The proportion of protected lands increased in both the Gulf of Guinea and Sudano-Sahelian zones probably indicating responsiveness by governments to global concerns for the preservation of natural resources. This contrasts with a decrease from 19.5 to 13.8% over the same period in protected lands in high-income countries [9, 13]. Lands under arable plus permanent crops were 31.8 and 9.7% in the Gulf of Guinea and Sudano-Sahelian zones, respectively, in 2003 and 33–11% in 2013. Land under arable crops increased to a much greater extent in the Sudano-Sahelian than the Gulf of Guinea. Overall, land under arable crops in ECOWAS increased by 7.3%. Area under permanent crops increased more in the Sudano-Sahelian than in the Gulf of Guinea, with an average increase of 10.5% for ECOWAS. Arable land plus permanent crop lands as a proportion of total land was almost the same in 2003 and in 2013 in the Gulf of Guinea. In the Sudano-Sahelian, there was a small increase from 9.7 in 2003 to 11.3% in 2013. For ECOWAS as a whole, it was 18.7% in 2003 and

These findings suggest a high proportion of land reserves for agricultural production and variability between zones. However, it would have been more meaningful to express arable plus permanent croplands as a percentage of cultivable land, but the definition of cultivable land lacks clarity. According to [1] "cultivable area is an area of land potentially fit for cultivation. This term may or may not include part or all of the forests and rangeland. Assumptions made in assessing cultivable land vary from country to country. In this (FAO) survey, national figures have been used whenever available, despite possible large discrepancies in computation methods."

**68**

20.2% in 2013.

*Land use: areas under arable and permanent crops and carbon storage in forests in ECOWAS.*

Research is therefore needed on reliably estimating cultivable land to facilitate the assessment of key factors controlling climate change impacts.

Area under forests declined at the rate of 9.8 and 8.5% in the Gulf of Guinea and Sudano-Sahelian, respectively. Overall, there was a 9.4% decline in area under forests in ECOWAS over 10 years (**Table 1**). The decline in the Sudano-Sahelian zone may, however, not be uniform because the conversion of biomass into forest has taken place in the Sudano-Sahelian zone [15]. The increase in arable area and decrease in area under forests in ECOWAS associated with the release of carbon to the atmosphere would contribute to land degradation.

The estimated carbon stocks in forests of ECOWAS (**Table 1**) were substantial (6708 million t in 2003 and 6034 million t in 2013) but significantly greater in the Gulf of Guinea, where forests predominate, than the Sudano-Sahelian. The variation of carbon stocks with agroecosystems as measured in the field and laboratory [16] showed that for mangrove ecosystems, roots and soil together accounted for about 86% of the total ecosystem carbon stocks (463 and 1382 Mg C ha−1 in the low mangroves of semiarid Senegal and in the tall mangroves of humid Liberia, respectively).

ECOWAS countries have experienced significant land degradation [15, 17]. Based on land area data of the World Bank [10, 14] and areas of degraded land [17], it is estimated that up to 644,000 km2 (31%) of lands in the Gulf of Guinea and 1,124,000 km2 (38%) of lands in the Sudano-Sahelian zone and 1,768,000 km2 (35%) of lands in ECOWAS were severely to very severely degraded by 2000. Nkonya et al. [18], however, reported a lower but still important rate of 19% of lands excluding deserts for Niger. The primary causes of land degradation in ECOWAS are deforestation, agriculture, conversion of grasslands to croplands, and overgrazing [18, 19]. Deforestation in ECOWAS is associated with slash and burn agriculture as well as with fuelwood, charcoal, and timber production to meet basic livelihood needs of small-holder farmers and boost incomes of commercial enterprises. Moussa et al. [19] estimate that more than 60% of carbon dioxide emission in Africa is due to deforestation and land degradation. Land reclamation to combat degradation is therefore a climate change adaptation option requiring financing by governments and their international development partners.

#### **3.2 Crop production**

The FAO database on crop production is available on the basis of country and not on farming systems or agroecological zones (AEZ). Nevertheless, it is well known that millet and sorghum are the traditional cereals of the Sudano-Sahelian, and maize, the roots and tubers, and tree crops are mainly cultivated in the Gulf of Guinea. With the development and release of new varieties of crops and changing climate, the distribution of these crops across ECOWAS is changing. Rice cultivation is becoming ubiquitous throughout the region as the urban population and demand for rice increase.

The production and yield of the major food and cash crops—rice, maize, millet and sorghum grains, cassava and yam tubers, groundnut (unshelled), cotton lint, coffee green berries, cocoa beans and palm oil—for the Gulf of Guinea and the Sudano-Sahelian zones are shown in **Tables 2–4**; along these lines:

#### *3.2.1 Annual crops: cereals*

The production and yield of rice in the Gulf of Guinea zone increased by 99 and 17%, respectively; for the Sudano-Sahelian zone, the corresponding values were 113 and 24%. The production of maize in the Gulf of Guinea zone rose by 53%, but yield declined by 4%. The production and yield of millet in the Gulf of Guinea zone declined by 78 and 45%, respectively. In the Sudano-Sahelian, zone production increased by 9%, but yield declined by 3%. For sorghum, production and yield in the Gulf of Guinea decreased by 31 and 14%, respectively; contrary to this, production and yield increased by 54 and 8%, respectively, in the Sudano-Sahelian zone.

#### *3.2.2 Annual crops: roots and tubers and groundnut*

The production of cassava in the Gulf of Guinea zone increased by 37%, but yield declined by 19%. In the Sudano-Sahelian zone, production and yield increased by 12 and 42%, respectively; this was a desirable case of yield increasing substantially over time. The production of yam in the Gulf of Guinea increased by 27%, but yield decreased by 25%. Similarly, the production in the Sudano-Sahelian zone increased by 157%, but yield declined by 29%. The large increase in yam production in the Sudano-Sahelian may indicate evolving farming systems because yam was traditionally a major crop in the humid areas of ECOWAS. In the Gulf of Guinea,

**71**

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector…*

Gulf of Guinea 5912 11,739 1.48 1.73 Range 54.2–3116 209.7–4823 0.83–2.31 1.25–3.30 Sudano-Sahelian 1344 2863 2.35 2.91 Range 20.5–938.2 69.7–1978 1.77–3.10 1.05–6.28 ECOWAS 7256 14,602 1.59 1.88

Gulf of Guinea 8878 13,577 1.50 1.44 Range 16–5203 7.3–8423 1.00–2.24 0.49–2.15 Sudano-Sahelian 1569 3504 1.59 1.97 Range 2.2–665.5 5.8–1636 0.38–2.28 0.18–2.59 ECOWAS 10,447 17,081 1.51 1.52

Gulf of Guinea 6765 1474 1.32 0.72 Range 10–6260 18.1–910 0.65–1.38 0.61–1.29 Sudano-Sahelian 5938 6464 0.59 0.58 Range 120.3–2745 93.8–2922 0.48–1.09 0.41–0.94 ECOWAS 12,703 7938 0.84 0.60

Gulf of Guinea 8768 6079 1.13 0.98 Range 10–8016 26.9–5300 0.55–1.16 0.67–1.35 Sudano-Sahelian 3115 4792 0.65 0.70 Range 30.1–1610 30.4–1880 0.27–1.13 0.37–1.11 ECOWAS 11,883 10,871 0.95 0.83

**Production (×1000 t) Yield (t ha−1**

**2003 2013 2003 2013**

**)**

production and yield of groundnut declined by 10 and 28%, respectively. In the Sudano-Sahelian, production and yield increased by 50 and 1%, respectively.

Crop production was higher in the Gulf of Guinea than the Sudano-Sahelian zone, with Nigeria being the highest producer in ECOWAS of rice, maize, sorghum, cassava, yam, and groundnut in both 2003 and 2013. World Bank [20] also reported higher crop production in the humid and subhumid AEZs than the semiarid zone. The greater proportion of the humid and subhumid zones is within the Gulf of Guinea zone, and this may explain in part why production of the major crops was higher in this zone. The average yield of rice, maize, and yam in the Sudano-Sahelian zone was, however, higher than the average yield of these crops in the Gulf of Guinea zone in both years. Thus, the highest yield of rice in 2003 and 2013 was reported for Niger, and that of maize in 2003 was for Senegal, and in 2013 it was

*3.2.3 Zonal effects on crop production and yield*

*DOI: http://dx.doi.org/10.5772/intechopen.84378*

Rice (paddy)

Maize

Millet

Sorghum

*Source: [11, 13].*

*Cereal production in ECOWAS.*

**Table 2.**

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector… DOI: http://dx.doi.org/10.5772/intechopen.84378*

#### **Table 2.**

*Environmental Health - Management and Prevention Practices*

governments and their international development partners.

it is estimated that up to 644,000 km2

1,124,000 km2

**3.2 Crop production**

demand for rice increase.

*3.2.1 Annual crops: cereals*

Sahelian zone.

ECOWAS countries have experienced significant land degradation [15, 17]. Based on land area data of the World Bank [10, 14] and areas of degraded land [17],

The FAO database on crop production is available on the basis of country and not on farming systems or agroecological zones (AEZ). Nevertheless, it is well known that millet and sorghum are the traditional cereals of the Sudano-Sahelian, and maize, the roots and tubers, and tree crops are mainly cultivated in the Gulf of Guinea. With the development and release of new varieties of crops and changing climate, the distribution of these crops across ECOWAS is changing. Rice cultivation is becoming ubiquitous throughout the region as the urban population and

The production and yield of the major food and cash crops—rice, maize, millet and sorghum grains, cassava and yam tubers, groundnut (unshelled), cotton lint, coffee green berries, cocoa beans and palm oil—for the Gulf of Guinea and the

The production and yield of rice in the Gulf of Guinea zone increased by 99 and 17%, respectively; for the Sudano-Sahelian zone, the corresponding values were 113 and 24%. The production of maize in the Gulf of Guinea zone rose by 53%, but yield declined by 4%. The production and yield of millet in the Gulf of Guinea zone declined by 78 and 45%, respectively. In the Sudano-Sahelian, zone production increased by 9%, but yield declined by 3%. For sorghum, production and yield in the Gulf of Guinea decreased by 31 and 14%, respectively; contrary to this, production and yield increased by 54 and 8%, respectively, in the Sudano-

The production of cassava in the Gulf of Guinea zone increased by 37%, but yield declined by 19%. In the Sudano-Sahelian zone, production and yield increased by 12 and 42%, respectively; this was a desirable case of yield increasing substantially over time. The production of yam in the Gulf of Guinea increased by 27%, but yield decreased by 25%. Similarly, the production in the Sudano-Sahelian zone increased by 157%, but yield declined by 29%. The large increase in yam production in the Sudano-Sahelian may indicate evolving farming systems because yam was traditionally a major crop in the humid areas of ECOWAS. In the Gulf of Guinea,

Sudano-Sahelian zones are shown in **Tables 2–4**; along these lines:

*3.2.2 Annual crops: roots and tubers and groundnut*

(35%) of lands in ECOWAS were severely to very severely degraded by 2000. Nkonya et al. [18], however, reported a lower but still important rate of 19% of lands excluding deserts for Niger. The primary causes of land degradation in ECOWAS are deforestation, agriculture, conversion of grasslands to croplands, and overgrazing [18, 19]. Deforestation in ECOWAS is associated with slash and burn agriculture as well as with fuelwood, charcoal, and timber production to meet basic livelihood needs of small-holder farmers and boost incomes of commercial enterprises. Moussa et al. [19] estimate that more than 60% of carbon dioxide emission in Africa is due to deforestation and land degradation. Land reclamation to combat degradation is therefore a climate change adaptation option requiring financing by

(38%) of lands in the Sudano-Sahelian zone and 1,768,000 km2

(31%) of lands in the Gulf of Guinea and

**70**

*Cereal production in ECOWAS.*

production and yield of groundnut declined by 10 and 28%, respectively. In the Sudano-Sahelian, production and yield increased by 50 and 1%, respectively.

#### *3.2.3 Zonal effects on crop production and yield*

Crop production was higher in the Gulf of Guinea than the Sudano-Sahelian zone, with Nigeria being the highest producer in ECOWAS of rice, maize, sorghum, cassava, yam, and groundnut in both 2003 and 2013. World Bank [20] also reported higher crop production in the humid and subhumid AEZs than the semiarid zone. The greater proportion of the humid and subhumid zones is within the Gulf of Guinea zone, and this may explain in part why production of the major crops was higher in this zone. The average yield of rice, maize, and yam in the Sudano-Sahelian zone was, however, higher than the average yield of these crops in the Gulf of Guinea zone in both years. Thus, the highest yield of rice in 2003 and 2013 was reported for Niger, and that of maize in 2003 was for Senegal, and in 2013 it was

#### **Table 3.**

*Cassava, yam, and groundnut production in ECOWAS.*

for Mali. In both years, the highest yield of cassava was reported for Niger and that of yam in both years for Mali. These findings are relevant in exploring how climate change may impact production and productivity differently, in various parts of ECOWAS, and how this knowledge can be exploited for improved food security and poverty alleviation.

