Research Article
Impact of Vegetation Over Exploitation in Relation to Carbon Stock of Savannah Trees in Minawao, Mayo-Tsanaga Division
Ibrahima Wanié Sago*, Djibrilla Mana, Ranava Dieudonne, Tchobsala and Ibrahima Adamou
Corresponding Author: Ibrahima Wanié Sago, University of Maroua, Higher Teachers’ Training College, Department of Life and Earth Sciences, Cameroon.
Received: February 22, 2024; Revised: March 01, 2024; Accepted: March 04, 2024 Available Online: March 21, 2024
Citation: Sago IW, Mana D, Dieudonne R, Tchobsala & Adamou I. (2024) Impact of Vegetation Over Exploitation in Relation to Carbon Stock of Savannah Trees in Minawao, Mayo-Tsanaga Division. BioMed Res J, 8(1): 708-721.
Copyrights: ©2024 Sago IW, Mana D, Dieudonne R, Tchobsala & Adamou I. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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In order to resolve the various environmental problems such as global warming that the world is faced with today, international organizations have been deployed for decades to provide valuable solutions. In the same vein, this study could highlight its contribution in this context. Since refugees and displaced people have occupied Minawao, the loss of biodiversity has increased, many species are threatened with extinction, yet each of the plant species is a carbon stock. In order to promote phytodiversity, the evaluation of the amount of carbon stored by savannah trees in the Minawao area would be a development issue. Located in Mayo-Tsanaga Division, Far-North Cameroon precisely in the canton of Gawar, Minawao occupies the granite-gneissic base of Mount-Mandara. A floristic inventory in four (3.75 ha/CU) Collecting Units (CU) was made according to the cardinal points in relation to the Minawao camp. To analyze the data, Excel was used to input data and calculate, Stat graphics plus 5.0 was used to compare variable and ENVI 4.5 to realize the map of the study area. On an area of 15 ha, we inventoried 7143 individuals belonging to 59 species, distributed under 42 genera and 30 families. The most represented species are Anogeissus leiocarpus (22.33%), Acacia polyacantha (12.29%) and Balanites aegyptiaca (6.22%). The amount of carbon stored by all CU is on average 166.01 tC/ha, equivalent to 1660.08 $, corresponding to 457328.71 CFAF. CU4 has the highest carbon content (346.07 tC/ha) than other CUs that do not differ significantly (P> 0.05) between them (15.43-175.20 tC/ha). Given the heterogeneity of the various CU, certified by their different values, a dignified development plan would be welcomed.

Keywords: Carbon, Minawao, Stock, Vegetation
INTRODUCTION

Plants are living things that have varied important roles towards mankind. Previously, it was seen simply as a tool for socio-economic use, but nowadays, the various roles they play involve: medicinal role, beautifying role of a good architectural environment etc., and besides all, they protect nature against climate change and equally have a real carbon stock. To emphasize the importance of phyto diversity in the fight against global warming and biodiversity loss, summits are held in various countries with the aim of drafting resolutions against global warming and the loss in biodiversity. There is for example the United Nations Framework Convention on Climate Change [1], itself resulting from the Rio Summit (1992), the Kyoto protocol [2] and also the 2015 Paris Conference on Climate Change [3]. A repeated disorder of the seasonal cycle for a few years now has been observed, in addition to this, the demographic growth caused above all by the humanitarian crisis which has driven populations of Borno (Federal State of Nigeria) and some Cameroonians to flee from the border area following the abuses of boko haram to settle in secure areas such as the case of Minawao camp which currently requires an increase in vital resources for the vulnerable population.

This galloping demography is correlated by the destruction of the plant cover for the benefit of human activities (agriculture, animal husbandry, traditional medicine, and logging, etc.) [4-6].

In order to assess the impact of vegetation exploitation on the carbon stock, we will need to achieve a number of objectives. Firstly, estimate the total biomass of vegetation in the Minawao area and determine its amount of carbon stored in order to assess its credit.

MATERIALS AND METHODS

Presentation of the study area

The study took place in Mokolo Sub-division, Mayo-Tsanaga Division, specifically in the Minawao area which includes villages of Minawao, Gadala and Gawar. Mokolo Sub-division covers 1650 km2 and has two first degree chiefdoms, five second degree chiefdoms and 106 third degree chiefdoms. The study area is located between 10 ° 28'- 10 ° 38 ’N latitude and 13 ° 48’- 13 ° 55’ E longitude [7]. It is bounded to the north by the city of Mokolo, to the south by Hina Sub-division, to the east by Gazawa Sub-division, and to the west by Mogode Sub-division (Figure 1).


Methods

To determine the composition, structure and amount of carbon in the vegetation of Minawao, a floristic inventory was carried out in 3 plots of 5 m x 2500 m separated from each other by 2 m in each of the four plant formations corresponding to CU1, CU2, CU3 and CU4 (Figures 2 & 4), located 500 m from Minawao camp. The total area of each CU is 3.75 ha. The transect method used by Tchobsala [8] was adopted. It makes it possible to achieve and gain precision in the estimation by reducing the displacement efforts. It consists of carrying out stratified sampling by dividing the plot into 500 quadrats of 5 m x 5 m oriented towards the outside of the camp (Figure 3). The plot use is recommended for stands dominated by coppice, the arrangement of inventory plots in clusters is especially recommended in areas where access is difficult [9,10].




On a previously defined collection sheet, all the woody individuals in each of the 500 quadrats were inventoried. The dbh, height and mean diameter of the crown were measured. The dbh and the average crown diameter were measured with a tape measure and the height with a graduated pole. Traces of exploitation (cutting, burning, debarking, pruning, etc.) were noted for all individuals from all plots. Species were identified on site using the determination keys [11,12] or in the laboratory of biodiversity and sustainable development of the University of Ngaoundere. The names of some species have been retained in local languages.

The experimental set-up is a complete randomized block with three repetitions. The block is the treatment and the plots are replications.

Assessment of exploitation impact of plant resources on the carbon stock

Estimation of woody biomass

  1. Estimation of above-ground biomass

To determine the above-ground biomass, the indirect method was adopted using allometric equations considering the dbh. The equation used by Brown [13] was chosen for this study because the coefficient of determination is highly significant (R2 = 0.987).

