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Article

Influence of Production Strategy on Gross Waste Output and Temporal Pattern of Gilthead Seabream (Sparus Aurata) Farming: Implications for Environmental Management

1
Spanish National Research Council, National Center Spanish Institute of Oceanography, “El Bocal” Marine Aquaculture Station, Oceanographic Center of Santander, 39012 Santander, Spain
2
Piscifactorías Albaladejo S.L., San Pedro del Pinatar Port, 30740 Murcia, Spain
3
Department of Bioeconomy, Water, and Environment, Murcian Institute for Agri-Food Research and Development, 30150 Murcia, Spain
*
Author to whom correspondence should be addressed.
Water 2022, 14(5), 788; https://doi.org/10.3390/w14050788
Submission received: 17 December 2021 / Revised: 28 February 2022 / Accepted: 1 March 2022 / Published: 2 March 2022
(This article belongs to the Special Issue Recent Advances in Environmental Interactions of Marine Aquaculture)

Abstract

:
This study compares the farm management model used in the Mediterranean gilthead seabream (GHSB; Sparus aurata) industry (S1)—stepped entry of juveniles throughout the year with several production cycles and fish ages overlapping in a single farm—with that used in the salmon industry (S2)—the whole is farm filled with fish that are the same age at once with a fallowing period between rearing cycles—in terms of waste production by coupling digestibility coefficients with growth, feeding, and eating behavior models into a mass balance model. We considered the total C, N, and P content in the different waste fractions (particulate and dissolved wastes). The model, which simulated real farming conditions, showed relevant quantitative and qualitative differences between both strategies, with stocked biomass and water temperature as the main drivers, the amount of feed wasted by chewing as the most relevant fraction differentiating both strategies, and the fallowing period as the main distinguishing management feature. We discuss the influence of both farming strategies on some key performance and environmental aspects, such as benthic recovery, the breakdown of the life cycle of pathogens, and adaptability to climate change. Our results suggest that changing the GHSB industry’s production model is necessary for its sustainability.

1. Introduction

Aquaculture has achieved a prominent role in the provisioning of seafood for a growing and demanding human population in a scenario where most fishing resources are exploited to their maximum capacity or are overexploited [1,2]. Globally, major efforts are being made for the sustainable development of aquaculture under the Blue Growth Initiative [3] and the 2030 Agenda [4]. In developed countries, aquaculture works according to an economies of scale model [5], namely production costs are reduced by increasing the production scale to widen the profit margin. EU governments have implemented different initiatives for aquaculture promotion and, after several years of decreasing production, a moderate but encouraging increase has recently been experienced [1]. However, in the EU, aquaculture accounts for only 20% of the total aquatic production [6], and therefore, the current demand for aquatic products still relies on imports and fisheries. Hence, the EU not only needs to reduce its dependence on fisheries and imports, but it should also encourage aquaculture at the farm level to achieve a strong and competitive industry.
In the Mediterranean basin, fish farming is the main aquaculture practice, both in terms of biomass production and economic profit [6]. Most fish production takes place in floating cage facilities in the open sea [7], with gilthead seabream (Sparus aurata) and European seabass (Dicentrarchus labrax) as the main reared species [8]. The most common fish stock management method in Mediterranean farms entails a stepped input of fish batches throughout the year so that batches of fish of different ages overlap, market-sized fish are harvested throughout the year, and farms are never empty. This strategy implies an increased risk of disease transmission and environmental impact [9,10,11,12] and forces farmers to manage several types of feed and feeding schemes simultaneously and to combine the diverse maintenance and husbandry tasks that are typical of different rearing stages, thus hindering the organization of work. In the current global scenario, this productive model is evolving towards the one used by the salmon industry [13]: large companies with several farms (licenses), wherein each single farm works as a big batch. The rearing method used in this model entails filling all the cages with fish of the same age at once, carrying out the harvest in a shorter period of time—as the commercial size/s is/are reached—and completing a mandatory fallowing period of variable length prior to the introduction of a new generation of fish [13]. This allows for the biological cycle of many pathogens to be broken [11] and a total or partial recovery of the environment [8,14,15]. This rearing strategy is commonly known as “all in/all out.” The Mediterranean fish farming industry is expected to evolve towards the salmon model to enhance production while adapting the business model to a blue economy scenario, which would also enable efficient sanitary and environmental management.
The Mediterranean fish farming industry employs feed-based aquaculture practices, with carnivorous fish based far from the shore. This results in the consumption of a considerable amount of resources—mainly raw materials for feed manufacturing and fuel [16,17]—and the generation of huge amounts of waste of biological and dietary origin [18]—mainly in the form of dissolved organic and inorganic nutrients (urinary and branchial excretion) and particulate organic matter (feces and uneaten feed). How effectively aquaculture companies manage their waste has a considerable influence on long-term sustainability [19]. Waste output and environmental impact can be minimized through on-farm feeding management [20,21] and site selection [22]. The Mediterranean fish farming industry is in the process of changing its production strategy. In such a scenario, an important part of the planning phase is the prediction of waste output over time as a first step for subsequent environmental management actions, namely risk, carrying capacity, and environmental impact assessment and monitoring [23].
The expansion of aquaculture over the next few years should be accompanied by technical and management improvements to minimize environmental and social concerns [21,24]. Over the past few decades, several models have been developed to deal with different aspects related to the environmental management of aquaculture. In feed-based aquaculture, waste output models are based on the mass balance between feed input and fish feed consumption [19,25,26,27,28]. Because of their impact on fish metabolism, oceanographic and environmental conditions and welfare also influence waste production [29]. In the present study, we explore how the farming strategy can influence the amount of feed-derived waste that is delivered to the environment and its temporal pattern. We developed a series of simulations by coupling growth, feeding, and waste models. We used gilthead seabream (hereafter GHSB) as the model species because this is one of the most widely farmed fish in the Mediterranean, and it displays a particular eating behavior that involves some degree of feed waste. Simulations were carried out for both the classical Mediterranean stepped fish input and the forthcoming all in/all out strategy for the same annual harvest. These were then compared in terms of gross waste production and temporal variability.

2. Materials and Methods

2.1. Waste Output Modelling

Waste output from a fish culture can be estimated through a mass balance equation [19,25,28]. A fraction of the nutrients—carbon (C), nitrogen (N), and phosphorus (P)—included in the feed supplied to the fish (Fs) is retained as they grow (G), and the remaining nutrients are wasted in two ways: via dissolved waste (DW) as fish metabolism by-products (mainly urinary and branchial excretion) and as solid waste via, undigested feed, namely feces (F). In GHSB, a portion of Fs is wasted as a result of this type of fish’s peculiar eating behavior: how it chews and crushes its food results in fragments of feed being wasted before swallowing [30,31]. We denominate this fraction as feed wasted by chewing (Fch). Another portion of the Fs can be wasted due to inefficient control when dispensing the feed (feed supplied but unused: Fu). This is normally the consequence of inaccurate feed distribution over the cage surface or a feed delivery rate that is too rapid for the fish to ingest the feed properly [20,21]. The actual ingested feed (Fi) is equal to the difference between Fs and the sum of the feed wasted in both ways (Fch + Fu). Fch can be estimated as the function of pellet size and fish size, according to the method described by Ballester Moltó et al. [32]. Conversely, Fu cannot be predicted because this depends on feeding practices, which vary from farm to farm [18]; therefore, Fu was not considered in our model.
The general model used in the present work is as follows:
F i = F s F c h = G + D W + F
All of the terms in Equation (1) were calculated from specific submodels on a daily basis. Fs—the daily ration—was determined according to the specific feeding rate (SFR = % BWi day−1) equation developed by Aguado-Giménez [21] through multiple regression analysis from a feeding chart provided by a prominent GHSB feed manufacturer:
S F R = e 4.954 0.289   · L n B W i + 2.034   · L n T   ( Adj .   R 2 = 0.90 )
where BWi is the mean initial fish weight in g, and T is the water temperature in °C.
The SFR was recalculated daily according to growth predictions (G in Equation (1)). We used the thermal growth coefficients (TGC) [26] and the GHSB growth model developed by Mayer et al. [33]. This is the only GHSB model developed under real commercial conditions that is available. The authors showed that TGC varies over time and identified two growth stages, with 117 g acting as a critical value for fish weight. Therefore, the two following separate equations were used to estimate GHSB growth:
B W f = B W i 1 / 3 + T G C 1   · Σ t o t 3 ,   when   B W   <   117   g ;   T G C 1 = 0.001646   ( Adj .   R 2 = 0.97 ) ,
B W f = B W i 2 / 3 + T G C 2   · Σ t o t 3 / 2 ,   when   B W   >   117   g ;   T G C 2 = 0.016095   ( Adj .   R 2 = 0.98 )
where BWf and weight BWi are the mean final and initial fish weight in g, respectively, and Σ(tot) is the effective temperature in °C—summation of the temperature during a given time interval minus the temperature below which growth is zero: 12 °C for GHSB [34].
Fch (as a % of Fs) was estimated as a function of the fish body weight (BW, in g) and pellet size (Ps, in mm) according to the model of Ballester-Moltó et al. [32], as follows:
F c h = 3.9074 + 1.3869 · P s + 0.0029 · B W · P s     ( Adj .   R 2 = 0.89 )
This model can provide negative values when pellets and/or fish are too small. In this case, Fch is assumed to be zero [32].
Fi should be digested before the fish utilize it for G and maintenance. The undigested fraction is eliminated as feces (F) and can be estimated using the method of Cho and Bureau [26], as follows:
F = F i   ·   100 A D C   %
where ADC is the apparent digestibility coefficient. The digestibility of nutrients is species-specific and depends on their content in the diet and the bioavailability of said nutrients [19]. The digestibility of fish diets of different origins can therefore provide different digestibility values. In addition, for some fish species, digestibility tends to increase slightly as the fish grow [35]. However, Ballester-Moltó et al. did not find significant changes in the digestibility of elemental nutrients (C, N, and P) throughout the development of GHSB fed with commercial feeds [36]; therefore, we used the total C, N, and P ADC coefficients from these authors (Table 1) for the F estimates.
DW was solved from Equation (1) as follows:
D W = F i G + F

