Livestock Research for Rural Development 34 (12) 2022 | LRRD Search | LRRD Misssion | Guide for preparation of papers | LRRD Newsletter | Citation of this paper |
The objective of this study was to evaluate the effectiveness and efficiency of the automatic funnel feeding in cage aquaculture system ( karamba). The examined feeding systems were an automatic funnel and a conventional method. The experiment was run for 8 weeks. The weight of the fish samples was measured every 2 weeks. The observed variables were individual weight gain (WG), specific growth rate (SGR), mortality (M), and feed consumption (FC). The unpaired t-test was applied to compare the feeding methods. Results showed that there were no significant differences in WG, SGR, M and FC of the two feeding methods. Feeding with the automatic funnel provided more benefits because it can save labour costs and reduce offered feed. Moreover, automatic feeding reduced the amount of feed entering the aquatic ecosystem thus it reduced the pollution load due to cage aquaculture. It is suggested that automatic funnel feeding technology resulted in similar fish productivity while it is more environmentally friendly in the cage aquaculture production system.
Keywords: cage aquaculture, automatic funnel, conventional feeding, fish growth rate, fish weight
Fish is the main source of animal protein in the human diet in many countries in Southeast Asia, especially archipelagic countries such as Indonesia. Even fish will be the most affordable source of animal protein for millions of people in developed and developing countries. Besides being easy to get, fish also contains healthy levels of nutrients. and a good source of thiamine, riboflavin, vitamins A and D, phosphorus, calcium and iron (FAO 2020). The demand for fish as a source of animal protein continues to increase along with the increase in population, while the availability of fish tends to decrease. In Indonesia, the demand for fish is fulfilled by the supply from fish capture and aquaculture.
Aquaculture in the river by using cages (karamba system) has been practised by the community in many rivers in South Kalimantan, Indonesia. This fish farming has a large economic contribution to people in the wetland area of South Kalimantan. However, there are many difficulties in karamba system that affect aquaculture profits, such as high fish mortality which is ranging from 30% - 50% (Rahman et al 2017). High fish mortality in karamba system is mainly caused by the accumulation of organic matter from uneaten feed and fish excrete around the cages (Herliwati and Rahman 2022; Iqbal et al 2018).).
The uneaten feed and excrete from fish farming enter the aquatic environment which will reduce water quality, inhibit fish growth, and cause fish mortality. The accumulation of organic substances from the uneaten feed in the river will stimulate microbial activity which can cause deoxygenation of the substrate and water due to a reduction in oxygen concentration. Reducing dissolved oxygen levels to anaerobic conditions will stimulate the formation of ammonia, hydrogen sulfide, and nitrite which are toxic to the fish (Boyd et al 1998). Deoxygenation conditions and increasing levels of ammonia, hydrogen sulfide, and nitrite are the main causes of mass mortality in cage aquaculture (Herliwati and Rahman 2022; Baverage 2004). The high mortality of fish in fish farming indicates the carrying capacity of the water environment and pollution levels has exceeded the fish tolerance.
The price of commercial fish feed is also an important issue in aquaculture. The increasing price of fish feed reduces the profits of fish farmers. Feed is a key factor in the success and sustainability of the cage aquaculture business (Okoye and Sule 2001). Feed cost is considered the largest part of production costs that reach more than 65% of production cost (Pallaya-Baleta et al 2022; Ashley-Dejo et al 2017; Ahmed et al 2007).
The use of cage (karamba system) in aquaculture has spread to all regions in South Kalimantan Province. Besides being a source of animal protein and having economic benefits, this production system also has social benefits namely creating jobs.
In order to improve the benefits and sustainability of the cage aquaculture system in the river, a better fish feeding technology is needed that can improve feed efficiency, reduce water pollution, and reduce the fish mortality rate. Akinrotimi et al (2007) and Jamu and Ayinla (2003) stated that feeding management determines the viability of fish farming.
An automatic funnel innovation has been developed by cage fish farmers in several rivers in South Kalimantan to improve feed efficiency. However, it is necessary to evaluate the effectiveness of this feeding technology on fish growth and mortality. Thus, this tool can be promoted for more beneficial and sustainable cage aquaculture system.
This research was carried out in Hanyar river, a sub order of Tabalong River, South Kalimantan province, Indonesia.
