Allometric Models for Predicting Biomass and Carbon Pools of Boscia Senegalensis(Pers.) Lam. Ex Poir. (Capparaceae) Populations in Central Africa: A Case Study from Cameroon

This article examines the categorical problem that persons of ‘mixed-race’ background presented to British administrations in eastern, central and southern Africa during the late 1920s and 1930s. Tracing a discussion regarding the terms ‘native’ and ‘non-native’ from an obscure court case in Nyasaland (contemporary Malawi) in 1929, to the Colonial Office in London, to colonial governments in eastern, central and southern Africa, this article demonstrates a lack of consensus on how the term ‘native’ was to be defined, despite its ubiquitous use. This complication arrived at a particularly crucial period when indirect rule was being implemented throughout the continent. Debate centered largely around the issue of racial descent versus culture as the determining factor. The ultimate failure of British officials to arrive at a clear definition of the term ‘native’, one of the most fundamental terms in the colonial lexicon, is consequently suggestive of both the potential weaknesses of colonial state formation and the abstraction of colonial policy vis-à-vis local empirical conditions. Furthermore, this case study compels a rethinking of contemporary categories of analysis and their historical origins.

Download Full-text

Beach-Bar Sandstone Reservoir Prediction: A Case Study from Central Africa

10.3997/2214-4609.202010736 ◽

2020 ◽

Author(s):

W. He ◽

B. Chen ◽

J. Li ◽

L. Xue ◽

L. Ma ◽

...

Keyword(s):

Central Africa ◽

Reservoir Prediction ◽

Sandstone Reservoir ◽

Beach Bar

Download Full-text

Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili

Applied Sciences ◽

10.3390/app9183648 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3648

Author(s):

Casper S. Shikali ◽

Zhou Sijie ◽

Liu Qihe ◽

Refuoe Mokhosi

Keyword(s):

Language Processing ◽

Critical Role ◽

Language Model ◽

Central Africa ◽

Spoken Language ◽

Language Models ◽

Word Embeddings ◽

Word Representation

Deep learning has extensively been used in natural language processing with sub-word representation vectors playing a critical role. However, this cannot be said of Swahili, which is a low resource and widely spoken language in East and Central Africa. This study proposed novel word embeddings from syllable embeddings (WEFSE) for Swahili to address the concern of word representation for agglutinative and syllabic-based languages. Inspired by the learning methodology of Swahili in beginner classes, we encoded respective syllables instead of characters, character n-grams or morphemes of words and generated quality word embeddings using a convolutional neural network. The quality of WEFSE was demonstrated by the state-of-art results in the syllable-aware language model on both the small dataset (31.229 perplexity value) and the medium dataset (45.859 perplexity value), outperforming character-aware language models. We further evaluated the word embeddings using word analogy task. To the best of our knowledge, syllabic alphabets have not been used to compose the word representation vectors. Therefore, the main contributions of the study are a syllabic alphabet, WEFSE, a syllabic-aware language model and a word analogy dataset for Swahili.

Download Full-text

Assessment of generalized allometric models for aboveground biomass estimation: A case study in Australia

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2020.105610 ◽

2020 ◽

Vol 175 ◽

pp. 105610

Author(s):

Li Liu ◽

Samsung Lim ◽

Xuesong Shen ◽

Marta Yebra

Keyword(s):

Aboveground Biomass ◽

Biomass Estimation ◽

Allometric Models

Download Full-text

Development of Allometric Models for Estimating the Biomass of <i>Sclerocarya birrea</i> (A.Rich) Hoscht and <i>Boscia senegalensis</i> (Pers.) Lam. ex Poir.

Open Journal of Ecology ◽

10.4236/oje.2020.108035 ◽

2020 ◽

Vol 10 (08) ◽

pp. 571-584

Author(s):

Ramata Talla ◽

Moustapha Bassimbé Sagna ◽

Mariama Dalanda Diallo ◽

Aly Diallo ◽

Daouda Ndiaye ◽

...

Keyword(s):

Allometric Models ◽

Sclerocarya Birrea ◽

Boscia Senegalensis

Download Full-text

Closing a gap in tropical forest biomass estimation: accounting for crown mass variation in pantropical allometries

Biogeosciences Discussions ◽

10.5194/bgd-12-19711-2015 ◽

2015 ◽

Vol 12 (23) ◽

pp. 19711-19750 ◽

Cited By ~ 5

Author(s):

P. Ploton ◽

N. Barbier ◽

S. T. Momo ◽

M. Réjou-Méchain ◽

F. Boyemba Bosela ◽

...

