DOI: 10.1007/s00267-004-4020-z
Nutrient-Balance Modeling as a Tool for Environmental Management in Aquaculture: The Case of Trout Farming in France ELIAS PAPATRYPHON*,** JEAN PETIT HAYO M. G. VAN DER WERF Environmental Systems Analysis Research Group Unit Mixte INRA-ENSAR Sol Agronomie Spatialisation 65 rue de Saint Brieuc CS 84215 35042, Rennes Cedex, France KAUSHIK J. SADASIVAM Fish Nutrition Research Laboratory Unit Mixte INRA-IFREMER Unit dÕHydrobiologie 64310, Saint-Pee-sur-Nivelle, France KANYARUSHOKI CLAVER Inter-Professional Committee of Aquaculture Products 71 rue Fondary 75015, Paris, France
Agricultural production systems, aquaculture being no exception, require the use of external nutrient inputs (as feed, fertilizers, manure, etc.) to meet their production goals. However, nutrient recovered as product is invariably lower than total nutrient input, the difference representing the loss of nutrients to the environment. Nutrient losses can have adverse effects on the quality of surface water, groundwater, and the atmosphere, which can be manifested as specific environmental impacts such as eutrophication, acidification, and ozone layer depletion, to name a few.
KEY WORDS: Aquaculture; Fish farming; Nutrient emissions; Modeling; Environmental impact; Environmental monitoring; Policy development; Nutrient management Published online February 14, 2005. *Author to whom correspondence should be addressed; email:
[email protected] **Current address: Institute for Prospective Technological Studies, Joint Research Centre, European Commission, Edificio EXPO, Inca Garcilaso s/n, E-41092, Seville, Spain y Current address: Chambre dÕAgriculture de Bretagne, 65 rue de St Brieuc, 35042, Rennes Cedex, France
Environmental Management Vol. 35, No. 2, pp. 161–174
ABSTRACT / The control and prevention of nutrient pollution from fish farming plays an essential role in the French regulatory framework. Assessing nutrient emissions from fish farms is important in terms of farm authorization, taxation, and monitoring. Currently employed strategies involve both water sampling and empirical modeling. This article reports the work and outcomes of an expert panel that evaluated existing methodologies and their possible alternatives. The development and evaluation of a nutrient-balance approach was assessed as a potential alternative to currently used methodologies. A previously described nutrient-balance model was suggested and parameterized using expert choice, and its validity and applicability were assessed. The results stress that the nutrient-balance model provides more robust and relatively conservative waste estimates compared to the currently used methodologies. Sensitivity of the approach to the uneven data quality available at farm level, difficulties of on-farm measurements, as well as model requirements and limitations are discussed.
Nutrient emissions of agricultural origin have received increased attention as society becomes more and more aware of environmental problems and gives a high priority to ecological concerns and clean production in general (Tamminga 2003). The ecological consequences of nutrient emissions from intensive aquaculture have been a major focus of debate, as, unlike most other forms of agriculture, fish farms are considered a point source of pollution, affecting directly the receiving waterways (Fernandes and others 2001; Bergheim and Brinker 2003). Nutrient emission assessment is critical in French fish farming because it plays an important role in permitting and monitoring of the farms (Kanyarushoki 2003). To this end, the administrative authorities responsible for monitoring nutrient emissions and surface water quality have been charged with the task to find the best way to ensure an effective and scientifically valid management plan (Petit 1999). To address this need, an expert panel was formed to review the current strategies employed and make propositions for potential ameliorations. This article deals with the outcomes of the work of the expert panel. As a natural consequence of the panelÕs work, a large part of this ª 2005 Springer Science+Business Media, Inc.
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study consists of an evaluation of a nutrient-balance model, which was proposed for predicting nutrient emissions under practical farming conditions. The emphasis is put on trout farms because they follow a relatively common system of production that comprises approximately 70% of total aquacultured fish production in France (FAO 2001).