#### *3.2.4 Changes in crop production and yield over time*

The production of rice, maize, cassava, yam, and groundnut increased between 2003 and 2013, but that of millet and sorghum decreased. In contrast to this positive change in production, yields of millet, sorghum, cassava, yam, and groundnut decreased, and although those of rice and maize increased slightly in ECOWAS as a whole, yields were low and well below their potentials. Increases in crop production in ECOWAS are generally more closely related to the area harvested than to yield per unit area [22]. Lipton et al. [23] reported yields of 1.01 t ha−1 for rice, 0.78 t ha−1 for maize, 0.56 t ha−1 for millet, 0.76 t ha−1 for sorghum, and 7.63 t ha−1 for cassava in Western Africa for 1961–1963. These levels, compared to those shown in **Tables 2** and **3**, indicate only modest increases over four to five decades from a very low baseline. Thus, the evidence is that while crop production increased, the boosting of crop yields (productivity), a major concern of CAADP and the Maputo Accord did not take place during the period under review. This finding should be of serious concern given that the Consultative Group on International Agricultural

**73**

and large-scale investors.

**3.3 Perennial and cash crops**

*Source: authors calculations from [11, 13].*

*Perennial and cash crop production in ECOWAS.*

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector…*

Gulf of Guinea 192.3 172.6 0.37 0.21 Range 0.06–140.0 0.07–103.7 0.2–1.7 0.14–2.48 Sudano-Sahelian 0.062 0.21 ECOWAS 192.3 172.7 0.76

Gulf of Guinea 2263 2696 0.49 0.48 Range 0.1–1352 0.12–1449 0.17–0.68 0.14–0.86 ECOWAS 2263 2696 0.49 0.48

**Production (×1000 t) Yield (t ha−1**

**2003 2013 2003 2013**

**)**

Research (CGIAR) centers in collaboration with the National Agricultural Research and Extension Systems (NARES) have developed and released high-yielding, pestand disease-resistant crop varieties with associated improved crop and soil management practices over the past three to four decades and increase in crop yields is recognized as a major step toward poverty reduction and food security [23]. The impacts of climate change on agriculture have reinforced the need for achievement and maintenance of high crop yields as an adaptation measure both by small-scale

Data for coffee, cocoa, and palm oil (from oil palm) were available almost exclusively for the Gulf of Guinea zone where they are mainly cultivated. For coffee, in the Gulf of Guinea zone, production and yield decreased by 10 and 45%, respectively. Production of cocoa increased by 19%, while yield decreased by 3%. Cote d'Ivoire in 2003 and 2013 was the highest producer in ECOWAS of coffee green berries and cocoa beans. Like for annual crops, yields of cocoa and coffee were well below potentials, and their improvement should be a sector development priority

*DOI: http://dx.doi.org/10.5772/intechopen.84378*

Gulf of Guinea 573 368 Range 1.8–172 1.4–133.5 Sudano-Sahelian 447 422 Range 0.18–259.7 0.19–280 ECOWAS 1020 790

Gulf of Guinea 1528 1638 Range 4.9–1022 6–880 Sudano-Sahelian 9 17 Range 2.5–6 3–13.7 ECOWAS 1537 1655

Coffee green

Cocoa beans

Cotton lint

Palm oil

**Table 4.**

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector… DOI: http://dx.doi.org/10.5772/intechopen.84378*

#### **Table 4.**

*Environmental Health - Management and Prevention Practices*

for Mali. In both years, the highest yield of cassava was reported for Niger and that of yam in both years for Mali. These findings are relevant in exploring how climate change may impact production and productivity differently, in various parts of ECOWAS, and how this knowledge can be exploited for improved food security and

**Production (×1000 t) Yield (t ha−1**

Gulf of Guinea 55,647 76,215 10.28 8.37 Range 38–36,304 23–47,407 5.86–15.2 3.52–18.27 Sudano-Sahelian 344 384 6.86 9.75 Range 3.4–181.7 4.1–156.1 2.17–17.90 1.36–19.97 ECOWAS 55,991 76,599 10.25 8.37

Gulf of Guinea 41,017 52,197 10.45 7.83 Range 20.5–29,697 21.5–35,618 8.67–12.11 6.61–16.78 Sudano-Sahelian 67 171 12.36 8.77 Range 31–35 79.2–91.6 12.01–12.80 5.86–20.63 ECOWAS 41,084 52,368 10.45 7.83

Gulf of Guinea 4056 3664 1.34 0.97 Range 4.3–3037 6.5–2475 0.60–1.53 0.64–1.24 Sudano-Sahelian 1316 1979 0.77 0.78 Range 92.9–440.7 0.28–677.5 049–0.89 0.49–1.38 ECOWAS 5372 5643 1.13 0.89

**2003 2013 2003 2013**

**)**

The production of rice, maize, cassava, yam, and groundnut increased between 2003 and 2013, but that of millet and sorghum decreased. In contrast to this positive change in production, yields of millet, sorghum, cassava, yam, and groundnut decreased, and although those of rice and maize increased slightly in ECOWAS as a whole, yields were low and well below their potentials. Increases in crop production in ECOWAS are generally more closely related to the area harvested than to

for millet, 0.76 t ha−1

for cassava in Western Africa for 1961–1963. These levels, compared to those shown in **Tables 2** and **3**, indicate only modest increases over four to five decades from a very low baseline. Thus, the evidence is that while crop production increased, the boosting of crop yields (productivity), a major concern of CAADP and the Maputo Accord did not take place during the period under review. This finding should be of serious concern given that the Consultative Group on International Agricultural

for rice,

for sorghum, and 7.63 t ha−1

yield per unit area [22]. Lipton et al. [23] reported yields of 1.01 t ha−1

**72**

0.78 t ha−1

poverty alleviation.

Cassava

Yam

Groundnut

*Source: [11, 13].*

**Table 3.**

*3.2.4 Changes in crop production and yield over time*

*Cassava, yam, and groundnut production in ECOWAS.*

for maize, 0.56 t ha−1

*Perennial and cash crop production in ECOWAS.*

Research (CGIAR) centers in collaboration with the National Agricultural Research and Extension Systems (NARES) have developed and released high-yielding, pestand disease-resistant crop varieties with associated improved crop and soil management practices over the past three to four decades and increase in crop yields is recognized as a major step toward poverty reduction and food security [23]. The impacts of climate change on agriculture have reinforced the need for achievement and maintenance of high crop yields as an adaptation measure both by small-scale and large-scale investors.

#### **3.3 Perennial and cash crops**

Data for coffee, cocoa, and palm oil (from oil palm) were available almost exclusively for the Gulf of Guinea zone where they are mainly cultivated. For coffee, in the Gulf of Guinea zone, production and yield decreased by 10 and 45%, respectively. Production of cocoa increased by 19%, while yield decreased by 3%. Cote d'Ivoire in 2003 and 2013 was the highest producer in ECOWAS of coffee green berries and cocoa beans. Like for annual crops, yields of cocoa and coffee were well below potentials, and their improvement should be a sector development priority

for governments. Production of palm oil increased by 7% in the Gulf of Guinea and 96% in the Sudano-Sahelian zone (data only for Gambia and Senegal), but the production of cotton lint in the Gulf of Guinea and Sudano-Sahelian zone declined by 36 and 6%, respectively. This contrasts with the 7.3% per annum increase in cotton production between 1980 and 2006 in West Africa reported by [21]. Mali in 2003 and Burkina Faso in 2013 were the highest producers of cotton lint in ECOWAS. Data were not available on yield of palm oil and cotton.

#### **4. Economic factors: GDP, inflation, infrastructure, and trade**

The vulnerability of the ECOWAS agricultural system to climate change depends partly on certain economic factors such as income, price level, labor productivity, transportation, communication, innovation, and trade, which define ECOWAS' adaptive capacity to external shocks, including climate change. The greater the adaptive capacity of ECOWAS, the lower its vulnerability to climate change, particularly in the agricultural sector.

#### **4.1 Growth, inflation, and capital formation**

After several years of strong economic growth, ECOWAS faced a small increase in 2016 of 0.5%. It rebounded in 2017 to 2.5% and was projected to 3.8% in 2018 and 3.9% in 2019. Countries' performance varied, but because Nigeria, Cote d'Ivoire, Ghana, and Senegal contribute to about 90% of the regional gross domestic product (GDP), their patterns largely shape regional ones. In 2003, agriculture was the dominant sector in ECOWAS (about 40% of GDP), but services contributed most to GDP in Liberia and Sierra Leone soon after the end of their civil wars which devastated the agriculture sector; however, agriculture remains dominant in 2013 [24].

GDP per capita increased by 31.3% in ECOWAS, 34.8% in the Gulf of Guinea, and 26.9% in Sudano-Sahelian between 2003 and 2013 (**Table 5**), indicating an increase in standard of living. Furthermore, consumer prices almost double during the same period, threatening the positive effect of the increased income. Changes in the productive capacity of ECOWAS captured by the gross capital formation represented a larger share of the GDP in 2013 than 2003. The same trend is observed in the Gulf of Guinea and Sudano-Sahelian zones. Agricultural labor productivity increased by 22% between 2003 and 2013 in ECOWAS, while it decreased by 18% in Sudano-Sahelian zone. This may be explained partly by an increase in fertilizer consumption in ECOWAS and in both agroecological zones (**Table 5**).

Financing adaptation and mitigation are an important component of building resilient of the agricultural sector to climate change in ECOWAS. As observed in the previous paragraph, increasing income per capita, investment, and labor productivity are an indication that further economic efficiency in production may be realized in ECOWAS to support the financing of mitigation and adaptation efforts for climate change. However, higher consumer prices represent a significant challenge that may offset the positive effect of an improved efficiency in production for climate finance. Furthermore climate change compounds with other pressing socioeconomic development challenges such as education, sanitation, health and infrastructure, resulting to underinvestment in agriculture in ECOWAS. As reported by AfDB [25], the agriculture sector in ECOWAS suffers from chronic underinvestment. Only Burkina Faso, Mali, Niger, and Senegal have so far raised public expenditure to 10% of GDP, the target fixed by the Maputo Declaration in 2003. The Gambia, Ghana, and Togo are on the threshold of reaching this target. Nigeria devotes 6% of GDP to agriculture and the remaining ECOWAS countries

**75**

ment outcomes.

**4.2 Infrastructure development**

Gulf of Guinea and Sudano-Sahelian zones.

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector…*

**Gross capital formation (percentage of GDP)**

**2003 2013 2003 2013 2003 2013 2003 2013 2003 2013**

Mean 736.0 992.1 64.7 122.5 9.1–23.8 7.2–27.8 1030.9 1584.7 8.6 16.3

Mean 870.1 1104.0 80.7 108.4 1215.3 996.9 7.8 12.1

10.0– 22.3

ECOWAS 789.6 1036.9 72.1 116.9 234.5 330.7 1107.7 1349.6 8.2 14.4

17.8– 36.2

**Agriculture, value added per worker (constant 2010 US\$)**

420.0– 2445.3

544.7– 1960.8

521.5– 5041.1

470.5– 1298.5

0.8– 29.3

0.3– 10.9 2.9– 37.2

0.4– 31.5

**Fertilizer consumption (kilograms per hectare of arable land)**

**Consumer price index (2010 = 100)**

Total 144.3 165.2

40.8– 83.6

Total 90.2 165.5

67.7– 85.2

*Source: Authors calculations from the World Bank Open Data [23].*

108.2– 156.4

105.6– 115.5

less than 5%. This is not surprising because in the presence of limited resources and a range of objectives, financing agricultural development issues such as climate change should involve trade-offs among multiple policy goals. Therefore, financing climate policies, programs, and plans in the agricultural sector of ECOWAS should be prioritized not only in terms of their ability to reduce the impact of climate change but also in terms of their ability to deliver some develop-

Infrastructure development in ECOWAS can be analyzed in three key areas: communication, transport, and technology. The telecommunication priority is the development of a reliable and modern regional telecom broadband infrastructure including the INTELCOM II program, alternative broadband infrastructure, and submarine cables as well as the establishment of a single liberalized telecom market. By 2013, 11 coastal member states were connected to submarine cables with at least 1 new landing station, and the 3 landlocked countries (Burkina Faso, Mali, and Niger) now have at least 2 access routes to the submarine cables. This has substantially increased the percentage of people using the Internet (from 1.1% in 2003 to 9.9% in 2013) and number of subscriptions to mobile cellular (from 4 subscriptions in 2003 to 78.4 subscriptions in 2013 per 100 people). A similar trend is observed in

The transport program in ECOWAS has overseen the implementation of multimodal transport infrastructure and policies to promote physical cohesion among member states and to facilitate the movement of persons, goods, and services within the community with special emphasis on increased access to island and landlocked countries. However, the transportation infrastructure is still underdeveloped and of poor quality. For example, the quality of port infrastructure was estimated at 3.9 on a scale of 7, the latter representing a well-developed and efficient

*DOI: http://dx.doi.org/10.5772/intechopen.84378*

**GDP per capita (constant 2010 US\$)**

**Zone/ region**

Gulf of Guinea

Range 271.0–

Sudano-Sahelian

Range 335.7–

**Table 5.**

1426.9

2310.5

*Growth, inflation, and capital formation.*

383.1– 2475.9

373.7– 3389.9 *Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector… DOI: http://dx.doi.org/10.5772/intechopen.84378*

**Table 5.**

*Environmental Health - Management and Prevention Practices*

particularly in the agricultural sector.

