Soil-test N recommendations augmented with PEST-optimized RZWQM simulations

Pilot study of global endocrine disrupting activity in Iowa public drinking water utilities using cell-based assays

Some anthropogenic substances in drinking water are known or suspected endocrine disrupting compounds (EDCs), but EDCs are not routinely measured. We conducted a pilot study of 10 public drinking water utilities in Iowa, where common contaminants (e.g., pesticides) are suspected EDCs. Raw (untreated) and finished (treated) drinking water samples were collected in spring and fall and concentrated using solid phase extraction. We assessed multiple endocrine disrupting activities using novel mammalian cell-based assays that express nuclear steroid receptors (aryl hydrocarbon [AhR], androgenic [AR], thyroid [TR], estrogenic [ER] and glucocorticoid [GR]). We quantified each receptor's activation relative to negative controls and compared activity by season and utility/sample characteristics. Among 62 samples, 69% had AhR, 52% AR, 3% TR, 2% ER, and 0% GR activity. AhR and AR activities were detected more frequently in spring (p =0 .002 and < 0.001, respectively). AR activity was more common in samples of raw water (p =0 .02) and from surface water utilities (p =0 .05), especially in fall (p =0 .03). Multivariable analyses suggested spring season, surface water, and nitrate and disinfection byproduct concentrations as determinants of bioactivity. Our results demonstrate that AR and AhR activities are commonly found in Iowa drinking water, and that their detection varies by season and utility/sample characteristics. Screening EDCs with cell-based bioassays holds promise for characterizing population exposure to diverse EDCs mixtures.

Corn stover harvest N and energy budgets in central Iowa

Harvesting corn stover removes N from the fields, but its effect on subsurface drainage and other N losses is uncertain. We used the Root Zone Water Quality Model (RZWQM) to examine N losses with 0 (NRR) or 50% (RR) corn residue removal within a corn and soybean rotation over a 10-yr period. In general, all simulations used the same pre-plant or post-emergence N fertilizer rate (200 kg ha^-1 yr^-1). Simulated annual corn yields averaged 10.7 Mg ha^-1 for the post emergence applications (NRRpost and RRpost), and 9.5 and 9.4 Mg ha^-1 yr^-1 for NRRpre and RRpre. Average total N input during corn years was 19.3 kg N ha^-1 greater for NRRpre compared to RRpre due to additional N in surface residues, but drainage N loss was only 1.1 kg N ha^-1 yr^-1 greater for NRRpre. Post-emergence N application with no residue removal (NRRpost) reduced average drainage N loss by 16.5 kg ha^-1 yr^-1 compared to pre-plant N fertilization (NRRpre). The farm-gate net energy ratio was greatest for RRpost and lowest for NRRpre (14.1 and 10.4 MJ output per MJ input) while greenhouse gas intensity was lowest for RRpost and highest for NRRpre (11.7 and 17.3 g CO₂-eq. MJ^-1 output). Similar to published studies, the simulations showed little difference in N₂O emissions between scenarios, decreased microbial immobilization for RR compared to NRR, and small soil carbon changes over the 10-yr simulation. In contrast to several previous modeling studies, the crop yield and N lost to drain flow were nearly the same between NRR and RR without supplemental N applied to replace N removed with corn stover. These results are important to optimizing the energy and nitrogen budgets associated with corn stover harvest and for developing a sustainable bioenergy industry.

N loss to drain flow and N2O emissions from a corn-soybean rotation with winter rye

Anthropogenic perturbation of the global nitrogen cycle and its effects on the environment such as hypoxia in coastal regions and increased N₂O emissions is of increasing, multi-disciplinary, worldwide concern, and agricultural production is a major contributor. Only limited studies, however, have simultaneously investigated NO₃^- losses to subsurface drain flow and N₂O emissions under corn-soybean production. We used the Root Zone Water Quality Model (RZWQM) to evaluate NO₃^- losses to drain flow and N₂O emissions in a corn-soybean system with a winter rye cover crop (CC) in central Iowa over a nine year period. The observed and simulated average drain flow N concentration reductions from CC were 60% and 54% compared to the no cover crop system (NCC). Average annual April through October cumulative observed and simulated N₂O emissions (2004-2010) were 6.7 and 6.0kgN₂O-Nha^-1yr^-1 for NCC, and 6.2 and 7.2kgNha^-1 for CC. In contrast to previous research, monthly N₂O emissions were generally greatest when N loss to leaching were greatest, mostly because relatively high rainfall occurred during the months fertilizer was applied. N₂O emission factors of 0.032 and 0.041 were estimated for NCC and CC using the tested model, which are similar to field results in the region. A local sensitivity analysis suggests that lower soil field capacity affects RZWQM simulations, which includes increased drain flow nitrate concentrations, increased N mineralization, and reduced soil water content. The results suggest that 1) RZWQM is a promising tool to estimate N₂O emissions from subsurface drained corn-soybean rotations and to estimate the relative effects of a winter rye cover crop over a nine year period on nitrate loss to drain flow and 2) soil field capacity is an important parameter to model N mineralization and N loss to drain flow.

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha^-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha^-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

Modeling groundwater nitrate concentrations in private wells in Iowa

Contamination of drinking water by nitrate is a growing problem in many agricultural areas of the country. Ingested nitrate can lead to the endogenous formation of N-nitroso compounds, potent carcinogens. We developed a predictive model for nitrate concentrations in private wells in Iowa. Using 34,084 measurements of nitrate in private wells, we trained and tested random forest models to predict log nitrate levels by systematically assessing the predictive performance of 179 variables in 36 thematic groups (well depth, distance to sinkholes, location, land use, soil characteristics, nitrogen inputs, meteorology, and other factors). The final model contained 66 variables in 17 groups. Some of the most important variables were well depth, slope length within 1 km of the well, year of sample, and distance to nearest animal feeding operation. The correlation between observed and estimated nitrate concentrations was excellent in the training set (r-square=0.77) and was acceptable in the testing set (r-square=0.38). The random forest model had substantially better predictive performance than a traditional linear regression model or a regression tree. Our model will be used to investigate the association between nitrate levels in drinking water and cancer risk in the Iowa participants of the Agricultural Health Study cohort.

Verifiable metamodels for nitrate losses to drains and groundwater in the Corn Belt, USA

Nitrate leaching in the unsaturated zone poses a risk to groundwater, whereas nitrate in tile drainage is conveyed directly to streams. We developed metamodels (MMs) consisting of artificial neural networks to simplify and upscale mechanistic fate and transport models for prediction of nitrate losses by drains and leaching in the Corn Belt, USA. The two final MMs predicted nitrate concentration and flux, respectively, in the shallow subsurface. Because each MM considered both tile drainage and leaching, they represent an integrated approach to vulnerability assessment. The MMs used readily available data comprising farm fertilizer nitrogen (N), weather data, and soil properties as inputs; therefore, they were well suited for regional extrapolation. The MMs effectively related the outputs of the underlying mechanistic model (Root Zone Water Quality Model) to the inputs (R(2) = 0.986 for the nitrate concentration MM). Predicted nitrate concentration was compared with measured nitrate in 38 samples of recently recharged groundwater, yielding a Pearson's r of 0.466 (p = 0.003). Predicted nitrate generally was higher than that measured in groundwater, possibly as a result of the time-lag for modern recharge to reach well screens, denitrification in groundwater, or interception of recharge by tile drains. In a qualitative comparison, predicted nitrate concentration also compared favorably with results from a previous regression model that predicted total N in streams.

Simulating nitrate-nitrogen concentration from a subsurface drainage system in response to nitrogen application rates using RZWQM2

Computer models have been widely used to evaluate the impact of agronomic management on nitrogen (N) dynamics in subsurface drained fields. However, they have not been evaluated as to their ability to capture the variability of nitrate-nitrogen (NO(3)-N) concentration in subsurface drainage at a wide range of N application rates due to possible errors in the simulation of other system components. The objective of this study was to evaluate the performance of Root Zone Water Quality Model2 (RZWQM2) in simulating the response of NO(3)-N concentration in subsurface drainage to N application rate. A 16-yr field study conducted in Iowa at nine N rates (0-252 kg N ha(-1)) from 1989 to 2004 was used to evaluate the model, based on a previous calibration with data from 2005 to 2009 at this site. The results showed that the RZWQM2 model performed "satisfactorily" in simulating the response of NO(3)-N concentration in subsurface drainage to N fertilizer rate with 0.76, 0.49, and -3% for the Nash-Sutcliffe efficiency, the ratio of the root mean square error to the standard deviation, and percent bias, respectively. The simulation also identified that the N application rate required to achieve the maximum contaminant level for the annual average NO(3)-N concentration was similar to field-observed data. This study supports the use of RZWQM2 to predict NO(3)-N concentration in subsurface drainage at various N application rates once it is calibrated for the local condition.