State-of-the-Art Fish Farming in France Farmed fish production in France comprises mainly species that depend on an exogenous feed supply (mainly salmonids and marine fish), following the intensive animal production model. Furthermore, it is practiced in open water systems, namely raceways utilizing surface water or sea-based cages, that emit directly to the natural waterways with no or with minimal nutrient recycling or waste treatment taking place within the boundaries of the farm. As a result, and as far as nutrient emissions are concerned and not their potential downstream environmental impacts, there is no major need to account for the potential on-farm nutrient transformations. Legislation on Fish Farm Nutrient Emissions in France Nutrient management of fish farming for regulatory purposes has two main concerns. First, fish farms producing more than 10 metric tons annually have a status of Classified Installations for the Protection of the Environment (ICPE) and are required to obtain the necessary authorization to operate from local administrative agencies (i.e., law No. 76-63 of July 19, 1976 with its decree of application No. 77-1133 of September 21, 1977; for details see Kanyarushoki 2003). The principal elements toward obtaining the authorization are related to an environmental impact assessment, in which nutrient emissions play a significant role. Trout farming uses river water or groundwater as input and releases its effluent almost invariably to the river. Therefore, emission requirements need to meet the quality objectives of the surface waters of concern, so that nutrient concentrations do not exceed the predefined standards. These standards are based on European directives (i.e., 78/659/EC and the more recent 2000/60/ EC) and related French legislation (i.e., the law on the water No. 92-3 of January 3, 1992 with its decree of application No. 93-742 of March 29, 1993; for details see Kanyarushoki 2003) at the national and regional levels. However, the final decision regarding the regulations imposed on fish farms is left to the local administra-
tive authorities (prefectures) and, therefore, the requirements on nutrient emission reporting by fish farms vary among different administrative regions. In general, ammonia (NH4+) is the sole parameter monitored in all regions, followed by suspended solids (SS) and, to a lesser extent, total phosphorus (total-P). The most common limits set by the administrative agencies for NH4+, SS, and total-P in waters where trout is farmed are 0.5 mg/L, 25 mg/L, and 0.2 mg/L (‘‘blue’’ quality river) or 0.1 mg/L, 5 mg/L, and 0.05 mg/L (‘‘green’’ quality river), respectively, measured 50–100 m after the confluence of the fish farm effluent with the river (based on the latest system of water quality evaluation, SEQ-eau; Anonymous 1999). A second concern of nutrient management is that farms characterized as ICPE are taxed according to the ‘‘polluter pays’’ principle, which is based on nutrient emission estimations controlled by the regional water agencies. In these calculations, effluent treatment for solids removal is also taken into account by applying a correction factor depending on the solids removal technology used. Nutrient Emission Monitoring Review Monitoring of agriculturally related nutrient emissions of environmental concern is based on the use of indicators that can be defined at any point on the means-based to goal-based hierarchical continuum. Indicators closer to the goal-based end of the hierarchy are generally of greater relevance but are also more difficult to obtain (van der Werf and Petit 2002). Because regulations on nutrient emissions are based on a set of objectives, which in the case of this study mainly concern surface water quality, it follows that the best possible indicator would consist of a goal-based indicator, such as the nutrient concentration in the waterway of concern. However, such indicators are usually hard to use in monitoring individual farms, due to difficulties in obtaining representative measurements, and to complications in relating individual farm practices to the final indicator as well as in assigning specific cause–effect relationships. In order to deal with such problems, intermediate indicators have been proposed and applied, which require, however, the use of some form of modeling to relate them to the final objective of concern. Intermediate type indicators that have been extensively developed and used in agriculture are the ones based on nutrient budget modeling. Such nutrient balances are a form of accounting where inputs and outputs are summed and their difference quantified (Schroder and others 2003). Further knowledge on nutrient flows and transformations within and between the different farm and environmental compartments
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(e.g., crops, animals, soil, water, air, etc.) can be used to further define the nature and fate of the nutrient surplus (emissions). Depending on the objective of the indicator, nutrient-balance sheets can be drawn to represent either total ‘‘farm-gate’’ balances or balances relevant to the receiving compartment of concern, such as the soil in terrestrial agriculture or the water in aquaculture. Such an approach has been used in agriculture referring to a ‘‘soil surface’’ balance (Schroder and others 2003), and we propose a similar approach for aquaculture (i.e., a ‘‘surface water’’ balance). In most agricultural sectors, nutrient monitoring by administrative agencies is usually achieved by means of nutrient budget modeling because it is widely accepted that accurate, on-farm, direct measurements are impossible. This is due to the requirements of high analytical expertise on a frequent basis, which renders the operation cost and skill prohibitive (i.e., Neeteson 2000; Simon and others 2000; Corpen 2003). Furthermore, waste release (1) is often multifaceted, (2) has significant temporal and spatial variability, and (3) involves multiple receptor media, all of which make simple measurements unsuitable. Trout farming differs from other agricultural activities in that the majority of emissions take place directly in the waterway of concern, which limits spatial variation as well as the receptor media involved. Because of the latter, the notion exists that elementary water analysis can be representative of these emissions, and, consequently, instantaneous measurements are currently being used in fish farm monitoring in France. This tactic is further supported by the need to conform to the administrative surface water quality monitoring, whereby the aim of not surpassing predefined limits of water quality at any point in time can only be confirmed by measurements at the point of concern. In addition to the on-farm monitoring of emissions by instantaneous water analysis and because of the above-mentioned limitations of such measurements, administrative agencies currently utilize empirical formulas based on a series of studies conducted in the late 1970s, as a means of estimating nutrient release (Faure´ 1983). This method also forms the basis in the environmental impact assessments used for obtaining and renewing the permits of operation. The coefficients for each nutrient of concern were derived as the proportion of the measured emissions over the total amount of feed distributed. Although these coefficients might have been representative under a specific set of conditions at the time the studies took place, it is obvious that they are of limited value today because (1) they are outdated, (2) scientific knowledge has since considerably advanced, and (3) they do not take into account
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changes in feed composition, animal/farm variability, and farmersÕ practices. Furthermore, the reported variability associated with these coefficients makes it extremely difficult to decide on the exact value that should be applied to different farms. As these are general disadvantages associated with the use of empirical models, it seems that the development of such approaches, even if based on more recent data (i.e., Lemarie and others 1998), will always be of limited value compared with the more mechanistic type models, such as nutrient budget modeling.
