30 
 

 American Scientific Research Journal for Engineering, Technology,  and Sciences  (ASRJETS) 
ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 

© Global Society of Scientific Research and Researchers  

http://asrjetsjournal.org/  
 

The Analysis of Factors Influence Catfish Seed (Clarias 

Gariepenus) Production in Wonogiri District 

Moedjtahid Djoko Pramonoa, Minar Ferichanib, Endang Siti Rahayuc 

aPostgraduate Agribusines College Student, Sebelas Maret University, Surakarta, Indonesia 
b,cFaculty of Agriculture, Sebelas Maret University, Surakarta, Indonesia 

aEmail : mujtahidpramono@gmail.com 
bEmail: newminar_jomint@yahoo.co.id 

cEmail: buendang@yahoo.co.id 

 

 

 

Abstract 

This research aimed to: (1) analysis the cost and income of Catfish hatchery farm in Wonogiri District. (2) 

analysis the influence factors of Catfish hatchery production in Wonogiri District. (3) analysis the efficiency 

level of feed production, natural food, and labour. The basic method of the research is description analysis 

method and the conduct of the research is using a census method. This research had conducted in the district of 

Wonogiri which consists of 45 respondents, analyzed data included hatchery income, R/C ratio, elasticity, 

efficiency, and multiple linier regression test. The result of the research showed that the relation between factors 

and hatchery of African Catfish production showed in multiple linier regression model, they are : LnP = 7.472 - 

0.047LnX1 + 0.312LnX2 + 0.388LnX3 +  0.304LnX4  +  0.136LnX5  +  0.016LnX6  + 0.108LnD1  +  0.058LnD2.   

The result of the analysis showed the number of brood stock, feed, natural food, labour, hatchery technology and 

counseling which were significant influenced in Catfish hatchery production, meanwhile the land area and 

hatchery process were not really influences to the Catfish hatchery production. The result of the research had 

obtained the income of Catfish hatchery with the price of  Per rupiahs. 2.369.533,-, value of R/C ratio was 2,67, 

feed production value, natural food, and labour were >1 which showed inefficient, the elasticity value of all 

independent variable was elastic. 

Keywords: Income; efficiency production; the Factors of Hatchery Production.  

------------------------------------------------------------------------ 

* Corresponding author.  

http://asrjetsjournal.org/


American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

31 
 

1. Introduction 

African catfish (Clarias gariepenus) is a freshwater fish that has been grown commercially by the people of 

Indonesia. In addition, to maintain the species, cultivation activities need to be improved in order to meet market 

demand and the nutritional needs of the community, especially accompanied by high levels of domestic 

consumption of African catfish which make their business opportunities more open starting from hatchery 

operations, enlargement to the processing business. 

According to Susanto in [19], to support the successful cultivation of fish, one of the decisive factors is available 

for qualified good seed quality, quantity, or continuity. Seeds are available in large quantities but low quality 

will only burden the farmers enlargement because the result is not balanced with the quantity of feed that has 

been given. While the seeds have good quality, a limited number will not increase business production 

enlargement, because there will be a shortage of seeds that quite serious. 

The problems of African catfish hatchery yield in traditional way are the quality of African catfish hatchery 

production is not maximized and it is still lacking. 

This study aims to (1) determine the costs and farm income African catfish hatchery in Wonogiri district, (2) 

determine the factors that influence the production of African catfish seed in Wonogiri district, (3) determine the 

efficiency level of production feed, natural feed and labor factors. Hatchery is the initial activity in aquaculture. 

Without this seeding activity, other activities such as nursery and enlargement will not materialize. It is because 

the seeds used from nursery activities and grown from hatchery, an outline of hatchery activities include: 

maintenance of the parent, the parent is ready for spawn, spawning and larval treatment [8]. Land or pool 

maintenance to be provided by the fish farmer, in addition to soil the land, the water conditions must also be 

abundant. The location to be used must meet the technical requirements, such as the discharge of water is 

available and it is not contaminated by waste and easily obtained [20]. Techniques can be done naturally 

spawning and intensive, but farmers do more spawning in natural and semi-intensive. This is done to save the 

production costs [20]. Feed is a factor of production whose value can reach 60% of the cost production [10]. 

Therefore, the feed which has been used must be taken into the quality account and the amount of usage in order 

to achieve optimal efficiency for the growth of catfish seeds. The terms of good natural food is to have a high 

nutritional value, easy to obtain, easy to process, easy to digest, non-toxic and the prices are relatively cheap. 

Natural feed silk worm is most preferred by freshwater fish. Silk worm is very good for the growth of 

freshwater fish because of its high protein content. The nutritional content of silk worms is 54.72% protein, 

13.77% fat, 22.25% carbohydrate [5].  

When the application technology adjusted for stage activities, so the application of appropriate technology can 

be applied starting from the management of the parent, eggs, larvae and seeds [18]. Costs in farming activities 

are intended to generate high income for the farming activity. By removing the costs, the farmers expect the 

highest revenue through high production level. Suratiyah in [21] states, costs and revenues are influenced by 

two factors: internal - external and management factors. 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

32 
 

2. Material and Method 

The research was conducted in Wonogiri Regency, Central Java Province from March until July 2016. This 

research method is using a description method of analysis. The research technique used is the method of census 

data collection method when all elements of the population investigated one by one. 

