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    Modern agriculture uses large amounts of organic and inorganic nutrients to optimize productivity. Excessive nutrient applications sometime lead to adverse effects on the environment and human health. Precision agriculture is evolving with the abjectives of minimizing these adverse effects by enabling farmers to manage nutrient applications more efficiently while sustaining precious environmental resources. To develop a method that uses nutrients more efficiently on cotton, a field experiment involving three sources and three rates of nitrogen with and without nitrification inhibitor was carried out in four replications at Belle Mina, AL during the 1994-97 crop seasons. In 1997, these plots were used to determine if there was a relationship between remotely sensed hyperspectral reflectance data and three field measurements that included cotton leaf chlorophyll (defined as measurements of five leaves using a Minolta Chlorophyll SPAD Meter to represent cotton canopy), soil nitrate-nitrogen, and cotton yield. Our results showed that hyperspectral reflectance in the 807.6 nm region had the highest significant correlation with cotton leaf chlorophyll. Cotton leaf chlorophyll correlated significantly with soil nitrate-nitrogen and cotton yield. Because leaf chlorophyll is an indicator of nitrogen deficiency, our results suggest that hyperspectral reflectance may be used as a tool to help farmers determine nitrogen deficiency, which may subsequently lead to increased crop productivity and reduced environmental pollution.

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    Boggs, Johnny L.; Tsegaye, T.D.; Coleman, Tamula L.; Reddy, K.C.; Fahsi, Ahmed. 2003. Relationship between hyperspectral reflectance, soil nitrate-nitrogen, cotton leaf chlorophyll, and cotton yield: A step toward precision agriculture. Journal of Sustainable Agriculture 22(3): 5-16


    Remote sensing, hyperspectral data, GIS, poultry litter, precision agriculture

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