Three units of fish cages (karamba) were used in the experiment (L=2m, W=1.5m and D=1m) and each unit was divided into two parts so that it becomes 6 cage units (L=1m, W=1.5m and D=1m). Two feeding systems were evaluated, namely feeding using an auto funnel and conventional feeding. Fifty Oreochromis niloticus (Tilapia), which were procured from a fish hatchery in Kambitin Tabalong regency, were stocked per cage unit (average initial weight of 1.96±0.397g). The cages are placed sequentially in the direction of the water flow (Photo 1).
Photo 1. Cage aquaculture unit used in the experiment |
The automatic funnel is designed by placing the funnel frame at the top of the cage. A lever is inserted in the funnel that connects the inside of the funnel and the cage. If the end base of the lever, which is submerged in water, was touched by the fish, the valve on the funnel will be open and the feed will fall into the cage. Thus the feed will only fall from the funnel into the cage if the fish touch the lever on the inside of the cage. Every two days the feed in the funnel was controlled to check if the feed runs out.
The conventional feeding method is done by spreading the feed directly into the cage. Feeding times were twice per day namely at 08.00 AM and 05.00 – 06.00 PM. Feed was offered as much as 5% of body weight per day. The type of feed used in the experiment was the same feed ty for both feeding techniques, namely commercial feed in the form of floating pellets with a protein content of 30 - 33%, fat of 4%, the fibre of 5%, ash of 13%, and water content of 12%. The weight of feed given directly to the cage (conventional method) and entering the cage through an automatic funnel is calculated as the weight of the feed given to the cage fishery.
The experiment was run for 8 weeks and every 2 weeks the fish samples were weighed to collect the data. The observed variables were individual weight gain rate (WG), specific growth weight (SGR), mortality rate (MR), and feed consumption (FC).
The variables of two feeding methods were compared using unpaired t-test (t-test: Two Samples with Equal Variance Assumption) at (p < 0.05). All computation was performed using a statistical package in the excel software.
The individual weight gain and individual weight gain rate of tilapia during the 8 weeks of the experiment are shown in the Table 1 and Table 2 below.
Table 1. Individual weight gain (g) of tilapia during experiment |
|||||
Cage Unit |
Weeks of observation |
||||
W0 |
W2 |
W4 |
W6 |
W8 |
|
F1 |
2.14±0.25 |
8.52±2.10 |
12.20±1.55 |
39.10±3.07 |
69.50±3.81 |
C1 |
2.14±0.40 |
9.95±3.21 |
13.00±1.49 |
42.10±2.77 |
71.75±4.06 |
F2 |
1.97±0.29 |
4.85±0.88 |
7.05±0.76 |
13.50±1.08 |
38.60±2.01 |
C2 |
2.11±0.40 |
5.33±0.79 |
7.65±1.00 |
13.80±1.32 |
40.80±3.19 |
F3 |
1.57±0.44 |
4.82±0.76 |
7.95±0.86 |
15.90±1.37 |
38.90±1.79 |
C3 |
1.84±0.31 |
5.62±1.54 |
8.00±0.97 |
16.70±1.57 |
40.10±3.21 |
W0 = initial week; W2 = 2nd week; W4 = 4th week ; W6 = 6th weeks ; W8= 8th weekF1,2,3 = cage with automatic funnel; C1,2,3 = cage with conventional method |
The weight gain rate of individuals between caged units is highly variable. The highest individual weight gain rate was found in cage unit 1 and the lowest individual weight gain rate ware found in cage unit 2. Furthermore, the relative growth data was transformed for statistical test purposes. The results of the logarithmic transformation of individual relative growth data during the rearing period can be seen in Table 3.
Table 2. Individual weight gain rate (%) during experiment |
|||
Cage Unit |
Treatments |
||
Automatic funnel |
Conventional method |
||
1 |
3.147,66 |
3.252,80 |
|
2 |
1.859,39 |
1.833,65 |
|
3 |
2.377,71 |
2.044,39 |
|
Table 3. Individual tilapia weight gain rate (%) |
|||
Cage Unit |
Treatment |
||
Automatic funnel |
Conventional method |
||
1 |
3.50 |
3.51 |
|
2 |
3.27 |
3.26 |
|
3 |
3.38 |
3.31 |
|
The large SGR variation between sampling periods (2.19% d-1 - 2.19% d-1) could be seen in Table 4. The lowest daily growth of individuals was found in the 4th week of the sampling period and the highest occurred in the 6th week of the sampling period.
