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Quantifying the tropical forests’ carbon stocks is presently an important component in the implementation of the emerging carbon credit market mechanisms. This calls for appropriate allometric equations predicting biomass which currently are scarce. In this study, we aimed to estimate above-
and below-ground biomass and carbon stocks of trees, and to identify the variation in diameter-height allometry of
Ipendja
mixed terra firme lowland tropical forest’s trees. The study area is located at Ipendja forest management unit (UFA), close to Dongou district (Likouala Department), in Northern Republic of Congo. This study combined forest inventory data of 1340 trees recorded from eight studied plots distributed in two sites, respectively Mokelimwaekili (i.e., Old-growth forest) and Sombo (i.e., Selective logging forest). Trees measurements were done with rectangular plots, each 25
× 200 m (i.e., 0.5 ha, 5000 m^{2}). In eight studied plots (4 plots per site), only trees with DBH
≥
10
cm were measured and identified. 1340 trees founded were belonged 145 species and 36 botanical families (n = 733 and n = 607, for Sombo and Mokelimwaekili respectively). The analyses were conducted using allometric method for aboveground biomass (AGB) and belowground biomass (BGB) estimations. The results showed that in Ipendja forest ecosystem the mean biomass is built up for AGB (346 Mg
·ha<sup>-1</sup>) as well as for BGB (81.3 Mg·ha<sup>-1</sup>), with a significant difference between forest types (F = 23.46, df = 7.771, P = 0.001). It was obvious that biomasses in Mokelimwaekili (AGB: 559.7 Mg·ha<sup>-1</sup>, BGB: 131 Mg·ha<sup>-1</sup>) w
ere
higher than those of Sombo (AGB: 291.8 Mg
·ha<sup>-1</sup>, BGB: 68.5 Mg·ha<sup>-1</sup>). By this study, Ipendja forest ecosystem has clearly variations on the diameter-height relationship and biomass across the plots and the sites.

The importance of forests in carbon (C) cycling has gained increasing attention in recent years. With the current interest in greenhouse gas emissions and their impact on global climate change, accurate, precise, and verifiable estimation of carbon stocks in forests have become insistently required [

[

Aboveground biomass (AGB) of forests can be estimated from ground-based inventory plots, where allometric equations are used to estimate AGB from measured tree diameters [

One of the approach used to develop biomass models involved destructive sampling of trees [

Accurate estimation of forest ecosystem biomass needs reliable regression equations which can convert tree variables measured directly in the field, such as diameter and height, to aboveground biomass estimation. Up to 2010, only a few studies had been developed specifically to estimate with the contribution of African tropical forests biomass [

Above- and below-ground biomasses are important components of terrestrial ecosystem carbon stocks. Patterns of aboveground biomass distribution in terrestrial ecosystems are reasonably well understood, whereas knowledge of belowground biomass and its distribution is still quite limited [

The present study about the carbon stocks of forest biomass in the northern Republic of Congo, will allow us to estimate the carbon stocks in forest ecosystems of the Likouala Department (Northern Republic of Congo) using Allometric equations. The results of this study will be useful to the Republic of Congo’s national forest carbon quantification program, managed by the CN-REDD+ Congo Project, and the Republic of Congo’s Ministry of Forest Economy and Sustainable Development. The objectives of this study were to: 1) estimate above- and below-ground biomass and carbon stocks of trees in Ipendja evergreen forest using allometric equations; 2) compare carbon stocks between old-growth and selective logging forests, respectively Mokelimwaekili and Sombo; 3) assess the diameter-height relationship of trees in Ipendja mixed evergreen lowland forest.

The sites were located in northern Republic of Congo, in Likouala Department, close to Impfondo city and Dongou district [

The Republic of Congo’s climate is characterized by heavy precipitation and high temperature and humidity. The equator crosses the country just in north part, precisely at Makoua city in the Cuvette centrale Department. In the north a dry season extends from November through March and rainy season from April through October, whereas in the south the reverse is true [

However, the meteorological station that cover Ipendja is around Impfondo city, located about 60 kilometers of the southeast massif to be developed, shows

that the dry season tends to move to the northeast [

Data collection was conducted using eight rectangular plots (^{−}^{3}) and total tree height (m). Ipendja forest management unit (UFA) is a moist tropical evergreen lowland terra firme forest with a status of old-growth (Mokelimwaekili) and selective logging (Sombo) forests. The stems less than 10 cm would normally be measured in fairly young forest [

We used a laser Hypsometer (Brand Nikon vision Co., Ltd., Forestry Pro No WJ072214) to measure the teller trees with a DBH ≥ 10 cm each in the study

