Understanding the pattern-process relations of land use and land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based geographic species distribution models (SDMs) offer a novel approach to understanding land suitability and land use decisions. SDMs link species presence and location data with geospatial information and use machine learning algorithms to develop nonlinear and discontinuous species-environment relationships. Here, we apply the maximum entropy (MaxEnt) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in northeastern Thailand as a case study for examining the relationships among the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District-cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that although the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the noncontinuous and nonlinear nature of these relationships. This approach demonstrates a novel method of understanding nonlinear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops.
- land suitability
- machine learning