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Lei H, Fan Y, Xiao Z, Jin C, Chen Y, Pan H. Comprehensive Evaluation of Tomato Growth Status under Aerated Drip Irrigation Based on Critical Nitrogen Concentration and Nitrogen Nutrient Diagnosis. PLANTS (BASEL, SWITZERLAND) 2024; 13:270. [PMID: 38256824 PMCID: PMC10818335 DOI: 10.3390/plants13020270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/06/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024]
Abstract
In order to provide a theoretical basis for the rational application of nitrogen fertilizer for tomatoes under aerated drip irrigation, a model of the critical nitrogen dilution curve was established in this study, and the feasibility of the nitrogen nutrition index (NNI) for the real-time diagnosis and evaluation of the nitrogen nutrient status was explored. The tomato variety "FENOUYA" was used as the test crop, and aerated drip irrigation was adopted by setting three levels of aeration rates, namely, A1 (dissolved oxygen concentration of irrigation water is 5 mg L-1), A2 (dissolved oxygen concentration of irrigation water is 15 mg L-1), and A3 (dissolved oxygen concentration of irrigation water is 40 mg L-1), and three levels of nitrogen rates, namely, N1 (120 kg ha-1), N2 (180 kg ha-1) and N3 (240 kg ha-1). The model of the critical nitrogen concentration dilution of tomatoes under different aerated treatments was established. The results showed that (1) the dry matter accumulation of tomatoes increased with the increase in the nitrogen application rate in a certain range and it showed a trend of first increase and then decrease with the increase in aeration rate. (2) As the reproductive period progressed, the nitrogen concentration in tomato plants showed a decreasing trend. (3) There was a power exponential relationship between the critical nitrogen concentration of tomato plant growth and above-ground biomass under different levels of aeration and nitrogen application rate, but the power exponential curves were characterized by A1 (Nc = 15.674DM-0.658), A2 (Nc = 101.116DM-0.455), A3 (Nc = 119.527DM-0.535), N1 (Nc = 33.819DM-0.153), N2 (Nc = 127.759DM-0.555) and N3 (Nc = 209.696DM-0.683). The standardized root mean square error (n-RMSE) values were 0.08%, 3.68%, 3.79% 0.50%, 1.08%, and 0.55%, which were less than 10%, and the model has good stability. (4) The effect of an increased nitrogen application rate on the critical nitrogen concentration dilution curve was more significant than that of the increase in aeration rate. (5) A nitrogen nutrition index model was built based on the critical nitrogen concentration model to evaluate the nitrogen nutritional status of tomatoes, whereby 180 kg ha-1 was the optimal nitrogen application rate, and 15 mg L-1 dissolved oxygen of irrigation water was the optimal aeration rate for tomatoes.
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Affiliation(s)
| | | | | | | | | | - Hongwei Pan
- School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China; (H.L.); (Y.F.); (Z.X.); (C.J.); (Y.C.)
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Jiang J, Wang C, Wang H, Fu Z, Cao Q, Tian Y, Zhu Y, Cao W, Liu X. Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat. SENSORS 2021; 21:s21165579. [PMID: 34451022 PMCID: PMC8402299 DOI: 10.3390/s21165579] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022]
Abstract
The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha−1) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.
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Affiliation(s)
- Jie Jiang
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Cuicun Wang
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Hui Wang
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhaopeng Fu
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Qiang Cao
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Yongchao Tian
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Yan Zhu
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Weixing Cao
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiaojun Liu
- National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.J.); (C.W.); (H.W.); (Z.F.); (Q.C.); (Y.T.); (Y.Z.); (W.C.)
- MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China
- MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
- Correspondence: ; Tel.: +86-25-8439-6804; Fax: +86-25-8439-6672
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