This equation has also been developed in the Sahelian climate.

Where AGB is the above-ground biomass of trees (in kg) and dbh is the diameter at breast height of a man.

  1. Estimation of underground biomass

Root biomass was estimated using an equation described by Cairns [14] who showed that from the total phytomass, the root phytomass (UGB) can be obtained by the following equation:

Where UGB = underground biomass, B = Biomass corresponding to above-ground biomass (AGB).

Estimation of the amount of carbon in woody plants

  1. Estimation of the amount of carbon

The amount of carbon was calculated from the biomass and the carbon concentration. Under recommendations of IPCC [15] majority of studies use an average value of vegetation carbon concentration of 50 % when more precise data are not available. This quantity of carbon is calculated according to the following formula:

Where AC is the total amount of carbon (tC/ha), B = Biomass (aerial and root) (t/ha) and Cv is the carbon concentration of the vegetation (0.5).

  1. Carbon credit assessment

The carbon credit was calculated from the quantity of carbon previously estimated and the value of the ecological service according to the following formula:  The value of the ecological service is estimated at 10 $/tAC [16]. One USD ($) = 580 FCFA (CoinMill.Com-the currency converter; value of 02/14/2020).

RESULTS

Biomass production

Diameter at breast height of a man (dbh)

Table 1 shows the dbh of the different species. This dbh varies very significantly from one species to another (0.000<0.0001) but not between CU (0.07>0.05). Some species have a very high dbh than others. Regarding the first group there are: Anogeissus leiocarpus (66.32 m/ha), Acacia polyacantha (40.22 m/ha), Faidherbia albida (41.13 m/ha), Tamarindus indica (36.68 m/ha) and Balanites aegyptiaca (34.82 m/ha), while the second group includes among others: Acacia erythrocalyx (0.02 m/ha), Acacia hokki (0.05 m/ha), Combretum paniculatum (0.13 m/ha), Eucalyptus camaldulensis (0.11 m/ha).

Table 1. Variation of dbh between different CU.

