kochia)

Ambrosia artemisiifolia

(common ragweed)