Agricultural crops are frequently exposed to severe frost events in the spring, which can greatly impact yield quantity and quality. To manage frost effectively, early warning systems are often used to forecast potential spring frost in advance of an event. For the Massachusetts cranberry industry, the majority of growers use a frost forecasting model developed in the 1940s called the “Franklin model”, but many growers have questioned the accuracy of the model and are eager for a more robust and accurate approach. In this study, we aimed to improve spring frost forecasting in cranberries by combining machine learning techniques with field observations of canopy-level air temperature data from cranberry farms in southeastern Massachusetts. Nine random forest models were developed to predict daily minimum canopy-level air temperature 12 h in advance. We combined mesoscale forecasts from the High-Resolution Rapid Refresh model with meteorological observations from local weather stations to develop our models. Compared to the Franklin model, the random forest models reduced the root mean square error by 5.2–6.2 °C and achieved 91–95 % accuracy in frost classification. The top-performing random forest model achieved a Peirce’s skill score of 0.88, significantly outperforming the Franklin model (0.68) in identifying frost events. The Franklin model exhibited an inherent cold bias which was addressed by reducing false alarms between 56 and 89 % using random forest models. The random forest modeling framework offers a computationally efficient and easily transferable solution in cranberries to reduce frost-related losses of water, nutrients, and crop yields. The methods described in this paper can be adapted for other frost-sensitive crops and integrated with real-time sensor networks for dynamic updates.

Warmer temperatures associated with climate change have affected the phenology of most plants, but limited information exists for the American cranberry (Vaccinium macrocarpon Ait.), an important specialty crop. We examined long-term spatiotemporal trends in spring development of cranberry buds using field observations of cranberry bud stages over a 65-yr period, spanning from 1958–2022. A growing degree day (GDD) model was further used to interpret the observed trends in bud development over the study period. To assess spatial variability in cranberry bud development, the GDDs were computed using gridded weather data for four counties of Massachusetts, representing 85

This document examines the Chesapeake Bay watershed response to nutrient and sediment reduction efforts under the Clean Water Act's total maximum daily load (TMDL) regulation. As the 2025 Chesapeake Bay TMDL deadline approaches, water quality goals remain unmet, primarily because of nonpoint source pollution, the largest remaining source of nutrients and sediment, and the primary obstacle to meeting the TMDL. We focus on the factors influencing the gap between the expected effect of management to reduce nonpoint source loads reaching the Bay and empirical evidence suggesting that decades of effort have not produced the expected improvement. This gap may be caused by both insufficient scale and type of implemented water quality management practices and by an overestimation of practice effectiveness. Reasons water quality goals remain unmet include legacy nutrients and lag times masking or delaying the effects of management efforts, areas with large nutrient mass imbalances contributing disproportionate loads, and the difficulty of incentivizing behavior change in voluntary nonpoint source programs. Closing the response gap may require fundamental changes to nonpoint source programs. Apart from seeking additional funding, nonpoint source programs could develop policies to more effectively incentivize behavior change, identify and target treatment of high loading areas with appropriate management actions, and address nutrient mass imbalances.

ABSTRACT Tile drainage has been incorporated into many cranberry production operations. Given the potential water quality impacts of tile drainage, we quantified its contribution to surface water flows and nutrient loads for a 2 ha cranberry bed during the 2014 growing season. Our results revealed that tile drainage tracked surface water flow except during (1) two major daily storm events ( > 99th percentile and > 95th percentile based on local rainfall records), which caused enhanced overland and shallow subsurface flow, and (2) extended dry periods, when surface water was stored in ditches and/or recharged to groundwater. The combination of the two major storms contributed 44% of the total N (TN) export load in the growing season and 39% of the total P (TP) export load in the growing season. The TN load in tile drainage (4.3 kg ha −1 ) accounted for approximately half of that exported in surface water (7.5 kg ha −1 ), indicating a substantial release of N during major storm events in the form of runoff, shallow groundwater flow, and potentially from the release of ditch sediments. Conversely, the TP load in tile drainage (2.7 kg ha −1 ) was approximately twice that exported in surface water (1.5 kg ha −1 ), which was consistent with the retention of P in ditch sediments.