In this study, a population that is taken by the researchers was all African catfish farmers in Wonogiri totaling 

45 people.  

Primary data used in this study consisted of: 

a. Fish farmers revenue and costs production of African catfish hatchery operations consist of the feed 

cost, natural feed costs, labor costs and other costs that are used in one cycles production of African 

catfish hatchery. 

b. Data socioeconomic factors in African catfish farmers consist of education, the respondent's age, 

education level, work’s experience and hatchery technology usage. 

2.1  Farming Systems Analysis 

The income level of catfish hatchery farming can be expressed in a mathematical equation as follows: 

TR  = Yi x Pi 

Pd  = TR - TC 

Information : 

TR = Total Revenue of African Catfish Farmers (Per rupiahs) 

Yi = Production of Catfish Seed (per head) 

Pi = Price of Catfish Seed (Per rupiahs/head) 

Pd = Revenue of African Catfish Farmers (Per rupiahs) 

TC = Total Cost (expense) (Per rupiahs) 

 2.2  Efficiency Analysis of Farming System (R/C Ratio) 

Analysis of R/C ratio can be used as descriptive farming efficiency. To determine the feasibility of African 

catfish hatchery, the mathematical formula used as follows: 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

33 
 

 

Information : 

TR = Total Revenue (receipts) African Catfish Farmers (Per rupiahs) 

TC = Total Cost (incurred) African Catfish Farmers (Per rupiahs) 

2.3   Factor Production Analysis 

The procedure of analysis in this study is using Ordinary Least Squares (OLS) estimation regression model for 

catfish hatchery production factors. Estimation is using Ordinary Least Square (OLS) method that has been done 

by testing each parameter by calculating the value of the t statistic and the F statistic. To perform multiple linear, 

the analysis used computer assistance with E views program. 

The model of the equation as follows: 

Ln P = lnβ0 + β1lnX1 +β2lnX2 + β3lnX3 + β4lnX4 +β5lnX5 + β6lnX6 + β7lnD1+ β8lnD2 +µ 

Where : 

β0 is a constant, 

β 1, β 2, β 3, β 4, β 5, and β6 is intercept / coefficient, 

P : Production Of Catfish Hatchery (per head) 

X1 : Land Area (m2) 

X2 : Brood stock (per set) 

X3 : Feed (kg) 

X4 : Natural Food (per can) 

X5 : Number of Labor (hours) 

X6 : Farmers Experience (years) 

D1 : Figures Technology (Dummy variables using Induced Breeding  

Technology = 1 Do not use = 0) 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

34 
 

D2 : Extension (Dummy variables obtain counseling = 1, not getting  

counseling = 0) 

µ : Error Term 

1) Regression Model Test 

a. The coefficient of determination (R2) 

b. Test F (F-test) 

c. Test t (t-test) 

2) Classical Assumption Test 

a. Normality Test 

b. Heteroscedasticity Test  

c. Multicolinearity Test 

d. Autocorrelation Test 

2.4   Efficiency Production Analysis 

To assess whether the use of production factors has achieved economic efficiency or not, the ratio between the 

value of marginal production and the price of each factor of production with the following formula are using the 

formula as follows: 

   NPMx1 

     Pxi 

Information : 

NPMxi = Marginal Product Value for production factors xi 

PXI = Price Production Factors xi 

In this research, the factors of production in African catfish hatcheries has been analyzed, they are feed, the 

natural food and labor production factors. So that the value of their production efficiency can be seen. 

2.5   Analysis of Elasticity Production 

Elasticity calculations production can be obtained by the formula: 

          MPPi 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

35 
 

Bi = ----------- 

           APPi 

By transforming the function of Cobb-Douglass then the regression coefficient (bi) become the elasticity of 

production. 

Where: 

bi <1, means the proportion of input-i addition is beyond proportion addition  

production. 

bi =1, means the proportion of input-i addition is equal same with proportion addition production. 

bi> 1, means the proportion of input-i addition will make proportion addition production more bigger 

3. Result 

3.1  Characteristics of Catfish Farmers 

Characteristics of fish farmers is an overview of the background and circumstances which related to African 

catfish hatchery operations in Wonogiri that are shown in Table 1  

Table 1: Characteristics of Catfish Farmers in Wonogiri District 

No Description Survey Results 

1 The number of African Catfish Farmers (people)  45 

2 Education of African catfish Farmers  

 a. Not completed primary school (person) 0 

 b. Junior high school (person) 1 

 c. Senior high school (person) 36 

 d. College (Person) 8 

3 The average age of African Catfish Farmers (years) 44,87 

4 Average number of family members (person) 3 

5 Average experience as catfish Farmers (years) 4,7 

6 Average land owned (M2) 108,47  

Source: Primary Data Analysis, 2016 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

36 
 

The number of respondents African catfish farmers as many as 45 people. Inadequate number of fish farmers in 

Wonogiri district due to the geography and limited water resources, it affects the level of productivity of African 

catfish hatchery operations. In addition, market ability and continuity production is not maximized so farmers 

relatively not continuous in producing African catfish seed. The average age of African catfish farmers in 

productive age is about average age of 44.87 years. Productive age population is the population classified by age 

15-64 years. According to Rasyaf  in [13], that the age between 20-55 years is an age that is still productive, 

while below 20 years is an age that has not been productive and can be categorized as school age while the age 

of 55 years productivity level has passed the optimum point and going downhill in line with age. 

