Model-Based Evaluation of Crop Agronomic Traits
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".
Deadline for manuscript submissions: 15 October 2025 | Viewed by 16
Special Issue Editors
Interests: drought evolution; climate change; drought prediction; drought characteristics
Special Issues, Collections and Topics in MDPI journals
Interests: crop modeling; climate change; climate extremes; water stress; phenology
Special Issues, Collections and Topics in MDPI journals
Interests: agricultural ecological environment; crop model; climate change; management decision-making; disaster risk
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the increase in population, limited arable land resources cannot be expanded indefinitely. In this context, agronomic trait evaluation has become a key technical tool to break through the resource constraints and guarantee food security. In the past, crop improvement mainly relied on field trials and phenotypic observations, which were both time-consuming and resource-consuming; this greatly limited the efficiency and effectiveness of crop improvement. However, with the advent of crop modeling, coupled with the challenges of climate change, the model-based assessment of crop agronomic traits has become an important tool for modern agriculture, integrating advances in computational modeling, remote sensing, and data analysis. It has revolutionized the way researchers assess traits such as yield potential, drought tolerance, and nutrient use efficiency. The purpose of this Special Issue is to compile cutting-edge research on the development, validation, and application of models in crop science. We hope that the papers solicited will deepen our understanding of genotype–environment interactions, optimize agricultural management strategies, and improve prediction accuracy under different climatic conditions. The scope of research includes but is not limited to mechanistic and empirical models, machine learning models, and high-throughput phenotyping models.
Dr. Ning Yao
Dr. Jian Liu
Dr. Na Li
Dr. Linchao Li
Guest Editors
Manuscript Submission Information
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Keywords
- crop modeling
- agronomic traits
- climate resilience
- precision agriculture
- digital agriculture
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