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Development of an NDVI‐Based Nitrogen Rate Calculator for Cotton
Author(s) -
Arnall D. Brian,
Abit M. Joy M.,
Taylor Randal K.,
Raun William R.
Publication year - 2016
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2016.01.0049
Subject(s) - normalized difference vegetation index , sowing , yield (engineering) , growing season , vegetation index , vegetation (pathology) , mathematics , agronomy , remote sensing , biology , environmental science , leaf area index , geography , medicine , materials science , pathology , metallurgy
The use and adoption of optical sensors to determine midseason nitrogen (N) application rates in cereal grain production has gained increasing acceptance by producers in the past decade; however, the technology has yet to impact mainstream cotton ( Gossypium hirsutum L.) production. Overapplication of N in cotton leads to excessive growth and can result in a dramatic yield decrease. This study was designed to develop a sensor‐based cotton yield prediction model using normalized difference vegetation index (NDVI) readings from an optical sensor, incorporate the model into an algorithm used to determine midseason N rates, and determine if N response can be predicted using NDVI values collected midseason. A GreenSeeker hand held sensor was used to measure NDVI from seven studies at Lake Carl Blackwell (LCB), Stillwater, OK, and Southwest Research Station (SWR), Altus, OK, from 2006 to 2010. Normalized difference vegetation index readings taken at white flower stage had a good correlation with final yield. Final yield prediction was improved when NDVI was normalized using cumulative growing degree day (CumGDD) measured between planting and sensing. Sorting NDVI by CumGDD to 940 to 1200 and 1400 to 1500 at LCB and SWR sites, respectively, extended the critical sensing window from match‐head square to white flower stages. This study showed that yield potential in cotton could be predicted within the season using NDVI, which confirms the potential for using sensor‐based N rate recommendations in cotton.