3.2  Farming Systems analysis 

Cost of African catfish hatcheries farming in Wonogiri district includes the cost of feed, natural feed, labor, cost 

of electricity used and the cost of other expenses incurred by farmers African catfish farmers in the districts of 

Wonogiri 

Table 2: The cost of production and farmers' income of African catfish farmers 

Description Value 

- Average Total Cost of Production (Per rupiahs) 

- Average feed cost (Per rupiahs) 

- Averagenatural feed cost (Per rupiahs) 

- Average labor costs (Per rupiahs) 

- Average larva production (per head) 

Prices of larva (Per rupiahs) 

Average Total Revenue (Per rupiahs) 

Average Revenue (Per rupiahs) 

1.359.956 

698.888 

398.666 

105.777 

33.904 

110 

3.729.489 

2.369.533 

     Source: Primary Data Analysis, 2016 

 

The largest cost in African catfish hatchery business is the feed cost (both natural feed or from the 

manufacturer). The average feed costs incurred by farmers of African catfish is 698 888, - rupiahs for the cost of 

feed manufacturers, while natural feed (in the form of silk worms) is 398 666, -rupiahs. Afrianto and Liviawati 

in [2] the cost of feed used for intensive cultivation which may reach 60% of the total cost of production, 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

37 
 

therefore feeding in the number, frequency and composition must be precisely and efficiently to the growth and 

survival of fish awake [22]. 

The avarege income of African catfish farmers is. 3,729,489, - rupiahs per cycle. The average production cost is 

1,359,956, - rupiahs and the average of African catfish farmers income is  2,369,533, - rupiahs per period. 

3.3  Farming Efficiency Analysis 

Based on the research, to determine the magnitude of R / C ratio, it depends on the costs incurred and the results 

obtained. The following table is the result of the analysis of the costs used to determine the amount of 

efficiencies farming African catfish hatcheries. 

Table 3: Costs and Income of African catfish hatchery farm in Wonogiri district 

No Description Farming costs 

1 Average Cost of Production (Per rupiahs) 1.359.956 

2 The average seed production (per head) 33.904 

3 Prices of seeds per head (Per rupiahs) 110 

4 Total Revenue(Per rupiahs) 3.729.489 

Source: Primary Data Analysis, 2016 

The above table shows the value of R/C ratio of 2.74, it means any use of the input 1, - rupiahs with the costs 

ratio and revenues is 2.74 per unit and per period production. 

3.4  Regression Analysis Factors of African Catfish Hatchery Production 

In estimating the influence of factors production to the production of African catfish seed, this research used 

Ordinary Least Square (OLS) that has been done by testing each parameter by calculating the value of the t 

statistic and the F statistic. Regression test is done by the aid of analysis software Eviews 9.0 program. From the 

test results, it obtained the general equation of the model functions as follows: 

LnP = 7.472 - 0.047LnX1 + 0.312LnX2 + 0.388LnX3 +  0.304LnX4  +  0.136LnX5  +  0.016LnX6  + 0.108LnD1  

+  0.058LnD2   

3.5   The coefficient of determination (R2) 

The coefficient of determination (R2) essentially measures how far the ability of the model to explain variations 

in the dependent variable [9]. The coefficient of determination is between zero and one. R2 value is small, it 

means the ability of the independent variables in explaining the variation of the dependent variable is very 

limited. In this research, the value of R2 (adjusted R-square) of 0.978, which means 97.8 percent of the variation 

variable seed production can be explained by the variation of the independent variables included in the model, 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

38 
 

while the remaining 2.2% is explained by the variation of other variables not included in the model. 

Table 4: The Influence of Regression Analysis Factors in African Catfish Seed Production (Clarias gariepenus) 

in Wonogiri Regency. 

 

Sample: 1 45    

Included observations: 45   

     
     Variable Coefficient Std. Error t-Statistic Prob. 

     
     P (Production) 7.472995 0.261443 28.58368 0.0000*** 

LX1 (land area) -0.047232 0.030986 -1.524310 0.1362ns 

LX2 (broodstock) 0.312600 0.056922 5.491715 0.0000*** 

LX3 (Feed) 0.388333 0.079467 4.886747 0.0000*** 

LX4 (Natural Feed) 0.304969 0.089571 3.404767 0.0016** 

LX5 (Labour) 0.136062 0.052426 2.595335 0.0136** 

LX6 (Experience Farmers) 0.016431 0.036942 0.444794 0.6591ns 

D1(Dummy Tehnologi) 0.108746 0.032308 3.365897 0.0018** 

D2 (Dummy Counceling) 0.058580 0.026389 2.219910 0.0328** 

     
     R-squared 0.978462     Mean dependent var 10.34527 

Adjusted R-squared 0.973676     S.D. dependent var 0.408753 

S.E. of regression 0.066319     Akaike info criterion -2.411825 

Sum squared resid 0.158335     Schwarz criterion -2.050492 

Log likelihood 63.26605     Hannan-Quinn criter. -2.277123 

F-statistic 204.4342     Durbin-Watson stat 2.023207 

Prob(F-statistic) 0.000000    

     
 

 

 

 

 

 

    Source: Primary Data Analysis, 2016 

Information : ns =non significant 

        

       

 

 

 

 

3.6  Test F 

F-test analysis tool (Table 4) obtained calculated F value of 204.43 with a significance probability of 0.000, is 

greater than the value of F table 1.39 at α level of 0.05. It shows that all independent variables simultaneously 

significantly influence the dependent variable at α level of 0.05 or 95% confidence. 