Plots | Site | n | Species | DBH | Height | WSG | AGB | BGB | G |
---|---|---|---|---|---|---|---|---|---|

Plot1 | Mokelimwaekili | 137 | 68 | 30.33 | 21.04 | 0.631 | 656.1 | 154.1 | 28.93 |

Plot2 | Mokelimwaekili | 187 | 73 | 25.53 | 14.42 | 0.631 | 324.1 | 76.1 | 30.98 |

Plot3 | Mokelimwaekili | 134 | 61 | 28.38 | 14.83 | 0.608 | 395 | 92.8 | 26.24 |

Plot4 | Mokelimwaekili | 149 | 58 | 29.24 | 15.92 | 0.595 | 439.5 | 103.3 | 32.41 |

Plot5 | Sombo | 171 | 64 | 25.51 | 12.22 | 0.596 | 260.5 | 61.2 | 23.48 |

Plot6 | Sombo | 184 | 70 | 22.69 | 12.75 | 0.599 | 217.1 | 51 | 21.63 |

Plot7 | Sombo | 189 | 66 | 25.01 | 13.42 | 0.604 | 278.4 | 65.4 | 27.45 |

Plot8 | Sombo | 189 | 55 | 22.44 | 11.92 | 0.593 | 196.9 | 46.2 | 22.75 |

plots. Tree height is a fundamental geometrical variable for trees. Unfortunately, most measures are based on visual inspection, and they are almost always considerably biased, as it is difficult to assess the size of vertical objects 10 - 40 m in height. One no-biased height estimate makes use of automated distance measurement tools, as reported here. We then used a compass (model SILVA-2S, Scale 1:24,000) to determine cardinal points (Nord-South and East-West) or orientations of each plot. The double tape decameter was used (model Stanley-30 m, serial number 34 - 108) made by Forestry Suppliers Inc, USA to measure the diameter at breast height (DBH) for each tree at both the Mokelimwaekili and Sombo forests. Finally, a Global positioning system (GPS) model Garmin 62CSx has been used to record the plot location (coordinates) in minutes, degrees and seconds. Latitude, longitude and altitude were then recorded using GPS in each plot center and four sides of all rectangular plots studied. Data from each plot were recorded.

However, the measurements have been performed by taking into account the tree locations. For trees with obstacles, we added 30 cm to 1.3 m (the normal size measurements). The description of the approach used to measure trees of the study was incorporated into the data collection to allow measurements to be made with precision and accuracy. The following steps have been done: An enumerator responsible for recording data has been focused exclusively on measuring and marking trees. Registration took place at the center of the plot being measured. The enumerator also monitored those measuring trees and ensured no trees were omitted; to prevent double counting or omission of trees, the measurement start from north and the first tree was labeled. Any measured tree was immediately labeled with a permanent marker sign facing the center of the plot to allow the data enumerator to distinguish between measured and unmeasured trees; any tree of suitable size inside each nested plot has a numbered tag, preferably was the polyvinyl chloride plastic, and nailed to it. However, all trees positioned in the plot boundary at trunk diameter > 50% out of plot were excluded (not measured). Field inventory has been performed with accordance to forest plots (see http://www.forestplots.net) protocol [

Once a fieldwork campaign is finished, the data has been digitized in spreadsheets according to standard procedures outlined in the data organization section [

To estimate biomass and carbon stock in Ipendja forest, allometric methods from [

・ Total aboveground biomass (AGB) of each tree in the plots has been estimated using the following allometric model from [

AGB e s t = 0.0673 × ( ρ D 2 H ) 0.976 (1)

ρ = wood density (g・cm^{−}^{3}),

D = diameter at breast height (cm),

H = height of tree (m),

AGB = aboveground biomass (Mg・ha^{−}^{1}).

Aboveground biomass (AGB) of trees for each permanent rectangular sample plot was calculated from a combination of variables [

ln ( AGB ) = α + β × ln ( H × D 2 × ρ ) + ε (2)

With AGB (in Mg・ha^{−}^{1}) representing the aboveground tree biomass, α and β are the model coefficients (derived from least-squares regression), D (in cm) the tree trunk diameter, H (in m) the total tree height, ρ (in g・cm^{−}^{3}) the wood specific gravity and ε (epsilon) the error term, which is assumed to follow a normal distribution N(0, RSE^{2}), where RSE is the residual standard error of the model. This model, denoted by m 0 , was considered as the reference model [

・ Next, to estimate belowground biomass (BGB), we used equation from [

Y = 0.205 × AGB if AGB ≤ 125 Mg ⋅ ha − 1 (3)

Y = 0.235 × AGB if AGB > 125 Mg ⋅ ha − 1 (4)

where Y is belowground biomass (BGB, Mg・ha^{−}^{1}) and AGB is aboveground biomass (Mg・ha^{−}^{1}).

