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dc.contributor.authorGracia-Romero, Adrian
dc.contributor.authorKefauver, Shawn C.
dc.contributor.authorVergara-Diaz, Omar
dc.contributor.authorHamadziripi, Esnath
dc.contributor.authorZaman‑Allah, Mainassara A.
dc.contributor.authorThierfelder, Christian
dc.contributor.authorPrassana, Boddupalli M.
dc.contributor.authorCairns, Jill E.
dc.contributor.authorAraus Ortega, José Luis
dc.date.accessioned2020-12-03T10:30:47Z
dc.date.available2020-12-03T10:30:47Z
dc.date.issued2020-09-29
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10459.1/70008
dc.description.abstractEnhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs.ca_ES
dc.description.sponsorshipThis work was supported by the Bill & Melinda Gates Foundation and USAID funded project Stress Tolerant Maize for Africa (STMA) (grant number OPP1134248) and the CGIAR Research Program on Maize (MAIZE). The CGIAR Research Program MAIZE receives W1&W2 support from the Governments of Australia, Belgium, Canada, China, France, India, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Sweden, Switzerland, U.K., U.S., and the World Bank. A.G.-R. is a recipient of a FPI doctoral fellowship from the AGL2016-76527-R Project from the Ministerio de Economía y Competitividad of the Spanish Government. We also acknowledge the support from the Institut de Recerca de l’Aigua and the Universitat de Barcelona. J.L.A. acknowledges the funding support from ICREA, Generalitat de Catalunya, Spain.ca_ES
dc.language.isoengca_ES
dc.publisherNature Researchca_ES
dc.relationMINECO/PN2013-2016/AGL2016-76527-Rca_ES
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1038/s41598-020-73110-3ca_ES
dc.relation.ispartofScientific Reports, 2020, vol. 10, article number 16008ca_ES
dc.rightscc-by, (c) Gracia-Romero, Adrian et al., 2020ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAgroecologyca_ES
dc.subjectImaging and sensingca_ES
dc.subjectPlant physiologyca_ES
dc.subjectPlant stress responsesca_ES
dc.titleLeaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabweca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1038/s41598-020-73110-3


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cc-by, (c) Gracia-Romero, Adrian et al., 2020
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