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Spatial suitability assessment for vineyard site selection based on fuzzy logic
Authors:Golnaz Badr  Gerrit Hoogenboom  Michelle Moyer  Markus Keller  Richard Rupp  Joan Davenport
Affiliation:1.AgWeatherNet Program,Washington State University,Prosser,USA;2.Cornell Lake Erie Research and Extension Lab,Cornell University,Portland,USA;3.Institute for Sustainable Food Systems, University of Florida,Gainesville,USA;4.Department of Horticulture,Washington State University,Prosser,USA;5.Department of Crop and Soil Sciences,Washington State University,Pullman,USA;6.Department of Crop and Soil Sciences,Washington State University,Prosser,USA
Abstract:Developing a sustainable agricultural production system requires knowledge of the climate, soil, and topography of the area of interest. This is especially relevant for wine grape (Vitis vinefera L.) production. The main objective of this study was the development of a comprehensive system to aid in the selection of suitable areas for grapevine cultivation. Included in this system were several bioclimatic indices, such as Growing Degree Days (GDD), Frost Free Days (FFD), and the Huglin Index (HI) calculated over a period of 30 years using daily weather data obtained from the University of Idaho’s Gridded Surface Meteorological (UI GSM) dataset. Soil data and topographical data were also included in the system. The bioclimatic indices, soil, and topographic data were then transformed using fuzzy logic, and suitability maps with scores ranging from 0 to 1 were developed. The final vineyard-potential scores were obtained by combining the soil, weather, and topographic potential scores with a range from 0 to 1, where 0 pertained to non-suitable areas and 1 referred to optimal sites. The maps were evaluated by comparing the range of suitability scores of existing vineyards in Washington State. The evaluation indicated that 97% of the established vineyards have a vineyard-potential score that ranges from 0.8 to 1. The results of this study revealed that 11% of the total study area had a high potential for wine grape production. This study was able to successfully employ fuzzy logic to help decision-makers, growers, and others with conducting a precise land assessment for wine grape production.
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