The future of fish in Africa: Employment and investment opportunities.

One of the most pressing challenges facing food systems in Africa is ensuring availability of a healthy and sustainable diet to 2.4 billion people by 2050. The continent has struggled with development challenges, particularly chronic food insecurity and pervasive poverty. In Africa's food systems, fish and other aquatic foods play a multifaceted role in generating income, and providing a critical source of essential micronutrients. To date, there are no estimates of investment and potential returns for domestic fish production in Africa. To contribute to policy debates about the future of fish in Africa, we applied the International Model for Policy Analysis of Agriculture Commodities and Trade (IMPACT) to explore two Pan-African scenarios for fish sector growth: a business-as-usual (BAU) scenario and a high-growth scenario for capture fisheries and aquaculture with accompanying strong gross domestic product growth (HIGH). Post-model analysis was used to estimate employment and aquaculture investment requirements for the sector in Africa. Africa's fish sector is estimated to support 20.7 million jobs in 2030, and 21.6 million by 2050 under the BAU. Approximately 2.6 people will be employed indirectly along fisheries and aquaculture value chains for every person directly employed in the fish production stage. Under the HIGH scenario, total employment in Africa's fish food system will reach 58.0 million jobs, representing 2.4% of total projected population in Africa by 2050. Aquaculture production value is estimated to achieve US$ 3.3 billion and US$ 20.4 billion per year under the BAU and HIGH scenarios by 2050, respectively. Farm-gate investment costs for the three key inputs (fish feeds, farm labor, and fish seed) to achieve the aquaculture volumes projected by 2050 are estimated at US$ 1.8 billion per year under the BAU and US$ 11.6 billion per year under the HIGH scenario. Sustained investments are critical to sustain capture fisheries and support aquaculture growth for food system transformation towards healthier diets.

Introduction

Ensuring that a healthy and sustainable diet is available to 2.4 billion Africans by 2050 is one of the most pressing challenges facing Africa’s food systems [1-4]. The continent has struggled with a series of interconnected development challenges, particularly in fighting chronic food insecurity and overcoming pervasive poverty–the two foundational Sustainable Development Goals [5]. In Africa’s food systems, fish and other aquatic foods play a multifaceted role as a way of life, generating income, and providing a critical source of essential micronutrients, particularly for women and infants [1, 6–9]. Nevertheless, the current and future values of fish and aquatic foods in Africa are often overlooked in development research, policy and investment. It is argued that this oversight means multiple pathways to address malnutrition and food insecurity are underexplored [10].

Fish consumed in Africa are predominantly provided by capture fisheries sourced from rivers, large inland lakes and coastal systems [11]. Whereas aquaculture is one of the fastest growing food production sectors globally [12, 13], Africa contributed only 2.7% to the global aquaculture share in 2019. Nevertheless, the African aquaculture sector is maintaining double digit average annual growth rates in the last two decades in response to increasing fish demand in the continent [12]. Despite this growth, capture fisheries and aquaculture do not supply sufficient fish and there is a significant gap between fish supplies and consumer demand in Africa [1, 8]. Further, the fish supply gap is projected to widen due to a dramatic increase in fish demand, driven by rapid population and income growth, diet transformation resulting from urbanization, and changing consumer preferences [1, 8, 14]. In addition to these growing demands, unmet nutritional needs persist and continue to increase, particularly for women of reproductive age, children under the age of five and in the first 1000 days of life [15]. Increasing fish supply, reducing waste and loss, supporting intra-regional and international fish trade, and ensuring equitable distribution and access to fish are important strategies to address some dietary deficiencies and the costly individual and societal consequences [7, 16, 17].

Africa hosts regions that are amongst the most susceptible to global climate change [18]. Climate change projections [19-22] indicate that most of northern and southern Africa will experience high water stress while eastern, central, and western Africa will be subject to increasingly heavy rains and flooding [23, 24]. Changes in precipitation and temperature patterns due to climate change will create further stress in inland lake, river and oceanic ecosystems with ramifications on fish supply and the broader wellbeing of actors in the food systems. Climate change is projected to reduce the potential fisheries catch in the Exclusive Economic Zones (EEZs) in Africa [21]. Coupling with climate change impacts, the activities of foreign fishing vessels in African EEZs are also likely to impact fish availability and access in Africa [25]. In sum, there are growing uncertainties and daunting challenges associated with the future of Africa’s food systems associated with large-scale drivers that operate outside and within fisheries and aquaculture systems. These challenges, amplified by the unprecedented COVID-19 pandemic have led the governments of African and regional organizations to determine the potential investment opportunities and interventions in fisheries and aquaculture to address food and nutrition security [26, 27].

Public and private sector investment will be critical to secure diverse supplies of fish and other aquatic foods from capture fisheries and aquaculture. Whilst contributing to food and nutrition security in Africa will require four simultaneous strategies (i.e., increasing fish supply, reducing waste and loss, supporting fish trade, and ensuring equitable distribution and access), in this paper we focus on fish supply that could be achieved by increasing production. To date, there are no estimates of investment and potential returns for domestic fish production in Africa. To contribute to policy debates about the future of fish in Africa, we develop two scenarios; business-as-usual (BAU) and high capture fisheries and aquaculture with stronger GDP growth (HIGH). We first project future fish supply and demand in Africa to 2050 using the International Model for Policy Analysis of Agriculture Commodities and Trade (IMPACT). Second, we conduct post-model analysis to extrapolate future potential direct (capture fisheries and aquaculture) and indirect employment that would be associated with the BAU and HIGH scenarios. Finally, we estimate future aquaculture production value and investment costs required to achieve BAU and HIGH scenarios.

Materials and methods

To provide more comprehensive and consistent outlooks and prospects of fish and aquatic food systems, efforts have been made to integrate fish into foresight modeling of agriculture and livestock commodities. We apply the IMPACT fish model developed by International Food Policy Research Institute (IFPRI), which is a partial equilibrium economic model containing a system of equations for analyzing baseline and alternative scenarios for fish demand, supply, trade and prices at global, regional and country level in responding to future changes such as income, population and technological progress. [28, 29]. Previous application of the model by the World Bank in “Fish to 2030” report [29] used global historical data up through 2009 to develop business-as-usual (BAU) scenario. The projection from that model underestimated the 2010–2015 historical trend of capture fisheries and aquaculture production. To address these shortcomings, we re-calibrate the model with recent dataset and parameters of fish production, consumption, trade, population and GDP compiled from FAO, UN and IFPRI databases [4, 12, 30]. Specifically, we revisited the productivity growth assumptions of the model, using expert knowledge informed by fisheries and aquaculture specific biophysical and socio-economic factors and fish management and production targets defined by national governments [1]. The progressive improvement of IMPACT fish model used to project future Africa’s fish sector is illustrated in Fig 1.

Fig 1

Chronological model improvement and analysis using IMPACT fish model.

In this study, we focused on eight African nations: Egypt, Ghana, Kenya, Malawi, Nigeria, Tanzania, Uganda and Zambia. We selected these countries because they are 1) nations projected to face the largest shortfalls in fish supply relative to demands, 2) experience high rates of fish consumption, and 3) are amongst the nations experiencing relatively rapid growth in aquaculture (Table 1). These eight countries are home to 40% of Africa’s total population but produce over 95% of aquaculture and 30% of capture fisheries production (by volume) in the continent in 2019. About half of fish consumed in Africa is by these eight countries, suggesting slightly higher per capita fish consumption rates than elsewhere in Africa [31]. Among these eight countries, Uganda, Tanzania, Malawi, Kenya, and Ghana are classified by Food and Agriculture Organization (FAO) as low-income food-deficit countries [32].

Table 1

Contribution of fish to food security in Africa and the world.

Indicator	Year	Egypt	Ghana	Kenya	Malawi	Nigeria	Tanzania	Uganda	Zambia	Studied countries	Africa	World
Demographic and socio-economic status a
Population (million)	2020	102.3	31.1	53.8	19.1	206.1	59.7	45.7	18.4	536.3	1,340.6	7,794.8
Population average annual growth (%)	2010–2020	2.1	2.3	2.5	2.8	2.7	3.0	3.5	3.1	2.6	2.6	1.1
Urban population (%)	2020	42.8	57.3	28.0	17.4	52.0	35.2	25.0	44.6	42.5	43.3	56.2
GDP per capita (current US$)	2020	3,548	2,329	1,838	625	2,097	1,077	817	1,051	2,047	1,789	10,926
GDP average annual growth (%)	2010–2020	5.2	8.4	9.5	5.6	1.8	6.9	3.5	-0.5	4.0	1.6	2.5
Undernourishment (%)	2019	5.4	6.1	24.8	17.3	14.6	25.1	n.a.	n.a.	14.6	17.7	8.9
Unemployment (% total labor force)	2020	10.5	4.5	3.0	6.0	9.0	2.2	2.4	12.2	7.1	7.7	6.5
	Year	2017	2016	2015	2016	2018	2017	2016	2015			2017
Population below US$1.90 a day (%)		3.8	12.7	37.1	69.2	39.1	49.4	41.3	58.7	n.a.	n.a.	9.3
Contribution of fish to food supply b
Total fish production (thousand tonnes)	2019	2,039	445	144	163	1,115	487	706	136	5,235	12,385	177,834
Share of aquaculture production (%)	2019	80.5	11.8	12.9	5.1	26.0	3.4	14.6	28.3	41.4	18.4	48.0
Aquaculture average annual growth (%)	1999–2019	10.4	15.6	22.9	14.1	13.8	24.7	30.9	11.7	11.3	11.1	5.2
Capture fisheries average annual growth (%)	1999–2019	-0.3	-1.2	-2.4	6.3	3.0	2.1	5.0	1.9	1.6	2.3	0.04
Contribution of fish to food and nutritional status c
Fish consumption (kg/capita/year)	2018	23.2	24.8	3.0	11.9	8.9	6.8	10.9	11.7	12.1	10.3	20.2
Fish protein (g/capita/day)	2018	6.6	8.0	0.9	3.5	2.6	2.2	3.3	3.5	3.6	3.0	5.6
Animal protein (g/capita/day)	2018	26.4	15.4	14.9	9.3	7.3	12.1	12.3	13.7	13.5	15.2	32.9
Fish/animal protein (%)	2018	25.0	52.2	5.7	37.6	35.3	18.5	26.5	25.3	29.1	20.0	16.9
Contribution of aquaculture value b
Farm-gate value (million US$)	2019	2,862	190	64	38	833	62	242	105	4,395	4,857	259,548
Farm-gate price (US$/kg)	2019	1.7	3.6	3.5	4.6	2.9	3.8	2.3	2.7	2.0	2.1	3.0

Author’s computation from data source aUN [4]

aWorld Bank [33]

bFishStatJ [12, 31]

and

cFAO [34].

