Loading...
Publication
Reflectance-based assessment of nitrogen status in ryegrass and mixed ryegrass-clover intercropping fodder crops
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 10.22 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Effective nitrogen (N) management is essential for optimizing crop yields and minimizing environmental impacts, particularly in semi-arid regions where climate risks and natural resource constraints complicate decisionmaking. These low-energy systems require precise N strategies tailored to their unique challenges. This study
evaluated a sensor-driven data analysis workflow for assessing N status in ryegrass-based fodder crops under
semi-arid conditions and identified the most effective bands and vegetation indices (VIs) for use. Field trials
conducted at Herdade da Comenda in Portugal employed a split-plot design, testing three N topdressing rates (0,
120, and 200 kg ha⁻¹) across varying crop types and irrigation systems. Both physical and remote measurements
of crop parameters and N nutrition indicators were taken to address the limitations of current approaches in
these conditions. The study found that vegetation pixels dominate spectral imagery, making additional filtering,
such as ExG masks, unnecessary at ryegrass tillering and stem-elongation in ryegrass-based fodders. This
simplification reduces processing time, costs, and digital footprints. Key VIs—NDRE, RERVI, and CIRE—proved
robust for monitoring variables such as crop type, growth stage, and N treatments, showing strong correlations
with N status indicators (NNI and CNI). Additionally, the study contrasted the efficiency of the entirely remote
NNI method with the enhanced accuracy of the hybrid CCCI-CNI approach, providing valuable insights for
tailored N management in semi-arid systems.
Description
Keywords
Nitrogen nutrition index Mediterranean rainfed systems Data-driven approach Site-specific management Unmanned aerial vehicle (UAV) Data science
Pedagogical Context
Citation
Luís Silva, Sofia Barbosa, Teresa Carita, Paola D’Antonio, Fernando Cebola Lidon, Luís Alcino Conceição, Reflectance-based assessment of nitrogen status in ryegrass and mixed ryegrass-clover intercropping fodder crops, Smart Agricultural Technology, Volume 11, 2025, 101046, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2025.101046.