Desertification and Grazing On south Crete A model approach

Transcription

Desertification and Overgrazing on South Crete
Desertification and Grazing
On south Crete
A model approach
Raymond Sluiter
Department of Physical Geography
Faculty of Geographical Sciences
University of Utrecht
October 29 1998
I
A Model Approach
A Model Approach
Contents
LIST OF FIGURES AND TABLES
V
1-INTRODUCTION
1
Problem definition
1
Objectives
2
2 - GRAZING
3
Introduction
3
Grazing capacity modelling
4
Modelling grazing capacity using ISPD
4
3-WATER BALANCE
7
Introduction
7
Water balance models
8
BOWET
WATDYN
SWBBM
8
8
8
Conclusion
10
4-BIOMASS
11
Nutrients
11
Nitrogen Balance
Phosphor Balance
12
13
Modelling vegetation production using CENTURY
14
Introduction
Water balance submodel
Nutrient submodels
Plant production submodels
Event scheduler
14
15
15
16
16
5-THE STUDY AREA
17
Introduction
17
Geology
17
Climate
19
Vegetation
19
6-FIELD METHODS
23
Field observations
23
Selection of testplots
23
Soil water content measurements
24
Saturated conductivity
25
Line intercept method
26
II
A Model Approach
Soil sampling
26
Vegetation sampling
26
7-METHODS IN THE LABORATORY
27
Determination of N and P
27
Analysis of Carbon
27
Analysis of lignin
28
Grain size analysis
28
Measurements of soil moisture retention curves
28
Calibration FDR
29
8- MAPMAKING USING GEOSTATISTICS
31
Introduction
31
Interpolation of the field data
32
Summary & Discussion
35
9-FIELD & LABORATORY RESULTS
36
Soil nutrients
36
Vegetation nutrients
36
Grain size analysis
38
Soil moisture retention curves
39
Volumetric soil water content measurements
40
41
Statistical analysis field observations
41
45
46
10-RESULTS OF MODELLING
47
Introduction
47
Water balance model
47
Model input
SWBBM in PCRaster
Results
Sensitivity analysis
Introduction Monte Carlo Analysis
Results of conditional simulation of soil depth
47
48
49
51
52
53
Biomass Production Model
57
Plant and soil parameters
Executing CENTURY 4.0
58
58
Dynamic grazing model
60
Model description
Results modelling grazing
60
61
III
A Model Approach
11-DISCUSSION
66
Study area
66
The soil water balance model
66
The biomass production model
67
The grazing model
67
12- SUMMARY & CONCLUSIONS
68
Summary & conclusions concerning measurements
68
Summary & conclusions concerning modelling
68
13 - RECOMMENDATIONS
70
REFERENCES
71
APPENDIX 1 - MAPS OF THE STUDY AREA
77
APPENDIX 2 - WEATHER DATA
79
APPENDIX 3 - STATISTICS VEGETATION STUDY AREA
81
APPENDIX 4 - DATA TSIOURLIS (1990)
82
APPENDIX 5 - FIELD FORM
84
APPENDIX 6 - VISUAL ESTIMATION CHART
85
APPENDIX 7 - ANALYSIS C ACCORDING TO WALKLEY BLACK
86
APPENDIX 8 - SOIL NUTRIENTS
87
APPENDIX 9 - VEGETATION NUTRIENTS
88
APPENDIX 10 - VEGETATION NUTRIENTS PER SPECIES AND DATE
90
APPENDIX 11 - GRAIN SIZE ANALYSIS
93
APPENDIX 12 - PF CURVES
95
APPENDIX 13 - STATISTICAL ANALYSIS THETA-V TESTPLOTS
98
APPENDIX 14 – RESULTS KSAT MEASUREMENTS
99
APPENDIX 15- MODEL SCRIPT
100
APPENDIX 16 – MODEL CHANGES AND ADDITIONS
104
APPENDIX 17 - CENTURY 4.0 PARAMETERISATION
105
APPENDIX 18 - LIGNIN ANALYSIS CALIBRATION CURVES
106
IV
A Model Approach
List of figures and tables
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7
Table 8
Table 9
Table 10
Table 11
Table 12
Table 13
Table 14
Table 15
Photo 1
Increase number of sheep and goats in the Psilorites region.
Schematic presentation of the basic components of ISPD (Bosch et al., 1994).
Flowchart of ISPD (Steenekamp & Bosch, 1994).
Components of a waterbalance (Thornthwaite and Mather, 1957).
Waterdynamics in WATDYN (Walker & Langridge, 1996).
Flowchart SWBBM.
Processes affecting the nitrogen balance.
A model of the P transformations in the soil.
CENTURY 4.0 environment.
Flowchart CENTURY 4.0 nitrogen submodel.
Overview geology of Crete (Fassoulas et al, 1994).
Alpine development around Crete (Fassoulas et al, 1994).
Lithology map of the study area.
Morphology of phrygana species (Tsiourlis, 1990).
Map of vegetation types.
Ksat method.
Line intercept method.
Principles of pF measurements (Kutilek & Nielsen, 1994).
FDR calibration curves.
Variogram example.
The principle of kriging.
Histograms with expected normal curve.
Variograms of soil depth, grazing pressure and vegetation cover .
Datapoints, predictions and variances of soil depth.
Predicted maps of grazing pressure and vegetation cover.
Texture chart.
Bivariate histogram texture versus lithology.
Decrease of LAI in time.
Bivariate histogram vegetation type versus lithology.
Measured volumetric soil water content versus model prediction.
Results of ten-year model run (Plot 1 and Plot 4).
Average soil depth and variances after 100 realizations and 999 realisations.
Soil moisture content statistics for point 1 - 4.
Average Theta-v and standard deviation of Theta-v for point 1-4.
Overview of Theta-v variation.
Difference of volumetric water content between week 40 and week 1.
Relation between volumetric water content and production level.
Optimal vegetation production curves.
Grazing risk areas.
Results long time scenarios.
Maps of differences in vegetation cover.
Results scenario 7.
Biomass production of phrygana and related ecosystems (Tsiourlis, 1990).
Properties of the testplots.
Descriptive statistics soil depth.
Spearman rank order correlations.
Comparison of the texture classes in lab and field.
Summary of pF measurements in the laboratory.
Results soil conductivity for three plots.
Spearman rank correlations, bold when significant (p<0.01).
Results line intercept method.
Used input variables.
Results of the sensitivity analysis.
Basic statistics point 1-4.
Values of variables determining the proper use factor.
Grazing model input.
Grazing scenarios.
Three important phrygana species.
V
A Model Approach
1-Introduction
In the framework of the European Union project “DeMon-2” (Satellite Based Desertification
Monitoring in the Mediterranean Basin), a fieldwork was carried out on the Greek island Crete
in the period May - June 1997.
The Demon project is developing methods to monitor and to model Mediterranean land
degradation processes. Remote sensing techniques and Geographical Information Systems
play an essential role in these procedures.
The research on Crete is focused on landdegradation processes due to overgrazing by sheep
and goats.The study site is situated in south central Crete in the Asteroussia mountains, near
Lendas (34°55’36 N, 24°55’18 E) and covers an area of 4*6 kilometres. Maps are shown in
appendix 1. Elevation varies from sea level to 650 meter. The landscape is rugged and stony
with special shrub vegetation called phrygana (average height 0.3 m.).
The average annual rainfall in this area is about 450 mm. Most rain falls during the months
November until April. The mean annual temperature is 18.7° C.
Problem definition
The main agricultural activity in the Asteroussia Mountains is guarding sheep and goats.
Since 1961 and especially since 1981, when Greece joined the European Union and guarding
was subsidized, the number of animals increased fast (Vasilakis, 1994 in Lyrintzis &
Papanastis, 1995). See figure 1.
Figure 1 - Increase number of sheep and goats in the Psilorites region (Lyrintzis & Papanastis, 1995).
Due to high grazing pressure, the vegetation degenerates and so the slopes are insufficient
protected against erosion processes. This, in combination with a dry climate and high
temperatures, is called desertification.
To quantify desertification it is necessary to define the theoretical vegetation production and
compare this with the actual vegetation conditions. Important factors, which influence the
vegetation production, are availability of water and nutrients and grazing pressure.
To obtain this information we have done field observations of soil and vegetation properties.
1
A Model Approach
Objectives
The objectives of the fieldwork are:
•
•
•
The development of a spatial dynamic model of the waterbalance
The development of a spatial dynamic model of the vegetation production.
To combine the waterbalance model and the vegetation production model into a model
that calculates the grazing capacity (grazing capacity = total grazing that vegetation can
withstand without degenerating).
2
A Model Approach
2 - Grazing
Introduction
The term grazing has two definitions: The first definition is defoliation by animals of the
aboveground parts of plants (Hodgson, 1979, in Esselink et al., 1991). The second definition
is the sum of consumption by animals and the loss of biomass due to trampling of plants by
animals (‘t Mannetje, 1978, in Esselink et al., 1991). In this study we will use the first
definition.
Some authors distinguish grazing: grazing of grassland, and browsing: grazing of shrubs and
trees (Steenekamp & Bosch, 1994). Our definition of grazing combines grazing and browsing.
Grazing capacity is defined as total grazing that vegetation can withstand without producing a
downward trend of vegetation production, vegetation quality (health of vegetation and specific
species composition) and soil quality (Van Gils et al., 1984, in Esselink et al., 1991).
Grazing capacity is calculated as follows:
G=
F
*p
R
Where:
G = Grazing capacity in food units per unit area for a specified grazing
season.
F = Forage production (aboveground biomass production) per unit area
during the grazing season (kg. dry matter/area).
R = Animal requirement of dry matter in weight per “animal unit” (kg. dry
matter/animal).
p = proper use factor, the percentage of the forage production that can be
grazed without producing a downward trend of vegetation production,
vegetation quality and soil quality.
Values of p mentioned in literature are 0.4 to 0.8 for herbaceous vegetation and 0.5 to 0.7 for
shrubs and trees (Van Gils et al., 1984, in Esselink et al., 1991). Wissler & Guertin (1991)
used in their study to aboveground biomass production in semi-arid grassland a p-value of
0.5.
Some methods exist to measure F:
1. The difference method. Measuring net aboveground biomass production in areas that
remain ungrazed for a short time (‘t Mannetje, 1978, in Esselink et al., 1991).
2. Estimates of production by measuring turnover rates of plant leaves (Davies, 1981, in
Esselink et al., 1991). .
3. Measuring assimilation, respiration, and transpiration and use this information to model
plant growth.
The difference method is used most frequently. Using this method, the effect that low grazing
intensities can cause compensation growth of plants has to be taken into account(Ryle &
Powell, 1975). Plants can try to compensate for the loss of grazed parts by:
1.
2.
3.
4.
5.
Increasing photosynthesis rate in not affected leaves (Gifford & Marshall, 1973)
Re-allocation of substrates from other parts of the plant (Ryle & Powell, 1975).
Producing hormones that increase leave growth (Torrey, 1976).
Removing of (not optimal functioning) old leaves (Ludlow & Wilson, 1971).
Reduction the ageing speed of leaves, prolonging the period of active photosynthesis
(Mc. Naughton, 1979).
6. Conservation of soil moisture by reducing the transpiration surface (Thorne & Koller,
1974)
7. More efficient nutrient recycling from dung and urine (Reardon et al., 1974).
3
A Model Approach
Grazing capacity modelling
Several models are developed to model grazing capacity. In this paragraph we will discuss
the methods of Wissler & Guertin (1991), Braat & Opschoor (1990), Wu et al. (1996) and
Steenekamp & Bosch (1994).
The model of Wissler & Guertin (1991) is developed for semi-desert grassland. Many
parameters like precipitation, soil texture, slope, distance to water etc. were measured during
ten years. Multivariate regression techniques were used to draw up empirical equations to
predict aboveground biomass production and grazing intensity. Combination of the equations
and assumptions of the proper use factor led to a grazing capacity map.
The model of Braat & Opschoor (1990) is a large-scale model that describes range-cattle
interactions. The model calculates crazing capacity and takes into account the dynamical
aspects of cattle. Grazing capacity is calculated as function of precipitation, stocking rate and
PCC (Potential Carrying Capacity: available biomass for consumption of cattle). The PCC is
estimated from precipitation and soil quality data.
Wu et al. (1996) have developed a model that combines fuzzy imprecision with probabilistic
uncertainty to model climate-plant-herbivore interactions in grassland ecosystems. Fuzzy
logic is used because ecosystems are very complex and because relations in ecosystems are
often not fully understood. Precipitation is an important parameter in the model. Other
parameters are topography, vegetation cover and grazing intensity dependent mortality rate.
Primary aboveground production is calculated as follows
V (t ) = Vmax * K (t ) * Rc (t )
Where:
V(t) = Primary aboveground production (kg. dry matter/ha/year).
Vmax = Regional based maximum possible primary production (kg. dry
matter/ha/year).
K(t) = Time-dependent plant habitat characteristic index, reflecting general
terrestrial environmental conditions.
Rc(t) = Vegetation cover dependent soil moisture index, obtained by a
weighted harmonic fuzzy operation.
The model of Steenekamp & Bosch (1994) (ISPD, The Integrated System for Plant Dynamics)
is a grazing capacity model that takes many factors into account: precipitation, precipitation
distribution, aboveground biomass production, animal type, plant palatability and insect
consumption. We will use this model because of its completeness and availability of the
manual. Further aspects of the model are discussed in the next paragraph.
Modelling grazing capacity using ISPD
To model the grazing capacity we will use the principles of the model ISPD, version 1.03
(Bosch et al., 1994). ISPD uses an expert system approach in combination with production
simulation models for the determination of grazing capacity (Stols et al., 1992, in Steenekamp
& Bosch, 1994). ISPD defines relatively homogeneous grazing areas, constructs vegetation
models and stores the relevant data for each area in a data base.
The structure of the Integrated System for Plant Dynamics is outlined in figure 2.
ISPD consist of four modules, namely:
the data base module
the analytical module
the condition assessment module
the grazing capacity module
The first module of the system consists of a relational database that handles all the data for
the total system. The relational database contains different sub-databases in which various
4
A Model Approach
types of data and models are stored, that are required by the condition and capability
assessment modules (Bosch et al., 1994).
In these module there are six data groups (see figure 2):
-
survey data
ordination data
qualitative data
map data
grazing data (consist of two parts: inference models and inference inputs)
management data (contains management models aimed at maintaining and
improving the range condition)
Figure 2 - Schematic presentation of the basic components of ISPD (Bosch et al., 1994)
The second module uses ordination techniques to analyse the survey data in order to finally
construct degradation models for the specific area. All the sample plots are ordinated to
define possible subsets. The ordination techniques, which are used in this module, are the
Principal Component Analysis (PCA), Detrended Correspondence Analysis (DCA) and
Reciprocal Averaging (RA). PCA is used with short gradients, RA with intermediate gradients
and DCA with long gradients of a certain dataset. A gradient is a transect on which a certain
parameter varies, e.g. measurements on a transect from low to high degraded vegetation.
The condition assessment module deals with the condition assessment of a particular area by
means of either a quantitative or a qualitative approach (Bosch et al., 1994. The assessment
involves the mathematical incorporation of a new relatively homogeneous grazing area into
an existing degradation model. The position on the degradation gradient is an indication of the
condition of the site.
In the quantitative approach, the data and models in the statistical results section of the
database are used for the condition assessment. The procedure also includes statistical tests
to select an appropriate model from the database, and no prior knowledge of habitat
characteristics and homogeneous grazing areas are required (Bosch et al., 1994). If the
qualitative approach is to be used, habitat characteristics are used to select the most suitable
model for the condition assessment. The ecological data on species are extracted from the
database for the qualitative assessment of condition.
After the assessment of the condition of an area of vegetation, the expert system approach is
used to calculate the grazing capacity of the area. The appropriate inference model is
selected and the expert system, in combination with the inference inputs (species data,
simulation data, etc.) and various run time questions are used to first determine gross
production and then to subtract the various components of biomass loss or non-availability,
until the net biomass available to the grazing animal is reached. This result is used to
calculate the grazing capacity of the area (Bosch et al., 1994).
5
A Model Approach
The management module contains management models to improve the condition and
capability of a particular area of vegetation.
Due to the complexity of the ISPD package and compatibility with the other models, we will
not use the program itself but the equations of the model shown in figure 3. The multivariate
submodel of ISPD will not be included in our model.
Figure 3 - Flowchart of ISPD (Steenekamp & Bosch, 1994).
6
A Model Approach
3-Water Balance
Introduction
A waterbalance shows the relationship between the input of water from precipitation and the
output of water from evapotranspiration, runoff, groundwaterflow and storage.
The waterbalance of a schematic catchment is showed in figure 4 and can be calculated as
follows:
P = I + AET + OF + ∆SM + ∆GWS + GWR
Where:
P
= precipitation
I
= Interception
AET
= actual evapotranspiration
OF
= overland flow
∆SM = change of soil moisture content
∆GWS = change of groundwater storage
GWR = groundwater flow
(Thornthwaite and Mather, 1957)
Figure 4 - Components of a waterbalance (Thornthwaite and Mather, 1957)
The amount of rainwater that infiltrates in the soil depends on interception and the physical
properties of the soil.Precipitation can be intercepted by the vegetation, litter and stones at
the soil surface (Poesen, 1996). The intercepted precipitation will be evaporated, reducing the
amount of precipitation reaching the soil. Precipitation that reaches the soil has to infiltrate in
the soil surface before it reaches deeper soil layers. The rate of infiltration depends on the
physical properties of the soil like infiltration capacity, and on slope. The infiltration capacity of
a soil is the maximum rate at which water can infiltrate the soil. This rate depends on two
factors: the gravity force and the absorptive forces of the soil. If the rainfall intensity exceeds
the infiltration capacity, the excess water will pond at the surface and generate overland flow.
Percolation to deeper layers of the soil occurs through forces of diffusion and gravity.
The rate of percolation depends on the conductivity of the soil.
Evaporation, percolation and suction forces of the soil will determine the amount of water
available to plant growth. The rate of evaporation is determined by climatic conditions: air
humidity, air temperature, wind speed and solar radiation. Not all water is available for plant
growth, because plants cannot extract water from the soil with a suction force greater than pF
7
A Model Approach
4.2 (wilting point). Most of the water taken up by the roots is lost through transpiration, with a
small part directly used for plant growth (Goubitz, 1996). Potential evapotranspiration is
defined as the potential rate of plant transpiration and soil evaporation from an extensive
surface of actively growing uniform vegetation. The actual evapotranspiration is defined as
the amount of water needed to meet the water loss through evapotranspiration of a plant,
growing under restricted conditions. The actual evapotranspiration compensates for different
types of vegetation and site specific pedological conditions (Evans & Trevisan, 1995).
Water balance models
In the next paragraph three water balance models are discussed:
- BOWET
(Mirschel et al., 1995)
- WATDYN
(Walker & Langridge, 1996)
- SWBBM
(Evans & Trevisan, 1995)
BOWET
BOWET is a semi-empirical one-dimensional dynamic soilwater and evapotranspiration model
for locations not affected by groundwater. It consists of submodels for potential and actual
evapotranspiration, interception, melting of snow and water percolation. BOWET does not
consider soil aeration, surface runoff, hysteresis, macropore flow and capillary rise.
BOWET calculates soilwater dynamics, percolation, transpiration (crop) and evaporation (soil)
for 20 layers of 10 cm. thickness. The main model inputs are: weather data (precipitation,
average temperature, and global radiation), soil and crop parameters. In BOWET all
calculations are made separately for vegetated and bare soil surface conditions, hence crop
cover is taken into account for the calculations.
BOWET is well validated for several locations, soil types and species of agricultural crops and
vegetables.The time step of the model is one day (Mirschel et al., 1995).
WATDYN
WATDYN (Water Dynamics) is developed for modelling plant and soilwater dynamics in semiarid ecosystems with limited site data. It calculates water distribution in the soil profile and
models water uptake using a modified Penman-Monteith equation involving separate
calculations of soil evaporation and transpiration loss from each soil layer. Default data are
provided where site data are unavailable. Vegetation is modelled only to account for radiation
interception and for resistances to transpiration and evaporation. Growth is calculated based
on transpiration efficiency (adjusted for temperature, vapour pressure deficit, soil fertility and
the time of year) and death as a function of temperature, vapour pressure deficit, water stress
and the amount of green biomass. Dead biomass is reduced as a function of soil moisture,
temperature and the amount of litter. The main model input data are: weather data
(temperature, precipitation, windspeed, wind direction, cloudiness, solar radiation), canopy
height, biomass, soil depth, physical soil properties (pF, Ksat, texture) and soil fertility (Walker
& Langridge, 1996). Variables and processes that determine the waterdynamics in WATDYN
are shown in figure 5.
SWBBM
SWBBM (Soil Water Balance Bucket Model) predicts soil moisture on a daily basis, using a
limited number of input parameters. The model implements equations describing potential
evapotranspiration. These equations vary with vegetation cover and soil type, and separate
runoff and percolation. A flowchart of the model is shown in figure 6.
8
Figure 5 - Variables and processes that determine the waterdynamics
in WATDYN (Walker & Langridge, 1996).
Figure 6 - Flowchart SWBBM
9
A Model Approach
A Model Approach
The model is validated with evapotranspiration patterns for major vegetation types, with
percolation data from field lysimetric trials and tested for sensitivity to input parameters.
SWBBM is considered suitable for reconstructing soilwater content and runoff in
paleoclimatology.
SWBBM is called a “bucket” model because it considers the soil as one layer.
Evapotranspiration is calculated according to Ritchie (1972) who separates
evapotranspiration into plant transpiration and soil evaporation and has the advantage of
taking into account site-specific conditions.
The main model input data are weather (precipitation, temperature, cloudiness, radiation),
albedo, physical soil properties (pF, Ksat, texture), soil depth and LAI (Leaf Area Index). The
time step of the model is one day (Evans & Trevisan, 1995).
Conclusion
Considering the data input and complexity of the three models, we have chosen to use
SWBBM because of the following points:
- SWBBM is an one layer model, simplifying field observations and model calculations.
BOWET and WATDYN use a multi layer system.
- SWBBM uses limited climatic data and does not take into account the effects of wind speed
and canopy resistance for the calculation of potential evapotranspiration. Both wind speed
and canopy resistance have great variation in space and time and are difficult to measure.
- WATDYN uses biomass as input and BOWET specific crop parameters. SWBBM also takes
into account vegetation properties but the input variables are less complex.
- Detailed equations are available for SWBBM.
10
A Model Approach
4-Biomass
Nutrients
Nutrients have great influence on the primary biomass production. De Wit & Penning de Vries
(1982) distinguish four levels of primary biomass limiting factors. These levels are derived
from analysis of agricultural crops. Because the analysis is based on the effect of external
factors on physiological processes, it is also applicable to natural environments (Penning de
Vries, 1983).
Level one:
Plant growth in conditions with ample plant nutrients and soil water. Plant growth is only
limited by weather conditions.
Level two:
Plant growth in conditions with ample nutrients, but plant growth is limited by water shortage
part of the time.
Level three:
The same as level two, but the availability of N (Nitrogen) also limits plant growth.
Level four:
The same as level three, but the low availability of elements other than N, particularly P
(Phosphor) also limits production.
P
and
N
are
the
most
important
nutrientsnutrients
influencing primary biomass production and in practice, it is most important to distinguish
whether production occurs at level three or four. Much of the P in plant cells is structurally and
functionally related to N: much enzymes in plants contain N, and much proteins require P and
N. (Penning de Vries et al. 1980, in Esselink et al. 1991). Determination of P/N ratios can
indicate if there is an N or P deficit. Plants appear to regulate their P/N ratios between 0.04
and 0.15 (under and upper limits are species dependent). When the P/N ratio equals 0.04,
growth will be limited by P. When the P/N ratio equals 0.15 growth will be limited by N. A
deficit of P or N will limit growth, but will not always stop growth ().
Generally, P/N ratios are determined in plant leaves. P/N ratios differ for the several parts of
the plants. When determining the P/N ratios you have to take into account the differences
between plants but also for the growth stage of the plant. Esselink et al. (1991) showed that
the P/N ratio would increase during the growing season. Young plants with a small root
system will absorb N better than P because of the higher mobility of NO3- in the soil. These
stocks of N are used in a later stage when growth exceeds N uptake (after flowering stage).
At the end of the growing season plant available N can limit growth.
Grazing intensity, frequency and duration influence vegetation and soil This is reflected in C
and N levels in the soil. Light grazing can result in greater species diversity and production
compared to areas where grazing is excluded (Johnston 1961, in Manley et al., 1995). High
intensity grazing has been found to have a negative impact on litter and live plant biomass.
Moderate grazing of prairie in North Dakota resulted in faster litter decomposition and soil N
mineralisation than either heavy or no grazing (Shariff et al. in Manley et al., 1995).
11
A Model Approach
Nitrogen Balance
The processes affecting the nitrogen balance are shown in figure7.
The processes in the soil whereby N is transformed from one form into another are
mineralisation, immobilisation, nitrification, denitrification and uptake of NO3- and NH4+ by the
plants.The first three processes are microbiologic, and of these, mineralisation and
immobilisation interact particularly with the C balance in the soil. In nitrification, where NH4+ is
oxidised to NO3-, the C balance is not directly affected (Esselink et al. 1991).
A soil is often characterised by a C/N ratio. In soils of temperate, tropical and subtropical
zones, NO3- and NH4+ accumulate when the C/N ratio is less than 20 (and when there are no
+
plants to adsorb NO3 and NH4 ). If the C/N ratio is greater than 30, the inorganic N is
immobilised (Esselink et al. 1991, Krul et al. 1982, in Esselink et al. 1991).
Figure 7 - Processes affecting the nitrogen balance
The major sources of N entering the plant-soil system are:
1. N deposition from atmosphere
The most abundant forms of N in the atmosphere are N2 and N2O. The main sources of
deposition from the atmosphere are in the form of NO3-, NH4+ and NOx. Atmospheric N
originates from volatilisation and combustion of wood and fuels. Sources of NOx are
decomposition of NO2 in acid soils, combustion of wood and fuels, lightning and chemical
reactions in the troposphere.