We consulted 76 experts from Egypt (43%), Nigeria (32%), Tanzania (15%), Zambia (4%), Ghana (2%), Kenya (2%) and South Africa (2%), during five stakeholder consultation workshops organized in Egypt, Nigeria, and Tanzania from 2017 to 2019. We sought the input of experts from government (27%), non-governmental organizations (47%), academia (13%), and private sector (13%) from different fields of expertise, covering fisheries, aquaculture, nutrition, gender, trade and economics to update and refine the model, explore alternative scenarios, validate projection results, verify the employment and investment dataset, and verify the post-model employment and investment estimation. The consensus had reached when no further comments from the stakeholders during consultation process.

Scenario analysis

We developed two scenarios in this study. The first scenario was business-as-usual future (BAU) which was characterized by a set of model parameters that reflect a continuation of past trends into the future. We had determined these trends from the regional experts we had gathered in the consultation workshops. In our BAU scenario, we use the Shared Socioeconomic Pathway (SSP) 2 [35], which assumes economic development continues but is not uniform, environmental degradation continues, but at a slowing pace compared to historical trends, and climate change presents moderate challenges to both adaptation and mitigation. Under the BAU scenario, African economies are assumed to have a low annual income growth rate of 2.9% from 2015–2050. This BAU scenario replicated projection results reported in our previous study [1].

The second alternative scenario is called high capture fisheries and aquaculture with stronger GDP growth (HIGH) assumes high aquaculture growth rates being driven by substantial investment in the industry. The model was calibrated such that aquaculture output grows at 12.7% over the 2015–2030 period (a relative improvement compared to the 10.6% aquaculture output growth observed from the 2005 to 2015 period). This is achieved by adjusting the model’s exogenous productivity growth rates of the top five aquaculture producing countries in Africa (Egypt, Nigeria, Uganda, Ghana, and Zambia) for key selected species farmed in Africa (59% tilapia, 11% catfish and 11% mullet) from 2015–2050. For capture fisheries, FAO statistics [12] reported that, in 2017, Africa produced 7.0 million tonnes of marine capture fisheries and 3.0 million tonnes of inland fisheries. However, Kolding et al. [36] estimated substantially higher production (about 20 million tonnes) from inland fisheries based on the total freshwater resources available in Africa (e.g., lakes, rivers, reservoirs, flood plains, and swamps). Given this disparity in capture fisheries estimates, we postulate in this scenario that the potentially unaccounted capture fisheries quantities are accrued to the existing BAU projections. To investigate the low per capita fish consumption in Africa, this scenario also assumes an increase in per capita incomes. Under the HIGH scenario, we set a moderate optimistic annual income growth rate of 4.8% per year compared to SSP 2 of 2.9% under BAU.

Post-model estimation of employment

Employment is a key indicator for assessing socio-economic contributions of the fisheries and aquaculture sectors to food, incomes, and livelihoods. Yet, due to the informal and dispersed nature of much of the sector, quality employment data are limited for both capture fisheries and aquaculture and their value chains. To estimate direct and indirect employment in the BAU and HIGH scenarios, we reviewed national employment data from global data sets [37, 38] and national sources [39-54]. We adopted the definitions of direct and indirect employment used by the FAO [38] which suggest a full time employee is one that received 90% of their livelihood or spends 90% of their time in that occupation; a part time employee between 30–90%, an occasional employee less than 30%, and indirect jobs are “those associated with ancillary activities such as the building of infrastructure (ponds, cages, tanks, etc.), feed and seed production, manufacturing of fish processing equipment, packaging, marketing, and distribution”. We also take indirect employment equals direct employment (full-time equivalent number of jobs) times employment multiplier presented in Table 4.

Table 4

Estimated direct and indirect employment of Africa’s fish food system for BAU and HIGH scenarios in 2030 and 2050.

Country	Scenarios	Fish production (thousand tonnes)		Direct labor productivity (tonnes/worker)		Direct employment (thousand)		Average employment multiplier		Indirect employment (thousand)		Total direct and indirect employment (thousand)
		2030	2050	2030	2050	2030	2050	2030	2050	2030	2050	2030	2050
Africa	BAU	11,439	12,064	2.0	2.1	5,630	5,774	2.6	2.7	15,035	15,855	20,665	21,629
	HIGH	26,784	34,816	2.4	2.8	11,049	12,230	3.2	3.7	35,202	45,758	46,251	57,988
Egypt	BAU	1,924	2,169	12.4	13.4	156	161	1.8	2.0	288	324	443	485
	HIGH	4,977	7,632	12.8	14.1	389	540	1.7	2.0	680	1,078	1,069	1,617
Ghana	BAU	369	389	1.4	1.4	271	276	2.1	2.2	565	597	835	872
	HIGH	1,147	1,722	1.7	2.2	660	790	2.7	3.3	1,757	2,639	2,417	3,429
Kenya	BAU	209	213	1.9	1.9	111	113	2.0	2.0	217	222	328	335
	HIGH	618	630	2.7	2.6	228	242	2.8	2.7	644	656	872	897
Malawi	BAU	133	136	1.0	1.0	135	137	4.0	4.0	534	545	669	682
	HIGH	397	404	1.0	1.0	400	406	4.0	4.0	1,595	1,622	1,995	2,028
Nigeria	BAU	1,441	1,638	1.3	1.3	1,137	1,266	0.9	0.9	1,005	1,142	2,142	2,409
	HIGH	2,072	2,396	1.2	1.3	1,749	1,903	0.8	0.9	1,445	1,670	3,194	3,573
Tanzania	BAU	341	341	1.8	1.8	192	192	1.3	1.3	247	247	439	439
	HIGH	1,087	1,088	1.8	1.8	602	604	1.3	1.3	786	787	1,409	1,416
Uganda	BAU	639	669	3.6	3.6	179	183	3.9	4.0	693	726	872	909
	HIGH	2,070	2,151	3.4	3.4	616	632	3.6	3.7	2,245	2,333	2,861	2,965
Zambia	BAU	115	124	1.3	1.3	92	96	0.5	0.5	49	52	141	148
	HIGH	418	474	1.2	1.3	347	377	0.5	0.5	177	200	524	578

To account for inconsistent data, we further adjusted direct and indirect employment data to better reflect our experts’ assessment of labor productivity and average employment multiplier during stakeholder consultation. We compiled the labor productivity data and used it to estimate future employment through capture fisheries. Future employment in the aquaculture sector was based on the increasing trend of labor productivity in Africa’s aquaculture sector observed over the past three years [37]. Assumptions for direct employment in aquaculture include a labor productivity/efficiency increase of 10% from 2018 to 2030, and again from 2030 to 2050 in Africa and the studied countries. This assumption aligns with the potential technology advancement to reduce labor requirements in aquaculture and fisheries production in the future.

Post-model estimation of aquaculture investment costs

For investment cost extrapolation, due to data limitations in capture fisheries, we focused explicitly on aquaculture alone to determine the size of investment needed to meet the BAU and HIGH projections of production. Aquaculture production values for the base year 2016 (except Uganda and Zambia base year in 2014) were computed using commodity prices collected from literature for each country. Production values of the base and future projections in 2030, and 2050 under the BAU and HIGH scenarios were converted to 2010 constant US dollar using the World Bank’s consumer price index. The investment needed to support projected value was built on key variable input costs such as seed (i.e., the broodstock, hatchlings, or fry that are spawned or caught from the wild), feed (i.e., a combination of ingredients made into a single feed for growing fish), and labor (i.e., fish farmers with full time equivalent number of direct jobs). The magnitude of costs for each scenario was ascertained through literature review and validated through our expert consultation workshops. The costs of inputs were determined using farm-gate prices, average productivity, average market size, survival rate, stocking density, feed conversion ratio, average wages, input prices, and profit margins (Table 2). We present the variation of these inputs information in single value in Table 2 after validation via the stakeholder consultation process. Future input costs were converted to constant US$ in 2010 using the consumer price index (i.e., dollar values are divided by the consumer price index of that year, and then multiplied by the index of 2010). Fixed costs such as infrastructure investment costs, and public spending for aquaculture research, development and extension, were not included in our estimation due to data limitations.

Table 2

Key parameters used for estimating the quantity and cost of key inputs in studied countries.

Base year	Egypt	Ghana	Kenya	Malawi	Nigeria	Tanzania	Uganda	Zambia
	2016	2016	2016	2016	2016	2016	2014	2014
Farm-gate price (US$/kg)	0.95	1.45	2.12	0.98	1.41	1.96	1.99	1.78
Feed conversion ratio (FCR)	1.12	2	1.5	1.8	1.3	1.5	1.5	1.7
Productivity (tonne/ha)	10.8	2.9	5	1.8	4	10	10	1.1
Average market size (g/fish)	300	400	400	-	800	-	-	-
Survival rate (%)	90	80	80	-	80	-	70	90
Stocking density (1000 pieces/ha)	40	-	15.6	6	6.3	30	2.5	2.8
Seed price (US$/1000 pieces)	5.6	39	62.6	6.6	57	90	70	43
Feed price (US$/kg)	0.38	0.48	0.63	0.33	0.57	0.81	0.56	0.30
Average wage (US$/year)	713	145	188	33	509	175	211	339
Profit margin (%)	34	15	23	19	28	11	37	23
References	[51, 55]	[39, 56–64]	[44, 52, 65–73]	[40, 41, 54, 74–76]	[70, 77–81]	[45, 48, 49, 82–86]	[43, 53, 87]	[46, 50, 88–90]

All values are converted to constant US$ in 2010 based on World Bank’s consumer price index.

