Estimates Of Nutrient Loads Research Articles

Land use and runoff effects on tidal creek ecosystem metabolism

Tidal creeks connect coastal lands and estuaries and are important conduits of watershed runoff and pollutants. With expanding urban development in many coastal areas, increased stormwater runoff to tidal creeks has the potential to increase nutrient loading, shift salinity regimes, and change ecosystem functions. To better understand these linkages, we measured dissolved oxygen (DO), water levels, salinity, and temperature for over 1 yr at 6 tidal creeks in Alabama and western Florida, USA. We coupled these measures with watershed-model (Soil and Water Assessment Tool) estimates of nutrient (NO3-, NH4+, and mineralized P) loading, flow, and other available watershed data. We estimated gross primary productivity, ecosystem respiration (ER), and net ecosystem metabolism (NEM) and found that all creeks were heterotrophic throughout the study period and frequently experienced periods of low DO. Our results did not identify a direct relation between urban land use and metabolism measures. Stepwise regression models showed that temperature, salinity fluctuations, and nitrogen loading were associated with seasonal measures of ER and NEM, which may be related to agricultural land use in some watersheds. We also noted that severe declines in DO related to ER occurred in most creeks following rainfall events associated with tropical cyclones. While this study indicates that low- to moderate-density residential development in this region may not readily elicit a response in ecosystem metabolism, runoff factors associated with land use change and excessive precipitation were related to metabolism. Further research is needed to understand how these stressors may interact to influence tidal creeks and the larger estuary.

Lake Trafford Nutrients Budget and Influxes After Organic Sediment Dredging (South Florida, USA)

Lake Trafford, a 600-ha subtropical lake in southwestern Florida, has suffered from over 50 years of cultural eutrophication, resulting in the invasion of Hydrilla verticillata and organic sediment accumulation due to herbicide treatments. This study aimed to assess the effects of dredging on nutrient dynamics. A pre-dredging nutrient budget, developed using land use models and climatic data, estimated nutrient loads of 190 kg d−1 for total nitrogen (TN) and 18.6 kg d−1 for total phosphorus (TP), with total maximum daily loads (TMDLs) of 70.4 kg d−1 for TN and 4.15 kg d−1 for TP. Post-dredging analysis, using detailed spatiotemporal data, showed higher nutrient loads of 274.3 kg d−1 for TN and 24.2 kg d−1 for TP. While dredging reduced legacy nutrient accumulation, it led to increased nutrient influx from groundwater, caused by the exposure of organic sediment, as evidenced by increased lake water electrical conductivity. These findings demonstrate the importance of conducting thorough pre-dredging assessments to mitigate unintended consequences, offering practical insights for managing nutrient loads and improving restoration strategies in eutrophic lakes.

Reducing wastewater nitrogen loading by >90% with carbon-amended septic systems: A field demonstration in Barnstable (Cape Cod), Massachusetts

Onsite wastewater treatment systems (OWTS) are a major source of excess nutrients and co-pollutants in watersheds across the United States. In Barnstable County (Cape Cod), Massachusetts, effluent from septic systems and cesspools contributes approximately 80% of the controllable reactive nitrogen (N) load to numerous impaired estuaries and degrades water quality in the region's sole source aquifer, streams and ponds. In unsewered areas, wastewater N loads could be reduced substantially by Innovative/Alternative (I/A) septic systems designed for enhanced removal. Use, however, has been partly limited by the availability of high performing, cost effective options, while conventional septic systems continue to be installed in watersheds with well documented N impairments. This paper describes the strategic replacement of residential OWTS with two I/A models that incorporate woodchip bioreactors to enhance N removal. Systems were installed at 14 neighboring homes in Barnstable, MA, and monitored for field performance. Influent and effluent were sampled monthly and analyzed for N and phosphorus (P), among other water quality indicators. Flow to each system was continuously metered to estimate nutrient loads. Results from the first 25 months of monitoring for 13 systems with at least a full year of data are presented in terms of 1) reductions in nutrient concentrations and mass loads and 2) reliability of the systems for meeting a performance goal of total N (TN) < 10 mg/L. Discussion supports consideration of where and how these technologies may be successfully used to manage excess N in sensitive watersheds.