2. Fixation by symbiotic and non-symbiotic bacteria (Rhizobia, Azospirillum, Clostridium,
Azotobacter)
3. Fixation by blue green algae.
4. N input from fertilisers and dung.
12
A Model Approach
The major sources of N leaving the plant-soil system are:
1. Loss by grazing
Only two to four percent of the N in the consumed biomass disappears from the plant-soil
system through assimilation by the animals. The remaining part is returned to system by
excretion of urine and faeces, but a great part of NH3 will volatilise and does not return in
the soil. The loss of N from the plant-soil system can be estimated by the following
equation: Loss = consumed biomass * N percentage biomass * 0.6 (Esselink et al. 1991).
2. NH3 volatilisation from plants
Evolving by thermal decomposition of proteins during dry periods and through
reminiralisation of NH4+ to NH3 at a pH greater than seven.
3. Denitrification, the chemical en biologic reduction NO3- to N2O and N2.
4. Leaching.
5. Fire and erosion.
(Esselink et al. 1991, Krul et al. 1982, in Esselink et al. 1991).
Phosphor Balance
Compared to the N balance, the P balance is less complex. Processes like N fixation,
denitrification and volatilisation do not occur. However, the transformations between organic P
and inorganic P are much less understood (Krul et al. 1982 in Esselink et al. 1991). The
processes affecting the phosphor balance are shown in figure 8.
P can enter the plant-soil system by atmospheric deposition and by input from fertilisers. P
can leave the plant-soil system by grazing animals which use P in their assimilation and by
faeces which do not return to the plant-soil system but which are left at places where animals
rest (Esselink et al. 1991).
Figure 8 - A model of the P transformations in the soil and the adsorption of P by plants and microorganisms. The P
in the soil is in inorganic form (PIL, labile inorganic P, PIS, stabile inorganic P and PIM, mineral inorganic P) and
organic form (POS, organic stabile, POL, organic labile and PMC, incorporated in microorganisms). Plants and
bacteria are only able to adsorb inorganic P from the soil solution (PSOL) (Krul et al. 1982, in Esselink et al. 1991).
13
A Model Approach
Modelling vegetation production using CENTURY
Introduction
Several models are developed to model vegetation production. We have reviewed WOFOST
(Van Diepen et al., 1988), CROPWAT (FAO, 1992), SUCROS (Simane, 1994) and
CENTURY (Metherell et al., 1993). CENTURY has been chosen because it is less complex
than the other models and it can simulate natural vegetations.
To model the vegetation production we will use the CENTURY model version 4.0 (Metherell
et al., 1993). The CENTURY model simulates the long-term dynamics of C (Carbon), N, P
and S (Sulphur). The model can simulate the dynamics of grassland systems, agricultural
crop systems, forest systems and savannah systems. The grassland/crop and forest systems
have different plant production submodels, which are linked to a common soil organic matter
submodel. The savannah submodel combines the grassland/crop submodel with the tree
submodel to simulate shading effects and nitrogen competition. The soil organic submodel
simulates the flow of C, N, P and S through plant litter and the different organic and inorganic
pools in the soil. Major input variables are:
-
Monthly average maximum and mean air temperature.
Monthly precipitation.
Plant N, P, S contents.
Plant lignin content.
Soil texture.
pF properties.
Atmospheric and soil N inputs.
Initial soil C, N, P and S content.
It is possible to run the model only considering C and N dynamics, C, N and P dynamics or C,
N, P and S dynamics. Using the schedule utility it is possible to model complex (agricultural)
systems including crop rotations, tillage practices, fertilisation, irrigation, grazing and
harvesting. The CENTURY model environment is shown in figure 9.
CENTURY runs on a monthly time-step and is available for UNIX and MS-DOS systems. The
model is calibrated for different ecosystems and parameterisation for a Mediterranean shrub
ecosystem is already available and calibrated. In the following paragraph, we will give a short
presentation of some parts of the CENTURY model. For further details, we refer to the
CENTURY manual (Metherell et al., 1993).
Figure 9 - CENTURY 4.0 environment
14
A Model Approach
Water balance submodel
The CENTURY model includes a multi layer water budget model which calculates monthly
evaporation, transpiration water loss, water content of the soil layers and saturated flow
between soil layers. Potential evapotranspiration is calculated as a function of the average
1
maximum and minimum air temperature, according to Linacre (1977) . The model can read
monthly precipitation data from a file or generate stochastic precipitation using user defined
statistical values (Metherell et al., 1993).
Nutrient submodels
The CENTURY model has four nutrient submodels: a soil organic matter submodel, an N
submodel, a P submodel and an S submodel. The flowchart of the N submodel is shown in
figure 10. The other submodels have an identical structure, but differ at some parts from the N
submodel: the soil organic matter submodel does not have the mineral pool, the S and P
submodels are extended with a labile and sorbed nutrient pool. The S model could be set up
to simulate K dynamics instead of S dynamics if K is a limiting factor in particular soils
(Metherell et al., 1993).
Figure 10 - Flowchart CENTURY 4.0 nitrogen submodel
1
An Internet document about this formula is included on floppy disk in the Internet directory.
15
A Model Approach
Plant production submodels
The CENTURY model is developed to simulate dynamics of grasslands, agricultural crops,
forests and savannah (tree-grass) systems. The grassland/crop production model, which we
will use, simulates production for different herbaceous crops and plant communities. The
model assumes that the monthly production is controlled by moisture and temperature and
that maximum plant production rates are decreased if there are insufficient nutrient supplies.
The user has to specify the maximum potential production. Maximum potential production,
unlimited by temperature, moisture and nutrient stresses, is primarily determined by the level
of photosynthetically active radiation, assimilation and respiration. In CENTURY, the seasonal
distribution of production is primarily controlled by a crop specific temperature response curve
rather than by photosynthetically active radiation. Therefore, the potential production
parameter should reflect aboveground production in optimal summer conditions (Metherell et
al., 1993). The effects of grazing and fire on plant production can be modelled using a grazing
submodel, based on data of Holland et al. (1992).
Event scheduler
The program EVENT100 is the scheduling program for CENTURY. Using EVENT100 it is
possible to schedule crop growth controls and management events (grazing, irrigation,
fertilising etc. (See figure 9). EVENT100 produces a scheduling file that drives events in
CENTURY and which contains information about the simulation like timestep, starting time
and endtime. EVENT100 uses blocks. A block is a series of events that will repeat
themselves in sequence, until the ending time of the block is reached. It is possible for
example to model 2000 years of undisturbed ecosystem in block 1, 10 years of light grazing in
block 2 and finally 10 years of cultivation and growth of agricultural crops in block 3 (all in one
CENTURY run).
16
A Model Approach
5-The Study Area
Introduction
The study site is situated in south central Crete in the Asteroussia mountains, near Lendas
(see appendix 1). The Asteroussia Mountains are 50 kilometres long, 8 kilometres wide and
have a maximum height of 1231 meters. The study site that is used in this survey covers an
area of 24 km2. The highest part of the study site is 600 meters.
The main vegetation in the study area is a small shrub vegetation called phrygana. The
northeastern part of the area consists of agriculture, predominantly olive trees. At some
places, there are also fields with watermelons.
The whole area is affected by grazing of sheep and goats. In the southern part of the study
area only grazing by goats occurs. In the northern part, there are also goats, but more sheep.
The sheep are moving in large flocks and return to a feeding place every night, while goats
are moving alone or in small groups.
Geology
Crete belongs with the Peloponissos and Rhodos to the ‘Hellenic arc’. About 100-150 km
south of Crete lies the Hellenic subduction zone (AHSZ) (see figure 11). The geological
structure of Crete is characterised by a rather complex pattern of faults and massifs caused
by uplifting (Fassoulas et al., 1994). The predominant directions of faults are NW-SE in the
western half of Crete and NE-SW in the eastern half.
Figure 11 - Overview geology of Crete (Fassoulas et al, 1994).
On Crete a number of different tectonic units occur, exposing a distinct variety in their
petrologic and metamorphic features. These tectonic units are separated in the upper and
lower nappes, due to the Late Oligocene/Early Miocene, High Pressure/Low Temperature
metamorphism (HP/LT).
17
A Model Approach
The Plattenkalk series and the Phyllite-Qaurtzite unit constitute the lower nappes. The upper
nappes comprise the unmetamorphosed Gavrovo and Pindos nappes, as well as the
metamorphic tectonic ‘melange’ unit, which encompasses the Vatos-Arvi-Miamou nappe. The
high grade metamorphosed Asteroussia nappe and the Ophiolites complement the upper
nappes (Fassoulas, 1995) (see figure 11).
The area around Crete is formed by a cyclic process of alternate compression and extension.
In the Eocene compression tectonics produced underplating in Cyclades and caused the
formation of the Cycladic blueschists and the Vatos-Arvi-Miamou nappe (see figure 12a).
During the Oligocene-early Miocene compression, the upper nappes stacked southwards
over the lower nappes (see figure 12b). Due to underplating HP/LT metamorphism of the
lower nappes occurred (Fassoulas et al, 1994). In the Miocene, an N-S crustal extension
occurred to compensate the overthickening of the crust (Platt, 1986, in Fassoulas et al.,
1994). In the late Miocene-Pliocene the subduction zone moved further to the south, to the
place of the present day. In the Pliocene an extension followed the compression, which led to
the final uplift of the lower nappes, which is still continuing, to a height of more than 2000
meters (see figure 12c).
Figure 12 - Alpine development around Crete (Fassoulas et al, 1994).
Around Lendas the Asteroussia nappe, the Pindos nappe, the Gavrovo nappe and the VatosArvi-Miamou nappe occur. The following geological units occur at the study site: flysch,
limestone, ophiolites, coastal plain and alluvial plain. Flysch and limestone belongs to the
extern zone and ophiolite belongs to the intern zone.
Limestone is separated in two groups:
- Light limestone unit
- Dark limestone unit
The light limestone unit has been deposited in the upper Miocene. The top layer of the light
limestone originates from the Messinien and consists of lime with breccies and
conglomerates. The bottom layer originates from the Tortonien and consists of marine
deposits with conglomerates and fossils. The dark limestone has been deposited during the
upper Cretaceous. The dark limestone consists of black lime with radiolite. The formation of
flysch took place during the Priabonien-Oligocene. The flysch consist of grey calcareous
breccies, sandstone and claystone. The formation of the ophiolites took place late Jurassic early Cretaceous. The ophiolites are mafic and ultra-mafic rocks consisting of diorites,
gabbro-diorites and gabbros. The coastal plain was formed during the Pleistocene and
18
A Model Approach
consists of marine deposits with grey limestone and conglomerates. The alluvial plain has
been formed during the Holocene. The lithology map of the study area is shown in figure 13.
Figure 13 - Lithology map of the study area
Climate
At the study site the climate is Mediterranean. Most rain falls during wintertime from
November until April. The summer is very dry and the temperature can rise to 40 °C.
Sometimes there blows a strong hot south wind called the Sirocco. Climatic data have been
obtained from Gortis near Agii Deka. Gortis is the nearest weather station, about 12
kilometres northwest of the centre of the study site. Appendix 2 shows the climatic data
obtained from Gortis. The mean annual air temperature is 18.7 °C. July and August are the
warmest months with an mean annual air temperature of 28 °C respectively 27.6 °C. July,
August and June are the driest months. The mean annual rainfall is 453 mm. Relative air
humidity is low throughout the year with an average of 57.3 %.
Vegetation
Two groups of Mediterranean-type shrub ecosystems are usually distinguished: evergreen
sclerophylous formations, known as garrigue and maquis, and phrygana.
Phrygana ecosystems are dominated by cushion-shaped shrubs with a height of 0.5 - 1.0 m.
(Diamantopolous et al., 1994). Because phrygana is the only type of ecosystem in the study
area, we will discuss phrygana in more detail.
Phryganic ecosystems occupy more than 12 % of the total area of Greece. (Diamantopolous,
1983, in Diamantopolous et al., 1994). They are generally used as grazing land (Pantis, 1987,
in Diamantopolous et al., 1994). Characteristic species of Phrygana are Sarcopoterium
spinosum (Rosaceae), Thymus capitatus, Satureia thymbra, Phlomis fruticosa (Labiatae),
Genista acanthoclada, Anthyllis hermaniae (Leguminosae) and Euphorbia acanthothamnos
(Euphorbiaceae). A detailed list of the phrygana species observed in the study area is found
in appendix 3. A photograph of three important species is shown in photo 1. Aboveground
and subground morphology of nine species is shown in figure 14. Many of these species grow
19
A Model Approach
spherical, are woody and spiny, and have reduced or very resistant leaves. The shrubs are
usually separated by open eroded stony patches. On these patches often grow therophytes:
annual plants which, having completed their life cycles, survive periods of cold or drought as
seeds or spores and geophytes: perennial, herbaceous plants with underground food-storage
organs such as bulbs, rhizomes etc. (Tivy, 1993, Tsiourlis, 1990). Examples of geophytes in
the study area are Originea maritime and Asphodelus spec.
Figure 14 - Morphology of phrygana species (Tsiourlis, 1990).
Photo 1 - Three important phrygana species: 1=Thymus capitatus, 2=Sarcopoterium spinosum, 3=Phlomis spec.
Phrygana is adapted to the dry climate on different ways. The four most important are:
seasonal diphormism, adaptation of the external structure, increased percentages of volatiles
in the plant and a reduced root system. Seasonal diphormism means that a plant changes the
shape of the leaves every season: big, soft leaves during the wet winter time and little
20
A Model Approach
resistant leaves during the dry summer time (Orshan, 1972 in Tsiourlis, 1990). This strategy
produces during dry periods a transpiration reduction of 80 to 85 % (Margaris, 1981, in
Tsiourlis, 1990). By creating a spherical shape and by growing in cushion shaped structures,
the plants create a microclimate in which temperature and relative humidity differ less
(Tsiourlis, 1990). The volatilisation from oils, produced in the plants, create a higher
vaporisation pressure around the leaves, and decreases transpiration losses from plants
(Meidner & Sheriff, 1976 in Tsiourlis, 1990). Phrygana is characterised by a reduced root
system during dry periods, to decrease water losses by roots. After the first significant
precipitation, phrygana is able to produce a new root system in a short time, using the stored
nutrients (Mooney & Dunn, 1970, in Tsiourlis, 1990).
Phrygana ecosystems can have different origins. Some phrygana ecosystems are a form of
secondary succession of former agricultural land (Raus, 1979 in Bergmeier et al., 1996).
Other phrygana ecosystems are a form of secondary succession of forests or grasslands
destroyed by fire (Margaris, 1982, in Bergmeier et al., 1996). In drier regions, like south
Greece, another type of phrygana ecosystems exists, caused by intensive grazing by sheep
and goats (Bergmeier et al., 1996).
To classify the phrygana ecosystem in the study area, the entire dataset is analysed to find
specific groups. The analysis resulted in a classification system that divides the ecosystem in
eleven classes with enough observations per class. The map is shown in figure 15.
Figure 15 - map of vegetation types
Tsoiurlis (1990) has studied the biomass production of a phryganic ecosystem on Naxos,
Cyclades, Greece during a period of 3½ years. The study area is characterised by an
average precipitation of 380 ± 70 mm./year. Most precipitation falls in wintertime. The soil is a
chromic luvisol. The results are shown in appendix 4. After three years without grazing, the
average biomass increase is 21,4% (when Quercus coccifera is excluded: 12.6%). The
vegetation cover increased from 40 to 58 % and the average height from 0.3-0.5 to 0.5-1.0.
Tsiourlis (1990) concluded that a non-stressed phrygana would develop to the succession
stage of Maquis. The results of other biomass studies of phrygana and related ecosystems
are shown in table 1.
21
A Model Approach
Vegetation type
Location
Biomass ton/ha.
Phrygana
(Tsiourlis, 1990)
Naxos, Greece
7.9
1.6
Phrygana
(Margaris, 1981)
Mont Hymette, Greece
11.0
4.1
Phrygana
(Diamantopolous, 1983)
6 ecosystems,Greece
9.0
Tomillares
(Merino & Vincente, 1981)
Spain
4.4
12.9
21.9
0.9
2.1
3.8
Coastal sage
(Loissant & Rapp, 1971)
California
7.0 - 11.8
2.5
Matorral bas
(Mooney et al., 1977)
Chile
7.4
2.5
Heath
(Miller, 1982)
Australia
9.0 - 15.0
0.6 - 1.6
Renosterveld
South-Africa
11.0
Average
Average phrygana
10.3
9.1
Stdev +/-
1.7
2.1
0.8
Table 1 - Biomass production of phrygana and related ecosystems (Tsiourlis, 1990)
22
Production ton/ha./year
2.4 +/- 1.0
A Model Approach
6-Field methods
Field observations
The models need many input variables. The field form that was used to collect data
systematically is shown in appendix 5. In total, we visited 303 field observation points. The
average distance between the observation points is 200 meters. The observation points have
been used to create maps by means of geostatistics and interpolation (i.e. vegetation cover
map, soil depth map, etc., see chapter 8). In the field, each observation point was made by
checking the observation form. We determined our position in the field using a GPS (Global
Positioning System) with an UTM grid (zone 35) in combination with satellite image maps of
SPOT and LANDSAT. The typical spatial error of the GPS measurements is ± 30 meters.
Slope, exposition and altitude has been measured. Altitude was measured with a barometric
altitude device.
The field observations can be divided two parts: soil and vegetation observations.
The following observations were made on soil related characteristics:
lithology
% stones
% solid rock
average stone size
texture
size of aggregates
soil depth
The following observations were made on vegetation related characteristics
grazing pressure
total vegetation cover
vegetation cover per species
density of plants per species
vegetation height per species
total number of species
Percentage stones, solid stones, total vegetation cover and vegetation cover per species are
estimated using the estimation technique of Hodgon, 1974 (appendix 6). Hodgon’s chart
consists of different black cover percentages. These covers are compared with the cover of
the parameter observed in the field. The visual estimation is made for an area of about 100
m2 around an observation point. Grazing pressure has been determined as a sum of different
parameters. These parameters are vegetation cover, vegetation diversity, vegetation
composition, vegetation damage by animals and soil damage by animals. Density of plants is
a visual estimation of the area within the projection of the plant that is covered by the leaves
of the plants (alternative LAI index) using the chart of Hodgon (1974). Density and height of
plants are measured at five individuals of the three most important plant species. The texture
and size of the aggregates are determined using a manual test. Soil depth has been observed
by slamming an iron bar into the soil at three random chosen locations and measure the
penetration depth. The three measurements were averaged.
Selection of testplots
During two periods in May and June several field measurements took place. Plant samples
and soil samples were taken for nutrient analysis. Soil moisture was determined using a FDR
device (Frequency Domain Reflectory). However, not the whole study area can be sampled.
Because of this, we have chosen testplots in a way that the properties of the different
testplots are representative for the whole area. In total, we selected nine testplots with
different soils, vegetation cover, vegetation species, exposition and soil depth. Collection of
plant samples, soil samples and measurements of soil moisture content for the testplots 1 – 6
23
A Model Approach
took place on May 19&20, 1997 and June 6&7, 1997. Plot 7, 8 and 9 were only visited on
June 6&7, 1997.
The properties of the nine testplots are shown in table 2. The average stone size, texture, size
of the aggregates, soil depth, grazing pressure and species are classified in groups.
Testplot
1
2
3
4
5
6
7
8
9
Observation point
44
45
46
47
48
49
188
112
289
Coord. X
316602
316562
315828
316302
316114
315069
315442
314690
315739
Coord. Y
3871928
3871799
3871616
3870259
3867625
3867613
3869913
3869730
3871565
Lithology
light limestone light limestone flysch
flysch
coastal plain
flysch
dark limestone flysch
flysch
Slope
12
10
15
30
4
10
2
14
22
Exposition
NNE
S
NE
SSW
S
SW
SW
NE
S
% detached stones
40
30
70
30
75
40
5
25
30
% solid rock
10
10
0
3
0
0
50
0
0
Average stone size cm
10-15
10-15
0-5
10-15
0-5
5-10
5-10
0-5
0-5
Texture
Silt loam
silt loam
silt loam
silt loam
loamy sand
silt loam
Loam
silt loam
silt loam
Size aggregates mm
5-10
2-5
1-2
1-2
1-2
2-5
1-2
2-5
2-5
Soil depth cm
0-25
0-25
0-25
25-50
0-25
0-25
0-25
25-50
0-25
Grazing pressure
absent
moderate
moderate/high moderate moderate/high high
very high
moderate light/moderate
Vegetation cover %
50
60
50
60
40
30
5
50
50
Vegetation type
2
2
8
2
5
1
4
8
7
Table 2 - Properties of the testplots
Soil water content measurements
For measuring soil water content there are destructive and non-destructive procedures.
Destructive procedures include taking a soil sample from the field and determining the soil
water content in the laboratory (gravimetric method). The non-destructive methods rely upon
a sensor placed in the soil with an evaluation unit connected to its cables at the time of
measurement or the sensor being inserted in the soil each time an observation is desired
(Kutilek & Nielsen, 1994). With the gravimetric method a soil sample is taken and dried in an
oven (105° C). By comparing the weight of the sample before and after drying and measuring
the volume of the sample, the moisture content of the soil can be determined.
We have used a non-destructive method. For measuring the soil water content, a FDR
ThetaProbe has been used. The FDR (ThetaProbe) measures the dielectric constant (ε) of
the soil and gives an output in Volt. The relationship between the measured dielectric
constant of a soil and its soil water content depends on the particular composition of the soil.
The ThetaProbe is pushed into the soil until the rods are fully covered and the analogue
output can be read on the voltmeter. The output from the ThetaProbe in mili-volt has to be
converted into percentage soil water content by using conversion equations. The calibration
of the FDR ThetaProbe is explained in chapter 7.
24
A Model Approach
Saturated conductivity was measured using the Ksat-ring method. On three important lithology
classes we have taken 36 Ksat-ring samples. After saturating the samples, the Ksat values
were measured with the method illustrated in figure 16. During the experiment, a constant
water head h is maintained above the soil sample.
Under saturated conditions v is given by Darcy’s law:
v = − k sat * i
Where:
v
ksat
i
= velocity
= saturated conductivity
= hydraulic gradient
i = expressed by
l+h
l
When A is the surface of the soil in the cross-section of the ring, then Q, under saturated
conditions is given by:
Q = v * A = − K sat
l+h
*A
l
Where Q is the volume of water which flows through the sample in a time period.
When Q is constant then Ksat is given by:
K sat =
Q *l
A * (l + h)
Figure 16 - Ksat method
25
A Model Approach
To calibrate the visual estimations of vegetation cover we have carried out line intercept
measurements. Using the line intercept technique, data are tabulated on the basis of plants
lying on a straight line across the community under study. Because an area is not being
sampled, only density indices and relative estimates of density can be calculated. In cases
where relative estimates are sufficient, the method performs well (Brower et al., 1990).
At four observation points the vegetation cover was measured, using four transects per
observation point, orientated in a quadrangular pattern (figure 17). Each transect is 50 m. long
and every 0.5 m., the plant species is recorded, its height, the projection of its canopy on the
transect, the maximum length parallel to the transect and the maximum width.
The percentage cover for each species on a transect is calculated as follows:
C% =
Where:
Σdx
* 100
Z
C%
Σdx
Z
= Percentage cover
= Sum of projection of the plants canopy on the transect
= Total transect length
Figure 17 - Line intercept method
Soil sampling
To obtain soil nutrient data, soil samples were collected from the seven testplots on May 19
&20, 1997 and June 6&7, 1997. As with the vegetation samples, the samples were collected
within two days. On every testplot five samples were collected at a depth of 10-15 cm. Many
subsamples were taken to obtain bulked samples. In the laboratory the samples were sieved
in a 2-mm. sieve and pulverised to powder.
Vegetation sampling
To obtain plant nutrient data, different species were sampled on the seven testplots. The two
indicator species Thymus capitatus and Sarcopoterium spinosum were sampled on every
testplot except in the testplots where they were simply not present. Of every species leaves
were collected of different parts of the plant and of different plants. In this way bulked species
monsters were obtained. To exclude time-influence the samples were collected within two
days. To study time effects we sampled at: may 19&20, 1997 and June 6&7, 1997. After
collection of the samples, the samples were sun dried to conserve them for further analysis.
In the laboratory the samples were pulverised and ovendried at 80° C.
26
A Model Approach
7-Methods in the laboratory
Determination of N and P
For determination of N and P of the soil and vegetation samples, the Kjeldahl method has
been used. The Kjeldahl method involves the destruction of the soil and vegetation samples
with a mixture of 30 % hydrogen peroxide and concentrated sulphuric acid with Selenium as a
catalyst. In the extract, also other elements can be determined: Ca, Mg, Mn, Na and Zn. P, K,
Ca, Mg and Na are analysed by using the Inductively Coupled Plasma Emission
Spectrophotometer (ICP-AES). N is analysed (as NH4) by using the Flow Injection Analysis
(FIA).
Analysis of Carbon
For analysis of organic matter in the soil, the Loss On Ignition method (L.O.I.) has been used
(NEN 5739). Oven dried soil samples were heated during three hours at 550 °C, to combust
organic carbon, which leaves the soil as CO2. Weighting the sample before and after the
analysis indicates the amount of organic matter in the soil. At the temperature of 550 °C,
oxidation of other elements can produce loss of mass. Another source of loss of mass occurs
by release of cristallic-bound water from clay minerals. Thus correction for clay minerals and
iron (if the content is more than 5%) has to be executed.
The L.O.I. is calculated as follows:
LOI =
(W1 − W2 )
* 100%
(W1 − W )
LOI ' = LOI − (0.07 * f 2 µm + 0.15 * f Fe2O3 )
Where:
LOI
LOI’
W
W1
W2
f2µm
fFe2O3
= Loss On Ignition
= Loss On Ignition corrected
= Weight empty glow dish
= Weight glow dish + ovendried soil 105 °C.
= Weight glow dish + ovendried soil 550 °C.
= Clay content
= Iron content
To calculate organic matter from LOI’, LOI’ is multiplied by 1/0.58 (empirical value derived
from a lot of measurements from different soils).