Results

Scenarios

Previous projections of BAU scenario [1] had suggested that African capture fisheries and aquaculture production will grow at 0.2% and 1.3%, respectively, from 2015 to 2050. Despite the higher growth rate of aquaculture, capture fisheries in Africa will continue to be the main contributor to total fish production until 2050, though Egypt is a notable exception. Driven by high population growth and low GDP growth, per capita fish consumption in Africa is projected to gradually drop from 10.0 kg/year in 2015 to 7.7 kg/year in 2050 under this scenario (Table 3).

Table 3

IMPACT fish model scenario projection of fish production and per capita fish consumption for Africa in 2015, 2030, and 2050.

Region	Scenarios	Capture fisheries (million tonnes)			Aquaculture (million tonnes)			Per capita fish consumption (kg/person/year)
		2015	2030	2050	2015	2030	2050	2015	2030	2050
Africa	BAU	8.7	9.0	9.2	1.8	2.4	2.9	10.0	8.5	7.7
	HIGH		15.8	16.0		11.0	18.8		12.1	14.0

Under the HIGH scenario, the total capture fisheries production is projected to be 76% and 74% higher in 2030 and 2050 compared to the BAU scenario. This is driven by better accounting for inland capture fisheries production, rather than substantial increases in capture fisheries production. The total aquaculture production is projected to be 350% and 558% higher in 2030 and 2050, respectively, compared to the BAU projections (Table 3). With these high growth assumptions, the aquaculture production in Africa will likely surpass capture fisheries production by 2050. High GDP growth will enable purchasing power to increase per capita fish consumption from 10 kg in 2015 to 12 kg in 2030 and 14 kg in 2050 (Table 3).

Employment in fish sectors

Table 4 depicts that, overall, African capture fisheries and aquaculture sectors are estimated to sustain 20.7 million jobs (direct and indirect employment) in 2030, and generate 21.6 million jobs by 2050 under the BAU scenario. Direct employment in Africa’s fish sector is estimated to remain relatively constant and only grow from 5.6 million in 2030 to 5.8 million in 2050 in the BAU scenario. In contrast, under the HIGH scenario, direct employment in capture fisheries and aquaculture will be more than double in comparison to the BAU, reaching 12.2 million by 2050, where for every person directly employed in the sector, 2.6 people will be indirectly employed. By 2050, capture fisheries and aquaculture sectors will sustain 58.0 million jobs. Under the BAU and HIGH scenarios, the total direct and indirect employment for the fish sector will represent 0.9% and 2.4%, respectively, of the total projected 2.4 billion African in 2050 [4].

For direct employment in African aquaculture, even with increasing average labor productivity from 5.8 tonnes/worker in 2030 to 6.3 tonnes/worker in 2050, fish farmers are projected to increase to 0.3 million under BAU and sharply increase to 1.1 million under HIGH by 2050 due to the 71% increase in aquaculture production. Among the eight studied countries, Egypt has the highest labor productivity of 13–15 tonnes/worker, followed by Uganda, Nigeria, Ghana, and Zambia. Conversely, Malawi, Tanzania, and Kenya have relatively lower labor efficiency with less than one tonne/worker (Table 5).

Table 5

Estimated direct employment of Africa’s aquaculture sector for BAU and HIGH scenarios in 2030 and 2050.

Country	Scenarios	Aquaculture production (thousand tonnes)		Labor productivity (tonnes/worker)	Labor productivity (tonnes/worker)	Direct employment
		2030	2050	2030	2050	2030	2050
Africa	BAU	2,439	2,864	5.8	6.3	424,368	452,866
	HIGH	10,984	18,816			1,910,711	2,975,598
Egypt	BAU	1,594	1,843	13.3	14.6	120,303	126,449
	HIGH	4,550	7,210			343,318	494,569
Ghana	BAU	85	102	3.9	4.2	21,929	24,067
	HIGH	558	1,122			144,856	264,663
Kenya	BAU	33	38	0.6	0.6	60,080	62,722
	HIGH	33	45			60,063	73,717
Malawi	BAU	8	9	0.8	0.9	9,328	10,292
	HIGH	8	11			9,328	12,124
Nigeria	BAU	445	526	4.5	5.0	98,846	106,190
	HIGH	500	707			111,188	142,952
Tanzania	BAU	7	8	0.8	0.9	9,010	9,315
	HIGH	7	10			9,005	10,822
Uganda	BAU	134	154	5.4	5.9	25,071	26,207
	HIGH	136	184			25,468	31,305
Zambia	BAU	29	34	1.8	1.9	16,379	17,610
	HIGH	61	103			34,658	53,325

Employment generated by capture fisheries contributes to over 90% of the total jobs in the African fish sector under BAU. This is mainly due to higher capture fisheries output but lower labor productivity as compared to aquaculture. Among the studied countries, Egypt again has the highest labor efficiency in capture fisheries of 9.3 tonnes/worker (Tables 5 and 6). Egypt is the only country that has a higher proportion of jobs generated by aquaculture than capture fisheries in the HIGH scenario (Fig 2). Nigeria is among the studied countries that generates the highest total employment in the fish sector, particularly in the capture fisheries sector. Only two countries—Nigeria and Zambia—have average employment multipliers less than one, resulting in the proportion of indirect employment being less than half of the total employment in the fish sector (Fig 3).

Table 6

Estimated direct employment of Africa’s capture fisheries sector for BAU and HIGH scenarios in 2030 and 2050.

Country	Scenarios	Capture fisheries production (thousand tonnes)		Labor productivity (tonnes/worker)	Direct employment
		2030	2050		2030	2050
Africa	BAU	9,000	9,200	1.7	5,205,551	5,321,230
	HIGH	15,800	16,000		9,138,634	9,254,313
Egypt	BAU	330	326	9.3	35,328	34,869
	HIGH	427	422		45,690	45,231
Ghana	BAU	284	287	1.1	248,730	251,645
	HIGH	588	600		515,164	525,665
Kenya	BAU	176	175	3.5	50,456	50,415
	HIGH	585	585		168,211	168,170
Malawi	BAU	125	126	1.0	125,534	126,717
	HIGH	389	393		385,213	393,786
Nigeria	BAU	996	1,113	1.0	1,038,293	1,159,992
	HIGH	1,572	1,688		1,638,155	1,759,854
Tanzania	BAU	334	333	1.8	183,254	182,948
	HIGH	1,079	1,079		592,958	592,680
Uganda	BAU	505	515	3.3	154,162	157,281
	HIGH	1,934	1,967		590,683	600,731
Zambia	BAU	86	90	1.1	75,607	78,438
	HIGH	357	370		312,323	324,017

Fig 2

Direct employment of capture fisheries and aquaculture under BAU scenario in 2030 (A), BAU scenario in 2050 (B), HIGH scenario in 2030 (C), and HIGH scenario in 2050 (D).

Fig 3

Direct and indirect employment of fish sector under BAU scenario in 2030 (A), BAU scenario in 2050 (B), HIGH scenario in 2030 (C), and HIGH scenario in 2050 (D).

Aquaculture production values and investment costs

Under the BAU scenario, Africa’s aquaculture production is projected to reach 2.4 million tonnes, valued at US$ 2.8 billion, and 2.9 million tonnes, valued at US$ 3.3 billion in 2030 and 2050, respectively. Farm-gate investment costs for three key variable inputs of feed, labor, and fish seed to realize aquaculture production in 2030 and 2050 are shown in Table 7. The investment costs are projected to increase to US$ 1.6 billion and US$ 1.8 billion in 2030 and 2050, respectively, to achieve the projected aquaculture outputs in those years. Of the three key variable costs estimated, feed costs account for 81% to 84%, labor costs range from 10% to 12%, and fish seed costs a little over 6% (Fig 4). The investment cost structure is likely to remain the same, unless there are technological innovations in the fish feed and seed sectors, resulting in a substantial decrease in feed costs.

Table 7

Annual output value and key inputs costs of Africa’s aquaculture for BAU and HIGH scenarios in 2030 and 2050.

