MODELING THE FATE OF TOXIC CHEMICALS IN SOILS

Studies on the fate of toxic chemicals in soils are often reported with a minimum of descriptive statistics. Use of modeling techniques to describe the kinetics of chemical degradation provides a better understanding of the fate of chemicals in soil systems. When modeling nonlinear systems, assumptions made about the error term greatly influence the parameter estimation. Inappropriate use of linearization and failure to account for autocorrelated errors may result in inaccurate models. Information is also needed about the effects of the magnitude of autocorrelation on parameter estimation. The exponential decay function was chosen to fit the data obtained from a TNT (2, 4, 6-trinitrotoluene) degradation experiment in soil using four different error assumptions. Estimates of the rate constant (k) and other parameter estimates changed appreciably as assumptions about the error term changed. Simulation studies indicated that modeling data from chemical decomposition studies with an independent error assumption resulted in unreliable k estimates when the autocorrelation was large. A two-step procedure was used to fit an exponential autocorrelated (AR(1)) model. Overall, the exponential function with the additive-correlated error assumption provided the best fit for TNT degradation data. In essence, the kinetic rate constant obtained through model fitting in chemical decomposition studies provides a great deal of useful information to scientists. However, the researcher must be aware of the fact that making correct assumptions about the error term is extremely critical for obtaining accurate and precise estimates of k

OVERCOMING RESISTANCE TO MULTIVARIATE ANALYSIS OVER TIME

One aspect of statistical consulting is assessing a clients needs. Sometimes the need for simplicity beclouds the information contained in the experiment. As an example, an experiment was performed as a multivariate study with repeated measures, yet the client preferred numerous univariate analyses that ignored time. The challenge was to show how a more sophisticated analysis provided additional insight into the biological process. Various covariance structures were employed to illustrate the usefulness of progressively more complex analyses. Multivariate methods were performed to utilize the correlation among variables to illuminate biological concepts. To complicate the whole process, an additional problem occurred where extreme variability among experimental units within treatment groups led to the identification and clumping of homogeneous units to increase precision. Power studies were performed to determine required sample sizes to minimize potential error reoccurrences in future trials

Changes in soil microbial community structure with tillage under long-term wheat-fallow management

Fatty acid methyl esters (FAMEs) were used to `fingerprint\u27 soil microbial communities that evolved during 25 years of wheat-fallow cropping following native mixed prairie sod at Sidney, Nebraska, USA. Total ester-linked FAMEs (EL-FAMEs) and phospholipid-linked FAMEs (PL-FAMEs) were compared for their ability to discriminate between plots remaining in sod and those cropped to wheat or left fallow under no-till, sub-till or plow management. Cropped plots were higher in microbial biomass than their fallowed counterparts, and did not differ significantly with tillage for the 0±15 cm depth. Under fallow, microbial biomass was greatest in no-till and least in plow. Both cluster and discriminant analysis of PL- and EL-FAMEs clearly separated the remaining native sod plots from the existing wheat-fallow plots. This separation was particularly pronounced for the EL-FAMEs and was largely driven by high amounts in sod of a single FAME, C16:1(cis11), which has been cited as a biomarker for arbuscular mycorrhizal (AM) fungi. Within wheat-fallow, C16:1(cis11) declined significantly from no-till to plow, which supports the origin of C16:1(cis11) from extraradical mycelium and spores of AM fungi known to be sensitive to soil disturbance. Although discriminant analysis of PL- and EL-FAMEs differentiated wheat and fallow systems by tillage, discrimination among tillage treatments was expressed most strongly during fallow. FAME profiles from fallow plow were most dissimilar from cropped soils which suggests a relationship between tillage management and the long-term resiliency of the microbial community developed under the wheat crop

Changes in soil microbial community structure with tillage under long-term wheat-fallow management