The L.O.I. method gives a reasonable estimation of organic C in most cases. However, a
better method exists: Organic C analysis according to Walkley & Black. With this method
organic C is oxidised by adding K2Cr2O7 and concentrated sulphur acid (H2SO4). A titration
with Mohr’s salt (Fe(NH4)2(SO4)2.6H2O gives an indication of the loss of K2Cr2O7 and thus the
organic C content. Because this method is more labour intensive and harmful to the
environment we have used the L.O.I method, but we have investigated the correlation of the
two methods. Twelve samples from the Asteroussia and Psiloritis region on Crete were
2
analysed with both methods. The analysis is shown in appendix 7 and resulted in a R 0f
0.80.
27
A Model Approach
Analysis of lignin
Lignin is a complex aromatic polymer which occurs in plant cell walls in close association with
cellulose and the hemicellulosic polysaccharides (Morrison, 1972). Before determining the
lignin content of the plant leafs, soluble sugars, phenolics, lipids and starch are extracted until
a cell residue remains. Lignin has been determined with the acetyl bromide procedure
according to Morrison (1972):
Ground plant leafs are extracted two times with 80 % ethanol at 70 °C. The residue (after
centrifugation) is extracted two times with de-ionised water at 30 °C. After centrifugation the
residue is extracted with methanol:chloroform and after this with acetone. After these
extractions 3% hydrochloric acid is added and heated to a temperature of 125 °C. The cell
wall residue after centrifugation is used for the determination of lignin.
For the determination of lignin, a spectrophotometer is used. 25 % acetyl bromide is added to
the cell wall residue and heated to 70 °C. After cooling down, acetic acid, NaOH and
hydroxylammonium-chlorid are added. In the spectrometer, the optical densities of the
solutions are determined at 280 nm. in quartz cuvettes. P-coumaric acid was used as a
standard. The calibration of p-coumaric is shown in appendix 18.
Grain size analysis
Grain size distribution of the different soils has been analysed according to the NEN 5753
method. The analysis is done for soil material smaller than 2 mm. First 30 % hydrogen
peroxide is added to oxidise present carbon. Hydrogloric acid is added to dissolve the
carbonates. The grain size distribution in the range of 53µm-2 mm is determined by sieving
the soil with twelve sieves of different mesh size. The distribution of the material < 53µm is
determined by a silt analysis. The silt analysis measures the deposition rate of soil particles in
a silt cylinder. The amount of soil particles (measured as dry weights per volume) at different
time intervals after the soil sample has been made in total suspension, is a measure of the
different silt fractions.
Measurements of soil moisture retention curves
For determination of pF curves, the principle of hydrostatic equilibrium is applied. For the
analysis, an undisturbed soil sample is required. The soil sample is placed upon a layer of
fine sand saturated with water (Kutilek & Nielsen, 1994), as shown in figure 18. The tank with
fine sand is connected hydraulically to an outflow vessel. Positioning the outflow vessel at the
same level as the soil sample (pF 0) saturates the soil. Lowering the outflow vessel induces
the first negative pressure head h. After reaching a new equilibrium (i.e. pF 0.4), the soil water
content is determined gravimetric. This procedure is repeated for pF 1, 1.5 and 2. For pF 2.3,
2.5 and 2.7 the fine sand plate is replaced by a porous kaolin plate. The principle of
measurement remains the same.
Figure 18 - Principles of pF measurements (Kutilek & Nielsen, 1994).
28
A Model Approach
For smaller values of the pressure head, a so-called pressure plate apparatus (figure 18) is
used. Instead of lowering the pressure hydraulically below the plate, the air pressure is
increased. At a pressure of 2.5 bar, the soil water content for pF 3.4 is determined and at a
pressure of 16 bar, the soil water content for pF 4.2 (wilting point) is determined.
Calibration FDR
The FDR (ThetaProbe) measures the dielectric constant (ε) of the soil and gives an output in
Volt. The relationship between the measured dielectric constant of a soil and its soil water
content depends on the particular composition of the soil. ThetaProbe has two built-in
calibration curves for generalised mineral and organic soils (Eijkelkamp, 1997). The
calibration curves are shown in figure 19. Calibration curves for other soils differ slightly from
the built-in calibration curves but reduce soil water content errors from ± 5 % to ± 2 %.
Figure 19 - FDR calibration curves
Four equations are important to calibrate for specific soils:
ε = 1 + 6.25V − 5.96V 2 + 4.93V 3
(1)
ε = a0 + a1 * θ
(2)
εw − ε0
θw
(3)
a1 =
[1 + 6.25V − 5.96V 2 + 4.93V 3 ] − a 0
θ=
a1
Where:
√ε
√εw
√ε0
θ
V
A0
A1
=
=
=
=
=
=
=
(4)
Refractive index
√ε at known volumetric water content
√ε of dry soil
Calculated volumetric water content
Volt
Fitting parameter
Fitting parameter
29
A Model Approach
Equation (1) describes the relation between dielectric constant and Theta-probe output by a
rd
2
3 order polynomial (R =0.9993) (Eijkelkamp, 1997). Whalley (1993) showed there is a
simple linear relationship between refractive index (√ε) and volumetric water content,
expressed in equation (2).
To calibrate for a specific soil you have to measure the output in volts of the FDR of a wet soil
and the corresponding gravimetric water content. After drying the soil sample at 105 °C the
output in volts of the FDR is measured again. These values are used in equation (1) to
calculate √ε and √ε0. Now a1 can be calculated using equation (3). A0 equals √ε0. After this,
the soil water content can be calculated using equation (4). Typical values of a0 are 1.0 - 2.0
and typical values for a1 are 7.6 - 8.6. The following parameters of a0 and a1 are used in the
built-in calibration curves:
Mineral soils
Organic soils
a0
1.6
1.3
a1
8.4
7.8
(Eijkelkamp, 1997)
30
A Model Approach
8- Mapmaking Using Geostatistics
Introduction
Because the topographic map was of inferior quality and thus normal mapmaking was not
possible, we have decided to do as much point observations as possible. This strategy
resulted in 303 observation points, containing several attributes, with an average spacing of
200 meters. This space is needed because the used GPS device has a relative error of ± 3050 meters.
To interpolate such a dataset several methods exist: manual classification, Thiessen
polygons, trend surfaces, inverse distance interpolation, kriging etc. McDonnell & Burrough
(1998) have shown that, when data is sparse (but not too sparse) Kriging is the best
interpolation technique available.
Kriging interpolation starts with the recognition that the spatial variation of a continuous
attribute is often too irregular to be modelled by a simple function. The variation can be better
described by a stochastic surface with an attribute known as a regionalized variable.
The regionalized variable theory assumes that the value of a random variable Z at (x) is given
by:
Z ( x ) = m( x ) + ε ' ( x ) + ε ' '
Where:
m(x) = a deterministic function describing a structural component of Z at x.
ε’(x) = a random spatially correlated component.
ε’’(x) = a residual non-spatially correlated term, or noise (Nugget variance).
When structural effects have been accounted for and the variation is homogenous in its
variation, the semivariance γ (h) can be estimated by:
γˆ (h) =
1 n
{z (xi ) − z ( xi + h )}2
∑
2n i =1
Where:
n = number of pairs of sample points of observations of the values of attribute z
separated by distance h.
A plot of γ (h) against h is called a semivariogram and gives a quantitative description of the
regionalized variation (see figure 20). An important factor of the variogram is the range,
which describes the distance when the datapoints become spatially independent. The
variogram can be used to estimate the optimal weights λI needed for interpolation. The value
z(x) for an unsampled point is then calculated with:
n
zˆ ( x0 ) = ∑ λi * z ( xi )
i =1
The principle is shown in figure 21.
(McDonnell & Burrough, 1998)
31
A Model Approach
Figure 20 - Variogram example
Figure 21 - The principle of kriging
Interpolation of the field data
We have chosen to calculate only maps of soil depth, vegetation cover and grazing pressure.
Quick modelling of variograms showed that other variables produced bad variograms with too
much nugget variance (no spatial correlation).
Before starting the kriging procedure, the data were first analysed in SPSS and STATISTICA.
Descriptive statistics were calculated, and the data was tested on normality using the
Kolmogorov-Smirnov test. The results are shown in table 3 and in figure 22a-c.
Descriptive Statistics
N
Grazing pressure 303
Soil depth
303
Vegetation cover
303
Mean
4.52
20.34
38.01
Std. Deviation
1.34
17.51
17.34
Minimum
1
0
3
Maximum
7
112.5
85
K-S Z
3.4
7.6
2.4
2-tailed p
0.000
0.000
0.000
Table 3 - Descriptive statistics
SOILDEPT
GRAZPRES
260
120
240
110
220
100
200
90
180
80
No of obs
No of obs
160
140
120
100
70
60
50
40
80
60
30
40
20
10
20
0
-20
0
20
40
60
80
100
120
140
Expected
Normal
0
0
1
2
3
4
5
6
7
8
Expected
Normal
90
Expected
Normal
Upper Boundaries (x < boundary)
Figure 22a-c Histograms with expected normal curve
VEGCOV
80
70
60
No of obs
50
40
30
20
10
0
-10
0
10
20
30
40
50
60
32
70
80
A Model Approach
Due to the large variation in soil depth, the soil depth is divided in classes during the field
measurements: 0-25 cm, 25-50 cm, 50-75 cm, 75-100 cm and > 100 cm Because kriging
interpolation needs scalar values and not nominal values, average soil depth values were
assigned to the classes afterwards. Respectively 12.5 cm, 37.5 cm, 67.5 cm, 87.5 cm and
120 cm. It is obvious that the data measured in this way cannot be normal distributed. SPSS
analysis shows a 2-tailed p-level of 0.000 (normal distributed when p > 0.05) thus the data is
not normally distributed. Both grazing pressure and soil depth are not normally distributed
(p=0.000). Grazing pressure tends to have a left skewness. Vegetation cover is most normal
(lowest Kolmogorov-Smirnov Z) but the classes 30 to 60 % are over presented. However, we
will use the data in this form because the generated maps appear to match the observed
pattern in the field very well.
Variograms are calculated using GSTAT 2.0 (Pebesma, 1995) and VARIOWIN 2.4 (Panatier,
1996). The variograms modelled in GSTAT 2.0 showed high variances in the first lag for all
variables. By removing just few points in the first lag using VARIOWIN, the model fits better
and the nugget variance is decreased in case of soil depth and grazing pressure.
After modelling the variograms, shown in figure 23a-c, an ordinary block kriging interpolation
is executed in GSTAT 2.0. using the next variogram models:
Soil depth:
Grazing pressure:
Vegetation cover:
103 Nug(0) + 235 Sph (935)
0.62 Nug(0) + 1.27 Sph (1014)
175 Nug(0) + 214 Sph (750)
Figure 23a - Variogram of soil depth
Figure 23b – Variogram of grazing pressure
Figure 23c – Variogram of vegetation cover
Orinary block kriging has been used to obtain smoother maps without outliers. The used
blocksize is 50 meters. The datapoint-input map, the predicted map of soil depth and the
variance map of soil depth are shown in figure 24. Note the area with large variances in the
upper right part of the map. This is an agricultural area where no field measurements were
done. The area in the centre of the map was too steep to access.
Of grazing pressure and vegetation cover only the prediction maps are shown (figure 25).
Note the correlation between vegetation cover and grazing pressure. In the grazing pressure
map, some areas are left white; these are fenced agricultural areas where no grazing occurs.
33
Figure 24 - Datapoints, predictions and variances of soil depth.
Figure 25 - Predicted maps of grazing pressure and vegetation cover.
34
A Model Approach
A Model Approach
Because the topographic map was of inferior quality and thus normal mapmaking was not
possible, we have decided to do as much point observations as possible. This resulted in a
dataset of 303 observation points adequately covering the whole study area. The number of
observations made it possible to execute a statistically justified analysis and to use
geostatistical interpolation techniques. The relations described in table 8 show significant
correlations. Only maps of soil depth, vegetation cover and grazing pressure produced
reasonable semivariograms and are interpolated using kriging techniques. Semivariograms of
other variables showed too much nugget variance, thus kriging techniques were not used.
The maps of nominal data, lithology and vegetation type are interpolated using Thiessen
polygons. This method does not give error assessment and results in a less realistic
tessellation pattern.
However, kriging produces maps that are unrealistically smooth. For a more realistic result,
the rougher average soil depth map produced in the Monte Carlo simulation (figure 32) could
be used. See chapter 10 for more information about Monte Carlo Simulation.
35
A Model Approach
9-Field & Laboratory Results
Soil nutrients
The results of the soil nutrient analyses were within the range of other soils analysed in the
laboratory of physical geography. No large deviations occurred between the five bulksamples
per testplot. The average results per testplot are shown in appendix 8.
Compared to the data of Tsiourlis (1990) (appendix 4) our C and N contents are higher. C/N
ratios measured by Tsiourlis (1990) are somewhat lower. This indicates that the Cyclades
ecosystem has a faster nutrient cycle and is more fertile.
When we compare the testplots it is seen that N-contents on plot 1, 2 and 7 are higher than
the other plots. These plots are situated on light and dark limestone. C/N ratios are lower on
these plots, this indicates that the limestone soil has a faster nutrient cycle and is more fertile.
Note that soil P- and N-contents are higher on plot 7 than on plot 1 and 2, perhaps caused by
intensive grazing (input N and P from dung and urine (Esselink et al. 1991)). Soil P- and Ncontents show no difference between the May and June data. The C-content shows an
average increase, and thus an increase of C/N ratios. The higher C/N ratio in June suggests a
less active ecosystem in June. We have to make the critical remark that these changes could
be caused by measurement errors. In common, soil properties are not so variable in short
time.
Vegetation nutrients
In appendix 9 the raw data of the plant nutrient analyses are shown. In appendix 10 the data
are sorted to obtain detailed information. Just one sample (p27) was excluded because the
measured values were dubious. We have to mention that not all calculated data (average,
standard deviation) are statistically justified, due to the small number of observations.
Nevertheless, we will explore the data to possible trends.
The results of the lignin analysis can be found in appendix 18. This appendix shows the used
calibration curves of p-coumaric. From these curves we can conclude that acetyl bromide was
stable during the analysis. The analysis of two standard samples with known lignin content
suggested that the results are reliable.
Average lignin content ranges from 2.7 % to 5.2 %. Poorter & Bergkotte (1992) concluded in a
study of 24 (Dutch) wild herbaceous species, that fast growing species accumulate more Ncompounds, organic acids and minerals. Slow growing species accumulate more cellulose,
insoluble sugars and lignin. They found typical values of 1.4 % for fast growing species and
2.6 % for slow growing species. The phrygana values are somewhat higher. Possible causes
of the higher lignin content are:
•
•
The lower growth rate of a phryganic ecosystem compared to an average Dutch
ecosystem.
The different chemical composition of phrygana species due to adaptations to the climate
and grazing.
36
A Model Approach
In table 4 Spearman rank order correlations are shown for different vegetation parameters.
2
The correlations are calculated using STATISTICA, significant R ‘s (p<0.05) are shown in
bold.
From this table some conclusions can be drawn:
• N and C/N contents differ significantly between plots.
• K, Na, P, N and C/N show a significant decrease from May to June.
• K and Mg show a significant difference between plant species.
• Lignin does not show any correlation with other parameters (correlation with plant species
is best, but not significant).
Plot
Date
Plant species
Plot
Date
Plant species
Ca
K
1.00
0.32
-0.25
0.05
-0.18 0.29
1.00
0.09
1.00
Ca
K
P/N
C/N
0.28
0.50
-0.27 -0.45 -0.23 -0.60 -0.53 -0.51 -0.08
-0.02
0.52
-0.03 0.35
-0.70 -0.25 -0.10 0.04
-0.29
-0.15
-0.03
1.00
0.22
0.40
0.43
0.08
0.07
-0.09
0.01
-0.08
1.00
-0.02 0.46
0.48
0.22
-0.17
0.27
-0.23
0.15
Mg
Mg
1.00
Na
P
Na
P
0.00
-0.32 -0.50 0.04
N
Lignin
0.49
-0.05 -0.16 0.17
0.14
1.00
0.28
0.21
0.14
0.12
-0.21
1.00
0.47
0.19
0.55
-0.47
1.00
0.28
-0.40
-1.00
1.00
-0.04
-0.28
1.00
0.41
N
Lignin
P/N
C/N
1.00
Table 4 - Spearman rank order correlations.
A more detailed look at the data (Appendix 10) shows that most species have a decreasing
nutrient content from May to June except Olea europaea and Sarcopoterium spinosum. The
increase of nutrient content of the latter could be explained by the sampling of different types
of leaves (winter/summer leaves, see: seasonal diphormism chapter 4). The decrease in Ncontent of Rhamnus oleoides and Genista acanthoclada is larger than the other species.
Calicotome villosa shows high N-contents, which is also observed by the other researchers of
the project2. P/N ratios are low and decrease from May to June except for Phlomis spec. The
low P/N ratios (total average 0.06) suggest that the ecosystem is P-limited (When the P/N
ratio equals 0.04, growth will be limited by P. When the P/N ratio equals 0.15 growth will be
limited by N (Esselink et al. 1991)).
Compared to the data of Tsiourlis (1990) (Appendix 4), nutrient contents are higher and P/N
ratios are lower on average. Unfortunately it is not clear if Tsiourlis (1990) measured only
leaves or all plant parts, so differences could be caused by differences of methodology.
2
E. van der Giessen and A. Hendriks, personal communication
37
A Model Approach
Grain size analysis
Grain size distribution has been determined for the seven testplots. The results of the analysis
are shown in appendix 11. The results give an indication of the distribution of sand, silt and
clay. With this information a sample can be classified in a texture group, using a texture chart
(figure 26). The texture determined in the lab has been compared with the estimation of the
texture in the field. The result of the comparison is shown in table 5.
Plot
1
2
3
4
5
6
7
Texture class lab
Clay loam
Clay loam
Sandy clay loam
Sandy loam
Sandy clay loam
Sandy loam
Sandy clay loam
Texture class field
Loam
Loam
Silt loam
Silt loam
Loamy sand
Loamy sand
Loam
Table 5 - Comparison of the texture classes determined in laboratory and field.
Figure 26 - Texture chart
The determination in the lab compared to the estimation in the field does not give the same
results. The samples determined in the lab are very sandy (almost all more than 40 - 50 %),
while the manual estimation in the field results in a classification with a high silt fraction and
almost no sand fraction. A problem with the manual method is that it only distinguishes the
texture classes sand, loamy sand, silt loam, loam, light clay and heavy clay. Sandy loam and
sandy clay loam (five of the seven samples) are not classes in the manual method. The
deviation is not constant so a correction for the total database cannot be done based on the
seven samples analysed in the laboratory. So we have chosen to use the field estimations.
38
A Model Approach
Soil moisture retention curves
Detailed results of the pF measurements in the laboratory are shown in Appendix 12. A
summary of the results is shown in table 6.
The pF curves are fitted in the following way:
1. Volumetric moisture contents at different pF’s are measured in the laboratory.
2. The parameter θe , needed in the formula of Van Genuchten (1980) is calculated as
follows:
θe =
θ −θr
θs −θr
θ
θe
θr
θs
Where:
= measured volumetric water content
= effective water content ranging from zero to one.
= residual water content
= water content at saturation
3. According to van Genuchten (1980), θe is calculated:
[
]
θ e = 1 + (α h )
n −m
m = 1 − 1/ n
And thus:
(θ
h=
−1 / m
e
)
−1
1/ n
α
Where n and α are fitting parameters and pF = log|h|
4. To calculate the fitting parameters α and n, the program pF-fit (Waterloo, 1992) is used.
This program constructs a soil moisture retention curve from measured pF-Theta pairs by
fitting a curve using the nonlinear least squares method.
5. h is calculated in Excel ’97 and shown in appendix 12 (pF Genuchten). Other calculated
values are the volumetric moisture content at pF 2.0 and pF 4.2. On every plot 3 to 5
measurements were done, in appendix 12 only the average values per plot are shown.
The average is calculated with two methods: the first method averages all theta-v values
at a certain pF and a new pF curve is fitted. The second method calculates Theta-v at pF
2.0 and pF 4.2 from the average values from the curves without refitting. As shown in
appendix 12, the two results do not differ much. We will use the refitted average curves.
We experienced problems with the pF measurements. On all plots, the values at pF 3.4 and
pF 4.2 are too low compared to other measurements in the laboratory of physical geography.
This could be a cause of drying out of the samples, due to a malfunctioning pressure
apparatus. Moisture contents measured in the sand tank are too high at plot 1 and 7. It seems
they did not make good contact in the sand tank, but that is striking because the samples of
plot 1 and 7 were the best-taken samples. The high pF 2.0 values and low pF 4.2 values
cause a (too?) large soil water-supplying range of the soil.
39
A Model Approach
In general, differences of lithologies are reflected in the pF curves. Soils on limestone have a
larger water supply capacity compared to soils on sandstone. The pF 2.0 values of plot 1 and
7 will be set lower in the water balance model.
Plot 1
Plot 2
Plot 3
Plot 4
Plot 5
Plot 6
Plot 7
Moisture content pF 2
0.45
0.30
0.27
0.32
0.33
0.31
0.38
Moisture content pF 4.2
0.09
0.07
0.06
0.11
0.06
0.07
0.11
Water holding capacity
0.36
0.23
0.21
0.21
0.27
0.24
0.28
N
1.40
1.31
1.32
1.48
1.36
1.30
1.46
A
0.024
0.072
0.034
0.01
0.022
0.056
0.009
Texture class (field)
Loam
Loam
Silt loam
Silt loam
Loamy sand Loamy sand
Loam
Table 6 - Summary of pF measurements in the laboratory
Volumetric soil water content measurements
Soil moisture content has been measured on six testplots in May and on nine testplots in
June (properties of the testplots are shown in table 7). To obtain statistically useable data,
theta-v is measured 30 times on every testplot. Statistical analyses in SPSS showed that soil
moisture content values on all testplots were normal distributed (2 tailed p-level > 0.05) so the
T-test for independent samples could be used.
To calibrate the raw FDR data, normal calibration curves were used (see figure 19). Soil
specific calibration failed because the samples were too dry and too disturbed and they did
not produce reliable FDR calibration curves. We set A1 to 8.1 and A0 to 1.5 for all samples.
Unfortunately, error levels are higher now.
In appendix 13 the results of the soil moisture content measurements and the statistical
analysis are shown. To test if the plots differ significant for soil moisture content, a cross Ttest is executed using STATISTICA. Although it is difficult (and statistically impossible with
this dataset) to explore which factor causes the differences between the plots, we will try to
describe possible trends in the dataset.
Corresponding pF values are calculated for the mean soil moisture contents of every plot,
using the measured pF data. In May the pF range is from 3.0 to 4.2. Growth will be limited (pF
> 3.5) but the plants will not stop growing. In June the pF range is from 3.6 to 5.4. Plot 1, 3, 6
and 9 experience moisture stress and plant growth will be limited. This matches field
observations of the physical state of the vegetation in May and June.
From appendix 13 some conclusions can be drawn:
•
•
•
•
•
•
Most plots (especially in June) differ significant in measured soil moisture content
(p<0.05).
Plots 1 to 6 differ significant between May and June.
Plot 1 and 4 are significant different from the other plots in May. Plot 4 is wetter than plot
1. Plot 1 is situated on limestone; plot 4 has a thicker soil layer.
Plot 2 is not significant different from plot 3, 5 and 6 in May. In June plot 5 is significant
wetter than plot 2. The lower vegetation cover and thus less transpiration on plot 5 could
cause this.
In June plot 4, 5, 7 and 8 are significant wetter than the other plots. Plot 4 and 8 have
thicker soil layers, plot 5 has a lower vegetation cover and plot 7 is situated on limestone
and has a very low vegetation cover.
Plot 8 is significant wetter than plot 9. Both plots are situated in flysch. Plot 8 is east
facing; plot 9 is south facing. Plot 8 also has a thicker soil layer.
40
A Model Approach
Testplot
1
2
3
4
5
6
7
8
9
Observation point
44
45
46
47
48
49
188
112
289
Coord. X
316602
316562
315828
316302
316114
315069
315442
314690
315739
Coord. Y
3871928
3871799
3871616
3870259
3867625
3867613
3869913
3869730
3871565
Lithology
light limestone light limestone flysch
flysch
coastal plain
flysch
dark limestone flysch
flysch
Slope
12
10
15
30
4
10
2
14
22
Exposition
NNE
S
NE
SSW
S
SW
SW
NE
S
% detached stones
40
30
70
30
75
40
5
25
30
% solid rock
10
10
0
3
0
0
50
0
0
Average stone size cm
10-15
10-15
0-5
10-15
0-5
5-10
5-10
0-5
0-5
Texture
silt loam
silt loam
silt loam
silt loam
loamy sand
silt loam
Loam
silt loam
silt loam
Size aggregates mm
5-10
2-5
1-2
1-2
1-2
2-5
1-2
2-5
2-5
Soil depth cm
0-25
0-25
0-25
25-50
0-25
0-25
0-25
25-50
0-25
Grazing pressure
absent
moderate
moderate/high moderate moderate/high high
very high
moderate light/moderate
Vegetation cover %
50
60
50
60
40
30
5
50
50
Vegetation type
2
2
8
2
5
1
4
8
7
Ksat Average
8.39
2.73
3.46
Ksat standard deviation
7.89
3.02
2.70
Ksat max
0.69
0.17
0.44
Ksat min
30.27
11.35
10.55
Table 7 - Properties of the testplots
Saturated conductivity (Ksat) has been measured for three lithology classes: light limestone
(plot 1), dark limestone (plot 4) and flysch (plot 7). Properties of the testplots and Ksat-values
are shown in table 7. For all three lithology classes, soil conductivity has been measured with
more than 30 soil samples to obtain statistically usable data. Some soil samples have been
taken out due to too high conductivity caused by holes and cracks in the samples.
Plot 1 gives a higher value for soil conductivity than plot 4 and plot 7, respectively 8.39 for plot
1 and 2.73, 3.46 for plot 4 and plot 7. In addition, the standard deviation of plot 1 is very high
compared to plot 4 and 7.
The statistical analysis in STATISTICA shows that the distribution of the Ksat-values is not
normal, but more lognormal distributed. Although the p-level of the lognormal distribution is
still not significant (p>0.05), see appendix 14). Because the distribution is not normal and not
lognormal the Mann-Whitney U-test has been used to test if the plots differ significant.