Country	Scenarios	Aquaculture production values (million US$)		2030 farm-gate costs (million US$)				2050 farm-gate costs (million US$)
		2030	2050	Feed	Labor	Seed	Total	Feed	Labor	Seed	Total
Africa	BAU	2,799.4	3,290.0	1,313.6	170.6	101.5	1,585.7	1,545.9	182.4	120.1	1,848.4
	HIGH	11,862.8	20,373.9	5,670.3	661.7	421.5	6,753.5	9,859.1	1,010.8	751.7	11,621.6
Egypt	BAU	1,518.8	1,756.1	672.9	85.8	33.2	791.9	778.0	90.2	38.4	906.6
	HIGH	4,334.4	6,868.3	1,920.2	244.8	94.8	2,259.8	3,042.8	352.6	150.3	3,545.7
Ghana	BAU	122.7	148.1	81.8	3.2	10.2	95.2	98.7	3.5	12.3	114.5
	HIGH	810.2	1,628.4	540.1	21.0	67.5	628.6	1,085.6	38.4	135.7	1,259.7
Kenya	BAU	70.0	80.4	31.0	11.3	6.5	48.8	35.7	11.8	7.4	54.9
	HIGH	70.0	94.5	31.0	11.3	6.5	48.8	41.9	13.9	8.7	64.5
Malawi	BAU	7.7	9.3	4.6	0.3	0.2	5.1	5.6	0.3	0.2	6.1
	HIGH	7.7	11.0	4.6	0.3	0.2	5.1	6.6	0.4	0.2	7.2
Nigeria	BAU	627.6	741.7	330.2	50.3	39.5	420.0	390.2	54.0	46.6	490.8
	HIGH	706.0	998.4	371.4	56.6	44.4	472.4	525.3	72.7	62.8	660.8
Tanzania	BAU	14.5	16.5	8.9	1.6	2.0	12.5	10.2	1.6	2.3	14.1
	HIGH	14.5	19.2	8.9	1.6	2.0	12.5	11.8	1.9	2.6	16.3
Uganda	BAU	266.7	306.6	113.4	5.3	2.4	121.1	130.4	5.5	2.7	138.6
	HIGH	270.9	366.3	115.2	5.4	2.4	123.0	155.7	6.6	3.2	165.5
Zambia	BAU	51.5	60.9	14.5	5.6	3.2	23.3	17.1	6.0	3.8	26.9
	HIGH	109.0	184.3	30.7	11.8	6.9	49.4	51.9	18.1	11.6	81.6

All value costs are in millions of constant 2010 US$.

Fig 4

Cost structure of key input production costs of aquaculture in Africa and studied countries.

Under the HIGH scenarios, aquaculture production values in Africa are projected to reach US$ 11.9 billion in 2030 and US$ 20.4 billion in 2050 (Table 7). To maintain the aquaculture growth rate as projected in the HIGH scenario, investment costs in the three key variable costs of feed, labor, and seed need to increase to US$ 6.8 billion by 2030 and US$ 11.6 billion by 2050. These key investment costs will be invested by producers, including private aquaculture enterprises and farmers at different production scales. Similar to the BAU scenario, feed cost is the main component, accounting for more than 80% (Fig 4). Investing in aquaculture feed is critical to achieving the aquaculture production outputs by 2030 and 2050 projected in the HIGH scenario.

Our post-model estimation (Table 7) suggests that uneven distribution of future aquaculture production values and required investment costs will remain under both the BAU and HIGH scenarios. Under the BAU, the eight countries included in this study are projected to account for 96% of Africa’s aquaculture production values in 2030 and only slightly reduce to 95% by 2050. A similar pattern is observed for investment costs of the key variable inputs required to achieve aquaculture projection output levels. The top four African aquaculture producers–namely Egypt, Nigeria, Uganda, and Ghana–account for 90% of the production values while the group of Kenya, Zambia, Tanzania, and Malawi was projected to account for 5% throughout 2030 and 2050.

Discussion

One of Africa’s biggest development challenges is to meet the nutrition needs, within sustainable limits of 2.4 billion women, men and children by 2050 [91]. Experiences in Asia and other regions show that aquatic foods, capture fisheries, and aquaculture systems [92] offer important nutritional and sustainability values, in some cases outperforming nutritional qualities of dietary supplements [93] and a relatively lower environmental footprint than animal-source foods [94, 95]. However, the role of fisheries, aquaculture and aquatic foods in the transformation of food systems has remained relatively overlooked due to the lack of scientific data, metrics, and evidence to inform donors, governments, and private investors in decision making and investment planning [96]. Using a rigorous partial equilibrium economic modeling tool, the IMPACT fish model, we generate future fish supply-demand projections in Africa to 2050 under the BAU and HIGH scenarios. Fish supply projections are then used to extrapolate future direct and indirect employment and investment costs needed to achieve the projected output levels.

There is a global concern that current food systems are ill-equipped to deliver nutritious food, a challenge that will be exacerbated as demands of burgeoning populations and wealth will outpace supplies. In practice, limited supplies of quality food will affect different people to different degrees based on their economic status, geography, and gender, with women of reproductive age and children under the age of five being most vulnerable to nutrient deficiencies [97]. Our BAU scenario projection shows that per capita fish consumption in Africa will gradually drop from 10.0 kg/person/year (about half the global and Asian fish intake) in 2015 to 7.7 kg/person/year in 2050. The drop in fish consumption we see in our BAU scenario is a result of population growth outpacing growth in the fish sector. Decreasing per capita fish consumption is also the outcome of modest GDP growth. Lower economic growth will constrain governments’ and private sector’s ability to invest in supply infrastructure, technology, and management systems that might otherwise boost supplies. In the optimistic HIGH scenario, with GDP growth at 4.8% per year to 2050, per capita fish consumption is projected to increase from 10.0 kg/year in 2015 to 14.0 kg/year in 2050. Investments in sustainable fisheries management and aquaculture will boost total domestic production to 34.8 million tonnes in 2050, of which the share of capture fisheries in total fisheries production in Africa will decline from 82.7% in 2015 to 46.0% in 2050. These results show that there is potential to sustain capture fisheries and expand aquaculture to meet the growing demand for fish in Africa. This needs a sound enabling macro-environment, particularly moderate to high economic growth to stimulate fish demand increase and sustained investment from farmers, investors and governments to transform Africa’s capture fisheries and aquaculture into sustainable, productive, nutrition-sensitive and inclusive aquatic food systems.

Youth employment, and the future employment of current youth, is a growing opportunity and concern globally, and aquaculture and fisheries offer possible but evolving opportunities. With rapid population growth and a young population (60% of the African population below the age of 25), it is expected that 11 million young people will enter the job market in Sub-Saharan Africa every year, while only about 3 million new jobs are created annually on the continent [98-100]. Both capture fisheries and aquaculture are important sources of employment in Africa, particularly for smallholders and value chain actors in rural areas [100]. Creating jobs in rural areas at a large-scale is critical to address these unemployment issues and income generation in Africa. Our study results show that under the BAU scenario, with slow aquaculture growth and almost stagnant capture fisheries, Africa’s fish sector is projected to provide 22 million direct and indirect jobs by 2050. However, with the HIGH scenario, 58 million people will be directly and indirectly employed in fisheries and aquaculture sectors, representing 2.4% of the total projected population in Africa in 2050. The projection results indicate that growth in Africa’s fish sector will create considerable employment and has the potential to generate significant income growth and facilitate inclusive value chain development to address development barriers faced by Africa. About 60 million people (14% of whom are women) were engaged in the primary sector to produce 179 million tonnes of fish globally in 2018 [101], implying a global average labor productivity of 3.0 tonnes/worker. Our projection estimated that the overall African labor productivity of direct employment in both capture fisheries and aquaculture is 2.0 tonnes/worker, slightly lower than the world average. Furthermore, employment in the fish sector in Africa will continue to be dominated by small-scale fisheries, with lower labor productivity compared to aquaculture (1.7 tonnes/worker vs. 6.3 tonnes/worker). This result highlights the importance of sustainable capture fisheries management to generate employment opportunities and provide income for the portion of the African population depending on artisanal fishing. In order to achieve the desired sustainability transformation, public policy leadership and private sector technological innovation will be required [102].

Our projection results show that strong aquaculture growth has a high potential to generate income and jobs for rural communities in Africa. Under the HIGH scenario, aquaculture production in Africa is projected to reach 18.8 million tonnes, generating a revenue of US$ 20.4 billion in 2050. Projected farm-gate investment costs of three key aquaculture inputs (feed, labor, and fish seed) will reach US$ 11.6 billion in 2050. It is essential to highlight that these investments can be mobilized from farmers, private sector investors and enterprises, suggesting dynamic opportunities for market-led aquaculture business development. Given that feed accounts for a major share of aquaculture production cost, this suggests that there will be bright prospects for investing in the aquaculture feed industry in Africa. It will be essential to have more supportive policies and regulations to serve as an entry point for the private sector on more inclusive ways to engage smallholders in the fish value chains.

This study provides useful insights on how aquatic foods, fisheries and aquaculture systems in Africa might evolve into the future under complex and dynamic interactions of structural changes, technological progress, income growth, and urbanization in a climate crisis. The study findings also allow drawing policy implications of different impact pathways, drivers and interventions to enhance aquatic food systems’ contributions to sustainable development goals in Africa. As documented in a previous report [103], these results could be the practical usage by a wide range of stakeholders from international organizations, academic and national government. Notwithstanding these contributions, our study has several limitations due to data gaps. First, in aquaculture investment cost extrapolation, we do not estimate the required investments for farm or value chain infrastructure. Second, we are unable to project investment costs needed for capture fisheries monitoring, management and capacity building, which mostly come from public funding and development and conservation funding. Future follow-up studies should investigate aquaculture infrastructure cost requirements and investment costs for capture fisheries management in Africa. Third, our extrapolation of outcomes focuses only on employment opportunities. Further research is needed to extend the post-model analysis to examine implications on other outcome areas such as gender equity, nutrition and environmental sustainability associated with different future projected trends. Effective and efficient use of data collection tools for gender and youth assessment needs to be embedded in a future inclusive development process. Fourth, our estimation of investment costs does not include public investment in infrastructure, human capital and research capacity needed to create macro and micro enabling environment for aquaculture and fisheries sector performance. Finally, aquatic foods are relatively new in the realm of foresight modeling tools compared to crops and livestock, and further advancement of fish foresight modeling tools is essential to improve the quality of modeling projections and incorporating these outcomes in future analyses. Given the high diversity in wild-caught and cultured fish species in Africa and worldwide, the current IMPACT model is highly aggregated with sixteen fish categories on the supply side and nine categories on the demand side. The model is calibrated using data in 2000 as a base year. This is quite out-of-date given that fisheries and aquaculture are complex and very dynamic, experiencing rapid growth over the last two decades. The IMPACT fish model uses generic assumptions to obtain parameters for specifying the fish sector equations, whereas fish and aquatic food systems are highly heterogeneous and complex. There are numerous fish types, classification schemes, and production methods. It is necessary to conduct disaggregated modeling studies for specific fish types to capture the diversity of trends within specific sub-sectors. Follow-up foresight modeling analysis and projection could address these disaggregation and complexity issues.

