where i indexes the type of production line, and ai is the additional income per labor unit generated by the first credit. The income generated by further credits is then calculated using the same algorithm as in the dry season.
selection of the location of the dam, which should be directed towards a maximum benefit for all its users, is a critical issue.
Second, the size of irrigation capacity and number of dams to be constructed have to be carefully determined. As in some situations the construction of a single large dam could match the socio-economic needs of the population, in other cases a collection of several scattered small-scale dams is required. Thus, it is necessary to evaluate scenarios of different combinations of size and number of dams.
Third, to provide a maximum of potential users with the possibility to engage in irrigation farming, a regulation of area limitation could be taken into consideration, i.e. the prescribed maximum area one household is allowed to cultivate along a dam. The selection of this parameter is also a critical issue, as it should ensure a maximal number of dam users on the one hand, but also a full utilization of the irrigation capacity on the other. Accord-ing to these considerations, in GH-LUDAS, the followAccord-ing parameters of the policy of dam construction Policydamhave been included:
Policydam={Damlim, Damnumber, [Damloc, Damsize]}
where Damlimdenotes the size of maximum cultivated area, Damnumberthe number of dams, and Damlocand Damsizethe location and size for each of the single dams.
In GH-LUDAS, the single dams can be inserted into the landscape on the user in-terface via a mouse click, and a slider allows the user to define the size of the dam. Another slider defining the maximum cultivated area can be set according to the scenarios to be ex-plored.
In the model, these parameters are linked to the landscape as well as to the Human Module (Figure 3.6). On the household side, the locations of the dams (Damloc) regulates the distance to dams and water sources, while Damlim defines the upper limit for dam cul-tivation for the household (section 3.4). On the landscape side, the size and location of the dam modify the parameter Pirrigableof some of the landscape patches: The parameter Pirrigable of those patches that are located within the irrigable perimeter around the dam will be set to 1.
Policy Module
Damsize Damloc Damlim
Household Module
STATE BEHAVIOR
Decision Module
Hgross dry
Landscape Module
BEHAVIOR STATE
Yield Dynamics Pland-use dry
Pirrigable
Land-use actions
Generating income
Figure 3.6: Integration of the dam construction policy in GH-LUDAS 3.5.2 Credit access policy
Access to credit directly affects land-use-related household decisions, thus possibly exerting an influence on the local land-use and land-cover patterns. It was observed during field inter-views as well as by statistical analysis of the empirical data set, that farmers with access to credit schemes change their focus regarding their activities. They may intensify some of their income-generating activities with higher income generation possibilities (e.g. trading, irriga-tion), while some of the less productive activities (e.g. food processing) might be reduced.
The additional income generated by these investments of labor and cash stimulated by the credit may be reinvested in land-use-related and other activities, thus gradually changing the livelihood strategy and decision-making processes.
In the study area, the credit scheme managed by the Ministry of Food and Agricul-ture (MOFA) allows a credit of 200 000 Cedis (about 20 US $) per household. Since this credit amount obtained by local farmers is constant, the possible effects of a different credit rate cannot be assessed from the empirical data set. Thus, in GH-LUDAS, the credit rate must presently be regarded as constant at 20 Cedis. The same is valid for the period of credit access, i.e. the number of successive years a household obtains this amount from the credit scheme, which was observed to be constant at 2 years.
Nevertheless, the annual rate of households supplied with credit can be modified as a pa-rameter within the model. Apart from that, the credit scheme can be manually switched to a different kind of scheme than the one observed in the study area, called the ’revolving credit’
scheme. The idea of this kind of scheme is that, once the credit has been distributed among the population, it will be handed round until a certain period of time has elapsed. In other words, the credit a household obtains from the scheme at the beginning of this period will not be paid back to the scheme, but to another household. This household will then pay back the credit to a third household, and so on, until a certain period has elapsed. Then, the last household will pay its debts back to the donor. We will call the period of time the credit remains within the population as the ’revolving credit period’. The parameters defining the credit scheme policy Policycreditin GH-LUDAS can therefore be summarized as follows:
Policycredit={Creditperc, Creditscheme, Creditrev period, Creditdef}
where Creditpercis the annual percentage of households supplied with credit, Creditschemea dummy variable defining which kind of scheme is activated, Creditrev periodthe parameter of revolving credit period, which is only called by the model if the scheme is of the revolving type, and Creditdefthe credit deflating factor (see section 3.4).
As the effects of credit access on the environment are only of an indirect nature, the direct linkages of this policy to the other system components are among these policy param-eters and paramparam-eters of the household agents (Figure 3.7), and the paramparam-eters of this policy directly change the household variables Hcredit, Hnr creditsand Hgross income. Changes in any of the policy parameters result in a change of income, and ultimately show indirect effects on land-use choice and land productivity.
3.5.3 Population dynamics and climate change
Other external variables of the Global-policy Module, which are not related to policies, in-clude parameters describing population dynamics and the choice among possible future rain-fall scenarios. As no reliable population data for the study area were available, due to un-reliable and insufficient population surveys (only 4 surveys in 1965, 1975, 1984 and 2000), no reliable model could be established for projecting future population numbers. Instead, the
Policy Module
Creditrev period Creditscheme
Creditperc
Creditdef
Household Module
BEHAVIOR STATE
Decision Module Hnr credits
Hcredit Hgross income
Landscape Module
STATE BEHAVIOR Yield Dynamics
Pland-use dry
Pland-use rainy Credit =
Yes/No
--
Generating income
Land-use actions
Figure 3.7: Integration of the credit access policy in GH-LUDAS
parameters describing local population dynamics were chosen to be set externally. To repre-sent these dynamics we chose one of the most widely used models for population growth, the logistic growth model, which can be expressed as:
P(t)= CP0ert
C+P0(ert −1) (3.31)
where P(t) is the population size at time step t, P0 the initial population size at time 0, and the carrying capacity C and the growth rate r parameters describing the convergence behavior of the population. For t → ∞, the population size converges against the carrying capacity C with growth rate or ’speed’ r. These two parameters are set externally by the model user, according to the scenarios population growth to be explored (Figure 3.8). New agents are
created in each time step, dependent on the logistic growth model and the number of agents that were deleted due to the ageing process incorporated in the model.
Finally, scenarios of future annual rainfall can be selected, based on local climate data as simulated by the IPCC (International Panel on Climate Change), which is the leading research group with respect to global climate assessment. The annual data of the rainfall scenario selected by the model user are fed into the productivity functions for rainy-season land-use types. Furthermore, a model is developed (see Chapter 5) to calculate the forage availability for local livestock based on rainfall data in order to determine the annual carrying capacity for local livestock. This way, in GH-LUDAS, a decrease or increase in crop and forage productivity due to changing rainfall patterns indirectly influence land-use choice and livestock dynamics and thus livelihood strategies (Figure 3.9). The details of the integration of rainfall data into crop and forage productivity are given in Chapter 5.