European Journal of Sustainable Agroecosystems

Background: Climate change operates as a primary disruptive force in global agroecosystems, altering environmental drivers that dictate plant ontogeny. Crop phenology—the timeline of biological life-cycle phases—serves as a highly sensitive bioindicator of these environmental shifts. Objective of the Review: This critical review provides an exhaustive evidence synthesis of climate change impacts on the phenological development of four primary global field crops: wheat (Triticum aestivum), rice (Oryza sativa), maize (Zea mays), and soybean (Glycine max).

Literature Sources: A systematic approach guided by PRISMA principles was deployed to evaluate peer-reviewed, Scopus- and Web of Science-indexed literature published between 2015 and 2025 across major platforms, including ScienceDirect, SpringerLink, Wiley Online Library, and institutional repositories of the FAO and IPCC.

Major Findings: The critical analysis demonstrates that ambient temperature elevation accelerates thermal time accumulation, causing widespread compression of both vegetative and reproductive phases. Across the synthesized literature, a 1°C increase in mean seasonal temperature advances anthesis by 4.5 to 8.8 days in winter wheat and 3.2 to 6.5 days in spring maize. However, significant contradictions emerge when evaluating the interacting effects of elevated atmospheric carbon dioxide (eCO2) and moisture deficits. While eCO2 exhibits a tendency to slightly delay phenological transitions via enhanced water-use efficiency and prolonged greenness in C3 crops, concurrent severe water stress triggers emergency maturity pathways, bypassing typical thermal models.

Research Gaps: Methodological limitations identified in historical studies include an over-reliance on single-factor temperature tracking and a geographical bias toward Northern Hemisphere temperate zones, leaving tropical and subtropical multi-stress dynamics under-investigated. Conclusion: This review establishes that current phenological prediction models fail to capture non-linear, multi-variable climate interactions. We outline future research directions prioritizing the integration of high-resolution remote sensing, machine learning architectures, and Free-Air Carbon Dioxide Enrichment (FACE) multi-stress field data to safeguard global food security under accelerating climate instability