Total land suitability analysis for rice and potato crops through FuzzyAHP technique in West Bengal, India

• Published in: Cogent food & agriculture 2023-12, Vol.9 (1)
• Main Authors: Singha, Chiranjit, Chandra Swain, Kishore, Sahoo, Satiprasad, Ghassan Abdo, Hazem, Almohamad, Hussein, Abdullah Al Dughairi, Ahmed, Albanai, Jasem A
• Format: Article
• Language: English
• Subjects: Agricultural practices; Cereal crops; Criteria; Crop rotation; Crop yield; Crops; environment; FuzzyAhp; GIS; Laboratory methods; Learning algorithms; Machine learning; Mathematical models; Nutrient availability; Nutrients; Parameters; Potatoes; Rice; Sentinel 2B; Soil nutrients; Soils; Support vector machines; total land suitability; Vegetables
• ISSN: 2331-1932; 2331-1932
• DOI: 10.1080/23311932.2023.2257975

A total land suitability analysis was carried out through FuzzyAHP technique for rice and potato crops in West Bengal, India. Around 21 most relevant crop suitability parameters were selected and classified under five primary criteria, such as terrain distribution parameter, static soil parameter, available soil nutrient, agriculture practice parameter, and local variation parameter for the study. The factors such as NDVI and SAVI values were estimated from Sentinel 2B images in "SNAP" toolbox software environment, whereas soil nutrients were estimated through standard laboratory methods. Individual parameter weights were assigned through the FuzzyAHP technique for sub-criteria as well as for primary criteria. The final crop suitability map was developed showing nearly 20% of the total area as highly suitable for rice crop, whereas nearly 39% of the area was found suitable for the potato crop. Comparing the prediction map with yield distribution, it was found that the southwest region of the study area is very suitable for both rice and potato crop with higher crop yields in the range of 5 t/ha and 20 t/ha, respectively. Six different machine learning models, namely random forest, support vector machine, AdaBoost, extreme gradient boosting, logistic regression, and naïve Bayes, were utilized for validation of the suitability maps. The support vector machine (SVM) learning model with the highest AUC (~80%) was found efficient for testing both rice and potato crop suitability. The economic status of farmers can be rejuvenated by selecting the best crop rotation through land suitability analysis.