Abstract
Remote sensing data from optical canopy sensors has been successfully used for precision nitrogen (N) management. This study aimed to explore the potential of multispectral camera mounted on fixed-wing unmanned aerial vehicle (UAV) in guiding in-season N topdressing for regional wheat production. Wheat plot and field experiments were conducted with multiple cultivars and N rates during 2017–2019. Manual sampling and UAV photography were carried out at wheat key growth stages. A modified sufficiency index algorithm (MSIA), driven by UAV spectral data, was developed and used for variable rate N recommendation (VRNR). Firstly, data from plot experiment was used to determine the local optimum N rate (\({\text{N}}_{\text{OPT}}\)) for attaining the maximum available yield, and to quantify the relationships between vegetation index and above ground biomass, plant N uptake (PNU), and grain yield. The results suggested that local \({\text{N}}_{\text{OPT}}\) was 283.5 kg ha−1, and red-edge soil adjusted vegetation index (RESAVI) yielded the highest accuracy in constructing PNU monitoring model (R2 = 0.78) and dynamic curves of UAV spectrum (R2 > 0.95). Secondly, using UAV spectral data of field experiment, the calculated real-time PNU and sufficiency index, along with \({\text{N}}_{\text{OPT}}\), were imported into MSIA for in-season VRNR. Compared to local empirical N fertilization, MSIA gave accurate N recommendation rate according to wheat growth status, with harvest index and nitrogen agronomic efficiency averagely increased by 3.1% and 11.9%, respectively under 15.4% reduced N input and undiminished yield return. This study supplies a new approach to implement VRNR for regional wheat production supported by UAV imagery.
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Data availability
The datasets and materials used and/or analyzed in this study are available from the corresponding author on reasonable request.
Code availability
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Abbreviations
- AGB:
-
Above ground biomass
- DAS:
-
Days after sowing
- DW:
-
Dry matter weight
- Exp.:
-
Experiment
- G:
-
Green
- GRF:
-
Gross return above fertilizer
- GY:
-
Grain yield
- HI:
-
Harvest index
- MSIA:
-
Modified sufficiency index algorithm
- N:
-
Nitrogen
- \({\text{N}}_{\text{OPT}}\) :
-
Optimum nitrogen rate
- \({\text{N}}_{\text{PreFert}}\) :
-
Applied nitrogen rate before topdressing
- \({\text{N}}_{\text{Soil}}\) :
-
Soil residual nitrogen rate
- \({\text{N}}_{\text{Comp}}\) :
-
Compensation nitrogen rate
- NAE:
-
Agronomic efficiency of applied nitrogen
- NC:
-
Nitrogen concentration
- NDVI:
-
Normalized difference vegetation index
- NDRE:
-
Normalized difference red-edge index
- NFOA:
-
Nitrogen fertilizer optimization algorithm
- NU:
-
Nitrogen uptake
- NIR:
-
Near-infrared
- PNU:
-
Plant nitrogen uptake
- R:
-
Red
- RE:
-
Red-edge
- RESAVI:
-
Red-edge soil adjusted vegetation index
- RMSE:
-
Root mean square error
- RRMSE:
-
Relative root mean square error
- RS:
-
Remote sensing
- SI:
-
Sufficiency index
- TFC:
-
Total fertilizer cost
- UAV:
-
Unmanned aerial vehicle
- VI:
-
Vegetation index
- VRN:
-
Variable rate nitrogen
- VRNR:
-
Variable rate nitrogen recommendation
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Acknowledgements
We would like to thank Yan Liang, Jie Jiang, Fanglin Xiang, Yan Yan, and Meng Zhou for their help with the field data collection.
Funding
This work was supported by the National Natural Science Foundation of China (No. 32071903), the Fund of Jiangsu Agricultural Science and Technology Innovation (No. CX(20)3072), the Earmarked Fund for Jiangsu Agricultural Industry Technology System, China (Nos. JATS (2021)467 and JATS (2021)164), and the Jiangsu Provincial Key Technologies R&D Program of China (No. BE2019386).
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J.Z. and X.L. conceived and designed the experiments, B.K. and W.W. performed experiments, J.Z. analyzed the data, J.Z. and X.L. wrote the paper, Y.Z., W.C., and Q.C. provided advice and edited the manuscript. All of the authors have read and approved the final manuscript submitted to the editor.
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Zhang, J., Wang, W., Krienke, B. et al. In-season variable rate nitrogen recommendation for wheat precision production supported by fixed-wing UAV imagery. Precision Agric 23, 830–853 (2022). https://doi.org/10.1007/s11119-021-09863-2
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DOI: https://doi.org/10.1007/s11119-021-09863-2