3.7  t-Test 

The t-test used to test the independent variables individually to see whether the independent variables 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

39 
 

individually significant effect on the dependent variable. The t test used is two-sided test, if the t-count value 

that is greater or smaller than the value of the t-table (-1.68 or 1.68) used (in α 95%), then H0 accepted which 

means that the independent variable are not exhibited significantly different from zero at α 5%, meaning that the 

independent variables did not significantly affect the dependent variable on α 95%. Conversely, if the t-count 

acquired smaller or larger than t-table on α 5% (-1.68 or 1.68), then H0 rejected, which shows that independent 

variables were significantly different from zero at α 5% means that the independent variable influence 

significant on the dependent variable on α 5%. 

T test results of this research variable land area (X1) and experience farmers (X6) did not significantly affect the 

results of African catfish fish seed production as indicated by the probability value of 0.1362 and 0.6591, while 

for a number of parents (X2) , the amount of feed (X3), natural feed (X4), labor (X5), the use of technology 

(D1) and extension (D2) showed highly significant probability values, respectively X2 = 0.0000, X3 = 0.0000, 

X4 = 0.0016, 0.0136 X5, D1 = D2 = 0.0018 and 0.0328. 

3.8  Classical Assumption Test 

1. Normality Test 

Normality test is done in testing by using E views application, (see the residual of the equation with the Jarque-

Bera), it is said to have a normal distribution when the value of the Jarque-Bera significantly above 5% and it is 

not normally distributed if the value of the Jarque-Bera significantly below 5%. 

0

2

4

6

8

10

12

-0.15 -0.10 -0.05 0.00 0.05 0.10

Series: Residuals
Sample 1 45
Observations 45

Mean       3.77e-15
Median   0.012301
Maximum  0.110526
Minimum -0.139754
Std. Dev.   0.059988
Skewness  -0.303400
Kurtosis   2.471777

Jarque-Bera  1.213547
Probability  0.545107

 

Figure 1: Graph Normality Test Results 

Based on Figure 1 shows that the value of JB's equation models were observed to have significant value 1.213 

above 0,05, thus, it can be concluded that the model equations were observed to have normal distribution. 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

40 
 

2. Multicoloniarity 

Multicoloniarity is a situation where one or more independent variables can be expressed as a linear 

combination of other free variable. As a result of this kind of relationship can be perfect or imperfect, i.e. by 

correlating between the explanatory variables, when the correlation is large then it showed signs of 

multicoloniarity. 

To determine whether there is multicoloniarity or not, then this research used the method which proposed by 

Klein L.R. Klein. This method compares r2 Xi, Xj (correlation between each independent variable) with a value 

R2y Xi, Xj, ...., Xn (coefficient) of the results of OLS regression models.  

The provisions used to determine the presence or absence of multicoloniarity is; if R2y Xi, Xj, ... .., Xn> r2 Xi, 

Xj then there is no problem multicoloniarity. If all the coefficient of determination from a simple regression 

model between the independent variable values is smaller than the coefficient of determination of the OLS 

model, then it means there is no problem with multicollinearity in the use of estimation OLS model. The test 

results multicollinearity can be seen below: 

Table 5: Klein Test Results 

 Nilai R-square Keterangan 

R2y 0.978462  

r2x1 0.825918 R2y > r2x1 

r2x2 0.818911 R2y > r2x2 

r2x3 0.859422 R2y > r2x3 

r2x4 0.812302 R2y > r2x4 

r2x5 0.832219 R2y > r2x5 

r2x6 0.693682 R2y > r2x6 

r2D1 0.620775 R2y > r2D1 

r2D2 0.389807 R2y > r2D2 

  Source: Primary Data Analysis, 2016 

 

3. Heterokedasticity Test (White Test) 

A good regression model is not going on heteroskedasticity. To determine whether there is heteroskedasticity 

situation or not, it can be seen in the value of the coefficient α2 above.  

If the value of the coefficient α2 is not significant (t <t-table), then H0 Ha accepted or rejected in other words 

indicating the absence heteroskedasticity, otherwise if H0 or Ha accepted, it showed heteroskedasticity. 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

41 
 

Table 6: Heteroskedasticity Method Test Results: White Test 

Heteroskedasticity Test: White  

     F-statistic 2.845834     Prob. F(42,2) 0.2942 

Obs*R-squared 44.25941     Prob. Chi-Square(42) 0.3765 

Scaled explained SS 20.84479     Prob. Chi-Square(42) 0.9974 

Test Equation:    

Dependent Variable: RESID^2   

Method: Least Squares   

Date: 09/24/16   Time: 14:06   

Sample: 1 45    

Included observations: 45   

Collinear test regressors dropped from specification 

     
Variable Coefficient Std. Error t-Statistic Prob.   