Therefore, Models developed by [

Mokelimwaekili (Old-growth forest) | Sombo (Selective logging forest) | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Species | Family | Pygmy name | CN | PT | n | DBH | Height | WSG | Plot | AGB | BGB | n | DBH | Height | WSG | Plot | AGB | BGB |

Anonidium mannii (Oliv.) Engl. & Diels | Annonaceae | Mobey | Ebom | TA | 10 | 46.54 | 20.41 | 0.297 | P4, P3, P2, P1 | 703.77 | 165.4 | 10 | 16.19 | 8.6 | 0.297 | P8, P6, P5 | 38.543 | 7.9 |

Blighia unijugata Baker | Sapindaceae | Blighia | Blighia1 | WA | 8 | 46.17 | 23.53 | 0.516 | P4, P3 | 1364.9 | 320.7 | 2 | 31.45 | 16.4 | 0.516 | P8, P7 | 453.53 | 107 |

Caloncoba mannii (Oliv.) Gilg | Achariaceae | Kouatolo | Caloncoba | TA | 19 | 14.81 | 8.9 | 0.5 | P4 | 55.685 | 11.42 | 4 | 14.17 | 9.1 | 0.5 | P8, P7, P6 | 52.205 | 10.7 |

Carapa procera DC. | Meliaceae | Bopessi | Crabwood | TA | 11 | 13.95 | 7.1 | 0.56 | P4, P3, P2, P1 | 44.39 | 9.1 | 20 | 14.68 | 7.8 | 0.56 | P8, P7, P6, P5 | 53.75 | 11 |

Celtis mildbraedii Engl. | Ulmaceae | Ngombe | Ohia | MA | 54 | 27.35 | 17.24 | 0.648 | P4, P3, P2, P1 | 452.81 | 106.4 | 36 | 20.88 | 12.49 | 0.648 | P8, P7, P6, P5 | 195.2 | 45.9 |

Celtis tessmannii Rendle | Ulmaceae | Ekekiele | Diania | TA | 18 | 25.33 | 15.02 | 0.704 | P4, P3, P2, P1 | 369.46 | 86.82 | 9 | 49.34 | 21.33 | 0.704 | P8, P7, P5 | 1911.9 | 449 |

Coelocaryon botryoides Vermoesen | Myristicaceae | Ebondo | Ekoune2 | TA | 12 | 26.9 | 14.87 | 0.65 | P4, P3, P2, P1 | 380.6 | 89.44 | 6 | 17.63 | 11.31 | 0.65 | P7, P6, P5 | 127.73 | 30 |

Coelocaryon preussii Warb. | Myristicaceae | Dissako | Ekoune1 | TA | 6 | 15.35 | 10.26 | 0.5 | P4, P3, P2 | 68.608 | 14.06 | 7 | 15.58 | 10.4 | 0.5 | P8, P7, P6 | 71.569 | 14.7 |

Corynanthe pachyceras K. Schum | Rubiaceae | Kania | Kangue | TA | 7 | 33.54 | 24.97 | 0.663 | P4, P2, P1 | 989.9 | 232.6 | 3 | 32.03 | 14.3 | 0.663 | P8, P7, P5 | 525.13 | 123 |

Dacryodes pubescens (Vermoesen) H.J. Lam | Burseraceae | Musafousafou | Safoukala | SE | 3 | 24.36 | 13.36 | 0.595 | P3, P2 | 259.13 | 60.9 | 13 | 20.34 | 12.08 | 0.595 | P8, P7, P6, P5 | 165.17 | 38.8 |

Dialium dinklagei Harms | Caesalpiniaceae | Mbasso | Eyoum3 | TA | 4 | 29.5 | 14.17 | 0.772 | P3, P2, P1 | 514.23 | 120.8 | 8 | 22.88 | 14.83 | 0.772 | P8, P7, P6 | 327.36 | 76.9 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Diospyros perrieri (Hiern) Jumelle | Ebenaceae | Nzete ya mino | Ebene5 | SE | 6 | 17.3 | 7.9 | 0.5 | P4, P3 | 67.137 | 13.76 | 9 | 13.24 | 7.7 | 0.5 | P7, P6, P5 | 38.846 | 7.96 |

