Indian Journal of Science and Technology

Abstract

Objectives: Commonly, agriculture in many areas of the world represents an example of fields that are affected hardly by climate changes. This work attempts to find and rank candidate plantation plans of a given set of crops that can be planted in a specific area with regard to the climate change. Methods/Statistical Analysis: This work represents a fuzzy-based approach. Firstly, a prediction process takes place in order to predict the incoming year values of climate variables like temperature, humidity and sun shining. In consequence, the crops climatic requirements are represented using the flexibility of fuzzy set theory. After that, the year under planning is scanned to find the more suitable plantation periods for each crop. Finally, the proposed approach finds a set of candidate plantation plans each is associated with an overall suitability degree greater than a predefined threshold. Findings: Many previous works attempt to find the more suitable areas for planting a specific crop or to modify the plantation dates to be more suitable. These works don’t care about the consequent planted crops in the same area which almost affect the overall outcome of the plantation process. In contrary, the proposed approach gives a ranked set of candidate plantation plans based on their overall suitability degrees. Accordingly, it helps farmers in selecting the more suitable plantation plan respecting climate change. Application/Improvements: An application is built respecting the proposed approach. Its database includes the area historical climatic data and the crops climatic requirements. The application starts predicting the incoming year climatic values. Consequently, it finds each crop suitable plantation periods which are used to get the candidate plantation plans with an overall suitability degree. An illustrative case study is presented showing the added value of the proposed approach compared with some other previous ones.

Keywords: Climate Change, Crops Plantation Planning, Crops Requirements, Fuzzy Set Theory, Prediction