The Delmarva Peninsula contributes significantly to nutrient loading of the Chesapeake Bay predominantly from excessive Phosphorus (P) historically applied to low-relief agricultural fields with artificial drainage. Subsurface P transport is recognized as a primary pathway for P loss from these artificially drained agricultural systems, especially during high intensity rainfall events. We used time-lapse electrical resistivity imaging (ERI) to monitor movement of an injected salt tracer in a low-relief, artificially drained agricultural field in Princess Anne, MD during a simulated 25-year rainfall event. Conductivity breakthrough curves (BTCs) from the time-lapse electrical datasets were compared against relative concentration BTCs of a solute transport model to assess the geophysically-estimated solute transport behavior. We identified rapid lateral transport via high permeability pathways, emphasizing the role of soil heterogeneity to increase the rate of groundwater flux from the field to ditch waters with the potential for subsurface P transport during high intensity rainfall events. Based on our results, we suggest that critical source areas for P loss (i.e., where high soil P concentrations coincide with hydrologic connectivity) in ditch-drained fields may be farther from field edges than previously recognized.

AbstractTailwater recovery (TWR) systems, which divert phosphorus‐rich drainage water from cranberry (Vaccinium macrocarpon Ait.) farms into reservoirs, have the potential to improve water quality of freshwater lakes in Massachusetts. However, residents and environmentalists have questioned the potential water quality benefits of TWR systems. In the southeastern United States, research shows that TWR systems decrease agricultural inputs of phosphorus (P) to surface water by 23%–92%. Additionally, a case study in Massachusetts demonstrated the efficacy of TWR and other best management practices in reducing P concentrations in White Island Pond. Although TWR systems appear effective as part of a P management strategy, more research is needed to quantify their environmental benefits and allay public concerns. We propose filling three critical research gaps to strengthen and broaden support for TWR systems in cranberry agriculture in Massachusetts: (1) quantifying physical properties, (2) quantifying water storage potential, and (3) quantifying P retention capacity.Core Ideas Excess phosphorus (P) from cranberry farms may contribute to the eutrophication of freshwater lakes. Agricultural tailwater recovery (TWR) systems are increasingly used to conserve water and improve water quality. Earth removal, critical to create TWR ponds and provide sand for cranberry farms, has drawn public opposition. Research and case studies indicate TWR systems may decrease P inputs from cranberry farms to surface water. Long‐term monitoring, research, and stakeholder engagement are needed to assess efficacy of cranberry TWR systems.

AbstractExtreme short-duration rainfall is intensifying with climate warming, and growing evidence suggests that subhourly rainfall extremes are increasing faster than more widely studied durations at hourly and daily timescales. In this case study, we used 55 years (1968–2022) of 5-min precipitation data from Mahantango Creek, a long-term experimental agricultural watershed in east-central Pennsylvania, United States, to examine annual and seasonal changes in subhourly (15-min), hourly, and daily rainfall extremes. Specifically, we evaluated temporal trends in the magnitude and frequency of subhourly, hourly, and daily rainfall extremes. We then estimated apparent scaling rates between rainfall extremes and dew point temperature (Td) and compared these rates to the Clausius-Clapeyron (CC) rate (∼ 7% per °C). We also determined the coincidence of extreme rainfall trends with indicators of atmospheric instability and convective-type precipitation. Overall, we found the most significant changes in rainfall extremes at 15-min durations during the spring, with magnitudes of these subhourly extremes increasing by 0.6 to 0.9% per year, and frequencies rising by 3.4% per year. Apparent scaling rates in the spring showed that 15-min rainfall extremes transitioned from sub-CC scaling to greater than 2CC scaling when Td reached 11° C, implying a possible shift from stratiform rains to more intense convective rains above this Td threshold. Notably, trends in maximum hourly convective available potential energy (CAPE) increased during spring, as did the ratio of 15-min rainfall extremes to their corresponding daily rainfall totals. Findings indicate that convective-type precipitation may be playing an increasing role in the intensification of springtime 15-min rainfall extremes in Mahantango Creek.

Compared to conventional crops, less is known about how genetic and environmental variability affect the yield and quality of specialty crops like cranberry (Vaccinium macrocarpon Ait.). Herein, we performed a multifaceted analysis of six commercial cranberry beds planted to the Stevens cultivar. The six beds included three with above-average multiyear yields and three that were lower than average. We considered genotype, edaphic factors, and plant nutrient content as driving variables of yield and fruit quality. We found that genetic purity within beds raised the odds of obtaining above-average yields over an 8-year period. The highest levels of genetic contamination (38%-75%) were found at the low-yield beds, where significant differences in yield and fruit quality were observed between genotypes, within beds. Across all beds, focusing only on plots genetically confirmed to be Stevens cultivar, we also found that plot-scale yield in 2020 was significantly higher for two of three high-yield beds, suggesting other factors besides genetic contamination influenced differences in bed-scale yield. A factor analysis of mixed data that jointly included genotype, edaphic variables, and plant tissue nutrient content revealed complex relations among these variables that were tied to grouping plots based on long-term yield. Findings highlight the need for further research into the complex genetic and environmental factors that control cranberry yield and fruit quality.