Species

CU1

CU2

CU3

CU4

Mean±sd

Acacia ataxacantha

24.09

49.77

3.93

27.3

26.27±12.27a

Acacia erythrocalyx

0.04

-

-

0.04

0.02±002b

Acacia hokki

0.17

-

-

0.03

0.05±0.06b

Acacia macrostachya

2.73

3.26

0.12

30.54

9.16±10.69c

Acacia nilotica

6.68

9.4

0.86

37.14

13.52±11.81d

Acacia polyacantha

10.6

1.04

6.69

142.55

40.22±51.16e

Acacia seyal

6.06

61.33

0.47

11.49

19.84±20.75g

Acacia sieberiana

1.4

-

13.84

0.07

3.83±5.01h

Adansonia digitata

0.47

2.57

-

1.93

1.24±1.01i

Adenium obesum

0.33

-

-

-

0.08±0.13b

Agave sisalana

1.93

-

-

7.2

2.28±2.46j

Annona senegalensis

5.84

36.54

4.61

0.3

11.82±12.36k

Anogeissus leiocarpus

119.38

43.04

22.13

80.74

66.32±33.74l

Azadirachta indica

13.9

10.2

1.55

15.5

10.29±4.41m

Balanites aegyptiaca

38.88

23.04

17.93

59.45

34.82±14.34n

Bauhinia rufescens

0.06

0.48

0.04

1.03

0.40±0.35b

Borassus aethiopum

13.26

0.37

15.12

-

7.19±7.00p

Boswellia dalzielii

1.63

-

0.09

-

0.43±0.60b

Bridelia ferruginea

-

-

0.02

-

0.01±0.01b

Calotropis procera

-

20.41

2.73

1.29

6.11±7.15o

Capparis sepiaria

-

0.32

0.11

1.51

0.48±0.51b

Cassia siamea

0.2

-

-

-

0.05±0.08b

Cassia singueana

5.88

5.47

0.31

13.4

6.26±3.57o

Celtis integrifolia

-

1.54

0.7

2.9

1.29±0.94i

Combretum aculeatum

0.91

6.81

3.74

1.77

3.31±1.97h

Combretum collinum

14.48

3.97

1.1

6.91

6.61±4.08o

Combretum paniculatum

-

-

0.5

-

0.13±0.19b

Combretum tomentosum

-

-

0.81

0.03

0.21±0.30b

Dalbergia melanoxylon

0.07

0.71

0.81

1.64

0.81±0.42b

Diospyros mespiliformis

43.63

9.71

2.62

15.68

17.91±12.86u

Entada Africana

-

-

0.17

-

0.04±0.06b

Eucalyptus camaldulensis

-

0.21

0.21

-

0.11±0.11b

Faidherbia albida

35.91

65.69

18.57

44.34

41.13±13.89f

Feretia apodanthera

2.18

0.97

1.23

4.05

2.11±1.01j

Ficus platiphylla

27.48

6.17

10.82

28.24

18.18±9.68q

Ficus sycomorus

11.5

-

34.89

26.41

18.20±12.45q

Ficus thonningii

0.48

-

6.99

-

1.87±2.56i

Gardenia aqualla

6.06

16.21

0.5

4.47

6.81±4.70o

Grewia flavescens

0.23

-

-

-

0.06±0.09b

Haematostaphis barteri

-

-

-

0.31

0.08±0.12b

Ipomoea carnea

0.23

-

-

-

0.06±0.09b

Isoberlina doka

-

3.17

-

-

0.79±1.19b

Jatropha curcas

-

0.19

0.2

0.06

0.11±0.08b

Lannea barteri

6.43

7.37

1.6

0.96

4.09±2.81r

Lannea velunita

0.37

-

-

-

0.09±0.14b

Parkia biglobosa

-

-

0.16

-

0.04±0.06b

Piliostigma thonningii

15.46

28.55

0.2

9.29

13.38±8.63s

Sclerocarya birrea

5.23

3.3

6.35

12,71

6.90±2.91o

Steganotenia araliaceae

-

-

0.03

-

0.01±0.01b

Sterculia setigera

26.78

5.13

0.68

12.31

11.22±8.32k

Strychnos spinosa

-

0.04

-

-

0.01±0.02b

Tamarindus indica

47.16

30.74

0.81

67.99

36.68±20.90t

Terminalia glauscesens

1.87

-

-

-

0.47±0.70b

Vernonia thomsoniana

0.06

14.14

0.1

1.22

3.88±5.13h

Vitex doniania

1.27

0.05

1.16

-

0.62±0.59b

Ximenia americana

-

-

-

1.95

0.49±0.73b

Ziziphus mauritiana

4.46

26.91

0.36

9.3

10.26±833m

Ziziphus mucronate

-

0.6

-

-

0.15±0.23b

Ziziphus spina-christi

0.35

6.13

0.24

2.48

2.30±2.00j

Mean±sd

8.58±10.93a

8.57±10.75a

3.15±4.22b

11.64±14.91c

7.98±2.41p

sd: standard derivation; Number with the same letter in the same row or column are not statistically different at 5 % level of probability; CU1: wooded savannah; CU2: shrub savannah; CU3: degraded savannah; CU4: wooded savannah

 

Production of above-ground biomass

Table 2 shows the above-ground biomass of the different species. This biomass varies from one species to another (p<0.0001) but not really between CU (0.23>0.05). Some species have a very high production capacity than others. Regarding the first group there are: Anogeissus leiocarpus (3393.74 t/ha), Acacia polyacantha (3386.10 t/ha), Faidherbia albida (951.70 t/ha), Acacia seyal (488.99 t/ha) and Acacia ataxacantha (422 t/ha) while the second group includes among others: Adansonia digitata (0.62 t/ha), Adenium obesum (0.01 t/ha), Bauhinia rufescens (0.04 t/ha), Capparis sepiaria (0.12 t/ha).

Table 2. Variation in Above ground Biomass Production (t/ha).

Species

CU1

CU2

CU3

CU4

Mean±sd

Acacia ataxacantha

218.28

1175.20

3.25

291.77

422±376.54a

Acacia macrostachya

1.40

2.11

-

378.48

95.50±141.49b

Acacia nilotica

1113

24.59

0.10

595.91

157.93±218.99c

Acacia polyacantha

32.50

0.15

11.17

13500.57

3386.10±5057.24d

Acacia seyal

8.88

1907.87

0.02

39.18

488.99±709.44e

Acacia sieberiana

0.30

-

60.34

-

20.21±26.75f

Adansonia digitata

0.02

1.21

-

0.62

0.62±0.40g

Adenium obesum

0.01

0.01

0.01

0.01

0.01±0.00g

Agave sisalana

0.62

-

-

13.25

6.64±6.31h

Annona senegalensis

8.15

573.81

4.71

0.01

146.67±213.57i

Anogeissus leiocarpus

8946.01

838.94

179.27

3610.72

3393.74±2884.63j

Azadirachta indica

60.95

29.72

0.38

78.47

42.38±27.33k

Balanites aegyptiaca

662.69

196.84

110.02

1774.93

686.12±544.40l

Bauhinia rufescens

-

0.02

-

0.15

0.04±0.05g

Borassus aethiopum

54.63

0.01

74.08

-

42.91±28.60m

Boswellia dalzielii

0.42

-

-

-

0.21±0.21g

Calotropis procera

-

148.59

1.40

0.25

50.08±65.67n

Capparis sepiaria

-

0.01

-

0.35

0.12±0.15g

Cassia singueana

8.28

7.00

0.01

55.98

17.82±19.08o

Celtis integrifolia

-

0.37

0.06

1.65

0.69±0.64g

Combretum aculeatum

0.11

11.64

2.90

0.51

3.79±3.93p

Combretum collinum

67.01

3.33

0.17

12.04

20.64±23.19f

Combretum paniculatum

-

-

0.03

-

0.03±0.00g

Combretum tomentosum

-

-

0.08

-

0.04±0.04g

Dalbergia melanoxylon

-

0.06

0.08

0.43

0.14±0.14g

Diospyros mespiliformis

865.86

26.52

1.27

80.60

243.56±311.15q

Faidherbia albida

551.12

2237.41

119.34

898.91

951.70±642.86r

Feretia apodanthera

0.83

0.13

0.22

3.49

1.17±1.16s

Ficus platiphylla

296.26

9.26

34.08

315.61

163.80±142.13ù

Ficus sycomorus

39.26

-

515.48

270.18

274.97±160.34t

Ficus thonningii

0.02

-

12.37

-

6.20±6.17h

Gardenia aqualla

8.88

87.07

0.03

4.38

25.09±30.99u

Haematostaphis barteri

-

-

-

0.01

0.01±0.00g

Isoberlina doka

-

1.98

-

-

1.98±0.00s

Lannea barteri

10.19

13.99

0.40

0.12

6.18±5.91h

Lannea velunita

0.01

-

-

-

0.01±0.00g

Piliostigma thonningii

78.00

323.71

-

23.93

106.41±108.65v

Sclerocarya birrea

6.31

2.17

9.90

49.52

16.97±16.27w

Sterculia setigera

279.04

6.03

0.06

45.98

82.78±98.13x

Tamarindus indica

1037.14

384.26

0.08

2423.38

961±769.05y

Terminalia glauscesens

0.58

-

-

-

0.58±0.00g

Vernonia thomsoniana

-

63.41

-

0.22

15.91±23.75z

Vitex doniania

0.24

-

0.19

-

0.14±0.10g

Ximenia americana

-

-

-

0.64

0.64±0.00g

Ziziphus mauritiana

4.36

282.19

0.01

23.99

77.64±102.28µ

Ziziphus mucronate

-

0.04

-

-

0.04±0.00g

Ziziphus spina-christi

0.01

9.12

-

1.12

2.56±3.28β

Mean±sd

308.36±489.35a

220.23±313.77b

25.94±41.41c

612.43±957.32d

291.74±168.66γ

Ba: Above ground biomass; sd: standard derivation; Number with the same letter in the same row or column are not statistically different at 5 % level of probability; CU1: wooded savannah; CU2: shrub savannah; CU3: degraded savannah; CU4: wooded savannah

