Weed mapping tools and practical approaches – a review

Prague February 2014

Weed mapping tools and practical approaches – a review

Prague February 2014

Brainstorming Presentation

Hansjörg Krähmer

Slide 2

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)

- - ORDERED WEIGHTED AVERAGING (OWA) METHOD: to record the

- spatial weed distribution

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

Slide 4

Objectives

Demonstrate difference between plant community research and agri- environment 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 5

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 6

Jens Streibig, Michaela Kolářová, Edita Stefanic, Falia Economou, Hansjörg Krähmer, Josef Soukup

Contributors

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

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