**4.1 Growth, inflation, and capital formation**

for governments. Production of palm oil increased by 7% in the Gulf of Guinea and 96% in the Sudano-Sahelian zone (data only for Gambia and Senegal), but the production of cotton lint in the Gulf of Guinea and Sudano-Sahelian zone declined by 36 and 6%, respectively. This contrasts with the 7.3% per annum increase in cotton production between 1980 and 2006 in West Africa reported by [21]. Mali in 2003 and Burkina Faso in 2013 were the highest producers of cotton lint in

ECOWAS. Data were not available on yield of palm oil and cotton.

**4. Economic factors: GDP, inflation, infrastructure, and trade**

consumption in ECOWAS and in both agroecological zones (**Table 5**).

Financing adaptation and mitigation are an important component of building resilient of the agricultural sector to climate change in ECOWAS. As observed in the previous paragraph, increasing income per capita, investment, and labor productivity are an indication that further economic efficiency in production may be realized in ECOWAS to support the financing of mitigation and adaptation efforts for climate change. However, higher consumer prices represent a significant challenge that may offset the positive effect of an improved efficiency in production for climate finance. Furthermore climate change compounds with other pressing socioeconomic development challenges such as education, sanitation, health and infrastructure, resulting to underinvestment in agriculture in ECOWAS. As reported by AfDB [25], the agriculture sector in ECOWAS suffers from chronic underinvestment. Only Burkina Faso, Mali, Niger, and Senegal have so far raised public expenditure to 10% of GDP, the target fixed by the Maputo Declaration in 2003. The Gambia, Ghana, and Togo are on the threshold of reaching this target. Nigeria devotes 6% of GDP to agriculture and the remaining ECOWAS countries

The vulnerability of the ECOWAS agricultural system to climate change depends partly on certain economic factors such as income, price level, labor productivity, transportation, communication, innovation, and trade, which define ECOWAS' adaptive capacity to external shocks, including climate change. The greater the adaptive capacity of ECOWAS, the lower its vulnerability to climate change,

After several years of strong economic growth, ECOWAS faced a small increase in 2016 of 0.5%. It rebounded in 2017 to 2.5% and was projected to 3.8% in 2018 and 3.9% in 2019. Countries' performance varied, but because Nigeria, Cote d'Ivoire, Ghana, and Senegal contribute to about 90% of the regional gross domestic product (GDP), their patterns largely shape regional ones. In 2003, agriculture was the dominant sector in ECOWAS (about 40% of GDP), but services contributed most to GDP in Liberia and Sierra Leone soon after the end of their civil wars which devastated the agriculture sector; however, agriculture remains dominant in 2013 [24]. GDP per capita increased by 31.3% in ECOWAS, 34.8% in the Gulf of Guinea, and 26.9% in Sudano-Sahelian between 2003 and 2013 (**Table 5**), indicating an increase in standard of living. Furthermore, consumer prices almost double during the same period, threatening the positive effect of the increased income. Changes in the productive capacity of ECOWAS captured by the gross capital formation represented a larger share of the GDP in 2013 than 2003. The same trend is observed in the Gulf of Guinea and Sudano-Sahelian zones. Agricultural labor productivity increased by 22% between 2003 and 2013 in ECOWAS, while it decreased by 18% in Sudano-Sahelian zone. This may be explained partly by an increase in fertilizer

**74**

*Growth, inflation, and capital formation.*

less than 5%. This is not surprising because in the presence of limited resources and a range of objectives, financing agricultural development issues such as climate change should involve trade-offs among multiple policy goals. Therefore, financing climate policies, programs, and plans in the agricultural sector of ECOWAS should be prioritized not only in terms of their ability to reduce the impact of climate change but also in terms of their ability to deliver some development outcomes.

#### **4.2 Infrastructure development**

Infrastructure development in ECOWAS can be analyzed in three key areas: communication, transport, and technology. The telecommunication priority is the development of a reliable and modern regional telecom broadband infrastructure including the INTELCOM II program, alternative broadband infrastructure, and submarine cables as well as the establishment of a single liberalized telecom market. By 2013, 11 coastal member states were connected to submarine cables with at least 1 new landing station, and the 3 landlocked countries (Burkina Faso, Mali, and Niger) now have at least 2 access routes to the submarine cables. This has substantially increased the percentage of people using the Internet (from 1.1% in 2003 to 9.9% in 2013) and number of subscriptions to mobile cellular (from 4 subscriptions in 2003 to 78.4 subscriptions in 2013 per 100 people). A similar trend is observed in Gulf of Guinea and Sudano-Sahelian zones.

The transport program in ECOWAS has overseen the implementation of multimodal transport infrastructure and policies to promote physical cohesion among member states and to facilitate the movement of persons, goods, and services within the community with special emphasis on increased access to island and landlocked countries. However, the transportation infrastructure is still underdeveloped and of poor quality. For example, the quality of port infrastructure was estimated at 3.9 on a scale of 7, the latter representing a well-developed and efficient infrastructure by international standards. Rail lines decreased by 72% between 2003 and 2013. This is mainly due to a sharp decrease of rail lines in the Gulf of Guinea zone (**Table 6**).

ECOWAS countries still have a long way to go to reach the African Union's target of devoting 1% of GDP to gross domestic expenditure on research and development (GERD). Mali comes closest (0.66% in 2010), followed by Senegal (0.54% in 2010), according to the UNESCO Science Report [25]. There is a lack of researchers in ECOWAS in general. AfDB [25] highlighted that only Senegal stands out, with 361 full-time equivalent (FTE) researchers per million population in 2010, while other countries, where data was available, have less than 60. However, the number of trademark applications, as an indicator of innovation, has increased substantially between 2003 and 2013 from 1671 to 25,294. Almost all trademark applications are from the Gulf of Guinea zone (99%).

Infrastructure development is an important means to build resilience to climate change, particularly in the agricultural sector, because it also delivers vital development outcomes. We should recognize that the availability and quality of infrastructure are never substitutes to agriculture-specific policies such as an increase in fertilizer use, irrigation, and improved seeds. However, inadequate infrastructure may be a significant constraint to growth and productivity. Indeed, recent literature indicates a significant positive link between infrastructure and agricultural productivity [26]. For example, transport infrastructure provides important connectivity with growing markets and also reduces input costs and transaction costs for producers and consumers, particularly in the context of climate change. Furthermore, information acquisition costs can represent a significant obstacle, for instance, when climate and weather data are costly or difficult to access. Improvement in communication infrastructure represents an opportunity to reduce information acquisition costs, thereby strengthening farmers' responses to external shocks such as climate change.

**77**

**Table 7.**

*Trade development.*

**Zone/ region**

Gulf of Guinea

Range 43.3–

Range 30.7–

Sudano-Sahelian

102.5

98.8

31.0– 131.0

61.7– 95.3

*Source: authors calculations from the World Bank Open Data [23].*

0.4– 4.0

0.7– 4.3

Total

Total

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector…*

Recent literature also recognizes that innovation (technological, managerial, and institutional) in agriculture is clearly an important response for effective and equitable adaptation to and mitigation of climate change. As underlined by Llanto [27], innovation can enhance technology adoption, may prevent or facilitate migration of production/population, enhance trade and aid, and increase efficiency of insurance and feasibility of inventories. More importantly, innovation will be the

ECOWAS has a huge potential for trade both in global and intra-regional terms mainly because of its large natural resource endowment, agricultural potential, and intra-regional complementarities. Trade (the sum of exports and imports of goods and services) was estimated at almost 65% of GDP in 2003 and 78% of the GDP in 2013, representing an increase of 13 points in 10 years. A similar trend is observed in Gulf of Guinea and Sudano-Sahelian zones. The export profile of ECOWAS shows little product diversity, with a heavy reliance on extractive products (e.g., petroleum, natural gas) and a few agricultural commodities (e.g., cocoa, rubber, cotton). Agricultural raw material exports measured as a share of merchandise exports decreased by 12 points between 2003 and 2013. The decrease is substantial in Sudano-Sahelian zone (20 points) and moderate in the Gulf of Guinea zone (5 points). However, food exports measured as a share of merchandise exports increased by 19 points in the Sudano-Sahelian zone and decreased by 15 points in the Gulf of Guinea zone (**Table 7**). This is an indication of an improvement in the industrialization of ECOWAS and/or building of adaptive capacity to environmen-

ECOWAS imports are more diversified, with a high share of industrialized products (e.g., refined petroleum, vehicles, ships, telecommunication equipment)

> **Agricultural raw material exports (percentage of merchandise exports)**

**2003 2013 2003 2013 2003 2013 2003 2013 2003 2013**

3.2– 49.1

0.0– 44.4 15.5– 62.4

18.0– 37.6

14.2– 40.3

12.1– 37.3

0.0– 95.4

6.4– 85.9 4.9– 38.5

8.0– 92.0

Mean 67.9 81.1 1.3 1.3 17.9 13.1 26.3 20.9 37.4 22.9

Mean 60.4 72.9 2.0 1.3 29.4 9.8 28.1 27.7 32.5 51.3

ECOWAS 64.9 77.8 1.7 1.3 23.6 11.6 27.2 24.0 34.9 35.8

3.3– 79.5

0.0– 74.2

0.4– 2.8

0.4– 2.6

**Food imports (percentage of merchandise imports)**

**Food exports (percentage of merchandise exports)**

**Agricultural raw material imports (percentage of merchandise imports)**

*DOI: http://dx.doi.org/10.5772/intechopen.84378*

key in moving toward climate-smart agriculture.

tal and economic shocks, such as climate change.

**Trade (percentage of GDP)**

**4.3 Trade development**

*Source: Authors' calculations from the World Bank Open Data [23].*

**Table 6.** *Infrastructure development.*

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector… DOI: http://dx.doi.org/10.5772/intechopen.84378*

Recent literature also recognizes that innovation (technological, managerial, and institutional) in agriculture is clearly an important response for effective and equitable adaptation to and mitigation of climate change. As underlined by Llanto [27], innovation can enhance technology adoption, may prevent or facilitate migration of production/population, enhance trade and aid, and increase efficiency of insurance and feasibility of inventories. More importantly, innovation will be the key in moving toward climate-smart agriculture.

#### **4.3 Trade development**

*Environmental Health - Management and Prevention Practices*

Guinea zone (**Table 6**).

as climate change.

Gulf of Guinea

Range 0.0–

Sodano-Sahelian

Range 0.2–

*Infrastructure development.*

1.4

4.3

3.1– 19.1

1.15– 37.5

*Source: Authors' calculations from the World Bank Open Data [23].*

0.1– 7.2

0.7– 11.6

**Zone/ Region**

from the Gulf of Guinea zone (99%).

**Individuals using the Internet (percentage of the population)**

**Mobile cellular subscriptions (per 100 people)**

infrastructure by international standards. Rail lines decreased by 72% between 2003 and 2013. This is mainly due to a sharp decrease of rail lines in the Gulf of

of devoting 1% of GDP to gross domestic expenditure on research and development (GERD). Mali comes closest (0.66% in 2010), followed by Senegal (0.54% in 2010), according to the UNESCO Science Report [25]. There is a lack of researchers in ECOWAS in general. AfDB [25] highlighted that only Senegal stands out, with 361 full-time equivalent (FTE) researchers per million population in 2010, while other countries, where data was available, have less than 60. However, the number of trademark applications, as an indicator of innovation, has increased substantially between 2003 and 2013 from 1671 to 25,294. Almost all trademark applications are

ECOWAS countries still have a long way to go to reach the African Union's target

Infrastructure development is an important means to build resilience to climate change, particularly in the agricultural sector, because it also delivers vital development outcomes. We should recognize that the availability and quality of infrastructure are never substitutes to agriculture-specific policies such as an increase in fertilizer use, irrigation, and improved seeds. However, inadequate infrastructure may be a significant constraint to growth and productivity. Indeed, recent literature indicates a significant positive link between infrastructure and agricultural productivity [26]. For example, transport infrastructure provides important connectivity with growing markets and also reduces input costs and transaction costs for producers and consumers, particularly in the context of climate change. Furthermore, information acquisition costs can represent a significant obstacle, for instance, when climate and weather data are costly or difficult to access. Improvement in communication infrastructure represents an opportunity to reduce information acquisition costs, thereby strengthening farmers' responses to external shocks such

> **Trademark applications, total**

**2003 2013 2003 2013 2003 2013 2003 2013 2003 2013**

14– 19332.0

406.0– 406.0

0.0– 0.0

0.0– 0.0

3.2– 4.5

3.5– 4.8

977– 3505.0

> 0.0– 0.0

639– 639.0

622.0– 622.0

Total 1313.0 24,888.0 4482.0 639.0

627– 686.0

Total 358.0 406.0 0 622.0

25– 298.0

ECOWAS 1.1 9.9 4.0 78.4 1671.0 25,294.0 0.0 3.9 4482.0 1261.0

Mean 0.7 7.8 2.9 73.7 0.0 3.7

55.9– 106.4

Mean 1.6 13.1 5.8 85.4 0.0 4.1

38.0– 119.9

*a: 1, extremely underdeveloped, to 7, well developed and efficient by international standards).*

**Quality of port infrastructure, WEFa**

**Rail lines (total route-km)**

**76**

**Table 6.**

ECOWAS has a huge potential for trade both in global and intra-regional terms mainly because of its large natural resource endowment, agricultural potential, and intra-regional complementarities. Trade (the sum of exports and imports of goods and services) was estimated at almost 65% of GDP in 2003 and 78% of the GDP in 2013, representing an increase of 13 points in 10 years. A similar trend is observed in Gulf of Guinea and Sudano-Sahelian zones. The export profile of ECOWAS shows little product diversity, with a heavy reliance on extractive products (e.g., petroleum, natural gas) and a few agricultural commodities (e.g., cocoa, rubber, cotton). Agricultural raw material exports measured as a share of merchandise exports decreased by 12 points between 2003 and 2013. The decrease is substantial in Sudano-Sahelian zone (20 points) and moderate in the Gulf of Guinea zone (5 points). However, food exports measured as a share of merchandise exports increased by 19 points in the Sudano-Sahelian zone and decreased by 15 points in the Gulf of Guinea zone (**Table 7**). This is an indication of an improvement in the industrialization of ECOWAS and/or building of adaptive capacity to environmental and economic shocks, such as climate change.