Production of underground biomass

Underground biomass of different species is shown in Table 3. Among these species, those with a high production capacity of root biomass are: Anogeissus leiocarpus (431.45 t/ha), Acacia polyacantha (389.65 t/ha), Faidherbia albida (143.25 t/ha), Balanites aegyptiaca (106.17 t/ha). On the other hand, those with a low capacity for producing root biomass are the most numerous and in reality, constitute the rest of the forest species. Among them, are: Adenium obesum, Combretum paniculatum, Lannea velunita, with substantially zero biomass value (0.01 t/ha), Adansonia digitata (0.22 t/ha), Agave sisalana (1.81 t/ha). The underground biomass production of the species thus demonstrated is closely linked to the aerial biomass, as shown by Cairns [14]. To this end, the same species known for the high production of aboveground biomass are those known for the high production of underground biomass. This suggests that species with an enormous aerial part (trunk, branches, leaves) actually have well-developed root systems. The underground biomass (UGB) varies very significantly from one species to another (P <0.0001) but not between CUs (P = 0.19> 0.05).

Table 3. Variation in Underground Biomass Production (t/ha) between CUs.

Species

CU1

CU2

CU3

CU4

Mean±sd

Acacia ataxacantha

40.45

179.05

0.98

52.28

68.19±55.43a

Acacia macrostachya

0.47

0.67

-

65.79

16.73±24.53b

Acacia nilotica

2.92

5.88

0.04

98.26

26.77±35.74c

Acacia polyacantha

7.52

0.06

2.93

1548.07

389.65±579.21d

Acacia seyal

2.39

274.73

0.01

8.87

71.50±101.61e

Acacia sieberiana

0.12

-

12.99

-

4.37±5.75f

Adansonia digitata

0.01

0.41

-

0.23

0.22±0.14g

Adenium obesum

0.01

-

-

-

0.01±0.00g

Agave sisalana

0.23

-

-

3.40

1.81±1.59h

Annona senegalensis

2.21

95.03

1.36

0.01

24.65±35.19i

Anogeissus leiocarpus

1076.15

132.93

33.99

482.73

431.45±347.99j

Azadirachta indica

13.10

6.95

0.15

16.38

9.14±5.60k

Balanites aegyptiaca

107.93

36.92

22.08

257.74

106.17±76.67w

Bauhinia rufescens

-

0.01

-

0.06

0.02±0.02g

Borassus aethiopum

11.90

0.01

15.57

-

9.16±6.10v

Boswellia dalzielii

0.16

-

-

-

0.08±0.08g

Calotropis procera

-

28.80

0.47

0.10

9.79±12.67k

Capparis sepiaria

-

0.01

-

0.14

0.05±0.06g

Cassia singueana

2.25

1.94

0.01

12.16

4.09±4.03f

Celtis integrifolia

-

0.14

0.03

0.54

0.24±0.20g

Combretum aculeatum

0.05

3.04

0.89

0.19

1.04±1.00h

Combretum collinum

14.25

1.00

0.07

3.13

4.61±4.82f

Combretum paniculatum

-

-

0.01

-

0.01±0.00g

Combretum tomentosum

-

-

0.04

-

0.02±0.02g

Dalbergia melanoxylon

-

0.03

0.04

0.16

0.06±0.05g

Diospyros mespiliformis

136.69

6.28

0.43

16.78

40.04±48.32l

Faidherbia albida

91.70

316.26

23.73

141.29

143.25±86.51m

Feretia apodanthera

0.29

0.06

0.09

1.05

0.37±0.34g

Ficus platiphylla

52.99

2.48

7.84

56.04

29.84±24.68n

Ficus sycomorus

8.88

-

86.44

48.84

48.06±26.12o

Ficus thonningii

0.01

-

3.20

 

1.61±1.59h

Gardenia aqualla

2.39

17.96

0.01

1.28

5.41±6.27p

Haematostaphis barteri

-

-

-

0.01

0.01±0.00g

Isoberlina doka

-

0.63

-

-

0.63±0.00g

Lannea barteri

2.70

3.57

0.16

0.05

1.62±1.51h

Lannea velunita

0.01

-

-

-

0.01±0.00g

Piliostigma thonningii

16.30

57.30

-

5.74

19.83±18.73q

Sclerocarya birrea

177

0.69

2.63

10.91

4.00±3.45f

Sterculia setigera

50.26

1.70

0.03

10.22

15.55±17.35r

Tamarindus indica

160.33

66.68

0.04

339.38

141.61±108.25s

Terminalia glauscesens

0.21

-

-

-

0.21±0.00g

Vernonia thomsoniana

-

13.57

-

0.09

3.42±5.08x

Vitex doniania

0.10

-

0.08

-

0.06±0.04g

Ximenia Americana

-

-

-

0.23

0.23±0.00g

Ziziphus mauritiana

1.27

50.76

0.01

5.75

14.45±18.16t

Ziziphus mucronata

-

0.02

-

-

0.02±0.00g

Ziziphus spina-christi

0.01

2.45

-

0.38

0.71±0.87g

Mean±sd

42.05±64.27a

34.42±47.36b

4.92±7.36c

79.71±119.46d

40.27±20.60u

Bs: Underground biomass; sd: standard derivation; Number with the same letter in the same row or column are not statistically different at 5 % level of probability; CU1: wooded savannah; CU2: shrub savannah; CU3: degraded savannah; CU4: wooded savannah


Total biomass production capacity

Total biomass production varies significantly between CUs (P <0.05) and between species (P <0.0001) (Table 4). Anogeissus leiocarpus stands out from other species by its total biomass production which is equal to 3825.19 t/ha. This species is followed by Tamarindus indica (1102.82 t/ha), Faidherbia albida (1094.94 t/ha), Acacia ataxacantha (490.32 t/ha), Diospyros mespiliformis (283.61 t/ha) and Ficus platiphylla (193.64 t/ha). However, other species have a substantially nil biomass production, namely Acacia sieberiana, Calotropis procera, Adenium obesum and Celtis integrifolia. We can also note Adansonia digitata (0.84 t/ha) and Agave sisalana (8.75 t/ha) to quote.

Table 4. Variation of Total Biomass Production Between CUs (t/ha).

