Effects of Potassium Source and Secondary Nutrients on Potato Yield and Quality in Southcentral Alaska.

Calcium (Ca), magnesium (Mg), and sulfur (S) are required for the growth and development of all higher plants. They are commonly referred to as secondary nutrients because they are less often limiting to plant growth than the primary nutrients nitrogen (N), phosphorus (P), and potassium (K), although secondary nutrients are as critical for crop growth and development as the primary nutrients. There is limited information available concerning secondary nutrient requirements of potatoes grown in southcentral Alaska. Laughlin (1966) conducted studies between 1961 and 1963 comparing potassium chloride (KCl) and potassium sulfate (K2SO4) as potassium sources for Green Mountain potatoes, and determined the effects of varying rates of magnesium sulfate (MgSO4) and K2SO4 on Kennebec potatoes. Since these studies were conducted without irrigation and at production levels about one-half those obtained by top producers in the Matanuska Valley today, it was considered appropriate to expand upon the previous work using current production practices. Potassium was supplied as KCl and K2 SO4 to explore the need for additional S under local potato production conditions and to determine the effects of the chloride (Cl) and sulfate (SO4) anions on production and quality of potato tubers. In addition, Mg and Ca were added to determine whether the background levels of these nutrients were adequate for optimum production

Rates and Methods of Application of Nitrogen and Phosphorus for Commercial Field Production of Head Lettuce in Southcentral Alaska

Head lettuce (Lactuca sativa L.) is one of the major agricultural crops grown in Alaska. In 1992, its wholesale value was approximately $314,000, second only to potatoes among Alaska’s commercially field grown vegetables (Brown et al., 1992). The quality of head lettuce is as important as yield, as lettuce heads that do not meet minimum size and weight standards are unmarketable. Head size and weight are strongly influenced by management practices, dictating a high level of management for successful commercial production. Among manageable cultural variables, rate of fertilizer application and the method of fertilizer placement are two of the most critical. Despite the value of the head lettuce crop to Alaska vegetable growers and the importance of fertilization as a management practice, little research has been published on rates of application and method of applying nitrogen and phosphorus to commercially grown head lettuce

Effects of shear on eggs and larvae of striped bass, morone saxatilis, and white perch, M. americana

Shear stress, generated by water movement, can kill fish eggs and larvae by causing rotation or deformation. Through the use of an experimental apparatus, a series of shear (as dynes/cm2)-mortality equations for fixed time exposures were generated for striped bass and white perch eggs and larvae. Exposure of striped bass eggs to a shear level of 350 dynes/cm2 kills 36% of the eggs in 1 min; 69% in 2 min, and 88% in 4 min; exposure of larvae to 350 dynes/cm2 kills 9.3% in 1 min, 30.0% in 2 min, and 68.1% in 4 min. A shear level of 350 dynes/cm2 kills 38% of the white perch eggs in 1 min, 41% in 2 min, 89% in 5 min, 96% in 10 min, and 98% in 20 min. A shear level of 350 dynes/cm2 applied to white perch larvae destroys 38% of the larvae in 1 min, 52% in 2 min, and 75% in 4 min. Results are experimentally used in conjunction with the determination of shear levels in the Chesapeake and Delaware Canal and ship movement for the estimation of fish egg and larval mortalities in the field

Modeling trait anxiety:from computational processes to personality

Computational methods are increasingly being applied to the study of psychiatric disorders. Often, this involves fitting models to the behavior of individuals with subclinical character traits that are known vulnerability factors for the development of psychiatric conditions. Anxiety disorders can be examined with reference to the behavior of individuals high in “trait” anxiety, which is a known vulnerability factor for the development of anxiety and mood disorders. However, it is not clear how this self-report measure relates to neural and behavioral processes captured by computational models. This paper reviews emerging computational approaches to the study of trait anxiety, specifying how interacting processes susceptible to analysis using computational models could drive a tendency to experience frequent anxious states and promote vulnerability to the development of clinical disorders. Existing computational studies are described in the light of this perspective and appropriate targets for future studies are discussed

Extreme Ground Motion Studies

This project consists of two separate investigations into extreme ground motions due to seismic events. First, it includes field studies of geological formations that should put an upper bound on extreme ground motions that have happened at the site of the formations. The locations are critically selected to provide the most effective constraints possible on the validity of the probabilistic seismic hazard analysis for Yucca Mountain. Second, this project surveys recorded ground motions from around the world, and aims to draw general conclusions from these as to the conditions where extreme ground motions are observed

Genome-Wide Association and Genomic Prediction for Host Response to Porcine Reproductive and Respiratory Syndrome Virus Infection

Host genetics has been shown to play a role in porcine reproductive and respiratory syndrome (PRRS), which is the most economically important disease in the swine industry. A region on Sus scrofa chromosome (SSC) 4 has been previously reported to have a strong association with serum viremia and weight gain in pigs experimentally infected with the PRRS virus (PRRSV). The objective here was to identify haplotypes associated with the favorable phenotype, investigate additional genomic regions associated with host response to PRRSV, and to determine the predictive ability of genomic estimated breeding values (GEBV) based on the SSC4 region and based on the rest of the genome. Phenotypic data and 60 K SNP genotypes from eight trials of ~200 pigs from different commercial crosses were used to address these objectives. Across the eight trials, heritability estimates were 0.44 and 0.29 for viral load (VL, area under the curve of log-transformed serum viremia from 0 to 21 days post infection) and weight gain to 42 days post infection (WG), respectively. Genomic regions associated with VL were identified on chromosomes 4, X, and 1. Genomic regions associated with WG were identified on chromosomes 4, 5, and 7. Apart from the SSC4 region, the regions associated with these two traits each explained less than 3% of the genetic variance. Due to the strong linkage disequilibrium in the SSC4 region, only 19 unique haplotypes were identified across all populations, of which four were associated with the favorable phenotype. Through cross-validation, accuracies of EBV based on the SSC4 region were high (0.55), while the rest of the genome had little predictive ability across populations (0.09). Traits associated with response to PRRSV infection in growing pigs are largely controlled by genomic regions with relatively small effects, with the exception of SSC4. Accuracies of EBV based on the SSC4 region were high compared to the rest of the genome. These results show that selection for the SSC4 region could potentially reduce the effects of PRRS in growing pigs, ultimately reducing the economic impact of this disease

Using Latent Profile Regression to Explore the Relationship between Religiosity and Work-related Ethical Judgments

Utilizing social structural symbolic interactionist theorizing about self-identity as presented by Weaver and Agle (2002) we obtained data related to five key measures of religiosity believed to be critical for understanding religiosity’s influence on ethical judgments. Using our five key religiosity measures we then fit a latent profile regression model to explore whether and how these constructs related to one another and to work-related ethical judgments. Results revealed that both our analytic and theoretical frameworks (latent profile regression and symbolic interactionism) were helpful in identifying religious profiles which are helpful for understanding the relationship between religiosity and work-related ethical judgments. More specifically, results indicated that extrinsic religious motivation orientation (RMO) may represent a ‘dark side’ to religiosity given higher levels of extrinsic RMO were found in a subgroup who judged unethical situations more favorably than those with lower levels of extrinsic RMO