Plot 1 differs significantly from plot 4 and plot 7. This could be caused by high grazing
pressure on plot 4 and 7 and no grazing pressure on plot 1. The plots with high grazing
pressure have a more compact soil (crust) and thus lower soil conductivity. Plot 4 and plot 7
do not differ significantly.
Statistical analysis field observations
To explore the dataset to possible trends, Spearman rank order correlations are calculated
using STATISTICA. All measured parameters at the 303 datapoints are used in the analysis.
The results are shown in table 8. The correlations significant at the 5% level (p=<0.05) are
shown. The correlations with a significance level of 1% (p=<0.01) are marked bold. When
correlations are logical to expect, the correlation is marked red.
Correlation between Y co-ordinate and vegetation cover / grazing pressure can be observed
in figure 25. Grazing pressure decreases to the north, vegetation cover increases to the north.
The correlation between Y co-ordinate and vegetation type can be observed in figure 15. In
the Northern part of the study area a more Sarcopoterium spinosum dominated vegetation
type occurs. In the southern part Thymus capitatus dominates.
Altitude correlates with texture and lithology. This can be explained by the occurrence of
limestone at higher altitudes (see figure 13). Altitude also correlates with vegetation cover,
41
A Model Approach
vegetation cover increases with altitude. This can be caused by the fact that most sheep are
grazing in the lower parts of the area near places accessible to farmers.
Spearman Rank Order Correlations
MD pairwise deleted
DATE
X
Y
ALT
LITHO
SLOPE
ASP
%ST
%SR
STSZ
TEXT
SAG
SOILD
GRAP
VCOV
VTYP
DATE
1.00
X
Y
ALT
-0.26
1.00 -0.20
1.00 0.57
1.00
LITHO SLOPE ASP %ST %SR
0.14
-0.14
0.18
-0.14 -0.17 -0.16 -0.28
-0.19
-0.19 0.15
1.00
0.23 -0.45
1.00
0.18 0.13
1.00 0.19 -0.09
1.00 -0.12
1.00
STSZ TEXT
0.12
0.13 0.22
-0.14 0.20
-0.16 0.24
-0.34 -0.46
-0.13
-----0.27
1.00
-0.23
0.24
0.24
1.00
SAG
0.16
SOILD
0.18
-0.11
0.12
GRAP
0.31
-0.36
VCOV
-0.31
-0.28
0.40
0.20
VTYP
-0.14
-0.15
------
-0.14
-0.34
-0.34
-0.12
0.18
-0.65
1.00
0.14
-0.14
0.24
1.00
------
0.32
0.19
0.24
-0.20
-0.18
-0.16
-0.19
-0.18
0.17
-----1.00
0.13
0.12
-0.12
1.00
1.00
Explanation Codes:
DATE
X
Y
ALT
LITHO
SLOPE
ASP
%DS
%SR
STSZ
TEXT
SAG
SOILD
GRAP
VCOV
VTYP
Observation date
X co-ordinate
Y co-ordinate
Altitude
Lithology
Slope
Aspect
% Stones
% Solid rock
Stone size
Texture
Size of aggregates
Soil depth
Grazing pressure
Vegetation cover
Vegetation type
Table 8 - Spearman rank order correlations, R shown when significant at 95% level (p<0.05), bold when significant at
99% level (p<0.01), red when the correlation is expected.
Lithology correlates with altitude, % stones, % solid rock, stone size and vegetation type as
expected. It does not correlate with the size of the aggregates, which is expected too. The
correlation of lithology and percentage stones is explained by low percentages of stones on
limestone and relative higher percentages stones on metamorphic claystone.
The correlations between lithology and texture are shown in figure 27.
Figure 27 - Bivariate histogram texture versus lithology
42
A Model Approach
From figure 27 some conclusions can be drawn:
•
•
•
Loamy sand is primarily found in metamorphic sandstone and is not found on limestone.
Clay loam is primarily found on limestone.
Silt loam is primarily found on metamorphic sandstone and metamorphic claystone.
Slope correlates with soil depth, on steeper slopes the soil depth decreases. Aspect
correlates with vegetation cover as expected. There is no correlation between aspect and
vegetation type, which could be expected.
Vegetation cover and grazing pressure are very good correlated because vegetation cover is
one of the parameters for estimating grazing pressure in the field.
Vegetation type and lithology are correlated. The Spearman rank correlation is just 0.24 but a
closer look to the bivariate histogram, figure 29 shows some interesting correlations.
Figure 29 - Bivariate histogram vegetation type versus lithology
From figure 29 some conclusions can be drawn:
•
•
•
•
•
Vegetation types 1, (3), 5 and 6, all poor in species, do not occur on light limestone.
Vegetation type 1, poor in species without domination of Thymus capitatus or
Sarcopoterium spinosum, is mostly found on dark limestone.
Vegetation types 3 and 4, 40-60 % Thymus capitatus, are mostly found on metamorphic
sandstone.
Vegetation type 8, >60 % Sarcopoterium spinosum, does hardly occur at light limestone.
Vegetation type 5, >60 % Thymus capitatus is mostly found on metamorphic sandstone.
43
A Model Approach
A χ2 analysis processed in SPSS also showed a significant correlation between vegetation
type and lithology. An indication of the strength of the correlation, Cramer’s V, showed a value
of 0.3 (in a range from zero to one, where one is perfect correlation).
To study the decrease of vegetation density we have sampled density of different plants using
an alternative LAI-index (aLAI) described in chapter 6. The decrease in aLAI-index for
different species is shown in figure 28a-d.
y = 0.0321x2 - 2282.4x + 4E+07
R2 = 0.851
Calicotome villosa
60
50
50
40
40
aLAI
aLAI
Sarcopoterium spinosum
60
30
R2 = 0.8732
30
20
20
10
10
0
0
10/5
y = 0.0337x2 - 2397.8x + 4E+07
20/5
30/5
9/6
19/6
10/5
29/6
15/5
20/5
25/5
30/5
4/6
9/6
14/6
19/6
24/6
Figure 28a +b, decrease of LAI of Sarcopoterium spinosum and Calicotome villosa (SS: n=155, CV: n=79)
y = 0.0265x2 - 1883.2x + 3E+07
60
50
50
40
40
30
30
20
20
10
10
0
0
10/5
y = 0.0266x2 - 1891.1x + 3E+07
R 2 = 0.8656
Phlomis spec.
R2 = 0.483
60
aLAI
aLAI
Thymus capitatus
20/5
30/5
9/6
19/6
10/5
29/6
20/5
30/5
9/6
19/6
29/6
Figure 28c+d - decrease of aLAI of Phlomis spec. and Thymus capitatus (PH: n=39, TC: n=111)
The decrease of aLAI of Phlomis spec. and Calicotome villosa is very clear. The rate of
decrease is fast until the end of May and approximates zero in the second half of June, the
trendline of the decrease of aLAI of Thymus capitatus does not fit as good as Phlomis spec.,
Sarcopoterium spinosum and Calicotome villosa.
44
A Model Approach
measurements. The data are analysed in Microsoft Excel using the Pivot table command.
Results are shown in table 9.
Observation point 45
SPECIES
Total
% Cover
Relative % cover Estimated relative % cover Estimated total: 60%
Thymus capitatus
3062
15.3
27.4
40
Calicotome villosa
2577
12.9
23.1
20
Genista acanthoclada
1984
9.9
17.8
20
Sarcopoterium spinosum 1276
6.4
11.4
Thymelaia hirsuta
931
4.7
8.3
Phlomis spec.
694
3.5
6.2
Rhamnus oleoides
440
2.2
3.9
Satureja thymbra
196
1.0
1.8
others: 20
Grand Total
11160
55.8
100.0
100.0
Total
% Cover
Relative % cover Estimated relative % cover Estimated total: 50%
29.67
94.06
Calicotome villosa
343
1.72
5.44
Satureja thymbra
32
0.16
0.51
others: 5
Grand Total
6309
31.55
100.00
100.0
Total
SPECIES
95
SPECIES
% Cover
Relative % cover Estimated relative % cover
27.4
90.0
Calicotome villosa
558
2.8
9.2
Phlomis spec.
30
0.2
0.5
Satureja thymbra
22
0.1
0.4
others: 10
Grand Total
6083
30.4
100.0
100
Estimated total: 25%
90
Table 9 - Results line intercept method
On observation point 45 the estimated cover per species agrees with the measured relative
percentage cover. Only plants with low relative vegetation cover are somewhat
underestimated. The estimated total cover agrees with the line intercept method.
On observation point 46 and 82 the relative estimated cover per species matches the
measured relative percentage cover very well. The estimated total cover of observation point
82 also matches with the line intercept method. The estimated total cover of observation point
46 is too high compared to the line intercept method. The line intercept method suggest the
same total cover for observation points 46 and 82, although the physical state of the
vegetation is different between the observation points. The plants on observation point 46 are
less stressed compared to observation point 82.
We can conclude, that the visual estimations give a good indication of total cover and species
composition when the species composition is not too complex. The line intercept method is
more accurate but also much more time consuming.
45
A Model Approach
The results of the soil nutrient analyses were within the range of other soils analysed in the
laboratory of physical geography. No large deviations occurred within the testplots. Analysis
of the data suggests that two soil types can be distinguished: fertile limestone soils (with
faster ecosystems) and less fertile flysch soils. The carbon values are determined by the
L.O.I. method. A comparison has been made between the results of the L.O.I. method and
the Walkley & Black method. The results of the two methods give a good correlation (R2
=0.80), thus the results of the L.O.I. method are reliable.
The results of the analysis of vegetation nutrients and lignin were plausible compared to other
measurements executed in the laboratory. The values of all standard samples to test the
accuracy of the analytical method were within range. K, Na, P and N concentration show a
significant decrease in time, so species have a decreasing nutrient content from May to June.
P/N ratio’s are low and decrease from May to June for most species. The low P/N ratios
suggest a P limited ecosystem. Average lignin content ranges from 2.7% to 5.2 % and does
not show correlation with any of the nutrients. In a study of Dutch herbaceous species
(Poorter & Bergkotte, 1992), typical values of 1.4% for fast growing species and 2.6% for slow
growing species are found. The phrygana values are somewhat higher. Possible causes of
the higher lignin content are the lower growth rate and different chemical compositions of
phrygana.
We could not match the results of the field and laboratory methods to determine texture.
However, the deviation is not constant so a correction for the total database cannot be done
based on the seven samples analysed in the laboratory. So we have chosen to use the field
estimations. We have to notice that the method used in the field does not cover the whole
range of the texture chart (figure26), thus not all field estimations can be compared with the
laboratory analysis.
We experienced problems with method to determine the soil moisture retention curves. In a
stony area like the Asteroussia Mountains, it is very difficult to get good undisturbed samples
for determining pF characteristics. The high pF 2.0 values and low pF 4.2 values cause a
(too?) large soil water-supplying range of the soil. In general, differences of lithologies are
reflected in the pF curves, thus the data can be used in the water balance model.
The measurements of volumetric soil water content resulted in reasonable values. The values
within a testplot do not show large variances and show a normal distribution. Significant
differences are found between plots and in time. Soil specific calibration failed. The FDR
calibration samples were too dry and too disturbed and they did not produce reliable FDR
calibration curves, therefore the estimated error is ± 5%.
Measured saturated conductivity varies considerably within a testplot. Standard deviations are
high. However, there is significant difference between testplot 1 and testplot 4 and 7. The
large variations in measured saturated conductivity are inherent to the method that was used.
It is difficult to take undisturbed soil samples and cracks and macropores can cause large
differences between samples. To improve the estimates on saturated conductivity, more
samples should have been taken. However, time constraints made this impossible.
measurements. On two plots, the visual estimations of total vegetation cover match the line
intercept measurements. On one plot, the visual estimation was higher than the line intercept
measurements, but this could be caused by the placement of the measure lines. The visual
estimation of the fractions of the different species matched the line intercept measurements.
We can conclude, that the visual estimations give a good indication of total cover and species
composition when the species composition is not too complex. The line intercept method is
more accurate but also much more time consuming.
46
A Model Approach
10-Results of Modelling
Introduction
Modelling is a very dynamic exercise. The model under “construction” is constantly changed,
improved and revised again. Like V.G. Jetten remarks in his introduction of the model SOAP
(Soil Atmosphere Plant model): “ The acronym SOAP is chosen for the association between
modelling and television soap series: as soon as you think that everything is going smoothly
the next problem presents itself ! “ (Jetten, 1994). When the model changes then the output
changes and so the contents of this report change. In most cases this is not a problem
because new model output is easy generated. In other cases like Monte Carlo Simulation it is
very time consuming to generate new model output. I have chosen only to generate new
model output in these situations when model results are substantially different. To improve
readability, for each generated result the model version is mentioned. The versions of the
waterbalance submodel are W1.0, W1.1 and W1.2. The versions of the grazing submodel
are: G1.0 and G1.1. The improvements of the latest model versions are described in
appendix 16.
Water balance model
Model input
This paragraph describes the input variables needed for SWBBM when modelled in the
PCRaster Dynamic Modelling package (Wesseling et al., 1996). The cellsize used for the
model is 50*50 m., The area covered is 4*6 km. The timestep of the model is one week.
An important input for the model is a Digital Elevation Model (DEM). The used DEM is
produced by the French satellite SPOT (Système Pour l’Observation de la Terre). SPOT has
stereoscopic imaging possibilities due to the off-nadir viewing capabilities. That is, images of
an area recorded on different satellite tracks can be viewed in stereo. The original cellsize of
the DEM is 20*20 m., to reduce dataset size and calculation time we have resampled to a
cellsize of 50*50 m.
The weather data used in the model are from the weather station near Gortis (11 km from the
centre of the study site). The dataset consists of monthly values of precipitation, temperature
and cloudiness over the period 1984 - 1993 (Appendix 2)
.
Insolation is calculated using the program SUN1 (Version 1.1, additional program for
PCRaster, programmed by L. Hazelhoff). SUN1 calculates net insolation from slope and
aspect maps (derived from the DEM) for a given time of the year and latitude of the study site.
Albedos for different lithologies and vegetation types are derived from literature (Evans &
Trevisan, 1995), the values used are shown in table 10. It is possible to calculate albedo from
Landsat TM images (Dugay & LeDrew, 1991) but this requires radiometric corrected images,
which were not available. Another possibility is measuring albedo in the field with a
pyranometer.
Lithology
class
Description
pF 2.0
Moisture content
pF 4.2
Moisture content
alfa Genuchten n Genuchten Ksat
m./day
Albedo
1
Light limestone
0.30
0.08
0.069
1.26
8.4
0.27
2
Dark limestone
0.33
0.06
0.008
1.33
3.5
0.23
3
Metamorphic sandstone 0.29
0.07
0.052
1.28
2.7
0.20
4
Metamorphic claystone 0.26
0.06
0.039
1.30
2.7
0.20
5
Alluvial
0.06
0.009
1.41
2.7
0.20
6
Coastal plain
0.32
0.06
0.013
1.37
2.7
0.20
7
Schists
0.26
0.06
0.039
1.30
2.7
0.20
0.35
Table 10 - Input variables (W1.2)
47
A Model Approach
SWBBM in PCRaster
The model SWBBM is modelled in the PCRaster Dynamic Modelling package (Wesseling et
al., 1996). The Dynamic Modelling module provides a meta-language within which the user
can build a dynamic model in a script. The script consists of the parts binding, areamap,
timer, initial and dynamic. The binding section allows one to use a name for a variable in the
script that is different from the file name of that variable in the database. In the binding
section, the constants are defined. The areamap section contains the name of the map that is
used as clone map in the model. The timer section gives the time dimension of the model and
consists of three values starttime, endtime and timeslice. The iterative part of the model is run
between the starttime and the endtime. The initial section is meant to prepare the set of input
variables, which are needed to run the dynamic section at timestep 1. The dynamic section
contains pcrcalc operations that are performed at each timestep i (Wesseling et al., 1996). In
the next paragraph, we will discuss the most important parts of the different sections (see
modelscript in appendix 15).
Binding, timer and initial sections
In the binding section of SWBBM the constants taken from Evans & Trevisan (1995) are:
alpha, b, he, Ic, kc, LAI, Ne and gamma. Used values for albedo, Ksat, pF, alpha-Genuchten
and n-Genuchten are shown in table 10.Timeseries of precipitation, temperature and
cloudiness are taken from weather station Gortis.
The timeslice used in the model is one week over a period of one year (52 weeks) starting in
January.
Initial soilwater is calculated after several runs starting with a “full bucket”. A full bucket is
needed to get soil depth independent volumetric water contents. (That is the difference of
volumetric water content between 20 mm. of water in 10 cm. soil and 20 mm. of water in 40
cm. soil).
To maintain a minimum quantity of soilwater and avoid empty buckets a “Bucketlimit”
parameter is used. The Bucketlimit is the volumetric water content at pF 4.2 minus the
bucketparameter (set at 0.02).
Dynamic section
An important variable in the model is the potential evapotranspiration calculated according to
Ritchie (1972): E∅t= (1.28*∆t*Rnt)/(∆t+γ)
Where:
∆t
= rate of change of saturation vapour pressure with temperature (mbar/Kelvin)
= net solar radiation in equivalent evaporation (mm/week)
Rnt
γ
= psychrometric constant (mbar/Kelvin)
To calculate Rn, insolation derived from the DEM is corrected for cloudiness and soil albedo.
Values for cloud albedo are 0.1 for very clear atmosphere, 0.3 for fair weather cumulus and
0.8 for large cumulonimbus (Walker & Langridge, 1996). These values were combined with
the Gortis cloud data to calculate average weekly cloud albedos.
E∅ is separated into soil evaporation and plant transpiration.
Soil evaporation is a function of E∅, LAI and alpha. Alpha is used to calculate an upper limit
of soil evaporation.
Plant transpiration is a function of E∅ and LAI and is limited as described on page 55. Above
wilting point, no plant transpiration occurs.
Actual evapotranspiration is calculated as the minimum of E∅ and soil evaporation plus plant
transpiration, where plant transpiration is corrected for vegetation cover. Actual
evapotranspiration may be corrected into Eveg by the vegetation correction factor kc.
48
A Model Approach
Precipitation is decreased by interception of plants. Interception is a function of the vegetation
cover and the interception constant Ic. Ic is defined as the interception of a 100 % vegetation
cover.
Percolation is a function of Ktheta and field capacity. Ktheta is the conductive capacity of the
soil type being considered and depends on soilwater content and Ksat. Ktheta is calculated
according to Hutson & Wagenet (1989). Percolation occurs when soilwater content plus
precipitation is greater than field capacity.
Runoff occurs when soilwater content plus precipitation is greater than maximum percolation.
Soilwater content is defined as the maximum of soilwater content in the previous timestep
plus precipitation minus actual evapotranspiration, percolation, runoff and the Bucketlimit.
Soilwater content is converted to volumetric soilwater content using the soil depth.
The volumetric soilwater content is used to calculate pF according to Van Genuchten (1980):
Θe = [1+(α|h|) n]-m
Where:
M = 1 - 1/n
Θe = (θ - θr) / (θs - θr)
θ
= measured volumetric water content
θr = residual volumetric water content
θs = saturated volumetric water content
Results
The results of one model run with average monthly weather values are compared to
volumetric water contents (Theta-v) of six testplots in spring/summer 1997. On the testplots
volumetric water content was measured using an FDR-device (Frequency Domain Reflectory)
with an accuracy of ± 5 percentage volume. During week 21 (half May) and week 26 (end
June) 30 measurements were randomly taken at every testplot.
The measured volumetric water contents for the different plots, compared to the model
predictions are shown in figure 30.
In general, the predicted volumetric soilwater content shows an incline during the first weeks
when it rains. From week 19 there is no significant rainfall and the soil dries out. From week
45 it begins to rain again and the volumetric soilwater content increases.
Plot 1:
Measured volumetric soilwater content is constant for the period week 21-week 26. The
model shows a decline in volumetric water content during these weeks. Week 21 is
overestimated, while the prediction of week 26 fits well.
Plot 2:
Measured volumetric soilwater content shows the same pattern as Plot 1. Estimated values
are lower, probably because Plot 2 has a south-facing slope.
Plot 3:
The decline of the measured values is conform the decline of the estimated values of the
model.
Plot 4:
The model overestimates the volumetric soilwater content of plot 4. The decline of the
measured values is conform the decline of the estimated values of the model. It seems that
the model predictions occur four weeks too late.
49
A Model Approach
-
Plot 5:
The decline of the measured values is less than the decline of the estimated values of
the model. The model overestimates the volumetric water content.
-
Plot 6:
Plot 6 shows the same pattern as Plot 4. Plot 4 and Plot 6 both have thicker soils.
Plot 2
0.3
0.3
0.25
0.25
Model
Week
51
46
41
36
Plot 4
0.3
0.25
0.25
0.2
0.2
Theta-v
Measured
0.15
Model
0.1
0.15
Measured
Model
0.1
0.05
0.05
0
Week
51
46
41
36
31
26
21
16
11
1
51
46
41
36
31
26
21
16
11
6
1
0
6
Week
Plot 5
Plot 6
0.3
0.3
0.25
0.25
Theta-v
0.2
Measured
0.15
Model
0.1
0.05
0.2
Measured
0.15
Model
0.1
0.05
Week
Week
Figure 30 - Measured volumetric soil water content versus model prediction (W1.2)
50
51
46
41
36
31
21
16
11
6
1
51
46
41
36
31
26
21
16
11
6
0
1
0
26
Theta-v
31
Week
Plot 3
Theta-v
26
21
1
51
46
41
36
31
26
21
0
16
0.05
0
6
0.05
11
Measured
0.1
16
Model
0.1
0.2
0.15
6
Measured
11
Theta-v
0.2
0.15
1
Theta-v
Plot 1
A Model Approach
The ten-year model run (1984-1993) shows the following results (Figure 31).
Plot 1 is more sensitive for climatological changes than Plot 4. Plot 1 has a thinner soil
compared to Plot 4 and less soilwater buffer capacity. Drier conditions during the last four
years are well shown in the graph. After reversing the weather data (1993-1984), the graph
shows the reversed pattern, so the model is stable and does not dry out.
Theta-v model 1984-1993
0.35
0.3
Theta-v
0.25
0.2
Plot 1
Plot 4
0.15
0.1
0.05
505
481
457
433
409
385
361
337
313
289
265
241
217
193
169
145
97
121
73
49
1
25
0
timeslice (week)
Figure 31 - Results of ten-year model run Plot 1 and Plot 4 (W1.2).
Sensitivity analysis
To test the model sensitivity of the prediction of volumetric soilwater content to different
variables, we have carried out a sensitivity analysis. The analysed variables are: soil depth,
field capacity, wilting point, albedo, interception constant, cloudiness, vegetation cover, LAI
and alpha.
Each variable is varied a certain percentage while other variables remained constant.
Percentage difference of volumetric soil water content is calculated between initial and new
values for each week. The average and standard deviation of the percentage difference over
one year are also calculated. The results of Plot 1, 3 and 4 are shown in table 11.
Variable
% change
% change plot 1
STDEV
% change plot 3
STDEV % change plot 4
STDEV
Interception constant -20
1.15
1.10
1.09
1.18
1.16
0.57
Albedo
-20
-2.97
4.49
-2.25
3.63
-2.84
2.53
LAI
+100
4.08
6.23
7.14
11.62
3.69
3.29
LAI
-50
-3.39
5.10
-4.49
6.83
-4.42
3.93
Field capacity
-20
-0.03
0.07
-0.06
0.13
-24.58
11.03
Field capacity
+20
0.05
0.11
0.07
0.19
0.00
0.00
Field capacity
-10
-0.02
0.04
-0.03
0.08
-11.31
5.92
Wilting point
+20
9.50
11.76
11.99
13.53
0.00
0.00
Vegetation cover
-20
3.06
4.02
2.78
3.93
2.96
2.25
Alpha
-20
0.00
0.00
0.00
0.00
0.00
0.00
Alpha
+20
0.00
0.00
0.00
0.00
0.00
0.00
Cloudiness
+20
4.05
5.44
4.79
7.21
2.91
2.29
Cloudiness
-20
-3.40
4.49
-3.96
5.64
-3.18
2.49
Soil depth
+20
-12.91
9.17
-6.90
5.19
-17.61
0.24
Soil depth
-20
1.20
6.91
10.35
7.78
-10.80
9.21
Table 11 - Results of the sensitivity analysis (W1.0)
51
A Model Approach
Table 11 indicates overall insensitivity to changes of interception constant and alpha,
moderate sensitivity to changes of albedo, LAI, vegetation cover and cloudiness and high
sensitivity to field capacity, wilting point and soil depth.
To study the sensitivity of the waterbalance model for soil depth we have executed a Monte
Carlo simulation.
Introduction Monte Carlo Analysis
Dynamical models used to simulate spatial processes are often liable to errors. Sources of
these errors are: the quality of input data, quality of the model and the way data and the
model interact (Burrough & McDonnell, 1998). Most dynamical models require a large number
of spatially distributed data. These data are often not exactly known and error values can be
large (De Roo et al., 1992).
A method to investigate error propagation through models is the use of simulation or
stochastic imaging. Stochastic imaging does not result in a single estimated map, like a map
obtained from kriging, but it results in a set of alternate maps all consistent with the used data
and the specific correlation between the data (Journel, 1996). A common method to execute
simulations is the Monte Carlo Method (Hammersley & Handscomb, 1979, in Heuvelink,
1993). The idea of the method is to calculate the result of an input map repeatedly, with input
values that are randomly sampled from their joint distribution and run the model with the
different input maps (Heuvelink, 1993). The technique is called Monte Carlo method, because
of its reliance on change. When the model under exploration is non-linear, more information is
obtained from Monte Carlo simulation than by simulation using e.g. ± 2 standard deviations
(De Roo et al., 1992).
Two types of simulation are distinguished: conditional simulation and unconditional
simulation. Both require that the variogram is known. With unconditional simulation, a surface
is simulated that has similar spatial characteristics (nugget, sill, range, and mean) to the
original surface, but which does not match the spatial pattern of the data. Conditional
simulation combines data at the observation points with the variogram to compute the most
likely outcomes per cell (Burrough & McDonnell, 1998). Thus maps are produced that match
the pattern of a simple kriging map, but which vary randomly between the borders of the data
specific normal probability distribution.