Species

CU1

CU2

CU3

CU4

Mean±sd

Acacia ataxacantha

258.73

1354.25

4.24

344.05

490.32±431.97a

Acacia macrostachya

1.86

2.78

-

444.27

112.23±166.02b

Acacia nilotica

14.05

30.47

0.14

694.17

184.71±254.73c

Acacia polyacantha

40.02

0.21

14.10

15048.65

3775.74±5636.45d

Acacia seyal

11.27

2182.60

0.04

48.05

560.49±811.06e

Acacia sieberiana

0.42

-

73.32

-

24.58±32.50f

Adansonia digitata

0.04

1.63

-

0.85

0.84±0.53g

Adenium obesum

0.02

-

-

-

0.02±0.00g

Agave sisalana

0.85

-

-

16.65

8.75±7.90h

Annona senegalensis

10.37

668.84

6.07

0.01

171.32±248.76i

Anogeissus leiocarpus

10022.16

971.87

213.26

4093.45

3825.19±3232.62j

Azadirachta indica

74.05

36.67

0.52

94.86

51.52±32.93k

Balanites aegyptiaca

770.62

233.76

132.10

2032.67

792,29±620.19p

Bauhinia rufescens

-

0.04

-

0.21

0.06±0.07g

Borassus aethiopum

66.53

0.02

89.65

-

52.07±34.70l

Boswellia dalzielii

0.58

-

-

-

0.29±0.29g

Calotropis procera

-

177.39

1.86

0.35

59.87±78.35q

Capparis sepiaria

-

0.02

-

0.49

0.17±0.21g

Cassia siamea

0.01

-

-

-

0.01±0.00g

Cassia singueana

10.53

8.94

0.01

68.14

21.90±23.12m

Celtis integrifolia

-

0.51

0.09

2.18

0.93±0.84g

Combretum aculeatum

0.16

14.68

3.79

0.70

4.83±4.92n

Combretum collinum

81.26

4.33

0.24

15.17

25.25±28.00o

Combretum paniculatum

-

-

0.04

-

0.04±0.00g

Combretum tomentosum

-

-

0,12

-

0.06±0.06g

Dalbergia melanoxylon

-

0.09

0,12

0,59

0.20±0.20g

Diospyros mespiliformis

1002.56

32.80

1,70

97,38

283.61±359.47r

Eucalyptus camaldulensis

-

0.01

0,01

-

0.01±0.00g

Faidherbia albida

642.82

2553.68

143,07

1040,20

109.94±729.37s

Feretia apodanthera

1.12

0.18

0,31

4,53

1.54±1.50u

Ficus platiphylla

349.24

11.74

41,93

371,65

193.64±166.81v

Ficus sycomorus

48.15

-

601,93

319,02

185.93±185.93w

Ficus thonningii

0.04

-

15,57

-

7.80±7.77x

Gardenia aqualla

1127

105.02

0,04

5,66

30.50±37.26y

Grewia flavescens

0.01

-

-

-

0.01±0.00g

Haematostaphis barteri

-

-

-

0,01

0.01±0.00g

Ipomoea carnea

0.01

-

-

-

0.01±0.00g

Isoberlina doka

-

2.61

-

-

2.61±0.00z

Lannea barteri

12.89

17.55

0,56

0,18

7.80±7.43Ʊ

Lannea velunita

0.02

-

-

-

0.02±0.00g

Piliostigma thonningii

94.30

381.01

0,01

29,67

126.25±127.38ù

Sclerocarya birrea

8.08

2.86

12,53

60,43

20.97±19.73γ

Sterculia setigera

329.30

7.73

0,08

56,19

98.33±115.49β

Tamarindus indica

1197.47

450.93

0,12

2762,76

1102.82±877.29û

Terminalia glauscesens

0.80

-

-

-

0.80±0.00g

Vernonia thomsoniana

-

76.99

-

0,30

19.32±28.83ƛ

Vitex doniania

0.33

-

0,27

 

0.20±0.13g

Ximenia americana

-

-

-

0,87

0.87±0.00g

Ziziphus mauritiana

5.64

332.95

0,02

29,74

92.09±120.43µ

Ziziphus mucronate

-

0.06

-

-

0.06±0.00g

Ziziphus spina-christi

0.02

11.57

0,01

1,50

3.27±4.15£

Mean±sd

350.41±552,73a

254.65±361.00b

30.86±49.06c

692.14±1075.95d

332.02±189.26ɣ

Bt: Total biomass; sd: standard derivation; Number with the same letter in the same row or column are not statistically different at 5 % level of probability; CU1: wooded savannah; CU2: shrub savannah; CU3: degraded savannah; CU4: wooded savannah

Carbon stock estimate

The total amount of carbon stored by all the CUs is estimated on average at 166.01 tC/ha (Table 5). It varies significantly (P = 0.03 <0.05) between CUs and between species. CU4 is the one that has a quantity of carbon (346.07 tC/ha) greater than those of other CUs which do not differ significantly (P> 0.05) from each other (15.43-175.20 tC/ha). The difference between UC4 and other UCs is due to the low logging activity due to the difficult accessibility of UC4 to neighboring populations. In terms of species in general, those that sequestrate the most carbon is: Anogeissus leiocarpus (1912.59 tC/ha), Acacia polyacantha (1887.87 tC/ha) Tamarindus indica (551.41 tC/ha), Faidherbia albida (547.47 tC/ha), Balanites aegyptiaca (396.14 tC/ha), Ficus sycomorus (161.52 tC/ha), and Diospyros mespiliformis (141.80 tC/ha).

Table 5. Variation in the Quantity of Carbon Stored (tC/ha).