Keyword(s):

Tropical Forest ◽

Reference Model ◽

Central Africa ◽

Carbon Stocks ◽

Forest Carbon ◽

Biomass Estimation ◽

Systematic Bias ◽

Allometric Models ◽

Original Dataset ◽

Large Trees

Abstract. Accurately monitoring tropical forest carbon stocks is an outstanding challenge. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference in the coming years. However, this reference model shows a systematic bias for the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass dataset on 673 trees measured in five tropical countries (101 trees > 100 cm in diameter) and an original dataset of 130 forest plots (1 ha) from central Africa to quantify the error of biomass allometric models at the individual and plot levels when explicitly accounting or not accounting for crown mass variations. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Accounting for a crown mass proxy in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error from −23–16 to 0–10 %. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by accounting for a crown mass proxy for the largest trees in a stand, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

Download Full-text

Climate Change Adaptation and Mitigation Options through strengthening carbon Management in Central Africa: Biomass allometric models of Khaya senegalensis (Desr.) A. Juss (meliaceae) in Cameroon

10.21203/rs.3.rs-815091/v1 ◽

2021 ◽

Author(s):

Djongmo Victor Awé ◽

Bi Tra Aimé Vroh ◽

Noumi Valery Noiha ◽

Moussa Ganamé ◽

Djawé Yannick Wanguili ◽

...

Keyword(s):

Central Africa ◽

Information Criterion ◽

Carbon Stocks ◽

Carbon Management ◽

Allometric Models ◽

Far North ◽

Breast Height ◽

Khaya Senegalensis ◽

Below Ground ◽

Adaptation And Mitigation

Abstract This study focused on the development of allometric models of Khaya senegalensis in order to establish a basis for calculating carbon stocks. The research was carried out in the Adamawa, North and Far North region of Cameroon. The weighing of the samples of each part (stem, branch, leaf) of the sample of trees composed of 60 feet of Khaya senegalensis and their drying in the oven made it possible to know the dry biomass of each subject Adjusted coefficients of determination (Adj.R2), residual standard error (RSE) and Akaike's information criterion (AIC) were used to choose the best models. Thus, the tested models presented varied performances. The various analyzes have confirmed that the diameter at breast height (DBH) is the variable that offers the best correlation with above-ground (AGB) and below-ground (BGB) biomass. The best selected models are: ln (AGB) = 1.004–0.054 * ln (D) and ln (BGB) = -3.009 + 0.016 * ln (D) (Adamawa); ln (AGB) = 0.004 + 0.054 * ln (D) and ln (BGB) = -1.301 + 0.116 * ln (D) (North); ln (AGB) = 0.004 + 1.084 * ln (D) and ln (BGB) = -0.002 + 0.016 * ln (D)( Far North). The best models selected for the global equations for the three regions are: ln (AGB) = 0.504 + 3.048*ln (D) and ln (BGB) =-0.109 + 0.306*ln (D). Models were proposed to estimate the carbon of Khaya senegalensis in Cameroon.

Download Full-text

Why do Logging Firms Reject Environmental Labeling in Central Africa? A Case Study of Forest Certification in Cameroon

Nile Journal of Business and Economics ◽

10.20321/nilejbe.v6i16.05 ◽

2020 ◽

Vol 6 (16) ◽

pp. 59-79

Author(s):

Isaac Bernard NDOUMBE BEROCK ◽

◽

Neba Cletus YAH ◽

Symphorien ONGOLO ◽

◽

...

Keyword(s):

Central Africa ◽

The Other ◽

Forest Certification ◽

Intermediate Case ◽

Environmental Labeling ◽

First Case ◽

Negative Case ◽

Logging Industry ◽

Empirical Case Studies

This article aims to understand why extractive firms in the industrial logging industry in central Africa are reluctant to certify or label their activities. The methodology is based on three empirical case studies of logging companies in Cameroon: one opposed to certification and labeling (the model), the other is in the process of being certified (intermediate case) and the last is certified (negative case). The preferred option followed by this study was to avoid the copying of the first case by prospecting an intermediate case. The "negative" case permitted the model to be saturated. The comparative analysis of data collected highlighted some key obstacles to the commitment to environmental labeling: corruption, low turnover, high certification cost and the source of capital.

Download Full-text