Abstract
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.
Highlights
Rapid assessment of crop yield is critical in order to monitor and address yield gaps [1,2], yet robust sampling is often cost-intensive across agricultural fields or landscapes
This study provides further insight into the potential use of unmanned air vehicle (UAV)-derived vegetational indices to assess yield variability for rapid agronomic monitoring and robust decision-support in smallholder farming systems
By setting up multilocational nutrient omission trials close to several farmers’ fields, our findings showed that nutrients, not genotype, significantly explained the observed yield variability
Summary
Rapid assessment of crop yield is critical in order to monitor and address yield gaps [1,2], yet robust sampling is often cost-intensive across agricultural fields or landscapes. Technologies and methods that can provide leverage for quick and non-destructive data collection are critical for monitoring changes both spatially and temporally. Agronomy 2020, 10, 1934 the acquisition and processing of remotely-sensed imageries is considered valuable for the assessment of yield or other agronomic variables at various scales [2,3]. Access to high-quality imageries is generally constrained by associated costs and time-lag in processing, posing limitations to the prospects of utilizing imagery-derived data for various agronomic use-cases, both in-season and out-of-season. There is a critical need to evolve reliable and rapid approaches for timely decision support within smallholder farming systems. Smallholder farming systems of sub-Saharan Africa (SSA) are often characterized by fragmented farmlands and differentiated management practices [3,4,5,6]
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