Abstract
Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R2 = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods.
Highlights
The North China Plain (NCP) is one of the most intensive agricultural regions in China and produces50% and 30% of the country’s wheat (Triticum aestivum L.) and maize (Zea mays L.), respectively [1].It is a representative smallholder farming system in China, with each household managing about 0.3 to0.5 hectares [2,3]
This study evaluated the potential of using eBee unmanned aerial vehicle (UAV)-based multispectral remote sensing to estimate winter wheat Nutrition Index (NNI) at the Feekes 6 stage for guiding topdressing N application in small farming systems of NCP
The top vegetation indices (VIs) of the eBee UAV remote sensing were significantly related to aboveground biomass (AGB) (R2 = 0.70–0.72) and plant N uptake (PNU) (R2 = 0.64) across fields, varieties, and years
Summary
The North China Plain (NCP) is one of the most intensive agricultural regions in China and produces50% and 30% of the country’s wheat (Triticum aestivum L.) and maize (Zea mays L.), respectively [1].It is a representative smallholder farming system in China, with each household managing about 0.3 to0.5 hectares [2,3]. Active canopy sensors can be used to guide in-season N management for non-destructive real-time diagnosis of crop N status, overcoming the limitations of soil Nmin test-based PNM strategy [12,13]. For practical on-farm applications across a large area like a village, active canopy sensors can be mounted on the fertilizer application machines for on-the-go sensing and variable rate application of N fertilizers in the wheat and maize fields. Such technologies are used in developed countries, they are not available in China yet. Satellite remote sensing is potentially more efficient for monitoring crop growth status across large areas [14,15], but the long revisit cycles, coarse spatial resolution, and bad weather conditions often limit their applications for guiding in-season topdressing N application, especially in small farming systems [12,16,17]
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