mapping of european weeds - ewrs · eu weed mapping meeting, july 12th, 2010 in kaposvar. hansjörg...
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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)
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