Conditional simulation is carried out as follows:
1. Set up equations for simple kriging with an overall mean.
2. Select an unsampled datapoint at random. Compute kriging prediction and variance using
data from the neighbouring points.
3. Draw a random value from the probability distribution defined by the prediction and
standard deviation. Add the calculated point to the dataset.
4. Repeat step 1-3 until all points are visited and one realisation is complete.
5. Repeat step 1-4 until sufficient realisations have been created.
6. Run the environmental model with each realisation to see how results vary with the
different inputs (Burrough & McDonnell, 1998).
The advantages of the Monte Carlo method are:
• The Monte Carlo method produces not only information about the mean and variance, but
about the entire distribution. Estimates of median, quantiles and percentiles can be easily
obtained from the simulation.
• Implementation of the model is easy because the method is not affected by the exact
operation of the model. The Monte Carlo Method treats the model as a black box.
(Heuvelink, 1993)
52
A Model Approach
The disadvantages of the Monte Carlo Method are:
• To analyse how a reduction of input error will influence the output, the entire simulation
has to be executed again (Heuvelink, 1993).
• Monte Carlo simulation is computer intensive at the moment. In this study it took 35 hours
on a Pentium 133 to produce thousand input maps (realisations) and run the model
thousand times.
Due to the heavy computing load, the number of simulations is limited on practical reasons.
However, it is important to know how many simulations are required to produce reasonable
results. It is stated that 100 simulations are sufficient to obtain a reasonable estimate of the
mean, 1000 simulations are minimum required to obtain a reasonable estimate of the
variance. More than 10.000 runs are needed to estimate the 1 % quantile. (Peck et al., 1988
in Heuvelink, 1993). The mean surface of conditional simulation should be very similar to
ordinary point kriging interpolation. Too few realisations could result in standard deviations
that are different from the ordinary kriging interpolation, producing a mean conditional
simulation map that is different from the ordinary kriging map (Burrough & McDonnell, 1998).
Results of conditional simulation of soil depth
999 realisations of the soil depth have been made, with the Conditional Simulation technique.
The values are not totally tied down at the observation points because a nugget variogram
model is used. The average soil depth of 100 and 999 realisations and the standard deviation
of 900 realisations are shown in figure 32.
The average soil depth is also reported for 200, 300, …,900 realisations. From these maps it
can be clearly seen that when more realisations are used for calculating the average, the
maps become smoother and match the ordinary kriging map (figure 24), but after 999
realisations the map is still rougher than the ordinary kriging map.
Figure 32 - Average soil depth after 100 realisations (left), 999 realisations (middle) and the variance of soil depth
after 999 realisations.
53
A Model Approach
As output, the soil depth at four (semi-) random points is reported in timeseries. These points
do not have any relation with the testplots. The basic statistics for these four points are
summarised in table 12 and showed in figure 33.
Point 1
Average soil depth cm.
16,8
Stdev soil depth cm.
12,7
Median soil depth cm.
16,8
Min soil depth cm.
-21,8
Max soil depth cm.
54,7
Lithology
Light Limestone
PF 2.0 Theta-v
0.30
PF 4.2 Theta-v
0.08
Albedo
0.27
23
Aspect °
13
Slope °
Table 12 - Basic statistics point 1-4
Point 2
25,2
12,6
25,2
-20,9
67,1
Dark limestone
0.35
0.06
0.23
247
32
Point 3
17,5
13,6
17,8
-33,0
58,7
Sandstone
0.29
0.07
0.20
156
26
Point 2
0.450
0.400
0.350
0.300
0.250
0.200
0.150
0.100
0.050
0.000
0.500
thetaV
0.400
0.300
0.200
0.100
Week
51
46
41
36
31
26
21
16
11
6
1
51
46
41
36
31
26
21
16
6
11
0.000
1
Week
Mean
Stdev
Min
Mean
Stdev
Min
Max
Mean+2SD
Mean-2SD
Max
Mean+2SD
Mean-2SD
Point 3
Point 4
0.450
0.400
0.350
0.300
0.250
0.200
0.150
0.100
0.050
0.000
0.350
0.300
thetaV
0.250
0.200
0.150
0.100
0.050
Mean
Week
Stdev
Min
Mean
Stdev
Min
Max
Mean+2SD
Mean-2SD
Max
Mean+2SD
Mean-2SD
51
46
41
36
31
21
16
11
6
1
51
46
41
36
31
26
21
16
6
11
1
0.000
26
thetaV
Point 1
thetaV
Point 4
35,0
13,9
35,0
-20,6
82,1
Claystone
0.26
0.06
0.20
170
16
Week
Figure 33 - Soil moisture content statistics for point 1 - 4. (W1.0)
The minimum, maximum, average, standard deviation and the 95% confidence interval
around the average are given (Mean ± 2 SD). For each point, an area can be indicated where
the maximum is higher than the confidence interval around the average. In these areas, the
model is unreliable. The maximum is only reached for those realisations with very thin soils.
The average and the standard deviation for each point are given in figure 34.
54
A Model Approach
Stdev of Theta-V
Average Theta-V
0.400
0.090
0.350
0.080
0.070
week
Point 1
Point 2
Point 3
51
46
41
36
31
26
1
49
45
41
37
33
29
25
21
17
9
0.000
13
0.000
5
0.020
0.010
1
0.050
21
0.100
0.040
0.030
16
0.150
11
0.200
0.060
0.050
6
0.250
thetaV
Theta-V
0.300
week
Point 4
Point 1
Point 2
Point 3
Point 4
Figure 34 - Average Theta-v and standard deviation of Theta-v for point 1-4 (W1.0)
Overall, each point shows the same behaviour. In the first weeks the moisture content is high
due to winter-rain (and in this simulation the fixed values of week 1 on field capacity). When
the summer comes, the moisture content drops, only to rise slowly again in October. This is
conforming the results from the single model run on the ordinary block kriging map. Point 2
has higher moisture content throughout the whole year. This is because point 2 is situated on
dark limestone. These soils can contain significantly more water than the soils on the other
points.
The standard deviation shows different behaviour for two groups of points. Point 2 and 4 are
situated on relatively thicker soils, where the soil moisture content can fluctuate largely. The
thinner soils on point 1 and 3 are completely dry in summer. The averages are the same in
those weeks for all realisations, so the standard deviation will decrease.
For an overview of all data, for each point two 3D plots are made on basis of all values. One
with “raw data” as produced by GSTAT and one with the same data, but ordered (using a
spreadsheet). For both plots, not every row is used but every tenth row. Therefore, the plot is
not complete but gives an overall impression of the data. In figure 35 these plots are given for
point 1. The horizontal axis represents weeks (52) and the vertical axis represent the
realisations. The left graph gives the ordered realisations with increasing soil water content
(and soil depth) from top to bottom. The blue zone represents the minimum model value,
which lies slightly below the wilting point. This results from a realisation that gives a negative
soil depth on point 1. It is clear that thicker soils react more gradual to changes. Thinner soils
dry more quickly and are faster saturated, represented by abrupt changes in the upper part of
figure (left). The middle graph shows the randomness of the conditional simulations. Notice
that this is a selection (every tenth line from the original data). On the right, two drapes of
these maps are displayed to get a spatial impression.
In figure 35 it is shown that thicker soils react more gradual to changes and that variation is
less. To give an spatial impression of the variation of volumetric water content, a map is
calculated of the difference of the volumetric water content between week 40 (very dry) and
week 1 (very wet). The map is shown in figure 36. It is clear that the pattern correlates to the
average soil depth in figure 24.
55
Figure 35 - Overview of Theta-v variation (W1.0)
Figure 36 - Difference of volumetric water content between week 40 and week 1 (W1.0).
56
A Model Approach
A Model Approach
Biomass Production Model
CENTURY 4.0, which was used to calculate biomass production, is not a spatial model. It is
only possible to model vegetation production for one (sample) point. To include CENTURY
4.0 in our grazing model we had two options. The first option was to build CENTURY 4.0 in
PCRaster using the available source code. The second option was to model different
vegetation types and include them in PCRaster using lookup tables. We have chosen for the
latter option because rebuilding CENTURY 4.0 in PCRaster should take several months
because of the complexity of CENTURY 4.0.
A problem of using CENTURY 4.0 with our other models is that CENTURY 4.0 has a build-in
water balance model, which cannot be switched off. We have solved this problem by using
the irrigation option of CENTURY 4.0. Irrigation was set in the irri.100 file to maintain soil
water level at field capacity and compensate for transpiration. However, the modelled
vegetation production is not optimal at field capacity. Analysis of the model results without
irrigation and the source code of the model proved that vegetation production at field capacity
has to be multiplied by 2.1 in all cases to obtain monthly optimal vegetation production
curves.
The optimal vegetation production curves are incorporated in the PCRaster model by linking
them directly to the transpiration level of the vegetation. When transpiration is maximum
vegetation production is maximum, when transpiration level is zero, vegetation production is
zero. Vegetation production level is dependent from the volumetric water content as shown in
figure 37. Between field capacity and A (Maxtranslimithighpf, appendix 15) production level is
limited by water abundance, between B (Maxtranslimitlowpf, appendix 15) and wilting point
production is limited by water shortage. The biomass production model is calibrated to the
CENTURY output without irrigation by adjusting A and B. Transpiration level is calculated in
the same way. The minimum value of A is pF 2.0, this is different from other water balance
models, but used because of the incorporation of CENTURY 4.0 in the model. The A-traject
can be switched off by setting Maxtranslimithighpf to 2.0.
Figure 37 - Relation between volumetric water content and production level (G1.1)
The parameterisation of all CENTURY parameters is done using the standard CENTURY
parameters, calibrated CENTURY parameters of another study of a Mediterranean shrub
ecosystem, our own nutrient data and vegetation production data of Tsiourlis (1990).
Because of the large number of parameters, we will not include them in this dissertation but
on floppy disk. The most important parameters are shown in appendix 17. Soil and vegetation
nutrient data are derived from appendix 8, 10 and 11.
57
A Model Approach
Plant and soil parameters
Analysis of the soil nutrient data has shown that limestone and flysch units (metamorphic
sandstone / claystone) differed significantly. Thus, we have made a distinction in site input
files: Gortis.lim for soils on limestone and Gortis.fls for soils on flysch and other lithologies.
The most important parameters are shown in appendix 17.
The crop.100 file contains information about the vegetation. Because the vegetation
classification system (chapter 5) is based on the percentage Sarcopoterium spinosum and
Thymus capitatus in combination with species diversity, three crops have been distinguished:
SS
TC
PHRYG
- Based on average Sarcopoterium spinosum nutrient data
- Based on average Thymus capitatus nutrient data.
- Based on average nutrient data of all phrygana species.
PHRYG is used to model vegetation types 1, 2 and 9. The other vegetation types are
modelled using SS and TC in fractions according to the classification system. For example,
vegetation type 5 is 1,0 * TC, vegetation type 3 is 0.4 * SS + 0.6 * TC.
Executing CENTURY 4.0
The use of two soil types and three crop types resulted in six CENTURY 4.0 model runs to
obtain optimal vegetation production curves:
1.
2.
3.
4.
5.
6.
PHRYG on limestone
TC on limestone
SS on limestone
PHRYG on flysch and other lithologies
TC on flysch and other lithologies
SS on flysch and other lithologies
The runs are executed with the following basic options:
-
The model output time is monthly.
The model is run with mean weather values of Gortis.
The used submodel is the grassland / crop submodel.
The used biome specific fix file is dryffix.100, dry forest, according to the calibrated
Mediterranean shrub ecosystem parameterisation.
Burke’s equations are used to initialise soil C pools (ivauto=2) (Burke et al., 1989 in
Metherell et al, 1993).
The simulated elements are C and N (nelem=1).
Although, P/N levels indicate that the ecosystem is P limited, we were forced to use only C
and N simulation because of a runtime error of the model when modelling P. Rechecking
almost every parameter and contacting the authors of CENTURY 4.0 did not result in a
working model with P dynamics.
With Event100 a simple schedule is developed. Growth of the specific crop and irrigation to
field capacity during all months. The total modelling time is 200 years with a repeating
sequence of one year The schedule consists of two blocks: a first block of 190 years to obtain
equilibrium values of C and N (longer runs than 190 years did not show significant
differences) and a second block of 10 years to report the aboveground biomass production
(cprodc) with 100 % vegetation cover. This resulted in the optimal vegetation production
curves shown in figure 38.
58
A Model Approach
Optimal vegetation production TC
160.0
140.0
140.0
120.0
120.0
100.0
100.0
g/m2
g/m2
Optimal vegetation production PHRYG
160.0
80.0
80.0
60.0
60.0
40.0
40.0
20.0
20.0
0.0
0.0
1
2
3
4
5
6
7 8
Month
PHRYG limestone
1
9 10 11 12
2
3
4
5
6
7
TC limestone
PHRYG flysch
8
Month
9 10 11 12
TC flysch
Optimal vegetation production SS
160.0
140.0
120.0
g/m2
100.0
80.0
60.0
40.0
20.0
0.0
1
2
3
4
5
6
7
Month
SS limestone
8
9 10 11 12
SS flysch
Figure 38 - Optimal vegetation production curves
The average net production per year of the whole study area is 151 g/m2 with an average
vegetation cover of 36%. Tsiourlis (1990) measured a net production per year of 160 g/m2
with an average vegetation cover of 46 %. Our modelled vegetation production is higher, but
still in the range of the measurements of Margaris (1981) (Mont Hymette, Greece, see table
1).
59
A Model Approach
Dynamic grazing model
Model description
For modelling the grazing capacity the package ISPD has not been used, but the principles of
the model ISPD have been used (shown in figure 3). Using the package ISPD it was too
difficult to see what really happened with the variables. For modelling the grazing capacity in
PCRaster Dynamic Modelling package (Wesseling et al., 1996) we build the basic principles
of ISPD in combination with field observations and data derived from literature.
For modelling the grazing capacity we used the formula:
G=
F
*p
R
Where:
G = Grazing capacity in “food units” per unit area for a specified grazing
season.
F = Forage production (aboveground biomass production) per unit area
during the grazing season (kg. dry matter/area).
R = Animal requirement of dry matter in weight per “animal unit” (kg. dry
matter/animal).
p = proper use factor, the percentage of the forage production that can be
grazed without producing a downward trend of vegetation production,
vegetation quality and soil quality.
The biomass production per year (Netbiomass, shown in appendix 15) has been determined
by the CENTURY module. The daily need (Dailyneed) of sheep and goats (700 grams dry
matter per day) is derived from Grant et al. 1997 3). The available biomass (Availbiomass)
and the net biomass determine the proper use factor (Properuse). The available biomass
depends on the palatability of the vegetation, the defoliation percentage of the vegetation and
the insect consumption. The values of palatability, defoliation and insect consumption are
shown in table 13. For calculating the available biomass, the palatability is the main factor
influencing the available biomass.
Palatability
Defoliation
Phrygana
50 %
60 %
Thymus Capitatus
70 %
50 %
30 %
30 %
Table 13 - Values of variables determining the proper use factor
Insect
10 %
10 %
10 %
The grazing pressure observed in the field is corrected by using the grazing intensity
(Grazingintensity) throughout the year. In the study area around 3500 sheep and goats
occur. During the winter months sheep from the Psiloritis Mountains come to the Asteroussia
Mountains. The grazing intensity is set to 4500 sheep and goats for the months December,
January, February and the first two weeks of March. The resulting grazing pressure is a linear
index, where 1 is grazed and 0 is non-grazed (Grazpres1).
The grazing pressure index is used to calculate areas of risk in the study area
(Grazindextotal). The risk is calculated by multiplying the grazing capacity with the grazing
pressure. A high grazing capacity multiplied with a low grazing pressure gives a low-risk area.
Low grazing capacity and high grazing pressure gives a high-risk area.
Some long time scenarios have been calculated to see how the study area reacts on the
grazing pressures. The condition of the vegetation is taken into account. The total vegetation
consumption by goats and sheep has been calculated (Browse), which depends on the
grazing intensity and the weekly need.
3
This article is included on floppy disk in the Internet directory
60
A Model Approach
According to Tsiourlis (1990) the vegetation cover will incline with 4 % in one year for a nongrazed (grazing pressure = 1) area. The vegetation cover will decline with 8 % (2*4%) in one
year in a fully grazed area (grazing pressure = 0). For the calculation of the incline or decline
of the vegetation cover, an index has been made (Growindex). The index is linked to the
grazing pressure index. For a non-grazed area the vegetation cover will incline with 4 %, then
the grow-index is 1. The grow-index is -2, for a grazed area where the vegetation cover will
decline with 8 % (-2*4 %).
The change in vegetation cover is calculated using the grow-index (Vegcov). When the
vegetation cover becomes zero for certain areas, grazing will no longer be possible. The area
accessible for grazing of sheep and goats becomes smaller, while the grazing pressure stays
the same. This will lead to stronger decline of the vegetation cover. A correction has been
made for the change of the area accessible for grazing (Area1, effective grazing area). When
grazing intensity decreases, the exclude are can become available again.
The grazing model (G1.1) includes a random regrowth option. The random regrowth option
simulates fast regrowth of a pioneer species with a spatial different rate. When vegetation
cover is low (adjustable by Returnlimit) and growing conditions are good, it is possible to
return a random percentage of vegetation cover (with a maximum of Maxreturn). The
returned vegetation type is a pioneer species (Sarcopoterium spinosum). Vegpause sets the
minimum time between random events at the same location. The random regrowth option can
be switched off by setting Returnlimit to zero.
Results modelling grazing
To explore and calibrate the grazing model we have done many model runs of the one-year
(52 timesteps) version. When this model ran stable, we used the long time scenario module to
calculate the hypothetical effects of different grazing strategies. The different scenarios are
shown in table 14. The principle of the long time scenario module is that vegetation cover will
increase at most with 4% per year when grazing is absent and will decrease at most with 8%
when grazing is at maximum. The increase of 4% is based on the data of Tsiourlis (1990).
The increase of vegetation cover is a function of grazing capacity and grazing intensity, the
decrease of vegetation is a function of grazing capacity, grazing intensity and palatability, so
the maximum and minimum levels are seldom reached.
The parameters of the water balance submodel reported earlier (table 10) Some new
parameters of version W1.2 included in table 14 are described in appendix 16. The values of
the parameters used in the biomass/grazing model are shown in table 14, where palatability is
the percentage of palatable parts of the plant and defoliation is the percentage of leaves that
have to remain on the plant to function properly. These parameters are estimated based on
the field observations. The daily dry weight animal food need is calculated as 2.3 * animal
weight according to Grant et al. (1997).
PHRYG
TC
SS
Palatability
50
70
30
Maxtranslimitlowpf:
Maxtranslimithighpf:
Insect consumption:
Gammachange
Maxrootdepth
Dwf
Dailyneed:
Maxchange:
Returnlimit
Maxreturn
Vegpause
Defoliation
60
50
30
Modelyear
1
2
3
4
5
6
7
8
9
10
3.5
2.3
10 %
-8%
25 cm.
0.2
700 g/animal
4%
10%
2%
20 weeks
Table 14 - grazing model input (G1.1)
61
Weather data year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
Scenario
Grazing intensity (nr. Of animals)
Dec-Feb
Mar-Jul
Aug-Nov
1
2
4500
2000
3500
1500
3500
1500
3
4
1
4500
1
3500
1
1500
5
6
1000
3500
1000
2500
1000
2500
7
*
*
*
A Model Approach
Description
Present situation with different winter and summer grazing.
Reduced grazing intensity with different winter and summer
grazing.
No grazing.
Present situation with different winter and summer grazing
and additional feeding during Aug-Nov.
Removing sheep, only grazing by goats.
Reduced grazing intensity. 1000 animals less than present
situation.
4 years heavy grazing (7000 animals), 1 year light grazing
(1000) animals and 5 years recovering without grazing.
Table 15 - grazing scenarios (G1.1)
One-year model run
The grazing model is run with the average weather values of the period 1984-1993. This
resulted in an average production of 151 g/m2 with an average vegetation cover of 36 %. The
calculated grazing capacity is 2875 animals per year for the total study area, when the
animals are homogenous spread over the study area. The grazing capacity converted to ha.
is 1.26 animal per ha. To explore the effects of the sensibility of the calculated grazing
capacity per year for soil depth, we have executed a Monte Carlo simulation with varying soil
depth. Hundred realisations were calculated with an initial soil water content of 0.6 * field
capacity for all cells. After hundred realisations, the average value of grazing capacity per
year is 2382 with a standard deviation of 17 (1.04 animal/ha) (G1.0).
To indicate areas that are sensible for overgrazing a grazingindex is calculated as follows:
Grazingindex = (1 − Grazingcapacity) * Observed grazing pressure
Where grazing capacity and observed grazing pressure are converted to a range of one to
zero, thus the grazingindex can have a value in the range of zero (low risk) to one (high risk).
The grazing risk areas are shown in figure 39.
Figure 39 - Grazing risk areas (G1.0).
62
A Model Approach
Long time scenarios
To evaluate the results of the long time grazing scenarios the following parameters are
reported:
•
•
•
•
Yearly grazing capacity of total study area.
Weekly total area with vegetation cover greater than 1 % (effective grazing area).
Weekly average vegetation cover of total study area.
Weekly vegetation cover maps.
From the weekly vegetation cover maps difference maps were calculated between begin and
end time of the simulation. These maps of scenario 1, 2 and 3 are shown in figure 41. The
changes of vegetation cover >1 %, vegetation cover and grazing capacity are shown in figure
40a-c
Figure 40a- Total area with vegetation cover >1 (G1.1)
Figure 40-b Total average vegetation cover (G1.1)
Figure 40c - Yearly grazing capacity (G1.1)
If we look to the grazing capacity per year (figure 40-c), it is clear that total grazing capacity is
very dependent of precipitation. Modelyear 5 is very wet and modelyears 7 and 9 are dry.
Note that additional feeding during August - November does not have much impact on total
grazing capacity. It is also shown that one dry year can have great impact on total grazing
capacities of the following years, after modelyear seven the grazing capacities are reduced by
an average factor of 1.5.
The effective grazing area shows for all scenarios a decline, except for the non-grazing
scenario 3 and the light grazing scenario 5. At the begin of the simulation, some areas are
already in such a state that even lower grazing intensities than the present situation (for
example scenario 2) lead to degradation. These areas are red in the grazing risk map (figure
39). Note that the effective grazing area decreases faster after model year seven for scenario
1 and 4.
Average vegetation cover shows an increase for scenario 3 (of course), scenario 2 and
scenario 5. Scenario 6 results in equilibrium average vegetation cover: the decrease of
vegetation cover in high-risk areas is compensated with the increase of vegetation cover in
low risk areas. This effect is well shown in figure 41. In the North, where less palatable
Sarcopoterium spinosum rich vegetation types occur, the vegetation cover increases. In the
south, where good palatable Thymus capitatus vegetation types occur, the vegetation cover
decreases. Nonetheless, scenario 6 shows a decrease of effective grazing area so this is till
not a “proper” management option.
63
A Model Approach
In figure 41 it is clearly seen that present grazing capacities are too high: the whole area
shows a decrease in vegetation cover (figure 40) and effective grazing area decreases fast.
The fast decrease of effective grazing area causes a higher grazing pressure for the
remaining area, so the decrease rate is speeded up. Scenario 2 shows better results, the
effective grazing area is not decreasing. In most areas, the vegetation cover increases.
Figure 41- Maps of differences in vegetation cover: left scenario 1, middle scenario 2 and right
scenario3 (G1.1).
Figure 42 shows the model results of scenario 7. This scenario has been run to give an extra
indication of grazing risk areas and to demonstrate the random regrowth option. After five
years of very heavy grazing it is clear that the southern part of the study area will be highly
desertificated. When grazing stops the pioneer vegetation (Sarcopoterium spinosum) will
return with in a random pattern, which can be observed in the right figure. After five years of
recovering the average vegetation cover is higher than the initial situation, but the vegetation
has been changed to a Sarcopoterium spinosum dominated vegetation. The percentage
covered with vegetation type 8 (>60 % Sarcopoterium spinosum) in the initial situation is 597
ha. (29% of study area). After five years heavy grazing and five years recovering this number
is 935 ha. (45 % of study area).
Modelling more than 10 years using the random regrowth option is not recommended. The
grazing model (G1.1) cannot model real succession of vegetation, because detailed data of
succession of phryganic ecosystems are not available.
64
A Model Approach
Figure 42 – Scenario 7: left initial situation, middle after 5 years intensive grazing and right
after five years without grazing (G1.1)
65
A Model Approach
11-Discussion
Study area
The study area consists of well distinguishable geologic and lithologic units. Lithologic units
show significant statistical correlations with texture and other soil properties. Because texture
and other soil properties did not produce good variograms for kriging and is not directly used
in the waterbalance model, we have chose to use the lithology map as “base” map. Texture is
indirectly linked to this map by using the pF and Ksat properties of the different lithologies.
However, this is not an optimal solution, but it makes the problems with the different methods
of texture analyses less important.
Another advantage of using the lithology map as base map is the good correlation with
vegetation types. It gives in combination with higher soil N-contents on the limestone units
good insight in fertile and less fertile areas. This information and water availability are linked
in the model. It results in a spatial pattern of on average slower growing vegetation in the
southern part of the study area and faster growing vegetation in the northern part of the study
area. This can be observed in figure 42 and matches the field observations.
A problem that occurs is that some variables observed in the field vary in time, like vegetation
cover, vegetation density and grazing pressure. Although we have tried to compensate for the
time dependence of vegetation cover, by sampling different parts of the study area at different
times, the statistical analysis shows the method was not totally adequate.
The soil water balance model
The soil water balance model is sensitive for the variables field capacity, wilting point and soil
depth. Field capacity and wilting point are important inputs but also very difficult to measure.
The soil depth is also an important input but hard to measure and very variable in space.
Monte Carlo analysis is used to get better insights in the sensitivity of the model to soil depth.