**Zone/ region Trade (percentage of GDP) Agricultural raw material imports (percentage of merchandise imports) Agricultural raw material exports (percentage of merchandise exports) Food imports (percentage of merchandise imports) Food exports (percentage of merchandise exports) 2003 2013 2003 2013 2003 2013 2003 2013 2003 2013** Gulf of Guinea Total Mean 67.9 81.1 1.3 1.3 17.9 13.1 26.3 20.9 37.4 22.9 Range 43.3– 102.5 31.0– 131.0 0.4– 4.0 0.4– 2.8 0.0– 74.2 3.2– 49.1 15.5– 62.4 14.2– 40.3 0.0– 95.4 4.9– 38.5 Sudano-Sahelian Total Mean 60.4 72.9 2.0 1.3 29.4 9.8 28.1 27.7 32.5 51.3 Range 30.7– 98.8 61.7– 95.3 0.7– 4.3 0.4– 2.6 3.3– 79.5 0.0– 44.4 18.0– 37.6 12.1– 37.3 6.4– 85.9 8.0– 92.0 ECOWAS 64.9 77.8 1.7 1.3 23.6 11.6 27.2 24.0 34.9 35.8 *Source: authors calculations from the World Bank Open Data [23].*

ECOWAS imports are more diversified, with a high share of industrialized products (e.g., refined petroleum, vehicles, ships, telecommunication equipment)

**Table 7.** *Trade development.* and food products (e.g., rice, wheat). Agricultural raw material imports measured as a share of merchandise imports decreased slightly by 0.4 points between 2003 and 2013. The same trend is observed in Sudano-Sahelian and Gulf of Guinea zones. On the other hand, food imports measured as a share of merchandise imports decreased in ECOWAS (3.2 points), Sudano-Sahelian zone (0.4 points), and Gulf of Guinea zone (5.4 points) between 2003 and 2013. ECOWAS main trading partners are highly industrialized countries such as China, India, the USA, EU countries, and Brazil, which mainly buy raw materials and sell industrialized products from/to the region. Intra-regional trade takes place, but is mainly, unreported, informal and generally considered to be well below its potential [29].

Trade is crucial for economic growth and food security, thereby representing an important controlling factor for climate change impacts in the agricultural sector [28]. Torres and van Seters [29] showed that while some countries in ECOWAS may be experiencing good crop harvest, others may not due to stochastic patterns in climate. The authors demonstrated that acreage and crop production in ECOWAS countries are sensitive to climate change and climate change will lead to a shift in land use for agricultural production within and among countries as a rational response to its impact on crop yield by farmers seeking to maximize the profit of their farm activities. A structural transformation of the agricultural sector is, therefore, inevitable to offset the negative impacts of climate change to achieve a better level of livelihood for the population. This means, therefore, that one of the ways to resolve the issue of food availability may be through food trade across countries. Several arguments have been made in favor of trade of agricultural commodities as a means of adapting to climate change [30, 31]. First, trade operates as an insurance against the risk of climate change. Going by this view, trade is the means by which food availability can be preserved for regions that are affected by reduced agricultural productivity. Second, trade can spread the cost of adaptation if free trade flows are allowed. In fact, if free trade is allowed between countries, countries that are net exporters of food may face food price increase in order to allow other food-deficit regions to survive.

#### **5. Economics of adaptation to climate change in the agriculture sector**

Recent literature on the impact of future climate change on West African crop yields showed a large dispersion of yield changes ranging from −50 to +90%, with the median yield loss near −11% [32]. The predicted impact is larger in Sudano-Sahelian region (−18% median response) than in the Gulf of Guinea (−13%). Roudier et al. [32] highlighted that the consistently negative impact of climate change results mainly from the temperature whose increase projected by climate models is much larger relative to precipitation change. As discussed in previous sections, the vulnerability of the biophysical system and the underdeveloped economic system of ECOWAS will exacerbate this impact on agriculture and food security. Economic analysis of adaptation in the agriculture sector of ECOWAS is still in its infancy state, and information even on costs benefit analysis of adaptation options in the agriculture sector of ECOWAS is scarce. Nevertheless, some valuable information on the magnitudes of costs and returns on a country-wide and technologyspecific basis has emerged.

Butt et al. [33], through modeling studies, showed that for Mali, policy changes that result in reduction in soil productivity loss, cropland expansion, adoption of improved cultivars, and changes in trade patterns altogether, under climate change, showed an annual gain of \$252 million in economic benefits as opposed to a \$161 million loss without policy adjustment. Roudier et al. [32] examined the

**79**

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector…*

benefits of climate change adaptation in agriculture for Ghana when the whole adaptation resource envelope is spent on gradual expansion of irrigated land area from 2012 onwards. The assumed upfront investment cost of irrigation is \$18,000 per ha, taking account of Ghana-specific cost estimates for recent and planned irrigation projects plus the need for complementary investment in water harvesting, etc. Under the general circulation model (GCM)/Special Report on Emissions Scenarios (SRES) of the IPCC modeling global wet scenario, the share of irrigated land rises gradually from less than 0.4 to 15% of the current total cultivated area. The resulting average annual factor productivity increase for agriculture as a whole is an additional 0.36% above baseline productivity growth. They also pointed out that this scenario can also be interpreted as representing other productivity-rising agricultural adaptation measures with a comparable yield impact per dollar spent. It is well known that technological adaptations such as adoption of high-yielding

pest and disease-resistant varieties of climate-smart varieties of cassava can double crop yields even without the use of expensive inputs (and therefore positive returns), in the immediate term. On the other hand, adaptation options in the form of soil conservation practices such as contour ridges and agroforestry require investments to pay off over longer periods, which small-scale farmers cannot afford in the typical situation of lack of long-term credit facilities by commercial banks. Persons with precarious hold on the land, such as women and strangers, are particularly constrained to make such investments. Thus, World Bank [34] reported that while farmers in Nigeria who were dependent on leased and/or communal lands expressed implicit dislike for climate-smart agriculture (CSA)-related investments, the majority with freehold titles, particularly those with registered titles, expressed positive willingness-to-accept incentives to embrace CSA and combat

The following examples illustrate the nature of the investments and returns in soil management adaptation options in ECOWAS: in the Sahel of ECOWAS, stone bunds associated with shallow pits, filled with compost or manure (Zai pits),

and costing \$200 ha−1

 for constructing the bunds on contours, have resulted in doubling of millet yields [36]. Bjorkemar [37] estimated the potential returns over 25 years from agroforestry, in a village in the Bombali District of Sierra Leone, as follows: \$15,470, \$135,812, \$5,427,800, and \$11,903,090 for dispersed interplanting, boundary planting, and woodlot and fruit orchard, respectively. A World Agroforestry Center study reported by [36] showed that farmer-managed natural regeneration raised

improvement in soil fertility, and increased supply of food, fodder, and firewood

Analysis of the economics of adaptation to climate change is moving from a focus on efficiency, market solutions, and cost-benefit analysis of adaptation to include "multi-metric" considerations of non-monetary and nonmarket measures, risks, inequities, and behavioral biases and barriers and limits and consideration of ancillary benefits and costs for providing support to decision-makers [31]. An understanding of the controlling factors of climate change impact chain is useful as inputs to these kinds of analysis. A few studies on economics of adaptation to climate change in ECOWAS have reached such sophistication. Recently, Aklesso et al. [38] tried to integrate a number of biophysical and economic factors of climate change impact chain in order to analyze the potential of intra-regional trade in ECOWAS for increasing food availability in the context of climate change. Several models were integrated: a regional climate model to predict temperature and precipitations from 2004 to 2100 with two representative concentration pathways (RCPs); an econometric crop simulator to simulate crop yield under RCPs; a

return each year.

and 150 person days of labor

in Maradi, Niger, taking into consideration

*DOI: http://dx.doi.org/10.5772/intechopen.84378*

land degradation [35].

was at least a \$56 ha−1

ha−1

requiring 30–50 t of stones ha−1

millet yields from 150 to 500 kg ha−1

#### *Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector… DOI: http://dx.doi.org/10.5772/intechopen.84378*

benefits of climate change adaptation in agriculture for Ghana when the whole adaptation resource envelope is spent on gradual expansion of irrigated land area from 2012 onwards. The assumed upfront investment cost of irrigation is \$18,000 per ha, taking account of Ghana-specific cost estimates for recent and planned irrigation projects plus the need for complementary investment in water harvesting, etc. Under the general circulation model (GCM)/Special Report on Emissions Scenarios (SRES) of the IPCC modeling global wet scenario, the share of irrigated land rises gradually from less than 0.4 to 15% of the current total cultivated area. The resulting average annual factor productivity increase for agriculture as a whole is an additional 0.36% above baseline productivity growth. They also pointed out that this scenario can also be interpreted as representing other productivity-rising agricultural adaptation measures with a comparable yield impact per dollar spent.

It is well known that technological adaptations such as adoption of high-yielding pest and disease-resistant varieties of climate-smart varieties of cassava can double crop yields even without the use of expensive inputs (and therefore positive returns), in the immediate term. On the other hand, adaptation options in the form of soil conservation practices such as contour ridges and agroforestry require investments to pay off over longer periods, which small-scale farmers cannot afford in the typical situation of lack of long-term credit facilities by commercial banks. Persons with precarious hold on the land, such as women and strangers, are particularly constrained to make such investments. Thus, World Bank [34] reported that while farmers in Nigeria who were dependent on leased and/or communal lands expressed implicit dislike for climate-smart agriculture (CSA)-related investments, the majority with freehold titles, particularly those with registered titles, expressed positive willingness-to-accept incentives to embrace CSA and combat land degradation [35].

The following examples illustrate the nature of the investments and returns in soil management adaptation options in ECOWAS: in the Sahel of ECOWAS, stone bunds associated with shallow pits, filled with compost or manure (Zai pits), requiring 30–50 t of stones ha−1 and costing \$200 ha−1 and 150 person days of labor ha−1 for constructing the bunds on contours, have resulted in doubling of millet yields [36]. Bjorkemar [37] estimated the potential returns over 25 years from agroforestry, in a village in the Bombali District of Sierra Leone, as follows: \$15,470, \$135,812, \$5,427,800, and \$11,903,090 for dispersed interplanting, boundary planting, and woodlot and fruit orchard, respectively. A World Agroforestry Center study reported by [36] showed that farmer-managed natural regeneration raised millet yields from 150 to 500 kg ha−1 in Maradi, Niger, taking into consideration improvement in soil fertility, and increased supply of food, fodder, and firewood was at least a \$56 ha−1 return each year.

