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ARS Home » Southeast Area » Jonesboro, Arkansas » Delta Water Management Research » Research » Publications at this Location » Publication #384528
Research Project: Preserving Water Availability and Quality for Agriculture in the Lower Mississippi River Basin

Location: Delta Water Management Research

Title: Determining nitrogen deficiencies for maize using various remote sensing indices

Author
item BURNS, B.W. - Arkansas State University
item GREEN, S.V. - Arkansas State University
item HASHEM, A.A. - Arkansas State University
item Massey, Joseph
item SHEW, A.M. - Arkansas State University
item Adviento-Borbe, Arlene
item MILAD, M. - Arkansas State University

Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/11/2021
Publication Date: 1/1/2022
Citation: Burns, B., Green, S., Hashem, A., Massey, J., Shew, A., Adviento-Borbe, A.A., Milad, M. 2022. Determining nitrogen deficiencies for maize using various remote sensing indices. Precision Agriculture. 23:791-811. https://doi.org/10.1007/s11119-021-09861-4.
DOI: https://doi.org/10.1007/s11119-021-09861-4

Interpretive Summary: Since nitrogen is the most important yield-limiting factor in crop production, accurate indicators of the N requirement during the crop growing period can improve farmers' fertilizer N management decisions to improve yield. A field study was conducted in two maize production fields that aimed to validate the accuracy of using plant metrics measured by high definition cameras and sensors in predicting the specific N requirement of maize. Remote sensing data were collected throughout the growth of maize and relationships among vegetation indeces (i.e. normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), chlorophlyll index-green (CIgreen)) and grain yield at different N fertilizer rates were analyzed. Three vegetation indeces namely Green NDVI, RENDVI, and CIgreen were the best predictors of maize yield. These findings can provide maize growers, extension agents, and industry an accurate decision support tool to devise best fertilizer N recommendation for optimal grain yield and nitrogen use efficiency with minimal N losses.

Technical Abstract: Determining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations, and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red-edge Normalized Difference Vegetation Index (RENDVI), Triangle Greenness Index (TGI), Normalized Area Vegetation Index (NAVI) and Chlorophyll Index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict maize grain yield. Strip trials were established at two locations in Arkansas USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an Unmanned Aerial System equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI, and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.