Species

CU1

CU2

CU3

CU4

Mean±sd

Acacia ataxacantha

129.37

677.12

2 .12

172.03

245.16±215.98a

Acacia macrostachya

0.93

1.39

-

222.14

56.11±83.01b

Acacia nilotica

7.03

15.23

0.07

347.08

92.35±127.37c

Acacia polyacantha

20.01

0.11

7.05

7524.32

1887.87±2818.23d

Acacia seyal

5.64

1091.30

0.02

24.03

280.25±405.53e

Acacia sieberiana

0.21

-

36.66

-

12.29±16.25f

Adansonia digitata

0.02

0.81

-

0.43

0.42±0.27g

Adenium obesum

0.01

-

-

-

0.01±0.00g

Agave sisalana

0.43

-

-

8.33

4.38±3.95e

Annona senegalensis

5.18

334.42

3.04

0.01

85.66±142.38f

Anogeissus leiocarpus

5011.08

485.94

106.63

2046.73

1912.59±1616.31g

Azadirachta indica

37.02

18.34

0.26

47.43

25.76±16.46h

Balanites aegyptiaca

385.31

116.88

66.05

1016.34

396.14±310.10i

Bauhinia rufescens

-

0.02

-

0.10

0.03±0.04g

Borassus aethiopum

33.26

0.01

44.83

-

26.03±17.35j

Boswellia dalzielii

0.29

-

-

-

0.15±0.15g

Calotropis procera

-

88.69

0.93

0.17

29.93±39.17k

Capparis sepiaria

-

0.01

-

0.25

0.08±0.11g

Cassia singueana

5.26

4.47

0.01

34.07

10.95±11.56l

Celtis integrifolia

-

0.26

0.04

1.09

0.46±0.42g

Combretum aculeatum

0.08

7.34

1.89

0.35

2.42±2.46m

Combretum collinum

40.63

2.17

0.12

7.59

12.63±14.00n

Combretum paniculatum

-

-

0.02

-

0.02±000g

Combretum tomentosum

-

-

0.06

-

0.03±0.03g

Dalbergia melanoxylon

-

0.05

0.06

0.30

0.10±0.10g

Diospyros mespiliformis

501.28

16.40

0.85

48.69

141.80±179.74o

Faidherbia albida

321.41

1276.84

71.53

520.10

547.47±364.68p

Feretia apodanthera

0.56

0.09

0.16

2.27

0.77±0.75g

Ficus platiphylla

174.62

5.87

20.96

185.82

96.82±83.40q

Ficus sycomorus

24.07

-

300.96

159.51

161.52±92.96r

Ficus thonningii

0.02

-

7.79

-

3.90±3.88s

Gardenia aqualla

5.64

52.51

0.02

2.83

15.25±18.63y

Haematostaphis barteri

-

-

-

0.01

0.01±0.00g

Isoberlina doka

-

1.30

-

-

1.30±0.00t

Lannea barteri

6.44

8.78

0.28

0.09

3.90±3.71u

Lannea velunita

0.01

-

-

-

0.01±0.00g

Piliostigma thonningii

47.15

190.51

-

14.83

63.12±63.69v

Sclerocarya birrea

4.04

1.43

6.26

30.21

10.49±9.86w

Sterculia setigera

164.65

3.87

0.04

28.10

49.16±57.74x

Tamarindus indica

598.74

225.47

0.06

1381.38

551.41±438.65y

Terminalia glauscesens

0.40

-

-

-

0.40±0.00g

Vernonia thomsoniana

-

38.49

-

0.15

9.66±14.42w

Vitex doniania

0.17

-

0.14

-

0.10±0.07g

Ximenia Americana

-

-

-

0.44

0.44±0.00g

Ziziphus mauritiana

2.82

166.48

0.01

14.87

46.04±60.22v

Ziziphus mucronate

-

0.03

-

-

0.03±0.00g

Ziziphus spina-christi

0.01

5.78

-

0.75

1.64±2.07t

Mean±sd

175.20±27636a

127,33±180.50b

15,43±24.53c

346.07±537.98d

166.01±94.63u

sd: standard derivation; Numbers with the same letter in the same row or column are not statistically different at 5 % level of probability; UC1: wooded savannah; CU2: shrub savannah; CU3: degraded savannah; CU4: wooded savannah

Carbon credit assessment in the different CUs

The average carbon credit value in all CUs is 1660.08 dollars ($), equivalent to 457328.71 FCFA (Table 6).

Table 6. Carbon Credit of Species According to CU ($).

