Improving maize nitrogen nutrition index prediction using leaf fluorescence sensor combined with environmental and management variables

Rui Dong, Yuxin Miao, Xinbing Wang, Zhichao Chen, Fei Yuan

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Precision nitrogen (N) management requires rapid and real-time technologies for in-season crop N status diagnosis. The leaf fluorescence sensor Dualex 4 is an effective and promising tool to monitor crop N status. N nutrition index (NNI) is the most widely recognized diagnostic tool for accurate in-season diagnosis of crop N status. However, studies focusing on revealing the relationships between fluorescence sensing indices and NNI and assessing the N status of maize is limited. The objectives of this study were to (1) evaluate the potential of using Dualex 4 indices measured on three differently positioned leaves to estimate NNI across different stages; and (2) determine if the incorporation of environmental (weather) and management information can significantly improve the in-season N status prediction and diagnosis of maize. In 2016 and 2017, a total of four experiments with six N rates and three plant densities were conducted in two fields in Northeast China. Dualex sensor readings – Chlorophyll (Chl) and N balance index (NBI) – were collected from three differently positioned leaves at three growth stages. Some external factors including weather and management conditions were included for in-season N status assessment. The results indicated that the two Dualex indices (Chl and NBI) had strong relationships with NNI at different growth stages, and both stage-specific and across-stage models could estimate NNI based on their values acquired from differently positioned leaves. Nevertheless, the N diagnostic accuracies based on the estimated NNI by the Dualex indices were not satisfactory with Kappa values all lower than 0.40. Likewise, similar results were found in the multiple linear regression (MLR) models only based on the Dualex readings (MLRChl, MLRNBI and MLRChl+NBI). However, when weather and management variables were used together with Dualex sensor measurements in MLR analysis, the prediction of NNI (R2 = 0.81 to 0.85) and the accuracy of maize N status diagnosis (areal agreement = 0.79 and Kappa = 0.52 to 0.55) were significantly improved. More studies are needed to develop strategies combining more environmental and management variables with sensor data to further improve in-season N status diagnosis and N management and/or combine proximal with remote sensing for large-scale crop N nutritional status diagnosis and in-season site-specific N management.

Original languageEnglish (US)
Article number108180
JournalField Crops Research
StatePublished - Jul 15 2021

Bibliographical note

Funding Information:
This work was supported by the Key National Research and Development Program ( 2016YFD0200600 ; 2016YFD0200602 ), Norwegian Ministry of Foreign Affairs (SINOGRAIN II, CHN-17/0019), Minnesota Department of Agriculture/Agricultural Fertilizer Research and Education Council (MDA/AFREC R2018-25, R2019-20 and R2020-32) and the USDA National Institute of Food and Agriculture (State project 1016571). We also would like to thank the help from Prof. Guohua Mi, Xuezhi Yue, Zheng Fang, Weidong Lou, Guiyin Jiang and Yue Li during the field experiments.

Publisher Copyright:
© 2021 Elsevier B.V.


  • Chlorophyll
  • Data fusion
  • Dualex 4
  • Nitrogen balance index
  • Nitrogen status diagnosis
  • Precision nitrogen management


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