ATI-RiceFTPredictor: A Tool for Predicting Rice Flowering Time Across Environments Based on Accumulated Temperature Index
This tool utilizes a redefined Accumulated Temperature Index (ATI) model to predict rice flowering times across diverse environments with high accuracy and stability.
How It Works:
The ATI model uses a redefined ATI, calculated from 1 Day After Sowing (DAS) to 26 Days Before Flowering (DBF). This optimal window was determined through an exhaustive search process, analyzing thousands of potential time windows to identify the optimal period that maximizes the correlation between predicted and observed flowering times.
The research behind this tool is based on:
- Genotypic data from 422 rice hybrids across three ecotypes
- Flowering time records from 178 field trials at 47 different locations across China
- Comprehensive environmental variables
The optimized ATI serves as a stable varietal parameter, enabling accurate flowering time predictions across various environments. Notably, for new cultivars, a single planting record is sufficient to estimate the ATI, making our model particularly valuable in real-world breeding and agricultural scenarios where data may be limited.
To enhance practicality for breeders, we've developed a simplified prediction tool using just 28 informative genetic markers (Click for Detailed Information of Markers).
Features:
- Estimate the ATI for any indica variety using genotypic data from the 28 markers provided by the user.
- Predict flowering time for any sowing date across 47 locations using historical daily temperature data from 2014 to 2016.
- Predict flowering time for any location by providing historical daily temperature data.
Citations: Xingbing Xu #, Qiong Jia #, Sijia Li #, Julong Wei, Luchang Ming, Qi Yu, Jing Jiang, Peng Zhang, Honglin Yao, Shibo Wang, Chunjiao Xia, Kai Wang, Zhenyu Jia, Weibo Xie. Redefining the accumulated temperature index for accurate prediction of rice flowering time in diverse environments. Plant Biotechnology Journal. 2024 Oct 29.