Conclusions

Our current food systems face severe challenges in achieving equitable access to healthy, nutritious food, maintaining environmental sustainability, and building resilience to shocks. Fish and aquatic foods offer significant potential in the transformation of food systems toward healthy and sustainable diets, sustaining livelihoods, and generating income. The fish sectors are important for employment creation in Africa, yet, their role has been overlooked, resulting in insufficient investment to support the sector growth and sustainable system transformation to meet the increasing demand for fish. This study provides insights into future fish supply and demand projections in Africa under the BAU and HIGH scenarios and provides first estimates of employment generated and the necessary input cost investments required to secure projected fish supplies in 2030 and 2050. The study suffers limitations that should be addressed in the future. Nonetheless, this is a key and first preliminary analysis to look at macro-level employment and investment scenarios of fish sectors in Africa.

15 Sep 2021

PONE-D-21-16152The future of fish in Africa: employment and investment opportunitiesPLOS ONE

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Reviewer #1: This is a well-written and balanced account to the much-neglected field of aquatic foods, fisheries and aquaculture systems in Africa. I really enjoyed reading it. Fact is that almost no sound data are available for capture fisheries and aquaculture investment costs , employment opportunities and farm-gate investment costs of key inputs. I hope more of these manuscripts are to follow.

I have made some additional grammatical and typographical corrections and restructured some sentences directly to the texts for consideration by the authors.

Finally, looking at the authorship and experience within it, I would like to see some clearer direction for decision makers on how the BAU and HIGH projection scenarios may be applied in management or policy decisions/implications. This would help a policy maker or even fisheries/aquaculture advocate be able to pull out a section and show how the data analyzed in this relevant is relevant to Continental National policy.

Reviewer #2: A good job. The attached review comment could contribute to its improvement especially the requested justifications. I wish to point that the article was of substantial information and should be published if adequate justifications are provided on the highlighted issues. The authors need to improve on tenses, grammar and the use of comma. There is the need to break quite a number of jaw breaking sentences.

Reviewer #3: “The future of fish in Africa: employment and investment opportunities” is an interesting and timely paper that addresses the future challenges and developments of the aquaculture and fisheries sectors in Africa. It does this using an existing tool, the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) developed by IPFRI (https://www.ifpri.org/project/ifpri-impact-model) to analyze two scenarios of developments in the fish production sector.

I suggest that the authors address a few issues with the paper ion its current form:

1. It is sometimes confusing whether the projections refer to the whole of Africa or the eight countries studied in detail. The current model is apparently an update from an earlier model based on detailed information from 8 African countries. The introduction then states that the projections are for eight African nations (line 100-101) while in several instances (Tables 3, 4 Line 241) the reference is made to the whole of Africa. This should be clarified

2. If projections are for the whole of Africa, the paper does not make clear how the detailed information is used to improved on the previous model. A flow chart detailing the steps made to do so is needed.

3. Some more detail on the main modelling assumptions is needed to be able to understand the scope and limitations of the analysis: the final; sentence in the discussion hints at these limitations – I suggest entering in more detail here and point out in what directions improvements are needed.

4. 76 experts were consulted in stakeholder sessions but its is not clear in what way this has improved the basic information collected for the model. For instance, what expertise was consulted? Which issues were discussed? How was consensus reached?

5. I have an issue with the conclusion that the results show that there is potential to sustainably increase capture fisheries. The high scenario, from which this conclusion derives I think, is based on a better accounting of current actual catches (rather than the estimated catches that are generally considered to be flawed) modelled as an increase in future catches. In other words, it basically assumes gradual better monitoring of otherwise stagnant (actual) fisheries production. In other words the model does not say anything about potential to sustainably increase catches. Many African fisheries experts consider todays freshwater catches as well as marine catches (especially in West Africa) unsustainable, though this can be disputed.

Detailed comments

Line 55-59 Rather awkward sentence. Please revise e.g. as

Whereas aquaculture is one of the fastest growing food production sectors globally, in Africa it supplies only 2.7% of the global share in 2019. Nevertheless, the African aquaculture sector is maintaining double digit average annual growth rates in the last two decades in response to continental market demand.

Line 112: “We apply the IMPACT model” - a few lines about the main features of this model are required for the reader to understand how projections are done.

Line 112: “This module”. Which module? “This” has no reference

Line 114: Previous application of the model: was this application limited to Africa or were these based on world historical trends?

Line 117: “The recent dataset” Which resent dataset is meant? Otherwise: “A recent data set compiled by ….”

Line 121 – Line 126: As the title of the paper is the future of fish in Africa, some discussion is required on how representative these 8 countries are for the rest of the continent, as the selection criteria as stated indicate a bias towards extremes in poverty, fish consumption and aquaculture growth

Line 124-126: Experts on what? Perhaps a list of (generalized) affiliations or expertise of the experts is useful?

Line 130 Table 1. Total fish production for Kenya, Tanzania and Uganda is 144+487+706 thousand ton = 1.337 million ton. The share of aquaculture production for these countries is 138.2 thousand ton leaving around 1.2 million ton of capture fisheries. Total catches of Lake Victoria alone over the three countries amounted to between 800 thousand to 1 million ton (between 2005 -2014, data LVFO), FAO Fishstat reports 1.1 million ton for total freshwater catches in 2019 for the three countries and 99.5 thousand ton Marine catches, which is around what is left after deducting freshwater catches and aquaculture. However, SeaAroundUs report reconstructed catches in 2018 as 140 thousand ton in Tanzania and Kenya 23 thousand ton totaling around 160 ton. Though this short review of data hat is available to me show that the estimates seem approximately what is agreed on it may be good to get a bit more insight in what basic data have been used. While for the HIGH scenario the Kolding et al.’s estimate of freshwater fisheries has been used, how is underreporting of marine capture estimates accounted for (for Tanzania and Kenya according to SeaAroudnUs captures in 2018 were 60% higher than the data used for BAU).

Line 142: “Environmental degradation continues” How is environmental degradation defined? What is meant by “at a slowing pace”? Compared to what?

Line 155: “Official statistics” as reported to FAO? Or in country official statistics? I.e. what data are used as data sets were also discussed with in country experts?

Line 151 - 154: “Exogenous productivity growth rates” What are these? “Exogenous” to what? It is not clear to me how this adjustment to reach an aquaculture output growth at 12.7%?

Line 161: “ Are accrued to the existing BAU projections” In other words the HIGH scenario assumes a gradual growth in freshwater towards the estimate by Kolding et al.? But this is an estimate of output the current freshwater capture fisheries. So in this scenario there is no room for additional growth in the freshwater sector. Also – African marine captures may also be underestimated (see SeaAroundUs estimates). How is that dealt with in this paper (for Ghana, Nigeria, Egypt, Tanzania, and Kenya).

Line 178-179 “…times employment multiplier.” Unclear – what multiplier is used here?

Line 236 – 241 – 277 In the two tables and text there is reference to African and Africa. How is the extrapolation from the 8 countries done to the African total? This is not clear from the methodology.

Line 357 – 359. “ These results show that there is potential to sustainably increase capture fisheries….” This is a strange conclusion as the HIGH scenario is based on a better accounting of current actual catches that – according to Kolding et al. paper cited, perhaps also SeaAroudnUs estimates – and is then not a gradual increase in actual catches from today’s underestimate. In other words the model does not say anything about potential to sustainably increase catches. On the contrary, though disputed, many African experts in fisheries consider todays freshwater catches as well as marine catches (West Africa) unsustainable.

Line 381 – 382 “ Global average labor productivity of 3.0 ton/worker” Apparently this is a weighted average over fisheries and aquaculture production. In an analysis of 16 African lakes Kolding, J., and van Zwieten, P.A.M. (2012) estimated a productivity of 3.0 ton per year per worker again indicating that the capture fisheries productivity of 1.7 over Africa may be way too low. (Kolding, J., and van Zwieten, P.A.M. (2012). Relative lake level fluctuations and their influence on productivity and resilience in tropical lakes and reservoirs. Fisheries Research 115-116, 99-109. doi:10.1016/j.fishres.2011.11.008)

Line 410 – 412. “We are unable to project investment costs needed to sustain capture fisheries, which mostly come from public funding and philanthropic investment” Not sure what investments are hinted at here? I guess this is investments in better monitoring and management capacity? What is meant by “philanthropic investment”: if by this is meant investment through e.g. Nature Conservation NGO’s then these are generally temporary investments that are not conducive to setting up permanent structures to maintain fisheries management, at most in capacity building.

Line 415-416. This sounds as a “must mention” line with limited or unclear links to preceding or following lines. The line also suggests that women are excluded from the employment opportunities mentioned in the previous sentence, which I don’t think will be or is the case. Please expand.

Line 423-426. As it is important to clarify the model assumptions in this study anyway, this final sentence should be expanded upon: what are the issues? What are avenues to improve the model based on your current insights.

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Reviewer #1: Yes: Dr Kevin Obiero

Reviewer #2: No

Reviewer #3: Yes: Paul A.M. van Zwieten

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Submitted filename: PONE-D-21-16152_reviewer-KO.pdf

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Submitted filename: future of fish for Africa reviewed aticle submitted to plusone review.pdf

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30 Oct 2021

RESPONSE TO REVIEWERS

We thank the reviewers’ comments and helpful advice. We responded to the critique, point-by-point as below, and made changes in the revised version (yellow highlighted).

Reviewer #1 Comments

This is a well-written and balanced account to the much-neglected field of aquatic foods, fisheries and aquaculture systems in Africa. I really enjoyed reading it. Fact is that almost no sound data are available for capture fisheries and aquaculture investment costs, employment opportunities and farm-gate investment costs of key inputs. I hope more of these manuscripts are to follow.

