Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains of the USA

Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains... A four-year study was conducted from 2000 to 2004 at eight field sites in Montana, North Dakota and western Minnesota. Five of these sites were in North Dakota, two were in Montana and one was in Minnesota. The sites were diverse in their cropping systems. The objectives of the study were to (1) evaluate data from aerial photographs, satellite images, topographic maps, soil electrical conductivity (ECa) sensors and several years of yield to delineate field zones to represent residual soil nitrate and (2) determine whether the use of data from several such sources or from a single source is better to delineate nitrogen management zones by a weighted method of classification. Despite differences in climate and cropping, there were similarities in the effectiveness of delineation tools for developing meaningful residual soil nitrate zones. Topographic information was usually weighted the most because it produced zones that were more correlated to actual soil residual nitrate than any other source of data at all locations. The soil ECa sensor created better correlated zones at Minot, Williston and Oakes than at most eastern sites. Yield data for an individual year were sometimes useful, but a yield frequency map that combined several years of standardized yield data was more useful. Satellite imagery was better than aerial photographs at most locations. Topography, satellite imagery, yield frequency maps and soil ECa are useful data for delineating nutrient management zones across the region. Use of two or more sources of data resulted in zones with a stronger correlation with soil nitrate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains of the USA

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Springer US
Copyright © 2010 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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  • The interpretation of trends from multiple yield maps
    Blackmore, S

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