Verdegem bio energetic

Bio-energetic Modeling of Growth and Waste Production of Nile Tilapia (Oreochromis niloticus L.) in Recirculation Systems Dr M.C.J Verdegem1, Dr Ir A.A van Dam1, M.Sc A.A Cabarcas-Nuñez2 and Dr L Oprea3 Fish Culture and Fisheries Group, Department of Animal Sciences, Wageningen University P.O.Box 338, 6700 AH Wageningen, The Netherlands Mark.verdegem@alg.venv.wau.nl Department of Marine Sciences, University of Puerto Rico, P.O.Box 9013 Mayagüez, PR 00681-5000, USA Fishing and Aquaculture Department, University of Galati “Dunarea de Jos”, Domneasca Str 47, 6200 Galati, Romania ABSTRACT A bio-energetic fish growth simulation (FGS) model was developed for Clarias gariepinus and subsequently adjusted for the culture of Oreochromis niloticus, Oncorhynchus mykiss and Colossoma macropomum The FGS model was extended with a fish waste module (FWM) to calculate the total waste production due to feeding by tilapias grown in indoor recirculation systems Wastes calculated included the amount of uneaten feed, feces and NH4+ production The amounts of wastes produced were expressed as g nitrogen (N) The model was calibrated and validated using independent data sets, together comprising 175 aquarium experiments, monitoring growth in all cases and changes in proximate body composition of O niloticus between stocking and harvesting in 51 cases Fishes were grown in the individual weight range of – 290 g using 32-54 % protein diets and feeding levels between and 35 g kg-0.8 d-1 The principal read-out parameters for calibration and validation of the model were final weight and final body fat level Because waste production is the result of the same metabolic processes that lead to protein and fat deposition, it was assumed that waste production was simulated well when protein and fat deposition are The calibrated model was used to review the effect of feeding level and dietary protein level on N-waste production per kg tilapia produced Finally, tilapias were grown in different types of recirculation systems and stagnant water ponds, quantifying N-inputs and the amount of Nwastes recovered from each system The latter was defined as the sum of N-waste discharged (sludge and sludge water drained) and within system accumulation of N-wastes (organic and inorganic nutrients) during culture After model calibration, the agreement between simulated and observed final weight and body fat level for all data sets was visualized In recirculation systems different types of N-wastes were estimated well by the model Care must be taken when applying the model to pond systems More insight is needed on feeding ecology of tilapias in these systems INTRODUCTION It is not possible to grow animals without producing wastes Only a small part of the nutrient input is retained in the animals raised (Wit de 1992), and farm management will always be partially geared towards improving the nutrient retention efficiency On average, the animals raised waste 50-85% of the nutrients received In aquatic systems, these non-retained nutrients or wastes end up in the same water column where the animals live The moment waste is produced within the water column its constitution and concentration change For example, some metabolic end products like NH4+ serve directly as nutrients for plants (Syrett 1981, McCarthy 1981), phosphates form insoluble compounds precipitating at the bottom (Boyd 1995) and wastes like feces or uneaten feeds are mineralized (Mohanti et al 1994) In addition, in outdoor systems extra nutrients are added to the water column through photosynthesis-based processes or N2-fixation (Olah et al 1994) Consequently, in aquatic systems it is not possible to measure the exact waste production by farmed animals Only the amounts of waste accumulating on-farm and the amounts of effluents discharged during a production cycle can be measured During farm operation, small volatile molecules like CO 2, NH3, O2 or N2 are lost from the systems (Addiscott 1995) In areas with a nutrient surplus, the biomass and nutrients lost in this way are no longer a waste management problem for the farmer, even though for example CO2 or NH3 will create environmental problems in farm-dense areas (Kelly et al 1994) Contrarily, in nutrient deficient areas, farmers should try to minimize nutrient losses In both cases, it is important to estimate the amount of nutrients lost during production In this study, N-waste was defined as the N present in uneaten feed, in feces and in ammonia gill excretion The objectives were (1) to model accurately waste production by tilapias grown in recirculation systems based on feed input (2) to analyze the effect of feed composition and feeding level on growth and N-waste production, and (3) to compare simulated waste production to the observed waste accumulation and discharge on-farm The farming systems used were an indoor recirculation systems, an outdoor recirculation systems and stagnant water ponds MATERIALS AND METHODS Fish Waste Generator Model A bio-energetic fish growth simulation model was developed for Clarias gariepinus (Machiels and Henken, 1986) and subsequently adjusted for the culture of Oreochromis niloticus and Oncorhynchus mykiss (van Dam and Penning de Vries 1995) and Colossoma macropomum (van der Meer and van Dam 1998) For a detailed description of parameters and equations used in the model see van Dam and Penning de Vries (1995) The model was extended with a fish waste module (FWM) to calculate the waste production as a result of feeding for O niloticus grown in an indoor recirculation system The types of wastes calculated included uneaten feed, feces and NH4+ production, and were expressed as weight (grams) of N produced in the system during the entire culture period The principal read-out parameters for calibration and validation of the model were protein and fat deposition Because waste production is the result of the same metabolic processes that lead to protein and fat deposition, it was assumed that waste production is simulated well when protein and fat deposition are The original model for O niloticus, written in Professional Dynamo Plus was translated to Turbo Pascal To see whether the two versions of the model work identically, the data set used by van Dam and Penning de Vries (1995) was used; results were equal Digestibility Commercially formulated diets are composed of quality ingredients with good digestibility The fractions of protein, fat or carbohydrate digested were determined for a group of tilapias raised in recirculation systems and fed at maintenance level (5 g kg -0.8 d-1) (Table 1), and were different from the original values used by van Dam and Penning de Vries (1995) The digestibility values obtained for recirculation systems were used in the model Table Digestibility of Nutrients in Tilapia Diets Within Parenthesis, the Proximate Composition (on a Dry Matter Basis) of the Commercial Diet Used in the Recirculation System is Given Proximate composition feed Protein (50 %) Fat (12 %) Carbohydrate (29 %) Digestibility used by van Dam and Penning de Vries (1995) 0.8 0.5 0.5 Digestibility in recirculation system 0.9 0.9 0.4 The data sets Three independent data sets were used for calibration and validation (Table 2, data set through 3) Commercial diets were used, ranging in protein level between 28 and 54% protein, with feeding levels in the range of – 32 g kg -0.8 d-1 Data set refers to growth and waste monitoring experiments in ponds (2 ponds), indoor recirculation systems (2) and outdoor recirculation systems (4) Ponds and outdoor recirculation systems were situated on-campus in Puerto Rico, while the Fish Culture and Fisheries Group in the Netherlands operated the indoor recirculation systems Table Data Sets Used for Validation and Calibration, Giving Minimum and Maximum Values for Each Set of Feeding/Growth Observations Information available Number of feeding/growth trials Fish Initial number of fish Initial individual weight of fish (g) Initial fat % Final number of fish Final individual weight of fish (g) Final fat % Feed % dry matter % protein % fat % carbohydrate Daily amount fed (g) Number days of feeding Average water temperature Data set Calibration Data set Validation Data set Validation 34 17 124 Data set Waste analysis 25-40 12-123 10-23 52-180 7-626 1-200 396-800 34-118 11-12 25-40 20-204 8-14 4-11 10-23 60-240 5-10 7-586 5-290 4-11 384-737 99-186 6-10 91 40-50 14 25-35 6-209 25-40 27-28 91-92 38-45 9-13 23-34 0-89 29-68 27-28 91 32-54 7-18 7-43 0-415 5-49 28 89-91 28-47 4-6 29-50 600-2392 30-47 28 Estimating the Amount of Uneaten Feed The fish growth model developed by van Dam and Pauly (1995) for O niloticus limited feed intake by O2 availability, the latter being a function of oxygen pressure in the water column and gill surface area The disadvantage of this method is that the oxygen concentration and temperature in the water column need to be known Farmers not record this type of information Therefore, maximum feed intake was estimated as a function of body weight Groups of tilapias growing in the weight ranges of 10-50, 40-140 and 60-210 g, respectively were fed different rations in the range of to 35 g kg -0.8 d-1 of diets with a protein level in the range of 40-50% protein on a dry weight basis These feeding experiments were part of data set (Table 1) To estimate the maximum feed intake for different size groups of tilapia a multiphasic allometric relation was used (Koops and Grossman 1993) (Figure 1): Y = a1+ b1X –(b1-b2) r ln[1+ e(X-C)/r] (Equation 1) -0.8 -1 Where: X = protein ration (g kg d ) Y = protein growth (g kg-0.8 d-1) a1, a2 = intercept equation and 2, respectively b1, b2 = slope equation and 2, respectively r = smoothness transition between equation and (nearly = abrupt transition, = smooth transition) C = central point of transition Figure Multiphasic Equation, When b2 = 0, the Second Part of the Multiphasic