Phenological Shifts and Optimization of the Sowing Date for Spring Maize Under Climate Change: A Framework Based on the 24 Solar Terms for Shanxi Province, China

The 24 solar terms (STs) comprise a traditional Chinese seasonal calendar that has long guided agricultural practices, but its agronomic relevance under climate change remains uncertain. We assessed 24ST applicability in Shanxi Province using daily temperature, precipitation, and sunshine duration records from 23 meteorological stations. Climate trends were analyzed at the ST scale, and the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Maize model was applied to simulate maize phenology and yield in evaluating phenological shifts and sowing-date effects. Results showed that (1) warming was concentrated during Yushui–Qingming and Xiaoxue–Dahan, with sunshine declines across Mangzhong–Xiaoshu and Bailu–Hanlu. Precipitation trends were weak and spatially variable. (2) The calibrated CERES-Maize model reliably reproduced regional yields and phenology. Simulated V3 growth stage, anthesis, and maturity dates advanced by 1.53, 1.88, and 3.11 days per decade, respectively. V3 was driven by early-spring hydrothermal radiation, while anthesis and maturity was closely associated with Xiazhi/Xiaoshu/Dashu summer heat. (3) Optimal sowing windows were Guyu-H2–H3, Xiaoman-H1–H2, and Xiaoman-H2–H3 for northern, central, and southern Shanxi, respectiely. Optimized sowing shifted V3 towards Xiaoman–Mangzhong, delayed anthesis from early Xiaoshu to late Dashu–Liqiu, and postponed maturity towards Qiufen. These findings provide a scientific basis for continued use of the 24 STs in guiding agricultural practices for maize.

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Publication Details

Journal
Agronomy
Published
2026-08-25
DOI
https://doi.org/10.3390/agronomy16171630
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Phenological Shifts and Optimization of the Sowing Date for Spring Maize Under Climate Change: A Framework Based on the 24 Solar Terms for Shanxi Province, China

Fen­wu Liu, Lanjun Li, Wencai Zhang, Lu Xia et al.
Agronomy
Remote Sensing in Agriculture
article

Phenological Shifts and Optimization of the Sowing Date for Spring Maize Under Climate Change: A Framework Based on the 24 Solar Terms for Shanxi Province, China

Fen­wu Liu, Lanjun Li, Wencai Zhang, Lu Xia, Yitong Chen, Meiting Yan
article en

Abstract

The 24 solar terms (STs) comprise a traditional Chinese seasonal calendar that has long guided agricultural practices, but its agronomic relevance under climate change remains uncertain. We assessed 24ST applicability in Shanxi Province using daily temperature, precipitation, and sunshine duration records from 23 meteorological stations. Climate trends were analyzed at the ST scale, and the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Maize model was applied to simulate maize phenology and yield in evaluating phenological shifts and sowing-date effects. Results showed that (1) warming was concentrated during Yushui–Qingming and Xiaoxue–Dahan, with sunshine declines across Mangzhong–Xiaoshu and Bailu–Hanlu. Precipitation trends were weak and spatially variable. (2) The calibrated CERES-Maize model reliably reproduced regional yields and phenology. Simulated V3 growth stage, anthesis, and maturity dates advanced by 1.53, 1.88, and 3.11 days per decade, respectively. V3 was driven by early-spring hydrothermal radiation, while anthesis and maturity was closely associated with Xiazhi/Xiaoshu/Dashu summer heat. (3) Optimal sowing windows were Guyu-H2–H3, Xiaoman-H1–H2, and Xiaoman-H2–H3 for northern, central, and southern Shanxi, respectiely. Optimized sowing shifted V3 towards Xiaoman–Mangzhong, delayed anthesis from early Xiaoshu to late Dashu–Liqiu, and postponed maturity towards Qiufen. These findings provide a scientific basis for continued use of the 24 STs in guiding agricultural practices for maize.

AgronomyVol. 16(17)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Climate action
Openalex Percentile: Top 16%
Remote Sensing in Agriculture
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