In figure 33 it is shown that the modelled soil water content exceeds the 95% confidence level
when the soil is very thin. In figure 33 it is also shown that thicker soils react more gradual to
changes in time than thinner soils, so it is reasonable to prevent running the model with too
thin soils. On the average the standard deviations of the predicted soil water contents are
high (figure 34). Unfortunately, this is inherent to the model structure of SWBBM.
A limitation of the model is the use of monthly weather data. When using daily weather data it
is possible to consider precipitation intensity. The model based on monthly values
underestimates runoff. Replacing the Ritchie equation for evapotranspiration with the
Penman-Monteith equation, which makes use of wind speed, could enhance the model.
Because wind speed is very variable in space and time, it is doubtful if it really enhances the
model. For good calibration and validation of the model, it is necessary to have the 1997
weather data, which are unknown. To calibrate the model, the average weather data of the
period 1984 – 1993 are used.
On the other side, advantages of the model are the use of limited site data and limited
weather data. So the model can be used when little time or money is available to obtain sitespecific data and detailed weather information is absent. Considering the limited knowledge of
the soil profile SWBBM is a good alternative for more complicated models described in
chapter 3.
66
A Model Approach
The biomass production model
CENTURY 4.0 is not a spatial model, it is only possible to model vegetation production for
one (sample) point. To incorporate the model in PCRaster we have modelled different
vegetation types, which are included by using lookup tables. We were forced to use only C
and N simulation because of a runtime error of the model when modelling P. This is not an
optimal simulation because the nutrient data suggest the ecosystem is P limited. The average
net production per year of the whole study area is 151 g/m2 with an average vegetation cover
of 36%. Tsiourlis (1990) measured a net production per year of 160 g/m2 with an average
vegetation cover of 46 %. Our modelled vegetation production is higher, but still in the range
of the measurements of Margaris (1981) (Mont Hymette, Greece, see table 1).
The grazing model
The calculated grazing capacity is approximately 2875 animals per year for the total study
area, when the animals are homogenous spread over the study area. The grazing capacity
converted to ha. is 1.26 animal per ha. The calculated grazing capacity is realistic compared
to the observed grazing intensity and the degradation stage of the vegetation. Both field
observations and calculated grazing capacity show that overgrazing occurs in the study area.
A Monte Carlo analysis with varying soil depth surprisingly shows that the calculated grazing
capacity per year for the total study area is hardly sensitive for variances in soil depth.
Long time scenario modelling shows that total grazing capacity is very dependent of
precipitation. It is also shown that one dry year can have great impact on total grazing
capacity. The vegetation degrades faster than it regenerates, because phryganic species are
slow growing. At the moment, some areas are already in such a state, that even low grazing
intensities lead to degradation. If we analyse the vegetation cover change after ten years of
the different scenarios, the total vegetation cover increases or stays equal for scenario 2,3
and 5. From this we can conclude that a “proper” management option could be scenario 2
(2000 animals during wintertime, 1500 animals during summer time). With this management,
the vegetation stays at equilibrium or increases at most places. Some high risk grazing areas
should be excluded from grazing to prevent these areas for further degradation and give them
chance to recover.
The grazing model is not perfect. It does not include compensation growth of vegetation (see
chapter 5). The total grazing capacity per year is calculated with homogenous grazing of the
total study area. Although when calculating the long time scenarios the model accounts for
the observed grazing pressure. However, this grazing pressure is not dynamic in time. A
major source of errors is the parameterisation of insect consumption, palatability and
defoliation, but these are very difficult to measure. The maximum change of vegetation cover
when grazing is absent is a deterministic value, time consuming to determine (long time
monitoring is needed) and varies in space. In addition, the growth of annuals during spring is
not included in the model due to lack of knowledge of these plants.
On the other hand, the model gives plausible results and gives reasonable long time
forecasts. The model considers many factors with relative few input data, so it could be used
in other areas and on other scales. The results of the model match the field observations and
expectations.
67
A Model Approach
12- Summary & conclusions
Summary & conclusions concerning measurements
•
•
•
•
•
•
•
•
•
•
•
•
•
Data of soil depth, vegetation cover and grazing pressure produced reasonable
semivariograms and are interpolated using kriging techniques. Semivariograms of other
variables showed too much nugget variance.
For a more realistic result than a smooth kriging soil depth map, the rougher average soil
depth map produced in the Monte Carlo simulation could be used.
The visual estimation of the fractions of the different species matched the line intercept
measurements.
The visual estimations of total vegetation cover almost match the line intercept
measurements.
Measured saturated conductivity varies considerably within a testplot. Between some
testplots there is a significant difference.
The measurements of volumetric soil water content resulted in reasonable values. The
values within a testplot do not show large variances and show a normal distribution.
Significant differences are found between plots and in time.
The results of the soil nutrient analyses were within the range of other soils analysed in
the laboratory of physical geography. Two soil types can be distinguished: fertile
limestone soils and less fertile soils.
The results of the analysis of vegetation nutrients and lignin were acceptable. K, Na, P
and N show a significant decrease in time.
P/N ratio’s are low and suggest a P limited ecosystem.
Average lignin content ranges from 2.7% to 5.2 % and does not show correlation with any
of the nutrients.
Lignin contents suggest that phrygana is slow growing.
We could not match the results of the field and laboratory methods to determine texture.
The deviation is not constant so a correction for the total database cannot be done. We
have chosen to use the field estimations.
The high pF 2.0 values and low pF 4.2 values cause a (too?) large soil water-supplying
range of the soil. In general, differences of lithologies are reflected in the pF curves, thus
the data can be used in the water balance model.
Summary & conclusions concerning modelling
•
•
•
•
•
•
•
•
•
•
The water balance model gives a reasonable prediction of volumetric soil water content
and can be used for the vegetation production model and grazing capacity model.
It seems that the water balance model predictions occur two weeks too late compared to
the measured values.
The annual pattern of volumetric soil water content matches the expected annual pattern.
The water balance model could be improved by further calibration with weather data of
1997.
The water balance model is sensitive for the variables field capacity, wilting point and soil
depth.
Thicker soils react more gradual to changes than thinner soils so it is reasonable to
prevent running the model with too thin soils.
The water balance model makes use of limited site data and limited weather data, so the
model can be used when little time or money is available.
The calculated biomass production by CENTURY 4.0 is in the range of other phrygana
ecosystems.
The calculated biomass production could be improved by solving the runtime error in the
P-submodel of CENTURY 4.0, because the ecosystem of the study area is probably P
limited during parts of the year.
The calculated grazing capacity is realistic compared to the observed grazing intensity
and the degradation stage of the vegetation. Both field observations and calculated
grazing capacity show that overgrazing occurs in the study area.
68
•
•
•
•
•
•
•
A Model Approach
A Monte Carlo analysis with varying soil depth shows that the calculated grazing capacity
per year for the total study area is hardly sensitive for variances in soil depth.
Long time scenario modelling shows that total grazing capacity is very dependent of
precipitation and that one dry year can have great impact on total grazing capacity.
With a management suggested by scenario 2, the vegetation stays at equilibrium at most
places.
The grazing model does not include compensation growth of vegetation.
A major source of errors is the parameterisation of insect consumption, palatability and
defoliation.
The grazing model gives reliable results and gives reasonable long time forecasts and the
results of the model match the field observations and expectations.
The grazing model considers many factors with relative few input data, so it could be
used in other areas and on other scales.
69
A Model Approach
13 - Recommendations
The objective of this study was to develop a dynamic grazing model. We have reached this
goal and have made a model on which grazing management can be defined for our study
area, nonetheless calculated values of grazing capacity should always be used as an
indication. The model is of course not perfect and many input variables and correlations
between variables have to be better determined. A major advance of the model should be a
when the source code of a biomass production model is included in this model.
Separate from advancing the model (a model is never “finished”) it could be a challenge to
use other data-sources for the model. Particularly data obtained by Remote Sensing can be
used in the model. Examples are: vegetation cover derived from infrared images, lithology
(when vegetation is sparse), albedo and information from thermal images to indicate soil
temperature and water status.
Only when the model can be directly linked to Remote Sensing data sources it is possible to
use the model in areas larger than the study area without doing too much fieldwork. Remote
sensing makes it also possible to calibrate the long time model by deriving vegetation cover
change from time series of (satellite) images.
70
A Model Approach
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75
A Model Approach
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DE
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76
Appendix 1 - Maps of the study area
77
A Model Approach
78
A Model Approach
A Model Approach
Appendix 2 - Weather Data
Weather Station Gortis 753
35° 03' N 24° 58' E
Height Barometer
182 m.
precipitation (mm.)
Year
jan
feb
mar
apr
may jun
jul
aug
sep
okt
nov
dec
146.0 87.2
total / year
1984
107.2 100.1 35.4
71.0 0.0
0.0
1.7
0.0
0.0
0.0
1985
304.0 76.3
42.0
56.3 0.0
0.0
0.0
0.0
0.0
49.0 49.4
51.8
628.8
1986
124.3 83.9
21.8
0.0
18.6 0.0
0.0
0.0
20.0 38.3 33.5
79.0
419.4
1987
58.2
80.0
105.6
61.3 2.8
0.0
0.0
0.0
0.0
19.0 117.8 58.8
503.5
1988
133.0 94.4
122.9
4.7
3.0
0.0
0.0
0.0
0.0
31.0 169.1 108.3 666.4
1989
34.5
7.5
92.0
30.0 3.0
0.0
0.0
0.0
0.0
36.0 99.5
1990
25.0
65.3
0.0
31.0 0.0
0.0
0.0
0.7
0.7
8.3
1991
63.6
82.4
15.1
28.4 14.1 0.8
0.0
0.0
0.0
63.7 45.0
45.6
358.7
1992
19.0
56.9
40.5
54.5 1.4
0.0
0.0
0.0
0.0
0.0
55.6
50.7
278.6
1993
52.0
92.0
26.0
11.5 16.5 0.0
0.0
0.0
0.0
0.0
116.1 55.5
369.6
Average
92.1
73.9
50.1
24.5 94.2
Maximum
304.0 100.1 122.9
Minimum
19.0
34.9 5.9
45.0
109.5 66.1
0.1
0.2
0.1
2.1
71.0 18.6 0.8
1.7
0.7
20.0 63.7 169.1 108.3
548.6
347.5
306.6
64.8
7.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
33.5
45.0
Standard Deviation 84.7
26.7
41.7
24.9 7.4
0.3
0.5
0.2
6.3
22.6 46.2
20.7
Average 1984-1993
442.8 mm./year
jul
aug
sep
okt
dec
Averg/year
Temperature °C
Year
jan
feb
mar
apr
may jun
nov
1984
11.1
11.2
12.9
14.7 22.3 24.7 27.5 26.5 24.7 21.4 15.4
11.6
18.7
1985
11.6
10.0
13.0
17.3 21.7 25.8 27.7 27.8 24.1 17.7 16.1
12.7
18.8
1986
11.0
12.1
13.1
17.9 20.4 25.5 28.1 28.2 24.6 18.9 13.8
11.6
18.8
1987
11.9
11.8
9.6
14.2 19.3 25.1 29.0 27.7 25.9 19.4 15.2
12.0
18.4
1988
11.5
10.8
12.2
16.8 21.9 26.4 30.1 28.2 24.7 18.8 13.4
11.7
18.9
1989
7.8
10.9
13.1
16.8 21.8 25.9 29.2 26.9 24.3 18.1 15.0
12.4
18.1
1990
9.8
11.4
14.0
16.9 21.7 25.4 28.3 27.2 24.2 20.6 17.3
13.4
19.2
1991
10.2
10.7
14.4
16.1 19.0 25.8 27.0 28.2 23.8 20.7 15.2
9.1
18.4
1992
9.6
8.5
11.8
15.6 19.5 25.0 26.6 27.6 23.4 22.0 16.2
10.5
18.0
1993
10.1
9.5
11.6
16.5 20.3 26.1 28.1 28.1 24.4 21.8 15.7
13.2
18.8
Average
10.5
10.7
12.6
16.3 20.8 25.6 28.2 27.6 24.4 19.9 15.3
11.8
Maximum
11.9
12.1
14.4
17.9 22.3 26.4 30.1 28.2 25.9 22.0 17.3
13.4
Minimum
7.8
8.5
9.6
14.2 19.0 24.7 26.6 26.5 23.4 17.7 13.4
9.1
Standard Deviation 1.2
1.1
1.4
1.2
1.3
1.2
0.5
79
1.1
0.6
0.7
1.6
1.1
A Model Approach
Monthly Average Precipitation
Monthy Average Temperature
100
90
80
30.0
25.0
70
60
m m50
.
40
30
20.0
C. 15.0
10.0
20
10
0
5.0
0.0
1
2
3
4
5
6
7
m onth
8
9
10
11
12
1
2
3
4
5
6
7
m onth
8
9
10
11
12
Annual Average Temperature
Annual Average Precipitation
19.4
19.2
19.0
700.0
600.0
500.0
18.8
18.6
C.18.4
18.2
18.0
400.0
mm.
300.0
200.0
17.8
17.6
17.4
100.0
0.0
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
year
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
year
80
A Model Approach
Appendix 3 - Statistics vegetation study area
Observed species
Patability Species
Code
Shrubs and trees
1
Sarcopoterium spinosum (SS)
2
3
Data
Total
Average of LAI
13.7
Average of height (cm.)
20.4
**
Max of height (cm.)
50
Calicotome villosa (CV)
****
Min of height (cm.)
4
Thymus capitatus (TC)
***
StdDev of height (cm.)
6.5
4
Genista acanthoclada (GA)
**
Number of observations
198
6
Phlomis spec.(PH)
***
Average of LAI
25.2
7
Olea europaea sylvestris (OS) grazed
***
54.0
8
Olea europaea sylvestris (OS) ungrazed
****
Max of height (cm.)
101
9
Pyrus amygdaliformis
Min of height (cm.)
5
13
Euphorbia characias (EC)
18.1
14
Rhamnus oleoides (RA)
167
16
Astragalus angustifolium
Average of LAI
21.9
19
Satureja thymbra (ST)
26.3
21
Oleander spec.
Max of height (cm.)
47
22
Thymelaia hirsuta (TH)
Min of height (cm.)
9
Salvia fruticosa (SF)
7.5
172
Average of LAI
27.9
Calicotome villosa
Thymus capitatus
Not determined shrubs
15
Dutch: Gewone brem
42.8
17
Dutch: Rechte stekel
Max of height (cm.)
68
18
Dutch: Zilvervetplant
Min of height (cm.)
19
20
Dutch: Vingerhennep
15.4
23
Dutch: Groene rechte stekel
42
Average of LAI
18.4
Phlomis spec.
Annuals
5
Originea maritime and Asphodelus spec.
32.6
*
Max of height (cm.)
67
19
11
Grasses
****
Min of height (cm.)
12
Thistles
*
11.9
10
Others
86
Average of LAI
14
Rhamnus oleoides
Patability
35
*
= not patable
Max of height (cm.)
38
**
= limited patable
Min of height (cm.)
32
*** = good patable
4.2
**** = very good patable
4
Average of LAI
3
Thymelea hirsuta
others: not observed
42.5
Max of height (cm.)
51
Min of height (cm.)
34
12.0
3
Total Average of LAI
19.6
Total Average of height (cm.)
30.0
Total Max of height (cm.)
101
Total Min of height (cm.)
4
Total StdDev of height (cm.)
16.2
Total Number of observations
692
81
A Model Approach
Appendix 4 - Data Tsiourlis (1990)
Chemical composition of soil under a phryganic ecosystem
Naxos, Cyclades, Greece
Tsiourlis 1990
Ph H20
CaCO3 %
OrgMatter % C %
N%
C/N
P ppm
S ppm
Depth
0 - 10 cm.
7.4
0.8
2.1
1.2
0.10
12
2
8
10 - 30 cm.
7.2
0.8
0.7
0.4
0.03
13
2
14
30 - 60 cm.
7.1
0.8
0.2
0.1
0.01
10
1
17
60 - 90 cm.
7.4
0.8
0.3
0.2
0.02
10
2
23
Ca2+
meq/100g.
Mg2+
meq/100g.
K+
meq/100g.
T
total
exch.
S
exch.
cation.
V
S/T*100
Fe
ppm
Mn
ppm
Zn
ppm
Cu
ppm
Depth
0 - 10 cm.
11.5
1.7
0.9
24.4
14.1
57.8
25
270
2
3
10 - 30 cm.
11.0
2.1
0.9
26.3
14.0
53.2
13
90
1
2
30 - 60 cm.
13.0
2.2
0.8
32.5
16.0
49.2
13
70
1
3
60 - 90 cm.
14.0
2.2
0.5
23.5
16.7
71.1
14
90
1
2
Chemical composition of vegetation in a phryganic ecosystem, Tsiourlis 1990
Naxos, Cyclades, Greece
N%
P%
K%
Ca %
Mg %
Fe ppm
Mn ppm
Zn ppm P/N
Aboveground
1.00
0.02
0.13
0.60
0.03
433
30
17
Underground
1.25
0.01
0.30
0.48
0.06
Aboveground
0.50
0.02
0.31
0.40
0.05
Underground
0.50
0.02
0.40
0.55
0.06
Leaves
1.15
0.06
0.60
0.80
0.09
155
272
21
0.056
Wood
0.50
0.02
0.26
1.08
0.06
130
102
16
0.036
Underground
0.50
0.02
0.35
1.11
0.05
966
60
11
0.042
Aboveground
0.60
0.02
0.22
0.30
0.07
0.027
Underground
0.45
0.02
0.12
0.25
0.03
0.036
Aboveground
1.35
0.04
0.62
1.45
0.18
0.028
Underground
0.50
0.02
0.20
1.05
0.07
0.040
Leaves
1.55
0.03
1.02
1.30
0.18
145
45
11
0.021
Wood
0.60
0.02
0.72
1.68
0.11
233
80
17
0.037
Underground
0.85
0.02
0.60
1.44
0.13
1132
43
24
0.025
0.015
0.008
Thymus capitatus
725
20
12
0.032
0.030
Quercus coccifera
Erica Manipuliflora
Cistus creticus
Pistacia lentiscus
Olea europaea sylvestris
Leaves
1.10
0.05
0.83
0.85
0.10
160
25
24
0.048
Wood
0.50
0.02
0.54
0.48
0.05
146
10
23
0.036
Underground
0.65
0.02
0.37
0.48
0.03
1500
30
11
Death biomass
0.50
0.01
0.13
0.69
0.04
0.024
Aboveground
0.85
0.02
0.15
0.54
0.06
0.025
Underground
0.65
0.05
0.12
0.75
0.08
Average phrygana
0.78
0.02
0.40
0.81
0.07
520
65
17
0.033
Standard deviation
0.34
0.02
0.26
0.42
0.04
486
74
5
0.015
Maximum
1.55
0.06
1.02
1.68
0.18
1500
272
24
0.078
Minimum
0.45
0.01
0.12
0.25
0.03
130
10
11
0.008
0.023
82
0.078
A Model Approach
Biomass data
Naxos, Cyclades, Greece, Tsiourlis 1990
A = Aboveground, R = Roots
Pct = percentage of total biomass of ecosystem
Species
A
R
A+R
g/m2
g/m2
g/m2
2673
220
2893
Thymus capitatus
1221
259
1480
R:A
A
A pct
R
R pct
A+R
A+R pct
ton/ha
%
ton/ha
%
ton/ha
%
0.08
4.10
51.70
0.34
4.29
4.44
40.66
0.21
1.10
13.87
0.23
2.90
1.33
12.18
Quercus coccifera
Leaves
327
Wood
2025
327
Total
2352
2114
4466
0.90
1.06
13.37
0.95
11.98
2.01
18.41
Erica manipuliflora
1440
1125
2565
0.78
0.51
6.43
0.39
4.92
0.90
8.24
Cistus creticus
486
200
686
0.41
0.14
1.77
0.06
0.76
0.20
1.83
1.41
0.48
6.05
0.68
8.58
1.16
10.62
2025
Pistacia lentiscus
Leaves
320
Wood
1370
Total
1690
320
1370
2380
4070
Leaves
686
Wood
3473
Total
4159
1807
5966
0.43
0.51
6.43
0.33
4.16
0.84
7.69
493
209
702
0.42
0.03
0.38
0.01
0.13
0.04
0.37
0.38
7.93
100.00
2.99
37.70
10.92
100.00
686
3473
0
Average phrygana
1988
750
Dead biomass on plant
g/m2
Quercus coccifera
240
Pistacia lentiscus
306
344
2738
Biomass data: difference grazed-ungrazed
Naxos, Cyclades, Greece, Tsiourlis 1990
Species
Grazed g/m2
Ungrazed g/m2 Difference
Difference % % of total
2310
2377
67
2.9
Thymus capitatus
1159
1231
72
6.2
2.3
Quercus coccifera
2545
4132
1587
62.4
51.6
Erica manipuliflora
2139
absent
Cistus monspeliensis
807
1041
234
29.0
7.6
Cistus creticus
728
775
47
6.5
1.5
Pistacia lentiscus
2062
2169
107
5.2
3.5
4422
5163
741
16.8
24.1
360
580
220
61.1
7.2
Total phrygana (C. monspeliensis excluded)
14393
17468
3075
21.4
Total phrygana
11848
13336
1488
12.6
Quercus coccifera
275
393
118
42.9
Pistacia lentiscus
309
223
-86
-27.8
603
620
17
2.8
(C. monspeliensis and Q. coccifera excluded)
Dead biomass on plant
83
2.2
A Model Approach
Appendix 5 - Field Form
Field Form Crete 1997
Niels Smit - Raymond Sluiter
Observation point
Date/Time
1997
GPS coordinates
Weather description
Lithology
(geology)
Slope
°
Aspect
°
Stone cover
% detached stones
% solid rock
Average stone size
0-5
5-10
10-15
15-20
25 - 50
>50
Texture
sand
loamy
sand
silt loam
loam
clay loam
light clay
Size aggregates
<1
1-2
2-5
Soil depth
absent
0-25
25-50
5-10
50-75
cm.
>10 mm.
75-100
>100
cm.
Grazing pressure
Vegetation cover
total
%
Vegetation cover
Species 1
%
Species 2
%
Species 3
%
Species 4
%
LAI
Species 1
LAI -1
LAI -2
LAI -3
LAI - 4
LAI–5 %
Species 2
LAI -1
LAI -2
LAI -3
LAI - 4
LAI–5 %
Species 3
LAI -1
LAI -2
LAI -3
LAI - 4
LAI–5 %
Species 4
LAI -1
LAI -2
LAI -3
LAI - 4
LAI–5 %
84
Appendix 6 - Visual Estimation Chart
85
A Model Approach
A Model Approach
Appendix 7 - Analysis C according to Walkley Black
Titter Mohr’s salt
M
c
g
M
solid1
12.36
0.25
0.412621
solid2
12.47
0.25
0.408982
mean:
0.410801
Titter K2Cr2O7
K
b
K
solution1
11.91
0.489264536
solution2
11.92
0.489675337
solution3
11.83
0.485978124
mean:
0.488305999
mcf
1
Sample
ml.
