Environmental Engineering Reference
In-Depth Information
cucumber species
Holothuria leucospilota
on Rarotonga, Cook Islands. Kobler and
Adami
ˇ
(1999) have used decision tree models to identify locations for construction
of wildlife bridges across highways in Slovenia. Decision trees were used to model
habitat suitability for red deer in Slovenian forests using GIS data, such as eleva-
tion, slope, and forest composition (Debeljak et al. 2001). Models of potential and
actual habitat for brown bears have been induced from GIS data and data on brown
bear sightings using decision trees (Jerina et al. 2003). Ogris and Jurc (2007)
applied decision trees to identify potential habitats for different tree species under
varying climate change scenarios. Decisions trees are used in habitat modelling of
soil organisms that are under the influence of different soil characteristics and crop
practices (Kampichler et al. 2000; Debeljak et al. 2007).
Habitat modelling is also becoming relevant in agriculture due to problems with
crops, such as oilseed rape, sunflower, wheat or sorghum, which can escape from
cultivation and colonise field margins as feral populations. To control the processes
leading to the formation of new feral populations, habitat models enable us to
identify suitable growing conditions for new potential feral populations. Such
research has been conducted on a 41 km
2
production area of winter oilseed rape
in Loir-et-Cher region, France (Pivard et al. 2008). Based on attributes describing
locations of all cultivated oilseed rape fields and feral populations and their
demographic properties, a habitat model for feral oil seed rape was developed
(Fig.
14.3
). The model predicts the probability of the presence of a feral population
in the studied area.
Side effects of oilseed rape (OSR) cultivation include volunteer plants that
emerge on the field after cultivation of OSR and may cause crop impurity or
weed control problems. To understand the suitable conditions for formation of
volunteer populations of OSR, a habitat model to predict presence and abundance
of volunteer oilseed rape (
Brassica napus
L.) was induced from a dataset about the
seedbank at 257 arable fields used for baseline sampling in the British Farm Scale
Evaluations of genetically modified herbicide tolerant (GMHT) crops (Debeljak
et al. 2008). Volunteer OSR was most likely present if a previous OSR crop had
been grown in the same field (Fig.
14.4
). However, machine learning also indicated
previously unknown correlations between the abundance of volunteer oilseed rape,
total seedbank and several other factors like the percent of nitrogen and carbon in
the soil. Once OSR has been cultivated at a site volunteers are not excluded
specifically from any part of the country or from sites having particular abiotic
characters such as high pH or low % of nitrogen. Volunteers had, moreover,
become present at 24% of sites where there had been no OSR crop in the last
8 years, presumably as a result of a previous crop (beyond the 8 years recorded) or
imported to the site with farm machinery. Their abundance, moreover, varied
systematically with factors that are generally associated with the intensity of
farming, notably total seedbank abundance, species number and plant life history
groups (Fig.
14.5
), and most consistently with percentage of nitrogen and carbon in
the soil. All these factors were linked to an extent with geographical region, being
smallest in the arable south-central and south-east and largest in the north and
south-west.
