Maize Cultivation and Improvement—2nd Edition

Dear Colleagues,

The development of maize germplasm is fundamental for the creation of cultivars with improved agronomic performance, adaptation to diverse environments, and enhanced resilience to emerging biotic and abiotic stresses. Germplasm resources provide the genetic basis for breeding programs and are essential for sustaining long-term genetic gain while addressing the challenges posed by climate change, evolving pathogens, and the increasing demand for global food production.

Modern maize breeding programs increasingly integrate classical genetic approaches with advanced technologies, including genomics, multi-omics integration, high-throughput phenotyping, and predictive modeling. The application of artificial intelligence, machine learning, and big data analytics has significantly improved the efficiency of selection processes, enabling breeders to evaluate large populations with greater precision and reduced breeding cycles. These approaches allow the identification of complex traits related to yield stability, stress tolerance, nutrient use efficiency, and plant architecture.

Furthermore, the integration of digital agriculture technologies, such as remote sensing, UAV-based monitoring, hyperspectral imaging, and Internet of Things (IoT) sensor networks, has transformed the way phenotypic and environmental data are collected and analyzed. These tools facilitate the development of data-driven breeding strategies and enhance the understanding of genotype × environment interactions across multiple environments.

Recent advances in crop modeling, enviromics, and climate-resilient breeding have also contributed to the identification of target population of environments (TPE) and the development of cultivars adapted to specific production systems. In addition, emerging research areas such as root system architecture, plant–soil–microbiome interactions, and sustainable crop management practices are expanding the scientific foundations of maize improvement and cultivation.

This Special Issue aims to present recent advances in maize germplasm development, breeding strategies, and crop management technologies that contribute to improving genetic gain, crop productivity, and sustainability. We welcome original research articles, reviews, and short communications addressing innovative methodologies, integrative breeding approaches, digital agriculture technologies, and sustainable maize production systems across diverse agroecological environments.

Prof. Dr. Glauco Vieira Miranda
Guest Editor

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• maize crop management
• maize breeding
• maize plant population
• germplasm diversity
• genetic resources
• genomic selection
• root system architecture
• plant microbiome
• soil fertility management
• sustainable maize production
• genomic prediction
• multi-omics integration
• high-throughput phenotyping
• plant phenomics
• remote sensing
• precision agriculture
• artificial intelligence
• machine learning
• genotype × environment interaction
• multi-environment trials
• enviromics
• climate adaptation
• abiotic stress tolerance
• disease resistance