cultivation

Aquaculture 213 (2002) 219 – 232 www.elsevier.com/locate/aqua-online

Bioeconomic analysis of production location of sea bream (Sparus aurata) cultivation Eucario Gasca-Leyva a,*, Carmelo J. Leoń b, Juan M. Hernańdez c, J.M. Vergara d a

Center of Research and Advanced Studies of the IPN, Me´rida, Apdo. Postal 73-CORDEMEX, 97310 Me´rida, Yucatań, Mexico b Department of Applied Economic Analysis, Universidad de Las Palmas de Gran Canaria, Edificio de Ciencias Economicas y Empresariales, Modulo D-3.16, Las Palmas de Gran Canaria 35017, Spain c Department of Quantitative Methods for Economics and Management, Universidad de Las Palmas de Gran Canaria, Edificio Departamental Modulo D-4 35017 Las Palmas de Gran Canaria, Spain d Department of Biology, Marine Science Faculty, Universidad de Las Palmas de Gran Canaria, Apdo. 550, Las Palmas de Gran Canaria 35017, Spain Received 30 August 2000; received in revised form 14 January 2002; accepted 21 January 2002

Abstract This paper develops a bioeconomic model to evaluate the production of gilthead sea bream in floating cages based on two locations, the Canary Islands and the Mediterranean. The model includes four sub-models: biologic, environment, production management, and economic. The biologic submodel contains a growth model based on this species physiology. A wide range of farm scales were considered, deriving the production and input costs for each scale and scenario. The results for average costs showed increasing returns, with a rising internal rate of return (IRR). The efficient farm size was obtained for alternative management decisions and location scenarios. The results revealed that input costs, and therefore product costs, were higher in the Canary Islands than in the Mediterranean. However, environmental conditions in the Canary Islands are shown to be more favorable than in the Mediterranean, resulting in a more rapid growth, which leads to higher returns. D 2002 Published by Elsevier Science B.V. Keywords: Economics; Bioeconomic model; Simulation; Sparus aurata

*

Corresponding author. Tel.: +52-999-98129660x507; fax: +52-999-9812334. E-mail addresses: [email protected] (E. Gasca-Leyva), [email protected] (C.J. Leoń), [email protected] (J.M. Hernańdez), [email protected] (J.M. Vergara). 0044-8486/02/$ - see front matter D 2002 Published by Elsevier Science B.V. PII: S 0 0 4 4 - 8 4 8 6 ( 0 2 ) 0 0 0 3 1 - 5

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E. Gasca-Leyva et al. / Aquaculture 213 (2002) 219–232

1. Introduction The aquaculture industry is continuously expanding, as it constitutes an alternative to declining and limited marine resources. This expansion has been favored by the successful adaptation of new species to commercial cultivation, as well as by the expectation of relatively high return rates. A crucial aspect of the decision to invest in the aquaculture industry is the localization of the production facilities. In this paper, we consider the economic implications of producing gilthead sea bream (Sparus aurata) in the alternative locations of the Mediterranean Sea and the Atlantic waters of the Canary Islands. Both regions offer different environmental and market conditions for sea bream production, which lead to different economic returns for the aquaculture firm. Sea bream is one of the most widely cultured marine fish species in the Mediterranean area, with an estimated total of 700 firms producing 57 000 Mt in 1999 (FEAP, 2000). The economic analysis is conducted with a bioeconomic model for gilthead sea bream which considers the effects of environmental factors on the firm’s competitiveness. The model is centered on production by offshore cages, and takes account of the effects of water temperature on fish growth and economic returns. Other studies for sea bream have partially looked at specific technical aspects, economies of scale, and alternative market strategies (e.g. Porter et al., 1986). These studies lack an integration of management decisions within a bioeconomic model reflecting the particular biological growth of sea bream. Economic models for other species can be found in the literature, such as for catfish (Cacho et al., 1990), salmon (Bjorndal, 1990), shrimp (Leung and Shang, 1989), rainbow trout (Sparre, 1977), and sea bass (Rizzo and Spagnolo, 1996). System dynamics is the methodology utilized in most of these models as it allows analysis of the relationships between biological and economic factors. Some studies utilize the optimum control methodology (Cacho et al., 1991), which involves obtaining the profit maximizing trajectories of the control variables. For sea bream, Mistiaen and Strand (1999) apply an optimal control model based on a very simplified growth function determined by the feeding rate. However, since this model fails to consider the role of environmental factors, it does not allow for a complete analysis of the interactions between the economic and biological subsystems. Bioeconomic models cannot be directly extrapolated between species as each species growth is determined by specific factors and parameters. Expansion and commercial analysis of sea bream cultivation is relatively recent, and thus, there is less scientific knowledge for this species than for other more developed species. The model in this paper is analyzed with a system dynamics methodology, which becomes appropriate for the available sea bream data and for the objective of the comparison between two alternative locations. The results of the economic analysis indicate that higher water temperature in the Canary Islands is an important competitive factor influencing fish growth to an extent that can overcome economic disadvantages, such as higher input and transportation costs. Thus, these favorable environmental conditions impinge greater biological growth, leading to higher revenues and lower production costs. It is also shown that these results depend on a


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market strategy focusing on the production of the larger market sizes. This means a departure from the normal size, which is only viable in the Mediterranean context. This size does not turn out to be profitable for the Canary Islands’ producers at normal market prices and input costs.

2. Bioeconomic model The bioeconomic model consists of four interrelated sub-models as shown in Fig. 1. Economic results are influenced by production management decisions in the cage subsystem (density, capacity, and feed), and by the parameters governing fish growth (water quality, temperature, and weight). Each sub-model has functional relationships dependent upon the assumptions employed and the specification of the parameters.

Fig. 1. Conceptual model of gilthead sea bream cage system.

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The growth rate is assumed to be a multiplicative function depending on weight, water temperature, and feed ration (Stauffer, 1973), that is: dw ¼ Cf1 ðwÞ f2 ðhÞ f3 ðrÞ; dt

ð1Þ

where dw/dt is weight increase with respect to time, or temporal growth rate, f1(w) is the weight function, f2(h) is the thermal function, f3(r) is the feed function, and C an exogenous parameter which incorporates all other factors influencing growth (correction parameter). Fish weight influences growth through its effects on metabolism (Brett, 1979). The weight function expression is assumed exponential, i.e. f1(w) = bwm, where b and m are specific parameters, with m < 1. As with other physiological processes, growth is affected by body temperature. Optimum water temperature for sea bream growth is near 25 jC. Growth progressively diminishes above and below this temperature, until halting at the minimum temperature of 12 jC, and the maximum of 32.9 jC (Ravagnan, 1984; Barnabe, 1991). This is represented by the following function: f2 ðhÞ ¼ DðeaðhM hÞ ebðhM hÞ Þ;

ð2Þ

where D is a correction parameter, a and b are thermal parameters, h is the water temperature during the growth period, and hM is the maximum water temperature supported by the fish. The ration r is assumed to be a relative measure of appetite, and is defined in a normalized form (Muller-Feuga, 1990). This definition gives us a range of standardized values between starvation and satiety. Thus, the normalized ration (r) is the quotient between the ration distributed within a time unit (dR/dt), and the maximum ration variation within this same time unit (dRM/dt). Therefore, r is the quotient (dR/dt)/(dRM/dt), which takes values between 0 and 1. Therefore, the ration size can be interpreted as a percentage of the saturation level. Similarly, the normalized growth rate ( g(r)) is the quotient between the growth rate (dw/dt) and the maximum growth rate (dwM/dt). Thus, the ration function is defined as: f3 ðrÞ ¼

gðrÞ ; gðrc Þ

ð3Þ

where rc is the culture ration. g(r) can be obtained from the normalized conversion rate, i.e. Y(r) = r/g(r). The function for Y(r) should have typical properties following fish growth physiology. That is, it should have a minimum in the optimum ration, a single point at the maintenance ration (rm), and take the unit value at the maximum ration. Fish growth has an effect on revenues and costs, thereby influencing the rate of return from investment. Cost and revenues under the alternative management strategies and scenarios are estimated for an investment horizon of 10 years. For comparison purposes,


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Table 1 Model parameter, description, values, and source Function f1 f2

f3

Parameter

Description

Value

Source

C m hM a b D rc rm

Influence of other factors Fish size Maximum water temperature (jC) Function parameters

1 0.23 32.9 0.12 0.15 4.93 0.8 0.12

Calibration Statistical estimation Barnabe (1991) Muller-Feuga (1990)

Parameter to adjust f2 to dw/dt Normalized cultivation ration Normalized maintenance ration parameter

Calibration Brett (1979) Brett et al. (1969); Hogendoorn (1983)

the economic returns are evaluated by computing the net present value (NPV) assuming a 6% interest rate, and the internal rate of return (IRR).

3. Model calibration and data The model was simulated using the POWERSIM 2.05 computer software program, which performed Euler integration for daily time steps. The model requires a calibration phase to specify the functional parameter values linking economic and biological variables. Growth and relevant economic data were obtained during sea bream cultivation in Melenara Bay, Grand Canary during 1994– 1996. These data were complemented with data from published sea bream studies in the literature (Table 1). Two alternative commercial sizes are considered as the final objective of the production cycle. The normal size weighs 400 g and is the predominant in the European market, while the extra large (700 g) is also relevant but involves larger production costs (Datsolopoulos, 1996). The principal biological, technical, and economic assumptions for the model were collected from primary sources, commercial data, and published experiments (Table 2). Both monthly water temperature and commercial price were assumed to be uniform random variables. Water temperature data were obtained from monthly observations taken during

Table 2 Assumptions for bioeconomic analysis for two scenarios Parameter

Survival rates Water temperature Juvenile cost Feed cost Distribution cost Commercial price—normal Commercial price—extra large

Unit measure

% jC [min, max] Ptas./unit Ptas./kg Ptas./kg Ptas./kg[min, max] Ptas./kg[min, max]

Quantity Canary Islands

Mediterranean

99 [17, 24] 100 100 250 [900, 1000] [1800, 2000]

92 [12, 26] 70 92 85 [900, 1000]

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Table 3 Initial investment costs for different farm sizes; indicated according to cage capacity Costs (Pesetas 1000) Principal system components

Net cages Anchors Boats Total initial investment

Cage capacity (m3) 4000

8000

16 000

24 000

32 000

48 000

64 000

6005 5815 3840 38 066

12 009 11 630 3840 54 885

22 096 21 278 26 479 113 677

34 105 32 908 26 479 140 835

41 348 37 902 26 479 161 590

56 484 52 883 26 479 208 145

71 621 67 864 49 118 291 259

cage experiments in Melenara Bay, Grand Canary and from Sahin (1995) for the Mediterranean waters. Intermediate inputs and distribution costs were considered to be higher for the Canary Islands than for the Mediterranean producers. It was also considered that distribution costs in the Canary Islands are subsidized at 40%. Final stocking density was assumed to be in the range between 15 and 16 kg/m3 according to published values (Blakstad et al., 1996). For comparison purposes, these values have been adjusted according to the hydrological characteristics at the alternative locations. It is assumed that currents and water quality were favorable for sea bream culture in the Canary Islands, due to the observed average water current velocity (about 6 cm/s). This can be sufficient to spread solid wastes, thereby avoiding some undesirable effects of organic sediments both on the farm and on the environment (Molina et al., 1997). Discounted financial returns must exceed initial costs for the firm’s investment to be profitable. Investment costs were estimated based on current data observed in the market, and include costs for net cages, anchorages, and boats. Initial investment costs increases proportionally according to farm size expressed in cubic meters of cage capacity (Table 3). The decision on farm size determines the maximum annual production (Mt/year), which can be obtained in the short run. It was assumed that a productivity relationship holds between annual production and the number of necessary employees as reported by Stephanis (1995).

4. Results 4.1. Growth and conversion rates The daily biomass in the cages follows from simulated fish growth as result from the interactions of the feed used, the conversion rate, fish mortality, water temperature, and the effects of management decisions. The biological simulation results for fish growth were contrasted with real data using validation tests (Sterman, 1984; Barlas, 1989) available from the authors. This proved that simulation results for the growth model adjusts the growth data observed in the field experiments. The simulated growth trajectories for sea bream in the Mediterranean and the Canary Islands were approximately linear. However, growth was faster in the latter scenario due to


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Table 4 Conversion rates generated by the model (Mediterranean (Med) Canary Islands (Can)) Ration (%)

80

100

Area

Med

Weight (g)

Conversion rate

100 200 300 400 500 600 700

0.88 1.18 1.68 1.83 1.97 2.1 2.2

120

Can

Med

Can

Med

Can

0.86 1.16 1.37 1.52 1.62 1.65 1.69

1.05 1.40 2.00 2.23 2.40 2.58 2.7

1.01 1.39 1.63 1.82 1.97 2.02 2.04

1.3 1.74 2.48 2.73 2.93 3.16 3.29

1.28 1.71 2.01 2.24 2.42 2.45 2.49

the effect of water temperature, which presents a wider variation in the Mediterranean. The simulation results showed that a juvenile weighing 13 g reaches 300 g in 334 days under the conditions provided by the Mediterranean scenario, whereas this weight is increased by 100 g in the Canary Island scenario within the same time span. Table 4 presents the conversion rates generated for the Mediterranean and Canary Islands scenarios considering three alternative values for the ration size as a percentage of the maximum ration for each weight level. The 80% ration is the one recommended

Table 5 Costs and benefits for fifth year of operation in the Canary Islands for seven farm sizes: joint normal and extra large size seabream production Pesetas ( 1000) Farm size (m3/farm)

4000

8000

16 000

24 000

32 000

48 000

64 000

Production (Mt/year)

(60)

(100)

(200)

(300)

(400)

(600)

(800)

Variable costs Juveniles Feed Labor Medicine Total variable costs

13 510 11 905 10 790 180 36 385

21 954 19 345 15 470 293 57 062

43 908 38 690 20 150 585 103 334

65 863 58 036 24 830 878 149 607

88 661 78 125 27 950 1182 195 918

130 881 115 328 34 190 1744 282 143

174 790 154 019 43 550 2330 374 689

Fixed costs Maintenance Insurance Total fixed costs Total costs Distribution costs Gross benefits Depreciation Net benefitsa IRR (%)

2498 4254 6752 43 137 14 292 24 049 4888 19 161 26

4060 6912 10 972 68 034 23 224 41144 6402 34 742 32

8119 13 825 21 944 125 279 46 448 93 077 14 220 78 857 38

12 179 20 737 32 916 182 523 69 672 145 011 17 810 127 201 46

14 812 29 499 44 311 240 229 93 790 200 682 20 511 180 171 53

24 200 41 211 65 411 347 554 138 451 303 314 26 801 276 513 59

28 642 58 713 87 355 462 043 184 900 407 181 37 992 369 189 58

a

Taxes not included.

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by feed suppliers. Statistical test results available from the authors showed that there were no significant differences for the conversion rates between the 100% and the 120% rations. The conversion rate values observed in Melanara Bay indicate that the actual ration offered is approximately 20% above the values recommended in the feed tables. Thus, for simulation purposes we assume a 100% ration as the baseline value. 4.2. Costs and benefits Production results are obtained for a 10-year period for different farm sizes, and economic returns are evaluated for the fifth year of operation (Tables 5 and 6). The fifth year is chosen because production, and thus, costs and benefits have already been stabilized. The Canary Islands’ farmers can take advantage from the production of the extra large size, which is less viable in Mediterranean waters. Thus, the Canary Islands scenario assumes joint production of the two commercial sizes considered, with a composition of 80% of the normal size and 20% of the extra large. Total variable costs increase as farm and production (Mt/year) scales increase, with labor costs showing a clear linear tendency. A similar pattern, although less accentuated, is shown for fixed costs, with insurance costs (directly related to plant capacity) experiencing the greatest increase. Fixed costs represent 13% of total costs, whereas variable costs are determined by feed and juvenile costs, which together account for 50% of total costs.

Table 6 Costs and benefits for fifth year of operation in the Mediterranean for seven farm sizes: normal size seabream production only Pesetas ( 1000) Farm size (m3/farm)

4000

8000

16 000

24 000

32 000

48 000

64 000

Production (Mt/year)

(60)

(100)

(200)

(300)

(400)

(600)

(800)

Variable costs Juveniles Feed Labor Medicine Total variable costs

11 900 9359 10 790 152 32 201

23 800 18 718 15 470 305 58 293

41 300 32 481 20 150 530 94 461

61 950 48 721 24 830 795 136 296

82 600 64 962 27 950 1060 176 572

123 900 97 443 34 190 1590 257 123

165 200 129 923 43 550 2120 340 793

Fixed costs Maintenance Insurance Total fixed costs Total costs Distribution Gross benefits Depreciation Net benefitsa IRR (%)

2119 3608 5727 37 929 4681 14 763 4888 9875 Neg.

4238 7217 11 455 69 748 9363 35 636 6402 29 234 7

7355 12 523 19 877 114 338 16 247 68 534 14 220 54 314 10

11 032 18 784 29 816 166 112 24 370 108 196 17 810 90 386 17

14 709 25 045 39 754 216 326 32 494 149 418 20 511 128 907 22

22 064 37 568 59 631 316 754 48 740 231 862 26 801 205 061 27

29 418 50 090 79 508 420 302 64 987 311186 37 992 273 194 26

a

Taxes not included.


227

Total costs for the various farm sizes are quite similar across the two alternative locations, even though the Mediterranean scenario has assumed only the small size, which weighs half the extra large. These results reflect the higher number of fish and greater feed consumption (higher conversion rates) required in the Mediterranean, which are caused by the lower water temperature and higher mortality. The estimated tendencies of net benefits for the alternative scenarios are linear, while cumulative investment fits an asymptotic logarithmic curve (Fig. 2). Investment costs will experience a significant change when farm size increases from 48 000 m3 (600 Mt) to 64 000 m3 (800 Mt), because of the substantial changes in the characteristics of the equipment, in particular the crane boats. Net benefits are larger in the Canary Islands, although the increasing pattern is similar in both scenarios. 4.3. Average costs, internal rate of returns, and efficient plant capacity The patterns for the average cost (AC) and the internal rate of return (IRR) follow from the biological response of sea bream and the particular product sizes assumed for the alternative locations (Fig. 3). That is, the lower AC values in the Mediterranean resulted from the lower relative costs obtained by producing only the normal size, instead of the combined production of normal and extra large sizes in the Canary Islands. However, the larger water temperature intervals in the Mediterranean causes greater conversion rates and slower growth, resulting in lower internal rates of return for all scales considered. The decreasing AC and increasing IRR with respect to farm size reveal economies of scale, i.e. as all inputs are simultaneously raised by a given proportion, total output would increase more than proportionally. This can be explained because of the particular discontinuities presented in the size of technical equipments, such as classification machines, service boats, and cages (Hill and Ingersent, 1982). The largest value for the IRR is reached for the 48 000-m3 scale (600 Mt/year), declining at the 64 000-m3 scale (800 Mt/year). This decrease is influenced by investment costs, which experience the

Fig. 2. Net benefits and cumulative investment.

228


Fig. 3. Average costs (AC) and internal rate of return (IRR) for the Canary Islands and Mediterranean.

greatest change within this range. A negative IRR is obtained for the smallest farm size of 4000 m3 (60 Mt/year) for the Mediterranean scenario, while it is always positive and larger than the 6% benchmark for the Canary Islands scenario. The presence of economies of scale in sea bream culture utilizing floating cages offers practical information in making long-term business decisions. Thus, it might be interesting to show how the pattern of scale economies and the efficient plant capacity change under alternative scenarios with respect to the decision-making variables. Thus, we have considered the sensitivity of the IRR to alternative scenarios defined by specific decisions regarding the commercial size, the stocking density, and the feed ration.

Fig. 4. Efficient farm sizes (indicated by arrows) for the Canary Islands (48 000 and 64 000 m3) and Mediterranean (48 000 m3).