Table 4. Specific growth rate (% d-1) of tilapia during 8 weeks |
|||
No. cage |
Sampling |
Automatic |
Conventional |
1 |
2 |
21.30 |
26.07 |
4 |
3.09 |
2.19 |
|
6 |
15.75 |
15.99 |
|
8 |
5.55 |
5.03 |
|
2 |
2 |
10.44 |
10.90 |
4 |
3.17 |
3.11 |
|
6 |
6.63 |
5.74 |
|
8 |
13.28 |
13.98 |
|
3 |
2 |
14.79 |
14.32 |
4 |
3.52 |
2.65 |
|
6 |
7.14 |
7.77 |
|
8 |
10.33 |
10.01 |
|
Calculation of dead fish was carried out every 2 weeks for 8 weeks of the experiment, along with sampling and weighing of fish. The number of live fish every 2 weeks and during the 8 weeks can be seen in Table 5, and fish mortality in Table 6.
Table 5. Number of live fish on sampling period (2 weeks) during the experiment |
||||||||
Cage |
Treatment |
Weeks |
Number of |
|||||
0 |
2 |
4 |
6 |
8 |
dead fish |
|||
1 |
Auto. Funnel |
50 |
49 |
47 |
47 |
45 |
5 |
|
Conventional |
50 |
50 |
50 |
49 |
47 |
3 |
||
2 |
Auto. Funnel |
50 |
47 |
45 |
44 |
44 |
6 |
|
Conventional |
50 |
50 |
47 |
46 |
43 |
7 |
||
3 |
Auto. Funnel |
50 |
46 |
45 |
44 |
42 |
8 |
|
Conventional |
50 |
49 |
46 |
45 |
43 |
7 |
||
Table 6. Mortality (%) of fish during the experiment |
||||||||
Cage |
Treatment |
Weeks |
Total |
|||||
2 |
4 |
6 |
8 |
|||||
1 |
Auto Funnel |
2,00 |
4,08 |
0,00 |
4,26 |
10,00 |
||
Conventional |
0,00 |
0,00 |
2,00 |
4,08 |
6,00 |
|||
2 |
Auto Funnel |
6,00 |
4,26 |
2,22 |
0,00 |
12,00 |
||
Conventional |
0,00 |
6,00 |
2,13 |
6,52 |
14,00 |
|||
3 |
Auto Funnel |
8,00 |
2,17 |
2,22 |
4,55 |
16,00 |
||
Conventional |
2,00 |
6,12 |
2,17 |
4,44 |
14,00 |
|||
The mortality of the 2-week sampling period for the automatic funnel feeding ranged from 0.00 – 4.55% (3.31% ± 2.345) and the conventional feeding ranged from 0 – 6.52% (2.95% ± 2.427). The total mortality during the 8-week of experiment ranged from 10.00 – 16.00% (12.67% ± 3.055) and 6 – 14% (11.33% ± 4.619), respectively.
The daily amount of feed consumed by tilapia is calculated based on the amount of feed offered through to the fish, either through the funnel or conventional feeding. The daily offered feed (kg cage-1) is presented in the Table 7.
Table 7. Daily offered feed (kg cage-1) |
|||
No. cage |
Automatic |
Conventional |
|
1 |
0.105 |
0.158 |
|
2 |
0.325 |
0.175 |
|
3 |
0.625 |
1.000 |
|
average |
0.352 |
0.444 |
|
The daily average amount of offered feed (kg cage-1) by conventional method was more than automatic funnel. Thus, the use of feed in the automatic funnel is more efficient than the conventional method.
The unpaired t-test is presented in the Table 8 below.
Table 8. Result of t-test |
||||||||
Variable |
Observation |
Mean |
Variance |
t-Test (p<0.05) |
||||
funnel |
convent |
funnel |
convent |
funnel |
convent |
tstat |
tcritical |
|
WGR |
3 |
3 |
3.381 |
3.362 |
0.013 |
0.017 |
0.1895 |
2.7764 |
SGR |
12 |
12 |
9.582 |
9.813 |
33.483 |
49.025 |
0.0879 |
2.0739 |
MRs |
12 |
12 |
3.313 |
2.956 |
5.493 |
5.893 |
0.3665 |
2.0739 |
MRt |
3 |
3 |
12.667 |
11.333 |
9.333 |
21.333 |
0.4170 |
2.7764 |
FC |
3 |
3 |
0.3516 |
0.4443 |
0.068 |
0.232 |
0.2931 |
2.7764 |
WGR = Weight gain rate; SGR = Specific growth rate; MRs = Mortality rate on sampling period; MRt = Mortality rate during experiment |
Statistical analysis showed that theres is no significance different of two feeding systems (p<0.05) on the weight gain rate of individual (t stat = 0.1895 < tcritical = 2.7764); the specific growth rate (tstat value = 0.0879 < tcritical 5% = 2.0739); mortality rate (tstat = 0.4170 < tcritical = 2.776); and the amount of offered feed (tstat = 0.2931 < tcritical = 2.776) to individuals tilapia during the 8-week experiment period.