Species

CU1

CU2

CU3

CU4

Mean±sd

Acacia ataxacantha

1293.66

6771.25

21.18

1720.26

2451.59±2159.83a

Acacia macrostachya

9.31

13.89

0.01

2221.37

561.14±830.11b

Acacia nilotica

70.25

152.35

0.70

3470.84

923.53±1276.65c

Acacia polyacantha

200.08

1.07

70.49

75243.23

18878.72±28182.26d

Acacia seyal

56.35

10913.02

0.18

240.26

2802.45±4055.28e

Acacia sieberiana

2.08

-

366.62

-

122.90±162.48f

Adansonia digitata

0.18

8.13

-

4.27

4.19±2.67g

Adenium obesum

0.08

-

-

-

0.08±0.00h

Agave sisalana

4.27

-

-

83.25

43.76±39.49ð

Annona senegalensis

51.83

3344.22

30.37

0.07

856.62±1243.80i

Anogeissus leiocarpus

50110.80

4859.36

1066,32

20467.25

19125.93±16163.09j

Azadirachta indica

370.25

183.36

2.61

474.28

257.62±164.62k

Balanites aegyptiaca

3853.10

1168.80

660.49

10163.35

3961.44±3100.96ñ

Bauhinia rufescens

-

0.19

-

1.04

0.31±0.37h

Borassus aethiopum

332.64

0.11

448.26

-

260.34±173.49l

Boswellia dalzielii

2.92

-

-

-

1.46±1.46m

Calotropis procera

-

886.94

9.31

1.73

299.33±391.74@

Capparis sepiaria

-

0.08

0.01

2.46

0.85±1.07h

Cassia siamea

0.03

-

-

-

0.03±0.00h

Cassia singueana

52.64

44.70

0.07

340.68

109.52±115.58n

Celtis integrifolia

-

2.57

0.44

10.92

4.64±4.18o

Combretum aculeatum

0.79

73.39

18.94

3.51

24.16±24.62p

Combretum collinum

406.29

21.67

1.21

75.85

126.25±140.02q

Combretum paniculatum

-

-

0.21

-

0.21±0.00h

Combretum tomentosum

-

-

0.61

-

0.31±0.30h

Dalbergia melanoxylon

-

0.45

0.61

2.96

1.01±0.98m

Diospyros mespiliformis

5012.78

163.98

8.49

486.89

1418.04±1797.37r

Entada Africana

-

-

0.02

-

0.02±0.00h

Eucalyptus camaldulensis

-

0.03

0.03

-

0.03±0.00h

Faidherbia albida

3214.12

12768.39

715.35

5200.98

5474.71±3646.84s

Feretia apodanthera

5.61

0.91

1.55

22.66

7.69±7.49t

Ficus platiphylla

1746.22

58.70

209.63

1858.24

968.20±834.03u

Ficus sycomorus

240.73

-

3009.63

1595.12

1615.16±929.65v

Ficus thonningii

0.19

-

77.85

-

39.02±38.83w

Gardenia aqualla

56.35

525.12

0.21

28.32

152.50±186.31x

Grewia flavescens

0.04

-

-

-

0.04±0.00h

Haematostaphis barteri

-

-

-

0.07

0.07±0.00h

Ipomoea carnea

0.04

-

-

-

0.04±0.00h

Isoberlina doka

-

13.04

-

-

0.13±0.00h

Jatropha curcas

-

0.02

0.03

-

0.02±0.01h

Lannea barteri

64.44

87.77

2.80

0.89

38.98±37.13y

Lannea velunita

0.11

-

-

-

0.11±0.00h

Parkia biglobosa

-

-

0.02

-

0.02±0.00h

Piliostigma thonningii

471.50

1905.06

0.03

148.33

631.23±636.92z

Sclerocarya birrea

40.39

14.28

62.64

302.13

104.86±98.63Ʊ

Sterculia setigera

1646.50

38.66

0.41

280.96

491.63±577.43ɣ

Tamarindus indica

5987.36

2254.67

0.61

13813.78

5514.11±4386.46£

Terminalia glauscesens

3.98

-

-

-

3.98±0.00µ

Vernonia thomsoniana

-

384.93

0.01

1.52

96.62±144.16ƛ

Vitex doniania

1.67

-

1.36

-

1.01±0.67m

Ximenia americana

-

-

-

4.37

4.37±0.00β

Ziziphus mauritiana

28.18

1664.77

0.10

148.70

460.44±602.17γ

Ziziphus mucronate

-

0.31

-

-

0.13±0.00h

Ziziphus spina-christi

0.09

57.84

0,04

7.50

16.37±20.73û

Mean±sd

1752.04±2763.63a

1273.26±1804.98b

154.31±245.28c

3460.70±5379.76d

1660.08±946.29ø

Number with the same letter in the same row or column are not statistically different at 5% level of probability; CU1: wooded savannah; CU2: shrub savannah; CU3: degraded savannah; CU4: wooded savannah

It varies very significantly from one species to another (0.000<0.0001) but not significantly between CUs (P=0.23>0.05). Among these CUs, CU4 has a carbon credit value (3460.70 $ ≈ 93026.41CFAF) higher than those of other CUs (154.31-1752.04 $). What shows the preponderant value of CU4 (wooded savannah), it is composition as a blossoming tree, the trunks of the trees that compose it are well developed, while in other CU (wooded savannah, shrub savannah and degraded savannah) the trees there are small in diameter. In addition, the exploitation of natural resources is regular in these CUs, because they are easily accessible to residents.

As for the species, those with the most carbon credit is for example Anogeissus leiocarpus (19125.93 $), Acacia polyacantha (18878.72 $), Acacia ataxacantha (2451.59 $), Ficus platiphylla (968.20 $), Annona senegalensis (856,62 $) and finally Balanites aegyptiaca (396144 $), those with the least carbon credit are among others Adansonia digitata (4.19 $), Cassia siamea (0.03 $) and finally Capparis sepiaria (0.85 $). The value of carbon credit evaluated in this work is higher than that of Kodji [17] who worked in the same area. This difference in value would probably be linked to the surface area of the Collection Units (15 ha) compared to the latter's study sites (12 ha), growth of other plants and human action. There is also the large number of large individuals (circumference) inventoried in this study.

DISCUSSION

Biomass production

What shows the difference in biomass production between individuals would be their density and size such as diameter (Table 1 & Table 4).

Among the CUs, CU4 is the one with the highest above-ground biomass (612.43 t/ha), followed by CU1 (308.36 t/ha), then CU2 (220.23 t/ha) and finally CU3 (25.94 t/ha).

The big difference between CU4 (wooded savannah) and other CUs in terms of biomass would be linked to the abundance of large woody plants compared to other CUs whose woody plants are much more exploited. The above-ground biomass of woody plants in CUs in the study area varies from 25.94 to 612.43 t/ha. In comparison to the values of dry areas such as that of Mozogo-Gokoro National Park. Sandjong [18] estimated an average variation of 19.02 to 35.61 t/ha or even that of West Africa. Valbuena [19] found values between 0.49 and 18.91 t/ha.

Total biomass production capacity based on Collection Units

The significant amount of biomass observed in some species can be explained by their well-developed vegetative part (trunks, roots, leaves, branches). With regard to CUs, CU4 has a higher total biomass production (692.14 t/ha) than that of other CUs (30.86-350.41 t/ha) which do not differ significantly between them. This large total biomass production in UC4 would be linked to the conservation of woody phyto diversity due to low exploitation (Table 4). The other three CUs (CU1, CU2 and CU3) have undergone heavy exploitation, this because of the uneven terrain well accessible to operators compared to CU4 which is not only at altitude but seems less accessible. Ibrahima and Abib Fanta [20] in their work on estimating the carbon stock in the tree and shrub faces of the Sudano-Guinean savannahs of Ngaoundere, Cameroon and Tchobsala [21] on carbon sequestration of anthropized vegetation in Ngaoundere (Adamaoua-Cameroon) showed that the differences between the faces of the savannahs are due to the differences in anthropogenic activities. As Herintsitohaina [22] confirmed in his work on the potential for carbon storage in the plant-soil system of Eucalyptus plantations in the Malagasy highlands.

