Using landscape typologies to model socioecological systems: Application to agriculture of the United States Gulf Coast. (May 2016)

• Record Type: Journal Article
• Authors: Surendran Nair, Sujithkumar; Preston, Benjamin L.; King, Anthony W.; Mei, Rui
• Is Part Of: Environmental modelling & software. Volume 79(2016:May)
• Page Start: 85
• Page End: 95
• Publication Date: 2016-05
• Subjects: Socioecological systems -- Landscape typology -- Socioecological typology -- High-dimensional statistics -- Agroecosystem -- Adaptive capacity; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.01.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The complexity of socioecological systems (SES) has posed a persistent challenge to the development of methods for diagnostic and prognostic analyses of global change. We developed a high dimensional statistical framework where cluster analysis was used to characterize regional landscape typologies and those typologies are linked to the outcome of interest through regression modeling. For demonstration, we applied the framework to agroecosystem of the United States Gulf Coast to evaluate the determinants of spatial variability in crop yield. Regional biophysical typologies (BPT; integrated climate, soil, and topography clusters) and socioecological typologies (SET; BPT combined with socioeconomic clusters) were developed. The SET corn model (R2 = 0.89) outperformed the BPT corn model (R2 = 0.72) and a county fixed-effect model (R2 = 0.53), which reflects the socioeconomic influence over agricultural productivity. The SET model also showed similar predictive skill for soybean and cotton yield. Therefore impact analysis for agroecosystems can lead to incorrect conclusions if biophysical factors are not examined jointly with socioeconomic factors. Highlights: The socioecological typology method was used for identifying and attributing regional crop yield variability. The models demonstrated high predictive skill. Useful for integrating multi-scaled characteristic of socioecological systems.