Journal of environmental quality最新文献

Saturated buffers are important edge-of-field conservation practices to reduce nitrate-nitrogen (NO₃-N) loading from subsurface (tile) drainage systems to downstream waters. The impact of seasonal management of weir elevations in the water control structure on NO₃-N removal has not been well studied. This study evaluated the effect of control box weir elevation management on nitrate removal and compared in situ flow treatment with design predictions from the USDA Natural Resources Conservation Service conservation practice standard 604. A 253 m long saturated buffer draining approximately 6 ha was monitored for flow and NO₃-N load from 2022 to 2024. The weir elevation was adjusted to “full drainage” (no treatment), “growing season” (reduced treatment capacity), and “fallow season” (full treatment capacity) settings according to weather conditions and field operations. During a 29-day full drainage period in 2022, the saturated buffer bypassed 25% of the annual drainage flow and 28% of the annual NO₃-N load. However, the fraction of flow treated and NO₃-N load removal efficiency was greater in 2022 than 2023 and 2024. Treated flow within the saturated buffer was greater than predicted, while peak drainage system flow was less, resulting in a greater percentage of drainage system capacity treated by the saturated buffer than designed. These discrepancies suggest that alternative design methods should be explored. While the saturated buffer removed substantial NO₃-N in the year with alternative weir management, careful consideration should be given for potential sites that may require extended full drainage periods, as large NO₃-N losses can bypass during such conditions.

This study evaluates the fate and variability of soil organic carbon (SOC) stocks and nitrogen species using the latest version of the Soil and Water Assessment Tool–Carbon (SWAT-C) and assesses how conservation practices influence their dynamics in the Raccoon River Watershed (RRW). Dominated by intensive agricultural production, the RRW is a significant contributor of sediment and nutrient loads to local rivers and the Mississippi River. This SWAT-C model simulates the export of SOC and nitrogen species and evaluates their responses under varying management scenarios. Model calibration was performed for streamflow, sediment, nitrate, total nitrogen, and organic carbon with monitoring data at both a sub-basin and the watershed outlet. The SWAT-C model achieved satisfactory to very good performance, with Nash–Sutcliffe efficiency values of 0.76–0.80, coefficients of determination (R²) of 0.78–0.86, and percent bias ranging from –18% to 12%. We assessed the effects of three conservation practices on SOC and nitrogen fate and transport: no-till, residue harvest with cover crop implementation, and residue harvest without cover crops. The SWAT-C results were compared with a historical baseline to quantify changes in the carbon and nitrogen loadings associated with each practice. This study highlights the role of conservation management and provides valuable insights for improving water quality and carbon sustainability in intensively farmed regions.

Amorim, H. C. S., Ashworth, A. J., Ducey, T. F., Brewer-Gunsaulis, V. B., Drescher, G. L., Owens, P. R., Patterson, A. H., DeBlasis, G., & van Straaten, I. (2025). Recycling waste via insect agriculture: Frass impacts on soil and plant health. Journal of Environmental Quality, 54, 1457–1469. https://doi.org/10.1002/jeq2.70089

A reader identified an inaccurate statement in the first sentence of the Conclusions section. The current text reads: “Insect manure or ‘frass’ is a promising amendment for organic and conventional systems, with 12 times greater concentrations of heavy metals and potentially toxic elements than poultry litter.” It should instead read: “Insect manure or ‘frass’ is a promising amendment for organic and conventional systems. Poultry litter had up to 12 times greater concentrations of heavy metals and potentially toxic elements than frass.”

We apologize for this error.