Saturated buffers: Improvements and issues.

Saturated buffers are a newly developed agricultural best management practice used to redirect tile flow away from waterways, thereby mitigating nutrient losses and downstream eutrophication. This study evaluated the potential benefits of a novel saturated buffer design, which included pitchfork-shaped (PF) dispersion lines and a backflow check valve, that was installed alongside a traditional or standard (ST) buffer on a field in Moultrie County, Illinois, in the spring of 2019. Daily flow measurements and routine water samples were used to monitor the movement of water through both buffers and estimate nutrient loads. During observation days in 2020 and 2021, the PF buffer diverted 35% and 1.9% of incoming tile flow, respectively, while the ST buffer increased effluent rates by 116% and 137% over the same period. Both the PF and ST buffers experienced backflow from 30% to 47% of the monitoring period, well above the often reported 5%. Ultimately, the efficacy of saturated buffers could be improved with minimal, low-cost additions to their designs. Check valves are a simple supplement to saturated buffer design that can enhance flow diversion and potential nutrient removal. Added dispersion lines provide more opportunity for diversion of tile flow; however, they require more land to be removed from agricultural production and could increase backflow volumes, so the costs and benefits should be weighed.

Investigating the applicability and assumptions of the regression relationship between flow discharge and nitrogen concentrations for load estimation

The regression relationship between water discharge rates and nutrient concentrations can provide a quick and straightforward way to estimate nutrient loads. However, recent studies indicated that the relationship might produce large biases in load estimates and, therefore, may not be applicable in certain types of cases. The goal of this study is to explore the theoretical reasons behind the selective applicability of the regression relationship between flow rates and nitrate + nitrite concentrations. For this study, we examined daily flow and nitrate + nitrite concentration observations made at the outlets of 22 watersheds monitored by the Heidelberg Tributary Loading Program (HTLP). The statistical relationship between the flow rates and concentrations was explored using regression equations offered by the LOAD ESTimator (LOADEST). Results demonstrated that the use of the regression equations provided nitrate + nitrite load estimates at acceptable accuracy levels (NSE≥0.35 and |PBIAS|≤30.0%) in 14 watersheds (64 % of 22 study watersheds). The regression relationships provided highly biased results at eight watersheds (36 %), implying their limited applicability. The heteroscedasticity of the residuals led to the high bias and resulting inaccurate regression, which was commonly found in watersheds where low flow had high nitrate + nitrite concentration variations. Conversely, the regression relationships provided acceptable accuracy for watersheds that had a relatively constant variance of the nitrate + nitrite concentrations. The results indicate that the homoscedasticity of residuals is the key assumption to be satisfied to estimate nitrate + nitrite loads from a statistical regression between flow discharge and nitrate + nitrite concentrations. The transport capacity (capacity-limited) concept implicitly assumed in the regression relationship between flow discharge and nitrate + nitrite concentrations is not always applicable, especially to agricultural areas in which nitrate + nitrite loads are highly variable depending on management practices (supply-limited). The findings suggest that the regression relationship should be carefully applied to areas in which intensive agricultural activities, including crop management and conservation practices, are implemented. Thus, the transport capacity concept is reasonably regarded to contribute to the homoscedasticity of residuals.

Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed

Rising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009–2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.

