The Landscape Module represents the state and processes of the environmental part of the coupled human-enviroment system of land-use/cover change. Just as the Human Module is represented in the form of a three-fold hierarchy, this module is also conceptualized as an or-ganization of three levels: the landscape agent or patch, the Landscape Vision, and the entire landscape (see Figure 3.4). The landscape agents are represented by congruent land patches of size 30 m x 30 m, consisting of two main components: the patch’s state variables and the internal ecological sub-models. The state variables comprise both biophysical/environmental attributes (e.g. soil texture, distances), which are independent of human actions, and vari-ables which are related to the human part such as land tenure and use. The internal ecological sub-models consist of i) productivity functions for all land-use types of both seasons, ii) a land-cover transformation model, which regulates the conversion of one land-cover type to the other, and iii) a livestock dynamics sub-model.
As already outlined in the previous section, the Landscape Vision is the environ-ment of a household agent in which he sets actions. Each household agent has his own Landscape Vision, which, in multi-agent-based terms, consists of a set of landscape agents located around the compound patch of the household agent. Within this environment, the household agent has (limited) insight into its features and attributes, makes land-use deci-sions and creates impacts on this environment. These impacts are accumulated over time and aggregately result in spatio-temporal dynamics of the overall landscape (Le, 2005).
The entire landscape is the collection of all landscape agents or patches, being the emergent result of both the changes and interactions of the single landscape agents. Due to these interactions, which can be either direct or indirect, i.e. mediated through household agents, the change of the entire landscape is not only the sum of the single changes of the patches, but must be rather regarded as an emergent phenomenon created by the interactive
A
B
####################### HH
Y Lorenz Curve Gini Coefficient = A
A + B
Figure 3.3: Lorenz curve and Gini index collective of landscape and household agents.
3.3.1 Structure of the landscape agent
The structure of the landscape agent can be formally expressed as:
Landscape Agent={Patch Profile, Eco-Sub-models}
where the Patch Profile is the state of the landscape agent, including both human-related and biophysical variables, and Eco-Sub-models is the collection of all ecological sub-models including the productivity functions and the land-cover transformation model. A detailed specification of these components is given below.
Patch Profile
The set of state variables of a patch consists of six components: biophysical variables (Pbiophys), environmental variables (Penv), tenure properties (Ptenure), the land-use/cover status (Pstatus), yield (Pyield), and irrigation-related parameters (Pirr):
Patch Profile={Pbiophys, Penv, Ptenure, Pstatus, Pyield, Pirr} Biophysical conditions comprise the following variables:
POLICY MODULE HOUSEHOLD
MODULE Decision
Module Entire Landscape
Statistics Landscape Agents
Landscape Vision Landscape Agents
Landscape Agent Patch Profile Ecological Sub-Models
6 6
-Figure 3.4: Integration of the Landscape Module in GH-LUDAS
Pbiophys={Psoil fertility, Psoil texture, Pgwl, Pgwr, Pwetness, Pupslope}
where Psoil fertility and Psoil textureare soil type and soil texture respectively, Pgwland Pgwr are average groundwater depth and groundwater recharge, respectively, during the dry sea-son. Pwetnessis the topographic wetness index, and Pupslopeis the upslope contributing area.
The environmental variables exclusively comprise distances to environmental features:
Penv={Pdist river, Pdist dams, Pdist water, Pdist border}
with Pdist river being distance of the patch to the main river, Pdist damsdistance to dams, and Pdist waterdistance to water sources, i.e. main river and dams. Thus, this variable is just the minimum of the two previous ones, similar to the calculation of the equivalent variable for household agents. Pdist border is the distance to the national border to Burkina Faso in the north.
The tenure properties of the patch can be summarized as follows:
Ptenure={Powner, Pdry-user, Prainy-user, Pdist user}
where Powner indicates the household agent who owns the patch. But since the user of the patch does not necessarily need to be the owner, we also included the variables of Pdry-user,
Pprofile
Pbiophys Penv Ptenure Pstatus Pyield Pirr
? ? ? ? ? ?
Soil Fertility Pfertility
Distance to Main River Pdist river
Owner Powner
Land Cover Dry Pcover dry
Yield Dry Pyield dry
Irrigation Coefficient
Pirr coeff
Soil Texture Ptexture
Distance to Dams Pdist dams
User in the Dry Season
Pdry-user
Land Use Dry Pland use dry
Yield Rainy
Pyield rainy Irrigability Pirrigable Land Cover
Rainy Pcover rainy
Compound Dummy Pcompound
?