Round Table Consultation An expert panel was formed, composed of (1) scientists from the French National Institute for Agricultural Research (INRA) and the French Research Institute for the Exploitation of the Sea (IFREMER), (2) industry representatives from the trout farming and feed manufacturing sectors, and (3) representatives of the French Aquaculture Federation (FFA) and the Inter-Professional Committee of Aquaculture Products (CIPA). The objective of this panel was to discuss the issues concerning nutrient emissions from trout farming in terms of their estimation and policy relevance. The group convened several times throughout a 10month period, from December 2002 through October 2003. Early discussions covered an evaluation of the current status regarding regulations and nutrient management of trout farms that would serve as a basis for further exchanges. During this phase, several concerns were expressed, the most important ones being the following: Variability in the control and monitoring between different regions Variability in farming practices and environmental conditions Variability and problems in the use of measurements Problems and difficulties in the use of modeling Following these discussions, a consensus was reached that a scientifically valid and robust approach in the assessment of nutrient emissions was needed and that such an approach would be to the benefit of everyone. Later discussions focused on the various options available, which in this case were limited to the following: Currently used empirical model (Faure´ 1983) ‘‘New’’ nutrient-balance model (based on Cho and Bureau 1998; Kaushik 1998)
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Figure 1. Flow diagram for a trout farming system as considered in this study. Rectangles correspond to compartments and circles to processes; processes and compartments filled in gray are considered either unimportant or nonpredictable and, therefore, were not included in the model; asterisks indicate the process steps considered in the model.
An agreement was reached that the currently used method of estimating nutrient emissions (Faure´ 1983) is outdated and therefore should be replaced by a more representative model. A previously described and validated nutrient-balance based model was proposed (Cho and Bureau 1998; Kaushik 1998). It was decided that the applicability of the model should be assessed, which would require the following: A definition of model parameters deemed most appropriate for the scope of this study An evaluation of the availability of data needed to run the model An evaluation of the modelÕs capability to provide the required output A comparison of model predictions with on-farm observations A discussion on the conditions of application of the model The study ended with the provision of final recommendations on the future directions and perspectives of this work.
Model Development, Parameterization, and Calibration The scientific principles underlying the suggested nutrient-balance based model have been previously described (Cho and Kaushik 1990; Cho and others
1991, 1994; Kelly 1995; Cho and Bureau 1997, 1998; Kaushik 1998; Lupatsch and Kissil 1998) and validated under controlled conditions (Cho and others 1991; Boujard and others 2002). It has also been suggested that in comparison to on-farm measurements, the balance approach is more reliable and a rather inexpensive way to quantify fish farming wastes (Cho and others 1991; Kaushik and Cowey 1991; Cho and others 1994; Kelly and others 1996; Cho and Bureau 1997; Boujard and others 2002). Although the validity, usefulness, and universality of the nutrient budget methodology has been previously shown in terrestrial farming systems (i.e., Neeteson 2000; Simon and others 2000; Corpen 2003) and despite the existence of bioenergetic models for fish (Cho and Bureau 1998; Kaushik 1998), their actual use in fish farming has lagged behind. Briefly, the model is based on a nutrient-balance approach, where calculation of nutrient emissions (in this case, of nitrogen, phosphorus, and total solids) is based on the difference between the amount of nutrient ingested via the feed during the growing period and that laid down as gain [for more conceptual details and the formulas, see Bureau and others (2002)]. Moreover, nutrient digestibility and whole-body composition coefficients are used to distinguish between solid (nonconsumed and undigested nutrients) and dissolved (absorbed but not retained nutrients) emissions (Figure 1). The expert panel defined the model parameters that were deemed most appropriate for the scope of
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Table 1. The description of variables used in the nutrient balance model Description
Notation
Unit
Value
Source
Feed distributed Feed consumed
FD FC
kg %
Variable 100 or variable
FarmersÕ records Common best estimate or farmersÕ best estimate if different
CPR CL CA CF CC CP CM
% % % % % % %
Variable Variable Variable Variable Variable Variable Variable
Feed manufacturers
ADCPR ADCL ADCA ADCF ADCC ADCP
% % % % % %
90 or variable 95 or variable 50 or variable 0 or variable 60 or variable 65 or variable
ScientistsÕ best estimate or feed manufacturers if information is available
BN BP FGR PRN NNH4
kg/kg kg/kg kg/kg % %
0.00272 0.004 Variable 16 80
