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THE USE OF SPECIFIC INDEXES AND GIS FOR DESERTIFICATION
RISK ASSESSING ON OLIVE CULTIVATED AREAS IN W. CRETE.
Kasapakis I.1, Kosmas C.2, Stylianaki G.1, and Chartzoulakis K.1
1: NAGREF – Institute for Olive Tree and Subtropical Plants, Soudas Av. 73100 Chania GREECE.
2: Agricultural University of Athens, Iera Odos 75, 118 55 Athens, GREECE
e-mail: [email protected]
ABSTRACT
Human activities, agriculture and climate affect ecosystems and their services, which may involve
continuous and discontinuous transitions from one stable state to another. In this study, we analyze
how olive orchards affect arid ecosystems, using both field data and a GIS modeling approach. The
study area is in the NW part of Crete Island which is completely covered with olive orchards. By
the input of specified desertification indicators in a GIS model we have the capability for regional
analysis and easy investigation of different scenarios combining various parameters like
management polices etc. setting the basis for future monitoring. Using this model we show that in
the investigated area, even though it is hilly, the desertification risk varies from low to moderate.
1. INTRODUCTION
Agriculture is still an important although of decreasing importance sector of the European Union’s
(EU) economy in terms of its share to the Gross Domestic Product (GDP) and the labour force
employed. The agricultural sector covers approximately 40 % of the land surface of the EU, ranging
from 6.5 % in Finland to 64.4% in the United Kingdom. It comprises many different types
and systems of farming from intensive arable production to extensive livestock systems, from dairy
production in northwest Europe to wine, fruit and olive production around the Mediterranean
coastline. [1]
Olive groves in Greece occupy an area of about 786.7 hectares with about 154 million trees
cultivated by about 686,000 families. Olive oil production in Greece represents about 23% of the
European Union production. In Crete in particular 65% of agricultural land (318.394 ha) is covered
by olive plantations [2]. Thus the silvery-green of the olive tree dominates the physiognomy of the
island and is the main feature of the natural environment in all farmed areas. Quite a few tourist
resorts are surrounded by olive plantations and many tourist complexes have their grounds
decorated with old or new olive trees. For this reason Crete is rightly known by many today as the
Olive Island. Olive plantations which have been established in hilly areas of the island and in areas
with extreme gradients using retaining walls contribute significantly to limiting soil erosion and
retaining the shallow soils which exist, which without the olive trees would be transported
downwards, denuding the mountains and hills and turning them into desert.
The olive, Olea europaea L., belongs to the Oleaceae family and includes several cultivars. Trees
are extremely long-lived (up to 1000 years) and tolerant to drought, and salinity. Where olive
groves are located in hilly areas, soils are moderately to severely eroded due to intensive
cultivation. Concerning topography, olives are cultivated in a variety of physiographic conditions.
Olive groves are found on flat areas, on gently sloping (slope 2-6%), moderately sloping (slope 612%), strongly sloping (slope 12-18%), steep (slope 18-35% and in very steep (slope >35%) areas.
Olive groves in Greece are usually intensively cultivated. Soils are usually ploughed once in midspring and may be treated once or twice a year with herbicides. Another management practice
which is related to biological olive oil production is zero tillage, with no pesticides. Fertilizers are
usually applied once during winter or early spring. In the case that water is available, olives are
irrigated 3 to 5 times during the dry period, applying the water usually by drip irrigation. Terracing
of land and cultivation of olive trees for conservation and soil erosion control is an ancient old
practice. Many uplands have been terraced for cultivating olives and other crops throughout the
country. In many cases terraces constructed with stones are some hundreds or even thousands of
years old [3].
The diversity of farming and farm structures in Greece leads to many different ways of interaction
with the environment. At the same time the relationship between agriculture and the environment
can be positive, as well as negative, mostly through the maintenance of many agricultural practices.
The European Environment Agency defines an indicator as "a parameter or value derived from
parameters, which provides information about a phenomenon. Indicators are quantified information
that helps to explain how things are changing over time and how they vary spatially. Indicators
generally simplify the reality in order to make complex phenomena quantifiable, so that information
can be communicated." [4] An indicator may be easy to measure and summarize in shorthand the
effects of complex processes that are more difficult to measure or observe [5], [6]. Its purpose is to
show how well or bad a system is working. If there is a problem, an indicator is useful in
determining what direction to take to address the issue. Indicators can also be useful as proxies or
substitutes for measuring conditions that are so complex that there is no direct measurement.
