Application of a hybrid neural-fuzzy inference system for mapping crop suitability areas and predicting rice yields

verfasst von

Kinh Bac Dang, Benjamin Burkhard, Wilhelm Windhorst, Felix Müller

Abstract

Environmental stressors and population growth have significantly affected terraced rice ecosystems, such as in the Sapa district in northern Vietnam. The question arises how natural and socio-economic components determine the amount of rice yields. This study combines a hybrid neural-fuzzy inference system (HyFIS) with GIS-based methods to generate two models that can map suitability areas for rice cultivation at a regional scale and predict actual rice yields at a plot scale. Semi-structured interviews, the “Integrated Valuation of Ecosystem Services and Tradeoffs” tool and different statistical models were used to investigate the impacts of eight environmental variables and three socio-economic variables on rice production. Subsequently, two HyFIS models were trained with an accuracy higher than 88%. Because the predictive power values of the two proposed HyFIS models were higher than those of benchmark models, they are considered as useful tools to assess and optimize land use and related rice productivity.

Organisationseinheit(en)

Institut für Physische Geographie und Landschaftsökologie
Arbeitsgruppe Physische Geographie

Externe Organisation(en)

Christian-Albrechts-Universität zu Kiel (CAU)
Vietnam National University
Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF) e.V.

Typ

Artikel

Journal

Environmental Modelling and Software

Band

114

Seiten

166-180

Anzahl der Seiten

15

ISSN

1364-8152

Publikationsdatum

04.2019

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Environmental engineering, Ökologische Modellierung

Ziele für nachhaltige Entwicklung

SDG 15 – Lebensraum Land

Elektronische Version(en)

https://doi.org/10.1016/j.envsoft.2019.01.015 (Zugang: Geschlossen)