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Pasturelands are inherently variable. It is this variability that makes sampling as well as characterizing an entire pasture difficult. Measurement of plant canopy reflectance with a ground-based radiometer offers an indirect, rapid, and noninvasive characterization of pasture productivity and composition. The objectives of this study were (i) to determine the relationships between easily collected canopy reflectance data and pasture biomass and species composition and (ii) to determine if the use of pasture reflectance data as a covariate improved mapping accuracy of biomass, percentage of grass cover, and percentage of legume cover across three sampling schemes in a central Iowa pasture. Reflectance values for wavebands most highly correlated with biomass, percentage of grass cover, and percentage of legume cover were used as covariates. Cokriging was compared with kriging as a method for estimating these parameters for unsampled sites. The use of canopy reflectance as a covariate improved prediction of grass and legume percentage of cover in all three sampling schemes studied. The prediction of above-ground biomass was not as consistent given that improvement with cokriging was observed with only one of the sampling schemes because of the low amount of spatial continuity of biomass values. An overall improvement in root mean square error (RMSE) for predicting values for unsampled sites was observed when cokriging was implemented. Use of rapid and indirect methods for quantifying pasture variability could provide useful and convenient information for more accurate characterization of time consuming parameters, such as pasture composition. Disciplines Agronomy and Crop Sciences | Statistics and Probability CommentsThis is an article from Crop Science 45 (2005): 996, doi:10.2135/cropsci2004.0004. Posted with permission. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.

The inherent variability of pasturelands makes it difficult to sample soils and accurately characterize a pasture. Indirect methods such as soil electroconductivity (EC) can be used to rapidly, noninvasively, and inexpensively quantify soil variability. The objective of this study was to determine if rapidly collected, georeferenced soil information could be used to propose an accurate, multistage sampling scheme for five soil variables in a central Iowa pasture. Results from this study suggest that the use of noninvasively collected soil EC and topographic data along with fuzzy k-means clustering can be used to delineate relatively homogeneous sampling zones. Consequently, these easily defined sampling zones can beneficially serve as a more directed approach to soil sampling.

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