Upscaling crop yield and nitrate-N leaching loss from experimental sites to large areas under alternative crop rotations is crucial for assessing strategies and setting goals to protect groundwater quality at a regional scale. Nitrogen (N) rate field trials were used to calibrate the Environmental Policy Integrated Climate (EPIC) model for continuous-corn (Zea mays L.) (C-C), corn-soybean (Glycine max L.) (C-Sb), and alfalfa (Medicago sativa L.)-corn (A-C), with or without rye (Secale cereale L.) cover crop. Satellite estimates of crop evapotranspiration (ET_c) were used to upscale the EPIC model for crop yield and nitrate-N leaching, using the irrigation-water permitting data from 2010 to 2017 for 13,375 ha of sandy soils in Bonanza Valley, central Minnesota. Four alternative management scenarios were evaluated with EPIC: (1) reducing N fertilizer rate from the maximum return to N value (MRTN) (of 0.05 to a value of 0.1 (for the N price/crop value ratio), (2) adding rye cover crop at MRTN of 0.1, (3) irrigating with EPIC auto-trigger in scenario 2, and (4) converting 50% of C-C acreage in scenario 3 to A-C. Nash-Sutcliffe coefficients, normalized root-mean-square error, and R² values based on ET_c/crop yield for calibration and validation of the EPIC model ranged 0.95–0.54, 4.67–19.4, and 0.96–0.74; and 0.74–0.41, 7.99–23.4, and 0.88–0.55, respectively. Results indicate that corn yield at MRTN of 0.05 averaged 12.5, 13.2, and 13.4 t ha⁻¹ under C-C, C-Sb, and A-C rotations, while yields at MRTN of 0.1 were reduced by 4.1%, 3.5%, and 3.3%, respectively. The baseline scenario of C-C, C-Sb, and A-C rotations at MRTN of 0.05 had annual nitrate-N leaching losses of 51.8, 45.5, and 31.4 kg ha⁻¹, while MRTN of 0.1 reduced these losses by 9.1%, 5.0%, and 3.8%, respectively. Rye after corn and soybean reduced nitrate-N leaching losses in the MRTN of 0.1 scenario by 5.8% and 13.6%, respectively. EPIC auto-irrigation of corn, soybean, and alfalfa at MRTN of 0.1 reduced nitrate-N leaching losses with rye (relative to conventional irrigation) by 9.6%, 9.1%, and 8.5%, respectively. Further, replacing half of the C-C acreage with A-C rotation would provide a 6.1% reduction, resulting in a total reduction of 27.4% in nitrate-N leaching to groundwater when all alternative practices are combined. Overall, augmenting EPIC model with field-observed ancillary data and remote sensing successfully predicted the yield and NO₃-N leaching losses under different crop rotations, indicating opportunities to upscale field-scale agroecosystem simulations, particularly if used to calculate NO₃-N leaching on a long-term basis at the regional scales.

To manage pests, including problematic weeds, farmers rely on an integrated pest management approach that encourages the safe and effective use of pesticides. As US federal regulators place restrictions on pesticides as a means of protecting endangered and threatened (known collectively as listed) species, it is paramount these decisions are based on empirical evidence. During 2022, restrictions placed on the Enlist Duo (glyphosate + 2,4-D choline) herbicide to protect listed species lacked scientific merit. As a means of protecting the reticulated or frosted flatwoods salamander, the ability to use the herbicide was lost to many farmers across the country, including those in 11 Georgia counties. County-wide restrictions in Georgia, based on the historical presence of these salamanders, prevented its use on over 366,000 ha (904,000 acres) of corn, cotton, and soybean. Using spatial datasets to identify specific salamander habitat and where it overlapped with potential pesticide applications, results documented that less than 1% of impacted hectares should even be included in the restrictions; over 99% of the restricted farmland did not provide suitable habitat features needed for the species’ survival. This research confirms the importance of utilizing a science-based mapping methodology for future pesticide restrictions being implemented to protect listed species.

Phosphorus (P) is essential for crop growth but leaches through subsurface drainage discharge, impacting water quality. This study's objectives are to (1) apply hybrid statistical-machine learning to quantify the contributions of incidental (new) and legacy (old) P in drainage discharge from organic site and inorganic site and (2) evaluate the effect of manure application timing on P loss. We collected data from two on-farm sites in southeast Michigan, USA. A linear regression equation was used to analyze P load based on drainage discharge and fertilizer application timing. The data were split into calibration and validation sets, and machine learning was used for training. The results showed strong model prediction performance. Organic fertilizers contributed approximately twice the observed total phosphorus (TP) loss (7.54 kg ha^{- 1} vs. 3.73 kg ha^{- 1}) and nearly four times the dissolved reactive phosphorus (DRP) loss (4.90 kg ha^{- 1} vs. 1.05 kg ha^{- 1}) compared to inorganic P loss, mainly due to the greater P application rate and higher soil test P. When applied during winter months (December-January), organic fertilizer contributed to greater new P loss, whereas early fall applications (October-November) resulted in lower new P loss, showing the importance of application timing. At the organic site, legacy P was the dominant contributor to TP and DRP losses, accounting for 84% and 79% of losses, respectively. At the inorganic site, legacy P was responsible for 97% of TP loss and the entirety (100%) of DRP loss. In conclusion, legacy P was the dominant source of P loss through drainage discharge, and winter organic fertilizer application significantly increased new P loss.