Fatty acid methyl esters (FAMEs) were used to `fingerprint\u27 soil microbial communities that evolved during 25 years of wheat-fallow cropping following native mixed prairie sod at Sidney, Nebraska, USA. Total ester-linked FAMEs (EL-FAMEs) and phospholipid-linked FAMEs (PL-FAMEs) were compared for their ability to discriminate between plots remaining in sod and those cropped to wheat or left fallow under no-till, sub-till or plow management. Cropped plots were higher in microbial biomass than their fallowed counterparts, and did not differ significantly with tillage for the 0±15 cm depth. Under fallow, microbial biomass was greatest in no-till and least in plow. Both cluster and discriminant analysis of PL- and EL-FAMEs clearly separated the remaining native sod plots from the existing wheat-fallow plots. This separation was particularly pronounced for the EL-FAMEs and was largely driven by high amounts in sod of a single FAME, C16:1(cis11), which has been cited as a biomarker for arbuscular mycorrhizal (AM) fungi. Within wheat-fallow, C16:1(cis11) declined significantly from no-till to plow, which supports the origin of C16:1(cis11) from extraradical mycelium and spores of AM fungi known to be sensitive to soil disturbance. Although discriminant analysis of PL- and EL-FAMEs differentiated wheat and fallow systems by tillage, discrimination among tillage treatments was expressed most strongly during fallow. FAME profiles from fallow plow were most dissimilar from cropped soils which suggests a relationship between tillage management and the long-term resiliency of the microbial community developed under the wheat crop

Nitrate Leaching in Irrigated Corn and Soybean in a Semi-Arid Climate

Nitrate-nitrogen leached from the root zone of land in intensive corn production is a major groundwater contaminant in some of the intensively irrigated regions of the western Cornbelt, including central and western Nebraska. To obtain a clearer understanding of the amount and timing of nitrate leaching losses from irrigated crops, 14 monolithic percolation lysimeters were installed in 1989-1990 in sprinkler irrigated plots at the University of Nebraska’s West Central Research and Extension Center near North Platte, Nebraska. The lysimeters were used to provide a direct measure of leachate depth from continuous corn and a corn-soybean rotation. Both cropping systems were sprinkler irrigated and used current best management practices (BMPs) in the region for water and nitrogen management. Leachate was collected from 1990 through 1998 and analyzed for nitrate-N concentration. Results for the period 1993- 1998 are reported here. In the semi-arid climate of West-Central Nebraska, the interaction of rainfall patterns with the period of active uptake of water by crops played a major role in defining leaching patterns. Careful irrigation scheduling did not eliminate leaching during the growing season. There was no significant difference in drainage depth between continuous corn and the corn-soybean rotation. The average drainage depth among the lysimeters was 218 mm yr-1. This was more than expected, and in part resulted from above normal precipitation during several years of the study. No water quality benefit was found for the corn-soybean rotation as compared to continuous corn. Nitrate-N concentration in the leachate from continuous corn averaged 24 mg L-1, while that from the corn-soybean rotation averaged 42 mg L-1. Total yearly nitrate leaching loss averaged 52 kg ha-1 for continuous corn and 91 kg ha-1 for the rotation. This represents the equivalent of 27% and 105% of the amount of N fertilizer applied over the six years of study. In calculating N fertilizer needs for corn in Nebraska, the recommended legume N credit of 50 kg ha-1 for a preceding crop of soybean may be too low under irrigated production

Electromagnetic Scanning of Beef Quarters to Predict Carcass and Primal Lean Content

