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Empirical yield predictive models for the fisheries of irrigation reservoirs in Sri Lanka

Authors:

Sareeha Nadarajah,

University of Kelaniya, LK
About Sareeha
Department of Zoology and Environmental Management, 
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W.M.H. Kelum Wijenayake,

Wayamba University of Sri Lanka, LK
About W.M.H. Kelum

Department of Aquaculture and Fisheries, Faculty of Livestock Fisheries and Nutrition

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Upali S. Amarasinghe

University of Kelaniya, LK
About Upali S.

Department of Zoology and Environmental Management, 

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Abstract

As fisheries production in reservoirs of most countries is a secondary use, challenges for improved management of fisheries should be addressed by building partnership between fisheries and other interested groups such as agriculture concerned with water management. Attempts were therefore made to develop empirical fish yield predictive models in ten irrigation reservoirs of Sri Lanka incorporating morphological, edaphic and hydrological parameters together with fishing intensity, with a view to investigating their influence on fish yields.

Reservoir fish yield was found to be significantly correlated with two formulations of morpho-edaphic index (i.e., conductivity in μS cm-1/mean depth in m [MEIc] and alkalinity in m. equiv. l-1)/mean depth in m [MEIa]), and a relative reservoir level fluctuation index (RRLF), defined as the mean amplitude of the annual reservoir level fluctuations divided by the mean depth of the reservoir. Both MEIc and MEIa also had significant positive ln-ln relationships with RRWL, indicating that RRWL can be used as an independent variable in reservoir fish yield prediction. Reservoir fish yield was also related to fishing intensity (FI in boat-days ha-1, yr-1) conforming to a ln-linear regression model (p<0.05). When MEIa, MEIc and RRWL were used as predctor variables together with FI, reservoir fish yield (FY) was multiply correlated as follows:

Ln FY = 3.245 + 0.327 Ln MEIa + 0.023 FI (R2 = 0.355; p< 0.01)

Ln FY = 3.403 + 0.249 Ln MEIc + 0.019 FI (R2 = 0.369; p< 0.01)

Ln FY = 1.330 + 0.650 Ln RRWL + 0.016 FI (R2 = 0.593; p< 0.001)

The empirical yield predictive model based on RRWL and FI as independent variables was more robust than those based on MEIa and MEIc, and the former has significant management implications because RRWL can be manipulated by irrigation authorities whereas control of FI is under the jurisdiction of fisheries authorities. Hence, through an effective dialogue between irrigation and fisheries authorities, there is a considerable potential to optimize fish yields in irrigation reservoirs of Sri Lanka.

How to Cite: Nadarajah, S., Wijenayake, W.M.H.K. and Amarasinghe, U.S., 2018. Empirical yield predictive models for the fisheries of irrigation reservoirs in Sri Lanka. Sri Lanka Journal of Aquatic Sciences, 23(2), pp.201–208. DOI: http://doi.org/10.4038/sljas.v23i2.7561
Published on 01 Sep 2018.
Peer Reviewed

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