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    Can a combination of UAV-derived vegetation indices with biophysical variables improve yield variability assessment in smallholder farms?

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    Journal Article (1.974Mb)
    Date
    2020-12-09
    Author
    Adewopo, J.
    Peter, H.
    Mohammed, I.
    Kamara, A.
    Craufurd, P.
    Vanlauwe, B.
    Type
    Journal Article
    Review Status
    Peer Review
    Target Audience
    Scientists
    Metadata
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    Abstract/Description
    The 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.
    https://dx.doi.org/10.3390/agronomy10121934
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/7726
    IITA Authors ORCID
    Julius Adewopohttps://orcid.org/0000-0003-4831-2823
    Alpha Kamarahttps://orcid.org/0000-0002-1844-2574
    bernard vanlauwehttps://orcid.org/0000-0001-6016-6027
    Digital Object Identifier (DOI)
    https://dx.doi.org/10.3390/agronomy10121934
    Research Themes
    Natural Resource Management; Plant Production and Health
    IITA Subjects
    Agronomy; Farming Systems; Maize; Plant Breeding; Plant Production; Smallholder Farmers
    Agrovoc Terms
    Multispectral Imageries; Maize; Drones; Trials
    Regions
    Africa; West Africa
    Countries
    Nigeria
    Hubs
    Central Africa Hub; Headquarters and Western Africa Hub
    Journals
    Agronomy
    Collections
    • Journal and Journal Articles4835
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