ScientistsÕ best estimate
A ESR R
kg/kg % L/sec
1.29 Variable Variable
Default value FarmersÕ records FarmersÕ records or regional water agency if information is available
RNH4
mg/L
Variable
FarmersÕ records or regional water agency if available
RP RSS
mg/L mg/L
Variable Variable
Feed nutrient content Protein Lipids Ash Fiber Carbohydrates Phosphorus Moisture Apparent digestibility coefficients Protein Lipids Ash Fiber Carbohydrates Phosphorus Whole-body nutrient content Nitrogen Phosphorus Feed: gain ratio Protein nitrogen content Proportion of ammonia-N in total dissolved N excretion Ammonia to ammonia-N ratio Solids removal efficiency River flow
Ambient river nutrient concentration Ammonia Total phosphorus Suspended solids
this study (Table 1). Some of these parameters were defined as constants; others were allowed to be modified according to the data supplied by farmers and feed manufacturers. Although some variability in the parameters defined as constant might be expected, it was decided that for the purpose of modeling, scientific knowledge was sufficient to propose fixed values under the current farming practices. The final decisions on these coefficients were based on expert choice stemming from research findings, the scientific literature, and scientific and industry expert opinion. This procedure was used for setting values for the nutrient apparent digestibility coefficients (ADCs), feed waste (set at zero), whole-body nutrient content of fish, and the proportion of ammonia-N in total dissolved N excretion (see Table 1). A similar procedure has been
FarmersÕ records Default value ScientistsÕ best estimate
previously used when definitive data do not exist or when some variability is unavoidable (i.e., Bureau and others 2002). For the evaluation of the applicability of the model under practical conditions, the model was modified so as to supply predictions for the emissions of concern, namely NH4+, total-P, and SS, in a format compatible with the instantaneous measurements. Specifically, four modifications were made to the originally described model: 1. A conversion of total dissolved N to dissolved NH4+, based on the fact that N-NH4+ excretion represents, on average, 80% of total dissolved N excretion (with the stoichiometric conversion factor of NNH4+ to NH4+ being 1.29).
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2. Calculated emissions were divided by the total river flow so that the final expression was concentration at the river in milligrams per liter. 3. Nutrients of farm origin were added to ambient nutrient concentrations for comparison with measurements made in the river. 4. To account for solid nutrient removal when effluent treatment was present, the nutrient recovered in the manure was subtracted from the total emissions calculation. Within the five farms that had some type of solids removal in place, two used rotary screen filters, one used settling ponds, and two were equipped with both. Because the efficiency of the solid removal technologies varies substantially due to system specifications, environmental conditions, and farm practices, we used data on manure production and composition provided by the farmers to calculate removal efficiencies. These data were only available for two farms using filters (estimated efficiency of solids removal was 27–33%; retained factor = 30%) and one using settling ponds (estimated efficiency of solids removal was 12%; retained factor = 10%), so they were also applied for the remaining three farms, whereas a factor of 40% was assumed for the two farms where both rotary filters and settling ponds were present. The same factors were applied in the estimation of solid P removal, assuming an even distribution of solid P in the SS. These values fall well within the range of removal efficiencies reported elsewhere with these types of aquaculture effluent treatment (Lamotte-Bandani 1992; Soulas 1992; Cripps and Bergheim 2000). Because surface water quality has been the only concern regarding fish farm emissions, the model has been developed to represent a ‘‘surface water’’ balance and not a total ‘‘farm-gate’’ balance. As a result, the solids collected after effluent treatment are considered as an output and not a potential emission. Similarly, any potential emissions to the air (as during storage of the solids or volatilization losses) are also not taken into account. The model predictions concern direct emissions by the fish and, therefore, do not make any attempt to estimate the fate or transformations that might take place once the nutrients are released in the raceways or the natural environment. However, on an annual average basis, trout farming is practiced under high-flow, highturnover-rate conditions, which avoids the occurrence of significant nutrient transformations within the boundaries of the farm (there are, however, situations where this might not be the case, such as dur-
