Comparing Inductive and Deductive Modeling of Land Use Decisions: Principles, a Model and an Illustration from the Philippines

Background

Land use changes due to complex interactions between underlying causal "driving forces." These forces operate at different scales and encompass various societal and scientific areas. The Cagayan Valley, located in northeastern Luzon, Philippines, exemplifies significant land use change, transforming from tropical lowland forest before the arrival of migrant groups into a gradient of intensive agriculture and residual and primary forest. Understanding the causes of this land use change is crucial for addressing issues like tropical deforestation, agricultural development, and biodiversity conservation. Most current integrated studies of land use change follow an inductive methodology. Although this approach may align with theoretical frameworks, it does not directly test the theory itself, as more deductive approaches do.

Goals and Methods

The main goal of this research is to illustrate the potential for using more deductive methods in land use modeling by comparing it to the more commonly used inductive methods. To achieve this, the authors build an integrated causal model based on the Action-in-Context (AiC) framework to predict land use deductively, using Cagayan Valley, Philippines as a case study. The researchers test this deductive model against observed land use data from 114 households managing 272 fields, which they collect through household-level interviews using a structured questionnaire between June and November 2002. They also conduct semi-structured interviews with farmers and key actors, guided by the AiC framework, to understand motivations and to help quantify the deductive model. They use the same dataset for an inductive model using multinomial logistic regression.

Conclusions and Takeaways

The study finds that both the deductive model and the inductive model performed equally well in predicting land use. However, the authors argue that the deductive model provides a more genuinely causal and theory-connected understanding of land use because it explicitly incorporates both causal factors and the causal mechanisms. They suggest that land use scholarship would benefit from adding more deductive, theory-driven approaches, as it better facilitates the accumulation of insight by testing full causal structures. The authors propose that the most effective way forward involves a conscious awareness of the spectrum between inductive and deductive extremes and a search for sequences and interactions between these approaches in land use change research.