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Reflectance-based assessment of nitrogen status in ryegrass and mixed ryegrass-clover intercropping fodder crops

datacite.subject.fosCiências Agrárias
datacite.subject.sdg01:Erradicar a Pobreza
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
dc.contributor.authorLuís Silvaen_US
dc.contributor.authorSofia Barbosaen_US
dc.contributor.authorTeresa Caritaen_US
dc.contributor.authorPaola D’Antonioen_US
dc.contributor.authorFernando Cebola Lidonen_US
dc.contributor.authorLuís Alcino Conceiçãoen_US
dc.date.accessioned2025-12-12T15:58:41Z
dc.date.available2025-12-12T15:58:41Z
dc.date.issued2025-08en_US
dc.date.updated2025-12-02T15:52:02Z
dc.description.abstractEffective 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.eng
dc.description.versionN/A
dc.identifier.citationLuís 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.
dc.identifier.doi10.1016/j.atech.2025.101046en_US
dc.identifier.slugcv-prod-4527195
dc.identifier.urihttp://hdl.handle.net/10400.26/60378
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectNitrogen nutrition index
dc.subjectMediterranean rainfed systems
dc.subjectData-driven approach
dc.subjectSite-specific management
dc.subjectUnmanned aerial vehicle (UAV)
dc.subjectData science
dc.titleReflectance-based assessment of nitrogen status in ryegrass and mixed ryegrass-clover intercropping fodder cropsen_US
dc.typeresearch articleen_US
dspace.entity.typePublication
oaire.citation.titleSmart Agricultural Technologyen_US
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
rcaap.cv.cienciaid9D12-7A76-F191 | Luís Miguel Roque da Silva
rcaap.rightsopenAccessen_US

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