Uncertainty in streamflow measurements significantly impacts estimates of downstream nitrate export

Across watershed science, two key variables emerge–streamflow and solute concentration–which serve as the basis for efforts ranging from basic watershed biogeochemistry research to policy decisions surrounding watershed management. However, we rarely account for how error in discharge (Q) impacts estimates of downstream nutrient loading. Here, we examined the impact of uncertainty in streamflow measurements on estimates of downstream nitrate export using publicly available data from the U.S. Geological Survey (USGS). We characterized how uncertainty in stage-discharge relationships impacts annual flux estimates across 70 USGS gages. Our results indicate the interquartile range of relative error in Q was 33% across these USGS sites. We documented a wide range in mean error in annual nitrate loads; some sites were underestimated (−105%), while predicted loads at other sites vastly overestimated (500%). Overall, any error in estimating Q leads to significant unpredictability of annual nutrient loads, which are often used as critical success benchmarks for governmental nutrient reduction strategies. Moreover, error in annual nitrate loads (as mass, kg) increases with mean Q; thus, as high flows become more unpredictable and intense under future climate change, error in estimates of downstream nutrient loading may also increase. Together, this indicates that error in Q may drastically influence our measures of water quality success and decrease our ability to accurately quantify progress towards algal bloom and ‘dead zone’ reduction.

A random forest approach to improve estimates of tributary nutrient loading

Estimating constituent loads from discrete water quality samples coupled with stream discharge measurements is critical for management of freshwater resources. Nutrient loads calculated based on discharge-concentration relationships form the basis of government nutrient load targets and scientific studies of the response of receiving waters to external loads. In this study, a new model is developed using random forests and applied to estimate concentrations and loads of total phosphorus, dissolved phosphorus, total nitrogen, and chloride, using data from 17 tributaries to Lake Champlain monitored from 1992 to 2021. I benchmark this model against one of the most widespread models currently used to estimate nutrient loads, Weighted Regressions on Time, Discharge, and Season (WRTDS). The random forest model outperformed both the base WRTDS model and an extension of the WRTDS model using Kalman filtering in the great majority of cases, likely due to the inclusion of rate-of-change in discharge and antecedent discharge over different leading windows as predictors, and to the flexibility of the random forest to model predictor-response relationships. The random forest also had useful visualization capabilities which provided important process insights. WRTDS remains a useful model for many applications, but this study represents a promising new approach for load estimation which can be applied easily to existing datasets, and which is easy to customize for different applications.

Estimation of long-term series of total nutrient loads flowing into a large perialpine lake (Lake Como, Northern Italy) from incomplete discrete data by governmental monitoring

Continuous series of nutrient loads released into a water body are essential for nutrient budgeting, water quality modelling and watershed management activities. A quintessential example of pursued data are the series of total nutrient loads flowing from multiple tributaries into a lake. However, except for extraordinary cases in which high-frequency monitoring (HFM) stations are installed for both discharge and concentrations, measured nutrient loads are available on a discrete basis. Such observations are typically obtained by governmental agencies for environmental monitoring purposes, with an at best monthly resolution, yet commonly with gaps spanning years. Usually, monitoring activities are limited to major inflows, neglecting minor ones. However, the latters can play a more relevant role in nutrient load budgets than in hydrological ones, in response to different natural features and pollution among tributary watersheds. In this work, we present a methodology we developed to estimate long-term series of total nutrient loads flowing into a water body, employing as case study Lake Como, a large deep lake in the Italian Alps with manifold monitored and unmonitored tributary watersheds. The method uses observed long-term relationships between discharges and concentrations (Q – C) and available discharge measurements and hydrological estimations to estimate continuous load series for the monitored basins. For the unmonitored watersheds, Q – C relationships are estimated from those of the monitored basins, given the observed dependence of the power-law coefficients on basin hydromorphological parameters. This equals to extending the regionalisation approach applied in hydrology for rainfall and discharges to the ecohydrological field for nutrient load estimation. The application of the method to the case study led to overall annual load estimations congruent to traditional techniques and revealed interesting Q – C watershed dynamics at interannual time scales which could not be disclosed through previous approaches. This work represents an exploratory development and application of ecohydrological regionalisation techniques, whose future development is fostered.

