Data-Driven Agronomic Research Across Scales for Food System Innovation

Agronomic research is fundamental to driving continuous innovation in global food systems. As data become more available with higher spatial and temporal resolutions, along with powerful computational capabilities, data-driven agronomic research is playing an increasingly vital role in tackling the evolving challenges of food production.

Long-term agricultural experiments provide critical insights into how food systems respond to changing environmental and management conditions, while modeling approaches allow researchers to better understand processes beyond the field scale. Moreover, emerging digital tools and technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), are transforming agricultural data collection, data analysis and evidence-based actions. These advancements are key to improving decision support in agricultural monitoring, production, and supply chains while minimizing the environmental impact of food systems.

At the same time, a deep understanding of agroecological principles remains crucial for interpreting the processes and outcomes of data-driven agronomic research. This ensures that innovations are relevant and applicable to farmers while translating scientific advancements into practical solutions for sustainable and efficient food production.

Therefore, we seek contributions that advance these efforts through original research, systematic reviews, and position papers. We particularly welcome submissions involving temporal and spatial data across agronomic systems focusing on;

• Long-term agricultural experiments evaluating sustainability outcomes and system resilience

• Spatiotemporal agronomic studies using open-source data, remote sensing, and UAV technology

• Innovations in precision agriculture, site-specific agronomic management, and smart farming

• Strategies for optimizing agricultural resources through modeling tools and AI

• Applications of digital agronomy in mixed crop-livestock systems

• Cropping system performance and its resilience under environmental extremes

• Other relevant topics that push the boundaries of agronomic research for innovation

-
Dr. Leonardo Bastos received financial support from Deere and Company. The other Topic Editors declare no competing interests with regard to the Research Topic subject

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Agroecology, Agricultural systems modeling, Artificial intelligence, Decision support systems, Digital agriculture

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.