Equation is a Horizontal Line 30 Growth (g kg-0.8 d-1) 25 EQUATION 2: Y = a2 + b2X 20 MULTIPHASIC EQUATION: Y = a1+ b1X –(b1-b2) r ln[1+ e(X-C)/r] 15 EQUATION 1: Y = a1 + b1X 10 C 10 14 18 22 Protein ration (g kg-0.8 d-1) The parameters a1 and b1 were assumed to be equal for each size group Parameters were estimated using the non-linear regression procedure of SAS 6.12 The value of C obtained for each size group was plotted against the average geometric mean body weight The resulting linear relation between body weight and maximum protein intake was incorporated into the model Calibration The parameters used for calibration were the digestibility of protein, fat and carbohydrate and the slope and intercept of the linear regression between body weight and maximum protein intake Read-out parameters for calibration were final body weight and final body fat content Agreement between simulated (Y) and observed (X) values was visualized by plotting simulated values against observed values The relative error (RE) for each simulation was calculated as (Y – X)/((X + Y)/2) (in %) (van der Meer and van Dam 1998) The average relative error (ARE) is the mean of the RE’s Validation Data set and were used for calibration Only final body weight was used as read-out parameter for data set because no data on body fat composition were available Estimation of N-waste Production Using the calibrated model, the amounts of N-waste generated per kg of tilapia produced were calculated considering two situations In the first simulation 50-g tilapias were fed a daily ration of 15-g kg-0.8 d-1 using feeds with protein levels varying between 15 and 50% of dry matter In the second simulation, 50-g tilapias were fed a 35% protein diet (% dry matter) at rations varying between to 40 g kg-0.8 d-1 Comparison of Waste Production to the Amount of Waste Recovered The simulated amounts of waste produced by the fish were compared to the amount of waste accumulated in and discharged from the systems during a production cycle of tilapia (data set 4, Table 2) The following systems were included in the analysis: indoor recirculation system, growth trials with a 47% protein diet at a feeding level of 12.5 g kg-0.8 d-1 stagnant water ponds, growth trials with 28% protein diet at a feeding level of 18.7 kg-0.8 d-1 outdoor recirculation systems with a 30% protein diet, and a feeding level of 15.7 kg 0.8 -1 d growth trials, with 24 hour solid waste removal growth trials, with 12 hour solid waste removal RESULTS Estimating the Amount of Uneaten Feed Figure reviews growth of size classes of tilapia in relation to protein ration The transition point above which growth does not increase further is given by the C values (Table 3) Figure Growth in Relation to Protein Ration for Size Groups of Tilapia MULTIPHASIC EQUATION Y = -0.174+3.259*X-3.259*ln(1+e 30 (X-C) ) 20 15 10 Slope b = 3.259 GROWTH (g kg- 0.8 d - 1) 25 3.3 5.8 7.2 C-values 0 -5 10 12 PROTEIN RATION (g kg 10-50 g fish 40-140 g fish 14 16 -0.8 -1 d ) 60-210 g fish 18 20 22 24 Table Average Individual Weight of Size Groups with the Corresponding Maximum Protein Intake Level and Maximum Growth Rate Mean body weight g 22 75 142 Maximum protein ration (C-value) g kg-0.8 d-1 7.2 5.8 3.3 Maximum growth rate g kg-0.8 d-1 23 19 11 The maximum protein ration (Y) was calculated as: Y = -0.032X + 8.041, where X = geometric mean body weight (g) (Equation 2) Calibration Adjusted parameter values as a result of calibration are given in Figure and Table Figure 4, gives plots of observed (X-axis) against simulated values (Y-axis) for data set The uncorrected plots compare observed to simulated values before inclusion of equation into the model Before correction, without a limitation to feed intake, simulated weights were higher than observed weights, especially for the larger fishes Simulated fat deposition was higher than observed fat deposition for the higher feeding levels within each size group The ARE’s for growth and fat deposition were 19.7 and 17.3, respectively After calibration the ARE’s became 0.0 and –3.2, respectively Figure Regression of Body Weight Against Maximum Dietary Protein Ration maximum protein ration (g kg -0.8 d-1) 8.0 7.0 After calibration: Y = -0.0273X + 8.0409 6.0 5.0 Calculated regression Before calibration: Y = -0.0324X + 8.0409 R2 = 0.9905 4.0 3.0 2.0 15.00 45.00 75.00 105.00 mean body weight (gram) 135.00 Table Model Parameters Before and After Calibration Parameter Protein digestibility Fat digestibility Carbohydrate digestibility Slope regression maximum protein intake against body weight Before calibration 0.9 0.9 0.4 -0.0324 After calibration 0.9 0.9 0.6 -0.0273 Figure Agreement Between Observed and Simulated Values for Fish Weight and Fat Content, Using Uncorrected (Before Calibration) and Corrected Parameters (After Calibration, Table 4), Based on Data Set Calibration (Data set 1) 250 15 uncorrected uncorrected 14 200 13 12 150 50 ARE = 19.7 -8.2