Information on optimal harvest periods and N fertilization rates for switchgrass (Panicum virgatum L.) grown as a biomass or bioenergy crop in the Midwest USA is limited. Our objectives were to determine optimum harvest periods and N rates for biomass production in the region. Established stands of ‘Cave‐in‐Rock’ switchgrass at Ames, IA, and Mead, NE, were fertilized 0, 60, 120, 180, 240, or 300 kg N ha−1. Harvest treatments were two‐ or one‐cut treatments per year, with initial harvest starting in late June or early July (Harvest 1) and continuing at approximately 7‐d intervals until the latter part of August (Harvest 7). A final eighth harvest was completed after a killing frost. Regrowth was harvested on previously harvested plots at that time. Soil samples were taken before fertilizer was applied in the spring of 1994 and again in the spring of 1996. Averaged over years, optimum biomass yields were obtained when switchgrass was harvested at the maturity stages R3 to R5 (panicle fully emerged from boot to postanthesis) and fertilized with 120 kg N ha−1. Biomass yields with these treatments averaged 10.5 to 11.2 Mg ha−1 at Mead and 11.6 to 12.6 Mg ha−1 at Ames. At this fertility level, the amount of N removed was approximately the same as the amount applied. At rates above this level, soil NO3–N concentrations increased.

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Appropriate indicators for assessing soil quality on a regional scale using the National Resource Inventory (NRI) are unknown. Our objectives were to (i) identify soil quality factors present at a regional scale, (ii) determine which factors vary significantly with land use, and (iii) select soil attributes within these factors that can be used as soil quality indicators for regional‐scale assessment. Ascalon (fine‐loamy, mixed, superactive, mesic Aridic Argiustoll) and Amarillo (fine‐loamy, mixed, thermic Aridic Paleustalf) soils were sampled from a statistically representative subset of NRI sample points within the Central and Southern High Plains Major Land Resource Areas (MLRA) and analyzed for 20 soil attributes. Factor analysis was used to identify soil quality factors, and discriminant analysis was used to identify the factors and indicators most sensitive to land use within each MLRA. In the Central High Plains, five soil quality factors were identified, with the organic matter and color factors varying significantly with land use. Discriminant analysis selected total organic C (TOC) and total N as the most sensitive indicators of soil quality at a regional scale. In the Southern High Plains, six factors were identified, with water stable aggregate (WSA) content, TOC, and soil salinity varying significantly with land use. Discriminant analysis selected TOC and WSA content as the most sensitive indicators of soil quality in the Southern High Plains. Total organic C was the only indicator that consistently showed significant differences between land uses in both regions.

of land management systems as related to agricultural production practices, and to assist government agencies Diversity of soil series present in a region may hinder identification in formulating and evaluating sustainable agricultural of soil quality factors and indicators at a regional scale. Our objectives and land use policies (Doran and Parkin, 1996). Howwere (i) to identify soil quality factors for a diverse population of soils at the regional scale, (ii) to determine which factors vary signifi-ever, soil quality cannot be measured directly, but must cantly with land use, (iii) to select indicators from these factors that be inferred from soil quality indicators. Soil quality indican be used with the National Resource Inventory (NRI) for monitorcators are measurable soil attributes that influence the ing soil quality, and (iv) to compare these results to a similar study capacity of soil to perform crop production or environinvolving only a single soil series. One hundred eighty-six points repremental functions and are sensitive to change in land use, senting 75 soil series in the Northern Mississippi Valley Loess Hills management, or conservation practices. However, many and 149 points representing 58 soil series in Palouse and Nez Perce soil attributes are highly correlated (Larson and Pierce, Prairies were sampled from a statistically representative subset of 1991; Seybold et al., 1997). Correlated soil attributes do NRI sample points and analyzed for 20 soil attributes. Factor analysis not change independently to changes in management, was used to identify soil quality factors and discriminant analysis was but respond as a group, integrating many complex interused to identify factors and indicators most sensitive to land use within actions among biological, chemical, and physical soil each region. In the Northern Mississippi Valley Loess Hills, five soil quality factors were identified. Discriminant analysis selected poten-processes. Single attribute indicators do not reflect intially mineralizable N (PMN), microbial biomass C (MBC), water teracting changes in soil quality that may occur with stable aggregates (WSA), and total organic C (TOC) as the most changes in management because of correlation among discriminating attributes between land uses. In the Palouse and Nez soil attributes. A more accurate assessment of soil qual-Perce Prairies, six factors were identified. Discriminant analysis seity may be achieved by evaluating several soil attributes lected TOC and total N as the most discriminating attributes between simultaneously using statistical procedures that account land uses. The soil quality factors were similar among three of the for correlation among soil attributes. four regions, but TOC was the only indicator common to all regions Multivariate statistical analyses, such as factor analyfor distinguishing among land uses. J.

Information on the probability distribution and variability of soil linization, excessive nutrient enrichment or depletion, properties at a regional scale could improve the ability of the USDA-Natural Resources Conservation Service (NRCS) to monitor soil con-chemical or heavy metal contamination, or reduced didition using the National Resources Inventory (NRI). Our objective versity and activity of soil organisms. Thus, an assesswas to evaluate the hypothesis that the probability distribution of 17 ment of soil condition must go beyond measuring soil physical, chemical, and biological soil properties are: (i) normally erosion and consider soil properties that are affected distributed, or (ii) log-normally distributed at a regional scale, and to by other forms of soil degradation. estimate the magnitude of change that may be detected assuming Currently, there is interest in collecting soil samples at either a normal or log-normal distribution. Samples were collected NRI sample points to improve NRCS' ability to monitor irrespective of soil series from two Major Land Resource Areas changes in soil and natural resource condition. The soil (MLRAs) (no. 9 and 105), and from the Ascalon (fine-loamy, mixed, samples could be analyzed for appropriate physical, superactive, mesic Aridic Argiustoll) and Amarillo (fine-loamy, chemical, or biological properties that serve as indicamixed, superactive, thermic Aridic Paleustalf) soils in MLRA 67 and 77, using the NRI sampling design. Most soil properties were non-tors of resource condition. However, the diversity of normally distributed, with the frequency of non-normality varying soils across the USA could hinder the success of a nabetween MLRAs. Confining sampling to a single soil series did not tional scale assessment to monitor changes in soil propconsistently improve the precision with which soil properties were erties using the NRI. Such an assessment may be more estimated. Log e transformation resulted in normal distributions for feasible at a regional scale if each region contains similar most soil properties and reduced variability two-to threefold. Howsoil and land-use patterns so that precise and accurate ever, a few soil properties remained non-normally distributed. Soil estimates of soil properties can be made. A regional-pH may be monitored at the regional scale with a high degree of scale assessment may be further improved if sampling precision. Small changes in soil C content (3-8% of the regional mean) was confined to key benchmark soils within a region. may be detected using log e transformed total organic C as the indicator. Major Land Resource Areas are geographic units of Sampling soil properties as part of the NRI should improve NRCS' ability to monitor soil condition on a regional scale.