TITLE

Study of Cobb-Douglass Function on Payang Catch Tools at Madura Strait

AUTHOR(S)
Primyastanto, Mimit; Soemarno; Efani, Anthon
PUB. DATE
July 2014
SOURCE
Australian Journal of Basic & Applied Sciences;Jul2014, Vol. 8 Issue 10, p420
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This study aim was to determine technical production factors affecting payang fishery catch in Madura Strait. Research was conducted at Gili Ketapang village, Sumberasih subdistrict, Probolinggo District, East Java Province. It is center of payang fishing activities in Probolinggo. This research was conducted during January to May 2014. This study uses descriptive method method with survey research techniques. A data analysis tool is Cobb-Douglass analysis. Production factors of payang fisheries in Madura Strait (X) that allegedly affect on production or catch in tones/year (Y) are experience (years), amount of catch trip (trip), amount of fuel required (Rp) and payang pockets length (m). Determination Coefficient (R2) obtained from analysis is 91.6%. It can be interpreted that increase or decrease amount of payang catch in Madura Strait that affected by production factors investigated are 91.6%, while 8.4% is determined by factors or other variables that not examined. Analysis results with F test values show Fcount = 68.008. This value is greater than Ftab = 2.690. This shows that all technical production factors significantly affect on payang catch. Meanwhile, effect of production factors on payang production is known by Student t test. Test results show that partially fishing experience (X1), amount of arrests trip (X2) and pocket payang length (X4) have significant direct effect on payang production at 95% confidence level. Analysis result of Cobb Douglas function obtains regression equation of payang production factor in Madura Strait, namely: Y =-8,580 + 0,454 X1 + 1,147 X2 + 0,239 X3 + 0,344 X4. To obtain optimum production, fishermen must intensify payang catch at peak fishing season and increases the size of payang pocket. Further research should include variable production factors outside these study variables in order to determine ratio of payang catch production.
ACCESSION #
97368509

 

Related Articles

  • Patrones espaciales del atún aleta amarilla (Tunnus albacares) en el Océano Pacífico Oriental: una exploración de perfiles de concentración. Sosa-López, A.; Manzo-Monroy, H. G. // Ciencias Marinas;dic2002, Vol. 28 Issue 4, p331 

    Presents information on a study which identified the spatial distribution patterns of yellowfin tuna (YFT) resource YFT in the Eastern Pacific Ocean, as an element of analysis of the relationship between catch-per-unit effort (CPUE) and abundance, steming on the concentration profile concept. ...

  • Socio-economic characteristics of the Cachoeira de Emas small-scale fishery in Mogi-Guaçu River, State of São Paulo, Brazil. Peixer, J.; Petrere Júnior, M. // Brazilian Journal of Biology;Nov2009, Vol. 69 Issue 4, p1047 

    Fishing in the area of Cachoeira de Emas dates from the aboriginal Painguás who inhabited its margins. The socioeconomic conditions of the fishers and fishing are described, derived from personal interviews with 33 fishers. Their mean age is 48.6 years and they have been fishing on average...

  • Forecasting the catch of Japanese anchovy larvae using a net survey prior to the fishing season. Yasue, Naotaka; Utsumi, Ryoichi; Gosho, Toyoho // Fisheries Science;Aug2008, Vol. 74 Issue 4, p938 

    The article discusses the catch forecasting of Japanese anchovy larvae applying a net survey prior to the fishing season. Shirasu is the larvae of the main Japanese anchovy or Engraulis japonicus and the Japanese sardine or Sardinop melanostictus which is commercially significant in the northern...

  • Is model selection using Akaike's information criterion appropriate for catch per unit effort standardization in large samples? Shono, Hiroshi // Fisheries Science;Oct2005, Vol. 71 Issue 5, p978 

    Akaike's information criterion ( AIC), which is widely used as a criterion of model selection in fish population dynamics, is known to have a bias in not only small samples but also large samples. Consistency was proposed as a property of the information criteria available in large samples. We...

  • The use of sampling weights in regression models of recreational fishing-site choices. Lovell, Sabrina J.; Carter, David W. // Fishery Bulletin;Oct2014, Vol. 112 Issue 4, p243 

    Improved methods for estimating saltwater recreational fishing catch and effort have been developed by the NOAA National Marine Fisheries Service. Sampling weights that account for a complex sample design in surveys of anglers are now available with NMFS catch and effort estimates. Previously,...

  • Fishing activity of tuna purse seiners estimated from vessel monitoring system (VMS) data. Bez, Nicolas; Walker, Emily; Gaertner, Daniel; Rivoirard, Jacques; Gaspar, Philippe; Walters, Carl // Canadian Journal of Fisheries & Aquatic Sciences;Nov2011, Vol. 68 Issue 11, p1998 

    In the lack of fishery-independent information, catch per unit of effort (CPUE) is the conventional abundance index. In the case of the tropical tuna purse seine fisheries, a critical difficulty lies in the definition of an effective fishing effort, because fishermen use two different fishing...

  • What Can Fisheries Historians Learn from Marine Science? The Concept of Catch per Unit Effort (CPUE). Poulsen, René Taudal; Holm, Poul // International Journal of Maritime History;Dec2007, Vol. 19 Issue 2, p89 

    The article discusses the concept of the catch per unit effort (CPUE) in the fishing industry in Sweden. It focuses on how to apply the CPUE concept in a historical analysis and the data requirements for, as well as its pitfalls and approaches. It outlines the significance of CPUE as a useful...

  • TAC.  // Europe: A Concise Encyclopedia;2004, p207 

    Information on the Total Allowable Catch, or TAC, the system which is part of the Common Fisheries Policy, is presented. As reported, TAC specifies the quantity of each species that each Member State of the European Union (EU) may catch. It is also reported that the system which is designed for...

  • Use of Principal Component Analysis with Instrumental Variables (PCAIV) to analyse fisheries catch data. Pech, Nicolas; Laloë, Francis // ICES Journal of Marine Science / Journal du Conseil;Feb1997, Vol. 54 Issue 1, p32 

    Principal Component Analysis with respect to Instrumental Variables (PCAIV) is a statistical tool for exploratory analysis combining both principal component analysis and multivariate regression analysis. This tool is used to analyse mean fortnightly catches obtained by Senegalese fishermen in...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics