A Meta-Analysis on the Effect of Agricultural Conservation Practices on Nutrient Loss.

Conservation practices are widely used to reduce N and P loads from agricultural fields and minimize their impact on water quality, but research using field-scale data to model the national average impact of conservation practices for different forms of N and P is needed. Thus, we quantified the effects of conservation practices (grassed waterways, terraces, contour farming, filter strips, and riparian buffers) on total, particulate, and dissolved N and P runoff from farmlands. Specifically, we conducted a meta-analysis of the Measured Annual Nutrient loads from AGricultural Environments (MANAGE) database using propensity score matching and multilevel modeling to remove the influence of confounding factors. There is no best method for addressing this influence, so we applied two alternative methods because similar results increase confidence in our findings. Propensity score matching found that conservation practices reduced total P, particulate P, and particulate N loading by an average of 67, 83, and 67%, respectively. Multilevel modeling estimated reductions of 58, 76, and 64% for the same nutrients. Although the propensity score method only yields a mean rate of reduction, multilevel modeling further estimates the reduction for different subgroups (i.e., different crops and fertilizer application methods) when such groupings are feasible. The multilevel models indicated that conservation practices affected row crops the most (e.g., corn [ L.] and soybean [ (L.) Merr.]) and fields with injected or surface-applied fertilizers. Our analysis used field-scale data to estimate the average effectiveness of conservation practices in reducing N and P runoff, providing valuable insight for regional and national decision making.

Updating the ELISA standard curve fitting process to reduce uncertainty in estimated microcystin concentrations.

This study is aimed at exploring the optimal ELISA standard curve fitting process for reducing measurement uncertainty. Using an ELISA kit for measuring cyanobacterial toxin (microcystin), we show that uncertainty associated with the estimated microcystin concentrations can be reduced by defining the standard curve as a four-parameter logistic function on the natural log concentration scale, instead of the current approach of defining the curve on the concentration scale. The model comparison method is outlined in this paper, allowing it to be transferable to test different statistical models for other ELISA test kits.

Quantifying and Reducing Uncertainty in Estimated Microcystin Concentrations from the ELISA Method.

We discuss the uncertainty associated with a commonly used method for measuring the concentration of microcystin, a group of toxins associated with cyanobacterial blooms. Such uncertainty is rarely reported and accounted for in important drinking water management decisions. Using monitoring data from Ohio Environmental Protection Agency and from City of Toledo, we document the sources of measurement uncertainty and recommend a Bayesian hierarchical modeling approach for reducing the measurement uncertainty. Our analysis suggests that (1) much of the uncertainty is a result of the highly uncertain "standard curve" developed during each test and (2) the uncertainty can be reduced by pooling raw test data from multiple tests. Based on these results, we suggest that estimation uncertainty can be effectively reduced through the effort of either (1) regional regulatory agencies by sharing and combining raw test data from regularly scheduled microcystin monitoring program or (2) the manufacturer of the testing kit by conducting additional tests as part of an effort to improve the testing kit.