2.2. Simulation Conditions

We performed simulations of the gross and temporal pattern waste output produced by GHSB farming in the form of total carbon (TC), total nitrogen (TN), and total phosphorus (TP) under two scenarios: (S1), stepped fish input, and (S2), all in/all out with a fallowing period of 8 months. We performed simulations for an annual production of around 1500 tons over a 4-year time period. The sizes of feed pellets based on the weight range of the fish are presented in Table 1, as are their content in TC, TN, and TP, ADC values, fish carcass composition [36], and other simulation conditions. To compare both strategies, gross waste output was estimated on a yearly basis for the duration of a full production cycle starting in April once production was stabilized. In S1, four fish inputs are carried out sequentially throughout the year. Fish of different ages (size) are therefore present within the farm at any given time. The dates of fish entry—four per year—were set according to a schedule resembling the Mediterranean GHSB stock management farming conditions [37]—i.e., in April, June, August, and September for a culture length of 16, 15, 15, and 17 months, respectively—predicted according to Mayer et al. [33]. The temperature regime is shown in Figure 1. In this system, the fish are harvested sequentially as different batches reach commercial size (500 g mean final weight). In S2, all of the fish needed to reach the required production quota enter the farm on the same date (in April). A fallowing period of 8 months was set between harvest and the following entry in April and was set within 2 years from the previous input date. At the end of each production cycle, all of the fish are harvested upon reaching commercial size.

3. Results

3.1. Feed Supplied and Stocked Biomass

The simulations of both strategies started at the beginning of the farming period (April year 1). Table S1 (in Supplementary Materials) shows the evolution of the biomass of the different batches, the biomass harvested at the end of each production cycle, and the cumulative biomass of both strategies. These simulations show that the Fs and stocked biomass (Figure 2A,B) increase gradually in both strategies. However, both the Fs and stocked biomass are higher in S2 up until the end of the first production cycle (end of July year 2). At this point, all of the stock is harvested in S2, while only a quarter of the stock is harvested in S1. In the following 8 months (August year 2 to April year 3), the Fs and fish biomass equal zero in S2 because of fallowing; In S1, the Fs and fish biomass ranges are 70–465 and 570–1150 tons/month, respectively, with ups and downs resulting from the successive fish inputs and harvests that are characteristic of this strategy during this period. Once production stabilizes in S1 (from year 2), Fs remains within the range of 70–470 tons month−1 indefinitely, and stocked biomass stays within the range of 570–1260 tons month−1, provided that the fish input and harvesting schedule are maintained within the simulation conditions. In S2, the stocked biomass increases from 60 tons on the fish-input date to a final biomass production of 1500 tons 16 months later; the Fs increases from 25 to 517  tons/month and follows the temporal patterns shown in Figure 2A,B.
In line with the simulation conditions, the temporal pattern shows that that the maximum Fs and stocked biomass occur in July in both strategies. In S1, minimum Fs and stocked biomass occur in January, while in S2, both equal zero for 8 months. In both strategies, Fs decreases during the winter months, and then gradually increases as the water temperature does.