          P (Production) -2.108785 0.722630 -2.918210 0.1001 

LX1(Land area) -0.617268 0.138238 -4.465260 0.0467 

LX2 (Parents) 0.551361 0.407896 1.351719 0.3090 

LX3 (Feed) 1.900035 0.411682 4.615303 0.0439 

LX4 (Natural Feed) 0.361192 0.347608 1.039078 0.4079 

LX5 (Labor) -0.915684 0.388350 -2.357885 0.1424 

LX6 ( Experience Farmers ) 0.452132 0.161361 2.801989 0.1073 

D1^2 (Dummy Tehnologi) -0.172269 0.169349 -1.017246 0.4161 

D2^2 (Dummy Councelling) -0.157284 0.078146 -2.012707 0.1818 

     
R-squared 0.983542     Mean dependent var 0.003519 

Adjusted R-squared 0.637935     S.D. dependent var 0.004317 

S.E. of regression 0.002598     Akaike info criterion -10.27092 

Sum squared resid 1.35E-05     Schwarz criterion -8.544552 

Log likelihood 274.0957     Hannan-Quinn criter. -9.627346 

F-statistic 2.845834     Durbin-Watson stat 2.548327 

Prob(F-statistic) 0.294242    

     
     

Source: Primary Data Analysis, 2016 

 

According to the Table 6, it shows that the value prob. (Chi-square) to the model equations was observed to 

have significant value above 0:05 (0.3765) thus can be concluded that the model equations were observed to 

walk away from heterokedasticity. 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

42 
 

0 dl 

1.13 

du 

1.95 

4-du 

2.04 

 

4-dl 

2.86 

 

4 

Autocorrelati
on is positive 

hesitate 

There has no 
autocorrelation  

Hesitate 
Autocorrelati
on is negative 

4. Autocorrelation Test 

Autocorrelation is the correlation between the variables error with another error variables. Autocorrelation often 

occurs in time series data and it can occur in cross section, but rarely. For the impact of their autocorrelation in 

the regression model is same as the impact of heteroskedasticity described above, the OLS estimators although 

still linear and unbiased, but it no longer has the minimum variance and standard error calculation methods that 

leads OLS truth cannot be trusted. Autocorrelation test results with E views device obtained the following 

results: 

 

  

  

 

 

 

Figure 2: The test results obtained value of D-W test amounted to 2.023207 in the area du <d <4 - du, the 

empirical models are not affected by autocorrelation. 

5. Discusion 

The use of factors of production in African catfish hatcheries farming in Wonogiri greatly affects the African 

catfish seed production. As for the use of each production factors has very important role in increasing 

production. In the aquaculture business, the goal is to maximize profits. Gains in farming are closely related to 

production inputs and production factors. Factors of production known as inputs, production factors and costs 

production. Soekartawi in [17] showed that the factors of production as land, capital, feed, labor and 

management aspects are the most important factors of production among other factors. 

5.1  Effect of Land against Fish Seed Production 

The land area or pond is one of the critical success factors of the production of African catfish hatcheries in 

Wonogiri, there are several types of land owned by African catfish farmers, there are earthen, concrete or plastic 

taper rupiahs, and the extent of each land or the pool depends on the size of the land that used by each African 

catfish farmers. The smallest land area African catfish fish hatchery in Wonogiri district is 24 m2, while the 

largest land is 700 M2. In addition, to differ extents types of ponds used is also different, some a pool taper 

rupiahs or  some concrete pool. Surely each catfish farmers deem themselves against this type of pond that used 

for African catfish hatcheries. Most pools are used by African catfish farmers in the districts of Wonogiri is a 

pool taper rupiahs. 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

43 
 

Each type has advantages and disadvantages pools that very depending on the type of use. In this research, after 

using regression analysis of land (X1, it does not significantly affect African catfish seed production, the results 

of regression analysis showed a probability value of 0.1362. 

Non Significant of land area factor is influenced by several factors including the use of more intensive farming 

systems, appropriate parent management and technical management of production which has been controlled by 

catfish farmers. Helfrich and Libey in [7] the use of the system hatchery closed with a recirculation system that 

has several advantages, namely: water use per unit of time that is relatively low, in the flexibility cultivation 

location, cultivation system that is controlled and more hygienic, it needs for space / land which is relatively 

small, easily control, maintain, and for maintaining the temperature and water quality. 

The use of mating injection system (induced breeding) is very helpful in increasing the fertility of fish eggs on 

African catfish. Parent fish were injected with pituitary hormones, LH-RH hormone injections can increase or 

enhance the gonadotropin hormone concentrations in the blood so as to induce the development of eggs in 

spawning. This proves that the intra muscular injection ova prim on African catfish parent in that mature gonads 

can stimulate ovulation. Some African catfish farmers in Wonogiri district have implemented intensive 

management in order to produce high quality seed. So that the effect of land area may be omitted if the fish 

farmers have mastered the technical management to produce seeds in quantity and quality maintained.   