Duboscia macrocarpa Bocp. | Tiliaceae | Ekaka | Akak | ML | 11 | 31.08 | 17.74 | 0.5 | P4, P3, P2 | 463.99 | 109 | 12 | 29.62 | 16.3 | 0.5 | P8, P7, P6, P5 | 388.9 | 91.4 |

Entandrophragma angolense (Welw.ex C. DC.) C. DC. | Meliaceae | Diboyo | Sapeli | TA | 6 | 61.5 | 29.16 | 0.508 | P4, P3, P1 | 2900.4 | 681.6 | 8 | 48.07 | 17.78 | 0.508 | P7, P5 | 1106.3 | 260 |

Entandrophragma candollei Harms | Meliaceae | Etembekesso | Kosipo | TA | 2 | 25.75 | 24.05 | 0.603 | P3, P1 | 519.29 | 122 | 7 | 17.27 | 10.75 | 0.603 | P8, P7, P6, P5 | 108.51 | 22.2 |

Eribroma oblonga (Mast.) Pierre ex A. Chev. | Sterculiaceae | Gboyo | Eyong | LA | 2 | 75.75 | 38.3 | 0.69 | P3, P1 | 7664.6 | 1801 | 9 | 26.27 | 15.93 | 0.69 | P6, P5 | 411.98 | 96.8 |

Funtumia africana (Benth.) Stapf | Apocynaceae | Ndembo | Dembo | TA | 6 | 24.7 | 17 | 0.416 | P4, P2, P1 | 237.53 | 55.82 | 8 | 23.17 | 11.83 | 0.416 | P8, P7, P6 | 147.17 | 34.6 |

Gambeya africana (A. DC.) Pierre | Sapotaceae | Bobambu | Longhi rouge | TA | 12 | 33.5 | 18.17 | 0.669 | P4, P3, P2, P1 | 730.55 | 171.7 | 22 | 26.44 | 15.59 | 0.669 | P8, P7, P6, P5 | 396.37 | 93.1 |

Gambeya beguei (Aubrev. & Pellegr.) | Sapotaceae | Monopi | Longhi blanc | TA | 2 | 15.6 | 7.6 | 0.5 | P3, P1 | 52.828 | 10.83 | 24 | 23.47 | 12.22 | 0.5 | P7, P6, P5 | 186.39 | 43.8 |

Ganophyllum giganteum (A.Cheval.) Haumann | Sapindaceae | Ekomou | Mokenjo | SE | 12 | 13.82 | 8.2 | 0.698 | P4, P2, P1 | 62.197 | 12.75 | 2 | 19 | 10.55 | 0.698 | P6, P5 | 148.06 | 34.8 |

Garcinia atroviridis Griff. ex T. Anderson | Clusiaceae | Mokata | Garcinia | ML | 3 | 16 | 10.66 | 0.5 | P4 | 77.222 | 15.83 | 33 | 17.32 | 10.15 | 0.5 | P8, P7, P6, P5 | 85.934 | 17.6 |

Guarea thompsonii Sprague & Hutch. | Meliaceae | Mbenia | Bosse fonce | TA | 20 | 18.2 | 9.3 | 0.56 | P4, P3, P2, P1 | 97.083 | 19.9 | 15 | 20.92 | 10.73 | 0.56 | P8, P6, P5 | 146.5 | 34.4 |

Khaya anthotheca (Welw.) C. DC. | Meliaceae | Deke | Acajou | SA | 7 | 32.3 | 15.04 | 0.53 | P4, P3, P2, P1 | 450.68 | 105.9 | 7 | 17.68 | 9.5 | 0.53 | P8, P7, P6, P5 | 88.767 | 18.2 |

Lannea welwitschii (Hiern) Engl. | Anacardiaceae | Gondo | Kumbi | TA | 5 | 26.65 | 13.58 | 0.469 | P2, P1 | 248.74 | 58.45 | 15 | 21.76 | 12.89 | 0.469 | P8, P7, P6, P5 | 159.15 | 37.4 |

Macaranga barteri Mull. Arg. | Euphorbiaceae | Mossomba1 | Mossomba1 | TA | 1 | 23.6 | 8.2 | 0.5 | P4 | 127.65 | 30 | 20 | 22.31 | 13 | 0.5 | P8, P7, P6, P5 | 179.35 | 42.1 |

Nesogordonia kabingaensis (K.Schum.) Capuron ex R. Germ. | Sterculiaceae | Moduka | Kotibe | TA | 14 | 29.01 | 20.77 | 0.681 | P4, P3, P2, P1 | 639.55 | 150.3 | 5 | 24 | 14.62 | 0.681 | P7, P6 | 313.57 | 73.7 |