Q1: I have made some additional grammatical and typographical corrections and restructured some sentences directly to the texts for consideration by the authors.

A1: Thank you very much for your corrections. We have revised all your suggestions in the main text of the manuscript.

Q2: Finally, looking at the authorship and experience within it, I would like to see some clearer direction for decision makers on how the BAU and HIGH projection scenarios may be applied in management or policy decisions/implications. This would help a policy maker or even fisheries/aquaculture advocate be able to pull out a section and show how the data analyzed in this is relevant to Continental National policy.

A2: Thank you for your suggestions. The analysis of this work will be helpful for policymakers at the national and continental levels. For example, when the government prepares a Masterplan for the fish sector and estimates the future investment cost, it is vital to refer to the scientific references analyzed in this study to project the future prospects and challenges in production, consumption, trade, employment, etc. One published report [104] documented the uses of and outcomes from the Policies, Institutions and Markets (PIM)-supported foresight modeling research from 2012-2018. It covers usage by a wide range of stakeholders from across the Consultative Group on International Agricultural Research (CGIAR) system, other international organizations, academia, and national governments.

This sentence is added in the Discussion section:

“As documented in a previous report [104], these results could be the practical usage by a wide range of stakeholders from international organizations, academic and national government.”

104. Lowder, S. K., Regmi, A. (2019) Independent Review: Assessment of outcomes based on the use of PIM-supported foresight modeling work, 2012-2018. Washington, DC: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/p15738coll2.133608

Reviewer #2 Comments

A good job. The attached review comment could contribute to its improvement especially the requested justifications. I wish to point that the article was of substantial information and should be published if adequate justifications are provided on the highlighted issues. The authors need to improve on tenses, grammar and the use of comma. There is the need to break quite a number of jaw breaking sentences.

Q1:The article is of importance to Africa’s future fish production. However, the following corrections/clarifications are needed. The objectives of the study is not clear under Abstract section.

Line 27 had different font characteristic

Line 30-31 not clear which one get 2.6 people, what system? And what specific contribution from each of feed, seed and labour

A1: Thank you very much for your comments. The objectives of the study are added in the Abstract:

“To date, there are no estimates of investment and potential returns for domestic fish production in Africa. To contribute to policy debates about the future of fish in Africa, we applied the International Model for Policy Analysis of Agriculture Commodities and Trade (IMPACT) to explore two Pan-African scenarios for fish sector growth.”

The font characteristic in Line 27 is revised to the same “Arial” font size 11.

In Line 30-31, it means that for every person directly employed in the fish sector (both capture fisheries and aquaculture), 2.6 people will be indirectly employed. Drawing evidence from the existing literature, the study provides an aggregated estimation of employment effects. We are unable to project employment for each input sectors owing to missing information. We have revised the sentence as follow:

“Approximately 2.6 people will be employed indirectly along fisheries and aquaculture value chains for every person directly employed in the fish production stage.”

Q2: Line 51: In Africa? Line 55-59: has too long sentence and there are such all over the manuscript. These has to be corrected. The use of comma, tenses and grammar as observed for instance in lines 65, 93, 117, 178, 202 (growth?), 246, 266, 336

A2: We have added “in Africa” to the sentence in Line 51:

“Nevertheless, the current and future values of fish and aquatic foods in Africa are often overlooked in development research, policy and investment”.

The sentence from lines 55-59 was revised as follow for clarity:

“Whereas aquaculture is one of the fastest growing food production sectors globally [12, 13], Africa contributed only 2.7% to the global aquaculture share in 2019; though the growth rate is faster in the continent and maintained an average double digit annual growth rate over the past two decades”.

As your suggestions, we carefully checked and corrected grammar, tenses, and sentences in lines 65, 117, 178, 202, 246, 266 and 336.

Q3: There is need for ref in citing in some cases such as line 107.

A3: A reference was added in line 107:

33. FAO. Low-Income Food-Deficit Countries (LIFDCs)-List updated June 2021. 2021. Available from: https://www.fao.org/countryprofiles/lifdc/en

Q4: Lines 100-107also 110-112 sounds line methods being presented in introduction

A4: Following your suggestion, we revised and moved the paragraph to the method section:

“We focused on eight African nations: Egypt, Ghana, Kenya, Malawi, Nigeria, Tanzania, Uganda and Zambia. These eight countries are home to 40% of Africa's total population but produce over 95% of aquaculture and 30% of capture fisheries production (by volume) in the continent in 2019. About half of fish consumed in Africa is by these eight countries, suggesting slightly higher per capita fish consumption rates than elsewhere in Africa [32]. Among these eight countries, Uganda, Tanzania, Malawi, Kenya, and Ghana are classified by Food and Agriculture Organization (FAO) as low-income food-deficit countries [33].”

Q5: It would be good to highlight the % of each country, the criteria for setting the ratio and how the experts look like (criteria for their selection /area of experts - aquaculture/fisheries?

A5: We revised the sentences to highlight the proportion of countries and experts’ backgrounds:

“We consulted 76 experts from Egypt (43%), Nigeria (32%), Tanzania (15%), Zambia (4%), Ghana (2%), Kenya (2%) and South Africa (2%), during five stakeholder consultation workshops organized in Egypt, Nigeria, and Tanzania from 2017 to 2019. We sought the input of experts from government (27%), non-governmental organizations (47%), academia (13%), and private sector (13%) from different fields of expertise, covering fisheries, aquaculture, nutrition, gender, trade and economics to update and refine the model, explore alternative scenarios, validate projection results, verify the employment and investment dataset, and verify the post-model employment and investment estimation. The consensus had reached when no further comments from the stakeholders during the consultation process.”

Q6: Table 1 seems not clear. Justify why population growth ranged 5 years, GDP growth ranged 10 years, undernourishment only captured 2018, unemployment 2018. These variation need to be backed up with good reasons

A6: We have revised table 1 for average annual growth ranging from the most recent ten years for population (2010-2020) and GDP (2010-2020). We also update the available latest year data for urban population (2020), GDP per capita (2020), undernourishment (2019), and unemployment (2020).

Q7: Data being referred in Line 137-139 need to be cited

A7: The two scenarios described in Line 137-139 are analysis of this study, therefore no citation in this paragraph. We have revised the sentence in Line 139:

“We had determined these trends from the regional experts we had gathered in the consultation workshops.”

Q8: Line141: what is the citation for the “Shared Socio Economic Pathways 2”

Line 142- need to explain the assuming “slow pace environmental degradation. Explain what informed moderate challenges from climate change adaptation.

A8: We have moved the citation next to Shared Socioeconomic Pathway (SSP) 2 and corrected the citation to the new reference of the original source for SSP. SSP 2 is a standard set of assumptions for socioeconomic development widely used in the modeling of future scenarios. All aspects of SSP 2 (environmental degradation, climate challenges, etc) are explained in O’Neli et al. 2017.

“In our BAU scenario, we use the Shared Socioeconomic Pathway (SSP) 2 [36], which assumes economic development continues but is not uniform, environmental degradation continues, but at a slowing pace compared to historical trends, and climate change presents moderate challenges to both adaptation and mitigation.”

36. O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K, et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change 2017;42:169-180. https://doi.org/10.1016/j.gloenvcha.2015.01.004

Q9: Line 147 seems to discuss aquaculture and fisheries together. Does it mean that data on the two were not separated. Check FAO data for separate figure for each of them.

A9: The HIGH scenario we analyzed combines aquaculture and capture fisheries impact by using separate assumptions and data on the growth of aquaculture and compensation of under-reported capture fisheries production.

Q10: Line 153-154. You need to support the choice of the listed species with good reason. You would need to provide information on the % Africas aquaculture being contributed by mullet and pangasius? What brought their importance for projection in Africa. There are similar issues in line 163-164, 193 (they all need justification).

A10: Thank you for your comments. In the IMPACT model, “Pangasius and other catfish” was defined as one fish group. To date, Africa only farms African catfish and does not farm Pangasius catfish. For clarity, we have revised the text and dropped Pangasius. Mullet is included in the projection as it is presently farmed in Egypt. We also describe the contribution in the percentage of farmed tilapia, catfish and mullet in Africa:

“This is achieved by adjusting the model’s exogenous productivity growth rates of the top five aquaculture producing countries in Africa (Egypt, Nigeria, Uganda, Ghana, and Zambia) for key selected species farmed in Africa (59% tilapia, 11% catfish and 11% mullet) from 2015-2050”

In Line 163-164, we have clarified the sentence:

“Under the HIGH scenario, we set a moderate optimistic annual income growth rate of 4.8% per year compared to SSP 2 of 2.9% under BAU.”

In Line 193, we have revised the sentence for clarity:

“For investment cost extrapolation, due to data limitations in capture fisheries, we focused explicitly on aquaculture alone to determine the size of investment needed to meet the BAU and HIGH projections of production.”

Q11: Line 200 rather use “broodstock” instead of “fertilized eggs”

A11: We have replaced “fertilized eggs” with “broodstock” as your suggestion.

Q12: Table 2; each column has cited many references but singler value was presented for each column, no variation/ standard deviation? In am curious why current values were not mentioned at all even in the discussion? Does it mean that the dynamics of these variants were held constant till now?

A12: We estimated the baseline (2014 or 2016) of key aquaculture input parameters by referring to various references in eight African countries. We need a single value to project the future cost of key inputs by 2030 and 2050. We have presented this variant information from various references and validated the final consensus of a single value as listed in Table 2 with stakeholders during the consultation process. We extrapolate the future costs by using a single value in the base year that was converted to constant US$ in 2010 using the consumer price index.

We added one sentence for clarity:

“We present the variation of these inputs information in single value in Table 2 after validation via the stakeholder consultation process.”