5.2 The number of Broodstock 

Each African catfish fish farmers in Wonogiri district has a number of different aircraft, resulting in an activity 

seeding aircraft used is also different. The regression analysis using mains (X2) indicates that the use of the 

mother affects the African catfish fish seed production with a probability value of  0.0000 and coefisien value of 

0.3126. This means if there are additional parent as much as 1% will boost seed production as much as 

0.3126%. Besides the quality factor of the parent and the season also affects the increase in seed production. 

One of the major limiting factor in the development of catfish on a mass scale is the parent spawning frequency 

is low, the quality and quantity of the parent mature gonads are limited so that the seeds produced are not 

sustainable. Main frequency of low spawning catfish, generally spawn in the rainy season so that the outside of 

the spawning season (dry season), this fish is difficult spawn, (Adebayo and Fagbenro, 2004). In addition to the 

number of parent breeders should be noted, Bramasta in [4] that the parent catfish mature gonads have a bulging 

stomach conditions, they will feel soft when they touched, the parent will look slow movement, body color a bit 

darker (females). Ideal posture between the length and weight, agile movement, and the body tends to bright 

colors or bright. Treatment of broodstock catfish also affect the quality of seeds produced. 

5.3  Feed 

Feed is a factor of production whose value reached 60% of total production costs [10]. Therefore, the use of feed 

on larval rearing must be considered in the quantity and quality feed. Regression analysis feed use (X3) shows 

very influential on African catfish seed production, with a probability value of 0.000 with a coefficient of 

0.3883, this means each additional 1% independent variable feed will increase African catfish seed production 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

44 
 

as much as 0 , 3883%. Feed also determine the costs to be incurred by the African catfish farmers, feed 

efficiency also showed a value above 1, it means approximately 2,070 use of feed is inefficient. Anggraini and 

Abdulgani in [3] growth is closely related to the availability of fish protein in the diet, because protein is a 

source of energy for fish and protein is a nutrient that is needed for growing fish. The level of protein in the diet 

is influenced by the content of non-protein energy is derived from carbohydrates and fats. Feed here is food 

made from a mixture of natural materials and processed materials that are subsequently be processed and 

prepared in a particular form so to create the appeal (stimulate) the fish to eat with ease and gusto. Feed pellets 

used flour containing 40% protein, 5% fat, and 30% carbohydrate. 

5.4  Feed Natural 

The use of natural feed silk worms in this case is one of the factors of production which are very important 

because natural food is the beginning of the larvae after egg yolk content of larvae have been exhausted. Usually 

the use of natural feed (silk worm) is given after the larvae 4-7 days old. The regression analysis showed the use 

of natural feed (X4) greatly affects the production of African catfish seed with a probability value of 0.0016 and 

the regression coefficient value of 0.3049. This means if there is additional 1% of natural feed will increase seed 

production of catfish as much as 0.3049%. Natural feed silk worms are most preferred by freshwater fish. Silk 

worms are very good for the growth of freshwater fish because of its high protein content. The nutritional 

content of silk worms is 54.725% protein, fat 13.770%, and 22.250% carbohydrate [5]. 

5.5  Labor 

The use of the labor factor in African catfish hatchery operations is not as much as in other farming, use of labor 

is only a certain time so that the costs used are also not great. The regression results labor (X5) indicates very 

influential on African catfish seed production, with a probability value coefficient of 0.0136 and 0.1360. This 

means each additional 1% of the workforce will increase 0.1360% African catfish seed production. The labor 

need in African catfish hatchery operations in Wonogiri district heavily influenced by the extensive effort 

catfish farmer. Labor requirements need for fish hatchery operations is much needed at the time of sorting seeds 

at harvest catfish because it requires a rapid handling the needs of the labor is needed. 

5.6  Farmers’ Experience 

Each farmers have a different experience, the average experience of African catfish farmers in Wonogiri district 

for 4.7 years, statistically experience farmer (X6) has no positive effect on the production of African catfish seed 

with a probability value of 0, 6591. The development of information technology makes fish farmers African 

catfish in the district of Wonogiri getting smarter in absorbing knowledge hatchery fish, especially with the 

internet, so that farmers can absorb available information directly, other than that their catfish fellow farmers 

communication make farmers more intelligent in exchanging experiences with fellow farmers horizontally. 

Munir in [11] describes these developments as a revolution that is taking the place in three waves, namely, the 

first wave with the advent of technology in agriculture, the second wave with the emergence of the technology 

industry and the third wave with the advent of information technology that encourage communication. 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

45 
 

5.7  Technology Usage 

Parameters the use of technology (D1) turned out to be statistically significant variable affecting the African 

catfish larva production in Wonogiri with a probability value of 0.0018 and the coefficient value of 0.1087. This 

means the input technology (induced breeding) by African catfish farmers are very influential on larva 

production. In this study of 45 African catfish fish farmers 20 people are already utilizing injection technology 

to stimulate spawning broodstock African catfish. This is in accordance with the opinion of Woynarofic and 

Horvard in [23] African catfish fish farming activities, availability of seed in sufficient quality and quantity is an 

essential factor that determines the success of a business. To obtain good quality seed in sufficient quantities and 

sustainable, through the hatchery must be controlled by performing the artificial spawning (induced breeding), 

followed by artificial insemination (artificial fertilization). Spawning fish can be accelerated by manipulating 

existing conditions, for example by providing stimulation of the pituitary gland or hormone can use ovaprim 

which is injected in the body. 