High fish mortality is the main issue in the cage aquaculture production system. Some factors might contribute on the fish mortality, such as dissolved oxygen depletion, increased levels of ammonia and nitrite around the cage (Daunda et al 2019). This poor water quality was caused by the accumulation of uneaten feed and fish excrete around the cage which undergoes biodegradation under anaerobic conditions. The high fish mortality in cage aquaculture or floating net was repeatedly occurred in Indonesia. It wa reported that 1,042 tons fishes died in the floating net at Saguling reservoir in 1993, 1,039 tons in the Cirata reservoir in 1994, 1,560 tons in the Juanda-Jatiluhur reservoir in 1996 as a result of water fertilization sources from aquaculture (Krismono 2004), and uncontrolled increase in the number of cage aquaculture (Machbub 2010). This phenomenon tends to reoccurred annually in reservoirs and lakes on the islands of Java and Sumatra. The same case has been experienced by cage aquaculture farmers in the Riam Kanan river at the end of 2012 which caused 2,340 tons were died (Herliwati and Rahman 2022) and repeated in October 2014. At the end of 2019, as many as 80 tons of fish cage aquaculture died in Martapura river. The high density of cages is one of the causes of the decrease in the carrying capacity of the river (Rahman et al 2017).
Therefore, feed is a key factor in ensuring the sustainability of the cage aquaculture business. Inadequate amount of feed given and the low nutrients content of the feed will inhibit the growth of the fish at a aquaculture (Abd El-Naby et al 2020). On the other hand, overfeeding will have an impact on reducing water quality which can lead to an increase in fish mortality and even large-scale mortality of cultured fish (Hasim et al 2017; Chitmanat et al 2016). Thus, controlled feeding technology is needed to avoid overfeeding.
The development of feeding technology can be a solution a reduce uneaten feed wasted into the aquatic environment (Yue and Shen 2022). Various feeding technologies from digital technology to nanotechnology have been developed (Abd El-Naby et al 2019; Fajardo et al 2022.). However, this technology is still not effective because it requires the support of electronic devices, technology control, additional business capital and there are still many locations for cage aquaculture business activities that have not been reached by the internet network. While cage aquaculture is mostly adopted by smallholder fish farmer. Thus the development of a simple technology that is in accordance with local conditions is the most appropriate choice.
We would like to thank Lambung Mangkurat University for the funding this research through the research grant No.: 026.2/UN8.2/PL/2022.
Abd El-Naby A S, Al-Sagheer A A, Negm S S and Naiel M A E 2020 Dietary combination of chitosan nanoparticle and thymol affects feed utilization, digestive enzyme, antioxidant status, and intestinal morphology of Oreochromis niloticus. Aquaculture, 515, 734577. doi.org/10.1016/j.aquaculture.2019.734577
Abd El-Naby F S, Naiel M A E, Al-Sagheer A A and Negm S S 2019 Dietary chitosan nanoparticle enhance the growth, production performance and immunity in Oreochromis niloticus. Aquaculture, 501, 82-89. doi.org/10.1016/j.aquaculture.2018.11.014
Akinrotini O A, Gabriel U U, Owhonda N K, Onunkwo D N, Opara J Y, Anyanwu P E and Cliffe P T 2007 Formulating an environmental friendly fish feed for sustainable aquaculture development in Nigeria. Agriculture Journal, 2(5), 606-612.
Alam M B, Islam M A, Marine S S, Rashid A, Hossain M A and Rashid H 2014 Growth performances of gift Tilapia (Oreochromis niloticus) in cage culture at the old Brahmaputra River using different densities. Journal of SylhetAgril. University, 1(2), 265-271.