Panda oleosa Pierre | Pandaceae | Mokana | Afan | TA | 3 | 22.46 | 17 | 0.565 | P3, P2 | 266 | 62.51 | 10 | 35.18 | 16.71 | 0.565 | P8 | 628.07 | 148 |

Petersianthus macrocarpus (P.Beauv.) Liben | Lecythidaceae | Bosso | Essia | WA | 20 | 32.05 | 19.3 | 0.769 | P4, P3, P2, P1 | 814.25 | 191.3 | 15 | 33.04 | 15.72 | 0.769 | P8, P6, P5 | 707.27 | 166 |

Polyalthia oliveri Engl. | Annonaceae | Motunga | Otungui | TA | 20 | 24.05 | 18.85 | 0.5 | P4, P3, P2, P1 | 298.44 | 70.13 | 21 | 24.14 | 14.37 | 0.5 | P8, P7, P6, P5 | 230.67 | 54.2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Pycnanthus angolensis (Welw.) Warb. | Myristicaceae | Nkolo | Ilomba | TA | 16 | 21.37 | 15.7 | 0.568 | P4, P3, P2, P1 | 224.51 | 52.76 | 11 | 27.41 | 13.94 | 0.568 | P8, P7, P6, P5 | 324.98 | 76.4 |

Staudtia kamerunensis (Warb.) Fouilloy | Myristicaceae | Malonga | Niove | LA | 20 | 18.63 | 12.6 | 0.8 | P4, P3, P2, P1 | 193.57 | 45.49 | 24 | 18.13 | 10.22 | 0.8 | P8, P7, P6, P5 | 149.64 | 35.2 |

Strombosia grandifolia Hook. f. ex Benth. | Olacaceae | Embongo | Afina | TA | 16 | 28.26 | 20.01 | 0.908 | P4, P3, P2, P1 | 775.91 | 182.3 | 21 | 19.61 | 11.79 | 0.908 | P8, P7, P6, P5 | 226.89 | 53.3 |

Strombosia pustulata Oliv. | Olacaceae | Mopipi | Mbazoa jaune | TA | 10 | 37.8 | 20.39 | 0.861 | P3, P2, P1 | 1323.8 | 311.1 | 5 | 46.88 | 22.72 | 0.861 | P8, P7, P5 | 2239.8 | 526 |

Strombosiopsis tetrandra Engl. | Olacaceae | Ebenge | Edip Mbazoa | TA | 7 | 43.37 | 20.78 | 0.663 | P4, P2 | 1366.6 | 321.1 | 14 | 21.45 | 11.74 | 0.663 | P8, P7, P6, P5 | 198.04 | 46.5 |

Synsepalum dulcificum (Schumach. & Thonn.) Daniell | Sapotaceae | Mokenzenze | Mokenzenze | MA | 2 | 21 | 14.5 | 0.5 | P2, P1 | 177.29 | 41.66 | 9 | 21.07 | 9.3 | 0.5 | P8, P7, P6, P5 | 115.68 | 23.7 |

The choice of a model is a crucial step because the largest source of error in estimating biomass is associated with it [

In this research, we compiled tree harvest studies that had been carried out in old-growth and selective logging forests, respectively in Mokelimwaekili and Sombo (excluding plantations and agroforestry systems). The rational for this choice is that the natural variability in plant allometry tends to be minimized in plantations. The fieldwork was conducted with help from by experienced botanists, ecologists and foresters who working in Thanry-Congo logging company.

To be included in the compilation, the following measurements had to be available for each tree: trunk diameter D (in cm), total tree height H (in m), wood specific gravity ρ (g・cm^{−}^{3}) and total oven-dry AGB (Mg・ha^{−}^{1}). We excluded trees with DBH < 10 cm because such trees hold a small fraction of aboveground biomass (AGB) in forests and woodlands [

For comparison, we tried to used [^{−}^{3}) using the model form (i.e., excluding tree height):

B = exp ( a + b × ln ( D ) + c ( ln ( D ) ) 2 − d ( ln ( D ) ) 3 + e × ln ( ρ ) ) (5)

Alternatively, using the H:D database developed by [^{−}^{3}) is:

B = exp ( a + b × ln ( ρ D 2 H ) ) (6)

According to [^{−}^{3}) and height (m) of trees (i.e., AGB = [^{−}^{1}) is calculated as a function of tree diameter and wood specific gravity (Wood density, g・cm^{−}^{3}) and estimated height (in m). Height has been calculated using the [

