Mapping High-Potential Areas Using Maximum Entropy Modeling Approach and Constraints Analysis of Pineapple (Ananas comosus) Cultivation in Sri Lanka
Wijegunawardhana, A.V., Abeynayake, N.R. and Kadupitiya, H.K.
ABSTRACT
Maximum entropy (MaxEnt) modeling is extensively tested high performing quantitative modeling technique which has great potential for identifying best ecological requirement of species based on “presence only data” together with environmental variables. This study was aim to model the high-potential areas for pineapple cultivation in Sri Lanka using MaxEnt model. Total of 215 locations of pineapple cultivation covering whole Sri Lanka and several environmental covariates namely monthly rainfall, monthly mean temperature, Digital Elevation Model (DEM), slope, slope aspect, Normalized Difference Vegetation Index (NDVI) were used as model drivers. The resulting model was validated by using area under the receiver operator characteristic curve analysis. In addition to mapping, a questionnaire survey was conducted with a sample of 60 farmers in four divisional secretariat divisions of Gampaha and Kurunegala districts to explore prevailing conditions and constraints for pineapple cultivation. Highly significant constraints were identified using Wilcoxon signed rank test. Probability prediction map developed by MaxEnt with high predictive power (AUC = 0.913) indicated that some parts of Ampara, Monaragala, Puttalam, Colombo, Kaluthara, Kegalle, Badulla districts as high- potential areas in addition to traditionally pineapple grown districts which are Gampaha and Kurunegala. Wilcoxon signed rank test proved that high cost of inputs, high price of mulching materials, high cost and shortage of labour, high investment, lack of government subsidy facilities, weed problem, threat of mealy bug attack as highly significant production constraints while lack of guaranteed price as the major marketing constraint for pineapple cultivation (p < 0.05). This information on high-potential areas important for investors as well as entrepreneurs to take information-based decisions and provide decisive guidance for farmers to expand their cultivation.
KEYWORDS: Environmental covariates (layers), Geographic information system, MaxEnt model, Pineapple cultivation, Probability mapping