Q13: The study has too many limitations listed. It could be referred “preliminary”

A13: We agree with your comments. We have revised the last sentence in the conclusions section:

“This is a key and first preliminary analysis to look at macro-level employment and investment scenarios in fish sectors in Africa.”

Reviewer #3 Comments

The future of fish in Africa: employment and investment opportunities” is an interesting and timely paper that addresses the future challenges and developments of the aquaculture and fisheries sectors in Africa. It does this using an existing tool, the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) developed by IPFRI (https://www.ifpri.org/project/ifpri-impact-model) to analyze two scenarios of developments in the fish production sector.

I suggest that the authors address a few issues with the paper in its current form:

Q1: It is sometimes confusing whether the projections refer to the whole of Africa or the eight countries studied in detail. The current model is apparently an update from an earlier model based on detailed information from 8 African countries. The introduction then states that the projections are for eight African nations (line 100-101) while in several instances (Tables 3, 4 Line 241) the reference is made to the whole of Africa. This should be clarified.

A1: Thank you for your comments. The IMPACT fish model can project at global, continental, regional, and national level. This study project both the whole Africa and also the eight studied countries in Africa.

Q2: If projections are for the whole of Africa, the paper does not make clear how the detailed information is used to improved on the previous model. A flow chart detailing the steps made to do so is needed.

A2: We have added one sentence and a flow chart (Fig 1) to illustrate the steps of improvement of IMPACT fish model used to project the future of Africa’s fish sector on this study:

“The progressive improvement of IMPACT fish model used to project future Africa’s fish sector is illustrated” in Fig 1.”

Figure 1. Chronological model improvement and analysis using IMPACT fish model

Q3: Some more detail on the main modelling assumptions is needed to be able to understand the scope and limitations of the analysis: the final; sentence in the discussion hints at these limitations – I suggest entering in more detail here and point out in what directions improvements are needed.

A3: Thank you for your comments. We have listed the IMPACT fish modeling assumptions and limitations in our previous publication [17] under the conclusion section, as shown in the paragraph below. This study less emphasize on the model improvement but more focus on the post-model analysis on future employment and investment costs in Africa’s fish sector. To avoid the manuscript being too lengthy, we did not repeat these points in this study.

“Modeling work for generating an evidence base for decision-making is crucial. While the IMPACT fish model has been greatly improved so that it can generate reasonable fish sector projections at both global and regional levels, the current setup suffers some limitations in terms of data and model structure. Available data is inconsistent and the lack of trade data at the desirable species classification leads to the inability to analyze bilateral trade flows, which makes the analysis of specific trade policy difficult without complementary work. Global markets with homogenized commodities is a necessary simplifying assumption that might be more contentious in the seafood market than, for example, in the cereal, meal or oil markets, where products are commoditized to a large degree. On the other hand, the sheer number of different fish species being caught and farmed requires some form of simplifications to be modelled at all. Other potential issues to address in the modeling framework include dealing with climate change and environmental stresses more explicitly, updating the underlying database with the latest available data and embedding the ability to develop new and alternative fisheries where they might not have previously been considered. The modeling efforts expose important data gaps and identifies the areas where improved data collection is needed.”

However, we have added the following sentences to highlight the issues and ways for improving fish foresight modelling and projection in the last paragraph in Discussion section:

“The current IMPACT model adopts highly aggregated with 16 fish categories in the supply side and 9 fish categories on the demand side. The model is calibrated using data in 2000 as a base year. This is quite out-of-date given that fisheries and aquaculture are complex and very dynamic, experiencing rapid growth over the last two decades. The IMPACT fish model uses generic assumptions to obtain parameters for specifying the fish sector equations, whereas fish and aquatic food systems are highly heterogeneous and complex. There are numerous fish types, classification schemes, and production methods. It is necessary to conduct disaggregated modeling studies for specific fish types to capture the diversity of trends within specific sub-sectors. Follow-up foresight modeling analysis and projection could address this disaggregation and complexity issues.”

Q4: 76 experts were consulted in stakeholder sessions but its is not clear in what way this has improved the basic information collected for the model. For instance, what expertise was consulted? Which issues were discussed? How was consensus reached?

A4: We revised the sentences to highlight the proportion of countries, experts’ background, issues being discussed and how consensus was reached during the consultation process:

“We consulted 76 experts from Egypt (43%), Nigeria (32%), Tanzania (15%), Zambia (4%), Ghana (2%), Kenya (2%) and South Africa (2%), during five stakeholder consultation workshops organized in Egypt, Nigeria, and Tanzania from 2017 to 2019. We sought the input of experts from government (27%), non-governmental organizations (47%), academia (13%), and private sector (13%) from different fields of expertise, covering fisheries, aquaculture, nutrition, gender, trade and economics to update and refine the model, explore alternative scenarios, validate projection results, verify the employment and investment dataset, and verify the post-model employment and investment estimation. The consensus had reached when no further comments from the stakeholders during the consultation process.”

Q5: I have an issue with the conclusion that the results show that there is potential to sustainably increase capture fisheries. The high scenario, from which this conclusion derives I think, is based on a better accounting of current actual catches (rather than the estimated catches that are generally considered to be flawed) modelled as an increase in future catches. In other words, it basically assumes gradual better monitoring of otherwise stagnant (actual) fisheries production. In other words the model does not say anything about potential to sustainably increase catches. Many African fisheries experts consider todays freshwater catches as well as marine catches (especially in West Africa) unsustainable, though this can be disputed.

A5: Thank you for your comment. We have revised the results conclusion to delete “sustainably increase” to “to sustain capture fisheries” :

“These results show that there is potential to sustain capture fisheries and expand aquaculture to meet the growing demand for fish in Africa.”

Q6: Line 55-59 Rather awkward sentence. Please revise e.g. as Whereas aquaculture is one of the fastest growing food production sectors globally, in Africa it supplies only 2.7% of the global share in 2019. Nevertheless, the African aquaculture sector is maintaining double digit average annual growth rates in the last two decades in response to continental market demand.

A6: Thank you for your correction. We have revised the sentence as you suggested.

Q7: Line 112: “We apply the IMPACT model” - a few lines about the main features of this model are required for the reader to understand how projections are done.

Line 112: “This module”. Which module? “This” has no reference

Line 114: Previous application of the model: was this application limited to Africa or were these based on world historical trends?

Line 117: “The recent dataset” Which resent dataset is meant? Otherwise: “A recent data set compiled by ….”

A7: Following your suggestions, we have revised the text describing the IMPACT fish model as follows:

In Line 112: “We apply the IMPACT fish model developed by International Food Policy Research Institute (IFPRI), which is a partial equilibrium economic model containing a system of equations for analyzing baseline and alternative scenarios for fish demand, supply, trade and prices at global, regional and country level in responding to future changes such as income, population and technological progress.”

In Line 114: “Previous application of the model by the World Bank in “Fish to 2030” report [29] used global historical data up through 2009 to develop business-as-usual (BAU) scenario.”

In Line 117: “To address these shortcomings, we re-calibrate the model with recent dataset and parameters of fish production, consumption, trade, population and GDP compiled from FAO, UN and IFPRI databases [4, 12, 30, 31].”

Q8: Line 121 – Line 126: As the title of the paper is the future of fish in Africa, some discussion is required on how representative these 8 countries are for the rest of the continent, as the selection criteria as stated indicate a bias towards extremes in poverty, fish consumption and aquaculture growth

A8: This study focus on the model projection and post-model analysis in Africa as a whole. Among 54 countries in Africa, we selected the top eight aquaculture and key capture fisheries producing countries to further analyze the future employment and investment costs. Among these eight countries, five countries (Uganda, Tanzania, Malawi, Kenya, and Ghana) are classified as low-income food-deficit countries. However, if we refer to the socio-economic indicators in Table 1, the performance of Egypt has a relatively above average both at global and at the continental level for extremes in poverty, fish consumption and aquaculture growth indicators. Therefore, the selection criteria still represent some country variations in poverty, per capita fish consumption, etc. Above all, our selection of these eight countries is not aim to represent the rest of the continent because we had conducted future projection and post-model analysis for Africa as a whole in this study.

Q9: Line 124-126: Experts on what? Perhaps a list of (generalized) affiliations or expertise of the experts is useful?

A9: Thank you for your comment. We have provided experts affiliation and expertise as shown in A4 above.

“We consulted 76 experts from Egypt (43%), Nigeria (32%), Tanzania (15%), Zambia (4%), Ghana (2%), Kenya (2%) and South Africa (2%), during five stakeholder consultation workshops organized in Egypt, Nigeria, and Tanzania from 2017 to 2019. We sought the input of experts from government (27%), non-governmental organizations (47%), academia (13%), and private sector (13%) from different fields of expertise, covering fisheries, aquaculture, nutrition, gender, trade and economics to update and refine the model, explore alternative scenarios, validate projection results, verify the employment and investment dataset, and verify the post-model employment and investment estimation. The consensus had reached when no further comments from the stakeholders during the consultation process.”

Q10: Line 130 Table 1. Total fish production for Kenya, Tanzania and Uganda is 144+487+706 thousand ton = 1.337 million ton. The share of aquaculture production for these countries is 138.2 thousand ton leaving around 1.2 million ton of capture fisheries. Total catches of Lake Victoria alone over the three countries amounted to between 800 thousand to 1 million ton (between 2005 -2014, data LVFO), FAO Fishstat reports 1.1 million ton for total freshwater catches in 2019 for the three countries and 99.5 thousand ton Marine catches, which is around what is left after deducting freshwater catches and aquaculture. However, SeaAroundUs report reconstructed catches in 2018 as 140 thousand ton in Tanzania and Kenya 23 thousand ton totaling around 160 ton. Though this short review of data hat is available to me show that the estimates seem approximately what is agreed on it may be good to get a bit more insight in what basic data have been used. While for the HIGH scenario the Kolding et al.’s estimate of freshwater fisheries has been used, how is underreporting of marine capture estimates accounted for (for Tanzania and Kenya according to SeaAroudnUs captures in 2018 were 60% higher than the data used for BAU).

