Abstract
Remote sensing has been used for assisting the precision nitrogen (N) management in wheat (Triticum aestivum) production. This study aimed to develop an efficient and energy saving N management strategy based on multi-source data for winter wheat at the farm scale. Five field experiments involving different cultivars and N treatments were conducted to establish and validate the N management strategy in 2017–2021. UAV multi-spectral images, plant sampling, weather and field management data collection were carried out synchronously at Feekes 6.0. Four machine learning methods were used to integrate multi-variate information to determine the optimal parameters in N regulation algorithm. The results showed random forest (RF) algorithm performed best for plant dry matter (R2 = 0.78) and plant N accumulation (R2 = 0.83) estimation, a N nutrition index optimized algorithm (NNIOA), driven by multi-source data, was developed and used for guiding in-season N application. The NNIOA efficiently regulated the deficient, optimal and excessive N status through up- (54.17%), fine- (0.67%) and downward- (18.18%) adjustment of N fertilizers, respectively, while the optimal N treatment achieved highest net profit, energy use efficiency (EUE) and energy productivity (EP). Compared with farmer’s practices, the NNIOA increased partial factor productivity (PFP), net profit, EUE and EP by 19.60–27.94%, 22.47–45.13 $ ha−1, 6.94–13.07% and 8.36–12.29%, respectively, while reduced N input (16.77–21.67%), energy input (8.13–10.74%) and CO2 emission (7.60–10.11%) without any yield reduction at study farms. In conclusion, this study supplied a precision N management strategy to implement variable N application for sustainable wheat production at farm scale.
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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.
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Abbreviations
- AGDD:
-
Accumulated growth degree day
- AND:
-
Accumulated N deficit
- ANN:
-
Artificial neural network
- CNDC:
-
Critical N dilution curve
- CO2 :
-
Carbon dioxide
- EP:
-
Energy productivity
- EUE:
-
Energy use efficiency
- Exp.:
-
Experiment
- FM:
-
Field management
- GYN :
-
Grain yield
- HI:
-
Harvest index
- LDM:
-
Leaf dry matter
- LNC:
-
Leaf N concentration
- N:
-
Nitrogen
- Nc :
-
Critical plant N concentration
- NE:
-
Net energy
- Nlocal :
-
Regional professional N recommendation rates
- NNIOA:
-
N nutrition index optimized algorithm
- NP:
-
Net profit
- Nr:
-
N recommendation rates
- NUE:
-
N use efficiency
- PDM:
-
Plant dry matter
- PFP:
-
Partial factor productivity
- PLSR:
-
Partial least squares regression
- PNA:
-
Plant N accumulation
- PNM:
-
Precision N management
- PNAc :
-
Critical plant N accumulation
- Prepsum :
-
Accumulated precipitation
- Radsum :
-
Accumulated radiation
- RE:
-
Relative error
- RF:
-
Random forest
- RMSE:
-
Root mean square error
- SDM:
-
Stem dry matter
- SNC:
-
Stem N concentration
- Tave :
-
Average daily temperature
- Tmin :
-
Average daily minimum temperature
- Tmax :
-
Average daily maximum temperature
- Tsum :
-
Accumulated daily average temperature
- UAV:
-
Unmanned aerial vehicle
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Acknowledgements
We would like to thank Xue Wang, Yan Liang, Jiayi Zhang, Fanglin Xiang, Yan Yan, Meng Zhou, Yang Gao, and Xinge Li for their help with the field data collection and chemical analysis of plant samples.
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 (Nos. JATS (2020)415 and JATS (2020)135), the Jiangsu Provincial Key Technologies R&D Program of China (No. BE2019386), and the Guidance Foundation of the Sanya Institute of Nanjing Agricultural University, China (No. NAUSY-ZD01).
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Jiang, J., Wu, Y., Liu, Q. et al. Developing an efficiency and energy-saving nitrogen management strategy for winter wheat based on the UAV multispectral imagery and machine learning algorithm. Precision Agric 24, 2019–2043 (2023). https://doi.org/10.1007/s11119-023-10028-6
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DOI: https://doi.org/10.1007/s11119-023-10028-6