Edge-of-field (EoF) water sampling methods play a crucial role in understanding non-point source nutrient fate and its environmental impacts, yet accurately interpreting water quality studies, remains challenging. This study evaluates and compares four EoF runoff water sampling techniques: (1) a commercial automated sampler (ISCO) with hourly sampling, (2) a low-cost internet of things sampler low-cost sampler with hourly sampling, (3) hourly hand sampling (grab hourly sampling), and (4) intermittent grab sampling (GB) in 2023 and 2024 at a surface irrigated agricultural site in Fort Collins, Colorado involving three levels of tillage intensity. Nine water quality parameters (nitrate-N, nitrite-N, total Kjeldahl nitrogen, orthophosphate-P, total phosphorus (TP), total suspended solids (TSS), total dissolved solids, pH, and specific conductivity) were measured over nine irrigation-driven and two rainfall storm runoff events. Resulting concentration values were modeled simultaneously using a Bayesian hierarchical generalized linear mixed model, enabling causal inference with uncertainty quantification while accommodating for missing data. Results show strong alignment across samplers for most analytes, confirming the validity of integrating diverse methods in long-term and widespread monitoring. However, ISCO samples exhibited consistently elevated TSS and TP due to a purge-induced sediment plume from the flume's stainless-steel bottom intake; excluding the first ISCO sample of each pair of sample draws restored agreement with other methods. These findings show the importance of flume morphology, intake placement, purge protocol, and selective data exclusion (if necessary) to ensure comparability across sampling methods.

In the Canadian prairies, spring snowmelt occurs rapidly and causes flooding in low-lying areas, inducing anaerobic soil conditions and exacerbating phosphorus (P) release to meltwater. Soil amendments can mitigate P loss from flooded soils soon after amendment application; however, their residual benefits are less understood. We examined the initial and residual benefits of alum (Al₂(SO₄)₃·18H₂O), gypsum (CaSO₄·2H₂O), and Epsom salt (MgSO₄·7H₂O) in a simulated snowmelt flooding experiment. Intact soil columns were taken from amended and unamended field plots in the same year and 1 year after the amendment application. The soil columns were flooded and incubated at a cold temperature. Porewater and floodwater samples were analyzed for dissolved reactive P (DRP), calcium (Ca), magnesium (Mg), iron (Fe), and manganese (Mn) concentrations, and pH. During the year of application, alum, gypsum, and Epsom salt decreased the mean porewater DRP by 68%, 29%, and 19%, and floodwater DRP by 69%, 51%, and 31%, respectively, relative to unamended treatment, with only alum showing significant differences. One year after applications, alum significantly decreased porewater DRP by 35%, but not floodwater DRP, whereas gypsum or Epsom salt did not decrease porewater or floodwater DRP. Correlation and principal component analysis revealed that porewater and floodwater DRP are positively related to pH and Fe, but only in alum-amended treatment, suggesting the influence of pH and Fe in stabilizing P. While alum was effective in mitigating P loss from flooded soils, its effectiveness decreased over time, with negligible residual benefits a year later.

The Annual Phosphorus Loss Estimator (APLE) model is a commonly used annual time-step model for predicting annual field-scale surface runoff and erosion losses of dissolved and particulate P as well as annual changes in total and Mehlich-3 extractable soil P. APLE was developed and coded as an Microsoft Excel workbook to provide a modeling option for users with limited modeling experience and lack of access to expensive software packages. The advantage of using Excel is that most users have both access and familiarity with Excel. However, the calculations within Excel require numerous calls to multiple cells, making it a challenge to modify and update the model. Moreover, the graphics are limited, and the current version does not have the ability to compare model predictions with observations. This limits the model's use and functionality. To address this, we have developed a Graphical User Interface application of APLE (APLE2026) to provide a cleaner and more intuitive user interface, improved graphics, and enhanced data analysis. This novel software package of APLE provides a more seamless way to run the model and view model output enhancing the functionality of APLE.

Phosphorus index (P-index) was developed to assess field vulnerability to phosphorus (P) loss and guide P management decisions. The original structure of the P-index was additive, and with continued refinement, multiplicative and component-based indices were developed. Alabama adopted the additive version in early 2000; however, the tool was never tested for its performance. The objectives of this study were to (i) evaluate the Alabama P-index using edge-of-field P loss data, (ii) test if multiplicative (Tennessee) and component-based (Georgia) P-indices perform better, and (iii) improve and test the performance of a modified Alabama P-index. We evaluated the performance by examining the strength and directional relationship between P-index scores and annual P loads. The Alabama P-index showed weak correlations (r < 0.50) between risk scores and measured dissolved reactive phosphorus (DRP), total particulate phosphorus (TPP), and total phosphorus (TP) loads. Additionally, directional inaccuracies were observed, indicating that the index misclassified the relative risk of P loss. Further, we evaluated multiplicative and component-based indices but found similar discrepancies between predicted risk scores and actual P loading. Subsequently, we modified the Alabama P-index by replacing soil test P with the phosphorus saturation ratio and substituting the underground outlet system factor with the timing of P application. Minor adjustments to weighting factors were made. The modified P-index demonstrated statistically significant correlations (r > 0.51) and directional alignment with DRP, TPP, and TP loads, suggesting it can serve as a reliable interim tool for assessing P losses. Future research should focus on restructuring and validating a component-based P-index tailored to Alabama's agricultural systems.