Working Paper Article Version 2 This version is not peer-reviewed

­­­Can Combination of UAV-derived Vegetation Indices with Biophysical Variable(s) Improve Yield Variability Assessment in Smallholder Farms?

Version 1 : Received: 12 October 2020 / Approved: 13 October 2020 / Online: 13 October 2020 (09:26:20 CEST)
Version 2 : Received: 19 October 2020 / Approved: 19 October 2020 / Online: 19 October 2020 (16:00:27 CEST)

How to cite: Adewopo, J.; Peter, H.; Kamara, A.; Mohammed, I.; Vanlauwe, B.; Craufurd, P. ­­­Can Combination of UAV-derived Vegetation Indices with Biophysical Variable(s) Improve Yield Variability Assessment in Smallholder Farms?. Preprints 2020, 2020100264 Adewopo, J.; Peter, H.; Kamara, A.; Mohammed, I.; Vanlauwe, B.; Craufurd, P. ­­­Can Combination of UAV-derived Vegetation Indices with Biophysical Variable(s) Improve Yield Variability Assessment in Smallholder Farms?. Preprints 2020, 2020100264

Abstract

Rapid assessment of maize yields in smallholder farming system is important to understand its spatial and temporal variability and for timely agronomic decision-support. Imageries acquired with unmanned air vehicles (UAV) offer opportunity to assess agronomic variables at field scale, however, it is not clear if this can be translated into reliable yield assessment on smallholder farms where field conditions, maize genotypes, and management practices vary within short distances. In this study, we assessed the predictability of maize grain yield using UAV-derived vegetation indices (VI), with(out) biophysical variables, in smallholder farms. High-resolution images were acquired with UAV-borne multispectral sensor at 4 and 8 weeks after sowing (WAS) on 31 farmers’ managed fields (FMFs) and 12 nearby Nutrient Omission Trials (NOT), all distributed across 5 locations within the core maize region of Nigeria. The NOTs included non-fertilized and fertilized plots (with and without micronutrients), sown with open-pollinated or hybrid maize genotypes. Acquired multispectral images were post-processed into several three (s) vegetation indices (VIs), normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), green-normalized difference vegetation index (GNDVI). Biophysical variables, plant height (Ht) and percent canopy cover (CC), were measured with the georeferenced plot locations recorded. In the NOTs, the nutrient status, not genotype, influenced the grain yield variability and outcome. The maximum grain yield observed in NOTs was 9.3 tha-1, compared to 5.4 tha-1 in FMF. Without accounting for between- and within-field variations, 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 relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMF (separately) through linear mixed-effects modeling, predictability of grain yield from UAV-derived VIs was generally (R2≤0.24), however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R2 ≥0.62, RMSEP≤0.35) in NOTs but not in FMF. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), than in actual farmer-managed fields where various confounding agronomic factors can amplify noise-signal within the vegetation canopy.

Subject Areas

UAV; multi-spectral imageries; multi-locational; Maize yield; smallholder; vegetation indices

Comments (1)

Comment 1
Received: 19 October 2020
Commenter: Julius Adewopo
Commenter's Conflict of Interests: Author
Comment: The Abstract was slighted edited for minor typo and clarity (The article in being prepared for submission to Journal of Agronomy on MDPI)
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