English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

zones in agricultural fields (Franzen et al., 2002). This approach has been applied in Illinois and Indiana where Recent precision-agriculture research has focused on use of man- 40% of grain yield variability was explained by topo- agement zones (MZ) as a method for variable application of inputs like N. The objectives of this study were to determine (i) if landscape graphical characteristics and selected soil properties attributes could be aggregated into MZ that characterize spatial varia- (Kravchenko and Bullock, 2000). Aerial photographs, tion in soil chemical properties and corn yields and (ii) if temporal crop canopy images, and yield maps have also been variability affects expression of yield spatial variability. This work was suggested as approaches to delineate MZ (Schepers et conducted on an irrigated cornfield near Gibbon, NE. Five landscape al., 2000). Remote sensing technology is especially ap- attributes, including a soil brightness image (red, green, and blue pealing to identify MZ because it is noninvasive and bands), elevation, and apparent electrical conductivity, were acquired low in cost (Mulla and Schepers, 1997). Additionally, for the field. A georeferenced soil-sampling scheme was used to deter- scientific evidence for suggesting practical use of remote mine soil chemical properties (soil pH, electrical conductivity, P, and sensing technology to delineate MZ is increasing (Varvel organic matter). Georeferenced yield monitor data were collected for et al., 1999). five (1997-2001) seasons. The five landscape attributes were aggre- gated into four MZ using principal-component analysis of landscape Another promising noninvasive approach to define attributes and unsupervised classification of principal-component the boundaries of MZ involves the use of electromag- scores. All of the soil chemical properties differed among the four netic induction to measure apparent electrical conduc- MZ. While yields were observed to differ by up to 25% between the tivity (ECa). This approach has been used to effectively highest- and lowest-yielding MZ in three of five seasons, receiving map variations in surface soil properties such as salinity, average precipitation, less-pronounced (5%) differences were noted water content, and percentage clay (Corwin and Lesch, among the same MZ in the driest and wettest seasons. This illustrates 2003; Kitchen et al., 2003). In a semiarid cropping sys- the significant role temporal variability plays in altering yield spatial tem, Johnson et al. (2003) showed that EC a-determined variability, even under irrigation. Use of MZ for variable application MZ could be used to characterize spatial variation in of inputs like N would only have been appropriate for this field in wheat (Triticum aestivum L.) and corn (Zea mays L.) three out of the five seasons, seriously restricting the use of this approach under variable environmental conditions. yields. Magnetic induction has also been used to track soluble nutrient levels in soil (Eigenberg et al., 2002). Caution is necessary when using this approach because of the extreme sensitivity to soil type and management

Appropriateness of Management Zones for Characterizing Spatial Variability of Soil Properties and Irrigated Corn Yields across Years