The model presented by [

AGB e s t = ρ × exp ( − 1.499 + 2.148 × ln ( D ) + 0.207 × ( ln ( D ) ) 2 − 0.0281 ( ln ( D ) ) 3 ) (7)

where AGB is aboveground biomass (in kg), est is an estimation, D is a diameter at breast height (in cm), ln is the natural logarithm, and ρ is the wood density (in g・cm^{−}^{3}). [

AGB e s t = exp ( − 2.977 + ln ( ρ D 2 H ) ) ≡ 0.0509 × ρ D 2 H (8)

where AGB is aboveground biomass (in kg), est is an estimation, D is a diameter at breast height (in cm), H is the height of tree (in m), and ρ is the wood density (in g・cm^{−}^{3}). Wood density is just wood specific gravity. These models already include the correction factor (7) and (8). The symbol ≡ (8) means a mathematical identity (i.e., equiv.): both formulas (7) and (8) can be used in the biomass estimation procedure. The standard error in estimating aboveground biomass (AGB) is around 12% if height predictor is available and around 19% if height predictor is not available [

To develop the H:D allometric relationships for inclusion in biomass models, height measurements has been used for individual trees made in eight plots in two study sites representing 1340 trees concurrent height (H) and trunk diameter (D) measurements. Nondestructive data has been used during our study. Only permanent plots trees have been used for processing.

Nevertheless, stand basal area (G) for each census was calculated as:

G = ( ∑ n × π × ( D i 2 ) 2 ) h a (9)

where G is basal area (in m^{2}・ha^{−}^{1}), D_{i} is diameter at breast height of individual i at 1.3 m above the ground (cm), π is 3.14 and n is the number of stems per plot. Basal area is the area of a given section of land that is occupied by the cross-section of tree trunk and stem (9) at the base [

However, the PAST program used includes standard statistical tests [

1340 trees were identified after analysis from the floristic inventory performed (

The Tropical Africa Area (EPFAT Area, country-based, south of Sahara, complementary to the following) species was the most recorded representative on phytogeographical level and corresponded with 75% and 72% of identified species respectively for Mokelimwaekili and Sombo sites (

Mean aboveground biomass (AGB) across eight measured plots ranged from 196.9 to 656.1 Mg・ha^{−}^{1} (^{−}^{1} with a standard error of 53.1%. One-way ANOVA analysis at P-level < 0.05 showed significant difference in means aboveground biomass for the studied forest (F = 23.46, df = 7.771, P = 0.00139). Levene’s test for homogeneity of variance for means shows a significant difference (P = 0.0184). Kruskal-Wallis test for equal median shows that there is a significant difference between Mokelimwaekili and Sombo (P = 0.0007). Two-sample paired test were applied on Mokelimwaekili and Sombo and shows a significant difference for t-test (Mean difference: 215.43, confidence interval at 95%: 0.54 - 430.32, P = 0.049), for Wilcoxin test (normal approximation inaccurate): P = 0.06. One-way ANOVA applied on Mokelimwaekili and Sombo revealed significant difference regarding the test for equal means (F = 8.48, df = 1, P = 0.0269), for the Welch F-test in the case of unequal variance: F = 8.481, df = 3.415, P = 0.0528. Kruskal-Wallis test shows that there is significant difference between Mokelimwaekili and Sombo (P = 0.02092). Levene’s test show a significant difference between Mokelimwaekili and Sombo (P = 0.0143).

In Mokelimwaekili forest, the model developed by [^{−}^{1}), while the model developed by [^{−}^{1}), and this was even much lower for the Sombo forest (AGB = 5.4 Mg・ha^{−}^{1}) (^{−}^{1}) but were much closer for the Sombo forest (AGB = 291.8 Mg・ha^{−}^{1}) to the values predicted by this most recent pantropical model but including site-specific height-diameter allometry. ^{−}^{1}) and belowground biomass (BGB, in Mg・ha^{−}^{1}) for Sombo using the reference model proposed by [

