Finding optimum climatic parameters for high tomato yield in Benin (West Africa) using frequent pattern growth...

Area of Science:

• Agricultural Science
• Climate Science
• Data Mining

Background:

• Tomato is a globally significant vegetable crop, crucial for food security.
• Optimizing tomato cultivation requires understanding the influence of climatic factors on yield.
• Climate variability poses challenges to consistent agricultural production.

Purpose of the Study:

• To identify optimal climatic conditions for maximizing tomato yield.
• To analyze associations between climate variables and tomato production using data mining techniques.
• To provide insights for farmers to adapt cultivation practices to changing environments.

Main Methods:

• Utilized the Frequent Pattern Growth (FPG) algorithm to analyze 26 years of climate and yield data from Benin.
• Aggregated monthly climate data (minimum/maximum temperature, humidity, sunshine, rainfall, evapotranspiration) with yield data.
• Transformed continuous climate data into discrete categories (low, medium, high) and applied association rule mining with specific support and confidence thresholds.

Main Results:

• High tomato yield was consistently associated with low evapotranspiration (ET) across all agro-ecological zones.
• Optimal conditions generally involved medium levels of minimum and maximum temperatures, maximum humidity, and sunshine.
• Rainfall patterns varied, being high in the Sudanian Zone and medium in others for high yields.

Conclusions:

• Climate variability significantly impacts tomato production, with ET being a key factor.
• The identified climate-yield associations can inform strategic cultivation decisions for farmers.
• Findings offer a basis for adapting agricultural practices to environmental changes and can be applied to other crops and regions.