Analysis of the economics of adaptation to climate change is moving from a focus on efficiency, market solutions, and cost-benefit analysis of adaptation to include "multi-metric" considerations of non-monetary and nonmarket measures, risks, inequities, and behavioral biases and barriers and limits and consideration of ancillary benefits and costs for providing support to decision-makers [31]. An understanding of the controlling factors of climate change impact chain is useful as inputs to these kinds of analysis. A few studies on economics of adaptation to climate change in ECOWAS have reached such sophistication. Recently, Aklesso et al. [38] tried to integrate a number of biophysical and economic factors of climate change impact chain in order to analyze the potential of intra-regional trade in ECOWAS for increasing food availability in the context of climate change. Several models were integrated: a regional climate model to predict temperature and precipitations from 2004 to 2100 with two representative concentration pathways (RCPs); an econometric crop simulator to simulate crop yield under RCPs; a

*Environmental Health - Management and Prevention Practices*

generally considered to be well below its potential [29].

allow other food-deficit regions to survive.

and food products (e.g., rice, wheat). Agricultural raw material imports measured as a share of merchandise imports decreased slightly by 0.4 points between 2003 and 2013. The same trend is observed in Sudano-Sahelian and Gulf of Guinea zones. On the other hand, food imports measured as a share of merchandise imports decreased in ECOWAS (3.2 points), Sudano-Sahelian zone (0.4 points), and Gulf of Guinea zone (5.4 points) between 2003 and 2013. ECOWAS main trading partners are highly industrialized countries such as China, India, the USA, EU countries, and Brazil, which mainly buy raw materials and sell industrialized products from/to the region. Intra-regional trade takes place, but is mainly, unreported, informal and

Trade is crucial for economic growth and food security, thereby representing an important controlling factor for climate change impacts in the agricultural sector [28]. Torres and van Seters [29] showed that while some countries in

ECOWAS may be experiencing good crop harvest, others may not due to stochastic patterns in climate. The authors demonstrated that acreage and crop production in ECOWAS countries are sensitive to climate change and climate change will lead to a shift in land use for agricultural production within and among countries as a rational response to its impact on crop yield by farmers seeking to maximize the profit of their farm activities. A structural transformation of the agricultural sector is, therefore, inevitable to offset the negative impacts of climate change to achieve a better level of livelihood for the population. This means, therefore, that one of the ways to resolve the issue of food availability may be through food trade across countries. Several arguments have been made in favor of trade of agricultural commodities as a means of adapting to climate change [30, 31]. First, trade operates as an insurance against the risk of climate change. Going by this view, trade is the means by which food availability can be preserved for regions that are affected by reduced agricultural productivity. Second, trade can spread the cost of adaptation if free trade flows are allowed. In fact, if free trade is allowed between countries, countries that are net exporters of food may face food price increase in order to

**5. Economics of adaptation to climate change in the agriculture sector**

Recent literature on the impact of future climate change on West African crop yields showed a large dispersion of yield changes ranging from −50 to +90%, with the median yield loss near −11% [32]. The predicted impact is larger in Sudano-Sahelian region (−18% median response) than in the Gulf of Guinea (−13%). Roudier et al. [32] highlighted that the consistently negative impact of climate change results mainly from the temperature whose increase projected by climate models is much larger relative to precipitation change. As discussed in previous sections, the vulnerability of the biophysical system and the underdeveloped economic system of ECOWAS will exacerbate this impact on agriculture and food security. Economic analysis of adaptation in the agriculture sector of ECOWAS is still in its infancy state, and information even on costs benefit analysis of adaptation options in the agriculture sector of ECOWAS is scarce. Nevertheless, some valuable information on the magnitudes of costs and returns on a country-wide and technology-

Butt et al. [33], through modeling studies, showed that for Mali, policy changes

that result in reduction in soil productivity loss, cropland expansion, adoption of improved cultivars, and changes in trade patterns altogether, under climate change, showed an annual gain of \$252 million in economic benefits as opposed to a \$161 million loss without policy adjustment. Roudier et al. [32] examined the

**78**

specific basis has emerged.

bioeconomic optimization model to predict agricultural land allocation and crop production under RCPs; and an intra-regional food trade module built from a classical transportation model to allocate food optimally from excess supply countries to excess demand countries. The authors showed that the climate-induced trade pattern in ECOWAS depends on the prevailing socioeconomic conditions during the century, and several countries may become dependent on food imports outside ECOWAS. However, doubling crop yields by 2050 and adjusting the ECOWAS common external tariffs could significantly reduce outside dependence. Although this study attempts to investigate the link between climate change and food security in ECOWAS, more research is needed to understand how the integration of factors of climate change impact chain could affect food security and promote sustainable agricultural development in ECOWAS.

#### **6. Conclusion**

This chapter assessed key biophysical and economic factors of the climate change impact chain and the economics of adaptation in the agricultural sector of ECOWAS.

As in many developing regions, land for agricultural production in ECOWAS has increased at the expense of forest land. These changes vary, however, by agroecological zones. Furthermore, ECOWAS countries have experienced significant land degradation. The primary causes of land degradation are deforestation, agriculture, conversion of grasslands to croplands, and overgrazing. Cropping systems are generally related to agroecological zones; however, the release of new varieties of crops and changing climate is significantly changing the distribution of crops across ECOWAS. Crop production was higher in the Gulf of Guinea than the Sudano-Sahelian zone. This is partly explained by the high proportion of humid and subhumid zones within the Gulf of Guinea. Increases in crop production in ECOWAS are more often related to the area harvested than to yield per unit area, which is the major concern of CAADP and the Maputo Accord. The boost in crop productivity expected from CAADP and the Maputo Accord did not happen between 2003 and 2013. Increasing crop yields through agricultural intensification while reducing land degradation is critical to reduce the impact of climate change in the agricultural sector of ECOWAS.

In recent years, ECOWAS has experienced an increase in income per capita, investment, and labor productivity, indicating that further economic efficiency in production may be realized. This may boost climate financing in the agricultural sector. However, further improvement in general production may not generate enough resources to address both climate change and other development challenges, particularly in the context of increasing prices. There is a need for policy makers to prioritize climate strategies that also have some development impacts. Infrastructure in ECOWAS is underdeveloped with poor quality. Although the availability and quality of infrastructure may never substitute for agriculturespecific policies such as an increase fertilizer use, irrigation, and improved seeds, infrastructure development can help build resilience to climate change. Indeed, infrastructure development provides important connectivity with growing markets and also reduces input, transaction, and information acquisition costs for farmers and other actors in the agricultural sector of ECOWAS. On the other hand, ECOWAS has a huge potential for trade both in global and intra-regional terms mainly because of its large natural resource endowment, agricultural potential, and intra-regional complementarities. However, intra-regional trade that takes place in ECOWAS is mainly informal and generally considered to be well below its potential.

**81**

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector…*

In the context of insufficient financial resources to support climate adaptation actions, stimulating trade appears as an appropriate measure to preserve food availability by allowing countries with excess food production to trade with food-deficit

Returns to adaptation to climate change in ECOWAS can vary widely depending on the technology with gains of up to \$252 million in economic benefits compared to \$161 loss without policy adjustment in a country in the Sudano-Sahelian zone. The economics of adaptation to climate change in the agricultural sector of ECOWAS has focused so far on the magnitude of costs and returns on a countrywide and technology-specific measures. There is a need, however, for further research to integrate biophysical and economic factors of climate change impact chain in sound analytical frameworks to provide "multi-metric" considerations of non-monetary and nonmarket measures, risks, inequities, and behavioral biases in

Although important results were obtained regarding the biophysical and economic opportunities and challenges for building the resilience of the agriculture sector of ECOWAS to climate change, our study presents some limitations. For example, a detailed year-to-year trend analysis from 2003 to 2013 is not done because of the wide scope of the study (mapping), and data are not available for some parameters for some countries, and some data are not available for the same years for all variables studied. In addition, the environmental and social factors that are not covered in the study are equally important in defining the climate change impact chain in the agricultural sectors. These issues will be addressed in further

*DOI: http://dx.doi.org/10.5772/intechopen.84378*

countries.

research.

**Author details**

Calvin Atewamba1

Canada

\* and Edward R. Rhodes2

2 Njala University, PMB, Freetown, Sierra Leone

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
1 United Nations University Institute for Natural Resources in Africa, Toronto,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

addressing climate change.

*Biophysical and Economic Factors of Climate Change Impact Chain in the Agriculture Sector… DOI: http://dx.doi.org/10.5772/intechopen.84378*

In the context of insufficient financial resources to support climate adaptation actions, stimulating trade appears as an appropriate measure to preserve food availability by allowing countries with excess food production to trade with food-deficit countries.

Returns to adaptation to climate change in ECOWAS can vary widely depending on the technology with gains of up to \$252 million in economic benefits compared to \$161 loss without policy adjustment in a country in the Sudano-Sahelian zone. The economics of adaptation to climate change in the agricultural sector of ECOWAS has focused so far on the magnitude of costs and returns on a countrywide and technology-specific measures. There is a need, however, for further research to integrate biophysical and economic factors of climate change impact chain in sound analytical frameworks to provide "multi-metric" considerations of non-monetary and nonmarket measures, risks, inequities, and behavioral biases in addressing climate change.

Although important results were obtained regarding the biophysical and economic opportunities and challenges for building the resilience of the agriculture sector of ECOWAS to climate change, our study presents some limitations. For example, a detailed year-to-year trend analysis from 2003 to 2013 is not done because of the wide scope of the study (mapping), and data are not available for some parameters for some countries, and some data are not available for the same years for all variables studied. In addition, the environmental and social factors that are not covered in the study are equally important in defining the climate change impact chain in the agricultural sectors. These issues will be addressed in further research.

#### **Author details**

*Environmental Health - Management and Prevention Practices*

agricultural development in ECOWAS.

**6. Conclusion**

sector of ECOWAS.

ECOWAS.

bioeconomic optimization model to predict agricultural land allocation and crop production under RCPs; and an intra-regional food trade module built from a classical transportation model to allocate food optimally from excess supply countries to excess demand countries. The authors showed that the climate-induced trade pattern in ECOWAS depends on the prevailing socioeconomic conditions during the century, and several countries may become dependent on food imports outside ECOWAS. However, doubling crop yields by 2050 and adjusting the ECOWAS common external tariffs could significantly reduce outside dependence. Although this study attempts to investigate the link between climate change and food security in ECOWAS, more research is needed to understand how the integration of factors of climate change impact chain could affect food security and promote sustainable

This chapter assessed key biophysical and economic factors of the climate change impact chain and the economics of adaptation in the agricultural sector of

As in many developing regions, land for agricultural production in ECOWAS has increased at the expense of forest land. These changes vary, however, by agroecological zones. Furthermore, ECOWAS countries have experienced significant land degradation. The primary causes of land degradation are deforestation, agriculture, conversion of grasslands to croplands, and overgrazing. Cropping systems are generally related to agroecological zones; however, the release of new varieties of crops and changing climate is significantly changing the distribution of crops across ECOWAS. Crop production was higher in the Gulf of Guinea than the Sudano-Sahelian zone. This is partly explained by the high proportion of humid and subhumid zones within the Gulf of Guinea. Increases in crop production in ECOWAS are more often related to the area harvested than to yield per unit area, which is the major concern of CAADP and the Maputo Accord. The boost in crop productivity expected from CAADP and the Maputo Accord did not happen between 2003 and 2013. Increasing crop yields through agricultural intensification while reducing land degradation is critical to reduce the impact of climate change in the agricultural

In recent years, ECOWAS has experienced an increase in income per capita, investment, and labor productivity, indicating that further economic efficiency in production may be realized. This may boost climate financing in the agricultural sector. However, further improvement in general production may not generate enough resources to address both climate change and other development challenges, particularly in the context of increasing prices. There is a need for policy makers to prioritize climate strategies that also have some development impacts. Infrastructure in ECOWAS is underdeveloped with poor quality. Although the availability and quality of infrastructure may never substitute for agriculturespecific policies such as an increase fertilizer use, irrigation, and improved seeds, infrastructure development can help build resilience to climate change. Indeed, infrastructure development provides important connectivity with growing markets and also reduces input, transaction, and information acquisition costs for farmers and other actors in the agricultural sector of ECOWAS. On the other hand, ECOWAS has a huge potential for trade both in global and intra-regional terms mainly because of its large natural resource endowment, agricultural potential, and intra-regional complementarities. However, intra-regional trade that takes place in ECOWAS is mainly informal and generally considered to be well below its potential.

**80**

Calvin Atewamba1 \* and Edward R. Rhodes2

1 United Nations University Institute for Natural Resources in Africa, Toronto, Canada

2 Njala University, PMB, Freetown, Sierra Leone

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Sustainable Development. Washington & Bonn: International Food Policy Research Institute & Center for Development Research (ZEF); 2016

[19] Moussa B, Nkonya E, Meyer S, Kato S, Johnson T, Hawking J. Economics of land degradation in Niger. In: Economics of Land Degradation and Improvement: A Global Assessment for Sustainable Development. Washington & Bonn: International Food Policy Research Institute & Center for Development Research (ZEF), University of Bonn; 2016

[20] World Bank. Africa's Development in a Changing Climate. Washington: International Bank for Reconstruction and Development; 2009

[21] Blein R, Soule B, Dupraigre B, Yerima B. Agricultural Potential of West Africa (ECOWAS). Presles: Fondation Pour l'agriculture et la Ruralité Dans le Monde; 2008

[22] Rhodes E, Tambi E, Bangali S. State of knowledge on CSA in Africa: Case studies from Burkina Faso, Senegal, and Sierra Leone. In: Forum for Agricultural Research in Africa, Accra. 2015

[23] Lipton M. Learning from Others: Increasing Agricultural Productivity for Human Development. Washington, USA: United Nations Development Programme; 2012

[24] World Bank. World Bank Open Data. The World Bank; 2018 [Online]. Available: https://data.worldbank.org/ indicator [Accessed: 1-12-2018]

[25] AfDB. West Africa Economic Outlook West Africa Economic Outlook. Abidjan, Côte d'Ivoire: AfDB; 2018

[26] UNESCO. UNESCO Science Report: Towards 2030. Paris, France: United Nations Educational, Scientific and Cultural Organization; 2015

[27] Llanto GM. The impact of infrastructure on agricultural productivity. Journal of Developing Areas. 2012;**28**(4):469-486

[28] Zilberman D, Lipper L, McCarthy N, Gordon B. Innovation in response to climate change. In: Climate Smart Agriculture: Building Resilience to Climate Change. Vol. 52. Springer; 2018

[29] Torres C, van Seters J. Overview of Trade and Barriers to Trade in West Africa: Insights in Political Economy Dynamics, with Particular Focus on Agricultural and Food Trade. Maastricht: European Center for Development Policy Management; 2016

[30] Lokonon BK, Egbendewe AYG, Atewamba C. Spatial Implications of Climate Change on Land Allocation and Agricultural Production in ECOWAS. Vol. 17. Accra, Accra, Ghana: United Nations University Institute for Natural Resources in Africa; 2016

[31] Stephan G, Schenker O. International Trade and the Adaptation to Climate Change and Variability. Mannheim, Germany: ZEW - Center for European Economic Research; 2012

[32] Roudier P, Sultan B, Quirion P, Berg A. The impact of future climate change on West African crop yields: What does the recent literature say? Global Environmental Change. 2011;**21**(3):1073-1083

[33] Butt T, McCarl B, Kergna A. Policies for reducing agricultural vulnerability to climate change in Mali. Climate Policy. 2006;**5**:583-598

[34] World Bank. The Economics of Adaptation to Climate Change: A Synthesis Report. Washington: International Bank for Reconstruction and Development; 2010

[35] Shittu A, Kehinde M, Ogunnaike M, Oyawole F. Effects of land tenure and

**82**

*Environmental Health - Management and Prevention Practices*

run off in West Africa: A review. Hydrology and Earth System Sciences.

