Journal of environmental quality最新文献

Decision support system for agrotechnology transfer (DSSAT) provides a robust platform for evaluating long-term management effects. A CROPGRO Cotton (Gossypium hirsutum L.) model (a component of DSSAT) was calibrated using 92 data points and evaluated using 232 data points to assess its performance in simulating phenology, growth, nitrogen (N) content, seed cotton yield (SCY), and nitrogen use efficiency (NUE). The calibrated model was then used to analyze the long-term (2003–2023) effects of three planting windows (May 15–30 [planting date 1, PD1], June 1–15 [PD2], and June 15–30 [PD3]), two irrigation strategies (rainfed and rule-based irrigation), and six N rates (0, 50, 101, 151, 202, and 252 kg ha⁻¹) on SCY, NUE, and N leaching. The calibrated and evaluated model showed a good agreement between simulated and observed data. Long-term seasonal analysis indicated that median SCY in PD1 was 21% and 46% higher than in PD2 and PD3, respectively. Delayed planting reduced NUE by 37% and increased N leaching by 85%. Irrigated cotton resulted in 38% and 29% higher SCY and NUE, respectively, than rainfed cotton, without significantly increasing N leaching. NUE declined by 19%–33% with each N rate increment from 0 to 252 kg N ha⁻¹, while N leaching increased by 45% at 252 kg N ha⁻¹ compared to 0 kg N ha⁻¹. However, leaching increased significantly only when N application exceeded 151 kg N ha⁻¹. These findings emphasize the importance of adjusting N rates based on PD and irrigation strategy to maximize yield and NUE while minimizing leaching losses.

This study develops a micro-level Ricardian model to assess how long-run climate patterns affect agricultural land values across the urban–rural gradient in the Chesapeake Bay Watershed. Using an 8-km gridded dataset that combines farmland prices, high-resolution climate data, and urban land cover, the analysis shows that seasonal temperature and precipitation affect land values nonlinearly, and urbanization significantly moderates the effects of precipitation. A climate simulation suggests heterogeneous impacts across urban grids. Our findings highlight the critical role of urban land cover in shaping climate adaptation strategies, offering new insights into how transitional urban-agricultural regions respond to climate stress. These results provide actionable guidance for policymakers seeking to enhance agricultural resilience in the face of continued urban expansion.

Accurate prediction of N₂O emissions in agricultural systems is essential for developing effective climate-smart practices. This study introduces a novel ensemble approach, termed the “Class-Swap” machine learning model, which employs two independent Random Forest (RF) models trained separately on background and hot-moment emissions. A statistical anomaly detection algorithm first classifies each flux observation, and the model then swaps between the two RF models accordingly, enabling emission-specific predictions based on distinct biogeochemical drivers. The objective was to evaluate the performance of this approach against traditional RF modeling in predicting N₂O fluxes from a long-term continuous cotton crop rotation in west Tennessee, which includes different tillage, N fertilization, and cover cropping treatments. The Class-Swap approach consistently outperformed traditional RF models on an independent unseen holdout dataset, achieving higher R² values (0.33–0.34 vs. 0.08–0.25) and lower root mean square error (9.8–9.9 vs. 10.5–11.6 g N₂O-N ha⁻¹ day⁻¹), while accurately capturing the magnitude and temporal dynamics of emissions—something traditional RF models failed to replicate. Key predictors varied by emission type: in the background emission model, moderate to high soil moisture (0.45–0.70 WFPS), soil $�{\mathrm{NH}}_4^{+}$, and increased soil CO₂ fluxes positively contributed to N₂O fluxes; in the hot-moment model, fluxes were primarily driven by large precipitation events and high soil moisture (>0.65 WFPS) conditions. This study underscores the effectiveness of differentiating between the distinct biogeochemical dynamics underlying background emissions and hot moments in predictive modeling. Further assessment across diverse sites, years, and agroecosystems remains necessary to validate the potential of the Class-Swap approach as a robust alternative for predicting dynamic soil N₂O fluxes.

The risk of water quality impairment from agricultural runoff depends on nutrient source, transport, and bioavailability. Phosphorus (P) spirals between dissolved and particulate forms as it is transported with suspended sediment (SS) from agricultural fields, through the stream network, to receiving water bodies. This dynamic sorption-desorption influences bioavailability. We quantified P form and abundance in samples collected during surface-runoff events from a farm field in the East River Basin, Wisconsin and compared them to those in stream water collected from the East River. We sampled five events between late March 2022 and June 2023. During most events, P in surface runoff was mainly in dissolved form, with particulate P sorbed to fine clay, the most abundant particle fraction transported from the field, whereas P in stream water was mainly in particulate form and sorbed to silt, even though fine clay was the most abundant particle fraction in the stream during events. Overall capacity for P sorption to SS in the stream varied among events. Total P and SS concentrations were lower during summer baseflow conditions and smaller surface runoff events; however, what SS was present was more P enriched. This shift in P form from field to stream indicates a potential for sorbing dissolved P to SS during transport through the stream network, which changes the bioavailability of P exported downstream with less bioavailable P as dissolved P binds to SS.

Injecting biostimulatory solutions around underground storage tanks (UGSTs) promotes petroleum hydrocarbon (PHC) bioremediation without the use of intrusive infrastructure. Yet, it is unclear how the interaction between porous bedding material (i.e., pea gravel) and biostimulatory solutions impacts PHC biodegradation rates. Consequently, we assessed whether pea gravel can act as an inert diffusion material that facilitates PHC bioremediation within UGST beds. First, we evaluated how biostimulatory solution elution through pea gravel (i.e., weathered) altered nutrient composition and benzene degradation rates. Suspected microbial activity decreased the proportion (i.e., nutrient concentration after pea gravel elution divided by initial biostimulatory solution concentration) of ammonium (0.45–0.84) and citrate (0.0–0.25) in effluents. In comparison, the proportion of anaerobic electron acceptors sulfate (0.91–1.08) and nitrate (0.78–1.0) were largely unaffected by pea gravel sorption. After 28 days, the weathered biostimulatory solution degraded 25 ± 0.50% of added benzene at rates similar (0.010 ± 0.002 days⁻¹) to fresh biostimulatory solution (0.008 ± 0.001 days⁻¹). Next, using positron emission tomography (PET) with [¹⁸F]-fluorodeoxyglucose and [¹⁸F]-fluoride, we evaluated the risks of biofilm fouling to gravel beds. In agreement with the reduced nutrient concentration, PET results show that the biostimulatory solution promoted microbial growth on the surface of the pea gravel. Yet, as the pore space occupied by bacteria remained low (between 8.8% and 13%) following biostimulatory solution elution, biofilm formation is not a concern. Therefore, pea gravel environments in tank nest areas can support PHC biostimulatory solution delivery with minimal consequences.

Reclaimed water (RW), which is treated municipal wastewater suitable for beneficial reuse, is increasingly recognized as a viable option for agricultural irrigation worldwide due to its numerous benefits. However, RW contains various contaminants whose fate in soils is variable and not fully understood. These contaminants may accumulate in soils, degrade, be taken up by plants and microbes, leach into groundwater, or be transported to surface waters via runoff. This review examines the impacts of RW contaminants on soil microbial communities, which play critical roles in nutrient mineralization, cycling, enzyme activities, and overall soil function. Evidence from literature is mixed: some studies report reductions in total bacterial and archaeal populations and decreased diversity of arbuscular mycorrhizal fungi, whereas others indicate enhanced microbial proliferation and enzyme activity due to increased availability of microbial growth substrates. These variable effects suggest that the impact of RW contaminants on soil microbial communities and their functions depends on soil properties (e.g., pH, organic matter content, texture, and mineralogy), the type and concentration of contaminants, and the wastewater treatment methods applied. Further research across diverse soil types and environmental conditions is needed to better understand how RW contaminants influence microbial communities, enzyme activities, and overall soil health.

Increasing agricultural and urban land use poses a growing threat to freshwater ecosystems worldwide, contributing to nutrient loading and degradation of surface water quality. Stream restoration, such as riparian vegetation, can help mitigate nonpoint source pollution, yet its ecological outcomes remain variable. Aquatic macroinvertebrates are widely used as bioindicators for water quality due to their sensitivity to environmental change, but their responses to stream impairment and restoration in the context of ongoing land-use pressures are less well understood. This study investigates associations between water quality parameters, biotic indices, and stream classifications using macroinvertebrate and water quality data collected from stream sites within an intensively managed agricultural watershed in the Midwestern United States during the autumn seasons of 2021 and 2022. Numerical analyses were used for water quality and macroinvertebrate indices, while stream classifications—impairment status, nutrient thresholds, and habitat quality—were analyzed categorically. Nonmetric multidimensional scaling assessed macroinvertebrate correlations with water quality, while Kruskal–Wallis tests identified statistical differences across stream classifications. Findings show that sites with stream improvement projects exhibit higher macroinvertebrate index scores, with strong correlations between higher index scores, higher dissolved oxygen, and lower total phosphorus. These results suggest macroinvertebrates and water quality parameters are effective indicators of stream impairment and habitat quality, though long-term links to restoration require further study. Findings contribute to a broader understanding of how biological indicators can inform restoration success and impairment conditions in agriculturally impacted watersheds across temperate regions.