Carbon stock estimate

The amount of carbon stored by individuals is influenced by their abundance and size. Mbow [23] who studied the challenges and hopes of REDD+ in Africa and Tchobsala [6] worked on the impact of logging and carbon sequestration in the Guinean savannah of Ngaoundere Region of Adamaoua-Cameroon demonstrated that human activities negatively influence the amount of carbon stored in vegetation, so as Herintsitohaina [22] for its part, but the emphasis rather on the taxonic side, i.e. the amount of carbon stored varies with the species. In our study, the two factors could explain the difference in carbon stock between CUs and between species.

Carbon credit assessment in the different CUs

The value of carbon credit evaluated in this work is higher than that of Kodji [17] who worked in the same area. This difference in value would probably be linked to the surface area of the Collection Units (15 ha) compared to the latter's study sites (12 ha) and growth of other plants and human action. There is also the large number of large individuals (circumference) inventoried in this study.

CONCLUSION

This study enabled us to enlighten everyone and raise awareness of the impact of vegetation exploitation of the Minawao zone on carbon stock. 7143 individuals divided into 59 species, 42 genera and 30 families were inventoried in the different CUs. The amount of carbon evaluated between these different CUs varies considerably between them. The quantity of carbon stored by all the Collection Units was estimated at an average of 166.01 tC/ha, or the equivalent of 1660.08 $, corresponding to 457328.71 FCFA. UC4 is the one with a quantity of carbon (346.07 tC/ha) greater than those of other CUs between them (15.43- 175.20 tC/ha), therefore it is the one with a carbon credit value (3460.70 $ ≈ 93026.41 FCFA) higher than those of other CUs (154.31- 1752.04 $). It is very important to protect our environment so as to permit the vegetation sequestrate more carbon.

ACKNOWLEDGMENT

We would like to thank the Lamido of Gawar with his notables who were useful to us for surveys. We cannot forget the willingness of the population to provide us with useful information for our work.

  1. UNFCCC (1992) United Nations Framework Convention on Climate Change New York, pp: 9.
  2. UNFCC (1997) Kyoto Protocol to the United Nations Framework Convention on Climate Change.
  3. Cop21 (2015) Conférence de Paris de 2015 sur les changements climatiques. pp: 5.
  4. Tchotsoua M, Mapongmetsem PM, Tago M (2000) Urbanisation, crise économique et dynamique de l’environnement en milieu soudanien d’altitude: le cas du plateau de Ngaoundéré. Revue géographique du Cameroun. Société et environment au Cameroun 14(2): 225-249.
  5. Mbolo T (2013) Characterization and impact of wood logging on plant formations in Ngaoundéré District, Adamawa Region, Cameroon. J Ecol Nat Environ 5(10): 265-277.
  6. Tchobsala, Mbolo M, Souare K (2014) Impact of wood logging on the phytomass and carbon sequestration in the guinea savanna of Ngaoundéré, Adamaoua Region, Cameroon. Glob Adv Res J Environ Sci Toxicol 3(3): 038-048.
  7. UNHCR (2018) Profil du camp pp: 4.
  8. Tchobsala (2011) Impact des coupes de bois sur la végétation naturelle de la zone périurbaine de Ngaoundéré (Adamaoua). [Doctoral dissertation, Université de Yaoundé I, Cameroun.]
  9. Lanly JP (1981) Manuel d’inventaire forestier, avec références particulières 41 aux forêts tropicales hétérogènes. Études FAO: Forêts 27, FAO, Rome, Italie. pp: 9.
  10. Schreuder HT, Banyard SG, Brink GE (1987) Comparison of three sampling methods in estimating stand parameters for a tropical forest. Forest Ecol Manag 21(1-2): 119-127.
  11. Arbonnier, M. Bonnet, Grard P (2008) Ligneux du Sahel. V.1.0 CIRAD.
  12. Maydell HJV (1990) Arbres et Arbustes du Sahel. Livre version francaise de Jean-Bernad Chappious. pp: 267.
  13. Brown S, Gilespie AJR, Lugo AE (1997) Biomass estimation methods for tropical forest with application to forest inventory data. For Sci 35(4): 881-902.
  14. Cairns MA, Brown S, Helmer EH, Baumgardner GA (1997) Root biomass carbon estimates for 1980. ORNL/CDIAC-92, DCP-055, Carbon Dioxide Information FAO Forestry 111: 1-11.
  15. IPCC (2000) Land use, land-use change and forestry (LULUCF). Watson, R.T. et al. (eds). Cambridge University Press pp: 375.
  16. Ecosystems Marketplace (2019) State of the voluntary carbon market. Ecosystem Services & Management pp: 25.
  17. Kodji P (2017) Impacts de l’installation des réfugiés sur la dynamique du couvert végétal du camp de Minawao dans le Département du Mayo-Tsanaga (Extrême Nord-Cameroun): implication pour une gestion durable. Mémoire de Master II, Université de Ngaondéré. pp: 47.
  18. Sandjong SRC (2018) Etude phytoécologique du ParcNational de Mozogo-Gokoro dans l’extreme-Nord Cameroun: implication pour une gestion durable. [Doctoral dissertation, Université de Maroua].
  19. Valbuena R, Heiskanen J, Aynekulu E, Pitkänen S, Packalen P (2016) Sensitivity of Above-Ground Biomass Estimates to Heigth-Diameter Modellingin Mixed-Species West Africa Woodlands. PLoS One 11(7): 1-2.
  20. Ibrahim A, Fanta AC (2008) Estimation du stock de carbone dans les faciès arborées et arbustives des savanes soudano-guinéennes de Ngaoundéré, Cameroun. J Experim Biol 04(01): 1-11.
  21. Tchobsala, Dongock ND, Nyasiri J, Ibrahima A (2016) Carbone storage of anthropoid’s vegetation on the Ngaoundere escarpment (Adamawa-Cameroon). J Adv Biol 9(2): 1-10.
  22. Herintsitohaina RR (2009) Potentialités de stockage des carbone dans le système plante-sol des plantations d’Eucalyptus des haute terres Malgaches. [Doctoral dissertation]. pp: 193.
  23. Mbow C, Skole D, Dieng M, Justice C, Kwesha D, et al. (2012) Challenges and Prospects for REDD+ in Africa: Desk Review Of REDD+ Implementation in Africa. GLP Report No. 3. GLP-IPO, Copenhagen; pp: 145.