High-Frequency Monitoring to Estimate Loads and Identify Nutrient Transport Dynamics in the Little Auglaize River, Ohio

New technologies allow for the in situ monitoring of nutrients, specifically nitrogen and phosphorus, in water systems at increasingly higher temporal frequencies. These technologies allow for the near-continuous monitoring of water quality, which can potentially provide new perspectives on temporal variations in nutrient concentrations and transport dynamics, ultimately supporting more targeted and sustainable water management. The current study investigated the utility of monitoring nitrate-N and soluble reactive phosphorus (SRP) in situ using wet analytical chemistry for one year at 2-h intervals in a small agricultural watershed located in northwestern Ohio. While we saw large variability in the estimated nutrient loads due to daily variations in the high-temporal resolution nutrient concentrations, the nutrient loads were fundamentally driven by high-flow events for this agricultural watershed. Concentration–discharge relations were then developed to help identify how nutrients are stored and released over time scales ranging from low-flow seasonal responses to event-driven high-flow storms. The patterns in the concentration–discharge relations indicated a potential shift in the timing of the mobilization responses for SRP at the event scale over the course of the year. These results suggest that SRP-targeted management practices would need to intercept the dominant delivery pathways of phosphorus in the watershed, such as the tile drainage runoff, to help reduce phosphorus loading. For nitrate-N, patterns in the concentration–discharge relations revealed an increased mobilization response, which was seen during the growing season with low-flow conditions, indicating the potential role of biological uptake instreams across the lowest flows and concentrations of the year. Collectively, high-frequency temporal nutrient data monitored over individual events and across seasons offer guidance for management decisions while allowing us to track progress toward water quality goals.

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<103 km2) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. However, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

Improving estimates of phosphorus loads using hierarchical models

Estimates of nutrient loads are critical for managing the effects of nutrient pollution in receiving waters. An accurate calculation of load requires sub-daily, concurrent measurements of flow and nutrient concentrations. Continuous flow measurements are common, but nutrient concentrations are often measured infrequently, and so, statistical estimates of flow – concentration relationships are often used to infer nutrient concentration on unsampled days. Contributions of diffuse, nonpoint sources to flow – concentration relationships in neighboring sites may be similar, and methods are available to apportion in-stream concentrations to contributions from point and nonpoint sources. Combining a source apportionment model with a hierarchical statistical model may be a useful approach for leveraging information from more intensively sampled sites to improve estimates of relationships at more sparsely sampled sites. The approach is tested using daily measurements of total phosphorus and flow from eight rivers in Ohio, USA. Data from seven of the rivers are randomly subsampled to a single sample from each month, and a hierarchical model fit to estimate flow – concentration relationships for all sites. Hierarchical model estimates of phosphorus loads in subsampled sites were compared with three site-specific models: a linear flow – concentration relationship, a quadratic flow – concentration relationship with seasonal corrections, and Beale’s ratio method. The hierarchical model improved substantially on the precision of estimated total phosphorus loads at sparsely sampled sites, yielding an interquartile range of prediction errors of 22%. Simple linear load – concentration models were the next best performing method with an interquartile range of 33%. The average bias in hierarchical model predictions of phosphorus loads was 8%, whereas the smallest biases associated with the comparison methods were –10% for both the linear load – concentration model and Beale’s ratio. These results suggest that a hierarchical statistical model can be valuable for improving nutrient load estimates when data from a small number of intensively sites are available.