Ashley-Dejo S S, Olaoye O J and Adelaja O A 2017 Analysis of profitability of small-scale catfish farmers in Oyo State, Nigeria. Malaysia Journal of Animal Science, 20(2), 11-24.
Ahmed N, Wahab M A and Thilsted S H 2007 Integrated aquaculture-agriculture systems in Bangladesh: potential forsustainable livelihoods and nutritional security of the rural poor. Aquaculture Asia, 12 (1), 14- 22.
Beveridge M C M 2004 Cage Aquaculture Third edition. Blackwell Publishing Ltd, Australia.
Boyd C E, Massaut L and Weddig L J 1998 Towards reducing environmental impacts of pond aquaculture. INFOFISH International 2/98, 27 – 33.
Chitmanat C, Lebel P, Whangchai N, Promya J and Lebel L 2016 Tilapia diseases and management in river-based cage aquaculture in northern Thailand. Journal of Applied Aquaculture, 28(1), 9-16.
Daunda A B, Ajadi A, Tola_F A S and Akinwole A O 2019 Waste production in aquaculture sources, components and managements in different culture systems. Aquaculture and Fisheries, 4(3), 81-88.
Fajardo D, Martinez-Rodriguez G, Blasco J, Mancera J M, Thomas B and De Donato M 2022 Nanotechnology in aquaculture: applications, perspectives and regulatory challenges. Aquaculture and Fisheries, 7, 185-200
Food and Agriculture Organization 2020 The State of World Fisheries and Aquaculture. Sustainability in action, Rome 224p.
Hasim, Koniyo Y and Kasim F 2017 Suitable location map of floating net cage for environmentally friendly fish farming development with geographic information systems applications in Lake Limboto, Gorontalo, Indonesia. AACL Bioflux, 10(2), 254-263.
Herliwati and Rahman M 2022 Loading capacity of water pollution from cage aquaculture in south Kalimantan River. International Journal of Wetland Management, 10(1), 1-11.
Iqbal M M, Shoaib M, Agwanda P and Lee J L 2018 Modeling approach for water-quality Management to Control Pollution Concentration: A case study of Ravi River, Punjab, Pakistan. Water, 10, 1068; doi:10.3390/w10081068. 1-20.
Jamu D M and Ayinla O A 2003 Potential for the development of aquaculture in Africa. NAGA, 693, 9-13.
Krismono 2004 Optimalisasi Budidaya Ikan dalam KJA di Perairan Waduk Sesuai Daya Dukung di dalam Pengembangan Budi Daya Perikanan di Perairan Waduk, Suatu Upaya Pemecahan Masalah Budidaya Ikan dalam Karamba Jaring Apung. Pusat Riset Perikanan Budidaya, Badan Riset Perikanan dan Kelautan, Departemen Kelautan dan Perikanan. Jakarta. p. 75 – 81
Machbub B 2010 Model daya tampung beban pencemaran air danau dan waduk. Jurnal Sumber Daya Air, 6 (2), 129-144.
Okoye F C and Sule O D 2001 Utilisation and Composition of Conventional and Non-conventional Fish Feedstuffs in Arid Zone of Nigeria. Journal of Arid Zone Fisheries, 1, 23-32.
Ogwu C 2020 Quantification of organochlorine pesticides content of Okumesi River Ebedei Uno Delta for cage aquaculture in schools: a pathway for youths empowerment and poverty eradication in Nigeria. International Research Journal of Curriculum and Pedagogy, 6(2), 133-139.
Omasaki S K, Charo-Karisa H and Kosgey I S 2013 Fish production practices of smallholder farmers in western Kenya. Livestock Research for Rural Development, 25(3), 1-13.
Pallaya-Baleta L J, Baleta F N, Magistrado-Candelaria P, Plantado L C, Baldo D E B, Navarro M C, and Encinas J L 2022 Growth performance and economic viability of dietary inclusion of Ipomea batatas L. shoot powder and extracts in the practical diets of Oreochromis niloticus L. Egyptian Journal of Aquatic Research, 48, 273-279.
Rahman M, Herliwati and Prihanto A A 2017 Phosphor-based carrying capacity of Riam Kanan river, South Kalimantan on cage fish farming. AACL Bioflux, 10(5), 1091-1097.
Yue K and Shen Y 2022 An overview of disruptive technologies for aquaculture. Aquaculture and Fisheries, 7, 111-120.