A long-term, analysis-ready dataset of rice phenology in middle and lower reaches of the Yangtze River Plain of China from 1981 to 2024.

Authors

Quan-Jun Zhang, Dong-Li Wu

Published in

Data in brief. Volume 69. Pages 113310. Epub Oct 07, 2026.

Abstract

Quantifying the impact of climate change on rice production, a cornerstone of global food security, requires long-term, spatially explicit, and readily analyzable phenological data. While the Middle and Lower Yangtze River Plain (MLYRP) is China's primary rice bowl, a critical gap persists: the lack of a publicly available, multi-decadal, station-based dataset that provides not only raw phenological dates but also curated statistical trends for the entire growth cycle, tailored for immediate reuse by the research community.
To bridge this gap, we present "MLYRP-rice phenology data", a foundational dataset characterizing rice phenology dynamics across the MLYRP from 1981 to 2024. Derived from rigorously quality-controlled records of 95 national agrometeorological stations, this dataset is architected for reuse, offering two synergistic products: (1) A visualization collection comprising 2,432 high-resolution graphs that intuitively depict the probability distribution and temporal trends of 11 phenological stages and 3 growth phase lengths via kernel density estimation and linear regression. (2) A machine-actionable statistical core, structured as an Excel spreadsheet, which provides the complete results of the linear trend analysis (including slope, intercept, R², F-value, and p-value) for every station and variable.
This dataset transcends a mere data compilation by delivering an analysis-ready product. It is designed as a community benchmark to directly facilitate crop model validation, climate-impact meta-analyses, remote sensing product verification, and data-driven agricultural adaptation planning. By providing pre-computed, statistically robust trends, "MLYRP-rice phenology data" establishes an indispensable empirical foundation for detecting and attributing climate impacts on one of the world's most critical agricultural systems.
The dataset is open-access and available at https://doi.org/10.57760/sciencedb.25297.

PMID:
42909410
Bibliographic data and abstract were imported from PubMed on 23 Oct 2026.

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