ing the dry season in regions where the intra-annual variability is significant). Waste outputs were calculated as follows (explanations for the abbreviations used in the formulas can be found in Table 1). (A) SS (mg/L in river) = ambient SS + produced SS, Produced SS = (Nondigested feed + Nonconsumed feed)/River flow, Nondigested feed = Nondigested proteins + Nondigested lipids + Nondigested carbohydrates + Nondigested ash + Nondigested fibers. Formula for SS: SS ¼RSS þ ðððFD FC RðCi ð1 ADCi ÞÞÞ þ ðFD ð100 FC Þ ð100 CM ÞÞÞ=RÞ ESR; where the subscript i corresponds to each of the following nutrient components: protein, lipids, carbohydrates, ash, and fiber. In the case where solids removal was present, the corresponding calculated efficiencies were used as multipliers to the final estimate of feed origin SS: 30% for rotary filters, 10% for sedimentation ponds, and 40% when both systems were present. The ambient SS was not multiplied by the ESR because the latter was expressed as a percentage of the produced SS (of feed origin) and not the total SS (which would include SS of river origin). By SS, we refer to the total solids because we assume that all solids will be suspended. (B) NH4+ (mg/L in river) = Ambient NH4+ + Produced NH4+, + Produced NH4 = (Soluble nitrogen production · N to NH4+ conversion)/River flow, Soluble nitrogen production = Digestible dietary nitrogen – Nitrogen export in product Formula for NH4+: NHþ 4 ¼R NH4 þðððFD FC ððCPr ADCPr PR N Þ ðB N =FGRÞÞ NNH4 AÞ=RÞ: (C) Total-P (mg/L in river) = Ambient total-P + Produced total-P, Produced total-P = (Total-P imports in feed – TotalP export in product)/River flow, Formula for total-P: TotalP ¼ RP þ ððFD FC ðCP ðBP =FGRÞÞÞ=RÞ: In the case where solids removal was present, the calculated efficiencies were used as multipliers (as
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Table 2. Availability (+) and quality of farm data for use as input to the nutrient balance model or for the comparison with the output of the model Farm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Measurement frequencya
Model input Feed quantity
Feed type
Feed: gainb
Solids removal
River flow
NH4+
Total-P
Suspended Solids
Monthly Annual Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Daily · 4 Daily · 5 Daily · 1 Daily · 3 Daily · 13 Daily · 1 Daily · 1
+ + + + + + + + + + + + + + + + + + +
Monthly Annual Monthly Monthly Monthly Annual Monthly Annual Monthly Monthly Monthly Monthly Annual Annual Monthly Monthly Annual Annual Annual
No No No Yes Yes No Yes No No No No Yes No No No No No Yes No
Monthly seasonally Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Daily · 4 Daily · 5 Daily · 1 Daily · 3 Daily · 13 Daily · 1 Daily · 1
Monthly Daily · 1 Daily · 1 Monthly Monthly Monthly Monthly Daily · 2 Monthly Monthly Monthly Monthly Daily · 4 Daily · 5 Daily · 1 Daily · 3 Daily · 13 Daily · 1 Daily · 1c
— — — Daily · 1 Daily · 3 — Monthly — — — — Monthly. Daily · 2 — — — — Daily · 1 Daily · 1c
— Daily · 1 Daily · 1 Daily · 1 Daily · 3 — Monthly — — — — Monthly Daily · 4 Daily · 5 — Daily · 3 — Daily · 1 Daily · 1
a
All measurements are instantaneous samples taken on a single day. The designation ‘‘monthly’’ indicates that the data provided were monthly averages of an unknown number of samples. Includes ambient nutrient measurements. b Feed: gain ratio was either provided by farmers or calculated from raw data as feed inputs divided by the fish gain, which is the difference between fish outputs (sales, transfers, mortalities) and the fish inputs plus the change in biomass for the period under concern. c Data not applicable; below the detection limit.
described earlier for SS) to the diet derived solid-P fraction. However, a different calculation needs to be used to take into account the different forms (solid and dissolved): Total-P (mg/L in river) = Ambient total-P + Produced total-P, Produced total-P = (Soluble-P production + Solid-P production)/ River flow, Soluble-P production = Digestible dietary phosphorus – Phosphorus export in product, Formula for soluble-P:
Soluble-P = (FD FC ððCP ADCP Þ ðBP =FGRÞÞÞ=R: Solid-P production = Nondigested phosphorus + Nonconsumed phosphorus, Formula for solid-P: Solid-P ¼R P þðððFD FC ðCP ð100 ADCP ÞÞ þ ðFD ð100FC ÞCP ÞÞ=RÞ ESR : Substituting nitrogen for phosphorus in the above formulas can be used for calculating total N losses (solid and dissolved).
Model Evaluation With the aid of industry contacts, the data required to run and evaluate the model were obtained from farmersÕ records (Table 2). Data quality was variable in terms of reliability, scope, and accuracy. Overall, we were able to obtain data from 19 farms, of which 18 had measurements for NH4+, 11 for SS, and 6 for total-P. The data used were either from measurements made by the farmers themselves or by regional accredited analytical laboratories. When both were available, the measurements made by the laboratories were used. The farmers were interviewed for ensuring the accuracy and relevance of the data provided (i.e., for ensuring that the measurements and calculations corresponded to the same period). Data obtained from the feed manufacturers (i.e., on nutrient composition) are based on their technical specifications. Ambient nutrient concentrations and river flow were preferably based on data from the water agencies; if that was not available, farmersÕ records were used. Some farms had only one single measurement per year, whereas others had several per month; for the presentation of the results, one data point per farm was used (the average was used when multiple pairs were available) so that they all share an equal representation in the evaluation.
