Transcript EWRS Scientific Committee (SciCom)

Weed mapping tools and practical
approaches – a review
Prague February 2014
Brainstorming Presentation
Hansjörg Krähmer
Objectives

Create the basis for a new seminar run by Michaela Kolářová, Edita
Stefanic, Falia Economou, Hansjörg Krähmer, Josef Soukup & Jens
Streibig

Summarize the purpose and applications of weed mapping

List available tools and equipment as used today
- Relative abundance index: RA = (RF+PD+RO)
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- ORDERED WEIGHTED AVERAGING (OWA) METHOD: to record the
spatial weed distribution
Slide 2

Compile experimental data supporting selected methods

Talk about assessment, documentation and evaluation

Include statistical evaluation tools

Come up with definitions for abundance, density, frequency,
distribution range vs. frequency maps
Slide 3
Objectives

Demonstrate difference between plant community research and agrienvironment research (continuous change/disturbance of habitat,
supplementation of resources by farmer…)

Come up with future use of tools: prediction of changes in arable
weed spectra – locally and globally, global warming and weeds (as
described by Ziska for example for the USA), description of the
biodiversity situation in agriculture

Include own field trial results

Make proposals for future trials
Slide 4
Try to answer questions

Why does a weed occur where?

Why do weed spectra change?

Can we predict future weed shifts?
- hierarchy of the factors which affect (at a regional level ?)
- the role of their interaction on weed shifts
- the role of unusual climatic events
- mathematical and not only statistical approach taking into account bioclimatic indices

Can we associate weeds with specific crops and with environmental
conditions?

Is it actually possible to prevent the occurrence of weeds?
Slide 5
Contributors
Jens Streibig, Michaela Kolářová, Edita Stefanic, Falia Economou,
Hansjörg Krähmer, Josef Soukup
Slide 6

Frequency and uniformity for each sampling site were
computed afterwards. In particular, the species’ presence or
absence in a sampling site was taken to indicate weed
frequency,and the time of a species’ occurrence in the
five quadrats of a sampling site was taken to indicate
weed uniformity.

Agronomic and soil /climatic Data (e.g applied
herbicides…..)
Slide 7
 Nonspatial Analysis.
included descriptive statistics (mean, maximum, standard deviation,
coefficient of variation, skewness), and Spearman’srank correlation
coefficients in order to examine the relationship between abiotic factors
and weed occurrence.
MODTTEST Fortran program developed by Legendre (2000).
The theoretical background was presented by Dutilleul (1993).
Slide 8
 The observed differences of the meteorological data made necessary to assess their
effect on weed occurrence further. The sampling sites were separated into three
groups, based on the distance from the three meteorological stations.
 Comparisons of means of weed densities of the three groups were performed with the
use of one-way analyses of variance, by incorporating an autocorrelated error term
through the use of generalized linear models. These tests were performed in R software,
with the use of the nlme package (Pinheiro et al. 2011).
Slide 9
 Spatial Outliers.
Local indicators of spatial associatio (LISA), like local Moran, belong to the
exploratory spatial data analysis (ESDA) techniques. It measures dependence
in only a part of the whole study area, and identifies the autocorrelation
between a single point and its neighboring ones in a specified distance from
that point (Ping et al. 2004).
 The local Moran’s I : can be used to study the spatial patterns of spatial
association like local clusters and spatial outliers. When spatial outliers are
detected for a variable, it means that they are differentiated by their neighboring
values.
Spatial outliers were defined with the use of the GIS software ArcMap ver 9.3.
Slide 10
 Geostatistical Methods
 Use of the semivariogram, whereas ordinary kriging and co-kriging were used for the
weed interpolation mapping.
 Ordinary kriging constitutes a spatial estimation procedure. It is the most commonly used
type of kriging and assumes a constant but unknown mean that may vary among neighboring
sampling sites within a study area.
 Co-kriging gives the best results in terms of theoretical foundation, because no assumptions are
made on the nature of the correlation between the two variables. It exploits more fully the auxiliary
information by directly incorporating the values of the auxiliary variable
Slide 11
Slide 12