5.8  Counseling 

Counseling is an activity undertaken by government agencies and private (non-extension) which aims to 

improve the quality and quantity of fish farmers to produce fish seeds of African catfish in Wonogiri. 

Counseling is done routinely and periodically, but with the number and spacing varying not all fish farmers 

African catfish get counseling. From the regression results are statistically counseling (D2) that affect the 

production of African catfish seed in Wonogiri with a probability value of 0.0328 and a coefficient of 0.0585. 

The extension factors is very real impact on the increased production of fish fingerlings of African catfish. 

Besides almost half over African catfish farmers in Wonogiri district are already getting counseling from 

relevant agencies especially the Animal Husbandry, Fisheries and Marine Department in Wonogiri. The 

principles of counseling and related concepts are generally applicable in the fisheries extension. However, 

differences in how farmers and fishermen to manage natural resources, demanding difference extension 

approaches for both the community groups [11]. Participatory implications approach in extension activities 

which based on the specific conditions of the region. Benchmark for the success of fisheries extension include 

capacity building of individuals/groups /communities managing fishing activities in the utilization of fishery 

resources; entrepreneurial skills, household income increases; social structure and social capital gained; and 

management and it is used appropriately and responsibly. 

5.9  Elasticity Production 

Elasticity of production is the degree sensitivity production which is mirrored by their percentage of the product 

for additional input additional one percent. From the results of the regression analysis, the use of factors 

production in African catfish hatcheries in Wonogiri shown multiple linear regression model with a logarithmic 

function as follows: 

Ln P = 7.472 - 0.047LnX1 + 0.312LnX2 + 0.388LnX3 +  0.304LnX4  +  0.136LnX5+  0.016LnX6  + 0.108LnD1  

+  0.058LnD2   



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

46 
 

Analysis of Cobb-Douglass production function used to determine the effect of factors production usage, 

through the process of transformation into a natural logarithm in a multiple linear regression equation. 

In the calculation of the production function, this study has defined as the dependent variable seed productions 

while the factors of production as independent variables. Efficient use of production factors is approached with 

the elasticity indicated by the exponent (rank) of a factor of production in the Cobb-Douglass. All factors of 

production in catfish farmers (food, natural food, hours of work, farmer experience, technology of cultivation 

and extension) is positive except for the input area of land that is negative, this means production factors is 

elastic. Factors of production land area is negative (-0.0472) not elastic. 

The elasticity of each factor of production as follows: The land area (-0.0472), breeders (0,312) feed (0,388), 

natural feed (0.304), working hours (0.136), farmers experience (0,016), technology (0.108) and extension 

(.058). Therefore, it can further state that the other factors of production equipment (ceteris paribus) are: 

1. If the average feed increased 1% (0.411), then the seed production increased by 0,388% (131.55). 

2. If the average natural food increased 1% (0.45) then the seed production increased by 0.304% 

(103.068). 

3. If the average hours of work plus 1% (0.1036) then the seed production increased by 0.136% (46.10). 

5.10  Efficiency Production Analysis 

Conditions of price efficiency is achieved when the marginal product value equal to the purchase price Xi Xi. 

Analysis of efficiency production economically performed using the indicator ratio value of marginal product 

(NPM) with respective prices of factors of production equally. If the ratio is not as great value (equal to one), 

the farming takes place is not efficient. Measurement of the efficiency of production by using the production 

function is a measure which produces an absolute value (absolute) either greater than or less than the worth of 

production efficiency is not economical. From the results of the regression analysis Cobb-Douglas and Marginal 

Product Value calculation can be obtained from the data in Table as follows: 

Table 7: Marginal Product Value Ratio (NPM) with Factor Prices (HFP) on African catfish Hatchery in 

Wonogiri 

Production Factors HFP NPM NPM/HFP Information 

Feed 698.889 1.447.023 2,070 yet efficient 

Natural Feed 408.867 1.133.750 2,772 yet efficient 

Labor 101.556 507.204 4,992 yet efficient 

Source: Primary Data Analysis, 2016 

The results of calculation of price efficiency (allocative) in Table 5 all factors of production (feed, natural feed, 

labor) indicate inefficient greater than 1 means the use of the three factors above is less then it needs to be added 

so that the product can be optimized. Based on the results of the condition assessment of the level of production 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

47 
 

efficiency yet achieved the above it can be concluded that the circumstances of the production efficiency of 

African catfish hatchery operations in Wonogiri district is not reached the maximum profit. This is similar to the 

research conducted in [6] that the level of efficiency  production costs (efficiency prices) on the business of 

enlarging catfish has not reached the maximum, it is indicated on the ratio of the value of the marginal product 

of each factor of production (NPMXi) with the value of each input prices (PXI) nothing is equal to one. 