3.2. Gross Waste Output

The annual gross waste production for the first year is higher in S2 than it is in S1 for all waste fractions and nutrients (Table 2). Thereafter, the waste discharge becomes higher in S1 for all waste fractions and nutrients and continues to be higher provided that the same farming conditions are maintained (Figure 3A–C). In the years in which fallowing takes place in S2 (even years for the simulation conditions), the gross annual waste production decreases for all nutrients and waste fractions. Then, during the first year of each production cycle (odd years for the simulation conditions), the differences between S1 and S2 become more marked, namely 12–16% higher in S1 than in S2.
In both strategies, DW is the greatest waste fraction (Figure 3C). In S1, once husbandry stabilizes (midsecond year), the annual gross DW output is 863, 163, and 6 tons year−1 for C, N, and P, respectively. In S2, the maximum DW download is 476, 95, and 3 tons year−1 for C, N, and P, respectively. This occurs during the first year of each production cycle.
Regarding particulate waste, the most abundant waste fraction differs depending on the nutrient, but regardless of the farming strategy (Figure 3A,B), there is more C and P waste in F (S1: 141 and 7 tons/year of C and P, respectively; S2: 79 and 3 tons/year of C and P, respectively) than in Fch (S1: 111 and 2 tons/year of C and P, respectively; S2: 71 and 1 tons/year of C and P, respectively), whereas the opposite is true for N waste, with Fch experiencing more N waste (S1: 18 tons/year of N; S2: 11 tons/year of N) than F (S1: 8 tons/year of N; S2: 5 tons/year of N). In year 1, the differences between S1 and S2 are particularly noticeable for the three nutrients in Fch, with S2 having concentrations that are about 60% higher (Figure 3B; Table 1).

3.3. Waste Output Temporal Pattern

The Simulations revealed a temporal pattern in the waste output (Figure 4A–C) that was related to fluctuations in Fs and in the stocked biomass (Figure 2A,B). Quantitatively, nutrient release was lower in S1 than it was in S2 for all of the waste fractions throughout the first production cycle. The waste output is only higher in S1 than in S2 at the beginning of the fallowing period and at the end of the winter period before the next production cycle in S2. From then on, and during the rest of the second part of the production cycle, waste production is higher in S2 than it is in S1 (Figure 4A–C).
The range of nutrient release per waste fraction for both farming strategies is presented in Table 3. According to the simulation conditions, maximum waste discharge occurs during the summer months for all waste fractions and nutrients in both strategies. In S1, waste output is also relevant during early autumn (Figure 4A–C); during the winter months, the waste discharge experiences a significant reduction until minimum values are reached (Figure 4A–C). In S2, minimum waste release occurs during the fallowing periods, as expected. Modelling showed the most obvious differences between both farming strategies in Fch, which were noticeably higher (about 30%) for S2 at the end of its production cycle (in the summers of even years during the simulation; Figure 4B).