A10: Thank you for your detailed calculation on fish production. Insights from hidden harvest/under-reporting in capture fisheries studies was used to develop HIGH assumptions, allowing capture fisheries to reach intermediate level accounting of under-report data. In IMPACT fish model, the model structure did not further disaggregate capture fisheries to marine capture and inland capture. Therefore, the capture fisheries production will be at aggregate level.

Q11: Line 142: “Environmental degradation continues” How is environmental degradation defined? What is meant by “at a slowing pace”? Compared to what?

A11: SSP 2 is a standard set of assumptions for socioeconomic development widely used in the modeling of future scenarios. All aspects of SSP 2 (environmental degradation, climate challenges, etc) are explained in O’Neli et al. 2017. We have corrected the citation to the new reference for the original source of SSP and revised the sentence for clarity:

“In our BAU scenario, we use the Shared Socioeconomic Pathway 2 [36], which assumes economic development continues but is not uniform, environmental degradation continues, but at a slowing pace compared to historical trends, and climate change presents moderate challenges to both adaptation and mitigation.”

36. O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K, et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change 2017;42:169-180. https://doi.org/10.1016/j.gloenvcha.2015.01.004

Q12: Line 155: “Official statistics” as reported to FAO? Or in country official statistics? I.e. what data are used as data sets were also discussed with in country experts?

A12: We revised the sentence to clarify official statistics reported by FAO:

“For capture fisheries, FAO statistics [30] reported that, in 2017, Africa produced 7.0 million tonnes of marine capture fisheries and 3.0 million tonnes of inland fisheries.”

Q13: Line 151 - 154: “Exogenous productivity growth rates” What are these? “Exogenous” to what? It is not clear to me how this adjustment to reach an aquaculture output growth at 12.7%?

A13: Thank you for your comments. In the IMPACT fish model, aquaculture production is modeled as:

Aquaculture productivity growth rates are decided outside the model system of equations and exogeneous to the model. In this study, we adjusted exogeneous growth rates so that the aquaculture growth rate achieved 12.7% over the 2015-2030 period.

Q14: Line 161: “ Are accrued to the existing BAU projections” In other words the HIGH scenario assumes a gradual growth in freshwater towards the estimate by Kolding et al.? But this is an estimate of output the current freshwater capture fisheries. So in this scenario there is no room for additional growth in the freshwater sector. Also – African marine captures may also be underestimated (see SeaAroundUs estimates). How is that dealt with in this paper (for Ghana, Nigeria, Egypt, Tanzania, and Kenya).

A14: In line with your Q5, in this study, we assume that capture fisheries will grow to the level that under-reported and presented by Kolding’s studies [37]. This allows the capture fisheries to grow from the current official statistics reported to FAO to the capture fisheries output level, incorporating the unreported. Given that this unreported data are one-point estimation, further studies are recommended to examine if after incorporating unreported quantity, capture fisheries can grow further. Similar to Q10, HIGH sceanrio is using assumptions to compensate unaccounted capture fisheries (both inland and marine).

Q15: Line 178-179 “…times employment multiplier.” Unclear – what multiplier is used here?

A15: We have revised the sentence for clarity:

“We also take indirect employment equals direct employment (full-time equivalent number of jobs) times employment multiplier presented in Table 4.”

Q16: Line 236 – 241 – 277 In the two tables and text there is reference to African and Africa. How is the extrapolation from the 8 countries done to the African total? This is not clear from the methodology.

A16: We first update the production quantity to 2015 for all 54 Africa countries and calibrate to 2050 as describe in our previous publication, Fish to 2050 in Africa [1]. In this study, we select 8 countries to further update more detailed production (by 16 fish groups) and consumption quantity (by 9 fish groups) and further calibrate to 2050. Therefore, we are not using 8 countries to extrapolate for all Africa countries as a whole. In fact we conduct this continental and national level updates and analysis in two stages.

Q17: Line 357 – 359. “ These results show that there is potential to sustainably increase capture fisheries….” This is a strange conclusion as the HIGH scenario is based on a better accounting of current actual catches that – according to Kolding et al. paper cited, perhaps also SeaAroudnUs estimates – and is then not a gradual increase in actual catches from today’s underestimate. In other words the model does not say anything about potential to sustainably increase catches. On the contrary, though disputed, many African experts in fisheries consider todays freshwater catches as well as marine catches (West Africa) unsustainable.

A17: In line with Q5, we have revised the results conclusion to delete “sustainably increase” to “to sustain capture fisheries” :

“These results show that there is potential to sustain capture fisheries and expand aquaculture to meet the growing demand for fish in Africa.”

Q18: Line 381 – 382 “ Global average labor productivity of 3.0 ton/worker” Apparently this is a weighted average over fisheries and aquaculture production. In an analysis of 16 African lakes Kolding, J., and van Zwieten, P.A.M. (2012) estimated a productivity of 3.0 ton per year per worker again indicating that the capture fisheries productivity of 1.7 over Africa may be way too low. (Kolding, J., and van Zwieten, P.A.M. (2012). Relative lake level fluctuations and their influence on productivity and resilience in tropical lakes and reservoirs. Fisheries Research 115-116, 99-109. doi:10.1016/j.fishres.2011.11.008)

A18: Thank you for your comments. In Line 381-382, yes, the global average labour productivity of 3.0 ton/worker combines capture fisheries and aquaculture. The SOFIA 2018 [38] reported that the labour productivity for capture fisheries in Africa ranged from 1.46 to 1.77 between 2000-2016. The labour productivity of aggregate capture fisheries 1.73 ton/worker in 2016 [38] was used as our base year to extrapolate to future employment in capture fisheries.

We appreciate your information very much from Kolding et al. 2012 to estimate the productivity 3.0 ton/worker in 16 African lakes. We recognize this reference showed higher labour productivity for inland capture fisheries in Africa. As explained in Q10, IMPACT fish model did not disaggregate capture fisheries into inland and marine. Therefore, we refer to labour productivity of aggregate capture fisheries (inland and marine) from the the SOFIA report in this study.

Q19: Line 410 – 412. “We are unable to project investment costs needed to sustain capture fisheries, which mostly come from public funding and philanthropic investment” Not sure what investments are hinted at here? I guess this is investments in better monitoring and management capacity? What is meant by “philanthropic investment”: if by this is meant investment through e.g. Nature Conservation NGO’s then these are generally temporary investments that are not conducive to setting up permanent structures to maintain fisheries management, at most in capacity building.

A19: Thank you. We have revised the sentence as below following your comments:

“Second, we are unable to project investment costs needed for capture fisheries monitoring, management and capacity building, which mostly come from public funding and development and conservation funding.”

Q20: Line 415-416. This sounds as a “must mention” line with limited or unclear links to preceding or following lines. The line also suggests that women are excluded from the employment opportunities mentioned in the previous sentence, which I don’t think will be or is the case. Please expand.

A20: Thank you for your comments. We deleted the sentence below to have a better link of preceding sentence:

“Enabling women to fully engage in and benefit from small-scale fisheries and aquaculture can boost production, reduce poverty, and improve food and nutrition security in Africa.”

Q21: Line 423-426. As it is important to clarify the model assumptions in this study anyway, this final sentence should be expanded upon: what are the issues? What are avenues to improve the model based on your current insights.

A21: We have added the following paragraph to highlight the issues and ways for improving fish foresight modeling and projection:

“The current IMPACT model adopts highly aggregated with sixteen fish categories on the supply side and nine fish categories on the demand side. The model is calibrated using data in 2000 as a base year. This is quite out-of-date given that fisheries and aquaculture are complex and very dynamic, experiencing rapid growth over the last two decades. The IMPACT fish model uses generic assumptions to obtain parameters for specifying the fish sector equations, whereas fish and aquatic food systems are highly heterogeneous and complex. There are numerous fish types, classification schemes, and production methods. It is necessary to conduct disaggregated modeling studies for specific fish types to capture the diversity of trends within specific sub-sectors. Follow-up foresight modeling analysis and projection could address these disaggregation and complexity issues.”

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

29 Nov 2021

PONE-D-21-16152R1The future of fish in Africa: employment and investment opportunitiesPLOS ONE

Dear Dr. Tran,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it is almost ready for publication. There is a final minor issue on page 23 lines 441-443, see comment of reviewer 3. Therefore, we invite you to submit a revised version of the manuscript that addresses the issue.

Please submit your revised manuscript by Jan 13 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Reviewers' comments:

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Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

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Reviewer #3: Yes

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Reviewer #1: All previous comments have been addressed by the authors. The manuscript is now ready for publication

Reviewer #3: The reviewer comments were adequately addressed. One small suggestion for an edit: the final paragraph contains the following confusing newly added sentence:

"The current IMPACT model adopts highly aggregated with sixteen fish categories on the supply side and nine fish categories on the demand side." Perhaps "Given the high diversity in fished and cultured species in Africa, the current IMPACT model is highly aggregated with sixteen fish categories on the supply side and nine fish categories on the demand side." At least, if this was what was meant.

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Reviewer #1: Yes: Dr Kevin Obiero

Reviewer #3: Yes: P.A.M. van Zwieten

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29 Nov 2021

A1: Thank you for your comments. We have revised the sentence as follow:

Given the high diversity in wild-caught and cultured fish species in Africa and worldwide, the current IMPACT model is highly aggregated with sixteen fish categories on the supply side and nine categories on the demand side.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

7 Dec 2021

The future of fish in Africa: employment and investment opportunities

PONE-D-21-16152R2

Dear Dr. Tran,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Gideon Kruseman, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

13 Dec 2021

PONE-D-21-16152R2

The future of fish in Africa: employment and investment opportunities

Dear Dr. Tran:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Gideon Kruseman

Academic Editor

PLOS ONE