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Fluid Journal : Spring 2011
9 The Fluid Journal Spring 2011 Summary: The remotely sensed normalized difference vegetation index (NDVI) can provide valuable information about in-field nitrogen (N) variability in maize. Significant relationships between sensor NDVI and maize grain yield have been found, suggesting that an N recommendation algorithm based on NDVI could optimize N application. Algorithms were developed for the GreenSeekerTM and Crop CircleTM sensors and the methodology of algorithm development was proven valid, as was the estimate of required N at maize growth stage V12. The algorithms developed for each sensor calculated very similar N recommendations. Our expectations are the integration of crop canopy sensors and the appropriate N application algorithms into an on-the-go fertilizer application system would increase the spatial accuracy of N application on fields that are spatially variable if these algorithms are shown to be stable over time and space. Current algorithm development methodology has proven sound and should be researched further for accuracy. More Research Needed On Remote Sensing For Nitrogen Management The Fluid Journal • Ofﬁcial Journal of the Fluid Fertilizer Foundation • Spring 2011 • Vol. 19, No. 3, Issue #73 Drs. T. Shaver, R. Khosla, and D. Westfall Precision farming has been a major research focus of agronomists for over a decade. Much of this research has been directed toward enhancing the efficiency of overall farm inputs (e.g., fertilizers, herbicides, insecticides, water) without negatively impacting farm productivity, profitability, and the environment. One way to achieve increased fertilizer efficiency could be through the application of nutrients based on remotely sensed data. An easy and effective way to obtain remotely sensed data is through the use of crop canopy sensors that can be used to calculate NDVI and ultimately nutrient recommendations, particularly N. Crop canopy sensors allow for the determination of NDVI at specific times and locations throughout the growing season without the need for ambient illumination. These sensors are relatively small in size and operate by directing sensor-produced visible light (VIS) as well as near infrared (NIR) light at the plant canopy. The amount of VIS and NIR light that is reflected off the plant canopy is collected by the sensor and an NDVI value is calculated using Equation 1: NDVI = (NIR -- VIS)/(NIR + VIS). Research has shown that a response index (RI) can be used to estimate N application rates. This RI uses the NDVI readings of an N-rich (reference) portion of the field divided by the NDVI of a target area of the field to give a response index that can then be used to determine an N recommendation. The RI equation is presented as Equation 2: RI = NDVIReference/NDVITarget, where NDVIReference = NDVI of N-rich plot, and NDVITarget = NDVI of managed plot. The wider the discrepancy in reflectance values from the reference and target areas the larger the RI, resulting in a higher N recommendation. Essentially, the RI indicates the difference in maize growth from a well-fertilized area of the field compared to a non-fertilized area of the field. The algorithm estimates the amount of N needed to make up this difference. Development of an N recommendation algorithm is the overall goal. However, initially we conducted a study to determine which sensor--Crop Circle (amber wavelength) or Green Seeker (red wavelength)--performed best in the semi-arid region of Colorado, and at
Early Spring 2011