2026-10-12T15:26:55Z https://www.scidb.cn/oai

10.57760/sciencedb.28863 2025-08-08T10:08:39Z

2025-08-08 Variations of Maize phenology Dataset in Shandong Province from 1981 to 2024 doi:10.57760/sciencedb.28863 en Based on maize phenological observation data from 12 national agro-meteorological stations in Shandong Province (1981–2024), this study established a high-precision, long-term maize growth stage dataset that systematically quantifies long-term variation patterns in the core Huang-Huai-Hai production region for the first time. The dataset comprises 192 high-resolution analytical charts (186 MB) organized into 12 station-specific groups, each containing one probability density plot for growth stages, one for growth stage durations, and fourteen linear trend fitting plots for stages and durations. Following a standardized protocol, we collected records of 10 key growth stages, implemented rigorous quality control (historical verification and expert validation), converted dates to Day of Year (DOY), and categorized stages into four developmental phases before applying kernel density estimation (KDE) and linear trend analysis. Jining case data revealed significant developmental delays (e.g., emergence delayed by 5.8 days per decade) and divergent duration changes (e.g., sowing-jointing shortened by 3.8 days per decade). Innovatively integrating multi-station validation with nonparametric statistics, this dataset transcends traditional mean-value limitations, providing scientific support for climate-crop interaction studies, crop model optimization, stress-resilient cultivar breeding, precision agronomic management, and strategic goals of national food security and agricultural carbon neutrality. Phenology; maize; climate change; kernel density estimation; univariate linear regression zhang quan jun Wu Dongli Cheng Zhaojin Wu Zexin Han Qian PUBLIC https://creativecommons.org/licenses/by-sa/4.0/ dataset Science Data Bank