期刊
ENVIRONMENTAL MODELLING & SOFTWARE
卷 114, 期 -, 页码 166-180出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2019.01.015
关键词
Agriculture; Crop; HyFIS; Neural network; Regional scale; Plot scale
类别
资金
- German Ministry of Research and Education [01LL0917]
- Vietnamese Government Scholarship [911]
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.
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