Linking drought indicators and crop yields through causality and information transfer: a phenology-based analysis

Area of Science:

• Agricultural Science
• Environmental Science
• Data Science

Background:

• Drought indicators are vital for agricultural sustainability and yield prediction.
• Understanding the causal links between climate variables, drought indices, and crop yields is essential.

Purpose of the Study:

• To identify the most representative drought indicator for agricultural productivity using causal inference and information theory.
• To ascertain the causal connection between precipitation, temperature, drought indices, and crop yields (corn and soybean).

Main Methods:

• Cross Convergent Mapping (CCM) was used to determine causal connections.
• Transfer Entropy (TE) was employed to quantify information transfer.
• The study focused on rainfed agricultural lands in Iowa, considering crop phenological stages.

Main Results:

• A causal connection was found between corn yield and precipitation and maximum temperature indices.
• SPEI-9m and SPI-6m showed the strongest causal relationship with corn yield during silking and doughing stages.
• Drought indices SPI-9m and SPI-6m were identified as effective predictors during specific crop phenological periods.

Conclusions:

• Specific drought indices (SPEI-9m, SPI-6m, SPI-9m) are effective predictors of crop yield.
• Phenological periods significantly influence the causal relationship between drought indices and crop production.
• Considering crop development stages is crucial for accurate yield prediction models.