Mean belowground biomass (BGB) across eight repeat measured plots ranged from 46.2 to 154.1 Mg・ha^{−}^{1} (^{−}^{1} with a standard error of 12.5% and the standard deviation of 35.3%. One-way ANOVA for BGB applied in 8 studied plots shows that there is a significant difference between plots and sites (F = 19.34, df = 7.096, P = 0.003). Test for equal means shows that there is a significant difference between sites (F = 19.34, df = 1, P = 0.0006). Levene’s test for homogeneity of variance from means showed that there is a significant difference between plots and sites (P = 0.0058). Levene’s test from medians showed that there is significantly different between plots and sites (P = 0.0224). Kruskal-Wallis test for equal medians showed that there is a significant difference between studied plots about BGB (P = 0.0007, H (chi^{2}): 11.29). One-sample test within t-test showed that there is not significantly different for BGB distribution in Ipendja forest (P = 0.999; 96% confidence interval: (−29.55 - 29.57; t = 0.0008). Wilcoxon test (one-sample test) showed that there is not significantly different for belowground biomass distribution in Ipendja lowland forest ecosystem (P = 0.64). F-test for equal variances shows for BGB the variance of 1250.3 and a significant difference (P = 0.001). Mann-Whitney test for equal medians applied to BGB shows a significant difference for eight studied plots (P = 0.0001). Fligner-Kileen test for equal coefficients of variation for BGB showed the follows results: CV= 43.48% with 95% of confidence intervals (32.497 - 67.95). Kolmogorov-Smirnov test for equal distribution shows a significant difference in eight plots for BGB (P = 0.0001).

distribution of belowground biomass in Mokelimwaekili and Sombo respectively site1 and site2 by number of trees. It was obvious that BGB in Mokelimwaekili were higher than those of Sombo (

Carbon stock was estimated from the total biomass (above- and below-ground

biomass) of tree and was estimated to be about 50% of total tree biomass [^{−}^{1} and belowground biomass (BGB) was 154.1 Mg・ha^{−}^{1} (^{−}^{1} and 77 Mg・ha^{−}^{1} respectively for aboveground biomass (AGB) and belowground biomass (BGB). Carbon stocks of AGB were higher than those of BGB. It was obvious that carbon stocks of AGB and BGB in Mokelimwaekili forest ecosystem were higher than those of Sombo forest ecosystem.

Mokelimwaekili (Old-growth forest) | Sombo (Selective logging forest) | |||||||
---|---|---|---|---|---|---|---|---|

Source | Location | Predictor | Allometric equation | AGB | BGB | AGB | BGB | |

[ | Gabon | D, H, ρ | A G B = [ − 2.5680 + 0.9517 × ln ( D 2 H ) + 1.1891 × ln ( ρ ) ] | 6.1 | 1.2 ( [ | 5.4 | 1.1 ( [ | |

[ | Pantropical | D, H, ρ | A G B = 0.0673 × [ ( ρ D 2 H ) ] 0.976 | 559.7 | 131.5 ( [ | 291.8 | 68.5 ( [ | |

[ | Cameroon | D, ρ | A G B = exp ( − 1.862 + 2.402 × ln ( D ) − 0.341 × ln ( ρ ) ) | 713.3 | 461.3 | |||

[ | Cameroon | D | A G B = exp ( − 2.331 + 2.596 × ln ( D ) ) | 730.4 | 455.3 | |||

[ | Madagascar | D, H, ρ | A G B = exp ( − 2.108 + 0.908 × ln ( ρ D 2 H ) ) | 538.7 | 293.7 | |||

[ | Ghana | D, H, ρ | A G B = 3.47 × 10 − 3 + 0.02 × ρ D 2 H | 207.6 | 106.4 | |||

[ | DR Congo | D | A G B = ( 36.3576 − 31.6591 × exp ( − 0.0221 × D ) ) | 20.4 | 18.5 | |||

[ | Africa | D, H, ρ | A G B = − 2.9205 + 0.9894 × ln ( D 2 ρ H ) | 6.2 | 5.5 | |||

[ | Tanzania | D, H | A G B = 0.076 × D 2.2046 × H 0.4918 | 613.9 | 354.2 | |||

[ | Tanzania | D, H | B G B = 0.176 × D 1.784 × H 0.343 | 218 | 142 | |||

[ | Mozambique | D | A G B = exp ( 2.601 × log ( D ) − 3.629 ) | 1.2 | 1 | |||

[ | Mozambique | D | B G B = exp ( 2.262 × log ( D ) − 3.370 ) | 1 | 0.8 | |||

[ | DR Congo | D, H, ρ | A G B = 1.603 × ρ ( D 2 H ) 0.657 | 697.7 | 449.8 | |||

[ | Benin | D, H | A G B = exp ( − 2.63 + 1.99 × ln ( D ) + 0.67 × ln ( H ) ) | 465.4 | 264.6 | |||

[ | Cameroon | D, H, ρ | A G B = exp ( − 2.436 + 0.139 × [ ln ( D ) ] 2 + 0.737 × ln ( D 2 H ) + 0.279 × ln ( ρ ) ) | 521.4 | 269.2 |

Stand-specific height-diameter regression model developed by [

Our database has been applied on the [^{−}^{1}) has been calculated as a function of tree diameter and wood specific gravity (ρ, g・cm^{−}^{3}) and estimated height. Height has been calculated using the [^{−}^{1}) follows by plot2 (424.78 Mg・ha^{−}^{1}), plot1 (378.40), plot7 (331.94 Mg・ha^{−}^{1}), plot3 (322.89 Mg・ha^{−}^{1}), plot8 (306.38 Mg・ha^{−}^{1}), plot5 (296.68 Mg・ha^{−}^{1}) and at a low AGB in plot6 (240.85 Mg・ha^{−}^{1}).