Manure amendments are widely used in agriculture to enhance crop productivity and maintain soil organic matter, but they also contribute to greenhouse gas emissions and reactive nitrogen (N) losses. Manure acidification and nitrification inhibitors (NIs) have been proposed as mitigation strategies, but their effectiveness likely varies with soil characteristics. To evaluate the influence of acidified and NI-treated manure on N cycling across different soil types, we conducted a 28-day laboratory incubation using soils collected from major agricultural regions in Wisconsin. We measured emissions of nitrous oxide (N₂O), nitric oxide (NO), carbon dioxide (CO₂), and methane (CH₄), along with soil ammonium (NH₄⁺) and nitrate (NO₃⁻) dynamics. Acidified manure enhanced short-term NH₄⁺ retention in all soils but increased cumulative N₂O emissions in the sandy soil compared to untreated manure, likely due to pH-driven disruption of nitrification and incomplete denitrification. In contrast, the NI dicyandiamide (DCD) consistently suppressed nitrification early in the incubation, resulting in significantly lower N₂O and NO emissions, often approaching levels observed in the no-manure controls, particularly in the sandy soil. Manure amendments increased CO₂ fluxes relative to the no-manure controls, but acidified manure emitted less CO₂ than DCD-treated manure, likely due to temporary suppression of microbial respiration. CH₄ emissions were minimal and largely unaffected by treatments. NIs offer consistent benefits in reducing N₂O and NO losses, while acidification can increase these emissions in certain soil conditions, highlighting the importance of tailoring management practices to specific soil characteristics.

The impact of biochar on soil nutrient pool has been well-studied in degraded and acidic soils, yet its effects in fertile soils such as Luvisols remain underexplored. To address this, two laboratory incubation experiments were conducted using biochar derived from wheat straw (Triticum aestivum)—Experiment 1 evaluated biochar produced at three pyrolysis temperatures (350°C, 500°C, and 650°C) with two residence times (1 and 2 h), whilst Experiment 2 examined the feasibility of different application rates (5 or 10 Mg ha⁻¹) and placements (thorough mixing or surface broadcast). Biochar significantly increased exchangeable Ca, K, Mg, and Na concentrations compared to both control and straw-amended soils, particularly with the higher temperature biochar. Soil available P and K were enhanced two- and fivefold, respectively, compared to control and straw-amended soils. The effects on soil available N were inconsistent, with no significant improvement observed and some treatments indicating possible immobilization. Soil cation exchange capacity (CEC) significantly increased with certain biochar compared to the control but did not differ from straw-amended soil, with occasional instances where biochar led to lower CEC. Soil available N was higher with biochar application than straw. However, these did not significantly differ from the control, except for biochar produced at 500°C with a 1-h residence time. Soil available N was notably higher when biochar was surface broadcasted than when thoroughly mixed into the soil. Consequently, this study highlights the influence of biochar pyrolysis conditions on soil nutrient pool, with outcomes also linked to some extent by the application rates and placements, suggesting careful consideration of these management factors for optimal biochar benefit in Luvisols.

Switchgrass (Panicum virgatum L.) has been identified as a “model” herbaceous species for bioenergy production by the United States Department of Energy. Switchgrass can provide several ecosystem services, including biodiversity support, soil erosion control, runoff filtering, and reclamation of marginal land. In addition to the reduction in greenhouse gas emissions from switchgrass-derived biofuel, soil carbon sequestration is of particular importance. The objective of this study was to evaluate the variability in soil carbon sequestration, particularly in response to water limitation, and to investigate the relationship between soil carbon sequestration and switchgrass yield. For this purpose, dry aboveground biomass yield and soil reactive carbon—specifically, permanganate oxidizable carbon (POXC)—content at three depths (0–15, 15–30, and 30–60 cm) were measured for 150 different switchgrass genotypes for three consecutive years. We found that drought significantly reduced yield compared to control plots, reduced the amount of soil POXC, and that POXC decreased with soil depth. A positive correlation (r = 0.27, p < 0.05) between POXC and yield was observed in the drought-stressed plots. This study provides insight into the impact of switchgrass on soil POXC over time and at different depths, offering a framework for future evaluation of root-related traits in switchgrass, particularly in relation to drought stress.