4. Discussion

By coupling growth, feeding, and chewing models with digestibility coefficients into a mass balance model, we resolved gross waste production and temporal pattern issues and compared GHSB farming strategies. The simulations revealed noteworthy differences in waste output in relation to production planning, which should be considered for the environmental management of the Mediterranean aquaculture industry, particularly in scenarios adapting to more efficient commercial strategies and climate change. Once again, modelling has proven itself as a useful tool that can be implemented to understand aquaculture processes [38]. Waste outputs largely depend on the nutrient composition of the feed, fish requirements, feed utilization, husbandry practices, and stock management [19,28,39,40]. New parameters of these variables that are more adjusted to particular farming conditions or that are related to new feed formulations can shift the final output of the different waste fractions; however, the usefulness of the model employed in the present work to make comparisons remains intact.
Our simulations revealed that the main driver of gross waste output estimation and to determine its temporal pattern is the stocked biomass at any time. Fish biomass varies over time because of fish growth; thus, changes in the stocked biomass also vary depending on the fish-size distribution frequency in the farm, which was different between the two farming strategies compared herein. Moreover, fish growth strongly depends on water temperature; therefore, the water temperature regime at a given location indirectly influences the waste output temporal pattern, with minimum and maximum discharge occurring during the coldest and warmest months, respectively. Differences among S1 and S2 in relation to the stocked biomass over time mirror their differences in terms of gross waste production and their temporal pattern. However, other issues related to farming strategy not only strongly influence waste output quantitatively, but also qualitatively; this is the case for the Fch fraction and the differences caused by the fallowing period.
For GHSB, the amount of Fch is directly related to the fish size and the pellet size [32]. Our results show that Fch is always larger in S2, especially during the last 3–4 months of the growing cycle, when all of the growing fish are larger in size and fed with larger pellets. Conversely, in S1, the portion of the population composed of large fish fed with large pellets is considerably smaller, resulting in the Fch fraction being smaller, too. The most relevant environmental consequence of cage fish farming is the organic enrichment of the seabed in the vicinity of the farm, which is mostly caused by accumulation of supplied but uneaten feed and feces [41,42], especially below the cages [43]. In the case of GHSB, the fraction corresponding to Fch should be added to the solid waste budget [18]. The uneaten feed fraction is difficult to estimate since this depends on the on-farm feeding methods [16]. On the one hand, this fraction of solid wastes can only be reduced by improving feeding control through precision fish farming [44]. On the other hand, the Fch fraction in GHSB cultures can only be minimized by changing the pellet size management, which was previously suggested by Ballester-Moltó et al. [32]. Both precision farming—emerging technologies and automated systems—and alternative feeding regimes—smaller pellets for the largest fish—are strongly recommended for GHSB farming to minimize its environmental footprint [20,21], and are recommended even more so if S2-type stock management is planned.
In the present work, the S2 strategy included a fallowing period of 8 months. A farm using this strategy would produce no waste during this time. Conversely, a farm employing an S1-type strategy would discharge waste during those 8 months. Fallowing is a management action with a dual objective: (i) to prevent/reduce the impact on the receiving environment [9,14,15] and (ii) to break the life cycle of diseases and parasites [11,12,45]. Both potential benefits can increase long-term farm productivity and sustainability [46]. From an environmental impact point of view, a full recovery of the receiving environment (the seabed being the main compartment that is affected) after the complete abatement of farming activity would be expected after some time—from months to years—although this process is not regularly monitored [14]. From a sustainability perspective, a periodic abatement between farming cycles would ensure the assimilation capacity of the environment, supporting long-term farming operations [15]; full environmental recovery, however, would not be expected. Moreover, cumulative deterioration could occur, and a loss of the functional capacity to assimilate the waste could put the continuity aquacultural activity at risk [47], regardless of the farming strategy. Because of the absence of production breaks in the S1-type farming strategy and the consequent continuous supply of organic matter and nutrients, the risk of the ecological capacity of the system collapsing could be higher compared to S2.