Effects of sampling strategy in rivers on load estimation for Nitrate-Nitrogen and total Phosphorus in a lowland agricultural area

The transport of nutrients into water bodies is one of the main causes of water eutrophication. It is therefore important to estimate the loads of nutrients. Discharge and nutrient concentrations are the fundamental elements to estimate the loads of nutrients, the latter can be affected by sampling strategies. As conducting sampling campaign and laboratory analysis are both expensive, it is necessary to find the best effective sampling strategy. The aim of this paper is to show how autocorrelation and standard statistical methods can be used to test the effects of different sampling strategies on the nutrient load estimation and to find the optimal sampling strategy. The data set in this study is from the 50 km² Kielstau catchment, a UNESCO demo site for ecohydrology in Northern Germany and consists of 14 years daily values of climate, hydrology, and water quality from 2006 to 2019. We calculated the autocorrelation (AC) of discharge (Q), precipitation, Nitrate-Nitrogen (NO3-N) and total Phosphorus (Ptot). Then we tested the effects of sampling intervals from 7 to 56 days (1–8 weeks) on the nutrient loads. Our results showed a high AC of Q and NO3-N for a long period, but the AC of Ptot and precipitation decreased very fast. An increase of the sampling interval (less frequent) increased the error of estimating the concentrations and loads. Consequently, we recommend that (1) the optimal sampling strategy for nutrient load estimation in an agriculture-dominant catchment should be continuously monitoring discharge combined with periodic grabbed samples; (2) the sampling frequency for NO3-N is suggested to be monthly (every 28 days) and for Ptot weekly (every 7 days). The information will help those tasked with catchment monitoring to design appropriate sampling strategy to ensure adequate data for nutrients load estimation in lowland rivers.

Estimation of nutrient loads with the use of mass-balance and modelling approaches on the Wełna River catchment example (central Poland)

Nitrogen and phosphorus budgeting is considered to be a key tool for policy makers and stakeholders when dealing with nutrient contamination issues, however no unified method has been employed in countries affected by this eutrophication problem. The current study offers a detailed insight into the estimations of nutrient loads and their distribution between different sources for a middle-sized agricultural catchment, with the use of two approaches: mass balance (static) and modelling (dynamic). Both methods revealed similar contributions of analysed nutrient sources, although the final estimates in the chosen calculation profile were divergent due to the various reasons related to the methods’ specificity. The advantages and disadvantages of both approaches have been specified in our study, and a hybrid solution on a local and country wide scale has been proposed.

Ultraviolet‐visual spectroscopy estimation of nitrate concentrations in surface waters via machine learning

AbstractHigh‐frequency acquisition of nutrient concentrations in rivers is needed to generate nutrient loading estimates commensurate with flow and discharge data. Although the combination of field sampling and laboratory analysis is the standard approach to riverine water quality analysis, this strategy is expensive and can miss important storm‐related events. Ultraviolet‐visual (UV–Vis) spectroscopy is widely used in drinking water and wastewater systems for high‐frequency concentration estimates. However, surface waters present a unique challenge as co‐occurring constituents in environmental samples cause spectral interference at the wavelengths used to measure concentrations of dissolved nutrients. Partial least squares regression (PLSR), Lasso regression (Lasso), and stepwise multivariate linear regression (Stepwise) models can be effective predictors of nitrate concentrations using UV–Vis absorbance and are used in many available in‐situ nitrate sensors; however, the proliferation of user‐friendly open‐source machine learning (ML) algorithms offers an opportunity to use sophisticated big‐data techniques to predict nutrient concentrations in surface waters. We collected samples from four rivers across southern Ontario with a variety of nitrate concentrations, flow regimes, and interfering co‐contaminants. We demonstrated that ML applications of random forest and gradient boosting models significantly outperformed PLSR, Lasso, and Stepwise methodologies to estimate nitrate concentrations in complex environmental samples via UV–Vis absorbance. Importantly, ML applications outcompete current models at low concentrations. This new predictive methodology provides regulators and stakeholders an opportunity to establish low cost, continuous monitoring environmental programs using UV–Vis approaches.

