@conference {50154,
	title = {Inference space vs. sampling requirements: A simulation study of soil properties on rangeland ecosites},
	booktitle = {62nd Society for Range Management Annual Meeting},
	year = {2009},
	month = {02/2009},
	pages = {90-7},
	publisher = {Society for Range Management},
	organization = {Society for Range Management},
	address = {Alb., NM},
	abstract = {<p>Planning a monitoring project requires careful planning. The inference space of a project must be balanced with the sampling requirements. Inference space is the population from which the samples in a study were drawn and the population to which results of a study apply. Increasing the number of conditions in a project increases the inference space. Sampling requirements are the number of samples or measurements required to detect a given level of change. The number of samples required to detect change depends on the variability of the conditions being evaluated. In general, the fewer conditions that are sampled the lower the variability that will be observed and the lower the measurements required to detect change. Thus increasing the inference space is usually in direct conflict with reducing sampling requirements. This is a problem when available resources allow only a limited number of measurements. The objective of this study is to explore the balance between detectable change, inference space, and sampling requirements for a dataset of soil and range measurements. We use resampling procedures to show these relationships for a dataset with 4 ecosites and 2 degradation conditions. Variability (measured as variance) increases as the number of ecosites increases, however, some ecosites add more variance than others. Limiting the number of ecosites and conditions can reduce the number of samples or measurements required to detect change.</p>
},
	keywords = {abstract, inference, monitoring, sampling, SRM},
	author = {S.A. Wills and Jeffrey E. Herrick and A.J. Tugel}
}
@conference {50303,
	title = {Minimum property dataset and sampling requirement tool for soil change studies in soil survey},
	booktitle = {Geological Society of America Meeting},
	series = {2008 Joint Meetings},
	year = {2008},
	month = {October 5-9, 200},
	pages = {679-9},
	address = {Houston, TX},
	abstract = {Dynamic soil properties (DSP) are those properties that change over human time scales. The new sampling guide {\textquotedblleft}Soil and Resource Inventory Guide for Dynamic Soil Properties and Soil Change{\textquotedblright} includes a minimum DSP dataset and an interactive tool to determine sampling requirements. The minimum dataset is a common set of properties that will be measured on all DSP projects. In order to help guide soil property selection, a ranking tool was developed and completed by 40 experts in soil survey and pedology, soil biology, agronomy, forestry, rangeland, and hydrology. The ranking tool asked experts to rank properties according to 4 criteria important to soil change and soil survey. They ranked sensitivity to change the most important criteria for selecting a minimum DSP dataset. The highest ranked properties were organic carbon, pH and EC. The multi-scale sample requirement evaluation tool (MSSRET) is an Excel spreadsheet-based tool that can be used to determine the necessary sample requirements at plot and landscape scales for DSP monitoring and comparison studies. It is intended to help in project planning and sample sufficiency assessment. The Guide recommends sampling DSPs in a multi-scale fashion (multiple samples on multiple plots) by soil map unit component phases. Sample and plot requirements are calculated from the desired minimum detectable difference (MDD), the variance of the property at plot and project scales and the level of acceptable error rates. The guide represents a new phase in soil survey that will document DSPs and soil change due to management.},
	keywords = {DSP, soil properties},
	author = {S.A. Wills and A.J. Tugel and Jeffrey E. Herrick and M.D. Remmenga}
}
