Reduced-complexity modeling for integrated assessment of land use systems

The traditional way of describing highly complex agricultural systems is to start with simple models and then gradually develop more complex models as understanding grows and new questions are raised. As a result, various agricultural systems models evolved into complex simulation models.

These models have numerous advantages as they are generic and they can handle a broad range of problems. However, in running these complex models, two problems emerge:

  • Firstly, data to run these models properly are often lacking. As a result many studies 1) use default values, 2) ignore variation, or 3) generate data through transfer functions rather than collecting required input data. These alternative options may negatively impact model output.
  • Secondly, running the complex models is difficult for other users (including scientists and policy analysts) because of its complexity in processes and parameters.

One can wonder whether the full complexity of these models is always required, especially in cases where we deal with very specific questions. Reduced-complexity modeling is an alternative approach to overcome the above problems but still answers the questions raised. The overall objective of this study is to develop a strategy for reduced-complexity modeling for the integrated assessment of agricultural systems and to make the models applicable for a wide variety of users to answer specific research questions.

Research Objectives
The objectives of this research are: to develop a strategy for reduced-complexity modeling to decrease data requirements and make the models applicable for a wide variety of users to answer specific research questions for agricultural systems. The study focuses on regional models for integrated assessment. Recently, the tradeoff analysis methodology (Stoorvogel et al., 2004), a complex model for integrated assessment, has been simplified in an ad-hoc manner into a reduced complex model dealing with payments for environmental services (Immerzeel et al., 2008). The success of this reduced-complexity model is an interesting case that allows models to become part of a participatory research efforts where scientists and stakeholders jointly define the specific research questions. These questions can form the basis for a more modeling effort that focuses on specific questions rather than on more generic models. In this research we will focus on regional questions that deal with the management decisions of a population of farmers and the respective environmental consequences. 

Fieldwork Area

A reduced-complexity model (RCM) will be developed for Ecuador to deal with the pesticide-related problems. However, the RCM is specific for the agro-ecological conditions in the Ecuadorian Andes and can, following the hypothesis of this study, be developed and tested using a complex simulation model after the development of a complex simulation model and the corresponding data collection. We will therefore test and apply the RCM under very different agro-ecological conditions in Iran to verify the generic character of the RCM. In other words, we will check whether the key processes and variables hold under these different conditions to deal with a pesticide related problem in Iran.

Current Research
In progress:
1 - Sensitivity analysis technique to determine core processes of Crop Growth Simulation Model for RCM
2 - Design a specify RCM to answer a specific question in Ecuador; Modeling the rate of adaption from IPM

In progress.