According to Winograd (1997) [7], in general, indicators should be useful to: (a) determine the
condition of, and change in, the environment in relation to society and the development process; (b)
diagnose the actual causes and effects of existing problems that have been detected, in order to
elaborate responses and actions, and (c) predict future impacts of human activities on the
environment and society to determine future and/or alternative strategies and policies.
Desertification can be analysed at different spatial scales, ranging from local to national and
international levels. The perception of the scale and the seriousness of land degradation will be
influenced by the timing of any investigation [8]. It is important to know which indicators are
appropriate at different scales, and which of them can be obtained directly and in a cost-effective
way at the desired scale. Some indicators can be locally analyzed and must be determined over
smaller space units, even when they can be aggregated at a national level. These indicators require a
precise definition of the areas in which they are meaningful, and may not be applicable at larger
mapping scales. Local indicators are derived from data collected in areas, which must be selected as
representative of larger areas.
The DESERTLINKS project partners conducted an exercise where, with all these criteria in mind,
they matched indicators to the main issues of desertification. The DESERTLINKS partners also
considered the need for indicators with respect to visible signs of desertification, the reasons for
what was seen and the consequences of what was seen. Indicators that could not be either described
or measured or used in a practical way were abandoned. The exercise also revealed gaps where new
indicators could be suggested. Gradually the indicator list has been optimized, retaining about 150
indicators that can be fully described. Many indicators have been proposed for determining the
desertification risk, some of them are the most significant in estimating desertification risk under
olives on the Greek islands [9], [10]. These indicators have been used for analysis in farm level and
in this study they were combined with GIS for analysis in landscape level. The necessity of using
models in environmental research arises from the fact that much detailed knowledge and data are
collected in environmental field research but in most cases it is not known what their consequences
are for the whole system. This is especially the case because practical field measurements are
usually only possible at the plot scale whilst providing information for environmental decisionmaking is usually a catchment or regional scale process. Models can combine these detailed
information and data with knowledge of processes in a logical manner and integrating behavior for
the whole system from them [9]. Environmental field data are often collected at some representative
locations and over a short period of time. In this context, models can use these local and short-term
data and can be used to deduce environmental dynamics for larger areas and over longer periods of
time. One of the objectives of modeling is to help to get a better understanding of the structures and
mechanisms operating in environmental systems [11]. Moreover, models can be used to develop
new hypotheses and reveal gaps in knowledge and consequently provide new input for
environmental research. Spatio-temporally variable systems of all scales can be handled by the
powerful method of grid-based modelling. This can be combined with Geographical Information
Systems (GIS) to model at the landscape scale [12].
2. METHODOLOGY
2.1. Study area
The study area is 22.5 Km2, located in the west part of Chania Prefecture and is consisted by part of
Kissamos and Mythimna municipalities. The landscape can be characterized as hilly, resulting in
the existence of plains only near the coast. The terrain is characterized by big slopes, which have
led to the creation of an expanded drainage network. The main parent material of the soils of study
area is marl. The mean annual rainfall of the area varies from 650 to 750mm. The climate is typical
Mediterranean with 6 months raining season.
Figure 1. More than 90% of the study area (22.5 Km2) is covered by olive groves
The economy of the area is based on agriculture and tourism. The most important agricultural
activity is olive cultivation. More than 90% of the area (22.5 Km2) is covered by olive groves
(Fig.1.). The cultivar grown in the study area is “Koroneiki” for olive oil production, mainly extra
virgin olive oil.
2.1. Methods
The analysis of desertification risk in olive orchards have been conducted according to the
methodology of DIS4ME program [3]. Soil quality and stage of land degradation and desertification
were defined, using a series of indicators related to the soil, topography, vegetation, climate, social,
economic, and management characteristics. These indicators are mainly related to the specific local
characteristics at farm level such as soil depth, soil texture, drainage, slope gradient slope exposure,
rainfall, present and previous types of land use, period of existing type of land use, application of
fertilizers and pesticides, tillage operations, tillage depth and direction, sustainable farming,
soil erosion control measures etc. Based on existing classification systems such as the
georeferenced database, classes have been defined for each indicator and presented in a
tabulated form. Classes have been defined for each indicator and numbers have been assigned for
each class according to its importance on desertification. The study was conducted in hilly areas in
which the main process of land degradation was soil erosion. Algorithms were defined for each land
use type that can be easily used for identifying land degradation and desertification risk at farm
level.
