Ground based active remote sensors for precision nitrogen management in irrigated maize production - Shaver, Timothy Michael, author; Westfall, Dwayne G., advisor; Khosla, Rajiv, advisor (Colorado State University. Libraries, 2009)
Precision agriculture can increase farm input efficiency by accurately quantifying variability within a field. Remotely sensed normalized difference vegetation index (NDVI) has been shown to quantify maize (Zea mays) N variability. Ground-based active remote sensors that can determine NDVI are commercially available and have been shown to accurately distinguish N variability in maize. There are several active sensors available but no studies directly comparing active sensors have been reported. Therefore, a study was conducted to evaluate active sensor performance and develop an in-season maize N recommendation algorithm for use in Colorado using NDVI. Previous studies have demonstrated an association of active sensor NDVI with maize N content and height. However, the NDVI from a GreenSeeker™ green NDVI prototype active sensor had not yet been tested when our study began. Therefore, the green sensor was evaluated to determine if differences in plant growth across MZ could be determined by the active sensor. Results show that the prototype active sensor did not record NDVI values that were associated with MZ. The NDVI from two different sensors (Crop Circle™ amber NDVI and GreenSeeker™ red NDVI) were then examined under greenhouse and field conditions. Results show that NDVI from the amber and red sensors equally distinguished applied N differences in maize. Each active sensor's NDVI values had high R2 values with applied N rate and plant N concentration. Results also show that each sensor's NDVI readings had high R2 values with applied N rate and yield at the V12 and V14 maize growth stages. An N recommendation algorithm was then created for use at the V12 maize growth stage for both the amber and red sensors using NDVI. These algorithms yielded N recommendations that were not significantly different across sensor type suggesting that the amber and red NDVI sensors performed equally. Also, each N recommendation algorithm yielded unbiased N recommendations suggesting that each was a valid estimator of required N at maize growth stage V12. Overall results show that the amber and red sensors equally determine N variability in irrigated maize and could be very important tools for managing in-season application of N fertilizer.
Keywords: active sensors; corn; management zone; NDVI; nitrogen management; precision agriculture; remote sensing; Zea mays; agronomy; remote sensing