229

For the Canary Islands, we have assumed (i) production of only normal size (assuming subsidization of distribution costs) (EC1), (ii) joint production of normal and extra large sizes (EC2) (baseline scenario), and (iii) increase in stocking density with joint production (EC3). For the Mediterranean, (iv) 100% ration (EM1) (baseline scenario), (v) 90% ration (EM2), and (vi) 15% mortality throughout sea bream weight gain cycle (EM3). The results reveal that the efficient farm scale is 48 000 m3 (600 Mt/year) for all Mediterranean scenarios, which is the plant generating the highest IRR values (Fig. 4). For the Canary Islands, the maximum IRR was given by the two largest scales in EC1, 48 000 m3 in EC2, and 64 000 m3 in EC3, showing that the most efficient levels are related to the largest scales. 4.4. Relative competitiveness Industry competitiveness depends on location, since this determines environmental factors influencing biomass growth, and also the relative input costs. The former means that the decision on location could imply significant changes in the time necessary to reach a given commercial size and the feed conversion rate. The Canary Islands location takes advantage in the performance of this biological process because the normal range of water temperature suits sea bream culture (Table 7). However, the large input costs make producing unviable in only the normal size in the Canary Islands. For a positive NPV, this scenario would require a subsidy of 100 Ptas./kg to distribution and commercialization. Even under this assumption, the economic returns are smaller than in the Mediterranean because of the larger input costs, as these inputs have to be brought from mainland Europe. Fig. 5 shows the IRR variation as a result of changes in the input and distribution costs, assuming the efficient farm size of 48 000 m3 and the production of only the normal size. Returns improve considerably by reducing the costs in the Canary Islands to the Mediterranean levels ( 30% juveniles, 8% feed, and 66% distribution), making production of the normal size commercially viable. An IRR above 6% is generated just with a 5% reduction in the input (juveniles and feed) and distribution costs. The reduction in juvenile costs has the largest effect on IRR, followed by the reduction in distribution and feed costs. Competitiveness in the Canary Islands can be enhanced by producing larger sizes. The bioeconomic model shows that the extra large size would take 30 months to grow in the Mediterranean conditions, whereas this is almost half in the Canary Islands, with a conversion rate of 2.04 for maximum rationing. Table 7 Biological – technical results generated by the bioeconomic model for the two scenarios, and two commercial sizes: 100% rationing assumed Scenario

Final weight (g)

Cultivation days

CR

Canary Islands Canary Islands Mediterranean Mediterranean

400 700 400 700

334 530 418 915

1.82 2.04 2.23 2.7

CR = Feed conversion rate.

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Fig. 5. Internal rate of return (IRR) sensitivity in the Canary Islands (Normal size production).

Considering the production of both normal and extra large sizes in the Canary Islands scenario, and assuming the 48 000 m3 efficient farm capacity, it can be seen that the IRR is always above the 6% benchmark for wide changes in the input (juveniles and feed) and distribution costs (Fig. 6). Even with input and distribution costs changing by over 100%, the bioeconomic model generated acceptable returns, in contrast to production of only the normal size. Economic viability becomes unfeasible only for changes above 120% in juvenile costs, 150% in distribution costs, or 170% in feed costs.

Fig. 6. Internal rate of return (IRR) sensitivity in the Canary Islands (Joint normal and extra large size production).


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5. Conclusions Production location influences the competitiveness of the aquaculture farm by conditioning environmental and economic factors. In this paper, we have looked at the economic implications of the location decision for the farm producing gilthead sea bream in offshore cages. The two alternative locations—the Canary Islands and the Mediterranean—have been compared by means of a simulated dynamic system model specific for this species. The results demonstrate the presence of economies of scale in this industry for the two locations considered. This is revealed by declining average costs and increasing internal rate of return with respect to plant capacity. The efficient farm size is given by the turning point in economic returns. It is obtained that this level of capacity is quite robust to alternative management strategies and location decisions. The Canary Islands location presents environmental characteristics that provide competitive advantages with respect to the Mediterranean area. These advantages follow principally from the lower range of water temperature, which favors more rapid biomass growth. In particular, the greatest opportunity comes from producing the extra large size in the Canary Islands waters, which is less viable in the Mediterranean because of the high costs involved. Despite the disadvantages of higher input and distribution costs, economic returns for joint production of normal and extra large sizes in the Canary Islands are always larger than production based only on the normal size in the Mediterranean.

Acknowledgements This study was supported by a PhD fellowship of the CONACYT Mexico for the first author. The authors acknowledge the information provided by Jose Luis Guersi in support of this study. We also thank the anonymous reviewers for their insightful comments.

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