However, in ^{−}^{1} [

Mean aboveground biomass (AGB) were 559.7 Mg・ha^{−}^{1} and 291.9 Mg・ha^{−}^{1} belong to Mokelimwaekili and Sombo respectively. Average belowground biomass (BGB) was 131.5 Mg・ha^{−}^{1} and 68.5 Mg・ha^{−}^{1} belongs to Mokelimwaekili and Sombo respectively (

Although many authors have suggested both ln-normal and ln-ln models as the most accurate for explaining allometric relationships, it is worth noting that the use of the power model is supported by growth that assumes a constant scaling rate across ontogenies. Comparing five forms of allometric relationships between tree diameter at breast height and tree height, [

For instance, the use of a common equation to predict the branch biomass and to further up-scale the biomass from branch to tree level implied that the tree biomass values were not exactly independent, and as such, the prediction error should be accounted for, especially by addressing the issue of error propagation from the branch to the tree level [

Recent studies [

There were confidence intervals around the mean aboveground biomass estimation for all studied sites (Mokelimwaekili and Sombo, respectively old-growth and selective logging forests) due to variability in aboveground biomass among plots. Equations integrating diameter, height and wood density provided the best estimators for estimation of total biomass in the two forest types and this study therefore suggests for biomass and carbon estimation of trees to always combine these variables whenever it is possible. For height estimations, the use of density as additional independent variable to tree diameter improved the quality of estimations, and this study recommends combining these variables when using these equations or when developing new tree height equations for tropical mixed forests. The choice of appropriate allometric models is crucial for reducing uncertainties in natural forest biomass estimates. The non-destructive sampling approach used here was dictated by the protected status of the forests, and could serve as an example for other places where trees are protected or where the wood resource is scarce. Nevertheless, the application of this non-destructive method requires an up-scaling of the biomass from branch to tree level, which is tied with some uncertainties. Therefore, specific future studies need to be undertaken in Republic of Congo’s forests by comparing non-destructive with some destructive preferably approaches targeting species that are not nationally protected. Outcomes of this research would also help to measure the level of accuracy attained with the application of non-destructive sampling, and thereby contribute to improve the reliability of the biomass stocks in natural forests for carbon economic initiatives. Finally, the present study on biomass and carbon stocks of trees in Ipendja terra firme mixed evergreen tropical forest from Likouala Department (Northern Republic of Congo) will allow Republic of Congo to receive the carbon credit under the CN-REDD Congo’s national strategy.

This study was supported by the National Key Research and Development Project of China (2017YFD0600106). The authors would like to thank China Scholarship Council (see http://www.csc.edu.cn) and Beijing Forestry University (see http://www.bjfu.edu.cn) for supporting this work. We greatly thank Seraphin Bikoumou, Martial Fomekong Tsakeu, Ghislain Teufack Sonna, Meroli Bokouaye, Freddy Iyoki, Wilfrid Bandakoulou, Roger Bassoukaka, Hermann, Jean and Benoit from Thanry-Congo logging company (STC) for them technical assistance during forest inventory data period at Ipendja forest management unit (UFA). Our warm thanks are to Republic of Congo’s Ministry of Forest Economy and Sustainable Development, CN-REDD, and Thanry-Congo logging company (STC, Vic-wood Group) for providing facilities about field measurements in Ipendja forest (Likouala Department, Northern Republic of Congo). Thanks are extended to Georges Claver Boundzanga from Republic of Congo’s Ministry of Forest Economy and Sustainable Development for his valuable contribution regarding this study. Different anonymous referees have provided substantial contribution and the authors address to them their heartfelt thanks.

The authors declare no conflict of interest.

Supplementary material related to this paper is available online at http://www.scirp.org/journal/oje/.

Ekoungoulou, R., Nzala, D., Liu, X.D. and Niu, S.K. (2018) Tree Biomass Estimation in Central African Forests Using Allometric Models. Open Journal of Ecology, 8, 209-237. https://doi.org/10.4236/oje.2018.83014