Postanesthetic Pulmonary Edema In an Arab Stallion

A six-year-old arabian stallion was admitted to The Ohio State University Veterinary Hospital for evaluation and repair of a comminuted fracture of the second phalanx. The horse developed impaired arterial oxygenation during surgey and pulmonary edema post-operatively. We postulate that impaired arterial oxygenation resulted from atelectasis of the dependent lung during annesthesia, and the pulmonary edema occurred following re-expansion of the atelectatic of the initiating cause of the edema, removal of excess lung water from the alveoli, and restoration of normal arterial oxygenation. The horse was fully recovered within 12 hours of initiation of clinical signs of respiratory compromise. The horse was fully recovered within 12 hours of initiation of clinical signs of respiratory compromise. This report describes re-expansion pulmonary edema due to reperfusion injury in a horse, treatment of the condition, and a possible explanation of the pathogenesis of this pulmonary pathology.     

CPIDS: a plant parameter selection program for erosion prediction modeling

Newly developed erosion prediction models require detailed input parameters including those describing cropping systems. Each model exhibits a different level of complexity for crop growth modeling, and many plant parameters for these models are difficult to obtain. Furthermore, numerous parameters require interpretation from other measurable plant characteristics. We developed a database containing measurable plant characteristics to supply users of these new erosion prediction technologies with the necessary parameters. The new database supports the Revised Universal Soil Loss Equation (RUSLE) and the Water Erosion Prediction Project (WEPP) erosion prediction models. The Crop Parameter Intelligent Database System (CPIDS) was developed to assist crop database builders and users of RUSLE and WEPP in interpretation of plant characteristics and selection of plant parameters. CPIDS is a Microsoft® Windows™-based program, currently running on a personal computer (PC) under Windows 3.1™, Windows 95™ and Windows NT 4.0™.     

Simulating landscape catena effects in no-till dryland agroecosystems using GPFARM

This study evaluated the site-specific applicability and efficacy of the GPFARM decision support system (DSS) based on underlying simulation model performance for dry mass grain yield, crop residue, total soil profile water content, and total soil profile residual NO₃-N across a landscape catena for dryland no-till experimental locations in eastern Colorado. Relative error of simulated mean, normalized objective function (root mean square error divided by the observed mean), and index of agreement evaluation statistics were calculated to compare modeled results to observed data. A one-way, fixed-effect ANOVA was also performed to determine differences among experimental locations and summit, sideslope, and toeslope landscape positions. GPFARM simulations matched observed data trends, with the model correctly distinguishing variations between the summit and toeslope landscape positions. In addition, experimental observations and GPFARM simulations both indicated that the toeslope landscape position was the most productive for grain yield and also exhibited higher amounts of crop residue, total soil profile water content, and total soil residual NO₃-N. The GPFARM crop model performed adequately but was inconsistent in simulating winter wheat, corn, and sorghum dry mass grain yield. GPFARM performance in simulating crop residue was poorer than for crop grain yield. GPFARM predicted mean total soil profile water content was generally within ±20% of the observed mean across locations and landscape positions, with the model somewhat biased towards overpredicting total soil profile water content at the summit and sideslope landscape positions. Total soil profile residual NO₃-N was underpredicted by GPFARM across all locations and landscape positions by an average of 30%. Although GPFARM appears to have reasonably simulated long-term output responses across a landscape catena for the eastern Colorado experimental locations (especially given the simplifying assumptions in many of the GPFARM simulation components and the inherent variability present at the experimental plot level), different interpretations of GPFARM performance can be made depending on the evaluation statistic of interest. Furthermore, the model cannot fully account for water and chemical movement across the landscape catena; simulation results suggest that addition of a spatially-distributed routing component should offer improvements in GPFARM prediction accuracy across a catena where surface runoff or lateral subsurface flow is occurring.     

Relationship between reproductive and productive traits in Holstein cattle using multivariate analysis

Multivariate procedures are used for the extraction of variables from the correlation matrix of phenotypes in order to identify those traits that explain the largest proportion of phenotypic variation and to evaluate the relationship structure between these traits. The reproductive traits (days from calving to first estrus [CFE], days from calving to last service [CLS], calving interval [CI] and gestation length [GL]) and milk production traits (milk yield at 305 days of lactation [MY305], peak yield [PY] and milk yield per day of calving interval [MYCI]) of 5,217 Holstein females (primiparous and multiparous) were measured. Principal component analysis (PCA) and factor analysis of the correlation matrix were used to estimate the correlation between traits. Analysis grouped the seven traits into three principal components and four latent factors that retained approximately 81.5% and 88.9% of the total variation of the data, respectively. The production variables exhibited positive phenotypic correlation coefficients of high magnitude (>.67). The phenotypic correlation estimates between the productive and reproductive traits were low, ranging from .13 to .22. A strong association (.99) was observed between CLS and CI. Our results indicate that multivariate analysis was effective in generating correlations between the traits studied, grouping the seven traits in a smaller number of variables that retained approximately 81% of the total variation of the data.