Cool-season turfgrass color and growth calibrated to leaf nitrogen

Tissue N analysis a tool available for N management of turfgrass. However, peer-reviewed calibration studies to determine optimum tissue N values are lacking. A field experiment with a mixed cool-season species lawn and a greenhouse experiment with Kentucky bluegrass (Poa pratensis L.) were conducted across 2 yr, each with randomized complete block design. Treatments were N application rates between 0 and 587 kg N ha-1 yr-1. In the field experiment, clipping samples were taken monthly from May to September, dried, ground, and analyzed for total N. Clippings samples were collected one to two mowings after plots were fertilized. Linear plateau models comparing relative clipping yield, Commission Internationale de l' Eclairage hue, and CM1000 index to leaf N concentrations were developed. In the greenhouse experiment, clipping samples were taken every 2 wk from May to October and composited across sample dates for leaf N analysis. Color and clipping yields were related to leaf N concentrations using linear plateau models. These models indicated small marginal improvements in growth or color when leaf N exceeded 30 g kg-1, suggesting that a leaf N test can separate turf with optimum leaf N concentrations from turf with below optimum leaf N concentrations. Plateaus in leaf N concentrations with increasing N fertilizer rates suggest, however, that this test may be unable to identify sites with excess available soil N when turf has been mowed before tissue sampling.

Empirical Analysis of Credit Risk Regime Switching and Temporal Conditional Default Correlation in Credit Default Swap Valuation: The Market liquidity effect

In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.