Davide Cammarano
Agricultural and Biological Sciences · Purdue University West Lafayette
Publications
218
Citations
11,690
Est. group size
~1
Recurring co-author estimate
Active years
23
Publishing since 2004
Davide Cammarano's research focuses on using crop simulation models, remote sensing, and drone (UAV) imagery to study how crops like wheat, maize, and potatoes respond to climate variability, nitrogen and organic fertilizer management, and drought. The work combines field data, satellite/UAV imagery, and AI-driven data analysis to improve predictions of crop yield, soil nitrogen levels, and water use, with applications ranging from Mediterranean and Sub-Saharan African farming systems to bioenergy potential on marginal lands in Italy.
Publication output has fluctuated over the past decade but shows an overall increase in recent years, rising from around 7-11 papers annually in 2018-2021 to 14-19 papers per year in 2023-2026.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Within-field variability in nitrogen losses: A case study in Denmark
Agriculture Ecosystems & Environment · 2026
- Assessing in-season crop nitrogen status based on UAV multispectral imaging and AI-driven model optimization
Computers and Electronics in Agriculture · 2026
- A long-term simulation of organic fertilizer's effects on wheat, barley and potato production
2026
- Disentangling environmental and structural drivers of evapotranspiration simulation errors in wheat of semi-arid and Mediterranean region
OpenAgrar · 2026
- Impacts of climate variability and multiple fertilization strategies on rainfed maize production in Sub-Saharan Africa
European Journal of Agronomy · 2026
- Three-dimensional reconstruction and light interception quantification of maize/soybean system based on UAV cross-surround photography
2026
- Novel methodologies for multi-sensor data fusion for high-resolution soil mapping
2026
- Supplementary files associated with the study: "Assessing different remotely sensed wheat biophysical variables for data assimilation in a crop model for yield mapping prediction".
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Supplementary files associated with the study: "Assessing different remotely sensed wheat biophysical variables for data assimilation in a crop model for yield mapping prediction".
Zenodo (CERN European Organization for Nuclear Research) · 2026
- An intelligent data-driven framework for optimizing wheat management toward high yields with reduced nitrogen inputs
Computers and Electronics in Agriculture · 2026
- Datasets on Data-Driven Nitrogen Management Framework
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Datasets on Data-Driven Nitrogen Management Framework
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Improving Vineyard Fractional Vegetation Cover Estimation through Phenology-Based Endmember Calibration Integrating Satellite and UAV Imagery
SSRN Electronic Journal · 2026
- Long-term crop model simulations and marginality layers for assessing oilseed bioenergy potential on Italian marginal lands
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Long-term crop model simulations and marginality layers for assessing oilseed bioenergy potential on Italian marginal lands
Zenodo (CERN European Organization for Nuclear Research) · 2026
- European Journal of Agronomy×9
- HAL (Le Centre pour la Communication Scientifique Directe)×9
- Zenodo (CERN European Organization for Nuclear Research)×9
- Field Crops Research×8
- Computers and Electronics in Agriculture×8
- German Mandrini
Agricultural and Biological Sciences · Purdue University West Lafayette
- Luis Vargas-Rojas
Agricultural and Biological Sciences · Purdue University West Lafayette
- Julio C. Postigo
Agricultural and Biological Sciences · Indiana University
- Xiaoshang Deng
Agricultural and Biological Sciences · Purdue University West Lafayette
- Amit Prasad Timilsina
Agricultural and Biological Sciences · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile