Fisheries science JSFS , tập 78, số 3, 2012 5

Fish Sci (2012) 78:491–501 DOI 10.1007/s12562-012-0484-6 ORIGINAL ARTICLE Fisheries Experimental study on the effect of waves on netting panels at a range of angles to the wave direction Wei-hua Song • Zhen-lin Liang • Rong Wan Fen-Fang Zhao • Kinoshita Takeshi • Liu-yi Huang • Jia-zhi Ma • Bo-hai Chen • Received: 31 July 2011 / Accepted: 13 February 2012 / Published online: 28 March 2012 Ó The Japanese Society of Fisheries Science 2012 Abstract The effect of horizontal waves on flexible netting panels placed at angles to the wave direction is studied with the aim of evaluating the testing method of pre-tensioned mooring and radial systems and flexible netting structures The netting was on a frame at a specific hanging ratio for ten types of polyethylene panels Regular waves were experimentally generated with a wave period varying from 0.8 to 2.0 s and wave height ranging from 50 to 250 mm The force on the netting structure was recorded by a tension transducer and a digital signal recorder The results showed that the horizontal wave force on the netting panel changed periodically and asymmetrically when it was back and left or right declinate to the wave direction; similar results were found for the surface wave elevation The opposite results were obtained when the sloping angle declinated front to the wave direction, with two obvious crests during each period The horizontal wave force was related to the height and width of the netting panel, wave height, wave length, twine diameter, bar length of the mesh, and sloping angle relative to the W Song (&) Á J Ma Department of Marine Fisheries, Zhejiang Ocean University, Zhoushan 316004, China e-mail: whsong6806@163.com W Song Á Z Liang Á R Wan Á F.-F Zhao Á L Huang College of Fisheries, Ocean University of China, Qingdao 266003, China W Song Á F.-F Zhao Á K Takeshi Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan B Chen Physical Ocean Key Open Lab, Ocean University of China, Qingdao 266003, China wave direction Using dual series relations, the least square approximation, and multiple stepwise regression analysis, the formula for estimating the maximum value of the wave force on the netting was obtained Keywords Netting panel Á Horizontal wave force Á Sloping angle Á Flume experiment Introduction Hydrodynamics of the fishing gear has been a main focus of researchers in fisheries science for many years and can be traced back to the work of Tauti [1, 2] and Baranow [3] in the 1930s, who used traditional formulae to calculate forces exerted on fishing gear and/or their components Various calculation methods have been developed during the intervening years based on a combination of empirical formulae and numerical computations, as described in She [4] and Matuda [5] Although physical experiments on fishing gear hydrodynamics have been conducted for many years, and many theories have evolved, most work has focused on the effect of water currents, with the effect of waves on fishing gears receiving less attention This is particularly the case of netting panels positioned not normal to the wave direction Fish cages are composed of relatively rigid parts, i.e., the frame, and more flexible parts, i.e., netting panels and mooring lines—with both rigid and flexible components considered to be fixed fishing gear These two main components react differently in terms of hydrodynamic properties to the actions of waves and currents In addition, research methods are different for currents and waves The dynamic behavior of cage frames usually belongs to the research field of ocean engineering, while the net falls 123 492 within the scope of fishing gear hydrodynamics A number of studies have been conducted in which the cage system was considered as a whole and the hydrodynamic forces of the spar cage determined using measurements made in the ocean and flume tanks, respectively, and by numerical simulations [6–8] Song et al [9–11] presented the iterative calculating method for the main cage’s individual components under different wave conditions and proved the validity of the method by wave flume experiments The results indicated that it is possible to separate the cage wave force into stiff and flexible forces Forces on the rigid frames have been calculated by the Morison equation [12], and forces on the flexible netting panels and ropes are tackled according to traditional formulae in fishing gear mechanics The nets on the fish cage are often the largest component and have been the focus of drag force research [13] Using theoretical and model test methods, Aarsnes et al [14] determined the force and blockage characteristics for individual net types Colbourne and Allen [15] conducted a field experiment measuring waves and the load and motion response of a gravity type net-pen and then compared the results with physical models; however, they focused on fishing gear mechanics only in terms of current response In contrast, some researchers have suggested that the wave forces could be calculated using the theory of fishing gear mechanics through the conversion of the water particle velocity according to different wave conditions [13] Song et al [16] also have carried out experiments on wave force on netting structures at positioned normal to the wave direction in the wave flume and determined the relationships between the wave force and various parameters under regular wave conditions and net panel dimensions The aim of the study reported here was to examine the effect of horizontal waves on netting panels located so as not to be normal to the wave direction and to examine the effect of horizontal waves on netting panels positioned at various angles to the wave direction and flexible netting structures The purpose of this study is to determine the expression of maximum wave force in terms of wave length, wave height, twine diameter, among other factors Materials and methods Regular wave theory Regular wave tests were conducted using physical modeling methods for seven monochromatic waves with frequencies spanning the expected forcing band The characteristics of these waves were approximated using linear wave theory as described by Dean and Dalrymple [17] and were characterized by the following velocity potential (u) 123 Fish Sci (2012) 78:491–501 u¼ gH cosh kðz þ d Þ sinðkx À 2pftÞ 4pf coshðkd Þ ð1Þ and surface elevation (g) g ¼ A0 cosðkx À 2pftÞ ¼ H cosðkx À 2pftÞ; ð2Þ where A0 is the wave amplitude (equals to H/2, H the wave height), g is the gravitational acceleration, f is the frequency (equals the reciprocal of T, which is the wave periods), k is the wave number (equal to 2p/L), L is the wavelength, d0 is the water depth, z is the vertical position in the water column, and x is the horizontal position In view of ð2pf Þ2 ¼ gk tanhðkd Þ; gT kd ; L¼ 2p sinhðkd Þ ¼ coshðkd Þ Á tanhðkd Þ; Equation can be derived as u¼ pH cosh kðz þ d0 Þ sinðkx À 2pftÞ Tk sinhðkd Þ ð3Þ with ux ¼ ou ; ox uz ¼ ou oz Therefore, the fluid element velocity of the position (x, z) is obtained as: pH cosh kðz þ d Þ > > cosðkx À 2pftÞ < ux ¼ T sinhðkd Þ ð4Þ > pH sinh kðz þ d Þ > : uz ¼ sinðkx À 2pftÞ T sinhðkd Þ where ux and uz are the horizontal and vertical velocity, respectively Based on the Morison equation, the hydrodynamic force (F) acting on a submerged cylindrical element with a diameter that is relatively small compared with the length of the wave is defined as the following [12, 18–20], ! dux dF ¼ d CD qAðux Þjux j þ CM qV Á ; ð5Þ dt where CD and CM are drag and inertial coefficients, respectively, A and V are the area projecting the verticalsection and volume of the submerged cylinder, respectively, q is fluid density, and dux =dt is the horizontal acceleration of the fluid element According to the experiments conducted by Kuznetsov [13] and Song et al [16], we consider that the apex value of the horizontal wave force is drag force when the fluid element velocity is at its maximum value in a period because the physical volume of the netting is smaller than that of the frame volume, and the inertial force is smaller Fish Sci (2012) 78:491–501 493 than that of the drag force [1–3] Combined with the dimensional analysis method, the horizontal wave forces on the differential area (DA) of the netting could be defined as X X dF ¼ dðCx qux jux jAÞ ð6Þ where Cx is the pending coefficient related to the netting characteristic coefficient d/a (d is diameter of the mesh twine, and a is bar length of the mesh), KC (waves periods parameters), Re (Reynolds number), and H/L [16] Table Characteristics of the ten netting panels used as physical models Panel type Mesh number Mesh size (mm) Twine diameter (mm) d/a A 32.0 28.5 40 0.75 0.038 B 53.5 47.5 24 0.50 0.042 C 37.5 33.5 34 0.90 0.053 D 53.5 47.5 24 0.75 0.063 E 85.5 76.0 15 0.50 0.067 F 53.5 47.5 24 0.90 0.075 G 21.5 19.0 60 1.55 0.052 H 26.0 23.0 50 1.55 0.062 I 32.0 28.5 40 1.75 0.088 J 32.0 28.5 40 1.95 0.098 d/a The netting characteristic coefficient (d is diameter of the mesh twine, and a is bar length of the mesh) Physical modeling In our study, there were ten experimental netting panels (Table 1), each of which needs to be fixed to a frame because the netting is flexible Each netting structure was sloped at a different angle to the wave direction under the regular wave condition (Fig 1) [16, 21–27] Based on the motions of the aquaculture fish cage in the open ocean field [6, 7] and the experimental conditions of Kuznetsov on the netting wave force (as described in [13]), the netting structures produced similar motions to the cage under our wave conditions [20, 21], including six components of responses (surge, sway, heave, roll, pitch, and yaw) To ensure that the netting structures could not produce any motion during the wave experiments, the method of pre-tension and radial moorings were selected to fix the netting structures onto the wave flume [22–25] (Fig 1) The wave experiments were conducted under a pre-tension which was approximately equal to the maximum horizontal wave force every time, and the mooring line was made of thin steel wire which could withstand tension without elongation (Fig 1) The horizontal distance between the mooring position of the front wave height meter and one of the netting panels was 5.0 m (Fig 1), and the distance between the mooring position of the rear wave height meter and one of the netting panels was 4.0 m; a and b are the front–back sloping angles and the right–left sloping angles, which were adjusted through the variation of four tension leg length Based on the wave tank conditions, these were Fig Installation of netting structure in the wave flume and experimental apparatus 123 494 Fish Sci (2012) 78:491–501 a1 = 30, 45, 60, 75, 90 (forward sloping), a2 = 90, 105, 120, 135, 150 (backward sloping), and b = 30, 45, 60, 75, 90 (right–left sloping), respectively The sensor can record the horizontal wave signal The wave force on the netting panel and the wave parameter were recorded synchronously so that it was possible to calculate the phase difference between the force and the wave Physical model tests using regular waves were conducted in the 65 1.2 1.7-m flume tank of the wind– wave–current Flume Laboratory of the Ocean University of China The working water depth was 0.70 m, the wave period was 0.8–2.0 s, and the wave height was 50– 250 mm The netting structures were moored 20 m away from the wave maker, which produced regular waves The instrument for measuring force consisted of a transducer, computer, and digital recorder, and it was able to measure forces ranging from to 40 ± 0.01 N The measuring technique was gradually improved in the field [13, 16, 25, 26] The bottom of the netting structure has a stated distance with the bottom of the pool When the sloping angle a1 (or a2) was 30° (or 150°), the distance between the center point of the frame and the bottom of the pool was the same as that between the point where the tension line and the frame junct and the bottom of pool Then the distance between the lower side of the frame and the bottom of the pool is 500 mm For example, when the wave height is 250 mm, the water surface cannot overflow the top of frame (i.e., the highest position of the water surface is 825 mm, and the highest position of the frame top is 900 mm) Song et al [9–11, 16, 25] showed that the horizontal wave force is influenced by the netting structures dimensions In their studies, the wave force had a linear relationship with the netting structure width, but an exponential relationship with the structure height sinking into the water, with the force decreasing dramatically with the sinking depth of the netting structure from the surface, being nearly equal to zero when the height was equal to the four-fifths of the flume depth from the surface Considering the above results and flume dimensions, the test frame was designed at 0.9 0.8 m in order to eliminate the interaction between the flume and the test structure The frame was made of high-strength polyvinyl chloride pipe (Qingdao ShenBon Sea cage engineering and technology Co., Qingdao, Shandong, China) The top of the frame was 150 mm above the water surface to ensure that the wave surface would not submerge the entire netting structures at maximum wave height The nets were constructed using knotted high-strength polyethylene (China National Fishery Yantai Marine Fisheries Corp, Yantai, Shandong Province, China) with the specifications shown in Table The hanging ratio of the netting structures was 0.707 Experimental data analysis Data were collected as part of the experiment program, including experiment surface waves, the horizontal wave force on the netting structures, and dimensions under different sloping angles The wave parameters were different in each experiment because the wave conditions were limited by the wave maker The wave heights in ten netting panels and the frame experiments were different, but the difference was small In order to attain the wave force on the netting panel, data processing techniques included the cubic spline method because the force recorded was effected by all the netting panel and the frame For example, in the wave experiment during the same period, the testing force Fn on the frame corresponding to the experiment wave height Hn (H1 \ H2 \ H3 \ ÁÁÁ \ Hn) is given by ! F ¼ ðHiþ1 À HÞ2 À ðHiþ1 À HÞ3 Fi hi hi ! þ ðH À Hi Þ2 À ðH À Hi Þ3 Fiþ1 hi hi ! 1 þ hi ðHiþ1 À HÞ2 À ðHiþ1 À HÞ3 Fi0 hi hi ! 1 þ hi ðH À Hi Þ2 À ðH À Hi Þ3 Fiþ1 ð7Þ hi hi where F is the horizontal force corresponding to the wave height H in the netting structures experiments, possibly implying the experimental sloping angles; hi = Hi?1 - Hi, i = 1, 2,…, n - 1; n is the wave height number in each period The critical condition is defined as F10 ¼ 0; Fiþ1 ¼ ðFiþ1 À Fi Þ=hi ; i ¼ 1; 2; ; n À 1, and F100 ¼ because the pre-tension force is constant Fi0 (i = 1, 2,…,n) differential coefficient is given by Table Wave length, wave period, and wave height number Variable The number of wave height Wave period (s) 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Wave length (m) 0.998 1.549 2.169 2.803 3.425 4.030 4.621 Wave height number 11 11 11 10 123 Fish Sci (2012) 78:491–501 6 6 ð1 À w2 Þ ð1 À w3 Þ 495 w2 ð1 À w4 Þ w3 ÁÁÁ 03 f F 36 7 F20 f3 7 76 76 7 6 76 ¼ 76 7 56 7 7 wnÀ1 5 Fn fnÀ1 w4 ð1 À wnÀ1 Þ HiÀ1 where wi ¼ HiÀ1 þHi , fi ¼ h i 1Àwi wi Hi À1 ðFi À FiÀ1 Þ þ Hi ðFiþ1 À Fi Þ , with i = 2, 3,…,n - According to the above analysis, the wave force on the netting panels under different angles can be dealt with through the iterative calculation method [9–11, 16] Results Variation of the horizontal wave force on netting panel relationship between the wave length and the wave period and also gives the wave height number Taking netting panel F as the subject of our experiment, the conduct wave experiment was conducted according to the front-and-back slope and right-and-left slope, respectively Since the wave is two-dimensional sine wave, the leftsloping and right-sloping waves are consistent (if the experimental components are symmetrical) Figures 2, 3, and show wave forces on the netting panel F at angle a1 = 30° and wave height 185.2 mm, angle a2 = 150° and The horizontal wave force of net-F structures in period 1.6s and heights 185.4mm Wave force (N) Wave force on the netting panel is similar to the variation of the experiment wave Table presents data on the 150 100 ð8Þ -1 50 -2 0 1.6 3.2 4.8 6.4 9.6 Time (s) -50 -100 1.6 3.2 4.8 6.4 9.6 Time (s) Fig Wave force on the netting panel (F) under the condition that the angle a2 is 150°, the wave height is 185.4 mm, and the wave period is 1.6 s The horizontal wave force of net-F structures in period 1.6s and heights 185.2mm The horizontal wave force of net-F structures in period 1.6s and heights 189.5mm 2.5 2.5 Wave force (N) Wave force (N) 1.5 0.5 -0.5 1.5 0.5 -0.5 -1.5 1.6 3.2 4.8 6.4 9.6 Time (s) Fig Wave force on the netting panel (F) under the condition that the angle a1 is 30°, the wave height is 185.2 mm, and the wave period is 1.6 s -1 1.6 3.2 4.8 6.4 9.6 Time (s) Fig Wave force on the netting panel (F) under the condition that the angle b is 30°, the wave height is 189.5 mm, and the wave period is 1.6 s 123 496 Fish Sci (2012) 78:491–501 Table Relationships between the wave force on the netting panel (F) and angle b at wave period 1.6 s Angle b Wave height (mm) 48.3 67.2 87.7 106.2 126.1 145.4 168.3 187.6 206.3 232.7 249.5 30° 0.107 0.222 0.330 0.367 0.483 0.693 0.753 0.880 0.938 0.869 0.762 45° 0.010 0.090 0.203 0.298 0.520 0.808 1.040 1.300 1.576 2.111 2.217 60° 0.023 0.097 0.124 0.102 0.171 0.352 0.683 1.166 1.687 2.432 2.964 75° 0.065 0.138 0.265 0.336 0.604 1.076 1.885 2.595 3.368 4.377 4.932 90° 0.136 0.374 0.782 1.155 1.833 2.795 3.990 4.985 5.923 7.287 8.212 Force: N Table Relationships between the wave force on the netting panel (F) and angle a at wave period 1.6 s Angle a Wave height (mm) 48.3 67.2 87.7 106.2 126.1 145.4 168.3 187.6 206.3 232.7 249.5 30° 0.053 0.084 0.092 0.042 0.028 0.053 0.271 0.881 – – – 45° 0.023 0.100 0.181 0.231 0.296 0.425 0.623 0.915 1.212 2.634 3.727 60° 0.003 0.051 0.075 0.037 0.120 0.324 0.832 1.449 2.218 3.854 4.922 75° 0.060 0.141 0.252 0.360 0.707 1.271 2.132 2.954 3.776 5.328 6.904 90° 0.136 0.374 0.782 1.155 1.833 2.795 3.990 4.985 5.923 7.287 8.212 105° 0.035 0.137 0.243 0.327 0.623 1.172 1.847 2.391 3.024 3.991 4.566 120° 0.029 0.046 0.033 0.054 0.068 0.098 0.115 0.346 0.723 2.350 2.736 135° 0.038 0.077 0.107 0.057 0.134 0.396 0.763 1.343 1.941 3.034 3.645 150° 0.215 0.358 0.524 0.560 0.840 1.200 1.391 1.899 – – – – No data Wave force: N wave height 185.4 mm, and b = 30° and wave height 189.5 mm, respectively under the same period 1.6 s Tables and presented the relationships between the wave force on the netting panel F and its angles of a or b According to data presented in these tables, the change in wave force was clearly different when the angle is mainly formed by forward–backward sloping (e.g., the angles a1 30°–75° corresponds to the angles a2 105°–150°) If each wave force during a cycle at a1 = 30° has two amplitudes, the wave force on the netting panel is fairly complicated Based upon our calculations, Figs and show the relationship between the horizontal wave force on the netting panel and wave height and length The relationship between wave force of the netting panel and wave height is fairly evident when there is an angle of attack, but the relationship between wave force of the netting panel and wave length cannot be clearly determined However, the reasons for such complicated variation were that the horizontal wave force on the netting panel from top to bottom was not in phase, and was even sometimes in reverse, which is different from the state when the netting panel is positioned so as not to be normal to the wave direction Therefore, the value for the wave 123 force on the whole netting panel exhibits several crests and asymmetry that are related to the sloping angle forms (angles a1, a2, and b), the measurement of the netting panel, wave length, among others Moreover, the experiment results revealed that the horizontal wave force on the netting panel had two or more wave apex values in a wave period at angle a1 forms because the wave force was affected by the effect of the out-phase wave force (referring to its drag and its inertia), and its wave apex value was less than the angle a2 ones Multiple stepwise regression analysis In general, the horizontal wave force on the netting was primarily related to netting dimensions, including the net width (l) and height (h), the twine diameter, bar length, wave characteristics, such as wave height and wave length, and the sloping angle relative to the wave direction The dimension of the net width was fixed, and the dimension of the net height should be attributed to that of the wave height Therefore, the dimensions of wave height H, length L, twine diameter d, bar length a, and sloping angle a especially were examined Fish Sci (2012) 78:491–501 497 Relations between the wave force and the wave height Relations between the wave force and the wavelength front 30° front 45° front 60° front 75° front 90° back 105° back 120° back 135° back 150° Wave force (N) Wave force (N) 50 100 150 200 front 45° front 75° back 105° back 135° 0 front 30° front 60° front 90° back 120° back 150° 250 2.5 3.5 Heights (mm) 4.5 Wave length (m) Relations between the wave force and the front-and-back Relations between the wave force and the front-and-back slopping angles Heights 67.2mm Heights 106.2mm Heights 145.4mm Heights 187.6mm Heights 232.7mm Wave length 2.17m Wave length 2.80m Wave length 3.43m Wave length 4.03m Wave length 4.62m 30 45 60 75 90 105 120 135 150 30 45 60 75 90 120 135 150 Slopping angle (°) Slopping angle (°) 105 Relations between the wave force and the right-and-left slopping Relations between the wave force and the right-and-left slopping angles Heights 67.2mm Heights 106.2mm Heights 145.4mm Heights 187.6mm Heights 232.7mm Wave length 2.17m Wave length 2.80m Wave length 3.43m Wave length 4.03m Wave length 4.62m 30 45 60 75 90 30 45 Based on the experimental data and Eqs and 6, the apex value expression of the horizontal force on the netting can be described as m m X X Fmax ¼ Fi ¼ ðC0 H Laiþ1 aaiþ2 daiþ3 laiþ4 aaiþ5 Þ ð9Þ i¼1 i¼1 where Fmax is the apex value of the wave force, m is number of components of the wave force, C0, and ai, ai?1, ai?2, ai?3, ai?4, ai?5 are pending coefficients Because the netting’s mass is small in the water, the inertial force which is affected by the mass is small compared with the drag force [1–3] According to the experimental results of Kuznetsov [13] and Song et al [16], the apex value of the 75 90 Slopping angle (°) Slopping angle (°) Fig Relations between the wave force on the net (F) and wave height at a period of 1.6 s under different the sloping angles 60 Fig Relations between the wave force on the net (F) and the wavelength at wave height 150 mm under different sloping angles horizontal wave force may be considered as drag force only in each period, with Eq derived as Fmax ¼ C0 H a1 La2 aa3 da4 la5 aa6 Following logarithmic equation is expressed as ð10Þ transformation, the above lnðFmax Þ ¼ lnðC0 Þ þ a1 lnðHÞ þ a2 lnðLÞ þ a3 lnðaÞ þ a4 lnðdÞ þ a5 lnðlÞ þ a6 lnðaÞ ð11Þ The coefficients were analyzed using the method of the least square approximation and multiple stepwise regression analysis based on actual experiments [16, 19] The formula for calculating the maximum value of the wave force on the netting is: 123 498 Fish Sci (2012) 78:491–501 F ¼ expa0 ðHÞa1 L0:79 aÀ1:48 d 1:06 l1:05 aa6 wave force and the wave length showed 0.8 power, which was related to the wave length and the measure of the netting panel Therefore, the calculation formula for the maximum coefficient of the wave force is as follows when the netting panel is perpendicular to the wave direction, ð12Þ The wave force on the netting panels was related to these three sloping angle forms in Table 5, which is a negative relation to the wave force at angle a2 increased from 90° to 150° The relationship between the wave force on the netting panel at these sloping angles and the wave height showed 2.5–3 power, with the smaller the sloping angle (only about an acute angle), the lower the coefficient of relationship The relationship between the F ¼ 2669:4ðH=2Þ2:62 L0:79 aÀ1:48 d1:06 l1:05 : Discussion According to Figs 2, 3, and 4, components of the wave force on netting F should be analyzed The fast Fourier transform (FFT) and integral were used, and the component of the wave force at the sloping angle (a1, a2, and b) of 30˚ when the the netting was not normal to the wave directions is shown in Table The relationship between the wave force ingredients on the net F and the wave height at wave periods 1.6 s and three sloping angles of 60° is shown in Fig 7, which is clearly a square relationship However, it is a linear Table Coefficient of relationships between wave force on the net and the two parameters of wave height and angles R2 Forms of sloping angle a0 a1 a6 Angle b 5.27 2.83 1.58 0.9527 Angle a1 5.17 2.85 2.04 0.9136 Angle a2 6.09 2.49 -2.15 0.8173 ð13Þ a1, a6, Coefficients of relationship between the wave force and the wave height or sloping angle, respectively Table Components of the wave force on netting panel (F) when the panels were in a position not normal to the wave directions at wave period 1.6 s Forms of sloping anglea The number of wave height 10 11 Height (mm) 4.82 6.59 9.01 10.90 f0 0.0080 0.0271 0.0560 0.1316 0.1842 0.2305 16.81 0.4389 18.52 0.7211 – – – – – f1 0.0520 0.0790 0.1288 0.1687 0.2205 0.2545 0.3265 – 0.4209 – – – f2 0.0088 0.0095 0.0364 0.0541 0.0878 0.1198 f3 0.0074 0.0054 0.0059 0.0017 0.0066 0.0158 0.2693 0.4529 – – – 0.1070 0.2337 – – Total 0.0762 0.1210 0.2271 0.3561 0.4991 0.6206 – 1.1417 1.8286 – – – Height (mm) 4.89 6.61 9.12 – – – f0 0.0614 0.1564 0.3185 0.3050 0.5217 0.6758 0.9291 f1 0.1425 0.2363 0.4044 0.5684 0.7337 0.8784 1.1334 1.1377 – – – 1.3242 – – f2 f3 0.0197 0.0143 0.0478 0.0292 0.0655 0.0436 0.0463 0.0287 0.0332 0.0300 0.1063 0.0905 – 0.1661 0.1411 0.2514 0.2190 – – – – – – Total 0.2379 0.4697 0.8320 0.9484 1.3186 1.7510 2.3697 2.9323 – – – Height (mm) 5.11 6.86 9.24 f0 0.0864 0.1096 0.1433 0.1476 0.2682 0.3809 0.5195 0.6700 20.67 23.06 25.31 0.9227 1.3165 f1 0.0769 0.1350 0.2455 0.3578 0.4751 0.5966 0.7730 1.5827 0.9211 1.0592 1.3452 f2 0.0065 0.0193 0.0387 0.0587 0.0998 0.1281 1.5308 0.1622 0.2151 0.3042 0.3580 f3 0.0023 0.0043 0.0352 0.0482 0.0558 0.3922 0.0717 0.1265 0.1444 0.2083 0.2227 0.2904 Total 0.1721 0.2682 0.4627 0.6123 0.8989 1.1773 1.5812 1.9506 2.4944 3.2424 3.7961 Angle a1 = 30° 12.81 14.29 Angle a2 = 30° 11.07 12.95 14.45 16.98 18.54 Angle b = 30° 11.28 13.49 15.05 17.31 18.95 Force: N – No data f0, Drifting force; f1, f2, f3, first-order, second-order, third-order wave excitation forces for which the encountering frequencies are 0.625x0, 1.25x0, 1.825x0 (x0 = 2p), respectively a 123 Fish Sci (2012) 78:491–501 Excitation forces f (N) The relation between drifting forces f and wave height a Drifting forces f (N) Fig Relationship between the wave force ingredients on the net (F) and the wave height at a wave period of 1.6 s under the sloping angle 60° 499 3.0 left 60° back 60° front 60° 2.0 1.0 0.0 10 15 20 25 30 b The relation between excitation forces f and wave height 3.0 left 60° back 60° front 60° 2.0 1.0 0.0 10 15 The relation between excitation forces f and wave height 2.0 1.5 left 60° back 60° front 60° 1.0 0.5 0.0 10 15 20 25 30 Wave height (cm) Fig Five pulses of excitation wave forces on the netting (F) at period 1.6 s, height 204 mm, and angle a2 60° relationship when the wave height is more than 150 mm and the wave excitation forces has a fourth-order and fifth-order relationship in addition to including three excitation forces and one drifting force (Fig 7b) For example, Fig showed five pulses of excitation wave forces on the netting F by the FTT method FFT and integral [16, 25] Based on the FFT analysis, the simulation formula for the wave force is as follows,     2p 2p FðtÞ ¼ f0 þ f1 cos  t þ f2 cos  t T T  2p þ f3 cos  t ð14Þ T 25 30 d The relation between excitation forces f Excitation forces f (N) Excitation forces f (N) c 20 Wave height (cm) Wave height (cm) and wave height 1.2 0.8 left 60° back 60° front 60° 0.4 0 10 15 20 25 30 Wave height (cm) where f0 is the drifting force, and f1, f2, and f3 are the firstorder, second-order, and third-order wave excitation forces, respectively The comparison between the wave force on the netting (F) obtained by the simulation method and those obtained by the experimental method at period 1.6 s, wave height 189.5 s, and angle b 30° is shown in Fig 9, which illustrates that the simulation wave force by FFT was similar to the results obtained by the experimental method These phenomena and variation are related to the sloping forms of the netting panels, the sloping angles, the wave periods, the measurements of the netting panels, among others The horizontal wave force on the netting panel changed periodically and asymmetrically when it was not normal to the wave direction, which was similar to the surface wave elevation The horizontal wave force is related to the following parameters: netting panel height (l) and width (h), wave height (H), wave length (L), twine diameter (d), bar length (a) of the mesh, and inclination angle (a or b) to the wave direction At the back slope, the wave force is affected by one wave apex value However, at the front and side slope, the wave force is affected by more than two wave apex values The variation resulted from the change in the height of netting panels, sloping angles, and wave lengths and has many components, such as excursion forces and excitation forces The different kinds of excitation forces were beyond the scope of this study In the future, we will be studying the rules and reasons for variations between the wave force 123 Fish Sci (2012) 78:735–742 removing the rice bran with 45 ml of sterilized saline solution containing 2.5 or 10% NaCl (w/v) for with a stomacher (400T; Organo, Tokyo, Japan) In order to count the facultative anaerobic halophilic lactic acid bacteria, decimal dilutions of the homogenate (0.05 ml) were inoculated onto a GYP agar plate containing 1% glucose, 0.5% yeast extract, and 0.5% peptone supplemented with 2.5 or 10% NaCl [8] The number of culturable bacteria on the plate was estimated after incubation at 25°C under aerobic conditions for days or under anaerobic conditions for 21 days For anaerobic cultivation, rectangular jars with AnaeroPacks (Mitsubishi Gas Chemical, Tokyo, Japan) were used Viable counts for each sample were also estimated by using plate count agar (PCA) for aerobes and potato dextrose agar (PDA) medium for fungi after incubation at 37°C for days and at 25°C for 21 days, respectively PDA medium was ready-made (Eiken Chemical, Tokyo, Japan) and was used without any supplementation Chemical analysis To determine organic acids, free amino acids, and acidsoluble peptides, the rest of each test piece subjected to microbial analysis was minced uniformly after removing the rice bran The minced meats were stored at -30°C for a few months prior to chemical analysis 2.5 g of the minced meats were homogenized with 25 ml of hot water at 100°C and the homogenates were incubated at 100°C for After the incubation, the homogenates were cooled down and centrifuged at 3,000g for 20 The precipitate was washed with 15 ml of hot water and the centrifugation was repeated The supernatants were collected, filtrated with filter paper (no 2; Advantec, Tokyo, Japan), and diluted to a total volume of 50 ml (hot water extract) Following the procedure of Furutani et al [9], protein components were removed from the hot water extract by acid precipitation with 5% trichloroacetic acid (TCA), and the supernatant was neutralized HCl was added to the supernatant at a final concentration of 0.02 M, and this was filtrated by a syringe-driven filter unit (MIlex-LH 0.45 lm; Millipore, Bedford, MA, USA) The aliquots of the filtrates were submitted to an amino acid analyzer (JCL-500/V2; JEOL, Tokyo, Japan) For the measurement of organic acids, hot water extracts were filtrated with a syringe-driven filter unit (MIlex-LH 0.45 lm; Millipore, Bedford, MA, USA) and then submitted to a capillary electrophoretic system (Agilent Technologies, Santa Clara, CA, USA) Lowry’s method [10] was applied for the determination of acidsoluble peptides after removing proteins by acid precipitation with 5% TCA Tests for significant differences in the production of free amino acids in samples B–F compared to that in sample A after aging for months were carried 737 out by Student’s t test using three test pieces from the respective barrels Results Suppression of microbial growth by antibiotics during processing Our previous paper [6] indicated that the dominant bacteria in heshiko preparation were facultative anaerobic halophilic lactic acid bacteria, and after culturing them on a 2.5% NaCl-GYP agar plate, these bacteria were identified as Tetragenococcus halophilus in our latest study [5] Accordingly, changes in the viable counts on fish flesh during the processing of heshiko with or without antibiotics were investigated using 2.5% NaCl-GYP agar plates, and the results are depicted in Fig To simplify the presentation of the data, the abscissas of all figures illustrating time-dependent microbial or chemical changes (Figs 1, 2, 3, and 5) indicate the aging time including the salting period, although the first sampling was carried out after salting for days—before aging with rice bran The detection limit for viable count in this study was 102 cfu/g Nevertheless, in Figs 1, 2, and 3, data points sitting on the line at 102 cfu/g correspond to viable counts that were under the detection limit, for the sake of convenience In the absence of antibiotics and ethanol (sample A), the viable count for raw mackerel with aerobic cultivation (Fig 1a) was 103 cfu/g, and this increased to 104 cfu/g upon salting A further increase in viable counts to over 106 cfu/g was observed during the early stages of the aging process Similar trends were observed in those that underwent anaerobic cultivation, except that the viable count of the raw material was 104 cfu/g (Fig 1d) These results correspond well to those reported in our previous papers [5, 6] The addition of 0.5% ethanol (the solvent used for the antibiotics) to sample B barely affected the changes in viable counts during the processing of heshiko whether aerobic or anaerobic cultivation was used The combined use of the three antibiotics (sample C) during the whole process suppressed microbial growth, and the viable counts were less than 103 cfu/g throughout the aging process, regardless of the cultivation conditions (Fig 1b, e) When the antibiotics were employed only during the aging process with rice bran (sample D), around 104 cfu/g of microbes were detected during the early stage of the aging process, but the viable counts decreased thereafter and were less than 102 cfu/g after aging for months, irrespective of the cultivation conditions used The same trends were observed in samples E and F, in which only chloramphenicol was employed (Fig 1c, f) Essentially the same results were obtained when the cultivation was 123 738 Fish Sci (2012) 78:735–742 8 Log of viable count (cfu/g) 6 4 2 8 d 6 4 4 6 e c b a 8 f Aging time (months) Fig Changes in viable counts on fish flesh during the processing of heshiko models in the presence and absence of antibiotics using 2.5% NaCl-GYP agar plates as the cultivation medium Heshiko samples were prepared in the presence or absence of antibiotics, as summarized in Table Samples A (open circles) and B (open triangles) were controls (no antibiotics) with or without 0.5% ethanol (a, d) In samples C (filled circles) and D (filled triangles), 0.43% 8 a Log of viable count (cfu/g) Fig Changes in viable counts on fish flesh during the processing of heshiko models in the presence and absence of antibiotics using 10% NaClGYP agar plates as the cultivation medium The same symbols were used for the samples without the antibiotics (a, d), those with 0.43% chloramphenicol, 0.02% penicillin, and 0.01% cycloheximide (b, e), and those with only 0.43% chloramphenicol (c, f) as in Fig Cultivation was carried out under aerobic (a–c) or anaerobic (d–f) conditions chloramphenicol, 0.02% penicillin, and 0.01% cycloheximide (w/w) were added to the fish flesh during the whole process (sample C) or only during the aging process (sample D) (b, e) In samples E (filled squares) and F (filled diamonds), only 0.43% chloramphenicol (w/w) was added to the fish flesh during the whole process (sample E) or only during the aging process (sample F) (c, f) Cultivation was carried out under aerobic (a–c) or anaerobic (d–f) conditions b c 6 4 2 2 d 4 4 6 8 f e 2 8 0 2 Aging time (months) carried out using 10% NaCl-GYP agar plates (Fig 2) Although the data are not shown here, few microbes were detected in heshiko prepared with the antibiotics when 2.5% NaCl-broth peptone glucose (BPG: another selective cultivation medium for lactic acid bacteria) agar plates were used These results suggest that the growth of facultative anaerobic halophilic lactic acid bacteria, which are 123 the dominant type of bacteria during the processing of heshiko, was completely inhibited by the addition of the antibiotics To confirm the effects of the antibiotics on the growth of the other aerobes and fungi, viable counts on the fish flesh during processing were estimated using PCA and PDA media The results are presented in Fig The viable count Fish Sci (2012) 78:735–742 Log of viable count (cfu/g) Fig Changes in viable counts on fish flesh during the processing of heshiko models in the presence and absence of antibiotics using PCA and PDA plates as cultivation media The same symbols were used for the samples without the antibiotics (a, d), those with 0.43% chloramphenicol, 0.02% penicillin, and 0.01% cycloheximide (b, e), and those with only 0.43% chloramphenicol (c, f) as in Fig Cultivation was carried out using PCA (a–c) or PDA (d–f) medium 739 a b 6 4 2 8 d 6 4 4 6 f 2 8 e c 8 Aging time (months) barrels (data not shown), suggesting that the antibiotics were homogeneously distributed in the barrels On the other hand, no inhibition rings were observed for the rice bran from samples A and B Effect of the antibiotics on the production of organic acids The production of organic acids in samples A–F after aging for months was measured and the results are presented in Fig A considerable amount of organic acid—largely lactic acid—accumulated to similar levels in samples A and B The levels of lactic acid production in samples A and B were analogous to those reported in previous papers [1–6] The lactic acid contents in samples C–F prepared with the antibiotics were much lower than those in the Organic acids (g/100g) for sample A (control) in PCA medium increased at the start of the aging process but decreased thereafter with aging, and was eventually less than 102 cfu/g after aging for months, suggesting that the growth of aerobic and salt-intolerant bacteria was suppressed during the aging process (Fig 3a) No difference between the change in the viable count on sample B and that on sample A was observed, confirming that adding 0.5% ethanol barely affected the viable count in the PCA medium Both the combined use of the antibiotics and the sole use of chloramphenicol during the whole process entirely suppressed the increase in the viable count on the fish flesh from the salting process (Fig 3b, c) When the antibiotics were only employed during the aging process, viable counts of around 104 cfu/g were detected after salting, but these decreased when the antibiotics were applied in combination or individually Similar trends were observed in the changes in viable counts in PDA medium, except that small viable counts were noted during the middle of the aging period in samples E and F, from which cycloheximide was removed (Fig 3d–f) This was expected, because PDA is a selective medium for yeast and mold, which are inhibited by cycloheximide These results indicated that microbial growth, including not only lactic acid bacteria but also other aerobes and fungi, was inhibited throughout heshiko processing by the antibiotics at the concentrations we employed in this study In addition, to confirm that the antibiotics were distributed homogeneously within the barrels, rice bran was removed from the various random positions within the barrels of samples C–F and subjected to the inhibition ring method using Escherichia coli Inhibition rings formed around the rice bran removed from every barrel for samples C–F, irrespective of their original positions inside the A B C D E F Samples Fig Effects of antibiotics on the production of organic acids in the heshiko models Levels of lactic acid (dotted bars), acetic acid (hatched bars), and other organic acids (filled bars) in the fish flesh after aging for months were measured by capillary electrophoresis 123 740 Fish Sci (2012) 78:735–742 Effect of the antibiotics on the production of free amino acids and acid-soluble peptides The production of free amino acids as well as acid-soluble peptides in the fish flesh during the processing of heshiko in the presence or absence of the antibiotics was investigated The results are shown in Fig The total free amino acid content is the sum of the contents of 17 major amino acids: Tau, Asp, Thr, Ser, Glu, Gly, Ala, Val, Met, Ile, Leu, Tyr, Phe, Lys, His, Arg, and Pro As demonstrated in Fig 5a, although microbial growth was completely inhibited by the antibiotics in samples C–F, the total free amino acid contents gradually increased during the aging process, irrespective of the sample The total free amino acid levels in samples A–F aged for months were 2.29 g/100 g, 2.28 g/100 g, 2.03 g/100 g, 2.06 g/100 g, 2.22 g/100 g, and 1.67 g/100 g, respectively, and these values did not vary significantly when ethanol and the antibiotics were added (p \ 0.05) The trends observed in the production of free amino acids were similar to those noted in previous papers [2, 6] The complete inhibition of bacterial growth by the antibiotics also barely affected the levels of acidsoluble peptide (Fig 5b) These results confirmed that the free amino acids and acid-soluble peptides are produced regardless of the bacterial growth that occurs during the processing of heshiko Irrespective of the samples, Glu, Leu, Lys, and Arg were dominant among the free amino acids produced Thus, to Amino acids (g/100g) a 0 Aging time (months) Fig Production of free amino acids and acid-soluble peptides in fish flesh during the processing of heshiko models in the presence and absence of antibiotics The same symbols are used as in Fig The 123 elucidate the effects of the inhibition of bacterial growth by the antibiotics on the amino acid composition, the contents of these four major amino acids in the fish flesh in the samples aged with rice bran for months were compared (Fig 6) Although there were variations in the content of each amino acid among the samples, no significant difference in the contents of these amino acids in the samples with and without the antibiotics was detected (p \ 0.05), suggesting that the inhibition of bacterial growth by the antibiotics barely affected the total production and the composition of the free amino acids produced during the aging process Discussion It is generally accepted that the production of free amino acids due to proteolysis during the aging process greatly contributes to the characteristic flavor of fermented seafoods, including fish sauce [12] and shiokara [7] Itou and Akahane [1, 2] also revealed that the characteristic flavor of heshiko was enhanced by the accumulation of free amino acids as well as acid-soluble peptides in the fish flesh during the aging process Therefore, many researchers have attempted to elucidate the contributions of microbial and/or endogenous proteases to the production of free amino acids Fujii et al [7] investigated the effects of the inhibition of microbial growth by antibiotics on the processing of shiokara, and they suggested that the role of microbes in the production of free amino acids is insignificant Furthermore, Taira et al [12] suggested that proteases derived from the koji mold play an important role in the hydrolysis of protein during the early stages of fish sauce fermentation On the other hand, Yatsunami and Takenaka [13] reported that brine proteases derived from viscera are possibly involved in proteolysis during the processing of sardine fermented with rice bran (using a Acid soluble peptides (g/100g) samples prepared without the antibiotics (samples A and B), and were in accord with the average values (around g/100 g) found in raw mackerel by Fukuda et al [11] The suppression of lactic acid accumulation corresponded to the inhibition of microbial growth (including the growth of lactic acid bacteria) by the antibiotics, thus confirming that the lactic acid was a fermentation product produced by lactic acid bacteria b 0 Aging time (months) free amino acid content (a) is the sum of the 17 major free amino acids measured by the amino acid analyzer, and the acid-soluble peptide contents (b) were determined using Lowry’s method Fish Sci (2012) 78:735–742 741 Amino acids (g/100g) 0.3 0.2 0.1 0.0 Glu Leu Lys Arg Fig Comparison of the contents of the major free amino acids in the fish flesh of heshiko models prepared in the presence and absence of the antibiotics The contents of Glu, Leu, Lys, and Arg in samples A (open bars), B (dotted bars), C (filled bars), D (hatched bars), E (bars with horizontal lines), and F (bars with vertical lines) were determined by an amino acid analyzer similar procedure to that employed to obtain heshiko) However, it is still unclear whether these endogenous brine proteases contribute to the production of free amino acids Thus, little information is available on the contributions of microbial and endogenous proteases to the production of taste-active components (including free amino acids) in fermented seafood In our previous study [6] on the effects of aging temperature and salt content of heshiko on the production of these taste-active components, we demonstrated that the production of lactic acid increased with the growth of halophilic bacteria during the aging process of heshiko, but that the accumulation of free amino acids as well as acidsoluble peptides occurred independent of bacterial growth These results suggest that endogenous proteases rather than microbial ones contribute to the accumulation of free amino acids and acid-soluble peptides In other words, presumably, endogenous proteases sluggishly hydrolyze muscle proteins during the processing of heshiko and are involved in the accumulation of these taste-active components, thus contributing to the improvement in the taste of heshiko with aging In the present study, to confirm the contribution of endogenous proteases to the production of taste-active components, heshiko models were experimentally prepared in the presence of antibiotics Consequently, despite the complete inhibition of microbial growth by the antibiotics during the salting and aging processes involved in heshiko preparation, differences in the production of free amino acids as well as acid-soluble peptides between the fish flesh processed in the absence of the antibiotics and that processed in the presence of antibiotics were minor, confirming that the contribution of endogenous proteases is critical to the formation of the characteristic flavor of heshiko Strictly speaking, because the PDA medium used in this study was not supplemented with NaCl, it is not possible to rule out the involvement of halophilic fungi in the production of taste-active components Although this appears to unlikely considering the anaerobic conditions in the barrel, further investigation is needed into the role of halophilic fungi in the formation of heshiko, including its characteristic aroma The addition of antibiotics barely affected the production of free amino acids and acid-soluble peptides during the processing of heshiko, but it did considerably suppress the production of lactic acid, confirming that lactic acid bacteria are responsible for the production of lactic acid as well as the decrease in pH Except for the production of lactic acid, little difference was noted sensorily in other characteristics such as the appearance and the aroma, although a sensory evaluation of the flavor was impossible due to the addition of the antibiotics However, the involvement of microorganisms including halophilic yeast in the production of volatile compounds is still unclear, as mentioned above, and this issue should be investigated in connection to the characteristic aroma of heshiko In this study, we did not investigate the origin of the active endogenous proteases (fish flesh or rice bran) However, it is unlikely that proteases derived from rice bran would permeate into fish flesh and hydrolyze muscle protein considering the molecular sizes of such proteases Furthermore, as previously reported, the production of free amino acids and acid-soluble peptides was affected by the NaCl content in the fish flesh rather than the rice bran [6] In addition to these issues, we performed a preliminary check of the production of free amino acids in a heshiko model prepared using heat-treated rice bran, and observed little difference between the amino acid content of this model and that of the usual heshiko (data not shown) Accordingly, endogenous proteases in fish flesh appear to be critical to the production of free amino acids and acidsoluble peptides, rather than those in rice bran On the other hand, hydrolysis of the protein in rice bran might occur, and the resulting degradation products may play some role in the formation of heshiko Therefore, further investigation of the chemical changes that occur in rice bran during the aging process are needed Nevertheless, the finding that lactic acid bacteria and endogenous proteases contribute independently to the production of taste-active components, lactic acid, and free amino acids and acid-soluble peptides suggests that quality formation in heshiko is a complicated process, and this information should prove useful for quality control purposes as well as in attempts to modify the processing based on the principle used in the conventional processing of heshiko Acknowledgments The authors are grateful to Dr Masataka Satomi, National Research Institute of Fisheries Science, Fisheries Research Agency, for his introduction to microbial analysis and 123 742 valuable discussions They also express sincere gratitude to Dr Yoshiaki Akahane, professor emeritus of Fukui Prefectural University, for his continuous encouragement This study was financially supported, in part, by a grant from the Fukui Prefectural Fund for the Promotion of Science References Itou K, Akahane Y (1999) Comparison of proximate and water extractive components in raw mackerel with those in fermented mackerel food heshiko Nippon Suisan Gakkaishi 65:878–885 (in Japanese with English abstract) Itou K, Akahane Y (2000) Changes in proximate composition and extractive components of rice-bran-fermented mackerel heshiko during processing Nippon Suisan Gakkaishi 66:1051–1058 (in Japanese with English abstract) Itou K, Akahane Y (2004) Antihypertensive effect of heshiko, a fermented mackerel product, on spontaneously hypertensive rats Fish Sci 70:1121–1129 Itou K, Akahane Y (2009) Effect of extracts from heshiko, a fermented mackerel product, on cholesterol metabolism in Wistar rats Fish Sci 75:241–248 Kosaka Y, Satomi M, Furutani Y, Ooizumi T (2012) Microfloral and chemical changes during processing of heshiko produced by aging of salted mackerel with rice bran by means of conventional practice in Wakasa Bay area, Fukui, Japan Fish Sci (in press) 123 Fish Sci (2012) 78:735–742 Kosaka Y, Kinoshita Y, Ooizumi T, Akahane Y (2010) Effects of temperature and NaCl content on production of taste-active components in heshiko during the aging process of salted mackerel with rice bran Nippon Suisan Gakkaishi 76:392–398 (in Japanese with English abstract) Fujii T, Matsubara M, Itoh Y, Okuzumi M (1994) Microbial contribution on ripening of squid shiokara Nippon Suisan Gakkaishi 60:265–270 (in Japanese with English abstract) Kozaki M, Uchimura T, Okada S (1992) Laboratory manual for lactic acid bacteria Asakura-shotenn, Tokyo (in Japanese) Furutani A, Ooizumi T, Akahane Y (2007) Elution and internal migration of free amino acids in fish meats by soaking in sodium chloride or sorbitol solution Fish Sci 73:1373–1382 10 Lowry O, Rosebrough N, Farr A, Randall R (1951) Protein measurement with the folin phenol reagent J Biol Chem 193: 265–275 11 Fukuda Y, Tarakida Z, Arai K (1984) Effect of freshness of chub mackerel on the freeze-denaturation of myofibrillar protein Nippon Suisan Gakkaishi 50:845–852 (in Japanese with English abstract) 12 Taira W, Funatsu Y, Satomi M, Takano T, Abe H (2007) Changes in extractive components and microbial proliferation during fermentation of fish sauce from underutilized fish species and quality of final products Fish Sci 73:913–923 13 Yatsunami K, Takenaka T (2000) Characterization of brine proteases as agents of hydrolysis during the ripening of fermented sardine with rice-bran Fish Sci 66:569–573 Fish Sci (2012) 78:743–751 DOI 10.1007/s12562-012-0488-2 ORIGINAL ARTICLE Social Science Estimation of productivity growth, technical progress, and efficiency changes in the Korean offshore fisheries Do-Hoon Kim • Ju-Nam Seo • Hyung-Seok Kim Kyounghoon Lee • Received: 14 May 2011 / Accepted: 28 February 2012 / Published online: 25 March 2012 Ó The Japanese Society of Fisheries Science 2012 Abstract In this study, changes in total factor productivity of 12 Korean offshore fisheries between 1997 and 2009 were estimated through the Malmquist productivity index, which is a nonparametric method Also, the cause of such changes in productivity of each fishery was analyzed more specifically by segmenting into factors for technological progress and technical efficiency As a result of this analysis, the total factor productivity change of the entire offshore fisheries was -6.0 % Changes in the technical efficiency and technological level factors, respectively, contributed 0.2 and -6.2 % to this rate of decrease in total factor productivity; that is, inactivity of technological progress led to the decrease in productivity of the offshore fisheries In addition, technological progress and technical efficiency were found to differently influence the change in total factor productivity of each fishery In order for each fishery to improve productivity, better rational fisheries management policies by the government and efforts by the fishing industry and individual fishing business units must accompany factors that promote productivity increase Keywords Total factor productivity Á Technical efficiency Á Technological progress Á Korean offshore fisheries Á Malmquist productivity index D.-H Kim (&) Á J.-N Seo Technology Management Center, National Fisheries Research and Development Institute, Busan 619-702, Korea e-mail: delaware310@yahoo.com H.-S Kim Division of Marine Production System Management, Pukyong National University, Busan 608-737, Korea K Lee Division of Fisheries System Engineering, Fisheries Resources Department, National Fisheries Research and Development Institute, Busan 619-702, Korea Introduction While global demand for marine products is increasing, production of marine fisheries has been stagnating or decreasing since 2000 [1] Market competition for global marine products is also increasing with the trend of free trade for marine products [2] In order to ensure stability of marine fishing business and to be equipped with market competitiveness with such changes in international marine environment, it is most important to improve productivity by removing inefficiency of fisheries In addition, rational fisheries management policies by the government and efforts of the fishing industry must be conducted effectively in the right direction Improvement in productivity of fisheries can be promoted from the perspective of resource management which increases productivity per unit effort through recovery or management of fishery resources However, in general, improvement of productivity for fishing business is possible through improvement of technological progress, which refers to development of new production technologies, and technical efficiency, which utilizes known technologies efficiently [3–5] Therefore, it is possible to understand factors of productivity change by analyzing changes in fisheries productivity separately through technological progress and technical efficiency In addition, based on this, a rational policy direction for future competitiveness and development of fisheries can be settled Despite such policy importance of analyses on productivity of fisheries, there have not been many studies on this topic Though recent studies analyzed short-term technical efficiency for measurement of fishing capacity [6–9], it is difficult to find studies with parallel analyses on technological progress for long-term productivity changes 123 744 Fish Sci (2012) 78:743–751 The purpose of this study is to estimate total factor productivity from 1997 until 2009 of 12 Korean offshore fisheries using a nonparametric method called Malmquist productivity index and to analyze the cause of such changes in productivity in terms of technological progress and technical efficiency [10] Based on the results of analysis, it is also aimed to provide policy implications for improvement of future fisheries productivity Materials and methods Determinants of productivity change: technological progress and technical efficiency When ðX t ; Y t Þ and ðX tþ1 ; Y tþ1 Þ are the respective input– output relationships at times t and t ? 1, ðX t ; Y t Þ F t and ðX tþ1 ; Y tþ1 Þ F tþ1 Here, F refers to production technology that transfers the input (X) into the output (Y), and the frontier of F is a production function The input–output relationship changes from ðX t ; Y t Þ to ðX tþ1 ; Y tþ1 Þ with flow of time, and these can be defined as changes in productivity Productivity change can occur from changes in technological level with development of new technology or from changes in technical efficiency Technical efficiency means how efficiently a given production technology is utilized, and change in technological level refers to movement of the production frontier function itself with time Technical efficiency and technological progress can be measured by estimating the distance function, which can be defined as in Eq based on the output That is, the output distance function at time t refers to the ratio between the maximum possible output from the input at time t and the actual output This is the same concept as the reciprocal of technical efficiency proposed by Farrell [11] Dt ðX t ; Y t Þ ¼ inffh : ðX t ; Y t =hÞ F t g: ð1Þ Dt ðX t ; Y t Þ is always less than or equal to because t ðX ; Y t Þ F t Using the same method, the distance function of ðX t ; Y t Þ and ðX tþ1 ; Y tþ1 Þ at time t and t ? can be defined by Eqs and Dt ðX tþ1 ; Y tþ1 Þ ¼ inffh : ðX tþ1 ; Y tþ1 =hÞ F t g; ð2Þ Dtþ1 ðX t ; Y t Þ ¼ inffh : ðX t ; Y t =hÞ F tþ1 g: ð3Þ Here, Eq is a distance function that evaluates ðX tþ1 ; Y tþ1 Þ using the production function at time t, and Eq is a distance function that evaluates ðX t ; Y t Þ using the production function at time t ? According to the definition, Dt ðX t ; Y t Þ and Dtþ1 ðX tþ1 ; Y tþ1 Þ are less than or equal to However, with the result of technological 123 progress, Dt ðX tþ1 ; Y tþ1 Þ may take a value larger than if ðX tþ1 ; Y tþ1 Þ exists outside F t Malmquist index approach The Malmquist index approach is an analysis method that measures periodic changes in productivity using panel-type data This approach can explicitly consider technical inefficiency based on the production frontier It is also advantageous in that productivity change can be considered separately in terms of efficiency change (EFFCH) and technical change (TECHCH) That is, this method is based on the concept of frontier by Farrell [11] The method can more specifically measure productivity and examine the cause of productivity change by dividing the productivity index into changes in production frontier with time and changes in the efficiency of each producing body The Malmquist productivity index between two consecutive periods (t, t ? 1) can be defined by Eq using the ratio of the distance functions [10–12] If M, which represents the total factor productivity change (TFPCH) between two consecutive periods, is greater than 1, productivity has increased between the two periods Productivity does not change if M = 1, and is reduced when M is less than !1 Dt ðX tþ1 ; Y tþ1 Þ Dtþ1 ðX tþ1 ; Y tþ1 Þ Mðt; t þ 1Þ ¼ Á : ð4Þ Dt ðX t ; Y t Þ Dtþ1 ðX t ; Y t Þ Equation can be segmented into EFFCH and TECHCH as shown in Eq That is, Dtþ1 ðX tþ1 ; Y tþ1 Þ= Dt ðX t ; Y t Þ is the ratio of distance functions of the two periods (t, t ? 1) that reflects EFFCH Also, since ½ÁŠ1=2 is the geometric mean of the change in output between X t and X tþ1 , this refers to TECHCH between the two periods Mðt; t þ 1Þ ¼ Dtþ1 ðX tþ1 ; Y tþ1 Þ Dt ðX t ; Y t Þ !1 Dt ðX tþ1 ; Y tþ1 Þ Dt ðX t ; Y t Þ Â tþ1 tþ1 tþ1  tþ1 t t : D ðX ; Y Þ D ðX ; Y Þ ð5Þ Therefore, efficiency can be written as TFPCH(t, t ? 1) = EFFCH(t, t ? 1) TECHCH(t, t ? 1) Since the Malmquist productivity index based on the distance function can be divided into many factors that cause changes in productivity, it has the advantage of specifically examining factors that contribute to productivity increase through such analysis Estimation of Malmquist index by DEA method The Malmquist productivity index for two consecutive periods can be measured by estimating four distance functions Fish Sci (2012) 78:743–751 745 ½Dt ðX t ; Y t Þ; Dt ðX tþ1 ; Y tþ1 Þ; Dtþ1 ðX t ; Y t Þ; Dtþ1 ðX tþ1 ; Y tþ1 ފ The data envelopment analysis (DEA) method, which mainly uses linear programming, is most widely used for estimation of distance functions using nonparametric methods Specifically, DEA is a method for measuring efficiency That is, DEA is a nonparametric technique for evaluating the technical efficiencies of a collection of ‘‘decision-making units’’ (DMUs) which consume common inputs to generate common outputs [13] Let us assume that n DMUs input m production factors (x) into each unit to produce a single output (y) When the distance functions Dt ðX t ; Y t Þ and Dtþ1 ðX tþ1 ; Y tþ1 Þ of the ith DMU are assumed to have constant returns to scale (CRS), they can be found from the solution of the following linear programming problem: max hi subject to: n X k j À hi y i À s ¼ j¼1 n X kj xki þ ek ¼ xki ; k ¼ 1; ; m j¼1 kj ! 0; s ! 0; ek ! 0: ð6Þ ½Dt ðXitþ1 ; Yitþ1 ފÀ1 ¼ max hi n X subject to: k j À hi y i À s ¼ j¼1 kj xki þ ek ¼ xki ; k ¼ 1; ; m j¼1 kj ! 0; s ! 0; j ¼ 1; ; n ek ! 0: j¼1 n X tþ1 t ktþ1 j xki þ ek ¼ xki ; k ¼ 1; ; m j¼1 ktþ1 ! 0; j s ! 0; j ¼ 1; ; n ek ! 0: ð8Þ A production frontier that assumes variable returns to scale (VRS) includes data points more flexibly and tightly than a production frontier that assumes CRS Therefore, the distance function (TECRS ) under the CRS assumption is always less than or equal to the distance function (TEVRS ) with VRS The scale efficiency (SE) can be obtained as shown in Eq using the results of CRS and VRS If SE = 1, there is scale efficiency If SE \ 1, there is scale inefficiency Accordingly, the distance function (technical efficiency) for CRS is represented as the product of the scale efficiency and pure efficiency, which is the distance function (technical efficiency) for VRS SE ¼ TECRS =TEVRS : j ¼ 1; ; n Here, hi represents the possible proportional increase of output for the ith DMU and regulates the relationship yFi ¼ hi yi between the maximum possible output (yFi ) and the actual output (yi ) Therefore, technical efficiency (TEi ) or the distance function of the ith DMU, defined as the ratio (yi =yFi ) of actual to maximum possible output, is the same as the reciprocal of hi s is the slack of the output, ek is the slack of the kth input, and kj is the weight of the jth DMU When the solution to the above linear programming problem is hi ¼ 1, ki ¼ 1, and kj ¼ 0, it is positioned at the production frontier of the ith DMU and is efficient An inefficient DMU has hi [ 1, ki ¼ 0, and kj 6¼ On the other hand, Dt ðXitþ1 ; Yitþ1 Þ and Dtþ1 ðXit ; Yit Þ can be found from solutions of the linear programming problems expressed in Eqs and 8, respectively n X ½Dtþ1 ðXit ; Yit ފÀ1 ¼ max hi n X tþ1 subject to: ktþ1 À hi yti À s ¼ j yj ð7Þ ð9Þ Using this result, as shown by Eq 10, the technical t EFFCH ½EFFCHðt; t þ 1Þ ¼ TEtþ1 CRS =TECRS Š between two consecutive periods (t, t ? 1) can be divided into a pure efficiency change (PECH) ½PECHðt; t þ 1Þ ¼ TEtþ1 VRS = tþ1 t TEVRS Š and a scale EFFCH ½SECHðt; t þ 1Þ ¼ SE =SEt Š EFFCHðt; t þ 1Þ ¼ PECHðt; t þ 1Þ Â SECHðt; t þ 1Þ: ð10Þ In this study, solutions to the linear programming problems of Eqs 6–8 were found to measure the distance functions and Malmquist productivity index (TFPCH) of Eqs and This was divided into TECHCH and technical EFFCH Also, determinants of productivity of Korean offshore fisheries were analyzed in detail by breaking technical EFFCH into PECH and scale efficiency change (SECH) according to Eq 10 The Korean offshore fisheries The annual production of Korean offshore fisheries within the Korean exclusive economic zone (EEZ) has remained at 800000 tons since the middle of the 1990s, corresponding to 65 % of total production of marine capture fisheries as shown in Fig These offshore fishing types include the large purse seine fishery, large pair otter trawl fishery, Danish seine large trawl fishery, offshore trawl fishery, west southern Danish seine fishery, eastern sea Danish seine fishery, anchovy drag net fishery, offshore trap fishery, offshore stow net fishery, offshore angling 123 746 Fish Sci (2012) 78:743–751 1,800,000 Total Offshore fisheries 1,600,000 Landings(ton) 1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 1995 1997 1999 2001 2003 2005 2007 2009 Year Fig Landings of total marine capture fisheries and offshore fisheries in the period 1995–2009 fishery, offshore drift gill net fishery, offshore long line fishery, etc Among these, the production of the large purse seine fishery is highest at 220000 tons annually, corresponding to about 30 % of total production of offshore fisheries Looking at the production activities by offshore fishing type, the large purse seine fishery catches chub mackerel, jack mackerel, squid, hairtail, Spanish mackerel, etc In particular, among these species, chub mackerel accounts for over 70 % of total production [14] The large pair otter trawl fishery mainly catches Spanish mackerel, hairtail, anchovy, croaker, etc., and the Danish seine large trawl fishery catches flatfish, brown croaker, monkfish, etc The offshore trawl fishery targets hairtail, Spanish mackerel, etc., the anchovy tow net fishery mainly targets anchovy, and the offshore trap fishery catches common conger, rockfish, etc In addition, the offshore angling fishery catches mainly squid, hairtail, and pacific saury, and the offshore long line fishery targets hairtail, puffer, etc As such, the Koran offshore fisheries can be typically characterized as ‘‘multi-species and multi-fisheries.’’ The offshore fisheries have been traditionally managed by an input control such as limited permit system, and technical measures, including closed season and time, mesh size regulation, etc Furthermore, major offshore target species such as chub mackerel, squid, hairtail, jack mackerel, blue crab, and red crab have been controlled by a total allowable catch (TAC) program, as one type of output control, since 1999 Analytical data In this study, panel data between 1997 and 2009 for 12 Korean offshore fisheries were used Specifically, target offshore fisheries for analysis include large pair otter trawl fishery (A), Danish seine large trawl fishery (B), offshore 123 trawl fishery (C), west southern Danish seine fishery (D), eastern sea trawl fishery (E), large purse seine fishery (F), anchovy tow net fishery (G), offshore trap fishery (H), offshore stow net fishery (I), offshore angling fishery (J), offshore drift gill net fishery (K), and offshore long line fishery (L) For analyzing the total factor productivity of offshore fisheries, production quantity per vessel of each fishing type was selected as the output variable Tonnage, horsepower, number of fishers, and number of fishing days—i.e., the physical elements of production directly related to the fishing activities of offshore vessels—were chosen as the input variables In addition, since production quantity is directly affected by stock fluctuations, considerations on fish stock biomass are needed for more accurate analysis of total factor productivity of offshore fisheries However, estimation of stock biomass can be very difficult and/or almost impossible in reality Therefore, this study included, as the input variable, catch per unit effort (CPUE, total production/total vessels) by fishery as an index of stock biomass that can control production quantity [15] The reason why the number of vessels by fishery was used in the calculation of the CPUEs is that the number of vessels by fishery has been controlled by a limited permit system by the Korean government Therefore, we believed that a CPUE (total production/total vessels) by fishery would properly indicate the change of fish stock biomass The mean values of the variables used in the analysis are summarized in Table Results Distance functions (technical efficiency) Distance functions (technical efficiency) estimated for each offshore fishery using the DEA method are presented in Table The distance function assumes the most efficient fishery as and shows the relative efficiency between fisheries When the value is closer to 1, it means that the fishery is operating near the production frontier During the sample period (1997–2009) for Korean offshore fisheries, the mean technical efficiency was about 0.836, and the technical efficiency of the anchovy drag net fishery (G) was highest at 0.881 The efficiencies of the large purse seine fishery (F), offshore trap fishery (H), and offshore stow net fishery (I) were found to be higher than 0.85 The technical efficiency of the large pair otter trawl fishery (A), offshore trawl fishery (C), west southern Danish seine fishery (D), eastern sea trawl fishery (E), offshore angling fishery (J), and offshore long line fishery (L) was estimated to be over 0.80, but less than 0.85 In Fish Sci (2012) 78:743–751 747 Table Target offshore fisheries and mean values of input and output variables Fishery Production (tons) Tonnage (tons) Horsepower (HP) Fishers (persons) Fishing days CPUE (tons) A 1,673 244 2,041 26 277 581 B 193 80 417 241 223 C 1,474 139 1,568 16 218 1,462 D 165 49 501 238 257 E 332 63 620 207 708 F 7,217 823 7,545 74 278 972 G 913 205 2,243 48 210 225 H 186 62 554 11 215 112 I 261 77 520 220 152 J 160 70 500 10 160 103 K L 153 66 32 34 396 427 169 212 69 25 Table Annual distance functions by offshore fishery Year A B C D E F G H I J K L Average 1997 0.391 0.414 0.961 0.858 0.800 0.762 1.000 0.891 0.996 1.000 0.933 0.911 0.826 1998 1.000 0.568 0.666 0.533 0.580 0.485 0.448 0.483 0.599 0.583 0.700 0.666 0.609 1999 0.723 0.846 0.917 0.999 1.000 0.812 0.843 1.000 1.000 0.915 0.912 0.932 0.908 2000 0.984 1.000 1.000 0.314 0.192 0.624 0.597 0.704 0.752 0.688 0.478 0.575 0.659 2001 1.000 0.941 1.000 1.000 1.000 1.000 0.902 0.610 0.316 0.347 0.441 0.441 0.750 2002 0.551 0.534 0.663 0.920 1.000 1.000 0.993 1.000 0.996 1.000 0.960 0.957 0.881 2003 1.000 0.988 1.000 0.909 0.935 1.000 0.831 0.791 0.964 1.000 0.885 0.923 0.936 2004 1.000 1.000 1.000 0.998 0.866 1.000 0.883 1.000 0.938 1.000 0.956 0.975 0.968 2005 1.000 0.916 0.874 1.000 0.963 1.000 0.950 0.924 0.850 0.791 0.807 0.994 0.922 2006 0.675 0.633 0.987 1.000 0.973 1.000 1.000 1.000 1.000 0.806 0.782 0.695 0.879 2007 2008 0.815 0.363 0.844 0.321 0.886 0.372 1.000 0.359 1.000 1.000 0.888 1.000 1.000 1.000 0.913 0.943 0.896 0.955 0.786 1.000 1.000 1.000 1.000 1.000 0.919 0.776 2009 0.902 0.678 0.661 0.669 0.646 0.525 1.000 1.000 0.960 1.000 1.000 0.904 0.829 Average 0.800 0.745 0.845 0.812 0.843 0.854 0.881 0.866 0.863 0.840 0.835 0.844 0.836 particular, the technical efficiency of the Danish seine large trawl fishery (B) was lowest at 0.745 Malmquist productivity index Results of estimating Eq using distance functions are presented in Table In Table 3, TFPCH represents the change in Malmquist productivity index between two consecutive periods If this value is greater than 1, productivity improved Also, since mean values by period and fishery were calculated as geometric means, the annual average rate of change in total factor productivity for the corresponding period can be calculated by subtracting from the value shown in the table The total factor productivity of Korean offshore fisheries was estimated to decrease annually by 6.0 % In terms of fisheries, the offshore long line fishery (L) showed the highest increase at 4.8 % The annual mean productivity rates for the offshore trap fishery (H), offshore angling fishery (J), and offshore drift gill net fishery (K) were estimated to have increased In contrast, the annual mean productivity of fisheries that use dragged gears and bottom trawls such as large pair otter trawl fishery (A), Danish seine large trawl fishery (B), offshore trawl fishery (C), west southern Danish seine fishery (D), and eastern sea trawl fishery (E) were reduced Among them, the annual mean productivity of the offshore trawl fishery (C) showed the largest reduction of -16.6 % As mentioned above, such changes in total factor productivity of each fishery can be analyzed more specifically in terms of TECHCH and EFFCH First looking at TECHCH, the annual mean rate of technological progress during the sample period was -6.2 % In terms of fishery, the technological level of the offshore trap fishery (H), offshore angling fishery (J), offshore drift gill net fishery (K), and offshore long line fishery (L) improved However, 123 748 Fish Sci (2012) 78:743–751 Table Malmquist productivity index by offshore fishery (TFPCH) A B C D E F G H I J K L Average TFPCH 2.063 1.072 0.541 0.473 0.559 0.480 0.299 0.431 0.454 0.426 0.568 0.551 0.570 TECHCH 0.807 0.781 0.780 0.762 0.771 0.755 0.666 0.796 0.754 0.731 0.757 0.753 0.759 EFFCH 2.556 1.372 0.693 0.621 0.725 0.636 0.448 0.542 0.602 0.583 0.751 0.731 0.752 TFPCH 0.434 1.126 1.850 1.677 2.114 1.782 2.228 1.953 1.947 1.592 1.264 1.528 1.517 TECHCH 0.600 0.756 1.345 0.894 1.226 1.064 1.185 0.943 1.167 1.014 0.971 1.092 1.000 EFFCH 0.723 1.489 1.376 1.875 1.724 1.675 1.880 2.072 1.668 1.570 1.302 1.399 1.517 TFPCH 1.461 1.369 0.938 0.329 0.175 0.666 0.595 0.590 0.619 0.724 0.557 0.622 0.630 TECHCH EFFCH 1.073 1.361 1.158 1.182 0.860 1.091 1.048 0.314 0.912 0.192 0.866 0.769 0.841 0.708 0.838 0.704 0.824 0.752 0.961 0.753 1.063 0.524 1.009 0.617 0.948 0.665 TFPCH 0.332 0.310 0.371 2.153 3.114 0.953 0.901 0.527 0.232 0.277 0.485 0.402 0.580 TECHCH 0.327 0.330 0.371 0.676 0.599 0.595 0.596 0.608 0.551 0.550 0.525 0.525 0.508 EFFCH 1.016 0.941 1.000 3.185 5.200 1.603 1.511 0.867 0.421 0.504 0.923 0.767 1.143 TFPCH 1.413 1.403 1.657 2.577 4.970 7.516 9.823 16.378 23.787 22.281 17.124 16.645 6.665 TECHCH 2.563 2.476 2.498 2.801 4.970 7.516 8.915 9.992 7.551 7.731 7.869 7.671 5.326 EFFCH 0.551 0.567 0.663 0.920 1.000 1.000 1.102 1.639 3.150 2.882 2.176 2.170 1.251 TFPCH 1.961 1.941 1.500 0.878 0.829 1.209 0.874 0.849 1.079 1.121 0.984 1.027 1.135 TECHCH 1.081 1.048 0.995 0.888 0.887 1.209 1.044 1.072 1.115 1.121 1.067 1.065 1.046 EFFCH 1.814 1.852 1.507 0.988 0.935 1.000 0.837 0.791 0.967 1.000 0.923 0.964 1.085 TFPCH 0.156 0.170 0.165 0.168 0.128 0.130 0.255 0.301 0.243 0.275 0.302 0.293 0.205 TECHCH EFFCH 0.156 1.000 0.168 1.012 0.165 1.000 0.153 1.098 0.138 0.926 0.130 1.000 0.240 1.062 0.238 1.264 0.250 0.973 0.275 1.000 0.280 1.079 0.277 1.056 0.198 1.036 TFPCH 0.775 0.880 0.791 0.889 1.120 1.019 1.108 1.347 1.228 0.924 0.974 1.081 0.998 TECHCH 0.775 0.960 0.905 0.887 1.007 1.019 1.029 1.458 1.355 1.169 1.154 1.060 1.049 EFFCH 1.000 0.916 0.874 1.002 1.113 1.000 1.076 0.924 0.906 0.791 0.844 1.020 0.951 TFPCH 0.917 0.977 1.561 1.346 1.283 1.197 1.323 1.435 1.616 1.352 1.336 0.951 1.255 TECHCH 1.358 1.414 1.382 1.346 1.270 1.197 1.257 1.326 1.374 1.327 1.378 1.359 1.331 EFFCH 0.675 0.691 1.130 1.000 1.011 1.000 1.052 1.082 1.176 1.019 0.969 0.699 0.943 TFPCH 0.845 0.891 0.648 0.647 0.711 0.603 0.625 0.552 0.584 0.733 0.989 1.094 0.726 TECHCH 0.700 0.669 0.722 0.647 0.692 0.679 0.625 0.605 0.651 0.752 0.774 0.760 0.688 EFFCH 1.207 1.332 0.897 1.000 1.027 0.888 1.000 0.913 0.896 0.975 1.278 1.439 1.056 TFPCH TECHCH 0.789 1.775 0.721 1.892 0.764 1.817 0.674 1.878 1.713 1.713 2.127 1.890 1.622 1.622 1.689 1.634 1.749 1.641 2.213 1.739 2.441 2.441 1.935 1.935 1.393 1.821 EFFCH 0.445 0.381 0.421 0.359 1.000 1.126 1.000 1.033 1.066 1.273 1.000 1.000 0.765 TFPCH 1.828 1.555 1.304 1.266 0.454 0.343 0.707 0.676 0.572 0.542 0.452 0.768 0.761 TECHCH 0.735 0.737 0.734 0.680 0.702 0.653 0.707 0.637 0.569 0.542 0.452 0.850 0.658 EFFCH 2.488 2.109 1.776 1.863 0.646 0.525 1.000 1.060 1.005 1.000 1.000 0.904 1.156 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07 07/08 08/09 123 Fish Sci (2012) 78:743–751 749 Table continued A B C D E F G H I J K L Average TFPCH 0.860 0.876 0.834 0.850 0.897 0.915 0.984 1.024 0.993 1.002 1.029 1.048 0.940 TECHCH 0.802 0.841 0.861 0.867 0.913 0.944 0.984 1.014 0.996 1.002 1.023 1.049 0.938 EFFCH 1.072 1.042 0.969 0.979 0.982 0.970 1.000 1.010 0.997 1.000 1.006 0.999 1.002 Average it was shown that the technological level of the large pair otter trawl fishery (A), Danish seine large trawl fishery (B), offshore trawl fishery (C), west southern Danish seine fishery (D), eastern sea trawl fishery (E), large purse seine fishery (F), anchovy drag net fishery (G), and offshore stow net fishery (I) decreased Looking at EFFCH, the annual mean change rate for technical efficiency during the sample period showed an increase of 0.2 % In terms of fishery, the technical efficiency of the large pair otter trawl fishery (A) increased annually by 7.2 % The Danish seine large trawl fishery (B), offshore trap fishery (H), offshore drift gill net fishery (K), and offshore trawl fishery (C) showed increases of 4.2, 1.0, and 0.6 %, respectively, implying a slight improvement in technical efficiency during the period On the other hand, the annual mean change rates of the other fisheries were found to be below % (-3.1 to %), meaning that technical efficiency either stayed the same or deteriorated Efficiency change can be divided into PECH and SECH as shown in Eq 10 Results are presented in Table During the period of analysis, the annual mean for PECH was -1.0 % In terms of fishery, the offshore trap fishery (H) and offshore drift gill net fishery (K) showed slight improvements with annual mean increases of 0.5 and 0.3 %, while the offshore trawl fishery (C), west southern Danish seine fishery (D), eastern sea trawl fishery (E), and large purse seine fishery (F) showed decreases of -2.6, -2.9, -2.7, and -4.6 %, respectively However, the annual mean change rate for scale efficiency showed an increase of 1.2 % In terms of fishery, the scale efficiency increased annually by 7.2 % for the large pair otter trawl fishery (A), by 4.2 % for the Danish seine large trawl fishery (B), by 0.8 % for the west southern Danish seine fishery (D), by 1.0 % for the eastern sea trawl fishery (E), by 1.6 % for the large purse seine fishery (F), by 0.5 % for the offshore trap fishery (H), and by 0.3 % for the offshore drift gill net fishery (K) In contrast, the scale efficiency of the offshore trawl fishery (C), offshore stow net fishery (I), and offshore long line fishery (L) deteriorated annually by -0.5, -0.3, and -0.1 %, respectively Analyzing the degree to which technological progress and technical efficiency contributed to change in productivity of offshore fisheries, EFFCH and TECHCH contributed 0.2 and -6.2 %, respectively, to the annual mean decrease of 6.0 % in total factor productivity during the sample period Inactivity of technical progress took the leading role in decreasing the total factor productivity In particular, the total factor productivity of fisheries that use dragged gears and bottom pair trawls was