TY - GEN
T1 - Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model
AU - Samani, Babak
AU - Samani, Saeideh
AU - Yang, Haishun
AU - Yu, Hongfeng
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
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.
KW - Multivariate Visualization
KW - Nitrogen Leaching
KW - Uncertainty Analysis and Visualization
UR - http://www.scopus.com/inward/record.url?scp=85103848134&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103848134&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9378105
DO - 10.1109/BigData50022.2020.9378105
M3 - Conference contribution
AN - SCOPUS:85103848134
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 3295
EP - 3303
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
Y2 - 10 December 2020 through 13 December 2020
ER -