salt
AS1
4.93
AS2
5.76
AS3
AS4
C
org C %
LOI
Clay content LOI'
0.499
2.2
3.8
7.5
17.2
6.3
0.498
2.0
3.4
8.6
11.1
7.8
7.57
0.498
1.4
2.4
7.1
15.6
6.0
3.49
0.251
5.4
9.2
11.5
3.8
11.2
22.9
23.9
21.2
AS6
5.11
0.498
2.2
3.8
13.1
35.8
10.6
AS7
4.56
0.252
4.7
8.0
17.3
32.3
15.1
AS8
6.22
0.498
1.8
3.1
11.4
55.2
7.6
PS1
4.27
0.499
2.4
4.2
10.0
23.0
8.4
PS2
5.43
0.489
2.1
3.6
10.5
23.9
8.9
PS3
3.26
0.498
2.8
4.8
10.9
27.7
9.0
PS4
6.35
0.250
3.5
6.1
14.2
17.3
13.0
PS5
5.64
0.248
4.0
7.0
15.2
24.0
13.5
AS5
Mohr’s G
0.252
2
Correlation AS+PS
R = 0.81
Correlation AS
R = 0.80
2
Samples collected by L. Brouwer October 1996
86
A Model Approach
Appendix 8 - Soil Nutrients
Plotnumber Date
5-19-97
Ca [g/kg] K [g/kg]
Mg [g/kg] Na [g/kg] P [g/kg]
N [g/kg]
C [g/kg]
C/N
P/N
C/P
1
2
3
4
5
6
Average
Plotnumber Date
6-26-97
1
2
3
4
5
6
7
Average
100.7
152.1
2.1
27.5
86.0
4.0
62.1
8.4
5.7
23.1
25.2
14.9
15.1
15.4
5.8
5.7
3.5
15.7
13.9
17.8
10.4
0.5
0.5
1.0
2.0
0.8
0.9
0.9
0.5
0.5
0.3
0.8
0.2
0.2
0.4
3.7
2.5
1.6
1.4
1.4
1.9
2.1
42.4
30.6
25.2
26.6
27.3
34.5
31.1
11.5
12.3
15.6
18.7
19.5
18.6
16.0
0.15
0.19
0.19
0.53
0.16
0.13
0.22
79.3
64.1
83.0
35.3
125.6
140.2
73.7
96.7
93.6
2.6
19.0
78.8
43.2
6.9
48.7
8.8
8.5
24.6
25.5
15.4
18.1
23.8
17.8
4.9
3.0
2.0
14.2
12.4
17.4
8.3
8.9
0.5
0.6
0.9
1.7
0.8
0.9
0.8
0.9
0.5
0.4
0.3
0.7
0.2
0.3
1.0
0.5
3.7
1.8
1.8
1.5
1.8
1.0
4.4
2.3
51.7
21.1
33.1
36.7
53.6
25.1
55.1
39.5
14.0
11.6
18.5
25.2
30.0
26.1
12.4
19.7
0.14
0.22
0.17
0.45
0.13
0.32
0.23
0.24
100.5
53.1
110.0
55.8
237.4
81.8
54.3
80.9
Plotnumber Difference
1
-3.9
2
-58.4
3
0.5
4
-8.5
5
-7.2
6
39.2
0.4
2.8
1.5
0.3
0.4
3.0
-0.9
-2.7
-1.4
-1.5
-1.6
-0.4
0.0
0.2
0.0
-0.2
0.0
0.0
0.0
-0.1
0.0
-0.1
0.0
0.1
0.0
-0.7
0.2
0.0
0.4
-0.9
9.3
-9.6
7.9
10.1
26.3
-9.3
2.5
-0.7
2.9
6.4
10.5
7.5
-0.01
0.03
-0.02
-0.08
-0.03
0.19
21.2
-11.0
27.0
20.5
111.9
-58.4
OS Point 95 Date
5-27-97
22.9
19.2
14.6
1.0
0.5
2.0
49.4
24.8
0.25
97.8
Total Average
1
2
3
4
5
6
7
Average
98.7
122.8
2.4
23.2
82.4
23.6
6.9
51.4
8.6
7.1
23.8
25.3
15.2
16.6
23.8
17.2
5.4
4.3
2.8
14.9
13.2
17.6
8.3
9.5
0.5
0.6
0.9
1.9
0.8
0.9
0.8
0.9
0.5
0.4
0.3
0.7
0.2
0.3
1.0
0.5
3.7
2.2
1.7
1.4
1.6
1.4
4.4
2.3
47.1
25.8
29.2
31.6
40.4
29.8
55.1
37.0
12.8
11.9
17.0
21.9
24.8
22.3
12.4
17.6
0.14
0.21
0.18
0.49
0.14
0.23
0.23
0.23
89.9
58.6
96.5
45.6
181.5
111.0
54.3
91.0
Total Average
Plot 1,2,7
Plot 3,4,5,6
76.2
32.9
13.2
20.2
6.0
12.1
0.6
1.1
0.7
0.4
3.4
1.5
42.7
32.8
12.4
21.5
0.2
0.3
67.6
108.6
87
A Model Approach
Appendix 9 - Vegetation Nutrients
Sample
p01
p02
p03
p04
p05
p06
p07
p08
p09
p10
p11
p12
p13
p14
p15
p16
p17
p18
p19
p20
p21
p22
p23
p24
p25
p28
p29
p30
p31
p32
p33
p34
p35
p36
p37
p38
p39
p40
p41
p42
OS Point
44
44
44
44
44
45
45
45
45
45
46
47
47
48
48
49
49
49
49
95
95
188
44
44
44
45
45
45
45
45
46
47
47
95
95
48
48
49
49
49
Plot
1
1
1
1
1
2
2
2
2
2
3
4
4
5
5
6
6
6
6
7
1
1
1
2
2
2
2
2
3
4
4
5
5
6
6
6
Date
19-5-97
19-5-97
19-5-97
19-5-97
19-5-97
19-5-97
19-5-97
19-5-97
19-5-97
19-5-97
20-5-97
20-5-97
20-5-97
20-5-97
20-5-97
20-5-97
20-5-97
20-5-97
20-5-97
27-5-97
27-5-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
26-6-97
Plant Species
Calicotome villosa
Phlomis
Thymus capitatus
Phlomis
Thymus capitatus
Rhamnus oleoides
Phlomis
Thymus capitatus
Thymus capitatus
Calicotome villosa
Thymus capitatus
Phlomis
Olea europaea sylvestris ungrazed
Olea europaea sylvestris grazed
Phlomis
Thymus capitatus
Phlomis
Rhamnus oleoides
Thymus capitatus
Phlomis
Thymus capitatus
Phlomis
Thymus capitatus
Phlomis
Thymus capitatus
Average
Stdev
Min
Max
88
Ca [g/kg]
14.8
13.4
19.9
26.0
9.3
15.5
21.3
16.7
6.8
17.9
16.1
20.8
24.2
17.7
19.3
11.9
21.6
19.3
10.6
13.6
10.4
12.0
16.3
6.2
9.0
13.1
34.0
21.3
8.6
6.1
13.6
26.5
12.5
10.5
11.9
11.7
18.5
8.3
18.7
11.4
15.43
6.13
6.1
34.0
K [g/kg]
11.3
10.5
15.5
19.1
16.7
10.0
13.1
14.0
12.8
8.2
7.5
12.4
12.9
6.0
14.9
6.1
13.4
7.7
15.8
10.4
9.7
7.4
9.7
7.7
8.8
7.1
6.0
13.3
7.3
5.8
5.8
10.1
9.2
13.2
14.3
5.5
10.4
17.4
9.1
4.8
10.52
3.70
4.8
19.1
Mg [g/kg]
3.6
1.7
1.8
2.3
1.3
4.2
1.9
1.6
1.0
0.8
3.8
1.7
2.4
3.1
2.0
1.9
2.5
4.4
2.6
1.3
0.6
0.8
1.8
0.5
0.6
3.3
1.6
2.0
0.4
0.6
3.0
2.6
1.0
0.9
0.9
2.7
1.6
2.5
1.6
2.7
1.95
1.05
0.4
4.4
Sample
p01
p02
p03
p04
p05
p06
p07
p08
p09
p10
p11
p12
p13
p14
p15
p16
p17
p18
p19
p20
p21
p22
p23
p24
p25
p28
p29
p30
p31
p32
p33
p34
p35
p36
p37
p38
p39
p40
p41
p42
Average
Stdev
Min
Max
Mg [g/kg]
3.6
1.7
1.8
2.3
1.3
4.2
1.9
1.6
1.0
0.8
3.8
1.7
2.4
3.1
2.0
1.9
2.5
4.4
2.6
1.3
0.6
0.8
1.8
0.5
0.6
3.3
1.6
2.0
0.4
0.6
3.0
2.6
1.0
0.9
0.9
2.7
1.6
2.5
1.6
2.7
1.95
1.05
0.4
4.4
Na [g/kg]
1.1
2.6
1.6
0.9
0.9
0.8
0.8
0.5
0.5
0.1
0.4
0.7
1.1
0.5
1.9
0.5
1.2
0.9
0.8
0.2
0.0
0.3
0.1
0.4
0.0
0.5
0.4
0.4
-0.1
0.5
0.4
0.6
0.2
0.1
0.1
0.5
1.5
0.5
0.4
0.2
0.62
0.55
-0.1
2.6
P [g/kg]
1.4
1.0
1.3
1.4
1.6
1.1
1.3
1.0
1.0
0.9
1.0
1.3
1.1
0.4
0.9
0.6
1.0
0.3
0.7
0.8
0.9
1.1
0.4
0.4
1.2
0.8
0.3
0.7
1.0
0.6
0.7
0.7
1.2
0.7
0.6
0.7
0.6
1.0
0.9
0.6
0.87
0.31
0.3
1.6
N [g/kg]
17.8
36.4
24.0
18.3
24.5
16.8
24.4
16.7
21.8
22.9
12.7
22.4
16.1
10.1
13.7
29.3
14.2
12.7
11.6
12.5
15.9
12.9
10.1
15.2
20.2
18.8
15.4
12.9
17.9
12.4
20.2
18.0
12.0
13.7
18.0
10.6
8.5
11.8
11.0
9.7
16.60
5.83
8.5
36.4
89
A Model Approach
Lignin %
6.3
4.9
1.2
3.4
5.7
6.1
6.0
0.2
4.5
0.8
1.5
5.0
4.3
6.3
5.9
5.6
6.5
1.2
3.2
4.5
4.5
6.3
0.1
4.8
1.0
6.8
4.7
4.7
6.3
4.1
8.3
0.9
3.1
3.6
3.8
5.9
0.2
3.3
0.9
1.4
3.94
2.21
0.1
8.3
P/N
0.08
0.03
0.05
0.07
0.06
0.07
0.05
0.06
0.05
0.04
0.08
0.06
0.07
0.04
0.06
0.02
0.07
0.03
0.06
0.06
0.05
0.09
0.04
0.03
0.06
0.04
0.02
0.06
0.06
0.05
0.03
0.04
0.10
0.05
0.03
0.06
0.07
0.08
0.08
0.07
0.06
0.02
0.0
0.1
C/N
53.4
25.7
39.1
51.0
38.5
56.5
38.4
57.0
43.8
41.5
75.7
42.1
58.6
94.9
69.2
32.4
66.8
74.9
82.8
77.0
60.7
74.6
95.1
63.7
47.7
51.0
61.3
73.8
53.8
78.5
47.3
52.2
80.2
70.3
53.1
91.4
113.2
81.0
87.2
99.7
63.88
20.27
25.7
113.2
A Model Approach
Appendix 10 - Vegetation Nutrients per Species and Date
Plant Species
Data
Total Plant Species
Data
Total
Calicotome villosa
Average of N [g/kg]
32.84 Sarcopoterium spinosum
Average of N [g/kg]
14.39
StdDev of N [g/kg]
0.27
StdDev of N [g/kg]
0.33
Average of P [g/kg]
0.77
Average of P [g/kg]
0.79
Average of C/N
29.05
Average of C/N
71.63
Max of C/N
32.43
Max of C/N
99.72
Average of P/N
0.02
Average of P/N
0.06
Max of P/N
0.03
Max of P/N
0.08
Average of Lignin %
5.23
Average of Lignin % 4.86
Average of N [g/kg]
18.50 Thymus capitatus
Average of N [g/kg]
13.93
StdDev of N [g/kg]
0.52
StdDev of N [g/kg]
0.27
Average of P [g/kg]
0.91
Average of P [g/kg]
0.86
Average of C/N
56.13
Average of C/N
72.41
Max of C/N
78.46
Max of C/N
113.19
Average of P/N
0.05
Average of P/N
0.06
Max of P/N
0.06
Max of P/N
0.08
Average of Lignin %
4.75
Olea europaea sylvestris Average of N [g/kg]
grazed
StdDev of N [g/kg]
Average of P [g/kg]
Average of C/N
16.60
0.31
0.18
0.74
Total Average of P [g/kg]
0.87
56.90
Total Average of C/N
63.88
Max of C/N
60.68
Total Max of C/N
113.19
0.04
Total Average of P/N
0.06
Max of P/N
0.05
Total Max of P/N
0.10
Average of Lignin %
4.14
Total Average of Lignin %
3.94
Average of P [g/kg]
Rhamnus oleoides
Total Average of N [g/kg]
Total StdDev of N [g/kg]
Average of P/N
Olea europaea sylvestris Average of N [g/kg]
ungrazed
StdDev of N [g/kg]
Phlomis spec.
Average of Lignin % 2.70
16.92
13.08
0.03
0.76
Average of C/N
73.64
Max of C/N
77.03
Average of P/N
0.06
Max of P/N
0.06
Average of Lignin %
4.09
Average of N [g/kg]
17.46
StdDev of N [g/kg]
0.19
Average of P [g/kg]
1.11
Average of C/N
59.97
Max of C/N
82.85
Average of P/N
0.07
Max of P/N
0.10
Average of Lignin %
3.94
Average of N [g/kg]
19.13
StdDev of N [g/kg]
0.43
Average of P [g/kg]
0.49
Average of C/N
51.38
Max of C/N
61.28
Average of P/N
0.03
Max of P/N
0.04
Average of Lignin %
2.76
Statistics of nutrients per species
90
Period
Data
June
Average of Ca [g/kg]
14.21
Average of K [g/kg]
May
A Model Approach
Data
-2.31
9.10
Average of K [g/kg]
-2.70
Average of Mg [g/kg]
1.64
-0.59
Average of Na [g/kg]
0.37
-0.49
Average of P [g/kg]
0.75
Average of P [g/kg]
-0.24
Average of N [g/kg]
14.17
Average of N [g/kg]
-4.62
Average of Lignin %
3.70
Average of Lignin %
-0.45
Average of P/N
0.06
Average of P/N
-0.00
Average of C/N
72.37
Average of C/N
+16.18
16.52
Average of K [g/kg]
11.80
2.23
0.86
Average of P [g/kg]
0.99
Average of N [g/kg]
18.79
Average of Lignin %
4.16
Average of P/N
0.06
Average of C/N
56.19
Difference
Average differences of nutrient content May-June
Plant comparison May -June
Total Average:
Average May:
Average June:
Plant Species
P
N
P/N
0.79
14.39
0.06
Phlomis
1.11
17.46
0.07
Thymus capitatus
0.86
13.93
0.06
0.75
15.00
0.05
0.74
16.92
0.04
0.76
13.08
0.06
Rhamnus oleoides
0.59
19.13
0.03
0.86
14.04
0.06
Phlomis
1.12
20.57
0.06
Thymus capitatus
1.06
15.77
0.07
0.74
16.92
0.04
0.87
15.86
0.05
0.78
12.48
0.06
Rhamnus oleoides
0.89
22.88
0.04
0.70
14.83
0.05
Phlomis
1.10
14.97
0.08
Thymus capitatus
0.65
12.09
0.06
0.76
13.08
0.06
0.62
17.97
0.03
0.74
13.68
0.05
Rhamnus oleoides
0.29
15.38
0.02
91
Difference May / June:
Fraction change
A Model Approach
-0.16
0.79
-0.01
Phlomis
-0.03
-5.59
0.02
Thymus capitatus
-0.41
-3.68
-0.01
0.02
-3.84
0.01
-0.25
2.10
-0.02
-0.04
1.20
-0.01
Rhamnus oleoides
-0.60
-7.50
-0.02
-0.19
+0.06
-0.13
Phlomis
-0.02
-0.27
+0.38
Thymus capitatus
-0.38
-0.23
-0.15
+0.02
-0.23
+0.31
-0.29
+0.13
-0.37
-0.06
+0.10
-0.14
Rhamnus oleoides
-0.68
-0.33
-0.52
Comparison of plant nutrients per plant May-June
92
A Model Approach
Appendix 11 - Grain Size Analysis
Sample
Total
Weight
2000 1400 1000 850
2
<2
1
11.74
0.03 0.28
0.39
0.24 0.46
2
15.97
0.01 0.18
0.19
0.10 0.24
0.36 0.30 0.21 0.20 0.25 0.50 0.31 1.12 0.48 0.88
2.48
3.23
0.34 0.46 0.47 0.56 0.85 1.43 0.68 1.14 1.12 1.84
2.76
3
19.55
0.05 2.97
2.37
3.63
1.16 2.15
1.48 1.00 0.78 0.62 0.54 0.49 0.23 0.68 0.64 0.72
1.36
4
17.95
0.05 1.59
2.31
1.50
0.74 1.67
1.49 1.22 1.00 0.95 0.83 0.74 0.36 0.84 0.68 0.72
0.88
5
15.74
2.71
0.05 1.57
1.08
0.60 1.35
1.11 0.84 0.66 0.59 0.50 0.46 0.22 0.56 0.60 0.84
1.20
6
3.51
18.71
0.02 1.42
1.25
0.72 1.69
1.41 1.10 0.97 0.99 1.00 1.02 0.49 1.12 0.68 0.96
1.44
2.43
7
18.60
0.03 0.50
0.62
0.46 1.33
1.55 1.53 1.31 0.99 0.74 0.54 0.26 0.40 0.96 0.92
2.00
4.47
b3
17.74
0.00 0.01
0.03
0.04 0.26
0.88 1.37 1.31 1.09 0.83 0.69 0.34 1.38 1.52 1.48
2.16
4.35
Perentual
Weights
Total
Weight
420
2
<2
Sampe
2000 1400 1000 850
600
600
420
300
300
210
210
150
150
105
105
75
75
53
32
53
32
16
16
8
8
1
11.74
0.24 2.40
3.36
2.07 3.89
3.10 2.59 1.82 1.70 2.16 4.23 2.63 9.54 4.09 7.50
2
15.97
0.05 1.12
1.16
0.64 1.48
2.11 2.85 2.92 3.50 5.29 8.95 4.24 7.14 7.01 11.52 17.28 22.73
3
19.55
0.26 15.19 12.12 5.93 11.00 7.57 5.11 3.96 3.19 2.76 2.50 1.20 3.48 3.27 3.68
6.96
11.82
4
17.95
0.25 8.86
8.36
4.09 9.30
8.30 6.80 5.55 5.29 4.61 4.09 2.02 4.68 3.79 4.01
4.90
15.10
5
15.74
0.34 9.97
6.86
3.82 8.58
7.05 5.34 4.21 3.73 3.16 2.90 1.42 3.56 3.81 5.34
7.62
22.30
6
18.71
0.13 7.59
6.68
3.84 9.03
7.53 5.88 5.19 5.31 5.32 5.45 2.61 5.98 3.63 5.13
7.69
12.98
7
18.60
0.16 2.68
3.34
2.46 7.15
8.33 8.22 7.04 5.34 3.95 2.89 1.40 2.15 5.16 4.95
10.75 24.03
b3
17.74
0.02 0.06
0.15
0.23 1.47
4.95 7.72 7.38 6.14 4.68 3.91 1.89 7.78 8.57 8.34
12.18 24.52
Distribution Plot 1
Distribution Plot 2
30.00
25.00
20.00
32
8
<2
8
<2
75
32
150
300
600
Distribution Plot 4
Distribution Plot 3
30.00
93
75
150
300
600
1000
2000
0.00
<2
8
32
75
150
300
600
1000
25.00
20.00
% 15.00
10.00
5.00
2000
30.00
25.00
20.00
% 15.00
10.00
5.00
0.00
1000
2000
0.00
<2
8
32
75
150
300
600
1000
% 15.00
10.00
5.00
2000
30.00
25.00
20.00
% 15.00
10.00
5.00
0.00
21.13 27.52
A Model Approach
25.00
20.00
% 15.00
10.00
75
150
300
600
1000
0.00
2000
5.00
94
<2
8
<2
<2
30.00
32
8
8
Distribution Plot 7
75
32
32
75
150
300
600
1000
0.00
2000
5.00
150
10.00
300
20.00
% 15.00
600
30.00
25.00
20.00
% 15.00
10.00
5.00
0.00
2000
30.00
25.00
1000
Distribution Plot 6
Distribution Plot 5
A Model Approach
Appendix 12 - pF curves
0
1.2
0.13
0.167
0.84
0.454
Moisture content pF 4.2 0.091
Difference
0.363
Theta-v measured
0.04
0.13
0.36
0.38
0.40
0.41
0.45
0.52
0.56
0.57
Theta-e
0.22
0.31
0.43
0.47
0.52
0.59
0.73
0.87
0.96
0.99
Theta-v Genuchten
0.12
0.18
0.25
0.27
0.30
0.34
0.42
0.49
0.55
0.56
Plot 1 - Average no new fit
Difference
n
a
0.455
0.093
0.362
1.398
0.024
Plot 1 - Average
0.60
0.50
0.40
Theta-v
pF
4.2
3.4
2.7
2.5
2.3
2.0
1.5
1.0
0.4
0.0
Plot 1 - Average
Theta-r
n
a
m
R2
0.30
0.20
0.10
0.00
0.0
1.0
2.0
3.0
4.0
5.0
pF
Theta-v measured
0
1.3
0.069
0.231
0.86
0.304
Difference
0.22
Theta-v measured
0.02
0.07
0.25
0.28
0.30
0.31
0.35
0.42
0.50
0.50
Theta-e
0.12
0.21
0.34
0.39
0.45
0.55
0.74
0.89
0.98
0.99
Theta-v Genuchten
0.06
0.11
0.17
0.20
0.23
0.28
0.37
0.45
0.49
0.50
Difference
n
a
0.302
0.071
0.231
1.306
0.072
Plot 2 - Average
0.60
0.50
0.40
Theta-v
pF
4.2
3.4
2.7
2.5
2.3
2.0
1.5
1.0
0.4
0.0
Plot 2-average
Theta-r
n
a
m
R2
Theta-v Genuchten
0.30
0.20
0.10
0.00
0.0
1.0
2.0
3.0
4.0
5.0
pF
Theta-v measured
0
1.3
0.039
0.231
0.83
Theta-v Measured
0.02
0.05
0.20
0.22
0.25
0.26
0.30
0.35
0.40
0.41
Theta-e
0.12
0.21
0.34
0.39
0.45
0.55
0.74
0.89
0.98
0.99
0.261
Difference
0.202
Theta-v Genuchten
0.05
0.09
0.14
0.16
0.18
0.22
0.30
0.36
0.40
0.40
Difference
n
a
0.265
0.056
0.209
1.317
0.034
Plot 3 - Average
0.60
0.50
0.40
Theta-v
pF
4.2
3.4
2.7
2.5
2.3
2.0
1.5
1.0
0.4
0.0
Plot 3 - Average
Theta-r
n
a
m
R2
Theta-v Genuchten
0.30
0.20
0.10
0.00
0.0
1.0
2.0
3.0
4.0
pF
Theta-v measured
95
Theta-v Genuchten
5.0
0.00
1.41
0.01
0.29
0.89
0.40
Difference
0.34
Theta-v Measured
0.03
0.11
0.30
0.32
0.35
0.36
0.40
0.45
0.47
0.48
Theta-e
0.13
0.28
0.52
0.61
0.71
0.83
0.96
0.99
1.00
1.00
Theta-v Genuchten
0.06
0.13
0.25
0.29
0.34
0.40
0.46
0.48
0.48
0.48
Difference
n
a
0.32
0.11
0.21
1.48
0.01
Plot 4 - Average
0.60
0.50
0.40
Theta-v
pF
4.2
3.4
2.7
2.5
2.3
2.0
1.5
1.0
0.4
0.0
Plot 4 - Average
Theta-r
n
a
m
R2
A Model Approach
0.30
0.20
0.10
0.00
0.0
1.0
2.0
3.0
4.0
5.0
pF
Theta-v measured
0
1.37
0.013
0.270
0.87
0.336
Difference
0.276
Theta-v Measured
0.02
0.09
0.25
0.27
0.30
0.31
0.34
0.39
0.42
0.43
Theta-e
0.14
0.27
0.49
0.57
0.66
0.79
0.93
0.98
1.00
1.00
Theta-v Genuchten
0.06
0.12
0.21
0.25
0.29
0.34
0.41
0.43
0.43
0.43
Difference
n
a
0.330
0.064
0.266
1.358
0.022
Plot 5 - Average
0.60
0.50
Theta-v
pF
4.2
3.4
2.7
2.5
2.3
2.0
1.5
1.0
0.4
0.0
Plot 5- Average
Theta-r
n
a
m
R2
Theta-v Genuchten
0.40
0.30
0.20
0.10
0.00
0.0
1.0
2.0
3.0
4.0
5.0
pF
Theta-v measured
0
1.28
0.052
0.219
0.89
0.293
Difference
0.222
Theta-v Measured
0.02
0.08
0.23
0.25
0.27
0.29
0.34
0.43
0.47
0.48
Theta-e
0.15
0.26
0.40
0.45
0.51
0.61
0.79
0.92
0.98
1.00
Theta-v Genuchten
0.07
0.12
0.19
0.22
0.24
0.29
0.38
0.44
0.47
0.47
Difference
n
a
0.312
0.071
0.242
1.300
0.056
Plot 6 - Average
0.60
0.50
0.40
Theta-v
pF
4.2
3.4
2.7
2.5
2.3
2.0
1.5
1.0
0.4
0.0
Plot 6 - Average
Theta-r
n
a
m
R2
Theta-v Genuchten
0.30
0.20
0.10
0.00
0.0
1.0
2.0
3.0
4.0
pF
Theta-v measured
96
Theta-v Genuchten
5.0
pF
4.2
3.4
2.7
2.5
2.3
2.0
1.5
1.0
0.4
0.0
Theta-v
0.03
0.09
0.34
0.36
0.38
0.39
0.44
0.51
0.53
0.53
0
1.45
0.008
0.310
0.87
0.449
Difference
0.388
Theta-e
0.11
0.26
0.51
0.61
0.71
0.84
0.96
0.99
1.00
1.00
Theta-v Genuchten
0.06
0.14
0.28
0.33
0.38
0.45
0.51
0.53
0.53
0.53
Difference
n
a
0.384
0.106
0.278
1.456
0.009
Plot 7 - Average
0.60
0.50
0.40
Theta-v
Plot 7 - Average
Theta-r
n
a
m
R2
A Model Approach
0.30
0.20
0.10
0.00
0.0
1.0
2.0
3.0
4.0
pF
Theta-v measured
97
Theta-v Genuchten
5.0
A Model Approach
Appendix 13 - Statistical Analysis Theta-v testplots
Descriptive Statistics (thetav.sta)
M=May, J=June, number=plot
All variables have a normal distribution
SPPS 2-tailed p-level >0.05 for all variables
Valid N Mean
Minimum
Maximum
Std.Dev. Df
pF van Genuchten
M1
30
0.11
0.02
0.18
0.03
58
3.0
M2
30
0.09
0.03
0.14
0.03
58
4.2
M3
30
0.08
0.03
0.16
0.03
58
3.8
M4
30
0.13
0.08
0.19
0.03
58
3.5
M5
30
0.09
0.05
0.14
0.02
58
3.7
M6
30
0.10
0.06
0.14
0.02
58
3.8
J1
30
0.06
0.01
0.10
0.02
58
3.8
J2
30
0.05
0.01
0.12
0.03
58
5.4
J3
30
0.05
0.02
0.09
0.02
58
4.5
J4
30
0.08
0.03
0.12
0.02
58
4.0
J5
30
0.09
0.04
0.12
0.02
58
3.8
J6
30
0.05
0.03
0.06
0.01
58
4.9
J7
30
0.12
0.08
0.16
0.02
58
3.6
J8
30
0.10
0.07
0.16
0.02
58
3.6
J9
30
0.03
0.01
0.05
0.01
58
5.1
T-test for Independent Samples (thetav.sta)
Note: Variables were treated as independent samples, significant values shown in bold
(p<0.05)
May
p-level June
p-level
Comparison May /June
p-level
0.003
0.000
M1 vs.
M2
J1 vs.
J2 0.015
M1 vs.
J1
0.000
0.000
M1 vs.
M3
J1 vs.
J3 0.012
M2 vs.
J2
0.037
0.000
M1 vs.
M4
J1 vs.
J4 0.001
M3 vs.
J3
0.004
0.000
M1 vs.
M5
J1 vs.
J5 0.000
M4 vs.
J4
0.028
0.000
M1 vs.
M6
J1 vs.
J6 0.000
M5 vs.
J5
0.000
M2 vs.
M3
0.598
J1 vs.
J7 0.000
M5 vs.
J6
0.000
0.000
M2 vs.
M4
J1 vs.
J8 0.000
M6 vs.
J6
M2 vs.
M5
0.514
J1 vs.