Regarding fish health, fallowing has been proven to be a useful tool that can be implemented to manage the spread of infections and diseases [11]. However, implementing this procedure is complex in areas where cage aquaculture has expanded since disease propagation can easily occur at the local level [48,49,50], making synchronization necessary among farms [11]. In Mediterranean countries, no fallowing period has been set, and virtually all GHSB farms operate under an S1-type strategy. This is probably the reason why general health status of the fish produced in Mediterranean cage fish farms, and that of the GHSB in particular, has deteriorated over the last decade [51], with sustained outbreaks of parasitic infestations of Sparycotyle chrysophrii and a high incidence of nodavirus (VER-VNN) infections [52]. These pathologies, which could actually be considered as chronic, lead to a considerable decrease in the performance and profitability of aquaculture farms. The management of fish health is a matter of concern for the Mediterranean aquaculture industry, as losses due to diseases are increasing [51]. Consequently, S2-type strategies with synchronized fallowing periods should be encouraged.
The fallowing duration depends on its goal. For Norwich pisciculture, Black et al. suggested a period of a few weeks between rearing cycles when the intention was to break the life cycle of pathogens, and much longer stops (one or more years) when the aim was to reduce the impact on the seabed to allow it to recover [10]. In Scotland, a routine sanitary fallowing of 4 weeks was recommended between successive rearing cycles, and longer periods (up to 6 months) were recommended if a notifiable disease was detected [12]. In Australia, a fallowing length of 3 months is regularly used [47] despite the lack of legislative requirements. Given that both the recovery capacity of the environment and disease transmission are site-specific [11,14], determining an effective duration for the fallowing period is challenging and can result in a period of time that is either too risky or too cautious. In many cases, the fallowing duration is largely at the discretion of the farm manager [47].
The chosen production strategy can also serve as a mechanism through which fish farms can adapt to the effects of climate change. In the case of GHSB growing in cage farms in the Mediterranean, sea water warming will enhance the growth rate, and the fish will reach commercial size faster, provided that the upper temperature tolerance threshold is not exceeded [37,53]. However, the increased temperature is expected to favor the spread of parasites and diseases and may even result in the emergence of new ones. This would have a strong negative effect on farming performance, forcing producers to use more chemotherapeutants to alleviate these detrimental effects. Hence, the adoption of farming strategies that include fallowing periods—such as S2-type strategies—would allow for better adaptation to global warming.
Wherever a marine farm is placed, the seabed will experience an impact. The seabed will never return to its original condition unless the activity ceases definitively; even so, the unpredictability of ecological succession phenomena makes it impossible to guarantee that the exact same state that existed before will be reached. Production strategies and husbandry practices must contribute to minimizing any affection in the environment, preserving its functionality and capacity to assimilate to the impacts for a long period while maintaining fish welfare and obtaining profitability. To achieve this, the carrying capacity of the farming area must be estimated with certainty in advance, and the producers must commit to the best aquaculture practices. Carrying capacity is site-specific and depends on a variety of physical, production, environmental, and social variables [54], and coupling it with our modelling would be very useful.
An S2-type strategy is in line with the salmon business model. The trend of this industry has been to reduce the number of companies while increasing production [12]. As a consequence, most of the remaining companies are growing, resulting in a greater number of farms. The Mediterranean fish industry is experiencing a similar evolution [55]. This type of business structure allows for the undertaking of a global S1-type strategy in which each separate farm follows an S2-type strategy: each single farm completely fills its facilities on a different date, so the company has products available throughout the year. Thus, the fallowing period at each individual farm can be longer—from the complete emptying of fish to the next input on the same date as the previous cycle—so that companies can satisfy both environmental and health requirements, becoming more sustainable.
In conclusion, a change is needed in the business model of the Mediterranean fish farming industry, adopting production strategies that enable better environmental and fish health management. This can be achieved with S2-type strategies, which will help to improve both profitability and sustainability.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w14050788/s1. Table S1: Changes in the biomass each fish batch over time according to the input date, biomass harvested at the end of each production cycle (in red), and cumulative stocked biomass. Biomass in tons. Fish input dates are at the beginning of the months in red.