Overland flow is water that flows over a land surface (sheet flow) or in rills and gullies (concentrated flow) in response to rainfall or melting snow. Measuring overland flow on croplands and rangelands is important because it represents a key water pathway that controls pollutant losses from agricultural fields. When overland flow is accurately measured along with other water inputs and outputs, scientists can develop budgets describing how water and associated pollutants traverse cropland and rangeland systems. Tracking these budgets over extended time frames (years, decades) helps quantify the beneficial effects of improved agricultural management. In many edge-of-field and small watershed studies, overland flow measurements typically involve control structures such as flumes or weirs. Several hydrology textbooks and agency manuals (USDA, USGS) provide detailed guidance on weir and flume development, installation, and operation. In general, weirs and flumes are located at the outlets of drainage areas where overland flow exits an agricultural field. The devices are sized based on the range of expected flows. In most instances, weirs and flumes have preset equations that calculate discharge (the volume of overland flow leaving a field over a given unit of time) based on measurements of water height, or stage. A wide variety of sensors are useful in measuring stage in weirs and flumes, including floats, bubblers, and pressure transducers. Stage data from these sensors are then recorded at frequent time steps (seconds to hours) to provide continuous estimates of overland flow.

The buffering of phosphorus (P) in the landscape delays management outcomes for water quality. If stored in labile form (readily exchangeable and bioavailable), P may readily pollute waters. We studied labile P and its intensity for >600 soils and sediments across seven study locations in the United States. Stocks of labile P were large enough to sustain high P losses for decades, indicating the transport-limited regime typical of legacy P. Sediments were commonly more P-sorptive than nearby soils. Soils in the top 5 cm had 1.3-3.0 times more labile P than soils at 5-15 cm. Stratification in soil test P and total P was, however, less consistent. As P exchange via sorption processes follows the difference in intensities between soil/sediment surface and solution, we built a model for the equilibrium phosphate concentration at net zero sorption (EPC0) as a function of labile P (quantity) and buffer capacity. Despite widely varying properties across sites, the model generalized well for all soils and sediments: EPC0 increased sharply with more labile P and to greater degree when buffer capacity was low or sorption sites were likely more saturated. This quantity-intensity-capacity relationship is central to the P transport models we rely on today. Our data inform the improvement of such P models, which will be necessary to predict the impacts of legacy P. Further, this work reaffirms the position of labile P as a key focus for environmental P management-a view Dr. Sharpley developed in the 1980s with fewer data and resources.

Phosphorus (P) budgets can be useful tools for understanding nutrient cycling and quantifying the effectiveness of nutrient management planning and policies; however, uncertainties in agricultural nutrient budgets are not often quantitatively assessed. The objective of this study was to evaluate uncertainty in P fluxes (fertilizer/manure application, atmospheric deposition, irrigation, crop removal, surface runoff, and leachate) and the propagation of these uncertainties to annual P budgets. Data from 56 cropping systems in the P-FLUX database, which spans diverse rotations and landscapes across the United States and Canada, were evaluated. Results showed that across cropping systems, average annual P budget was 22.4 kg P ha(-1) (range = -32.7 to 340.6 kg P ha(-1)), with an average uncertainty of 13.1 kg P ha(-1) (range = 1.0-87.1 kg P ha(-1)). Fertilizer/manure application and crop removal were the largest P fluxes across cropping systems and, as a result, accounted for the largest fraction of uncertainty in annual budgets (61% and 37%, respectively). Remaining fluxes individually accounted for <2% of the budget uncertainty. Uncertainties were large enough that determining whether P was increasing, decreasing, or not changing was inconclusive in 39% of the budgets evaluated. Findings indicate that more careful and/or direct measurements of inputs, outputs, and stocks are needed. Recommendations for minimizing uncertainty in P budgets based on the results of the study were developed. Quantifying, communicating, and constraining uncertainty in budgets among production systems and multiple geographies is critical for engaging stakeholders, developing local and national strategies for P reduction, and informing policy.

