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ARS Home » Plains Area » Lincoln, Nebraska » Agroecosystem Management Research » Research » Publications at this Location » Publication #150571
Title: APPROPRIATENESS OF MANAGEMENT ZONES FOR CHARACTERIZING SPATIAL VARIABILITY OF SOIL PROPERTIES AND IRRIGATED CORN YIELDS ACROSS YEARS

Author
item SCHEPERS, AARON - UNIV OF NE/LINCOLN
item Shanahan, John
item Liebig, Mark
item Schepers, James
item Johnson, Sven
item LUCHIARI, ARIOVALDO - EMBRAPA,BRAZIL @NE

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/12/2003
Publication Date: 2/2/2004
Citation: SCHEPERS, A., SHANAHAN, J.F., LIEBIG, M.A., SCHEPERS, J.S., JOHNSON, S.H., LUCHIARI, A. 2004. APPROPRIATENESS OF MANAGEMENT ZONES FOR CHARACTERIZING SPATIAL VARIABILITY OF SOIL PROPERTIES AND IRRIGATED CORN YIELDS ACROSS YEARS. AGRONOMY JOURNAL. 96:195-203.

Interpretive Summary: Recent research in precision agriculture has focused on the use of management zones as a method to more efficiently apply N across variable agricultural landscapes. Management zones, in the context of precision agriculture, are field areas possessing homogenous attributes in landscape and soil condition. When homogenous in a specific area, these attributes should lead to the same results in crop yield potential, crop input-use efficiency, and environmental impact. Approaches to delineate management zones vary. Topography has been suggested as a logical basis to define homogenous zones in agricultural fields. Aerial photographs of bare soil color, crop canopy images, soil electrical conductivity, and yield maps have also been suggested as approaches to delineate management zones. Remote sensing technology is especially appealing to identify management zones, because it is noninvasive, and low in cost. While characterizing spatial variability is important in site-specific studies, it is equally important to consider the year-to-year effects of climate variability on the expression of spatial yield variation. We reasoned that year-to-year variability in climate will greatly influence how spatial yield patterns are expressed in a given field, and hence, use of management zones as a method for variable application of crop inputs requires further evaluation before it can be successfully implemented. The objectives of this study were to determine 1) if landscape attributes such as soil color, elevation, and apparent electrical conductivity (ECa) could be aggregated into management zones characterizing spatial variation in soil chemical properties as well as corn grain yield, and 2) if year-to year variability affects expression of yield spatial variability. This work was conducted on an irrigated cornfield located near Gibbon, NE. Landscape attributes, including an aerial image of soil color, GPS-determined elevation, and ECa, map were acquired for the field. A georeferenced soil sampling scheme was used to obtain field information about yield determining soil chemical properties (soil pH, EC, P, and organic matter). Georeferenced yield monitor data were collected for five (1997-2001) seasons. The landscape attributes were aggregated into four management zones using statistical techniques that classified the soil variables into homogenous areas or management zones, producing four well-defined zones for the field. Soil chemical properties differed noticeably among the management zones. Marked yields differences were observed among management zones in three of five crop seasons under average precipitation conditions, with a maximum difference of 25% for lowest- vs. highest-yielding zones. However, less pronounced (<5%) yields differences were observed among management zones in the driest and wettest seasons, illustrating the significant role year-to-year variability plays in expression of yield spatial variation. Our results indicate that use of management zones alone for variable application of crop inputs like N may not always produce satisfactory results. Alternatively, a better strategy might be to combine the use of management zones along with crop-based in-season remote sensing systems to more efficiently apply crop inputs such as N.

Technical Abstract: Recent precision agriculture research has focused on use of management zones (MZ), which are field areas possessing homogenous landscape and soil attributes, as a method for variable application of crop inputs like N. The objectives of this study were to determine 1) if landscape attributes such as soil brightness (SB), elevation, and apparent electrical conductivity (ECa) could be aggregated into MZ characterizing spatial variation in soil chemical properties as well as corn grain yield, and 2) if temporal variability affects expression of yield spatial variability. This work was conducted on an irrigated cornfield located near Gibbon, NE. Five landscape attributes, including a SB image (red, green, and blue bands), elevation, and ECa, were acquired for the field. A georeferenced soil sampling scheme was use to obtain field information about yield determining soil chemical properties (soil pH, EC, P, and organic matter). Georeferenced yield monitor data were collected for five (1997-2001) seasons. The five landscape attributes were aggregated into four MZ using principal component analysis (PCA) and unsupervised classification of PC scores. The PCA showed 85% of the variability in the five landscape attributes was accounted for by only two PC¿s. Unsupervised classification of scores from the first two PC¿s in turn produced four well-defined MZ for the field. Soil chemical properties differed noticeably among MZ. Marked yields differences were observed among MZ in three of five crop seasons under average precipitation conditions, with a maximum difference of 25% for lowest- vs. highest-yielding MZ. However, less pronounced (<5%) yields differences were observed among MZ in the driest and wettest seasons, illustrating the significant role temporal variability plays in expression of yield spatial variation. Our results indicate that use of MZ alone for variable application of crop inputs like N requires further study.