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

Abstract

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 (EC a ) 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 EC a 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 EC a 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.

Challenges in Using Precision Agriculture to Optimize Symbiotic Nitrogen Fixation in Legumes: Progress, Limitations, and Future Improvements Needed in Diagnostic Testing

“…Application of inorganic N fertilizer to areas with high soil N availability not only reduces SNF rates, but can also lead to nitrate leaching, resulting in economic losses and environmental pollution. Therefore, it is important to identify the variability in soil N availability, especially residual N [22], within an agricultural field to optimize SNF. Plant tissue N diagnostics were described above (Table 1).…”

“…Typical practices used in precision agriculture include remote sensing, geographical information systems (GIS), spectral imaging, the global positioning system (GPS), data management, and sensors [18,20,21]. These techniques help to identify patterns of variability within a field to guide soil or plant sampling [22], which then directs optimal and rational spatial input use in conjunction with appropriate equipment (e.g., variable rate input applicators mounted on tractors). Precision agriculture technologies have been widely used for crop N management, especially with non-legumes (e.g., corn, wheat) [23][24][25][26].…”

Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China

“…There have been many approaches to delineating site‐specific management zones, which include the use of the ISODATA method (Host et al ., ), geostatistical techniques (Moral et al ., ) and k ‐means clustering (Altdorf & Dietrich, ). For precision agriculture, crop yield (Boydell & McBratney, ; Jaynes et al ., ), topographical features (Franzen et al ., ) and soil fertility measures (Davatgar et al ., ) have also been used to partition fields into homogeneous units for management. It has been demonstrated that the fuzzy k ‐means (FKM) cluster algorithm is useful for showing the continuous natures of landscapes.…”