- Ground-water Level
Pgwl
Distance to Water Sources Pdist water
User in the Rainy Season Prainy-user
Land Use Rainy Pland use rainy Distance to
Border Pdist border
Ground-water Recharge
Pgwr
Elevation Pelevation
Upslope Area Pupslope
Wetness Pwetness
'
&
$
%
Sub-Model:
Land-Cover Trans-formation Model
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&
$
%
Sub-Model:
Agricultural Yield Model
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&
$
%
HOUSEHOLD-POPULATION
MODULE
6
Figure 3.5: Patch Profile
indicating the agent who uses the patch in the dry season, and Prainy-user, the agent who uses it in the rainy season. If the patch is not used or owned by anybody, the variables will get the value ’nobody’. Pdist userdenotes the distance of the patch to its rainy-season user (this variable is only needed for the rainy season).
The land-use/cover status of a patch Pstatuscomprises the following variables:
Pstatus={Pcover dry, Pcover rainy, Pland use dry, Pland use rainy, Pcompound}
with Pcover dry and Pcover rainy indicating the land-cover type in the dry and rainy season, respectively, Pland use drythe land-use type in the dry season, and Pland use rainy the land-use
type in the rainy season. If a patch is not used during a specific season, the value of the land-use type is set to 0 for that season. Pcompoundis a dummy variable, indicating whether a compound house is present on the patch.
The yield status of the patch simply reports the amount of yield in the local currency (Ghana-ian Cedis) from the dry and the rainy season:
Pyield={Pyield dry, Pyield rainy}
The category irrigation includes the following two variables: Pirrigable, being a dummy vari-able indicating if a patch is irrigvari-able, and Pirr coeff, which is called the irrigation coefficient, with values between 0 and 1 indicating the irrigation potential of a patch. The calculation of this coefficient, the irrigability, as well as a detailed explanation of the other biophysical variables will be given in Chapter 5.
Ecological sub-models
As mentioned in the introduction, there are three kinds of ecological sub-models to be built into the model of the landscape agent: productivity functions for each land-use type, a live-stock dynamics model, and a land-cover transformation model. For further details, see Chap-ter 5.
i) Agricultural productivity functions
The agricultural productivity functions are patch sub-models calculating the variables Pyield dry and Pyield rainyin response to variables of the Patch Profile and the user’s land-use decisions.
Since the importance of biophysical attributes and the kind of land management differ be-tween the two seasons with respect to crop productivity, a yield model for each season was developed. Although the range of variables differ between the two seasons, the general form of the function is the same (see section 5.3.3).
ii) Livestock dynamics model
The livestock dynamics model is a sub-model to calculate the variable of Hlivestock in re-sponse to random annual variations and forage availability, the latter being dependent on both rainfall data and land-use behavior. The livestock index of a household is basically
modeled as being dependent on the livestock index of the previous year (with a random er-ror), reflecting changes in the stock due to sale, death, diseases, etc. The forage availability on the other hand restricts the total number of livestock within the study area, thus reducing the total number of livestock equally for all households, if the carrying capacity with respect to forage availability is reached (see section 5.3.4).
iii) Land-cover transformation model
The cover transformation model is a model to simulate the conversion of one land-cover type to another, whereby two variables describe land-land-cover distributions, one for the rainy season, Pcover rainy, and one for the dry season, Pcover dry. For the establishment of the model, changes of both variables should thus be analyzed and modeled if necessary. The range of land-cover types for both seasons comprises ’rock’, ’water’, ’bare land’, ’grassland’
and ’cropland’. Changes among these land-cover types are driven by both anthropogenic influence (land-use change) and natural processes independent of human interference (e.g.
grass growth), which both need to be considered in the analysis. In section 5.3.5, the full land-cover change analysis and the parameterization of the subsequent land-cover transfor-mation model will be presented.
3.3.2 Entire landscape
The entire landscape is the collection of all landscape agents, together with a database of statistical spatial parameters:
Entire landscape={{Landscape Agents}, Spatial-Stat}
The spatial statistical database Spatial-Stat comprises descriptive statistics about land-cover and land-use evolving over time. Percentages of the different land-use types of the total cultivated area are computed for both seasons, as well as the simulated land-cover fractions of the total area under study. The temporal dynamics of these parameters can be observed via graphs on the simulation interface of the GH-LUDAS model.