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Effect of Drought Stress on Sensing Nitrogen Requirements for Cotton
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Paper number 131620324, 2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: http://dx.doi.org/10.13031/aim.20131620324) @2013
Authors: Earl D Vories, Andrea S Jones, Kenneth A Sudduth, Scott S Drummond, N Ray Benson
Keywords: cotton, irrigation, water management, nitrogen, nitrogen management, NDVI
Abstract. A field experiment was conducted in 2010 - 2012 at the University of Missouri Fisher Delta Research Center to investigate crop sensors and the timing of sensor data collection for both irrigated and rainfed cotton receiving different nitrogen (N) rates and timings of N application. Eleven N treatments were included, with total N ranging from 0 – 134 kg ha-1 and timings from all preplant to all 10 days after first flower. Sensors for canopy reflectance, height, and temperature were driven through the plots once preflower and once postflower each season. While growing degree days indicated that air temperatures were similar all three years, rainfall varied, which is typical for the Mid-South region. Observation of significant in-season and end-of-season factors varied considerably among years. When the data from all three years were combined and subjected to a correlation analysis, most of the correlations were highly significant even if they were not numerically large. For yield, only correlations between both postflower NDVI and height had r > 0.5. However, as more data are collected and additional variables investigated the relationship between in-season and end-of-season observations should improve. While these data demonstrate that in-season sensor measurements can detect differences in water and nitrogen status for cotton that result in end-of-season differences in yield and fiber properties, further research, including looking at additional sensors, is needed before recommendations can be made for developing real-time N-application algorithms that account for the effects of drought stress.
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