TY - JOUR

T1 - Application of a hybrid neural-fuzzy inference system for mapping crop suitability areas and predicting rice yields

AU - Dang, Kinh Bac

AU - Burkhard, Benjamin

AU - Windhorst, Wilhelm

AU - Müller, Felix

N1 - Funding Information: The study was embedded in the LEGATO (Land-use intensity and Ecological EnGineering – Assessment Tools for risks and Opportunities in annual crop based production systems) project, funded by the German Ministry of Research and Education within their funding program Sustainable Land Management ; Funding No. 01LL0917 . We want to thank all our LEGATO colleagues, especially Anika Klotzbücher and Isoda Yuzuru, for providing relevant information and data. This study was co-financed by the Vietnamese Government Scholarship ( 911 ). Furthermore, we would like to thank our colleagues at the Institute for Natural Resource Conservation, Department of Ecosystem Management at Kiel University, Germany. We thank Angie Faust for language revision of the manuscript.

PY - 2019/4

Y1 - 2019/4

N2 - 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.

AB - 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.

KW - Agriculture

KW - Crop

KW - HyFIS

KW - Neural network

KW - Plot scale

KW - Regional scale

UR - http://www.scopus.com/inward/record.url?scp=85060952679&partnerID=8YFLogxK

U2 - 10.1016/j.envsoft.2019.01.015

DO - 10.1016/j.envsoft.2019.01.015

M3 - Article

AN - SCOPUS:85060952679

VL - 114

SP - 166

EP - 180

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

ER -