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Figure 2. Observed values compared with predictions from the nutrient mass-balance model for (A) ammonia (NH4+), (B) total-phosphorus (total-P), and (C) suspended solids (SS). The diagonal represents perfect agreement between predicted and observed data.
A validation-type approach was undertaken to evaluate the applicability of the model under practical conditions. This was not a ‘‘classic’’ model validation exercise because data and measurements were of variable quality that were obtained from commercial fish farms and because the nutrient-balance principles have been previously validated in controlled experiments. Plots of observed (Y-variate) or model-predicted (Xvariate) values and residual plots (the residuals plotted against predicted values) were used to analyze the data as described by Mitchell (1997) (Figures 2A–2C, 3A– 3C, and 4A–4C). In this approach, specific criteria based on measurement uncertainty are defined to test the adequacy of the model with reference to its purpose. In addition, mean bias (which was computed by dividing the mean of the predicted minus the mean of the observed by the mean of predicted) and the root of the mean square prediction error (RMSPE, computed as the square root of the sums of squares divided by the square root of the number of observations) were used as statistical indicators for the evaluation of the data (Bibby and Toutenburg 1977) (Table 3). The criteria for adequacy were defined by Mitchell (1997) as (1) the envelope of acceptable precision and (2) the proportion of points that must lie within it. Due to the lack of quantitative information regarding the acceptable precision, a more qualitative approach was implemented. Because the model is tested against observations made by farmers and/or laboratory personnel, we believe that an estimate of the uncertainty involved in these measurements can be used to evaluate model adequacy and expectations. There are at least two main sources of potential measurement error involved in these measurements: sampling error/variability (of human or environmental origin) and analytical error/variability (due to different analytical techniques and different analysts). Unfortunately, we were not able to find any data related to the first of these sources. However, we were able to obtain data on the analytical uncertainty from accredited laboratories that participate in Proficiency Testing Schemes (PTS) organized by BIPEA (Gennevilliers, France) and that are often involved in making the measurements for fish farms. They provided us with the following values of uncertainty: 77% for NH4+ for a value of 0.31 ± 0.24 mg/L; 11–15% (retained value: 13%) for total-P for the values of 4.7 ± 0.5, 4.8 ± 0.7, and 7.5 ± 1.0 mg/L (retained value: 0.7mg/L); and 40% for SS for a value of 5 ± 2 mg/L. Uncertainty in this case was defined as two times the standard deviation, which, in other words, will cover 95% of the variation. For NH4+ and SS, the uncertainty was representative for concentrations of similar range as found within our samples, but for total-
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Table 3. Summary statistics of model predictions versus observed values for 19 fish farms for average ammonia (NH4+), average total-phosphorus (total-P), and average suspended solids (SS) with and without adjustment for solids removal With adjustment for solids removal
Predicted (mg/L) Observed (mg/L) Mean bias (%) RMSPEa (mg/L)
Without adjustment for solids removal
NH4+
Total-P
SS
NH4+
Total-P
SS
0.58 0.43 25.00 0.21
0.36 0.29 18.20 0.20
7.55 6.82 9.76 1.60
0.58 0.43 25.00 0.21
0.38 0.29 21.64 0.22
7.92 6.82 14.00 2.11
a
RMSPE = root mean square prediction error.
P, it represented the uncertainty calculated for 10-fold higher concentrations, as the levels normally found in the river water (and as in our assessment) are very low and often near the detection limit. The lowest detection limits of the commonly used techniques according to the same source are 0.04 mg/L for NH4+, 0.05 mg/L for total-P, and 2 mg/L for SS. As this was the only available information, as uncertainty would only be expected to increase if sampling variability was also taken into account, and as the model cannot be expected to perform better than the measurements, we believe that the above-mentioned values can be used as a minimum envelope of acceptable precision where points must lie within and was used as a guide for drawing conclusions. As the number of points was limited (especially for the SS and total-P analysis), the proportion of points that must lie within the envelope was not defined, but, rather, an argumentative approach was used.