Legacy phosphorus concentrations resulting from historic additions of phosphorus (P) to the landscape may impede rapid remediation of P pollution and achievement of water quality management goals. Herein, we hypothesized that the capacity of stream biofilms to assimilate new polyphosphate (polyP) will vary as a function of stream legacy phosphorus. To test this hypothesis, we deployed a series of in situ enrichment experiments at five sites of varying land cover in central Pennsylvania, United States. Incremental P-loading was delivered using vials fitted with porous lids, that contained agar enriched with six levels of P (as Dissolved inorganic phosphorus, dissolved inorganic P) loading with rates ranging from 0 to 1,540 µg PO 4 −3 /day; these loading rates mimicked natural stream P loadings. Substrata were incubated at stream sites for a relatively short incubation period (12 days), to measure uptake rates; after which, biofilms growing on the lids were removed and their tissue content was analyzed for biomass (as chlorophyll) and various forms of particulate phosphorus. Polyphosphate (polyP) accumulated by stream biofilms at all sites closely tracked the release of dissolved inorganic P from experimental enrichment assays. Comparatively, biofilms accumulated relatively small amounts of Particulate inorganic phosphorus and other forms of organic P that we assume constitute a third group of P-rich biochemicals (e.g., DNA, RNA, lipids, proteins). Viewed at the watershed scale, land use appeared to affect P accumulation, where sites dominated by forest cover had a higher capacity for P storage, while sites dominated by agriculture did not; this underscores the importance of polyP storage as an indicator of legacy P pollution.

The advent of in situ optical sensors that can collect sub-daily measurements of nutrients and turbidity in flowing water bodies has yielded comparatively much larger water quality data sets than were previously available. With these newly available data sets, there has been increased interest in studying event-based concentration-discharge (c-Q) relationships to infer the sources and pathways of various watershed constituents during storms. With water quality data sets increasingly growing in size and scope, the need to automate the processing and analyses of such data has become apparent. However, consensus on storm event delineation methods as they pertain to c-Q analysis is currently lacking, and methodological details, including parameter values, are sometimes unreported in the literature. Here, we present an open-source workflow using the programming language R that automates the processing of sub-daily c-Q data to analyze event-based hysteresis patterns. Briefly, the workflow accepts a time series of concentration and discharge data, extracts stormflow from streamflow, delineates storm events and then evaluates c-Q relationships using widely applied metrics like the hysteresis index (HI) and the flushing index (FI). We applied the workflow to three watersheds in the mid-Atlantic United States, including a 0.4-km(2) agricultural watershed, a 150-km(2) urbanizing watershed and a 29 940-km(2) mixed land use river basin. Sub-daily sensor-based nutrient concentrations and discharge data were collected in each watershed. Using the small agricultural watershed as an example, we demonstrate the step-by-step application of the workflow. We then present results from the larger watersheds as a means for comparison and to illustrate the flexibility of the code. We believe that this rapid approach to event-based c-Q analysis will allow scientists and practitioners more time to focus on interpreting results, and promote greater scientific reproducibility. Likewise, we conclude this Scientific Briefing with suggested future improvements to the workflow to increase the automation of data analyses and reproducibility.

Accurate and reliable forecasts of quickflow, including interflow and overland flow, are essential for predicting rainfall-runoff events that can wash off recently applied agricultural nutrients. In this study, we examined whether a gridded version of the Sacramento Soil Moisture Accounting model with Heat Transfer (SAC-HT) could simulate and forecast quickflow in two agricultural watersheds in east-central Pennsylvania. Specifically, we used the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) software, which incorporates SAC-HT, to conduct a 15-yr (2003-17) simulation of quickflow in the 420-km(2) Mahantango Creek watershed and in WE-38, a 7.3-km(2) headwater interior basin. We directly calibrated HL-RDHM using hydrologic observations at the Mahantango Creek outlet, while all grid cells within Mahantango Creek, including WE-38, were calibrated indirectly using scalar multipliers derived from the basin outlet calibration. Using the calibrated model, we then assessed the quality of short-range (24-72 h) deterministic forecasts of daily quickflow in both watersheds over a 2-yr period (July 2017-October 2019). At the basin outlet, HL-RDHM quickflow simulations showed low biases (PBIAS = 10.5%) and strong agreement (KGE '' = 0.81) with observations. At the headwater scale, HL-RDHM overestimated quickflow (PBIAS = 69.0%) to a greater degree, but quickflow simulations remained satisfactory (KGE '' = 0.65). When applied to quickflow forecasting, HL-RDHM produced skillful forecasts (>90% of Peirce and Gerrity skill scores above 0.5) at all lead times and significantly outperformed persistence forecasts, although skill gains in Mahantango Creek were slightly lower. Accordingly, short-range quickflow forecasts by HL-RDHM show promise for informing operational decision-making in agriculture. Significance StatementDaily runoff forecasts can alert farmers to rainfall-runoff events that have the potential to wash off recently applied fertilizers and manures. To gauge whether daily runoff forecasts are accurate and reliable, we used runoff monitoring data from a large agricultural watershed and one of its headwater tributaries to evaluate the quality of short-term runoff forecasts (1-3 days ahead) that were generated by a National Weather Service watershed model. Results showed that the accuracy and reliability of daily runoff forecasts generally improved in both watersheds as lead times increased from 1 to 3 days. Study findings highlight the potential for National Weather Service models to provide useful short-term runoff forecasts that can inform operational decision-making in agriculture.