Nutrient loading and farm characteristics of giant gourami fish aquaculture systems in Lake Maninjau, Indonesia: basic knowledge of production performance

Background Aquaculture systems for giant gourami, Osphronemus goramy Lacepède (1801), have significantly improved fish production yields and food security in Indonesia. However, these systems also cause serious problems in terms of eutrophication in waterbodies. This study analysed the nutrient loading and farm characteristics of giant gourami in floating cages in Lake Maninjau. Method A total of 20 floating cages were used to record these nutrients in feed supply, female and male juvenile fish, dead fish and harvested fish to estimate nutrient loading. Data on the harvested fish, production cycle, stock number and cage capacity were used to estimate the stocking density, feeding rate, feed efficiency, and net fish yield, and the relationship between feed supply and nutrient loading and farm characteristics was analysed by least squares regression methods. Results A total of 20 floating cages released nutrients into waterbodies at an average rate of 236.27±60.44 kg/cycle for C, 84.52±20.86 kg/cycle for N and 8.70±3.63 kg/cycle for P. On average, fish production for each floating cage (±SD) was 1226±282 kg wet weight/cycle, and the net fish yield was 12.63±2.82 kg/m3/cycle. Survival rates ranged from 86.33 to 95.27%/cycle. The production cycles varied from 160 to 175 days with feed conversion ratios between 1.60 and 1.75, feed conversion efficiencies were between 0.58 and 0.63. The production parameters that had strong relationships with the net fish yield were feed supply (r2=0.960), stocking rates (r2=0.924) and feeding rates (r2=0.961). In contrast, the length of the production cycle was not strongly related to the net fish yield (r2=0.187). Conclusion Nutrient loading from the supplied feed was greater than that from the harvested fish, juvenile fish and dead fish. Increasing the net fish yield in floating cages was better predicted by the stocking densities and feeding levels than by the other factors.

Nutrient loading and farm characteristics of giant gourami fish aquaculture systems in Lake Maninjau, Indonesia: basic knowledge of production performance

Background Aquaculture systems for giant gourami, Osphronemus goramy Lacepède (1801), have significantly improved fish production yields and food security in Indonesia. However, these systems also cause serious problems in terms of eutrophication in waterbodies. This study analysed the nutrient loading and farm characteristics of giant gourami in floating cages in Lake Maninjau. Method A total of 20 floating cages were used to record these nutrients in feed supply, female and male juvenile fish, dead fish and harvested fish to estimate nutrient loading. Data on the harvested fish, production cycle, stock number and cage capacity were used to estimate the stocking density, feeding rate, feed efficiency, and net fish yield, and the relationship between feed supply and nutrient loading and farm characteristics was analysed by least squares regression methods. Results A total of 20 floating cages released nutrients into waterbodies at an average rate of 236.27±60.44 kg/cycle for C, 84.52±20.86 kg/cycle for N and 8.70±3.63 kg/cycle for P. On average, fish production for each floating cage (±SD) was 1226±282 kg wet weight/cycle, and the net fish yield was 12.63±2.82 kg/m 3/cycle. Survival rates ranged from 86.33 to 95.27%/cycle. The production cycles varied from 160 to 175 days with feed conversion ratios between 1.60 and 1.75, feed conversion efficiencies were between 0.58 and 0.63. The production parameters that had strong relationships with the net fish yield were feed supply ( r 2=0.960), stocking rates ( r 2=0.924) and feeding rates ( r 2=0.961). In contrast, the length of the production cycle was not strongly related to the net fish yield ( r 2=0.187). Conclusion Nutrient loading from the supplied feed was greater than that from the harvested fish, juvenile fish and dead fish. Increasing the net fish yield in floating cages was better predicted by the stocking densities and feeding levels than by the other factors.

Nutrient loading and farm characteristics of giant gourami fish aquaculture systems in Lake Maninjau, Indonesia: basic knowledge of production performance.

