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

authored by

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.

Organisation(s)

Institute of Physical Geography and Landscape Ecology
Physical Geography Group

External Organisation(s)

Kiel University
Vietnam National University
Leibniz Centre for Agricultural Landscape Research (ZALF)

Type

Article

Journal

Environmental Modelling and Software

Volume

114

Pages

166-180

No. of pages

15

ISSN

1364-8152

Publication date

04.2019

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Software, Environmental Engineering, Ecological Modelling

Sustainable Development Goals

SDG 15 - Life on Land

Electronic version(s)

https://doi.org/10.1016/j.envsoft.2019.01.015 (Access: Closed)