To study the use of electromagnetic scanning in prediction of lean content in beef carcasses and cuts, 100 beef cattle (60 steers and 40 heifers), representing a broad range in external fat thickness (. 1 to 2.9 cm) and live weight (414 to 742 kg), were selected. Chilled right sides were divided into streamlined (foreshank, brisket, and ventral plate removed) forequarters (FQ) and full hindquarters (HQ) and scanned. Primal rounds, loins, ribs, and chucks were fabricated from the right side, scanned, and physically separated into lean, fat, and bone. Prediction equations for dissected lean content and percentage of lean included the peak of the electromagnetic scan response curve (obtained from scanning the HQ or FQ), length, temperature and weight of the scanned cut, and fat thickness at the 12th rib. Using the coefficient of determination, root mean square error, and Mallows\u27 Cp statistic, the best model for each dependent variable (weight and percentage of lean) that included up to five independent variables was selected. Prediction equations for the HQ or FQ of steers accounted for 84 to 93% of the variation in lean weight of beef sides and quarters and 71 to 93% of primals. Sixty-one to 75% of the variation in percentage of lean in sides and quarters and 48 to 65% of primals was also explained. Similar results were obtained for heifer carcasses. Predicting percentage of lean in any scanned cut, rather than weight of lean, accounted for less of the variation. Weight and fat thickness contributed significantly when predicting percentage of lean. These data indicate that electromagnetic scanning is capable of objectively measuring lean content in beef quarters and primals

The personal and contextual contributors to school belongingness among primary school students

School belongingness has gained currency among educators and school health professionals as an important determinant of adolescent health. The current cross-sectional study presents the 15 most significant personal and contextual factors that collectively explain 66.4% (two-thirds) of the variability in 12-year old students' perceptions of belongingness in primary school. The study is part of a larger longitudinal study investigating the factors associated with student adjustment in the transition from primary to secondary school. The study found that girls and students with disabilities had higher school belongingness scores than boys, and their typically developing counterparts respectively; and explained 2.5% of the variability in school belongingness. The majority (47.1% out of 66.4%) of the variability in school belongingness was explained by student personal factors, such as social acceptance, physical appearance competence, coping skills, and social affiliation motivation; followed by parental expectations (3% out of 66.4%), and school-based factors (13.9% out of 66.4%) such as, classroom involvement, task-goal structure, autonomy provision, cultural pluralism, and absence of bullying. Each of the identified contributors of primary school belongingness can be shaped through interventions, system changes, or policy reforms

Multivariate Analysis of EcologicaI Data using CANCO

This book is about understanding and applying multivariate statistical methods useful for analyzing complex ecological problems. Researchers and students seeking to improve their ability to collect and analyze data from field observations and experiments, especially those interested in the response or variation of biotic communities to environmental conditions or experimental manipulation, will find this handbook helpful. The methods discussed are widely used in plant community ecology, as well as other areas in biology. The book is tutorial in nature. The authors provide advice on how to best apply the multivariate statistical methods using the CANOCO for Windows, a licensed computer program available to readers of the book on a trial basis. The authors also use Statistica for Windows to describe how to implement some methods not available in CANOCO. The primary focus of the book is on ordination methods although classification methods are mentioned by comparison. The book begins with a coherent introduction to simple ordination methods, followed by a lucid description of constrained ordination methods (RDA, CCA) and permutation tests for testing multivariate statistical hypotheses. Then, a discussion of similarity measures is followed by an overview of classification and regression methods. The book concludes with seven case studies of varying difficulty. These case studies are supported by data sets and project files available from the book Web site

Relationships Among Grain Sorghum Quality Factors

Correlations among grain sorghum quality factors (proximate composition, physical properties, and water absorption properties) were evaluated. Samples of 46 commercial hybrids (24 and 22 from crop years 1993 and 1994) were analyzed for starch, protein, crude free fat, test weight, absolute density, 1,000 kernel weight, percent kernel abraded, water absorption index, initial water absorption rate, and moisture saturation point. Test weight, absolute density, and percent kernel abraded were positively correlated among themselves (r \u3e 0.5). Protein was negatively correlated with both test weight and absolute density (r \u3c -0.5), while moisture saturation point showed negative correlations with test weight, absolute density, 1,000 kernel weight, and percent kernel abraded (r \u3c -0.4). Principal component factor analysis through the covariance matrix explained 95% of the total variation of quality factors among hybrids (two factors), and, through the correlation matrix, 85% of the total variation (five factors). Water absorption rate decreased with increasing starch content of grain sorghum kernels as water absorption rate increased and amount of water for saturation decreased with softening of kernels