Background Aquaculture systems for giant gourami, Osphronemus goramy Lacepède (1801), have significantly improved fish production yields and food security in Indonesia. However, these systems also cause serious problems in terms of eutrophication in waterbodies. This study analysed the nutrient loading and farm characteristics of giant gourami in floating cages in Lake Maninjau. Method A total of 20 floating cages were used to record these nutrients in feed supply, female and male juvenile fish, dead fish and harvested fish to estimate nutrient loading. Data on the harvested fish, production cycle, stock number and cage capacity were used to estimate the stocking density, feeding rate, feed efficiency, and net fish yield, and the relationship between feed supply and nutrient loading and farm characteristics was analysed by least squares regression methods. Results A total of 20 floating cages released nutrients into waterbodies at an average rate of 236.27±60.44 kg/cycle for C, 84.52±20.86 kg/cycle for N and 8.70±3.63 kg/cycle for P. On average, fish production for each floating cage (±SD) was 1226±282 kg wet weight/cycle, and the net fish yield was 12.63±2.82 kg/m 3/cycle. Survival rates ranged from 86.33 to 95.27%/cycle. The production cycles varied from 160 to 175 days with feed conversion ratios between 1.60 and 1.75, feed conversion efficiencies were between 0.58 and 0.63. The production parameters that had strong relationships with the net fish yield were feed supply ( r2=0.960), stocking rates ( r2=0.924) and feeding rates ( r2=0.961). In contrast, the length of the production cycle was not strongly related to the net fish yield ( r2=0.187). Conclusion Nutrient loading from the supplied feed was greater than that from the harvested fish, juvenile fish and dead fish. Increasing the net fish yield in floating cages was better predicted by the stocking densities and feeding levels than by the other factors.

Remediating Agricultural Legacy Nutrient Loads in the Baltic Sea Region

The Baltic Sea is considered the marine water body most severely affected by eutrophication within Europe. Due to its limited water exchange nutrients have a particularly long residence time in the sea. While several studies have analysed the costs of reducing current nutrient emissions, the costs for remediating legacy nutrient loads of past emissions remain unknown. Although the Baltic Sea is a comparatively well-monitored region, current data and knowledge is insufficient to provide a sound quantification of legacy nutrient loads and much less their abatement costs. A first rough estimation of agricultural legacy nutrient loads yields an accumulation of 0.5–4.0 Mt N and 0.3–1.2 Mt P in the Baltic Sea and 0.4–0.5 Mt P in agricultural soils within the catchment. The costs for removing or immobilising this amount of nutrients via deep water oxygenation, mussel farming and soil gypsum amendment are in the range of few tens to over 100 billion €. These preliminary results are meant as a basis for future studies and show that while requiring serious commitment to funding and implementation, remediating agricultural legacy loads is not infeasible and may even provide economic benefits to local communities in the long run.

Estimation of Nutrient Load from Aquaculture Farms in Manila Bay, Philippines

Waste from aquaculture is considered as one of the possible causes of water quality deterioration in Manila Bay. Aquaculture in the area accounts for almost 30% of the total production in the Philippines. This high production entails intensified application of inputs that could possibly contribute to the nutrient (nitrogen, N and phosphorus, P) load in the bay. Thus, estimation of the N, P and SO4 loaded from aquaculture farms is necessary to develop more responsive intervention to reduce nutrient load in Manila Bay. Water samples were collected throughout the rearing period from different aquaculture systems in Cavite, Bulacan, Pampanga, and Bataan. The annual estimated N and P loaded from aquaculture farms were 12, 696.66 MT and 2, 363.01 MT, respectively. Fish pens/cages recorded the highest contribution accounting for 88% N and 86% P of the total load. It can be attributed to the direct release of uneaten feeds into the bodies of water. Roughly, 12% N and 14% P were obtained from the fishponds. Furthermore, the annual SO4 loaded from fishponds was estimated at 36,917.54 MT. Results of the study suggested that there should be an extensive monitoring of the environmental impacts and annual load of aquaculture farms for the sustainable regulations and management of aquaculture activities to reduce nutrient load and improve the aquaculture production as well. Finally, strict compliance to the regulatory guidelines and ordinances must be imposed to achieve the effluent quality standards.