alexa Forecasting Copadichromis (Utaka) Production for Lake Malaŵi, Nkhatabay Fishery- A Stochastic Model Approach
ISSN: 2332-2608

Journal of Fisheries & Livestock Production
Open Access

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Research Article

Forecasting Copadichromis (Utaka) Production for Lake Malaŵi, Nkhatabay Fishery- A Stochastic Model Approach

Zaindi I*, Singini W and Mzengereza K

Mzuzu University, Department of Fisheries science Private Bag 201, Mzuzu 2, Malawi

*Corresponding Author:
Zaindi I, Mzuzu
University, Department of Fisheries
science Private Bag 201
Mzuzu 2, Malawi
T el: +265 1 320 722
[email protected]

Received Date: October 07, 2015; Accepted Date: January 11, 2016; Published Date: January 21, 2016

Citation: Zaindi I, Singini W, Mzengereza K (2016) Forecasting Copadichromis (Utaka) Production for Lake Malawi, Nkhatabay Fishery- A Stochastic Model Approach. J Fisheries Livest Prod 4:162. doi:10.4172/2332-2608.1000162

Copyright: © 2016 Zaindi I, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Due to the overexploitation of Chambo (Oreochromis species), other species such as Utaka (Copadichromis species) have become important part of Lake Malaŵi, Nkhatabay fishery. The shift to Copadichromis species has put the stocks on danger of being overexploited just as was the case with Chambo (Oreochromis species). The study was therefore conducted to forecast Copadichromis (Utaka) species yield for Lake Malaŵi Nkhatabay fishery from 2010 to 2019. The study was based on the data of fish catches during the years from 1976 to 2009. The study considered Autoregressive Integrated Moving Average (ARIMA) model to select the appropriate stochastic model for forecasting Copadichromis species yield. Maximum likelihood estimation (MLE) method was considered in estimating the parameters. Based on ARIMA (p, d, q) and its components Autocorrelation function (ACF), Partial autocorrelation (PACF), Normalized Bayesian Information Criterion (NBIC), Box – Ljung Q statistics and residuals estimated, ARIMA (1, 1, 1) was selected. Based on the chosen model, it could be predicted that the Copadichromis species yield would decrease from 424.9tons in 1976 to 174.13tons in 2019. As the study has shown that Copadichromisspecies production will decrease, stakeholders in the management of fisheries resources should use this study to make policies and formulate strategies that will sustain yield of Copadichromis species.


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