Results and Discussion Figure 2A–2C indicates that predictions are well correlated with observations for all three parameters. However, there seems to be a tendency of overestimation for predicted NH4+ values. Nevertheless, when the minimum envelope as defined earlier is used, all NH4+ predictions fall within the minimum acceptable range when the percentage uncertainty is used (Figure 4A) and 72% of the data points fall within the envelope when the sample deviation is used (Figure 3A). This is expected because (1) the points falling out of the envelope represent larger concentrations than the ones used to define the uncertainty and (2) the uncertainty interval in absolute value will have a tendency to increase as the average concentration increases, whereas the uncertainty interval in percentage value will have a tendency to decrease as the average concentration under question increases and vice versa. One potential explanation for the lower NH4+
measurements compared to the modelÕs predictions is that losses might occur rapidly before and after water sampling, as it is very sensitive to transformations via nitrification processes, uptake by plants/algae, and evaporative losses, which renders proper sampling and quick analyses a critical factor. Moreover, it has been suggested that dissolved nutrient emissions (N and P) might be underestimated even when measurements are made under experimental conditions (Kaushik and others 2004). Although part of the problem might be due to some error associated with the coefficients used in the model, such as the proportion of NH4+ in the total dissolved nitrogenous emissions, we believe that it is safer to err on the conservative side; this becomes even more important when considering the significant measurement uncertainty as observed herein and elsewhere (i.e., Hennessy and others 1996) and the potential unaccounted forms of emitted N (Kaushik and Cowey 1991; Heinsbroek and others 1993). In the case of SS (Figures 2C, 3C, and 4C), two outliers were depicted, where observations were much higher than predictions. When these two outliers are disregarded, all but two data points fall within the envelope of uncertainty on an absolute and on a percent deviation basis. The large variability in the SS can be explained by considering the properties of solids transport in aquaculture raceways. First, solid transport (and therefore SS and solid-P transport) is not continuous but highly variable, due to the slow precipitation of solids within the raceways (so-called ‘‘selfcleaning’’ properties of raceways) and to spikes in solid output when conditions are favorable. The latter is due to farm management and/or environmental variability such as high flow, increased turbidity, raceway cleaning, filter backwashing, fish harvesting, pond emptying, and others. Second, it is not uncommon for SS and solid-P levels to be relatively elevated in the natural waterway before further inputs from the fish farm are added, which could result in the proportion of added nutrients by the fish farm to be negligible compared to
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Figure 3. The graph of deviations from empirical validation of the nutrient mass-balance model for (A) ammonia (NH4+), (B) total-phosphorus (total-P), and (C) suspended solids (SS). The dashed line indicates the boundaries of the envelope of measurement uncertainty.
Figure 4. The graph of deviations in percentage from empirical validation of the nutrient mass-balance model for (A) ammonia (NH4+), (B) total-phosphorus (total-P), and (C) suspended solids (SS). The dashed line indicates the boundaries of the envelope of measurement uncertainty.
Nutrient Emissions from Fish Farming in France
background concentrations (background levels could represent up to 90% of the total SS after effluent release). Third, the very small particle size of fecal matter makes it difficult to truly account for all possible losses (Lemarie and others, personal communication). An analysis of the deviations between predicted and observed values for total-P is more difficult, because the envelope was calculated from a significantly higher concentration and as a result when the uncertainty as a percentage is used, only one data point falls within the envelope (Figure 4B), but when the absolute uncertainty is used, all data points are included in the envelope (Figure 3B). At low concentrations, it is expected that percentage uncertainty will increase, because of approaching the detection limit but also because even small differences in absolute value will be very important on a percent basis. Taking into account that total-P concentrations are very low and that a significant part is due to solid-P emissions, whose transport is unpredictable, it is evident that expectations of predictions could not be higher. Because it is highly improbable that a generic model could be developed to take into account the hydraulics of solid transport adequately, we conclude that the nutrient budget approach, although inadequate for accurate prediction of instantaneous concentrations, is suitable for predicting average emissions over time with an acceptable margin of error. The statistics shown in Table 3 indicate that, on average, predictions were higher than measurements for all three parameters studied. However, considering the nature of the nutrient emissions, the potential measurement error, and the variability associated with the environment and the farms, the differences between predictions and measurements do not seem very important. Furthermore, the mean bias for all parameters is within, or very close to, our minimum uncertainty limits when expressed either on an absolute or on a percent basis deviation. Adjusting for solids removal led only to a small improvement in the models predictive capacity for SS and total-P, indicating the importance of other factors (as those indicated previously) that influence the variability of SS concentration in the river. An important argument set forth by several stakeholders concerned the potential differences in the validity and applicability of the model among different farms or different time periods within a farm. This argument was particularly relevant in the case of NH4+, due to its variable transformation potential to other nitrogenous forms. Whereas possible solutions would be searching for a relationship between the NH4+ transformation potential and different farm types/
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conditions, or performing detailed studies in individual farms, a simpler approach might be to reason in terms of the model. For example, it would be much simpler to reason in terms of total N (like it is done for P) or of dissolved and solid N and avoid the extra (and unnecessary?) complexity of dealing with NH4+. In the following, we discuss in more detail why we think that the simple modeling approach is a better alternative, at least until new knowledge is produced. Farm variability can be significant with respect to management practices and environmental conditions. Management practice variability involves differences in input use (such as feed quantity and quality, fish quality and quantity, veterinary treatments, water quantity and quality, oxygen, and aeration), final product size, equipment use and sophistication, waste treatment, pond design/hydraulics, and so forth. It is obvious that it would be nearly impossible to develop a single and simple model able to accurately represent all different farm types and to follow the exact fate of nutrients for each different farm. The variability of the production system and the receiving milieu is large, which complicates both the accurate representation of reality and the appropriate measurements corresponding to reality. Although the latter has been previously suggested (Cho and others 1991; Kaushik and Cowey 1991; Kelly and others 1996), this is the first effort, to our knowledge, of evaluating the nutrientbalance methodology through the development of a generic parameterized model for its practical application in an inter-farm assessment. The overall model evaluation undertaken in this study indicated that the model can adequately predict nutrient emissions for the purposes of current use, either of administrative nature or not. Because the modelÕs output does not take into account nutrient transformations that might take place between the emission by the fish and the sampling (potential model error) and because it avoids the nutrient transformations taking place between the sampling and analysis (sampling error), especially for the very sensitive ammonia measurements, predictions are a conservative estimate that follow the precautionary principle. The modelÕs potential error in missing any transformations of NH4+ might not be that important because it is the total nitrogenous emissions that are of environmental concern (and which can be calculated by the model) and not just the emission of NH4+, which is used for administrative purposes due to its ease of analysis. The only form of nitrogen release that would not constitute a potential environmental hazard is gaseous N2, but it is highly improbable that significant denitrification occurs within these systems.