Bank for Reconstruction and

[11] Rhodes ER, Atewamba C. Climate change impact chain factors in ECOWAS. Journal of Agriculture and Environment for International Development. 2019;**113**(1):35-78

[10] World Bank. World Development Indicators. Washington: International

[12] FAO. Land Use. FAO; 2018 [Online]. Available: www.fao.org/statistics/en

[13] FAO. Agricultural Production Data. 2018 [Online]. Available: www.fao.org/ statistics/en [Accessed: 15-09-2018]

[14] World Bank. World Development Indicators. Washington: International

[15] Le Q, Nkonya E, Mirzabaev A. Biomass Productivity-Based Mapping of Global Level Degradation Hotspots. Bonn: ZEF, University of Bonn; 2014

[16] Kaufman JB, Bhomia R. Ecosystem carbon stocks of mangroves across broad environmental gradients in west-Central Africa: Global and regional comparisons. PLoS One. 2017;**12**(11)

[17] Bot A, Nachtergale F, Young A. Land resource potential and constraints at regional and country levels. In: World Soil Resources Report 90. Food and Agriculture Organization of the United

Bank for Reconstruction and

Development; 2015

Nations: Rome; 2000

[18] Nkonya E, Johnson T, Kwon H, Kato E. Economics of land

degradation in Sub-Saharan Africa. In: Economics of Land Degradation and Improvement—A Global Assessment for

2014;**18**:2789-2801

Development; 2005

[Accessed: 15-09-2018]

[1] FAO. Irrigation in Africa: AQUASTAT Survey. Rome: Food and Agriculture Organization of the United Nations;

[2] UNCDP. List of Least Developed Countries. New York: United Nations Committee for Development Policy; 2016

[3] FAO. Agroecological Map of West Africa. FAO; 2016 [Online]. Available from: www.fao.org/docrep/006/y4751e/ y4751e0p.htm [Accessed: 17-10-2018]

[4] Maplecroft. Climate change vulnerability index 2014. Maplecroft; 2013 [Online]. Available from: https:// reliefweb.int/sites/reliefweb.int/ files/resources/Climate\_Change\_ Vulnerability\_Index\_%202014\_Map\_0.

pdf [Accessed: 12-12-2016]

Bank; 2008

Institute; 2009

[5] Kurukulasuriya P, Mendelsohn R. How Will Climate Change Shift Agroecological Zones and Impact African Agriculture? Policy Research Working Paper 4717. Washington: World

[6] Nelson GC, Rosegrant M, Koo J, Robertson R, Sulser T, Zhu T, et al. Climate Change: Impact on Agriculture and Costs of Adaptation. Washington: International Food Policy Research

[7] Jalloh A, Nelson G, Thomas T, Zougmore R, Roy-Macauley H. West African Agriculture and Climate Change. Washington: International Food Policy Research Institute; 2013

[8] Rhodes E, Jalloh A, Diouf A. Review of Research and Policies for Climate Change Adaptation in the Agriculture Sector in West Africa. Brighton: AfricaInteract, Institute of

Development Studies; 2014

[9] Roudier P, Ducharne A, Feyen L. Climate change impacts on

2005

**References**

property rights on farm households' willingness to accept incentives to invest in measures to combat land degradation in Nigeria. Agricultural and Resource Economics Review. 2018;**47**(2):357-387

[36] Neate P. Water harvesting boosts yield in the Sahel. In: Climate Smart Agriculture Success Stories from Farming Communities Around the World. CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS) and Technical Center for Agriculture for Agricultural and Rural Cooperation (CTA); 2013

[37] Bjorkemar K. Agroforestry in Sierra Leone: Examining the Economic Potential with Carbon Sequestration. Sweden: Linnaeus University; 2014

[38] Aklesso YGE, Lokonon BOK, Atewemba C, Coulibaly N. Can intraregional food trade increase food availability in the context of global climatic change in West Africa? Climatic Change. 2017;**145**(1-2):101

**85**

**Chapter 6**

**Abstract**

**1. Introduction**

*Dominika Saniewska*

treated as a cleansing zone for the Baltic Proper.

**Keywords:** mass balance, sea, river, atmosphere, sediments, organisms

Mercury (Hg) is considered to be one of the most toxic metals found in terrestrial and aquatic ecosystems around the world. This is due to the specific nature of this element—volatility, persistence, and strong toxicity of its organic forms. It has no known essential biological function. Hg is toxic in every form; however, its toxicity depends mainly on its chemical form. The most dangerous form of Hg is methyl mercury (MeHg). Hg has neurotoxic, mutagenic, cytotoxic, nephrotoxic, and allergenic properties. It can also affect the functioning of the muscular system and many enzymes and proteins. Mercury is particularly dangerous for pregnant women because MeHg can easily pass through the placenta via active transport by amino acid carriers, causing embryotoxic and teratogenic effects in the fetus [1]. The aquatic environment is particularly sensitive to Hg contamination because this metal accumulates and biomagnifies with increasing trophic level [2]. Consequently, the Hg concentration in tissues of fish, birds, and water mammals can be even 100,000 times higher than in surrounding water [2]. The most common cause of Hg poisoning is consumption of predatory fish and seafood with high levels of this metal in its tissues [2]. Therefore, an understanding of mercury cycling in the aquatic environments is of fundamental importance, especially in

Mercury Cycling in the Gulf of

The most efficient way of mercury (Hg) transport to the Gulf of Gdańsk was river runoff. Therefore, hydrological conditions were the most important factors controlling the inflow of Hg to the sea. The second most important Hg source in the Gulf was atmospheric deposition, which transported seven times smaller load than rivers. The Hg wet deposition dominated in the warm season, while during the heating season the predominant was dry deposition of mercury. The Hg source, which should not be neglected during the creation of the mass balance of Hg in aquatic ecosystems, was the coastal erosion. In the Gulf of Gdańsk, it accounts for 6% of the Hg load reaching the sea. The main sink of Hg was bottom sediments. Other important processes that reduced the Hg load in the Gulf water were reemission of Hg to the atmosphere and export of this metal to the Baltic Proper. The mass balance of mercury in the Gulf of Gdańsk indicated that a larger load of this metal flowed into the Gulf than left it. Consequently, the Gulf of Gdańsk should be

Gdańsk (Southern Baltic Sea)

#### **Chapter 6**

*Environmental Health - Management and Prevention Practices*

property rights on farm households' willingness to accept incentives to invest in measures to combat land degradation in Nigeria. Agricultural and Resource Economics Review. 2018;**47**(2):357-387

[36] Neate P. Water harvesting boosts yield in the Sahel. In: Climate Smart Agriculture Success Stories from Farming Communities Around the World. CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS) and Technical Center for Agriculture for Agricultural and Rural Cooperation (CTA); 2013

[37] Bjorkemar K. Agroforestry in Sierra Leone: Examining the Economic Potential with Carbon Sequestration. Sweden: Linnaeus University; 2014

[38] Aklesso YGE, Lokonon BOK, Atewemba C, Coulibaly N. Can intraregional food trade increase food availability in the context of global climatic change in West Africa? Climatic

Change. 2017;**145**(1-2):101

**84**

## Mercury Cycling in the Gulf of Gdańsk (Southern Baltic Sea)

*Dominika Saniewska*

#### **Abstract**

The most efficient way of mercury (Hg) transport to the Gulf of Gdańsk was river runoff. Therefore, hydrological conditions were the most important factors controlling the inflow of Hg to the sea. The second most important Hg source in the Gulf was atmospheric deposition, which transported seven times smaller load than rivers. The Hg wet deposition dominated in the warm season, while during the heating season the predominant was dry deposition of mercury. The Hg source, which should not be neglected during the creation of the mass balance of Hg in aquatic ecosystems, was the coastal erosion. In the Gulf of Gdańsk, it accounts for 6% of the Hg load reaching the sea. The main sink of Hg was bottom sediments. Other important processes that reduced the Hg load in the Gulf water were reemission of Hg to the atmosphere and export of this metal to the Baltic Proper. The mass balance of mercury in the Gulf of Gdańsk indicated that a larger load of this metal flowed into the Gulf than left it. Consequently, the Gulf of Gdańsk should be treated as a cleansing zone for the Baltic Proper.

**Keywords:** mass balance, sea, river, atmosphere, sediments, organisms

#### **1. Introduction**

Mercury (Hg) is considered to be one of the most toxic metals found in terrestrial and aquatic ecosystems around the world. This is due to the specific nature of this element—volatility, persistence, and strong toxicity of its organic forms. It has no known essential biological function. Hg is toxic in every form; however, its toxicity depends mainly on its chemical form. The most dangerous form of Hg is methyl mercury (MeHg). Hg has neurotoxic, mutagenic, cytotoxic, nephrotoxic, and allergenic properties. It can also affect the functioning of the muscular system and many enzymes and proteins. Mercury is particularly dangerous for pregnant women because MeHg can easily pass through the placenta via active transport by amino acid carriers, causing embryotoxic and teratogenic effects in the fetus [1]. The aquatic environment is particularly sensitive to Hg contamination because this metal accumulates and biomagnifies with increasing trophic level [2]. Consequently, the Hg concentration in tissues of fish, birds, and water mammals can be even 100,000 times higher than in surrounding water [2]. The most common cause of Hg poisoning is consumption of predatory fish and seafood with high levels of this metal in its tissues [2]. Therefore, an understanding of mercury cycling in the aquatic environments is of fundamental importance, especially in

coastal areas of densely populated countries which are usually exposed to pollutants from various human activities.

The Gulf of Gdańsk is located in the southern part of the Baltic Sea (**Figure 1**). Its northern boundary is the straight line connecting Cape Rozewie (54**°**50'N, 18**°**20'E) with Cape Taran (54**°**58'N, 19**°**59'E). The Gulf of Gdańsk (average depth 50 m, maximum depth 118 m) is often considered a separate natural region [3, 4]. Its area is 4940 km2 , while the volume of water is estimated at 291.2 km3 [4, 5]. The hydrological conditions in the Gulf of Gdańsk are largely influenced by river inflow, of which the Vistula is the most important. It is the longest river that flows into the Baltic Sea and the second, after the Neva, in terms of the size of the catchment [6]. The Vistula catchment (194 thousand km2 ) constitutes over 88% of the whole drainage basin of it of Gdańsk (220 thousand km2 ) [4]. Therefore, the Gulf is considered as an important transition between land and sea. In addition, urbanized and industrialized regions within its coast affect the level of pollutants in the sea.

Investigations performed in the 1970s and 1980s revealed that the Gulf of Gdańsk was one of the most polluted water bodies in the world [7, 8]. However, the latest research indicates that the Gulf of Gdańsk is not exceptionally contaminated, and contaminant concentration in the water generally indicates a good environmental state [9]. Such considerable differences between results within the last 40 years were the effect of both analytical errors and reduction of Hg load introduced from anthropogenic sources [6]. However, this situation has caused numerous studies of environmental pollution being carried out in this area for many years, including those relating to mercury. This enabled the creation of a large database containing the results of determinations of this metal in practically all the abiotic and biotic matrices. Therefore, the objectives of this study were to construct a multi-year mass balance of Hg in the Gulf of Gdańsk. This mass balance was used to identify and better understand the dominant mercury sources and sinks in the gulf and can provide an initial basis for future research in this region.