J9 0.000
M2 vs.
M6
0.201
J2 vs.
J3 0.502
M3 vs.
M4
0.000
J2 vs.
J4 0.000
M3 vs.
M5
0.174
J2 vs.
J5 0.000
0.043
M3 vs.
M6
J2 vs.
J6 0.870
0.000
M4 vs.
M5
J2 vs.
J7 0.000
0.000
M4 vs.
M6
J2 vs.
J8 0.000
M5 vs.
M6
0.422
J2 vs.
J9 0.005
J3 vs.
J4 0.000
J3 vs.
J5 0.000
J3 vs.
J6 0.124
J3 vs.
J7 0.000
J3 vs.
J8 0.000
J3 vs.
J9 0.000
J4 vs.
J5 0.484
J4 vs.
J6 0.000
J4 vs.
J7 0.000
J4 vs.
J8 0.002
J4 vs.
J9 0.000
J5 vs.
J6 0.000
J5 vs.
J7 0.000
J5 vs.
J8 0.024
J5 vs.
J9 0.000
J6 vs.
J7 0.000
J6 vs.
J8 0.000
J6 vs.
J9 0.000
J7 vs.
J8 0.000
J7 vs.
J9 0.000
J8 vs.
J9 0.000
98
A Model Approach
Appendix 14 – Results Ksat measurements
Testplot 1
Sample
Testplot 4
Testplot 7
Ksat
(m/day)
(m(m/day)
3.92
19.47
1.40
3.29
1.76
3.64
26.05
24.94
16.71
7.24
16.96
3.88
2.23
5.74
0.72
2.82
11.21
3.77
2.10
6.98
4.56
1.39
4.31
13.32
0.69
3.34
12.63
30.27
2.95
10.39
13.31
9.00
5.80
Sample
Ksat (m/day)
Sample
Ksat (m/day)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0.34
1.97
0.44
2.41
2.56
5.16
0.17
4.83
0.26
3.84
0.23
1.78
0.28
0.22
0.39
5.18
11.01
3.83
11.35
1.60
2.36
0.65
0.38
0.93
6.92
6.14
1.53
1.46
0.27
0.96
5.19
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
8.04
3.23
5.32
4.13
4.37
10.55
3.74
0.74
8.27
0.56
2.13
0.99
4.16
2
3.32
3.31
3.79
9.87
0.44
8.42
2.69
4.54
4.03
0.87
1.16
0.79
4.68
0.97
1.35
1.75
1.7
2.7
3.49
1.94
1.08
stdev
average
min
max
7.89
8.39
0.69
30.27
stdev
average
min
max
3.02
2.73
0.17
11.35
Stdev
Average
Min
Max
2.70
3.46
0.44
10.55
99
A Model Approach
Appendix 15- Model script
#
#
#
#
#
#
Sluiter Dynamic grazing model 21/10/98
10 year version
Created and tested using PCRaster version Feb 10 1997
Waterbalance submodel version 1.2
Grazing/Biomass submodel version 1.1
One timeslice represents one week
binding
# Input water balance model
Mask=mask50.map;
alfa=31.5;
b=12.5;
he=0.1;
I=intercep;
Ic=16.8;
kc=1.05;
ks=ksat.map;
labda=albedo.map;
LAI=1;
LDtotal=soildept.map;
Ne=0.1;
gamma=0.67;
Gammachange=-8;
Thetasat=Thetasat.map;
ThetaFC=ThetaFC.map;
ThetaWP=ThetaWP.map;
Bucket=0.02;
FC=fieldc;
WP=wilting;
DT=deltat;
Rainseries=rain10r.tss;
Tempseries=temp10r.tss;
Cloudseries=cloud10.tss;
n=ngenuch.map;
a=agenuch.map;
Testplots=testplot.map;
Vegcov=vegetat;
Maxtranslimitlowpf=3.5;
Maxtranslimithighpf=2.3;
Maxrootdepth=25;
Dwf=0.2;
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
Study area without agriculture
Soil evaporation factor
mm/day
Soil retention factor
Hours per rain event per week
Interception
Interception constant
Vegetation correction factor
Hydraulic saturated conductivity
mm/hour
Albedo
Leaf area index
Soil layer thickness
cm
Number of events/month
Psychrometric constant
%change of gamma to calibrate PET (-20,+20)
Saturated volumetric water content
Field capacity volumetric water content
%
Wilting point volumetric water cont.
Model parameter to avoid empty buckets
Total water stored in soil at field capacity
mm/h2o
Total water stored in soil at wilting point
mm/h2o
Rate of change of saturated pressure with T
mbar/Kelvin
Rain Time Series
mm/week
Temperature Time Series
°C
Cloudcover Time Series
%
Genuchten parameter n
Genuchten parameter a
Location of the test sites
Vegetation cover map
%
pF value when Etrans is limited by water shortage
pF value when Etrans is limited by water abundance
Maximum rooth depth
cm.
Fraction of water available under maximum root depth (0-1)
#
#
#
#
#
#
#
Soil water content
Volumetric soil water content
Calculated pF according to van Genuchten
Map of values above pF4.2
Volumetric water content output series
pF time series
Plant transpiration output series
# Output water balance model
Soilwater=soilwat;
Thetav=volu;
Pfgenuchten=pfgenuch;
Limit42=limit;
Thetavtimeseries=thetav.tss;
Pftimeseries=pF.tss;
Etransseries=etrans.tss;
mm/h20/week
%
%
# Input biomass production model
Fertil=fertil50.map;
Century=centur10.tss;
# Soil fertility map
# Century optimal production curves
g/m2/week
#
#
#
#
#
#
Theta-v related production level
Prodlevel output serie
Netto produced biomass
Netto produced biomass output serie
Total Biomass cell/year
Average produced biomass study area/year
g/m2/year
g/m2/year
#
#
#
#
#
#
#
#
#
#
#
Daily animal dryweight food need
g/animal/day
Percentage Palatable of vegetation
%
Minumum required not removed produced biomass
%
Percentage of insect consumption
Observed grazing pressure map
Non dynamic grazing intensity
animals/study area
Dynamic grazing intensity time serie
animals/study area/week
Max change of vegetation cover without grazing %
Max % vegetation cover when random regrowth is possible
Max % possible random vegetation cover return
Weeks between random vegetation cover return events
#
#
#
#
#
#
#
#
#
#
Grazing
Grazing
Grazing
Grazing
Grazing
Grazing
Grazing
Grazing
Grazing
Grazing
# Output biomass production model
Prodlevel=produ;
Prodleveltimeseries=product.tss;
Netbiomass=netbio;
Netbiomasstimeseries=netbio.tss;
Biomasstotal=biototal.map;
Biomassaverage=bioav.map;
g/cell/week
# Input grazing model
Dailyneed=700;
Palatability=patab50.map;
Defoliation=defol50.map;
Insect=10;
Observedgrazpres=grazpr50.map;
# Grazingintensity=1;
Grazingseries=grazin10.tss;
Maxchange=4;
Returnlimit=10;
Maxreturn=2;
Vegpause=20;
# Output grazing model
Gracap=gracap.map;
Gracaptotal1=Gracapto.001;
capacity per year per cell
capacity year 1 total area
100
nr.
nr.
nr.
nr.
nr.
nr.
nr.
nr.
nr.
nr.
animals
animals
animals
animals
animals
animals
animals
animals
animals
animals
Weekgracap=gracapwk;
Properuse=propuse;
Grazpres1=pressure.map;
Grazindex=index;
Grazindextotal=indextot.map;
Availbiomass=avail;
Area1=arcov;
Area=area2.map;
Browse=browse.map;
Growindex=grow;
Averagegrowindex=avegrow.map;
#
#
#
#
#
#
#
#
#
#
#
#
A Model Approach
Grazing capacity year 10 total area
nr. animals
Potential Grazing capacity per year per week
nr. animals
Proper use factor
Linear observed grazing pressure (0-1)
Index to show risk areas per week
Index to show risk areas per year
For animal consumption available biomass
Total area with vegetation cover >0
m2
Total study area
Total vegetation consumption by animals g/cell
Grazing intenstity+Browse related grow index [-2,1]
Average grow index/year
areamap
dem50.map;
timer
1 520 1;
initial
# Calculating root depth dependent soil depth
LD1=min(Maxrootdepth,LDtotal);
LD2=max(LDtotal-Maxrootdepth,0);
LD=LD1+(Dwf*LD2);
# Calculating initial waterbalance submodel
FC=100*(ThetaFC)/(100/LD)*70;
WP=100*(ThetaWP)/(100/LD)*70;
UL=9*((alfa-3)**0.24);
Soilwater=iniwat.map;
Bucketlimit=100*(ThetaWP-Bucket)/(100/LD)*70;
m=1-(1/n);
Thetav=((Soilwater/70)*(100/LD))/100;
Counter=0;
Eplantlimithigh=Thetasat*((((10**Maxtranslimithighpf)*a)**(1/(1/n)))+1)**(1/(-1/m));
Eplantlimitlow=Thetasat*((((10**Maxtranslimitlowpf)*a)**(1/(1/n)))+1)**(1/(-1/m));
Gamma1=(1+(Gammachange/100))*gamma;
# Calculating initial grazing submodel
Vegcov=vegcov50.map+Mask;
Biomassyear=0;
Cumulgrowindex=0;
Yearneed=365*Dailyneed;
Weekneed=7*Dailyneed;
Maxchangeweek=Maxchange/52;
Vegtype=vegtyp50.map;
Vegpause1=0;
Vegpause2=1;
dynamic
WATER BALANCE SUBMODEL
# Calculating DT according to Ritchie (1972)
Temp=timeinputscalar(Tempseries,1);
DT=(5304/sqr(Temp+273))*exp(21.255-(5304/(Temp+273)));
# Calculating netto insolation
Ins=timeinput(inso);
Cloud=timeinputscalar(Cloudseries,1);
Inscloud=(1-Cloud)*Ins;
# Calculating PET according to Ritchie (1972)
RN=(1-labda)*((0.2408*Inscloud)/58.3);
EPSI=(1.28*DT*RN)/(DT*Gamma1);
# Calculating soil evaporation
Esoil1=EPSI*(exp(-0.4*LAI));
Esoil2=if (EPSI*(exp(-0.4*LAI)) gt UL then UL else Esoil1);
Esoil=max(Esoil2,0);
# Calculating plant transpiration
Eplant=if (LAI gt 3 then EPSI-Esoil else EPSI*LAI/3);
Limitetrans1=((((ThetaFC-Thetav)-(0.5*(ThetaFC-Thetav)))/(ThetaFC-Eplantlimithigh))+0.5)*Eplant;
Limitetrans2=((Thetav-ThetaWP)/(Eplantlimitlow-ThetaWP))*Eplant;
Etrans1=if (Thetav gt Eplantlimithigh then Limitetrans1 else 0);
Etrans2=if (Thetav lt Eplantlimitlow then Limitetrans2 else 0);
Etrans3=if (Thetav le Eplantlimithigh then boolean(1) else 0);
Etrans4=if (Thetav ge Eplantlimitlow then boolean(1) else 0);
Etrans5=if (Etrans3 and Etrans4 then Eplant else 0);
Etrans=max(Etrans1+Etrans2+Etrans5,0);
# Calculating actual evapotranspiration
Vegcovfr=Vegcov/100;
EVegcov=(Vegcovfr*Etrans)+Esoil;
Evapo=min(EVegcov,EPSI);
Evegt=Evapo*kc;
Eveg=if (Evegt gt 0 then Evegt else 0);
# Calculating netto precipitation
I=Ic*Vegcovfr;
Prt=timeinputscalar(Rainseries,1);
Precip = Prt*((100-I)/100);
101
A Model Approach
# Calculating percolation
Ktheta=ks*291.2*(Soilwater/FC)**2*b+3;
Percol=if (Soilwater+Precip gt FC then min(Ktheta*he*Ne,Soilwater+Precip-FC) else 0);
# Calculating runoff
Runoff=if (Soilwater+Precip gt FC then max(Soilwater+Precip-Percol-FC,0) else 0);
# Calculating netto soilwater
Soilwater=max(Soilwater+Precip-Eveg-Percol-Runoff,Bucketlimit);
Thetav=((Soilwater/70)*(100/LD))/100;
# Calculating pF according to van Genuchten
Thetae=Thetav/Thetasat;
Pfgenuchten=log10(((Thetae**(-1/m)-1)**(1/n))/a);
Limit42=if (Pfgenuchten gt 4.2 then scalar(1) else scalar(0));
BIOMASS PRODUCTION SUBMODEL USING CENTURY 4.0
# Production level calculated as function of Etrans
Prodlimit1=(((ThetaFC-Thetav)-(0.5*(ThetaFC-Thetav)))/(ThetaFC-Eplantlimithigh))+0.5;
Prodlimit2=((Thetav-ThetaWP)/(Eplantlimitlow-ThetaWP));
Prod1=if (Thetav gt Eplantlimithigh then Prodlimit1 else 0);
Prod2=if (Thetav lt Eplantlimitlow then Prodlimit2 else 0);
Prod3=if (Thetav le Eplantlimithigh then boolean(1) else 0);
Prod4=if (Thetav ge Eplantlimitlow then boolean(1) else 0);
Prod5=if (Prod3 and Prod4 then scalar(1) else 0);
Prodlevel= max(Prod1+Prod2+Prod5,0);
# Calculation vegetation and soil specific biomass production
Biomass=if (Vegtype eq 1 and Fertil eq 1 then timeinputscalar(Century,1)*Prodlevel else 0);
Biomass=Biomass+if(Vegtype eq 1 and Fertil eq 2 then timeinputscalar(Century,4)*Prodlevel else 0);
Biomass=Biomass + if (Vegtype eq 2 and Fertil eq 1 then Biomass +
timeinputscalar(Century,1)*Prodlevel else 0);
timeinputscalar(Century,2)*0.6+timeinputscalar(Century,3)*0.4*Prodlevel else 0);
# Calculating biomass corrected for actual vegetation cover cellsize (50*50)
Netbiomass=Biomass*Vegcovfr*2500;
GRAZING SUBMODEL
# Calculating Area with positive vegetation cover
Vegcov1=if(Vegcov gt 1 then (Mask+1) else 0);
report Area1=maptotal(Vegcov1)*2500;
# Calculating total Netbiomassproduction/year
Counter=Counter+1;
Biomassyear=Biomassyear+Netbiomass;
Biomasstotal=if(Counter eq 52, Biomassyear else Biomassyear);
Biomassyear=if(Counter eq 53 then Netbiomass else Biomassyear);
102
A Model Approach
# Calculating proper use factor
Availbiomass=((Palatability/100)*Netbiomass)*(1-((Defoliation+Insect)/100));
Properuse= max((Availbiomass)/(Netbiomass+0.00001),0);
# Calculating Grazingintensity
# Grazingintensity=timeinputscalar(Grazingseries,1);
Observedlinear=(Observedgrazpres-mapminimum(Observedgrazpres))/(mapmaximum(Observedgrazpres)mapminimum(Observedgrazpres));
Grazpres1=(Grazingintensity/3500)*Observedlinear;
# Calculating Grazing capacity/year
report Gracap= (Biomasstotal*Properuse)/(Yearneed);
report Gracaptotal1= if(Counter eq 52, maptotal(Gracap) else 0);
# Calculating Grazing capacity/week
Weekgracap=(Netbiomass*Properuse)/(Weekneed);
# Determining areas with high grazing pressure and low grazing capacity
Weekgracap1=(Weekgracap-mapminimum(Weekgracap))/(mapmaximum(Weekgracap)-mapminimum(Weekgracap));
Grazindex=(1-Weekgracap1*Grazpres1);
Gracaptota1= if(Counter eq 520, (Gracap-mapminimum(Gracap))/(mapmaximum(Gracap)mapminimum(Gracap)));
report Grazindextotal= if(Counter eq 520, (1-Gracaptota1)*Grazpres1);
LONG TIME SCENARIOS SUBMODEL
# Calculating grow index
Browse=((Grazingintensity*Weekneed)/(Area1)*2500);
Growindex=max((Availbiomass-Browse)/(Availbiomass+0.00001),-2);
Growindex2=if(Growindex ge 0 then (Growindex*(1-Grazpres1)) else
(Growindex*Grazpres1*(Palatability/100)));
Cumulgrowindex2=Cumulgrowindex+Growindex2;
report Averagegrowindex=if(Counter eq 520, Cumulgrowindex2/520);
# Calculating random vegetation cover return
Vegreturn= if(Vegcov lt Returnlimit and Growindex ge 0 and Vegpause2 eq 0 then
(uniform(boolean(1))*Maxreturn) else(0));
Vegpause1 =if (Vegreturn gt 0 then Vegpause else scalar(0));
Vegpause2 =if (Vegpause1 gt 0 then Vegpause1 else Vegpause2);
Vegpause2=Vegpause2-1;
Vegpause2= max (Vegpause2,0);
Vegtype= if(Vegcov lt Returnlimit and Growindex ge 0 then nominal(8) else (Vegtype));
# Calculating Vegetation cover change
Vegcov=Vegcov+Vegreturn;
Vegcova=max(Vegcov+(Growindex2*Maxchangeweek),1);
report Vegcov=min(Vegcova,100);
# Reporting timeseries of testplots
report Thetavtimeseries=timeoutput(Testplots,Thetav);
report Pftimeseries=timeoutput(Testplots,Pfgenuchten);
Etransseries=timeoutput(Testplots,Etrans);
Prodleveltimeseries=timeoutput(Testplots,Prodlevel);
103
A Model Approach
Appendix 16 – Model changes and additions
Changes / additions of the waterbalance submodel version 1.2
•
Plant transpiration and production are now a function of pF and not any more a function of
a fraction of the volumetric water content. It is set by Maxtranslimitlowpf and
Maxtranslimithighpf.
•
The bucket consists now of 2 layers. One layer for direct water supply in the rootzone and
a second layer for additional water from deeper layers. Maxrootdepth sets the depth of
the first layer. The available water of the deeper layer is set as a fraction of the total water
content in the deeper layer, by setting Dwf.
•
To calibrate the model to specific climatic conditions it is possible to change the
psychrometric constant (gamma). The maximum deviation is –20% for very dry climatic
conditions to +20% for very wet climatic conditions. The deviation is set by
Gammachange.
•
PF0, PF20 and PF42 are renamed to Thetasat, ThetaFC and ThetaWP.
Changes / additions of the grazing submodel version 1.2
•
A random regrowth option is included. The random regrowth option simulates fast
regrowth of a pioneer species with a spatial different rate. When vegetation cover is low
(adjustable by Returnlimit) and growing conditions are good, it is possible to return a
random percentage of vegetation cover (with a maximum of Maxreturn). The returned
vegetation type is a pioneer species (Sarcopoterium spinosum). Vegpause sets the
minimum time between random events at the same location. The random regrowth option
can be switched off by setting Returnlimit to zero.
•
An error is solved in the calculation of Grazpres1 (Linear observed grazing pressure).
Decrease of vegetation cover at very low grazing intensities does not occur any more.
104
A Model Approach
Appendix 17 - CENTURY 4.0 parameterisation
CROP.10
Parameter PHRYG TC
SS
Description
Prdx
220
Potential aboveground monthly production for crops (gC/m /month)
170
170
2
Pramn(1,1) 13
25
20
Minimum C/N ratio with zero biomass
Pramn(1,2) 26
50
40
Minimum C/N ratio with biomass => to biomax
pramx(1,1) 30
36
36
Maximum C/N ratio with zero biomass
pramx(1,2) 60
72
72
Maximum C/N ratio with biomass => to biomax
prbmn(1,1) 13
25
20
N intercept to compute minimum C/N as function of precipitation
prbmn(1,2) 0
0
0
N slope to compute minimum C/N as function of precipitation
prbmx(1,1) 60
72
72
N intercept to compute maximum C/N as function of precipitation
prbmx(1,2) 0
0
0
N slope to compute maximum C/N as function of precipitation
fligni(1,1)
0.039
0.027
0.048
Lignin intercept to compute aboveground lignin as function of annual precipitation
fligni(2,1)
0
0
0
Lignin slope to compute aboveground lignin as function of annual precipitation
fligni(1,2)
0.26
0.26
0.26
Lignin intercept to compute underground lignin as function of annual precipitation
fligni(2,2)
0
0
0
Lignin slope to compute underground lignin as function of annual precipitation
crprtf(1)
0.32
0.23
0.3
Fraction of elements retranslocated from crop leaves at death
GORTIS.100
Parameter
Limestone
Flysch
Description
rces1(1,1)
14
16
Initial C/N ratio in surface organic matter with fast turnover (active SOM)
rces1(2,1)
12
20
Initial C/N ratio in soil organic matter with fast turnover (active SOM)
rces2(1)
24
40
Initial C/N ratio in soil organic matter with intermediate turnover (slow SOM)
rces3(1)
10
16
Initial C/N ratio in soil organic matter with slow turnover (passive SOM)
aglcis(2)
658
658
Initial value for aboveground live C isotope (gC/m )
2
2
aglive(1)
12.5
10.5
Aboveground initial N value (g/m )
bglcis(2)
256
256
Initial value for belowground live C (g/m )
bglive(1)
7
6
Initial value for belowground live N (g/m )
2
2
105
A Model Approach
Appendix 18 - Lignin analysis calibration curves
Lignin Analysis
p-coumaric calibration curves
(stock solution)
- The p coumaric calibration curves are measured six times:
three time before the analysis and three times after the analysis,
to check if acetyl bromide is stable during the analysis.
Cuvette
Absorption Sample
Cuvette
Absorption cuvette
Absorption measured
Absorption Lignin mg/ml
A
0
Calib1-1_0
A
0.000
0.000
0.000
0.000
B
2.195
Calib1-1_0.1
B
2.195
2.293
0.098
0.002
C
2.126
Calib1-1_0.2
C
2.126
2.390
0.264
0.004
D
0.513
Calib1-1_0.4
D
0.513
1.550
1.037
0.017
E
0.699
Calib1-1_0.6
E
0.699
2.531
1.832
0.030
F
0.601
Calib1-1_0.8
F
0.601
2.535
1.934
0.032
G
-0.008
Calib1-1_1
G
-0.008
2.503
2.511
0.041
H
0.582
Calib1-2_0
H
0.582
0.569
-0.013
0.000
I
0.01
Calib1-2_0.1
I
0.010
0.271
0.261
0.004
J
2.198
Calib1-2_0.2
J
2.198
2.520
0.322
0.005
K
1.249
Calib1-2_0.4
K
1.249
2.553
1.304
0.021
L
0.033
Calib1-2_0.6
L
0.033
1.914
1.881
0.031
M
0.566
Calib1-2_0.8
M
0.566
2.427
1.861
0.030
N
0.025
Calib1-2_1
N
0.025
2.513
2.488
0.041
O
2.51
Calib1-3_0
A
0.000
0.002
0.002
0.000
Calib1-3_0.1
B
2.195
2.377
0.182
0.003
Calib1-3_0.2
C
2.126
2.381
0.255
0.004
Calib1-3_0.4
D
0.513
1.881
1.368
0.022
Calib1-3_0.6
E
0.699
2.528
1.829
0.030
Calib1-3_0.8
F
0.601
2.536
1.935
0.032
Calib1-3_1
G
-0.008
2.485
2.493
0.041
Calib2-1_0
A
0.000
0.004
0.004
0.000
Calib2-1_0.1
B
2.195
2.291
0.096
0.002
Calib2-1_0.2
C
2.126
2.382
0.256
0.004
Calib2-1_0.4
D
0.513
1.836
1.323
0.022
Calib2-1_0.6
E
0.699
2.553
1.854
0.030
Calib2-1_0.8
F
0.601
2.546
1.945
0.032
Calib2-1_1
G
-0.008
2.517
2.525
0.041
Calib2-2_0
H
0.582
0.633
0.051
0.001
Calib2-2_0.1
I
0.010
0.296
0.286
0.005
Calib2-2_0.2
J
2.198
2.526
0.328
0.005
Calib2-2_0.4
K
1.249
2.566
1.317
0.022
Calib2-2_0.6
L
0.033
1.823
1.790
0.029
Calib2-2_0.8
M
0.566
2.372
1.806
0.030
Calib2-2_1
N
0.025
2.368
2.343
0.038
Calib2-3_0
O
2.510
2.542
0.032
0.001
Calib2-3_0.1
A
0.000
0.309
0.309
0.005
Calib2-3_0.2
B
2.195
2.431
0.236
0.004
Calib2-3_0.4
C
2.126
2.523
0.397
0.006
Calib2-3_0.6
D
0.513
2.219
1.706
0.028
Calib2-3_0.8
E
0.699
2.539
1.840
0.030
Calib2-3_1
F
0.601
2.542
1.941
0.032
Calculation of p-coumaric calibration curves
106
Calibration 1-1
3
A Model Approach
Calibration 1-2
3
2.5
2.5
2
2
1.5
1.5
1
1
0.5
0.5
0
0
0
0.2
Calib1-1
0.4
0.6
0.8
1
1.2
-0.5
y = 2.5497x
R 2 = 0.9691
Linear (Calib1-1)
0.2
Calib1-2
Calibration 1-3
3
0
0.6
0.8
1
1.2
y = 2.5871x
R 2 = 0.9521
Linear (Calib1-2)
Calibration 2-1
3
2.5
0.4
2.5
2
2
1.5
1.5
1
1
0.5
0.5
0
0
0
0.2
Calib1-3
0.4
0.6
0.8
1
0
1.2
y = 2.604x
R 2 = 0.9518
Linear (Calib1-3)
Calib2-1
Calibration 2-2
3
0.2
0.6
0.8
1
1.2
y = 2.6169x
R 2 = 0.9543
Linear (Calib2-1)
Calibration 2-3
2.5
2.5
0.4
2
2
1.5
1.5
1
1
0.5
0.5
0
0
0
0.2
Calib2-2
0.4
0.6
Linear (Calib2-2)
0.8
1
0
1.2
y = 2.4809x
R 2 = 0.9432
0.2
0.4
Calib2-3
p-coumaric calibration curves
107
0.6
0.8
Linear (Calib2-3)
1
1.2
y = 2.1147x
R 2 = 0.8888

Desertification and Grazing On south Crete A model approach

Transcription

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