Author Contributions

F.A.-G. and M.B.-M. were responsible for the conceptualization, methodology, data curation, investigation, and visualization. F.A.-G., M.B.-M., and B.G.-G. were responsible for the validation, formal analysis, writing—original draft preparation, review, and editing. F.A.-G. and B.G.-G. were responsible for the supervision and project administration. F.A.-G. was responsible for the funding acquisition and project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Spanish Institute of Agro-Forestry Research (INIA; Ministry of Economy and Competitiveness; project RTA2011-00088-00- 00). The study was also partially sponsored by the student-grant Sub-Programme of Researcher Formation of the Spanish Institute of Agro-Forestry Research (FPI-INIA).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available within the article and in the Supplementary Materials at https://www.mdpi.com/article/10.3390/w14050788/s1.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Water temperature regime used for simulations.
Figure 1. Water temperature regime used for simulations.
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Figure 2. (A) Variation in the feed supplied (Fs) and (B) stocked biomass over time for both production strategies according to simulation conditions. S1: stepped inputs and harvests; S2: all in/all out.
Figure 2. (A) Variation in the feed supplied (Fs) and (B) stocked biomass over time for both production strategies according to simulation conditions. S1: stepped inputs and harvests; S2: all in/all out.
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Figure 3. Annual gross C, N, and P waste production in different waste fractions for both production strategies (S1: stepped inputs and harvests; S2: all in/all out). (A) feces (F); (B) feed lost by chewing (Fch); (C) dissolved wastes (DW).
Figure 3. Annual gross C, N, and P waste production in different waste fractions for both production strategies (S1: stepped inputs and harvests; S2: all in/all out). (A) feces (F); (B) feed lost by chewing (Fch); (C) dissolved wastes (DW).
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Figure 4. Temporal waste output pattern of C, N, and P in different waste fractions for both production strategies (S1: stepped inputs and harvests; S2: all in/all out). (A) feces (F); (B) feed lost by chewing (Fch); (C) dissolved wastes (DW).
Figure 4. Temporal waste output pattern of C, N, and P in different waste fractions for both production strategies (S1: stepped inputs and harvests; S2: all in/all out). (A) feces (F); (B) feed lost by chewing (Fch); (C) dissolved wastes (DW).
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Table 1. Simulation conditions to estimate the gross waste output and temporal pattern in GHSB farming. (BWi: mean initial weight; BWf: mean final weight; TN, TC, and TP: total nitrogen, carbon, and phosphorus content, respectively; d.m.: dry matter; ADC: apparent digestibility coefficient).
Table 1. Simulation conditions to estimate the gross waste output and temporal pattern in GHSB farming. (BWi: mean initial weight; BWf: mean final weight; TN, TC, and TP: total nitrogen, carbon, and phosphorus content, respectively; d.m.: dry matter; ADC: apparent digestibility coefficient).
BWi (g)BWf (g)Annual Harvest (Tons)Num. Fish Inputs Per YearNum. Fish Per InputPellet Sizes Per Fish Weight Range *Feed Composition
(% d.m.) **
GHSB Carcass Composition
(% d.m.) **
ADC
(%) **
TCTNTPTCTNTPTCTNTP
55.58.41.788.795.971.4
205001500S1: 4S1: 750,00020–70 g: 2 mm48.68.70.8
205001500 S2: 1S2: 3,000,00071–220 g: 4 mm50.18.90.7
221–500 g: 6 mm52.28.40.8
* As provided in commercial feeding charts. ** Data from Ballester-Moltó et al. [36]. Because of fallowing in S2, fish input and harvests occur every two years.
Table 2. Differences in % between S1 and S2 regarding gross annual waste output for each waste fraction and nutrient.
Table 2. Differences in % between S1 and S2 regarding gross annual waste output for each waste fraction and nutrient.
Feces (F)Feed Lost by Chewing (Fch)Dissolved Waste (DW)
CNPCNPCNP
S1 < S2Year 138.1737.5238.5158.8958.1258.6538.8137.8542.15
S1 > S2Year 255.9057.2356.0735.3035.9635.3254.4756.3749.74
S1 > S2Year 343.8941.5243.1861.9861.4562.0744.8041.7146.31
S1 > S2Year 455.9057.2356.0735.3035.9635.3254.4756.3749.74
S1 > S243.8941.5243.1861.9861.4562.0744.8041.7146.31
Table 3. Range of nutrient release (kg/month−1) and month in which minimum and maximum output were reached for different waste fractions in both farming strategies once production stabilized. For S2, minimum and maximum nutrient release occurred in even years under simulation conditions.
Table 3. Range of nutrient release (kg/month−1) and month in which minimum and maximum output were reached for different waste fractions in both farming strategies once production stabilized. For S2, minimum and maximum nutrient release occurred in even years under simulation conditions.
Feces (F)Feed Lost by Chewing (Fch)Dissolved Wastes (DW)
Min. (February)Max. (July)Min. (February)Max. (July)Min. (February)Max. (July)
S1C3.46022.8462.23020.52524.352138.528
N2091.3433683.3354.52825.620
P13791435322251961
Min. (August–April)Max. (July)Min. (August–April)Max. (July)Min. (August–April)Max. (July)
S2C024.461031.1630154.740
N01.41805.046027.942
P0971048901.217
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Aguado-Giménez, F.; Ballester-Moltó, M.; García-García, B. Influence of Production Strategy on Gross Waste Output and Temporal Pattern of Gilthead Seabream (Sparus Aurata) Farming: Implications for Environmental Management. Water 2022, 14, 788. https://doi.org/10.3390/w14050788

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Aguado-Giménez F, Ballester-Moltó M, García-García B. Influence of Production Strategy on Gross Waste Output and Temporal Pattern of Gilthead Seabream (Sparus Aurata) Farming: Implications for Environmental Management. Water. 2022; 14(5):788. https://doi.org/10.3390/w14050788

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Aguado-Giménez, Felipe, Mateo Ballester-Moltó, and Benjamín García-García. 2022. "Influence of Production Strategy on Gross Waste Output and Temporal Pattern of Gilthead Seabream (Sparus Aurata) Farming: Implications for Environmental Management" Water 14, no. 5: 788. https://doi.org/10.3390/w14050788

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