components which offer us further development in the model system. For example,
changes in land use activities and resource input are proportionate to productionrelated parameters of farm management. The model should always offer the chance
for improvement in the different modules of the whole model, according to the
changes in the real-world scenario (Kaechele and Dabbert 2002).
MODAM focuses on the multifunctionality of the land use/landscape of the
region. This article presents only the background from which the concept for
transmission of MODAM has been emerged and which, at present, is running in
developed countries (such as European countries, North America) and is now going
ahead to focusing on the multifunctionality of landscape in developing countries
like India (Zander and Kächele 1999; Kaechele and Dabbert 2002; Zander 2003;
Schuler and Kaechele 2003). But, in view of the complexity of the Indian
Himalayan landscape we have been developing the model and restructuring the tool
for the Himalayan landscape while following the methodological approaches and
major characteristics of the model developed by the researchers. Overall, we wish to
generate sound methodological approaches for the Himalayan landscape which is
diverse and has great importance in a global context. This also describes the factors
that determine the forces and relationship between different branches of the
ecosystems and concurrently provides a graphical representation of those relationships. Visualizing the great use of the remote sensing, we have applied and
successfully testified forest and pasture ecosystem analysis in multi-temporal
dimensions. We have found this approach supports the empirical evidence with
more precision in the framework of our current modeling approach MODAM
(Nautiyal and Kaechele 2007a, b, c).
8 An Overview of Conceptual Model Development
The whole MODAM consists of several database and linear programming
(LP) modules (Kaechele and Dabbert 2002; Zander 2003). As we are predominately
interested in the MODAM approach, hence, we have concentrated on the conceptual core of MODAM—the LP modules. Database development to LP is
interesting from a software point of view. Choosing LP seems to be adequate, and
hence we refer to the comments of some authors concerning this approach. Before
going in detail we would like to provide a basic overview of linear programming in
landscape modeling.
For multidisciplinary problem of the real world, we argue for bio-economic
models which are helpful to solve the problems of economics and ecological
complexities simultaneously. In the real world, when models are used to evaluate
possible changes in land use under different sets of technological and socioeconomic and ecological conditions, such models are known as mechanistic
bio-economic farm models (Ruben et al. 1998, cited in Janssen and van Ittersum
2007). Often, when such mechanistic models are implemented, linear programming
(LP) or some derivatives of LP are also used (Balasubramamiam et al. 1996;
616
S. Nautiyal et al.
changes in land use activities and resource input are proportionate to productionrelated parameters of farm management. The model should always offer the chance
for improvement in the different modules of the whole model, according to the
changes in the real-world scenario (Kaechele and Dabbert 2002).
MODAM focuses on the multifunctionality of the land use/landscape of the
region. This article presents only the background from which the concept for
transmission of MODAM has been emerged and which, at present, is running in
developed countries (such as European countries, North America) and is now going
ahead to focusing on the multifunctionality of landscape in developing countries
like India (Zander and Kächele 1999; Kaechele and Dabbert 2002; Zander 2003;
Schuler and Kaechele 2003). But, in view of the complexity of the Indian
Himalayan landscape we have been developing the model and restructuring the tool
for the Himalayan landscape while following the methodological approaches and
major characteristics of the model developed by the researchers. Overall, we wish to
generate sound methodological approaches for the Himalayan landscape which is
diverse and has great importance in a global context. This also describes the factors
that determine the forces and relationship between different branches of the
ecosystems and concurrently provides a graphical representation of those relationships. Visualizing the great use of the remote sensing, we have applied and
successfully testified forest and pasture ecosystem analysis in multi-temporal
dimensions. We have found this approach supports the empirical evidence with
more precision in the framework of our current modeling approach MODAM
(Nautiyal and Kaechele 2007a, b, c).
8 An Overview of Conceptual Model Development
The whole MODAM consists of several database and linear programming
(LP) modules (Kaechele and Dabbert 2002; Zander 2003). As we are predominately
interested in the MODAM approach, hence, we have concentrated on the conceptual core of MODAM—the LP modules. Database development to LP is
interesting from a software point of view. Choosing LP seems to be adequate, and
hence we refer to the comments of some authors concerning this approach. Before
going in detail we would like to provide a basic overview of linear programming in
landscape modeling.
For multidisciplinary problem of the real world, we argue for bio-economic
models which are helpful to solve the problems of economics and ecological
complexities simultaneously. In the real world, when models are used to evaluate
possible changes in land use under different sets of technological and socioeconomic and ecological conditions, such models are known as mechanistic
bio-economic farm models (Ruben et al. 1998, cited in Janssen and van Ittersum
2007). Often, when such mechanistic models are implemented, linear programming
(LP) or some derivatives of LP are also used (Balasubramamiam et al. 1996;
616
S. Nautiyal et al.