relatively lower than that of other fisheries with negative annual mean growth Moreover, deterioration in total factor productivity of these fisheries mainly resulted from reduction in technological progress This is exactly opposite to the case of other fisheries, in which technological progress led to increase in total factor productivity Discussion Changes in total factor productivity of 12 Korean offshore fisheries during the period between 1997 and 2009 were estimated using a nonparametric analysis method called Malmquist productivity index The cause of such productivity change was examined in terms of technological progress and technical efficiency Summarizing the results, the annual mean change in total factor productivity of the entire offshore fisheries was -6.0 % Efficiency change and technical change, respectively, contributed 0.2 and -6.2 % to the annual mean decrease in total factor productivity, leading to the analysis that productivity decrease resulted mainly from inactivity of technological progress The estimated change in total factor productivity varied for each fishery; i.e., according to the levels of technological progress and technical efficiency, the change in fishery total factor productivity varied For instance, the annual mean changes in total factor productivity of the large pair otter trawl fishery (A), Danish seine large trawl fishery (B), offshore trawl fishery (C), west southern Danish seine fishery (D), and eastern sea trawl fishery (E) that use dragged gears and bottom pair trawls were negative Also, deterioration in total factor productivity of these fisheries mainly resulted from decline in technical progress In contrast, the annual mean total factor productivity of the offshore trap fishery (H), offshore angling fishery (J), offshore drift gill net fishery (K), and offshore long line 123 750 Fish Sci (2012) 78:743–751 Table Changes in technical efficiency by offshore fishery (EFFCH) A B C D E F G H I J K L Average EFFCH 2.556 1.372 0.693 0.621 0.725 0.636 0.448 0.542 0.602 0.583 0.751 0.731 0.752 PECH 1.000 1.000 1.000 1.000 1.019 1.049 1.000 1.060 0.859 0.954 1.038 0.956 0.993 SECH 2.556 1.372 0.693 0.621 0.711 0.606 0.448 0.511 0.701 0.611 0.724 0.765 0.757 EFFCH 0.723 1.489 1.376 1.875 1.724 1.675 1.880 2.072 1.668 1.570 1.302 1.399 1.517 PECH 1.000 1.000 0.955 1.000 1.051 0.812 0.843 1.000 1.169 1.029 0.995 1.046 0.988 SECH 0.723 1.489 1.440 1.875 1.640 2.063 2.231 2.072 1.427 1.525 1.308 1.337 1.537 EFFCH 1.361 1.182 1.091 0.314 0.192 0.769 0.708 0.704 0.752 0.753 0.524 0.617 0.665 PECH SECH 0.984 1.383 1.000 1.182 1.047 1.042 0.541 0.580 0.300 0.642 1.081 0.711 1.187 0.597 1.000 0.704 1.000 0.752 1.018 0.740 1.005 0.521 1.000 0.617 0.881 0.754 EFFCH 1.016 0.941 1.000 3.185 5.200 1.603 1.511 0.867 0.421 0.504 0.923 0.767 1.143 PECH 1.016 1.000 1.000 1.847 3.338 1.140 1.000 1.000 0.383 0.404 0.551 0.551 0.913 SECH 1.000 0.941 1.000 1.725 1.558 1.406 1.511 0.867 1.098 1.246 1.676 1.393 1.252 EFFCH 0.551 0.567 0.663 0.920 1.000 1.000 1.102 1.639 3.150 2.882 2.176 2.170 1.251 PECH 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.610 2.473 1.774 1.807 1.287 SECH 0.551 0.567 0.663 0.920 1.000 1.000 1.102 1.639 1.207 1.165 1.226 1.201 0.972 EFFCH 1.814 1.852 1.507 0.988 0.935 1.000 0.837 0.791 0.967 1.000 0.923 0.964 1.085 PECH 1.000 1.000 1.000 0.934 0.948 1.000 1.000 0.928 1.000 1.000 1.023 1.005 0.986 SECH 1.814 1.852 1.507 1.058 0.986 1.000 0.837 0.853 0.967 1.000 0.902 0.960 1.100 EFFCH 1.000 1.012 1.000 1.098 0.926 1.000 1.062 1.264 0.973 1.000 1.079 1.056 1.036 PECH SECH 1.000 1.000 1.000 1.012 1.000 1.000 1.070 1.026 0.987 0.939 1.000 1.000 1.000 1.062 1.077 1.173 1.000 0.973 1.000 1.000 1.000 1.079 1.000 1.056 1.011 1.025 EFFCH 1.000 0.916 0.874 1.002 1.113 1.000 1.076 0.924 0.906 0.791 0.844 1.020 0.951 PECH 1.000 0.927 0.880 1.000 1.069 1.000 0.950 0.958 1.000 1.000 1.000 1.000 0.981 SECH 1.000 0.989 0.993 1.002 1.041 1.000 1.133 0.965 0.906 0.791 0.844 1.020 0.970 EFFCH 0.675 0.691 1.130 1.000 1.011 1.000 1.052 1.082 1.176 1.019 0.969 0.699 0.943 PECH 1.000 0.717 1.137 1.000 1.000 1.000 1.052 1.044 1.000 1.000 1.000 1.000 0.991 SECH 0.675 0.964 0.994 1.000 1.011 1.000 1.000 1.036 1.176 1.019 0.969 0.699 0.951 EFFCH 1.207 1.332 0.897 1.000 1.027 0.888 1.000 0.913 0.896 0.975 1.278 1.439 1.056 PECH 0.815 1.277 0.890 1.000 1.000 0.958 1.000 0.952 1.000 0.838 1.000 1.000 0.972 SECH 1.481 1.044 1.007 1.000 1.027 0.928 1.000 0.958 0.896 1.165 1.278 1.439 1.087 EFFCH PECH 0.445 0.770 0.381 1.180 0.421 1.123 0.359 0.537 1.000 1.000 1.126 1.044 1.000 1.000 1.033 1.010 1.066 0.984 1.273 1.194 1.000 1.000 1.000 1.000 0.765 0.968 SECH 0.578 0.323 0.375 0.668 1.000 1.078 1.000 1.023 1.083 1.066 1.000 1.000 0.790 EFFCH 2.488 2.109 1.776 1.863 0.646 0.525 1.000 1.060 1.005 1.000 1.000 0.904 1.156 PECH 1.593 1.000 0.733 1.315 0.669 0.545 1.000 1.040 1.016 1.000 1.000 1.000 0.957 SECH 1.562 2.109 2.421 1.417 0.966 0.964 1.000 1.020 0.989 1.000 1.000 0.904 1.207 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07 07/08 08/09 123 Fish Sci (2012) 78:743–751 751 Table continued A B C D E F G H I J K L Average EFFCH 1.072 1.042 0.969 0.979 0.982 0.970 1.000 1.010 0.997 1.000 1.006 0.999 1.002 PECH 1.000 1.000 0.974 0.971 0.973 0.954 1.000 1.005 1.000 1.000 1.003 1.000 0.990 SECH 1.072 1.042 0.995 1.008 1.010 1.016 1.000 1.005 0.997 1.000 1.003 0.999 1.012 Average fishery (L) increased In addition, the increase in total factor productivity of these fisheries mainly resulted from both technological progress and increase in technical efficiency As such, the productivity change was found to be various by offshore fishing type Technological progress and technical efficiency were found to operate differently in changing the total factor productivity for each fishery Increase in productivity of fishing business units must be promoted for development of more competitive and stable offshore fisheries in the future To increase productivity, better rational management policies by the government and efforts by the fishing industry and individual business units must be considered according to factors that can promote productivity increase for each fishery In addition, for fisheries, unlike manufacturing industry, production is affected by the natural environment Therefore, stable management and protection of fishery resources that target fisheries production are required for productivity improvement If fishery resources are underdeveloped, the productivity of fisheries could be improved by technological development However, if fishery resources are fully exploited and/or overfished, improving fishery productivity would be desirable through fish stock rebuilding and management, rather than by technological development Management measures that are currently implemented can also affect the productivity of fisheries and fishing business units; For instance, technical inefficiency of invested capital factors can occur when an output control such as TAC is applied alone For economically viable development of fisheries through productivity improvement of fishing business units, more integrated management measures should be implemented; For example, various input controls such as a vessel buyback program could be effectively integrated with output controls Furthermore, market-based management approaches such as individual transferable quotas (ITQs) that enable fishing business units to improve productivity as much as possible could be considered as alternative management measures References FAO (2010) The State of World Fisheries and Aquaculture FAO, Rome OECD (2003) Liberalising fisheries markets: scope and effects OECD, France Kang S, Jo S (2007) Change of total factor 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Euphausiacea Others 2,9 77 7,6 36 1 5, 5 68 5, 3 60 4,0 07 3 5, 5 48 1,9 51 7,2 25 1 2,6 77 4 ,5 44 3,2 64 2 9,6 61 17 8 1,4 11 155 67 1,6 58 1,0 09 403 1,4 80 661 676 4,2 29 , No catch a Towing applied a stair-step method at 24 0, 22 0, 20 0, and 170 m Table 2 Measured body lengths of Diaphus theta and Euphausia pacifica Species Categorya Date and layer Total August 24 Layer 2 D theta E pacifica a Layer 1 August 25 August 26 August... Pre – 56 – Fig 2b 6 Sep 2008 18°N, 136°E 23.4 Lepto 120 53 147 6 Sep 2008 18°N, 136°E 18.0? Lepto 106 56 140? Tail damaged 6 Sep 2008 18°N, 136°E 18.6 Lepto 113 55 1 35? Tail damaged 6 Sep 2008 18°N, 136°E 27.3 Lepto 114 53 141 6 Sep 2008 18°N, 136°E 40.0 Lepto 121 55 1 45 6 Sep 2008 18°N, 136°E 21.6 Lepto 113 55 142 6 Sep 2008 18°N, 136°E 19.7 Lepto 110 55 144 6 Sep 2008 18°N, 136°E 27 .5 Lepto 114 55 141... 2008 18°N, 136°E 18°N, 136°E 26.8 35. 5 Lepto Lepto 119 121 54 51 142 143 6 Sep 2008 18°N, 136°E 31.4 Lepto 117 57 142 6 Sep 2008 18°N, 136°E – Lepto – – – 146 6 Sep 2008 18°N, 136°E 21.3 Lepto 112 53 7 Sep 2008 21°N, 136°E 23.6 Lepto 121 55 147 7 Sep 2008 21°N, 136°E 30.9 Lepto 1 15 54 143 7 Sep 2008 21°N, 136°E 23.1 Lepto 116 54 149 7 Sep 2008 21°N, 136°E 25. 0 Lepto 116 55 143 7 Sep 2008 21°N, 136°E... Environmental Studies, Tokyo University of Marine Science and Technology, 4 -5- 7 Konan, Minato-ku, Tokyo 108-847 7, Japan e-mail: mtsr@affrc.go.jp K Uchikawa Japan Sea National Fisheries Research Institute, Fisheries Research Agency, 1 -59 39-22 Suido-cho, Chuo-ku, Niigata, Niigata 951 -812 1, Japan K Sawada National Research Institute of Fisheries Engineering, Fisheries Research Agency, 7620-7 Hasaki, Kamisu, Ibaraki... Singapore, pp 52 –7 1, 79–12 3, 326–3 45 Qiu DH (19 85) Wave theory and its applying to engineering Higher Education Press, Beijing, pp 140–14 6, 274–290 Zu YR (1991) Wave mechanics of ocean engineering Tianjin University Press, Tianjin, pp 9–1 9, 63–92 Huang XL, Lu XS (1992) marine engineering hydrodynamics and structural dynamic response Shanghai Jiao Tong University Press, Shanghai, pp 54 5 5, 78–123 Linfoot BT,... Hakozaki, Fukuoka 812- 858 1, Japan M Okazaki Á M Takahashi Á D Ambe National Research Institute of Fisheries Science, Fisheries Research Agency, Yokohama, Kanagawa 236-864 8, Japan M J Miller Á K Tsukamoto Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba 277- 856 4, Japan Present Address: S Katayama Faculty of Agriculture, Tohoku University, Sendai, Miyagi 981- 855 5, Japan larvae... 1 Towed depth, time, and number of micronektonic animals sampled by the Matsuda–Oozeki–Hu trawler during the 4-day study period Date: August 24 Towed depth (m): Towed time (h:min): Myctophid fish (total) 190 14:16–14:36 August 25 110 15: 39– 15: 59 190 14:43– 15: 13 August 26 a 170–240 14: 15 14 :57 August 27 Total 200 14:23– 15: 00 – – 847 – 1 75 1,0 42 1,3 83 3,4 47 846 – 1 75 1,0 42 1,3 79 3,4 42 Stenobrachius leucopsarus... Station, National Research Institute of Fisheries Science, Fisheries Research Agency, Yokosuka, Kanagawa 238-031 6, Japan Present Address: H Kurogi (&) Á S Chow Coastal Fisheries and Aquaculture Division, National Research Institute of Aquaculture, Fisheries Research Agency, Yokosuka, Kanagawa 238-031 6, Japan e-mail: hkuro@affrc.go.jp N Mochioka Faculty of Agriculture, Kyushu University, Hakozaki, Fukuoka... Interamericana-McGraw Hill, Madrid, Spain, pp 4 05 4 25 19 Yamamoto A, Yamazaki F (1961) Rhythm of development in the oocyte of the gold fish, Carassius auratus Bull Fac Fish Hokkaido Univ 12:93–110 20 Kume G, Yamaguchi A, Aoki I, Taniuchi T (2000) Reproductive biology of the cardinalfish Apogon lineatus in Tokyo Bay, Japan Fish Sci 66:947– 954 21 Yoneda M, Futagawa K, Tokimura M, Horikawa Matsuura H, Matsuyama SM... 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