**87**

*Mercury Cycling in the Gulf of Gdańsk (Southern Baltic Sea)*

The mass balance of mercury in the Gulf of Gdańsk was constructed on the basis of the analysis of Hg loads in precipitation, aerosols, river water, groundwater, sea water, cliff and marine sediment as well as in organism samples. In order to balance the loads of Hg in the gulf, it was assumed that the main sources of this metal were atmospheric deposition, river water, and coastal erosion, while the factors having impact on the decrease of Hg concentration in sea water were re-emission of gaseous Hg to the atmosphere, sedimentation processes, and exchange of water

Aerosols and precipitation samples were collected at a station located in Gdynia

(**Figure 1**), at a distance of about 500 meters from the sea (54**°**30'N, 18**°**32′E). A detailed description of the station can be found in the publications [10, 11]. Precipitation samples were collected from August 2008 to May 2009 and from January to December 2012. In the first period, samples were collected using a bulk collector composed of a Teflon funnel of 20 cm diameter connected directly to a borosilicate glass bottle. In the second period, precipitation samples were collected using an automatic collector of wet deposition. After collection, samples were stored at −20°C until analysis. In each case, information about the amount of precipitation was obtained. Detailed information about sampling and storage methods

Aerosol samples were collected from December 2007 to December 2008 in 24 h cycles using open-faced Teflon filter packs on 47 mm glass-fiber filters. Samples were then stored at −20°C until analysis. The collection and storage meth-

River water samples were taken from cross sections close to the mouth: the Vistula (12 km from mouth), the Reda (1.0 km from mouth), the Zagórska Struga (0.8 km from mouth), the Płutnica (0.4 km from mouth), the Kacza River (0.1 km from mouth), and the Gizdepka (0.6 km from mouth) (**Figure 1**). Sampling took place between 2008 and 2016 (with gaps). In each case, information about river flow was obtained. Water samples were collected directly into acid-washed borosilicate vials with Teflon screw cap. Samples were stored at 4°C until analysis. Detailed information on characteristics of the rivers as well as sampling and storage methods

The Hg concentration in groundwater was described by Szymczycha et al. [18]. The research was conducted in the Puck Bay (part of the Gulf of Gdańsk) in 2009–2010. Groundwater samples were collected directly into acid-washed borosilicate vials with Teflon screw cap and stored at 4°C until analysis. Detailed information on characteristics of the research area and sampling and storage methods can

Information about Hg concentration in cliff deposits was presented in the work by Bełdowska et al. [12] and Kwasigroch et al. [19]. Samples were collected in two periods: 2011–2014 [12] and 2016–2017 [19]. Materials within the colluvium were collected from three cliffs located on the west part of the Gulf of Gdańsk (Orłowo, Mechelinki and Osłonino cliffs) (**Figure 1**). Detailed information about sampling

and storage methods can be found in the publications [12, 19].

Seawater samples were collected between 2006 and 2016 (with gaps) [14, 15, 20–22]. As part of these studies, samples were taken from various regions of the Gulf of Gdańsk (coastal and offshore areas) as well as from different depths

*DOI: http://dx.doi.org/10.5772/intechopen.86159*

**2. Methods of mass balance calculations**

between the Gulf of Gdańsk and the Baltic Proper.

can be found in the publications [11, 12].

ods were described by Bełdowska et al. [10].

can be found in the publications [12–17].

be found in the publication [18].

**2.1 Sampling**

**Figure 1.** *The map of the study area.*

#### **2. Methods of mass balance calculations**

The mass balance of mercury in the Gulf of Gdańsk was constructed on the basis of the analysis of Hg loads in precipitation, aerosols, river water, groundwater, sea water, cliff and marine sediment as well as in organism samples. In order to balance the loads of Hg in the gulf, it was assumed that the main sources of this metal were atmospheric deposition, river water, and coastal erosion, while the factors having impact on the decrease of Hg concentration in sea water were re-emission of gaseous Hg to the atmosphere, sedimentation processes, and exchange of water between the Gulf of Gdańsk and the Baltic Proper.

#### **2.1 Sampling**

*Environmental Health - Management and Prevention Practices*

from various human activities.

catchment (194 thousand km2

research in this region.

it of Gdańsk (220 thousand km2

4940 km2

coastal areas of densely populated countries which are usually exposed to pollutants

cal conditions in the Gulf of Gdańsk are largely influenced by river inflow, of which the Vistula is the most important. It is the longest river that flows into the Baltic Sea and the second, after the Neva, in terms of the size of the catchment [6]. The Vistula

tant transition between land and sea. In addition, urbanized and industrialized

Investigations performed in the 1970s and 1980s revealed that the Gulf of Gdańsk was one of the most polluted water bodies in the world [7, 8]. However, the latest research indicates that the Gulf of Gdańsk is not exceptionally contaminated, and contaminant concentration in the water generally indicates a good environmental state [9]. Such considerable differences between results within the last 40 years were the effect of both analytical errors and reduction of Hg load introduced from anthropogenic sources [6]. However, this situation has caused numerous studies of environmental pollution being carried out in this area for many years, including those relating to mercury. This enabled the creation of a large database containing the results of determinations of this metal in practically all the abiotic and biotic matrices. Therefore, the objectives of this study were to construct a multi-year mass balance of Hg in the Gulf of Gdańsk. This mass balance was used to identify and better understand the dominant mercury sources and sinks in the gulf and can provide an initial basis for future

) constitutes over 88% of the whole drainage basin of

) [4]. Therefore, the Gulf is considered as an impor-

, while the volume of water is estimated at 291.2 km3

regions within its coast affect the level of pollutants in the sea.

The Gulf of Gdańsk is located in the southern part of the Baltic Sea (**Figure 1**). Its northern boundary is the straight line connecting Cape Rozewie (54**°**50'N, 18**°**20'E) with Cape Taran (54**°**58'N, 19**°**59'E). The Gulf of Gdańsk (average depth 50 m, maximum depth 118 m) is often considered a separate natural region [3, 4]. Its area is

[4, 5]. The hydrologi-

**86**

**Figure 1.**

*The map of the study area.*

Aerosols and precipitation samples were collected at a station located in Gdynia (**Figure 1**), at a distance of about 500 meters from the sea (54**°**30'N, 18**°**32′E). A detailed description of the station can be found in the publications [10, 11]. Precipitation samples were collected from August 2008 to May 2009 and from January to December 2012. In the first period, samples were collected using a bulk collector composed of a Teflon funnel of 20 cm diameter connected directly to a borosilicate glass bottle. In the second period, precipitation samples were collected using an automatic collector of wet deposition. After collection, samples were stored at −20°C until analysis. In each case, information about the amount of precipitation was obtained. Detailed information about sampling and storage methods can be found in the publications [11, 12].

Aerosol samples were collected from December 2007 to December 2008 in 24 h cycles using open-faced Teflon filter packs on 47 mm glass-fiber filters. Samples were then stored at −20°C until analysis. The collection and storage methods were described by Bełdowska et al. [10].

River water samples were taken from cross sections close to the mouth: the Vistula (12 km from mouth), the Reda (1.0 km from mouth), the Zagórska Struga (0.8 km from mouth), the Płutnica (0.4 km from mouth), the Kacza River (0.1 km from mouth), and the Gizdepka (0.6 km from mouth) (**Figure 1**). Sampling took place between 2008 and 2016 (with gaps). In each case, information about river flow was obtained. Water samples were collected directly into acid-washed borosilicate vials with Teflon screw cap. Samples were stored at 4°C until analysis. Detailed information on characteristics of the rivers as well as sampling and storage methods can be found in the publications [12–17].

The Hg concentration in groundwater was described by Szymczycha et al. [18]. The research was conducted in the Puck Bay (part of the Gulf of Gdańsk) in 2009–2010. Groundwater samples were collected directly into acid-washed borosilicate vials with Teflon screw cap and stored at 4°C until analysis. Detailed information on characteristics of the research area and sampling and storage methods can be found in the publication [18].

Information about Hg concentration in cliff deposits was presented in the work by Bełdowska et al. [12] and Kwasigroch et al. [19]. Samples were collected in two periods: 2011–2014 [12] and 2016–2017 [19]. Materials within the colluvium were collected from three cliffs located on the west part of the Gulf of Gdańsk (Orłowo, Mechelinki and Osłonino cliffs) (**Figure 1**). Detailed information about sampling and storage methods can be found in the publications [12, 19].

Seawater samples were collected between 2006 and 2016 (with gaps) [14, 15, 20–22]. As part of these studies, samples were taken from various regions of the Gulf of Gdańsk (coastal and offshore areas) as well as from different depths (surface water and profiles). Water samples were collected directly into acidwashed borosilicate vials with Teflon screw cap. Samples were stored at 4°C until analysis. Detailed information on characteristics of the stations as well as sampling and storage methods can be found in the publications [14, 15, 20–22].

The Hg concentration in sediments from several areas of the southern Baltic Sea was described by Bełdowski et al. [23], Jędruch et al. [24], and Kwasigroch et al. [19]. Samples were collected in the periods 1999–2002 [23], 2011–2013 [24], and 2016–2017 [19]. Detailed information about sampling and storage methods can be found in the publications [19, 23, 24].

Information about Hg in marine organisms was obtained from the literature [25–33]. The Hg concentration in phytoplankton was measured by Bełdowska and Kobos between 2011 and 2013 [25, 26]. Phytoplankton samples were collected with 20 μm nets and then stored at −20°C until analysis. At the same time, zooplankton samples were collected by Bełdowska and Mudrak-Cegiołak [27]. The samples were collected vertically using a zooplankton net (mesh size 50 μm). The collected samples were transported to the laboratory in lifesupporting conditions and after about 24 h (required for the acclimatization and defecation of the zooplankton) live zooplankton was separated from other material using the phototaxis phenomenon. Samples were then stored at −20°C until analysis. Detailed information about sampling and storage methods can be found in the publications [25–27].

The Hg concentration in macrophytobenthos was described by Bełdowska et al. [28]. Benthic plant samples were collected along the Gulf of Gdańsk coastal zone in the years 2006–2012. The collected material was taken to the laboratory, cleansed of epiphytes and organic and mineral particles, then rinsed in distilled water, and then stored at −20°C until analysis.

Information about Hg concentration in benthic macrofauna was presented by Jędruch et al. [29, 30] and Bełdowska et al. [31]. Samples were collected between 2011 and 2013 using a manual Van Veen grab sampler. In order to separate the benthic organisms, samples of marine sediments were sieved through a 0.5 mm mesh. Live biological material was placed in containers with sea water in situ and transported to the laboratory where they were stored at −20°C until analysis.

The Hg concentration in herring, sprat, and cod from the southern Baltic Sea was obtained from the study by Polak-Juszczak [32]. Samples were collected from 1994 to 2003. Fish were stored at −20°C until analysis.

Information about Hg concentration in gray seals (*Halichoerus grypus*) was presented by Bełdowska and Falkowska [33]. The studies were conducted in the Polish coastal zone of the Baltic Sea during the years 2001–2011. Seal's tissues were obtained from dead individuals found along the coast.

#### **2.2. Chemical analysis**

The Hg concentrations in solid samples were determined via pyrolysis with the atomic absorption technique using a direct mercury analyzer [34]. This technique did not require any sample preparation (e.g. extraction/digestion) which would pose a risk of contamination. The analysis of certified reference materials produced both satisfactory recovery and precision.

Water samples for mercury analysis were oxidized by the addition of BrCl and pre-reduced with hydroxylamine hydrochloride solution 1 h prior to analysis by the cold vapor atomic fluorescence technique, according to USEPA method 1631 [35]. Quality control procedures for water samples included blanks, and water spiked with mercury nitrate produced adequate rates of precision and recovery [11].

**89**

*Mercury Cycling in the Gulf of Gdańsk (Southern Baltic Sea)*

where Fwet is the wet deposition flux (ng m−2

where Fdry is the dry deposition flux of Hg (pg m−2

by the area of the Gulf of Gdańsk and time of sampling.

was used to calculate annual Hg load transported into the sea:

); and Q is the water flow during sampling (m3

Gulf of Gdańsk to the atmosphere was calculated.

in the profile at the Gdańsk Deep [20, 21].

volume of water in the gulf [4, 5].

Gdańsk was estimated.

The Hg concentration in precipitation was used to calculate wet deposition fluxes:

); and R is the amount of precipitation (mm = dm3

s−1

); C is the Hg concentration in the river water

s−1

) was multiplied by the total length of the cliff sections in the area of

. The Hg load introduced to the Gulf of

); C is the Hg concentration in

m−2

); C is the Hg concentra-

). Deposition

). The annual Hg load was

) [40].

). The

).

Fwet = C × R (1)

Hg load introduced to the Gulf of Gdańsk with wet deposition was calculated by multiplying the wet deposition flux by the area of the Gulf of Gdańsk (4940 km2

The Hg dry deposition fluxes were calculated using the formula previously

Fdry = C × Vd (2)

velocity was not measured during sampling. Therefore, this value was taken from

Gdańsk with dry deposition was calculated by multiplying the dry deposition flux

The Hg concentration in river water at cross sections close to the river mouth

Lr = C Q (3)

calculated assuming linear variability in time periods between measurements [39]. The Hg load associated with coastal erosion was calculated based on information presented by Bełdowska et al. [12]. The Hg load form the 1 km long section of the

The Hg load transported to the sea by the seeping groundwater was estimated by multiplying the Hg concentration in groundwater measured in the Puck Bay [18] by

Taking into account the annual air-water flux estimated by Wängberg et al. [41] and data presented by Marks and Bełdowska [42], the Hg load re-emitted from the

The yearly Hg sedimentation flux presented by Bełdowski et al. [23] was used to

Based on the model calculations [43], the export flux of water from the gulf was estimated. This value was assumed to be equivalent to the volume of water introduced into the gulf by rivers. The outflow of Hg from the Gulf of Gdańsk was calculated by multiplying this volume of water by the Hg concentration measured

The Hg load in the Gulf of Gdańsk water was calculated by multiplying the median values of the Hg concentration in the open water of the gulf [20, 21] and the

Based on the mean Hg concentration in the two types of sediment (sandy and silty), their wetness [24], density [44], and surface of the bottom of each type, the load of Hg deposited in the upper layer of the sediments (0–5 cm) in the Gulf of

the submarine groundwater discharge rate to the Gulf of Gdańsk (0.07 km3

calculate the load of mercury deposited in the Gulf of Gdańsk sediments.

