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dc.contributor.authorAdewopo, J.
dc.contributor.authorPeter, H.
dc.contributor.authorMohammed, I.
dc.contributor.authorKamara, A.
dc.contributor.authorCraufurd, P.
dc.contributor.authorVanlauwe, B.
dc.date.accessioned2022-09-09T09:33:05Z
dc.date.available2022-09-09T09:33:05Z
dc.date.issued2020-12-09
dc.identifier.citationAdewopo, J., Peter, H., Mohammed, I., Craufurd, P. & Vanlauwe, B. (2020). Can a combination of UAV-derived vegetation indices with biophysical variables improve yield variability assessment in smallholder farms?. Agronomy, 10(12): 1934, 1-21.
dc.identifier.issn2073-4395
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7726
dc.description.abstractThe rapid assessment of maize yields in a smallholder farming system is important for understanding its spatial and temporal variability and for timely agronomic decision-support. We assessed the predictability of maize grain yield using unmanned aerial/air vehicle (UAV)-derived vegetation indices (VI) with (out) biophysical variables on smallholder farms. High-resolution imageries were acquired with UAV-borne multispectral sensor at four and eight weeks after sowing (WAS) on 31 farmer managed fields (FMFs) and 12 nearby nutrient omission trials (NOTs) sown with two genotypes (hybrid and open-pollinated maize) across five locations within the core maize region of Nigeria. Acquired multispectral imageries were post-processed into three VIs, normalized difference VI (NDVI), normalized difference red-edge (NDRE), and green-normalized difference VI (GNDVI) while plant height (Ht) and percent canopy cover (CC) were measured within georeferenced plot locations. Result shows that the nutrient status had a significant effect on the grain yield (and variability) in NOTs, with a maximum grain yield of 9.3 t/ha, compared to 5.4 t/ha in FMFs. Generally, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r < 0.02, p > 0.1), but significant correlations were observed at 8WAS (r ≤ 0.3; p < 0.001). Ht was positively correlated with grain yield at 4WAS (r = 0.5, R2 = 0.25, p < 0.001) and more strongly at 8WAS (r = 0.7, R2 = 0.55, p < 0.001), while the relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMFs (separately), predictability of grain yield from UAV-derived VIs was generally low (R2 ≤ 0.24); however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R2 ≥ 0.62, Root Mean Square Error of Prediction, RMSEP ≤ 0.35) in NOTs but not in FMFs. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), compared to actual farmer managed fields where various confounding agronomic factors can amplify noise-signal ratio.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-21
dc.language.isoen
dc.subjectMultispectral Imageries
dc.subjectMaize
dc.subjectDrones
dc.subjectTrials
dc.titleCan a combination of UAV-derived vegetation indices with biophysical variables improve yield variability assessment in smallholder farms?
dc.typeJournal Article
cg.contributor.crpGrain Legumes
cg.contributor.crpMaize
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationBayero University, Nigeria
cg.contributor.affiliationInternational Maize and Wheat Improvement Center
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.hubCentral Africa Hub
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeNatural Resource Management
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidADEWOPO:2020
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectFarming Systems
cg.iitasubjectMaize
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.iitasubjectSmallholder Farmers
cg.journalAgronomy
cg.notesPublished online: 09 Dec 2020
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.3390/agronomy10121934
cg.iitaauthor.identifierJulius Adewopo: 0000-0003-4831-2823
cg.iitaauthor.identifierAlpha Kamara: 0000-0002-1844-2574
cg.iitaauthor.identifierbernard vanlauwe: 0000-0001-6016-6027
cg.futureupdate.requiredNo
cg.identifier.issue12
cg.identifier.volume10


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