Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil

The aim of the present study was to construct agroclimatic zoning for the conilon coffee crop in the state of Bahia, Brazil, using fuzzy logic. Historical data series on rainfall, mean air temperature, and relative air humidity were used. Analyses were carried out considering the mean values of the...

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Bibliographic Details
Published in:International journal of fruit science 2021-01, Vol.21 (1), p.205-217
Main Authors: Medauar, Caique Carvalho, Silva, Samuel De Assis, Galvão, Ícaro Monteiro, Franco, Laís Barreto, Carvalho, Luis Carlos Cirilo
Format: Article
Language:English
Subjects:
Agricultural management
agroclimatic zoning
Air temperature
Coffee
Crops
Cultivation
Decision making
Fuzzy logic
geostatistics
Historical account
Implantation
Mapping
Rainfall
Relative humidity
thematic maps
Variability
Zoning
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Summary:The aim of the present study was to construct agroclimatic zoning for the conilon coffee crop in the state of Bahia, Brazil, using fuzzy logic. Historical data series on rainfall, mean air temperature, and relative air humidity were used. Analyses were carried out considering the mean values of the accumulated variables for each month in the historical series. The data were subjected to geostatistical analysis to verify and quantify the existence of spatial dependence between the values of the studied variables. Subsequently, maps with representations of the monthly means of the variables were subjected to continual classification using fuzzy mapping to identify suitable areas and areas of climate favorability for the implantation of conilon coffee in the state of Bahia. Bahia presents great spatial variability in regard to suitability for conilon coffee cultivation, with highly favorable areas, but no totally unsuitable region. The south and extreme south of Bahia were the regions with the lowest temporal-spatial variability for climate favorability for the development of conilon coffee trees, these being the most suitable regions for this crop. The zoning through fuzzy logic assisted in decision-making on which regions of the state had the highest suitability for crop implantation.
ISSN:1553-8362
1553-8621
DOI:10.1080/15538362.2020.1864698