Abstract
India's increasing population growth and unsystematic land cover transformation have led to land degradation and a decline in agricultural production. To achieve optimum advantage from the land, proper exploitation of its resources is necessary. Remote sensing, advanced fuzzy logic, and multi-criteria decision-making like analytical hierarchy process (AHP) integrated agricultural land suitability analysis (ALAS) may facilitate identifying and formulating effective agricultural management strategies required for smart agriculture.
The present study was conducted to construct India's robust agricultural suitability model by developing hybrid fuzzy logic and the AHP based model.
Fourteen topographical, climatological, soil-related, land-use, and land-cover-related factors were prepared and employed to model agricultural suitability. Agricultural suitability models predicted multi-parameters based agricultural suitable zones for the entire country using three fuzzy operators (AND, Gamma 0.8, Gamma 0.9) and a hybrid fuzzy-AHP model. Sensitivity analysis was conducted to test the models' reliability using Moris technique-based global sensitivity analysis, random forest (RF), and correlation coefficient. The best agricultural suitable model was compared with the production of major crops in India.
Results showed that 19.8% of the study area was permanently not suitable in the northernmost region, 19.7% was currently not suitable in the northernmost region, while 20.1% and 20.2% areas were predicted as moderately suitable and highly suitable zones, respectively. The rainfall, elevation, slopes, evapotranspiration, and aridity index had a prime influence on the output of the agricultural suitability model.
The adopted method and its application processes can analyze agricultural land suitability and recommend optimal farming methods. It is also comprehended as a promising option for meeting food, nutrition, energy, and job demands while still protecting our threatened environment.
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•Agriculture land suitability (ALS) mapping for the country has not been yet done due to data scarcity and lack of technology.•e developed and applied fuzzy-AHP and fuzzy logic-based ALS models, as well as several sensitivity analyses.•20.2% and 19.78% area were identified as highly suitable and permanently unsuitable for agriculture.•The integrated fuzzy-AHP based ALS model was found to be the best model for ALS mapping.•A robust ALS model could aid agricultural decisions that support smart agricultural practices with greater production.