Development of error correction techniques for nitrate-N load estimation models

Excess nutrient loads carried by streams and rivers are a great concern for environmental resource managers. In agricultural regions, excess loads are transported downstream to receiving water bodies, potentially causing algal blooms, which could lead to numerous ecological problems. To better understand nutrient load transport, and to develop appropriate water management plans, it is important to have accurate estimates of annual nutrient loads. This study used a Monte Carlo sub-sampling method and error-corrected statistical models to estimate annual nitrate-N loads from two watersheds in central Illinois. The performance of three load estimation methods (the seven-parameter log-linear model, the ratio estimator, and the flow-weighted averaging estimator) applied at one-, two-, four-, six-, and eight-week sampling frequencies were compared. Five error correction techniques; the existing composite method, and four new error correction techniques developed in this study; were applied to each combination of sampling frequency and load estimation method. On average, the most accurate error reduction technique, (proportional rectangular) resulted in 15% and 30% more accurate load estimates when compared to the most accurate uncorrected load estimation method (ratio estimator) for the two watersheds. Using error correction methods, it is possible to design more cost-effective monitoring plans by achieving the same load estimation accuracy with fewer observations. Finally, the optimum combinations of monitoring threshold and sampling frequency that minimizes the number of samples required to achieve specified levels of accuracy in load estimation were determined. For one- to three-weeks sampling frequencies, combined threshold/fixed-interval monitoring approaches produced the best outcomes, while fixed-interval-only approaches produced the most accurate results for four- to eight-weeks sampling frequencies.

Knowledge management capabilities and organizational performance: an investigation into the effects of knowledge infrastructure and processes on organizational performance

Knowledge is one of the most important assets for surviving in the modern business environment. The effective management of that asset mandates continuous adaptation by organizations, and requires employees to strive to improve the company's work processes. Organizations attempt to coordinate their unique knowledge with traditional means as well as in new and distinct ways, and to transform them into innovative resources better than those of their competitors. As a result, how to manage the knowledge asset has become a critical issue for modern organizations, and knowledge management is considered the most feasible solution. Knowledge management is a multidimensional process that identifies, acquires, develops, distributes, utilizes, and stores knowledge. However, many related studies focus only on fragmented or limited knowledge-management perspectives. In order to make knowledge management more effective, it is important to identify the qualitative and quantitative issues that are the foundation of the challenge of effective knowledge management in organizations. The main purpose of this study was to integrate the fragmented knowledge management perspectives into the holistic framework, which includes knowledge infrastructure capability (technology, structure, and culture) and knowledge process capability (acquisition, conversion, application, and protection), based on Gold's (2001) study. Additionally, because the effect of incentives ̶̶ which is widely acknowledged as a prime motivator in facilitating the knowledge management process ̶̶ was missing in the original framework, this study included the importance of incentives in the knowledge management framework. This study also identified the relationship of organizational performance from the standpoint of the Balanced Scorecard, which includes the customer-related, internal business process, learning & growth, and perceptual financial aspects of organizational performance in the Korean business context. Moreover, this study identified the relationship with the objective financial performance by calculating the Tobin's q ratio. Lastly, this study compared the group differences between larger and smaller organizations, and manufacturing and nonmanufacturing firms in the study of knowledge management. Since this study was conducted in Korea, the original instrument was translated into Korean through the back translation technique. A confirmatory factor analysis (CFA) was used to examine the validity and reliability of the instrument. To identify the relationship between knowledge management capabilities and organizational performance, structural equation modeling (SEM) and multiple regression analysis were conducted. A Student's t test was conducted to examine the mean differences. The results of this study indicated that there is a positive relationship between effective knowledge management and organizational performance. However, no empirical evidence was found to suggest that knowledge management capabilities are linked to the objective financial performance, which remains a topic for future review. Additionally, findings showed that knowledge management is affected by organization's size, but not by type of organization. The results of this study are valuable in establishing a valid and reliable survey instrument, as well as in providing strong evidence that knowledge management capabilities are essential to improving organizational performance currently and making important recommendations for future research.