European Journal of Sustainable Agroecosystems

Maize (Zea mays L.) is an essential crop for providing food, animal feed, and bioenergy. Climate change has increased the frequency, severity, and impact of droughts affecting maize productivity. The root system architecture (RSA)—the way in which the roots are distributed in time and space—constitutes the first interface through which plants obtain water and serves as a focus of breeding efforts aimed at obtaining drought-resistant plants. Aim of the review: The purpose of this literature review was to show the state of the art in quantitative measures of maize RSA, formulate ideotypes, as well as emphasize the importance of non-destructive phenotyping in closing the gap between genotype and phenotype. Literature sources: The review is based on peer-reviewed studies published from 2015 to 2026 in the databases of Scopus, Web of Science, PubMed, and Google Scholar, as well as foundational regulatory documents. Key findings: The results of the review show that the idea of using a 'steep, cheap and deep' ideotype minimizes the use of resources at the root level while enabling efficient water extraction from deep soil layers. Furthermore, non-destructive three-dimensional imaging techniques allow researchers to obtain reliable data on RSA.X-ray Computed Tomography, Magnetic Resonance Imaging, combined with artificial intelligence, makes high-dimensional topological trait extraction possible. Genetically essential loci such as ZmDRO1 and qSOR1 cooperate with hormonal signaling networks to control the root growth angle and plasticity in conditions of water deficit. Research gap: Among the most crucial unresolved issues are the low translation of controlled-environment phenotyping to field conditions, high operating and computational costs of advanced 3D systems, and a lack of correlation between macro-architectural traits and microscopic anatomical changes. Conclusion: The optimization of maize RSA by integrated multi-scale phenotyping and genomic selection opens the way for climate-smart breeding. Future research should focus on automated field-scale solutions and multi-omics fusion to maintain yields in conditions of resource scarcity.