The algorithm used for olive cultivated areas is
DR = (4.32) - (0.68*number of parcels) + (1.57*tillage operations) - (0.68*period of
existing land use) - (0.56*soil depth) + (0.44*slope gradient) + (0.19*parent
material) - (0.79*rainfall) + (0.67*aspect) + (0.65*frequency of flooding) +
(0.69*sustainable farming) + (0.44*policy enforcement)
All the above indicators have a Spatio-temporally distribution and easy can be handled by the
powerful method of grid-based modelling. These indicators can be combined with Geographical
Information Systems (GIS) to model the desertification risk at the landscape scale [12]. Aerial
photograph interpretation, topographic maps and field-survey techniques were used to recognize
these indicators along above described olive cultivated study area.
Figure 2. Factors of the GIS model
The incorporation of a modelling component to a GIS was needed for the prediction of potential
outcomes and evaluating alternatives. The GIS model is composed of several interrelated factors,
which contribute the main land-degradation processes identified in the study area. The components
of the model are: Climate, Soil, Topographic and Management factors (Fig.2.). The GIS model was
created integrating the mathematical equations of these processes in the ArcGIS software, which
combines GIS tools and spatial modeling (Fig. 3.). The run of the GIS was individually performed
on a one-cell basis (30X30 m) per each factor in the study site.
Figure 3. The GIS model
The main factors of land degradation identified at plot scale, could be applied as input variables in
the GIS model, and so they seem to be also relevant at catchment scale [13]. In an extended olive
covered area, like the above described in which each farm is next to the other, there is a lot of farm
management practices and other indicators which could be grouped and analyzed in landscape level.
The same indicators were used with a scaling up from farm level to cartography unit level. The
cartography units were identified by interpretation of satellite images. With the use of DTM
obtained the data of slope and aspect of the study area. From the historical data of 5 meteorological
stations inside and around the area we took a grid with the annual rainfall of the area. The other
parameters of the model were identified by field survey. All the required information about
management practices and soil parameters were investigated and integrated to the GIS by field
survey with a GPS connected to a laptop. During the field survey it was taken into account the
topography, the hydogeological map and the interpretation of the satellite images of the study area
in order to recognize areas with same values of the indicators. Farmer’s interviews were used to
find out or validate indicators like number of parcels and sustainable farming.
3. RESULTS
The analysis of data for the area, in which the main cultivation is olive groves, showed that the most
important indicators for desertification risk were tillage operation, policy enforcement, number of
parcels, period of existing land use etc. Although the area is mainly hilly (TABLE 1.) the
desertification risk is low as result of land management policies.
TABLE 1. Slope categories of olive orchards.
Sllope Cat.
<6%
6-18%
18-35%
>35%
SUM
Area (Km2)
3.54
7.32
7.05
2.53
20.44
Area %
17.3
35.8
34.5
12.4
100.0
The results of running the model are presented in Figure 4.
Figure 4. The distribution of Desertification Risk (DRI) in the study area.
The results indicate that the desertification risk varies from low to moderate in the area. In the
bigger part of the area (70.9%) there is no risk of desertification (TABLE 2.).
TABLE 2. Summarized results for desertification risk.
Desert. Risk
No risk
Low risk
Moderate risk
SUM
Area (Km2)
14.50
4.85
1.09
20.44
Area %
70.9
23.8
5.3
100.0
Even though the study area is hilly with extreme gradients, the desertification risk is low because of
policy enforcement. The use of retaining walls (terraces) contributes significantly to limiting soil
erosion - land degradation. The use of herbicides instead of cultivating the orchards is another
practice that protects soils with extreme gradients.
A wide range of alternative scenarios may be developed with the GIS model, based on policies of
particular relevance to the region and which help to determine the potential desertification
consequences of these policies. By the use of the GIS model we have the ability to easy investigate
many scenarios about anyone of the indicators or combination of those. An interesting scenario will
be the estimation of desertification risk if the farmers of the hilly areas will stop cultivation, or if
there are no any Policy enforcement in the area.
ACKNOWLEDGEMENTS
This study was carried out with funds of DESERTNET II (“Implementation of a Platform of
Services to combat desertification and drought through a system of pilot actions in the
Mediterranean Regions”), which is a project co-financed by the EU and the Greek Ministry of
Financial.
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