Productivity and profitability of small-scale aquaculture in Malawi

ABSTRACT

Small-scale aquaculture in Malawi contributes to nutrition and food security, and serves as a rural income diversification strategy. Nevertheless, its adoption is low. Drawing on a recent survey of 732 small-scale fish farms across the country, this study assesses the production, yield, profit, and profit per square metre of small-scale fish farms and explores their determinants using regression analysis. Most fish farms are owned and managed by individual farm-households, though communally owned farms are also present. Small-scale aquaculture is found to be profitable, though the gross margins are slim. Regression results reveal that production and profit are positively associated with the use of farms for both fingerling and grow-out production and the number of years the farm has existed, while yield and profit/m² are positively associated with the use of high-quality inputs such as commercial feed and inorganic fertilisers. These results suggest that small-scale fish farmers in Malawi and other similar settings should adopt improved technologies and follow best on-farm management practices to increase production and profits. This study contributes to the discourse on the pathway through which aquaculture in Malawi can best contribute to the country’s development.

KEYWORDS:

• Gross margins
• aquaculture
• small-scale farm
• fish farming
• Malawi

Acknowledgment

We gratefully acknowledge the support rendered by Dr. Friday Njaya and staff from the Department of Fisheries headquarters, DADOs, DFOs, AEDOs and Lead Farmers before and during the survey. The authors gratefully acknowledge the work of the enumerators and supervisors who made this study a success. Many thanks to the MwAPATA staff for their logistical and technical support. We also express our heartfelt appreciation to the farmers who dedicated time to share their fish farming experiences with us. The authors further express their gratitude to Foundation for a Smoke-Free World (FSFW) through Agricultural Transformation Initiative (ATI) for funding the study. The views and opinions expressed herein are those of the authors and do not necessarily reflect those of MwAPATA Institute.

Consent for publication

Consent for publication has been obtained from all authors.

Consent to participate

The participants in this study were given a thorough explanation of the objectives of the study. The study adhered to confidentiality and informed consent from the participants.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

Human and animal ethics

The study adhered to the confidentiality of the participants. No animals were handled.

Notes

1 This period of production overlaps with the start of the COVID-19 pandemic. However, aquaculture production in Malawi did not drop during the pandemic (Figure 1), with total production in 2020 and 2021 continuing a steady, upward trend. This suggests that a study during this somewhat unusual time period can yield insights that are broadly relevant.

2 In EPAs in which we sampled fish farms from within a larger population of fish farms, each farm received an initial weight of greater than “1” within the EPA, reflecting that it represents a larger number of farms in the EPA. In EPAs in which all fish farms were included in the sample, each fish farm received an initial weight of “1” within the EPA. Beyond this level of sampling, the final weights additionally account for the likelihood of selection at the level of EPA and district.

3 The dependent variables are transformed using an inverse hyperbolic sine transformation (IHST).

4 Dropping these farms from analysis did not have any noticeable effect on our results.

5 The exchange rate between the U.S. dollar and Malawian kwacha as of 30 June 2021 was US$1 =  MK813.49.

6 In a semi-log model in which the dependent variable is logged, the effect of a 0–1 change in a binary regressor is $[100\ast (e^{\beta }-1)]\text{\%}$.

Additional information

Funding

This research work was supported by a grant from the Foundation for a Smoke-Free World (FSFW) through the Agricultural Transformation Initiative (ATI).