Nonpoint sources of nitrogen (N) and other nutrients are a major source of water pollution within the Chesapeake Bay watershed and other basins around the world. Human activities associated with agricultural practices can account for a large percentage of N loadings delivered to streams and rivers. This work aims to improve understanding of N transport from groundwater to surface waters, quantifying the principal hydrological processes driving water and N fluxes into and out of a headwater agricultural stream reach. The study site is a 175-m stream reach in a heavily cultivated 40-ha watershed in east-central Pennsylvania. This subwatershed is underlain by fractured shale bedrock, and receives most of its baseflow from groundwater, either by diffuse matrix discharge through the streambed or by localized discharge through riparian seeps. Samples of stream, seep, and shallow groundwater were collected approximately monthly under steady hydrologic conditions in 2017. Calculated matrix flow from hydraulic head and conductivity measurements paired with differential stream gauging was used to solve for the riparian seep flux using a mass balance approach. Riparian seep fluxes ranged from 45 to 217 m(3)/d, transporting 0.6-4.2 kg N d(-1) of nitrate-N from the fractured bedrock aquifer to the stream. Hydrochemical data suggest that the stream is mainly disconnected from the underlying aquifer and that seeps supply essentially all water and N to the system. Seeps are likely sourced with N in nearby agricultural fields and accelerated through the system with shorter residence times than shallow groundwater. Water isotope data reinforced this notion. This study underscores the importance of agriculture as a source of N to ground and surface waters. Identifying source areas that are causing groundwater enrichment of N and seep areas where N discharges to streams is beneficial for developing N pollution mitigation strategies and implementing management practices that aim to reduce nutrient loads to the Chesapeake Bay.

Despite ongoing management efforts, phosphorus (P) loading from agricultural landscapes continues to impair water quality. Wastewater treatment research has enhanced our knowledge of microbial mechanisms influencing P cycling, especially regarding microbes known as polyphosphate accumulating organisms (PAOs) that store P as polyphosphate (polyP) under oxic conditions and release P under anoxic conditions. However, there is limited application of PAO research to reduce agricultural P loading and improve water quality. Herein, we conducted a meta-analysis to identify articles in Web of Science on polyP and its use by PAOs across five disciplines (i.e., wastewater treatment, terrestrial, freshwater, marine, and agriculture). We also summarized research that provides preliminary support for PAO-mediated P cycling in natural habitats. Terrestrial, freshwater, marine, and agriculture disciplines had fewer polyP and PAO articles compared to wastewater treatment, with agriculture consistently having the least. Most meta-analysis articles did not overlap disciplines. We found preliminary support for PAOs in natural habitats and identified several knowledge gaps and research opportunities. There is an urgent need for interdisciplinary research linking PAOs, polyP, and oxygen availability with existing knowledge of P forms and cycling mechanisms in natural and agricultural environments to improve agricultural P management strategies and achieve water quality goals.

Agricultural communities of New Mexico regularly redistribute manure nutrients from dairies to nearby croplands to fulfill agronomic nutrient needs and protect water quality. Yet competition for water resources can result in land use change that affects these cooperative manure transfers. Focusing on three clusters of New Mexico dairy farms and their surrounding lands (three manuresheds), we calculated the magnitude of land use changes in 2008-2019 and the balance between manure nutrient supply and crop demand in 2019 to assess how past change may predict future prospects for sustainable management. The overall magnitude of change was small, with each manureshed experiencing a different complement: an exchange of cropland and rangeland in the Roosevelt manureshed (7,975 ha rangeland to cropland; 7,624 ha cropland to rangeland), a 464-ha gain in cropland but a 1,187-ha loss of "spreadable" land (cropland, rangeland, fallow) to developed land in the Doña Ana manureshed, and relatively minor changes in the Chaves manureshed. Nutrient supply and demand were mainly in balance, but a surplus of manure phosphorus (P) in the Chaves manureshed and a thin margin of P assimilation by croplands in the Roosevelt manureshed point to the need for preserving existing croplands and understanding of effects of dairy manure on shortgrass rangeland. Our assessment suggests that an ideal scenario would entail manure being generated in landscapes with portfolios of productive lands that can sustainably use the manure nutrients to minimize environmental quality concerns and agronomic tradeoffs. Coordinated, participatory, and interdisciplinary research and planning are needed.

HighlightsWe used SWAT-VSA to assess the effects of climate change with rising CO2 on the water balance of a karst basin.For future climate, SWAT-VSA with rising CO2 yielded 7.1% less ET and 6.3% more runoff than standard SWAT-VSA.Rising CO2 also affected variable source areas, with greater ET declines and runoff increases in the wettest soils.Findings suggest CO2 effects on water balance should be included in future climate change studies with SWAT-VSA.Abstract. Characterizing the effects of climate change on hydrology is important to watershed management. In this study, we used SWAT-VSA to examine the effects of climate change and increasing atmospheric CO2 (CO2) on the water balance of Spring Creek watershed, a mixed land-use karst basin in the Upper Chesapeake Bay watershed. First, we modified the stomatal conductance and leaf area index (LAI) routines of SWAT-VSA’s Penman-Monteith evapotranspiration (ET) procedure and enabled the model to accept daily CO2 data. Using downscaled climate projections from nine global climate models (GCMs), we then compared water balance estimations from baseline SWAT-VSA against two modified versions of SWAT-VSA. One SWAT-VSA version integrated daily CO2 levels (SWAT-VSA_CO2), while another version added flexible stomatal conductance and LAI routines (SWAT-VSA_CO2+Plant) to the dynamic CO2 capacity. Under current climate (1985-2015), the three SWAT-VSA models produced generally similar water balance estimations, with 51% of precipitation lost to ET and the remainder converted to runoff (10%), lateral flow (9%), and percolate (30%). For future climate (2020-2065), water balance simulations diverged between baseline SWAT-VSA and the two modified SWAT-VSA models with CO2. Notably, variable stomatal conductance and LAI routines produced no detectable effects beyond that of CO2. For the 2020-2065 period, baseline SWAT-VSA projected ET increases of 0.7 mm year-1, while SWAT-VSA models with CO2 suggested that annual ET could decline by approximately -0.4 mm year-1 over the same period. As a result, the two CO2-based SWAT-VSA models predicted streamflow increases of almost 1.6 mm year-1 over the 2020-2065 period, which were roughly double the streamflow increases projected by baseline SWAT-VSA. In general, SWAT-VSA models with CO2 effects produced 22.4% more streamflow in 2045-2065 than the SWAT-VSA model without CO2. Results also showed that adding daily CO2 to SWAT-VSA reduced ET in wetter parts of Spring Creek watershed, leading to greater runoff losses from variable source areas compared to baseline SWAT-VSA. Findings from the study highlight the importance of considering increasing atmospheric CO2 concentrations in water balance simulations with SWAT-VSA in order to gain a fuller appreciation of the hydrologic uncertainties with climate change. Keywords: Carbon dioxide, Climate change, Hydrologic model, Water balance, Watershed.

The midwestern United States is a highly productive agricultural region, and extended crop-free periods in winter/spring can result in nitrogen (N) and phosphorus (P) losses to waterways that degrade downstream water quality. Planting winter cover crops can improve soil health while reducing nutrient leaching from farm fields during the fallow period. In this study, we used linear mixed effects models and multivariate statistics to determine the effect of cover crops on soil nutrients by comparing fields with cover crops (n = 9) versus those without (n = 6) in two Indiana agricultural watersheds: the Shatto Ditch Watershed, which had >60% of croppable acres in winter cover crops, and the Kirkpatrick Ditch Watershed, which had ∼20%. We found that cover crops decreased soil nitrate-N by >50% and that the magnitude of reduction was related to the amount of cover crop biomass. In contrast, cover crops had variable effects on water extractable P and Mehlich III soil test P. Finally, cover crop biomass significantly increased soil N mineralization and nitrification rates, demonstrating that cover crops have the potential to supply bioavailable N to cash crop after termination. Our study showed that widespread implementation of winter cover crops holds considerable promise for reducing nutrient loss and improving soil health. The degree to which these results are generalizable across other systems depends on factors such as climate, soil characteristics, and past and current agronomic practices.