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Nutrient budget modeling has been previously shown to enhance the understanding and awareness of farmers regarding nutrient management, which, in turn, might further result in an increase of farmer operational management skills (Neeteson 2000; Schroder and others 2003). Model development and improvements in awareness and management skills can be thought of as having a cyclic cause-and-effect relationship. An improvement in data quality and accounting procedures at the farm level will provide the basis for an improved model evaluation, which might lead to improvements in the model itself and further improve farmer awareness and so forth. The use of nutrient accounting would be of great benefit to fish farming, as very few farms were capable of supplying good quality data, and as accounting approaches previously developed for terrestrial farming systems have not been fully adapted in the aquaculture sector. Developments in nutrient-balance accounting can provide information on the environmental impacts of the farming activity, as well as on management quality and efficiency of resource utilization. This is particularly relevant because feeds and feed-related nutrient emissions are associated with the most important environmental burdens resulting from intensive aquaculture (Papatryphon and others 2004a, b). Furthermore, such developments might facilitate and provide a sound basis for the integration and comparison of fish farming with the other agricultural sectors, which is a critical factor for effective administrative management. An additional benefit of the adoption of the proposed approach is that, because the model is based on mechanistic principles, it can serve as the basis for applying it to other farmed fish species, provided that species-specific and system-specific coefficients are available. Moreover, as feed manufacturers are directly implicated in the parameterization of the model, they will need to be clear on the specifications for the different feeds found in the market, such as nutrient content and digestibility coefficients. Most importantly, as this work stems from a collaborative effort between multiple stakeholders, it might be used to orient further work in the areas where information is lacking and to form the basis of future propositions to the ministries and government agencies regarding the regulations on nutrient emission monitoring of fish farms.
estimate of the various forms of nutrient surpluses that have a relatively direct relationship with the specific environmental objectives. The disadvantage of not being able to accurately represent the emissions on a temporal scale is overridden by the fact that it provides more robust waste estimates; this is especially true when compared to the highly variable on-site measurements, indicating that basing annual emissions on limited instantaneous measurements would entail the danger of significant overestimations or underestimations, depending on the particular conditions during sampling. Because the model yields average emission values for specific periods of time (i.e., day, month, year), it follows that the agreement between predictions and observations will increase as the time scale increases, because temporal variability will be avoided. Furthermore, nutrient accounting could be used to provide incentives for farmers to reduce their emissions and to increase their efficiency of resource utilization through improved awareness and management practices. For the above-described reasons, we suggest that the nutrient-balance modeling approach should be the preferred method for predicting nutrient emissions in the various forms of environmental impact assessments of fish farming. The scientific validity of the model has been previously reported, and its parameterization and practical applicability for trout farming have been developed and demonstrated herein. However, it should be noted that: model coefficients might need to be adapted as feeds, animals, and systems evolve; the model could be further developed in the future to include more processes if sufficient knowledge is generated and as long as model complication is justified by significant prediction improvements; because regulations are based on defined environmental objectives, predictions will not replace on-site measurements as these will continue to be used for confirmation purposes. finally, we believe that the participation of multiple stakeholders is a promising way toward understanding and interpreting issues in environmental management, by ensuring that a minimum of common understanding is reached while considering a wide diversity of opinion and expertise.
Acknowledgments Recommendations The primary purpose of the current environmental monitoring of fish farms is to meet the goals of surface water quality. Nutrient-balance modeling provides an
We would like to thank all those who kindly provided us with the data that were used in this study and the members of the experts group for their stimulating discussions and contributions throughout the study.
Nutrient Emissions from Fish Farming in France
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