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

Drought indicators are essential for agricultural sustainability. This research employs causal inference and information theory to identify the most representative drought indicator (index or variable) for agricultural productivity. The causal connection between precipitation, maximum air temperature, drought indices and corn and soybean yield are ascertained by cross convergent mapping (CCM), while the information transfer between them is determined through transfer entropy (TE). This research is conducted on rainfed agricultural lands in Iowa, considering the phenological stages of crops. The results uncover both the causal connection between corn yield and precipitation and maximum temperature indices. Based on the analysis, the drought indices with the strongest causal relationship to crop production are SPEI-9 m and SPI-6 m during the silking period, and SPI-9 m and SPI-6 m during the doughing period. Therefore, these indices may be considered as the most effective predictors in crop yield prediction models. The study highlights the need to consider phenological periods when estimating crop production, as the causal relationship between corn yield and drought indices differs for the two phenological periods.