The total mass of nitrate passing a gauging station during a period of study may be estimated based on infrequently monitored nitrate concentration data. To examine the accuracy and precision of these nitrate load estimates, a Monte Carlo sub-sampling study was conducted using six years of daily nitrate concentration and daily average discharge data at one gauging station on a medium size river in central Illinois draining predominantly agricultural lands. The daily nitrate concentration data set was sub-sampled repetitively to generate multiple sub-samples that might have been drawn if a different sampling frequency was used. Load estimation methods from three broad classes were used to calculate nitrate loads based on the generated sub-samples. It was found that the regression-based estimators are all positively biased when applied for the subject study site. The simple ratio estimator and the flow-weighted average estimator, however, are almost bias free and have an overall performance similar to that of the rating curve estimator. Copyright ASCE 2004.