6. Conclusion 

Based on the research, the factors that influence African catfish seed productions  in Wonogiri and after doing 

the analysis, it can be concluded as follows: 

a. Revenue of African catfish farmers in Wonogiri district in the research was an average of 2,369,533, - 

rupiahs per cycle. 

b. R / C ratio of 2.67 was obtained, this means African catfish hatcheries feasible to be developed due to 

the value of R / C ratio greater than 1. 

c. Almost all independent variables is elastic, except for the variable area of land is inelastic. It is shown 

that positive regression coefficients were negative except the land. 

d. Production efficiency variable feed, natural feed and labor has a value above 1 each variable feed = 

2.07, 2.77 variable natural feed and variable workforce of 4.999 indicates that the variable is not 

efficient so it needs to be added in order to optimize production. 

e. Regression analysis showed that variable land area (X1) and farmers’ experience (X6) did not 

significantly affect seed production of catfish while the other variable number of sires (X2), feed (X3), 

natural feed (X4), labor (X5) , hatchery technology (D1) and extension (D2) significantly affect the 

African catfish seed production with a probability value of less than 0.05 at α 5%. 

References 

[1] Adebayo, OT & Fagbenro, OA,. Induced ovulation and spawning of pond raised African giant catfish, 

Heterobranchus bidorsalis by exogenous hormones. Aquaculture. 242. 2004. 

[2] Afrianto, E dan E Liviawati, Pakan Ikan, Penerbit Kanisius, Yogyakarta. 2005. 

[3] Anggraini, N.M dan N. Abdulgani,. Pengaruh Pemberian Pakan Buatan dan Pakan Alami Terhadap 

Pertumbuhan Ikan Betutu pada Skala Laboratorium. Jurnal Sains dan Seni Pomits Vol: 2 (1) : 197-201. 

2013. 

[4] Bramasta, Tehnik Pemijahan Ikan Lele Sangkuriang.http://hobiikan.blogspot.com/2009/01/teknik-

pemijahan-lele-sangkuriang.html.(diakses 20 maret 2016) 1: 1 kolom. 2009. 

[5] Buwono, I. D.. Kebutuhan Asam Amino Esensial dalam Ransum Ikan. Penerbit Kanisius. Yogyakarta. 

2000. 

[6] Fitriani dan Zaini,. EfisiensiEkonomis Usaha Pembesaran Ikan Lele. Jurnal Ilmiah ESAI Volume 6, 

Nomor 2. 2012. 

[7] Helfrich, L. A. and Libey, G). Fish Farming In Recirculating System (RAS), Departement of Fisheries 

and Wildlife Sciences, Virginia. 2000. 

http://hobiikan.blogspot.com/2009/01/teknik-pemijahan-lele-sangkuriang.html.(diakses
http://hobiikan.blogspot.com/2009/01/teknik-pemijahan-lele-sangkuriang.html.(diakses


American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2017) Volume 28, No  1, pp 30-48 

48 
 

[8] Khairul Amri dan Khairuman, Budidaya Lele African Secara Intensif, Agromedia Pustaka. 2002. 

[9] Kuncoro, M. Metode Kuantitatif, Teori dan Aplikasi Untuk Bisnis dan Ekonomi.  UPP. AMP. YKPN. 

Yogyakarta.  2001. 

[10] Mahyuddin, K,  Panduan Lengkap Agribisnis Lele. Jakarta; Penebar Swadaya 2008. 

[11] Munir, Manajemen Kelas Berbasis Teknologi Informasi dan Komunikasi. Mimbar pendidikan XXIV 

(2). Universitas pendidikan Indonesia. 2005. 

[12] Nazir, Moh. Metode Penelitian Cet.9. Penerbit Ghalia Indonesia. Bogor. 2014. 

[13] Rasyaf, M. Pengelolaan Usaha Peternakan Ayam Pedaging. Gramedia Pustaka Utama. Bogor. 1995. 

[14] Siti Amanah dan Agung Yulianto, Profil Penyelenggaraan Penyuluhan Perikanan Menunjang Kinerja 

DKP di Era Globalisasi, Jakarta: STP. 2002. 

[15] Soekartawi,  Ilmu Usaha Tani dan Penelitian Untuk Pengembangan Petani Kecil, UI – Press, Jakarta. 

1990. 

[16] Soekartawi, Analisis Usahatani, Jakarta. Penerbit Universitas Indonesia. 1994. 

[17] Soekartawi, Pengantar Agroindustri. Raja Grafindo Persada, Jakarta. 2003. 

[18] Sukadi, F. Peningkatan Teknologi Budidaya Perikanan. Jurnal lchtiologi lndonesia Vol 2 (21:61-66). 

2002. 

[19] Susanto, H. Teknik Kawin Suntik Ikan Ekonomis. Penerbit Penebar Swadaya. Jakarta. 1996. 

[20] Susanto, H. Teknik Kawin Suntik Ikan Ekonomis. Penerbit Penebar Swadaya. Jakarta. 2011. 

[21] Suratiyah, K.. Ilmu Usahatani. Edisi Revisi. Penebar Swadaya. Jakarta. 2015. 

[22] Tahapari, EPembuatan Pakan Ikan Berbahan Baku Lokal. Makalah disampaikan pada acara 

pengawalan Tehnologi Untuk Peningkatan Kapasitas Balai Penelitian Pemuliaan Ikan, Sukamandi. , 

2012. 

[23] Woynarovich E,Horvarth,. The Artificial Propagation of Warm Water Finfishes A Manual For 

Extention. FAO Fisheries Technical PaperNo.201.FIR/T 201. 1981.