Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model

Babak Samani; Saeideh Samani; Haishun Yang; Hongfeng Yu

doi:10.1109/BigData50022.2020.9378105

Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model

Babak Samani, Saeideh Samani, Haishun Yang, Hongfeng Yu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Nitrogen (N) is an essential nutrient for many crops including corn and soybean. However, its leaching into groundwater is a serious cause of concern for environmental and public health. The amount of N leaching is closely linked to soil water drainage and rainfall. Prediction of N leaching in cropping systems is critical to the improvement of crop management. Maize-N is a model for maize yield and N rate recommendation. However, uncertainties in many parameters, such as weather predictions, soil properties, and information entered by users (e.g., applied N fertilizer), can incur uncertainties in N leaching simulation results. We have developed a platform to assist comprehending the relationship between various input parameters and N leaching. Our platform can reveal N leaching with uncertainty analysis and visualization of different parameters.

Original language	English (US)
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3295-3303
Number of pages	9
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9378105
State	Published - Dec 10 2020
Externally published	Yes
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

Keywords

Multivariate Visualization
Nitrogen Leaching
Uncertainty Analysis and Visualization

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/BigData50022.2020.9378105

Cite this

Samani, B., Samani, S., Yang, H., & Yu, H. (2020). Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 3295-3303). [9378105] (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData50022.2020.9378105

Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model. / Samani, Babak; Samani, Saeideh; Yang, Haishun et al.

Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao Wu; Chris Jermaine; Li Xiong; Xiaohua Tony Hu; Olivera Kotevska; Siyuan Lu; Weijia Xu; Srinivas Aluru; Chengxiang Zhai; Eyhab Al-Masri; Zhiyuan Chen; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. p. 3295-3303 9378105 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Samani, B, Samani, S, Yang, H & Yu, H 2020, Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9378105, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., pp. 3295-3303, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9378105

Samani B, Samani S, Yang H, Yu H. Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 3295-3303. 9378105. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378105

Samani, Babak ; Samani, Saeideh ; Yang, Haishun et al. / Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao Wu ; Chris Jermaine ; Li Xiong ; Xiaohua Tony Hu ; Olivera Kotevska ; Siyuan Lu ; Weijia Xu ; Srinivas Aluru ; Chengxiang Zhai ; Eyhab Al-Masri ; Zhiyuan Chen ; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 3295-3303 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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title = "Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model",

abstract = "Nitrogen (N) is an essential nutrient for many crops including corn and soybean. However, its leaching into groundwater is a serious cause of concern for environmental and public health. The amount of N leaching is closely linked to soil water drainage and rainfall. Prediction of N leaching in cropping systems is critical to the improvement of crop management. Maize-N is a model for maize yield and N rate recommendation. However, uncertainties in many parameters, such as weather predictions, soil properties, and information entered by users (e.g., applied N fertilizer), can incur uncertainties in N leaching simulation results. We have developed a platform to assist comprehending the relationship between various input parameters and N leaching. Our platform can reveal N leaching with uncertainty analysis and visualization of different parameters.",

keywords = "Multivariate Visualization, Nitrogen Leaching, Uncertainty Analysis and Visualization",

author = "Babak Samani and Saeideh Samani and Haishun Yang and Hongfeng Yu",

note = "Funding Information: ACKNOWLEDGMENT This research has been sponsored in part by the National Science Foundation through grants IIS-1423487 and CNS-1739705, and by the Layman award of the University of Nebraska Foundation. Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",

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doi = "10.1109/BigData50022.2020.9378105",

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editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",

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AU - Samani, Saeideh

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AU - Yu, Hongfeng

N1 - Funding Information: ACKNOWLEDGMENT This research has been sponsored in part by the National Science Foundation through grants IIS-1423487 and CNS-1739705, and by the Layman award of the University of Nebraska Foundation. Publisher Copyright: © 2020 IEEE.

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N2 - Nitrogen (N) is an essential nutrient for many crops including corn and soybean. However, its leaching into groundwater is a serious cause of concern for environmental and public health. The amount of N leaching is closely linked to soil water drainage and rainfall. Prediction of N leaching in cropping systems is critical to the improvement of crop management. Maize-N is a model for maize yield and N rate recommendation. However, uncertainties in many parameters, such as weather predictions, soil properties, and information entered by users (e.g., applied N fertilizer), can incur uncertainties in N leaching simulation results. We have developed a platform to assist comprehending the relationship between various input parameters and N leaching. Our platform can reveal N leaching with uncertainty analysis and visualization of different parameters.

AB - Nitrogen (N) is an essential nutrient for many crops including corn and soybean. However, its leaching into groundwater is a serious cause of concern for environmental and public health. The amount of N leaching is closely linked to soil water drainage and rainfall. Prediction of N leaching in cropping systems is critical to the improvement of crop management. Maize-N is a model for maize yield and N rate recommendation. However, uncertainties in many parameters, such as weather predictions, soil properties, and information entered by users (e.g., applied N fertilizer), can incur uncertainties in N leaching simulation results. We have developed a platform to assist comprehending the relationship between various input parameters and N leaching. Our platform can reveal N leaching with uncertainty analysis and visualization of different parameters.

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KW - Uncertainty Analysis and Visualization

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ER -

Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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