); and Vd is the deposition velocity (m s−1

*DOI: http://dx.doi.org/10.5772/intechopen.86159*

described by Seinfeld and Pandis [36]:

the literature [37, 38] and was 0.005 m s−1

where Lr is the Hg load (kg s−1

the Gulf of Gdańsk (36 km).

**2.3. Calculations**

precipitation (ng dm−3

tion in aerosols (pg m−3

(μg m−3

cliff (kg year−1

#### **2.3. Calculations**

*Environmental Health - Management and Prevention Practices*

found in the publications [19, 23, 24].

found in the publications [25–27].

stored at −20°C until analysis.

**2.2. Chemical analysis**

sion and recovery [11].

1994 to 2003. Fish were stored at −20°C until analysis.

obtained from dead individuals found along the coast.

both satisfactory recovery and precision.

(surface water and profiles). Water samples were collected directly into acidwashed borosilicate vials with Teflon screw cap. Samples were stored at 4°C until analysis. Detailed information on characteristics of the stations as well as sampling

The Hg concentration in sediments from several areas of the southern Baltic Sea was described by Bełdowski et al. [23], Jędruch et al. [24], and Kwasigroch et al. [19]. Samples were collected in the periods 1999–2002 [23], 2011–2013 [24], and 2016–2017 [19]. Detailed information about sampling and storage methods can be

Information about Hg in marine organisms was obtained from the literature [25–33]. The Hg concentration in phytoplankton was measured by Bełdowska and Kobos between 2011 and 2013 [25, 26]. Phytoplankton samples were collected with 20 μm nets and then stored at −20°C until analysis. At the same time, zooplankton samples were collected by Bełdowska and Mudrak-Cegiołak [27]. The samples were collected vertically using a zooplankton net (mesh size 50 μm). The collected samples were transported to the laboratory in lifesupporting conditions and after about 24 h (required for the acclimatization and defecation of the zooplankton) live zooplankton was separated from other material using the phototaxis phenomenon. Samples were then stored at −20°C until analysis. Detailed information about sampling and storage methods can be

The Hg concentration in macrophytobenthos was described by Bełdowska et al. [28]. Benthic plant samples were collected along the Gulf of Gdańsk coastal zone in the years 2006–2012. The collected material was taken to the laboratory, cleansed of epiphytes and organic and mineral particles, then rinsed in distilled water, and then

Information about Hg concentration in benthic macrofauna was presented by Jędruch et al. [29, 30] and Bełdowska et al. [31]. Samples were collected between 2011 and 2013 using a manual Van Veen grab sampler. In order to separate the benthic organisms, samples of marine sediments were sieved through a 0.5 mm mesh. Live biological material was placed in containers with sea water in situ and transported to the laboratory where they were stored at −20°C until analysis. The Hg concentration in herring, sprat, and cod from the southern Baltic Sea was obtained from the study by Polak-Juszczak [32]. Samples were collected from

Information about Hg concentration in gray seals (*Halichoerus grypus*) was presented by Bełdowska and Falkowska [33]. The studies were conducted in the Polish coastal zone of the Baltic Sea during the years 2001–2011. Seal's tissues were

The Hg concentrations in solid samples were determined via pyrolysis with the atomic absorption technique using a direct mercury analyzer [34]. This technique did not require any sample preparation (e.g. extraction/digestion) which would pose a risk of contamination. The analysis of certified reference materials produced

Water samples for mercury analysis were oxidized by the addition of BrCl

and pre-reduced with hydroxylamine hydrochloride solution 1 h prior to analysis by the cold vapor atomic fluorescence technique, according to USEPA method 1631 [35]. Quality control procedures for water samples included blanks, and water spiked with mercury nitrate produced adequate rates of preci-

and storage methods can be found in the publications [14, 15, 20–22].

**88**

The Hg concentration in precipitation was used to calculate wet deposition fluxes:

$$\mathbf{F}\_{\text{wet}} = \mathbf{C} \times \mathbf{R} \tag{1}$$

where Fwet is the wet deposition flux (ng m−2 ); C is the Hg concentration in precipitation (ng dm−3 ); and R is the amount of precipitation (mm = dm3 m−2 ). The Hg load introduced to the Gulf of Gdańsk with wet deposition was calculated by multiplying the wet deposition flux by the area of the Gulf of Gdańsk (4940 km2 ).

The Hg dry deposition fluxes were calculated using the formula previously described by Seinfeld and Pandis [36]:

$$\mathbf{F\_{dry} = C \times V\_d} \tag{2}$$

where Fdry is the dry deposition flux of Hg (pg m−2 s−1 ); C is the Hg concentration in aerosols (pg m−3 ); and Vd is the deposition velocity (m s−1 ). Deposition velocity was not measured during sampling. Therefore, this value was taken from the literature [37, 38] and was 0.005 m s−1 . The Hg load introduced to the Gulf of Gdańsk with dry deposition was calculated by multiplying the dry deposition flux by the area of the Gulf of Gdańsk and time of sampling.

The Hg concentration in river water at cross sections close to the river mouth was used to calculate annual Hg load transported into the sea:

$$\mathbf{L\_r = C \ Q} \tag{3}$$

where Lr is the Hg load (kg s−1 ); C is the Hg concentration in the river water (μg m−3 ); and Q is the water flow during sampling (m3 s−1 ). The annual Hg load was calculated assuming linear variability in time periods between measurements [39].

The Hg load associated with coastal erosion was calculated based on information presented by Bełdowska et al. [12]. The Hg load form the 1 km long section of the cliff (kg year−1 ) was multiplied by the total length of the cliff sections in the area of the Gulf of Gdańsk (36 km).

The Hg load transported to the sea by the seeping groundwater was estimated by multiplying the Hg concentration in groundwater measured in the Puck Bay [18] by the submarine groundwater discharge rate to the Gulf of Gdańsk (0.07 km3 ) [40].

Taking into account the annual air-water flux estimated by Wängberg et al. [41] and data presented by Marks and Bełdowska [42], the Hg load re-emitted from the Gulf of Gdańsk to the atmosphere was calculated.

The yearly Hg sedimentation flux presented by Bełdowski et al. [23] was used to calculate the load of mercury deposited in the Gulf of Gdańsk sediments.

Based on the model calculations [43], the export flux of water from the gulf was estimated. This value was assumed to be equivalent to the volume of water introduced into the gulf by rivers. The outflow of Hg from the Gulf of Gdańsk was calculated by multiplying this volume of water by the Hg concentration measured in the profile at the Gdańsk Deep [20, 21].

The Hg load in the Gulf of Gdańsk water was calculated by multiplying the median values of the Hg concentration in the open water of the gulf [20, 21] and the volume of water in the gulf [4, 5].

Based on the mean Hg concentration in the two types of sediment (sandy and silty), their wetness [24], density [44], and surface of the bottom of each type, the load of Hg deposited in the upper layer of the sediments (0–5 cm) in the Gulf of Gdańsk was estimated.

The amount of Hg bound to the phytoplankton was estimated by multiplying the median of the Hg concentration in phytoplankton in the coastal zone of the Gulf of Gdańsk [25] by the mean phytoplankton biomass in the Gulf [26, 45] and the volume of the water to a depth of 20 meters (where phytoplankton occurs most abundantly).

Taking into account the median of the Hg concentration in zooplankton in the coastal zone of the Gulf of Gdańsk [27], the mean zooplankton biomass in the Gulf [45] and the volume of water in the Gulf [4, 5], the Hg pool in zooplankton was estimated.

The calculation of the Hg load accumulated in macrophytobenthic plants was carried out based on the research conducted by Bełdowska et al. [28] (the Hg concentration in macrophytobenthos), Saniewski and Zalewska [46] (biomass of particular macrophytobenthic plants from the Gulf of Gdańsk) and the area on which these plants occur [4].

The amount of Hg accumulated in macrozoobenthos in the Gulf of Gdańsk was estimated based on the research conducted by Jędruch et al. [29, 30] (the Hg concentration in macrozoobenthos) and Włodarska-Kowalczuk et al. [47] (biomass of particular macrozoobenthic organisms from the Gulf of Gdańsk) and the area on which these organisms occur [4]. Jędruch et al. [29, 30] observed that the macrozoobenthos occurred most abundantly up to a depth of 80 m as well as the Hg concentration in macrozoobenthic organisms was dependent on the type of bottom. Therefore, the bottom area of the Gulf of Gdańsk to a depth of 80 m (3350 km2 ) was divided into two types: bottom covered by macrophytobenthos (100 km2 ) and bottom not covered by macrophytobenthos (3250 km2 ) [Jędruch, personal comm.].

The fish resources of the Gulf of Gdańsk have not yet been reliably estimated. However, information about the biomass of the most common caught fish was available. Therefore, the Hg load exported from the sea with the fish was calculated by multiplying the median values of the Hg concentration in the particular species of the most commonly caught fish [32, 33] and the biomass of these fish [48].

Taking into account the median Hg concentration in the muscle [33], the wetness of the tissue (55%), the average weight of the gray seal (250 kg), and the size of population in the Gulf of Gdańsk [49], the load of Hg accumulated in the gray seals was estimated.

#### **3. External sources and sinks of mercury in the Gulf of Gdańsk**

An important source of mercury in the sea is atmospheric deposition. The Hg load introduced into the Gulf of Gdańsk with wet and dry deposition was 17.7 kg year−1 and 9.5 kg year−1 , respectively (**Figure 2**) [10–12, 16]. The inflow of Hg with precipitation dominated in the warm season (May–September), while during the heating season (October-April) dry deposition of mercury associated with aerosols was predominant [10, 11, 16]. The key role in Hg dry deposition was played by the concentration of mercury bound in aerosols, while the particle deposition velocity was of marginal significance [10, 16]. Hg wet deposition flux depended mainly on the amount of precipitation. There was a significant statistical relationship between Hg wet deposition flux and the Hg concentration in the precipitation, only in the warm season [11, 16].

Another important source of Hg in the sea is riverine input. The average annual Hg load carried out into the Gulf of Gdańsk by rivers was estimated to be 200 kg (**Figure 2**) [12–16]. The riverine input of Hg to the Gulf varied between 180 and 220 kg per year [12–16]. A total of 99% of Hg carried out by the rivers was transported by the Vistula (average water flow 1047 m3 s−1 ), the longest river that flows into the Baltic Sea [6]. The small rivers (average water flow < 5 m3 s−1 ) provided about 2 kg of Hg [13, 15, 16]. The Hg load transported by rivers to the

**91**

**Figure 2.**

*Mercury Cycling in the Gulf of Gdańsk (Southern Baltic Sea)*

sea depended on the amount of precipitation and retention capacity of the catchment. In areas of natural character, only 10–30% atmospheric Hg deposited to the catchment outflow from the soil to the river [13, 16, 50, 51]. However, in areas where the catchment was transformed by humans (fields and urban and industrialized areas), Hg outflow was considerably greater than in the natural areas and may have reached up to 65% [13, 16, 52–54]. Saniewska and colleagues [13] showed that in urbanized and agricultural areas, river water was two to three times more polluted with mercury than in the forests. This was influenced not only by the increased Hg outflow from the catchment, but also by the inflow of Hg from anthropogenic sources (e.g. combustion of fossil fuels and point sources). In this area, Hg is emitted into the atmosphere. Consequently, more Hg could be deposited in the catchment and then could be washed out to river water [13, 16, 17]. The Hg load transported by rivers to the sea depended on the river flow [13–16]. This was particularly visible during floods, downpours, and thaws, when river flow was much higher than the average value. At that time, water pours out of the riverbed, which causes local flooding and increased soil erosion [13–16]. During high flow, the Hg daily outflow from the catchment could increase over tenfold than during the period with average [13–16]. This was clearly visible during the Vistula flood of 2010. During the month of the flood, over 1197 kg of mercury flew into the Gulf of Gdańsk, which accounted for 75% of the Hg load transported through 2010 with the Vistula water [14]. During the flood, a very large load of dissolved mercury (around 314 kg) reached the sea. This can have a negative impact on the marine

environment, especially on organisms inhabiting river estuaries [14].

An important source of mercury in the Gulf of Gdańsk, usually neglected during the creation of the mass balance of Hg in aquatic ecosystems, is the coastal erosion. Bełdowska et al. [12] estimated that the 1-km long section of the cliff

*DOI: http://dx.doi.org/10.5772/intechopen.86159*

*The mass balance of mercury in the Gulf of Gdańsk.*

*Mercury Cycling in the Gulf of Gdańsk (Southern Baltic Sea) DOI: http://dx.doi.org/10.5772/intechopen.86159*

*Environmental Health - Management and Prevention Practices*

which these plants occur [4].

was estimated.

17.7 kg year−1